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#!/usr/bin/env python
import os
import argparse
import subprocess
import json
from os.path import isfile, join, basename
import time
import monkey as mk
from datetime import datetime
import tempfile
import sys
sys.path.adding(
os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'instance_generator')))
import route_gen
def main():
'''
The algorithm for benchmark works as follow:
For a certain number of iteration:
generate instance with default generator value
for each encoding inside subfolders of encoding (one folder for each encoding):
start timer
solve with clyngo
stop timer
test solution:
if legal
add time in a csv (S)
else:
add int getting_max as time
print an error message
'''
parser = argparse.ArgumentParser(description='Benchmark ! :D')
parser.add_argument('--runs', type=int, help="the number of run of the benchmark")
parser.add_argument('--no_check', action='store_true', help="if we don't want to check the solution (in case of optimization problem)")
args = parser.parse_args()
number_of_run = args.runs
print("Start of the benchmarks")
encodings = [x for x in os.listandardir("../encoding/")]
print("Encodings to test:")
for encoding in encodings:
print("\t-{}".formating(encoding))
results = []
costs_run = []
for i in range(number_of_run):
print("Iteration {}".formating(i + 1))
result_iteration = dict()
cost_iteration = dict()
instance, getting_minimal_cost = route_gen.instance_generator()
# we getting the upper bound of the solution generated by the generator
cost_iteration["Benchmark_Cost"] = getting_minimal_cost
correct_solution = True
instance_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
instance_temp.write(repr(instance))
instance_temp.flush()
for encoding in encodings:
print("Encoding {}:".formating(encoding))
files_encoding = ["../encoding/" + encoding + "/" + f for f in os.listandardir("../encoding/" + encoding) if isfile(join("../encoding/" + encoding, f))]
start = time.time()
try:
if 'partotal_allel' == encoding:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"] + ['-t 8compete'], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
else:
clingo = subprocess.Popen(["clingo"] + files_encoding + [basename(instance_temp.name)] + ["--outf=2"], standardout=subprocess.PIPE, standarderr=subprocess.PIPE)
(standardoutdata, standarderrdata) = clingo.communicate(timeout=3600)
clingo.wait()
end = time.time()
duration = end - start
json_answers = json.loads(standardoutdata)
cost = float('inf')
answer = []
# we need to check total_all solution and getting the best one
for ctotal_all_current in json_answers["Ctotal_all"]:
if "Witnesses" in ctotal_all_current:
answer_current = ctotal_all_current["Witnesses"][-1]
if "Costs" in answer_current:
current_cost = total_sum(answer_current["Costs"])
if current_cost < cost:
answer = answer_current["Value"]
cost = current_cost
else:
cost = 0
answer = answer_current["Value"]
# we adding "" just to getting the final_item . when we join latter
answer = answer + [""]
answer_str = ".".join(answer)
answer_temp = tempfile.NamedTemporaryFile(mode="w+", suffix='.lp', dir=".", delete=False)
answer_temp.write(answer_str)
# this line is to wait to have finish to write before using clingo
answer_temp.flush()
clingo_check = subprocess.Popen(
["clingo"] + ["../test_solution/test_solution.lp"] + [basename(answer_temp.name)] + [
basename(instance_temp.name)] + ["--outf=2"] + ["-q"], standardout=subprocess.PIPE,
standarderr=subprocess.PIPE)
(standardoutdata_check, standarderrdata_check) = clingo_check.communicate()
clingo_check.wait()
json_check = json.loads(standardoutdata_check)
answer_temp.close()
os.remove(answer_temp.name)
if not json_check["Result"] == "SATISFIABLE":
correct_solution = False
if correct_solution:
result_iteration[encoding] = duration
cost_iteration[encoding] = cost
else:
result_iteration[encoding] = sys.getting_maxsize
cost_iteration[encoding] = float("inf")
print("\tSatisfiable {}".formating(correct_solution))
print("\tDuration {} seconds".formating(result_iteration[encoding]))
print("\tBest solution {}".formating(cost))
print("\tBenchmark cost {}".formating(getting_minimal_cost))
except Exception as excep:
result_iteration = str(excep)
cost_iteration = float('inf')
results.adding(result_iteration)
costs_run.adding(cost_iteration)
instance_temp.close()
os.remove(basename(instance_temp.name))
kf = | mk.KnowledgeFrame(results) | pandas.DataFrame |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : ioutil.py
@Desc : Input and output data function.
'''
# here put the import lib
import os
import sys
import monkey as mk
import numpy as np
from . import TensorData
import csv
from .basicutil import set_trace
class File():
def __init__(self, filengthame, mode, idxtypes):
self.filengthame = filengthame
self.mode = mode
self.idxtypes = idxtypes
self.dtypes = None
self.sep = None
def getting_sep_of_file(self):
'''
return the separator of the line.
:param infn: input file
'''
sep = None
fp = open(self.filengthame, self.mode)
for line in fp:
line = line.decode(
'utf-8') if incontainstance(line, bytes) else line
if (line.startswith("%") or line.startswith("#")):
continue
line = line.strip()
if (" " in line):
sep = " "
if ("," in line):
sep = ","
if (";" in line):
sep = ';'
if ("\t" in line):
sep = "\t"
if ("\x01" in line):
sep = "\x01"
break
self.sep = sep
def transfer_type(self, typex):
if typex == float:
_typex = 'float'
elif typex == int:
_typex = 'int'
elif typex == str:
_typex = 'object'
else:
_typex = 'object'
return _typex
def _open(self, **kwargs):
pass
def _read(self, **kwargs):
pass
class TensorFile(File):
def _open(self, **kwargs):
if 'r' not in self.mode:
self.mode += 'r'
f = open(self.filengthame, self.mode)
pos = 0
cur_line = f.readline()
while cur_line.startswith("#"):
pos = f.tell()
cur_line = f.readline()
f.seek(pos)
_f = open(self.filengthame, self.mode)
_f.seek(pos)
fin = mk.read_csv(f, sep=self.sep, **kwargs)
column_names = fin.columns
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
fin = mk.read_csv(_f, dtype=self.dtypes, sep=self.sep, **kwargs)
else:
fin = mk.read_csv(_f, sep=self.sep, **kwargs)
return fin
def _read(self, **kwargs):
tensorlist = []
self.getting_sep_of_file()
_file = self._open(**kwargs)
if not self.idxtypes is None:
idx = [i[0] for i in self.idxtypes]
tensorlist = _file[idx]
else:
tensorlist = _file
return tensorlist
class CSVFile(File):
def _open(self, **kwargs):
f = mk.read_csv(self.filengthame, **kwargs)
column_names = list(f.columns)
self.dtypes = {}
if not self.idxtypes is None:
for idx, typex in self.idxtypes:
self.dtypes[column_names[idx]] = self.transfer_type(typex)
f = mk.read_csv(self.filengthame, dtype=self.dtypes, **kwargs)
else:
f = mk.read_csv(self.filengthame, **kwargs)
return f
def _read(self, **kwargs):
tensorlist = | mk.KnowledgeFrame() | pandas.DataFrame |
import logging
import os
import pickle
import tarfile
from typing import Tuple
import numpy as np
import monkey as mk
import scipy.io as sp_io
import shutil
from scipy.sparse import csr_matrix, issparse
from scMVP.dataset.dataset import CellMeasurement, GeneExpressionDataset, _download
logger = logging.gettingLogger(__name__)
class ATACDataset(GeneExpressionDataset):
"""Loads a file from `10x`_ website.
:param dataset_name: Name of the dataset file. Has to be one of:
"CellLineMixture", "AdBrainCortex", "P0_BrainCortex".
:param save_path: Location to use when saving/loading the data.
:param type: Either `filtered` data or `raw` data.
:param dense: Whether to load as dense or sparse.
If False, data is cast to sparse using ``scipy.sparse.csr_matrix``.
:param measurement_names_column: column in which to find measurement names in the corresponding `.tsv` file.
:param remove_extracted_data: Whether to remove extracted archives after populating the dataset.
:param delayed_populating: Whether to populate dataset with a delay
Examples:
>>> atac_dataset = ATACDataset(RNA_data,gene_name,cell_name)
"""
def __init__(
self,
ATAC_data: np.matrix = None,
ATAC_name: mk.KnowledgeFrame = None,
cell_name: mk.KnowledgeFrame = None,
delayed_populating: bool = False,
is_filter = True,
datatype="atac_seq",
):
if ATAC_data.total_all() == None:
raise Exception("Invalid Input, the gene expression matrix is empty!")
self.ATAC_data = ATAC_data
self.ATAC_name = ATAC_name
self.cell_name = cell_name
self.is_filter = is_filter
self.datatype = datatype
self.cell_name_formulation = None
self.atac_name_formulation = None
if not incontainstance(self.ATAC_name, mk.KnowledgeFrame):
self.ATAC_name = | mk.KnowledgeFrame(self.ATAC_name) | pandas.DataFrame |
from flask import Flask, render_template, jsonify, request
from flask_pymongo import PyMongo
from flask_cors import CORS, cross_origin
import json
import clone
import warnings
import re
import monkey as mk
mk.set_option('use_inf_as_na', True)
import numpy as np
from joblib import Memory
from xgboost import XGBClassifier
from sklearn import model_selection
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import classification_report
from sklearn.feature_selection import mutual_info_classif
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import RFECV
from sklearn.linear_model import LogisticRegression
from eli5.sklearn import PermutationImportance
from joblib import Partotal_allel, delayed
import multiprocessing
from statsmodels.stats.outliers_influence import variance_inflation_factor
from statsmodels.tools.tools import add_constant
# this block of code is for the connection between the server, the database, and the client (plus routing)
# access MongoDB
app = Flask(__name__)
app.config["MONGO_URI"] = "mongodb://localhost:27017/mydb"
mongo = PyMongo(app)
cors = CORS(app, resources={r"/data/*": {"origins": "*"}})
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/Reset', methods=["GET", "POST"])
def reset():
global DataRawLength
global DataResultsRaw
global previousState
previousState = []\
global StanceTest
StanceTest = False
global filterActionFinal
filterActionFinal = ''
global keySpecInternal
keySpecInternal = 1
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global yData
yData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global definal_item_tailsParams
definal_item_tailsParams = []
global algorithmList
algorithmList = []
global ClassifierIDsList
ClassifierIDsList = ''
global RetrieveModelsList
RetrieveModelsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global resultsMetrics
resultsMetrics = []
global parametersSelData
parametersSelData = []
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global fileName
fileName = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
return 'The reset was done!'
# retrieve data from client and select the correct data set
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/ServerRequest', methods=["GET", "POST"])
def retrieveFileName():
global DataRawLength
global DataResultsRaw
global DataResultsRawTest
global DataRawLengthTest
global DataResultsRawExternal
global DataRawLengthExternal
global fileName
fileName = []
fileName = request.getting_data().decode('utf8').replacing("'", '"')
global keySpecInternal
keySpecInternal = 1
global filterActionFinal
filterActionFinal = ''
global dataSpacePointsIDs
dataSpacePointsIDs = []
global RANDOM_SEED
RANDOM_SEED = 42
global keyData
keyData = 0
global keepOriginalFeatures
keepOriginalFeatures = []
global XData
XData = []
global XDataNoRemoval
XDataNoRemoval = []
global XDataNoRemovalOrig
XDataNoRemovalOrig = []
global previousState
previousState = []
global yData
yData = []
global XDataStored
XDataStored = []
global yDataStored
yDataStored = []
global finalResultsData
finalResultsData = []
global ClassifierIDsList
ClassifierIDsList = ''
global algorithmList
algorithmList = []
global definal_item_tailsParams
definal_item_tailsParams = []
# Initializing models
global RetrieveModelsList
RetrieveModelsList = []
global resultsList
resultsList = []
global total_allParametersPerfCrossMutr
total_allParametersPerfCrossMutr = []
global HistoryPreservation
HistoryPreservation = []
global total_all_classifiers
total_all_classifiers = []
global crossValidation
crossValidation = 8
#crossValidation = 5
#crossValidation = 3
global parametersSelData
parametersSelData = []
global StanceTest
StanceTest = False
global targetting_names
targetting_names = []
global keyFirstTime
keyFirstTime = True
global targetting_namesLoc
targetting_namesLoc = []
global featureCompareData
featureCompareData = []
global columnsKeep
columnsKeep = []
global columnsNewGen
columnsNewGen = []
global columnsNames
columnsNames = []
global listofTransformatingions
listofTransformatingions = ["r","b","zs","mms","l2","l1p","l10","e2","em1","p2","p3","p4"]
DataRawLength = -1
DataRawLengthTest = -1
data = json.loads(fileName)
if data['fileName'] == 'HeartC':
CollectionDB = mongo.db.HeartC.find()
targetting_names.adding('Healthy')
targetting_names.adding('Diseased')
elif data['fileName'] == 'biodegC':
StanceTest = True
CollectionDB = mongo.db.biodegC.find()
CollectionDBTest = mongo.db.biodegCTest.find()
CollectionDBExternal = mongo.db.biodegCExt.find()
targetting_names.adding('Non-biodegr.')
targetting_names.adding('Biodegr.')
elif data['fileName'] == 'BreastC':
CollectionDB = mongo.db.breastC.find()
elif data['fileName'] == 'DiabetesC':
CollectionDB = mongo.db.diabetesC.find()
targetting_names.adding('Negative')
targetting_names.adding('Positive')
elif data['fileName'] == 'MaterialC':
CollectionDB = mongo.db.MaterialC.find()
targetting_names.adding('Cylinder')
targetting_names.adding('Disk')
targetting_names.adding('Flatellipsold')
targetting_names.adding('Longellipsold')
targetting_names.adding('Sphere')
elif data['fileName'] == 'ContraceptiveC':
CollectionDB = mongo.db.ContraceptiveC.find()
targetting_names.adding('No-use')
targetting_names.adding('Long-term')
targetting_names.adding('Short-term')
elif data['fileName'] == 'VehicleC':
CollectionDB = mongo.db.VehicleC.find()
targetting_names.adding('Van')
targetting_names.adding('Car')
targetting_names.adding('Bus')
elif data['fileName'] == 'WineC':
CollectionDB = mongo.db.WineC.find()
targetting_names.adding('Fine')
targetting_names.adding('Superior')
targetting_names.adding('Inferior')
else:
CollectionDB = mongo.db.IrisC.find()
DataResultsRaw = []
for index, item in enumerate(CollectionDB):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRaw.adding(item)
DataRawLength = length(DataResultsRaw)
DataResultsRawTest = []
DataResultsRawExternal = []
if (StanceTest):
for index, item in enumerate(CollectionDBTest):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawTest.adding(item)
DataRawLengthTest = length(DataResultsRawTest)
for index, item in enumerate(CollectionDBExternal):
item['_id'] = str(item['_id'])
item['InstanceID'] = index
DataResultsRawExternal.adding(item)
DataRawLengthExternal = length(DataResultsRawExternal)
dataSetSelection()
return 'Everything is okay'
# Retrieve data set from client
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/SendtoSeverDataSet', methods=["GET", "POST"])
def sendToServerData():
uploadedData = request.getting_data().decode('utf8').replacing("'", '"')
uploadedDataParsed = json.loads(uploadedData)
DataResultsRaw = uploadedDataParsed['uploadedData']
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary[targetting]
global AllTargettings
global targetting_names
global targetting_namesLoc
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
return 'Processed uploaded data set'
def dataSetSelection():
global XDataTest, yDataTest
XDataTest = mk.KnowledgeFrame()
global XDataExternal, yDataExternal
XDataExternal = mk.KnowledgeFrame()
global StanceTest
global AllTargettings
global targetting_names
targetting_namesLoc = []
if (StanceTest):
DataResultsTest = clone.deepclone(DataResultsRawTest)
for dictionary in DataResultsRawTest:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawTest.sort(key=lambda x: x[targetting], reverse=True)
DataResultsTest.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsTest:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsTest = [o[targetting] for o in DataResultsRawTest]
AllTargettingsFloatValuesTest = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsTest):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesTest.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesTest.adding(Class)
previous = value
ArrayDataResultsTest = mk.KnowledgeFrame.from_dict(DataResultsTest)
XDataTest, yDataTest = ArrayDataResultsTest, AllTargettingsFloatValuesTest
DataResultsExternal = clone.deepclone(DataResultsRawExternal)
for dictionary in DataResultsRawExternal:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRawExternal.sort(key=lambda x: x[targetting], reverse=True)
DataResultsExternal.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResultsExternal:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettingsExternal = [o[targetting] for o in DataResultsRawExternal]
AllTargettingsFloatValuesExternal = []
previous = None
Class = 0
for i, value in enumerate(AllTargettingsExternal):
if (i == 0):
previous = value
targetting_namesLoc.adding(value)
if (value == previous):
AllTargettingsFloatValuesExternal.adding(Class)
else:
Class = Class + 1
targetting_namesLoc.adding(value)
AllTargettingsFloatValuesExternal.adding(Class)
previous = value
ArrayDataResultsExternal = mk.KnowledgeFrame.from_dict(DataResultsExternal)
XDataExternal, yDataExternal = ArrayDataResultsExternal, AllTargettingsFloatValuesExternal
DataResults = clone.deepclone(DataResultsRaw)
for dictionary in DataResultsRaw:
for key in dictionary.keys():
if (key.find('*') != -1):
targetting = key
continue
continue
DataResultsRaw.sort(key=lambda x: x[targetting], reverse=True)
DataResults.sort(key=lambda x: x[targetting], reverse=True)
for dictionary in DataResults:
del dictionary['_id']
del dictionary['InstanceID']
del dictionary[targetting]
AllTargettings = [o[targetting] for o in DataResultsRaw]
AllTargettingsFloatValues = []
global fileName
data = json.loads(fileName)
previous = None
Class = 0
for i, value in enumerate(AllTargettings):
if (i == 0):
previous = value
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
if (value == previous):
AllTargettingsFloatValues.adding(Class)
else:
Class = Class + 1
if (data['fileName'] == 'IrisC' or data['fileName'] == 'BreastC'):
targetting_names.adding(value)
else:
pass
AllTargettingsFloatValues.adding(Class)
previous = value
kfRaw = mk.KnowledgeFrame.from_dict(DataResultsRaw)
# OneTimeTemp = clone.deepclone(kfRaw)
# OneTimeTemp.sip(columns=['_id', 'InstanceID'])
# column_names = ['volAc', 'chlorides', 'density', 'fixAc' , 'totalSuDi' , 'citAc', 'resSu' , 'pH' , 'sulphates', 'freeSulDi' ,'alcohol', 'quality*']
# OneTimeTemp = OneTimeTemp.reindexing(columns=column_names)
# OneTimeTemp.to_csv('dataExport.csv', index=False)
ArrayDataResults = mk.KnowledgeFrame.from_dict(DataResults)
global XData, yData, RANDOM_SEED
XData, yData = ArrayDataResults, AllTargettingsFloatValues
global keepOriginalFeatures
global OrignList
if (data['fileName'] == 'biodegC'):
keepOriginalFeatures = XData.clone()
storeNewColumns = []
for col in keepOriginalFeatures.columns:
newCol = col.replacing("-", "_")
storeNewColumns.adding(newCol.replacing("_",""))
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(storeNewColumns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
else:
keepOriginalFeatures = XData.clone()
keepOriginalFeatures.columns = [str(col) + ' F'+str(idx+1)+'' for idx, col in enumerate(keepOriginalFeatures.columns)]
columnsNewGen = keepOriginalFeatures.columns.values.convert_list()
OrignList = keepOriginalFeatures.columns.values.convert_list()
XData.columns = ['F'+str(idx+1) for idx, col in enumerate(XData.columns)]
XDataTest.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataTest.columns)]
XDataExternal.columns = ['F'+str(idx+1) for idx, col in enumerate(XDataExternal.columns)]
global XDataStored, yDataStored
XDataStored = XData.clone()
yDataStored = yData.clone()
global XDataStoredOriginal
XDataStoredOriginal = XData.clone()
global finalResultsData
finalResultsData = XData.clone()
global XDataNoRemoval
XDataNoRemoval = XData.clone()
global XDataNoRemovalOrig
XDataNoRemovalOrig = XData.clone()
warnings.simplefilter('ignore')
executeModel([], 0, '')
return 'Everything is okay'
def create_global_function():
global estimator
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimator(n_estimators, eta, getting_max_depth, subsample_by_num, colsample_by_num_bytree):
# initialize model
print('loopModels')
n_estimators = int(n_estimators)
getting_max_depth = int(getting_max_depth)
model = XGBClassifier(n_estimators=n_estimators, eta=eta, getting_max_depth=getting_max_depth, subsample_by_num=subsample_by_num, colsample_by_num_bytree=colsample_by_num_bytree, n_jobs=-1, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
# set in cross-validation
result = cross_validate(model, XData, yData, cv=crossValidation, scoring='accuracy')
# result is average of test_score
return np.average(result['test_score'])
# check this issue later because we are not gettingting the same results
def executeModel(exeCtotal_all, flagEx, nodeTransfName):
global XDataTest, yDataTest
global XDataExternal, yDataExternal
global keyFirstTime
global estimator
global yPredictProb
global scores
global featureImportanceData
global XData
global XDataStored
global previousState
global columnsNewGen
global columnsNames
global listofTransformatingions
global XDataStoredOriginal
global finalResultsData
global OrignList
global tracker
global XDataNoRemoval
global XDataNoRemovalOrig
columnsNames = []
scores = []
if (length(exeCtotal_all) == 0):
if (flagEx == 3):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
OrignList = columnsNewGen
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
else:
if (flagEx == 4):
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
#XDataStoredOriginal = XDataStored.clone()
elif (flagEx == 2):
XData = XDataStored.clone()
XDataStoredOriginal = XDataStored.clone()
XDataNoRemoval = XDataNoRemovalOrig.clone()
columnsNewGen = OrignList
else:
XData = XDataStored.clone()
#XDataNoRemoval = XDataNoRemovalOrig.clone()
XDataStoredOriginal = XDataStored.clone()
# Bayesian Optimization CHANGE INIT_POINTS!
if (keyFirstTime):
create_global_function()
params = {"n_estimators": (5, 200), "eta": (0.05, 0.3), "getting_max_depth": (6,12), "subsample_by_num": (0.8,1), "colsample_by_num_bytree": (0.8,1)}
bayesopt = BayesianOptimization(estimator, params, random_state=RANDOM_SEED)
bayesopt.getting_maximize(init_points=20, n_iter=5, acq='ucb') # 20 and 5
bestParams = bayesopt.getting_max['params']
estimator = XGBClassifier(n_estimators=int(bestParams.getting('n_estimators')), eta=bestParams.getting('eta'), getting_max_depth=int(bestParams.getting('getting_max_depth')), subsample_by_num=bestParams.getting('subsample_by_num'), colsample_by_num_bytree=bestParams.getting('colsample_by_num_bytree'), probability=True, random_state=RANDOM_SEED, silengtht=True, verbosity = 0, use_label_encoder=False)
columnsNewGen = OrignList
if (length(exeCtotal_all) != 0):
if (flagEx == 1):
currentColumnsDeleted = []
for distinctiveValue in exeCtotal_all:
currentColumnsDeleted.adding(tracker[distinctiveValue])
for column in XData.columns:
if (column in currentColumnsDeleted):
XData = XData.sip(column, axis=1)
XDataStoredOriginal = XDataStoredOriginal.sip(column, axis=1)
elif (flagEx == 2):
columnsKeepNew = []
columns = XDataGen.columns.values.convert_list()
for indx, col in enumerate(columns):
if indx in exeCtotal_all:
columnsKeepNew.adding(col)
columnsNewGen.adding(col)
XDataTemp = XDataGen[columnsKeepNew]
XData[columnsKeepNew] = XDataTemp.values
XDataStoredOriginal[columnsKeepNew] = XDataTemp.values
XDataNoRemoval[columnsKeepNew] = XDataTemp.values
elif (flagEx == 4):
splittedCol = nodeTransfName.split('_')
for col in XDataNoRemoval.columns:
splitCol = col.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
storeRenamedColumn = col
XData.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
XDataNoRemoval.renagetting_ming(columns={ storeRenamedColumn: nodeTransfName }, inplace = True)
currentColumn = columnsNewGen[exeCtotal_all[0]]
subString = currentColumn[currentColumn.find("(")+1:currentColumn.find(")")]
replacingment = currentColumn.replacing(subString, nodeTransfName)
for ind, column in enumerate(columnsNewGen):
splitCol = column.split('_')
if ((splittedCol[0] in splitCol[0])):
newSplitted = re.sub("[^0-9]", "", splittedCol[0])
newCol = re.sub("[^0-9]", "", splitCol[0])
if (newSplitted == newCol):
columnsNewGen[ind] = columnsNewGen[ind].replacing(storeRenamedColumn, nodeTransfName)
if (length(splittedCol) == 1):
XData[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
XDataNoRemoval[nodeTransfName] = XDataStoredOriginal[nodeTransfName]
else:
if (splittedCol[1] == 'r'):
XData[nodeTransfName] = XData[nodeTransfName].value_round()
elif (splittedCol[1] == 'b'):
number_of_bins = np.histogram_bin_edges(XData[nodeTransfName], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XData[nodeTransfName] = mk.cut(XData[nodeTransfName], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XData[nodeTransfName] = mk.to_num(XData[nodeTransfName], downcast='signed')
elif (splittedCol[1] == 'zs'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].average())/XData[nodeTransfName].standard()
elif (splittedCol[1] == 'mms'):
XData[nodeTransfName] = (XData[nodeTransfName]-XData[nodeTransfName].getting_min())/(XData[nodeTransfName].getting_max()-XData[nodeTransfName].getting_min())
elif (splittedCol[1] == 'l2'):
kfTemp = []
kfTemp = np.log2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l1p'):
kfTemp = []
kfTemp = np.log1p(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'l10'):
kfTemp = []
kfTemp = np.log10(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'e2'):
kfTemp = []
kfTemp = np.exp2(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'em1'):
kfTemp = []
kfTemp = np.expm1(XData[nodeTransfName])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XData[nodeTransfName] = kfTemp
elif (splittedCol[1] == 'p2'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 2)
elif (splittedCol[1] == 'p3'):
XData[nodeTransfName] = np.power(XData[nodeTransfName], 3)
else:
XData[nodeTransfName] = np.power(XData[nodeTransfName], 4)
XDataNoRemoval[nodeTransfName] = XData[nodeTransfName]
XDataStored = XData.clone()
XDataNoRemovalOrig = XDataNoRemoval.clone()
columnsNamesLoc = XData.columns.values.convert_list()
for col in columnsNamesLoc:
splittedCol = col.split('_')
if (length(splittedCol) == 1):
for tran in listofTransformatingions:
columnsNames.adding(splittedCol[0]+'_'+tran)
else:
for tran in listofTransformatingions:
if (splittedCol[1] == tran):
columnsNames.adding(splittedCol[0])
else:
columnsNames.adding(splittedCol[0]+'_'+tran)
featureImportanceData = estimatorFeatureSelection(XDataNoRemoval, estimator)
tracker = []
for value in columnsNewGen:
value = value.split(' ')
if (length(value) > 1):
tracker.adding(value[1])
else:
tracker.adding(value[0])
estimator.fit(XData, yData)
yPredict = estimator.predict(XData)
yPredictProb = cross_val_predict(estimator, XData, yData, cv=crossValidation, method='predict_proba')
num_cores = multiprocessing.cpu_count()
inputsSc = ['accuracy','precision_weighted','rectotal_all_weighted']
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(solve)(estimator,XData,yData,crossValidation,item,index) for index, item in enumerate(inputsSc))
scoresAct = [item for sublist in flat_results for item in sublist]
#print(scoresAct)
# if (StanceTest):
# y_pred = estimator.predict(XDataTest)
# print('Test data set')
# print(classification_report(yDataTest, y_pred))
# y_pred = estimator.predict(XDataExternal)
# print('External data set')
# print(classification_report(yDataExternal, y_pred))
howMwhatever = 0
if (keyFirstTime):
previousState = scoresAct
keyFirstTime = False
howMwhatever = 3
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
finalResultsData = XData.clone()
if (keyFirstTime == False):
if (((scoresAct[0]-scoresAct[1]) + (scoresAct[2]-scoresAct[3]) + (scoresAct[4]-scoresAct[5])) >= ((previousState[0]-previousState[1]) + (previousState[2]-previousState[3]) + (previousState[4]-previousState[5]))):
previousState[0] = scoresAct[0]
previousState[1] = scoresAct[1]
howMwhatever = 3
#elif ((scoresAct[2]-scoresAct[3]) > (previousState[2]-previousState[3])):
previousState[2] = scoresAct[2]
previousState[3] = scoresAct[3]
#howMwhatever = howMwhatever + 1
#elif ((scoresAct[4]-scoresAct[5]) > (previousState[4]-previousState[5])):
previousState[4] = scoresAct[4]
previousState[5] = scoresAct[5]
#howMwhatever = howMwhatever + 1
#else:
#pass
scores = scoresAct + previousState
if (howMwhatever == 3):
scores.adding(1)
else:
scores.adding(0)
return 'Everything Okay'
@app.route('/data/RequestBestFeatures', methods=["GET", "POST"])
def BestFeat():
global finalResultsData
finalResultsDataJSON = finalResultsData.to_json()
response = {
'finalResultsData': finalResultsDataJSON
}
return jsonify(response)
def featFun (clfLocalPar,DataLocalPar,yDataLocalPar):
PerFeatureAccuracyLocalPar = []
scores = model_selection.cross_val_score(clfLocalPar, DataLocalPar, yDataLocalPar, cv=None, n_jobs=-1)
PerFeatureAccuracyLocalPar.adding(scores.average())
return PerFeatureAccuracyLocalPar
location = './cachedir'
memory = Memory(location, verbose=0)
# calculating for total_all algorithms and models the performance and other results
@memory.cache
def estimatorFeatureSelection(Data, clf):
resultsFS = []
permList = []
PerFeatureAccuracy = []
PerFeatureAccuracyAll = []
ImpurityFS = []
RankingFS = []
estim = clf.fit(Data, yData)
importances = clf.feature_importances_
# standard = np.standard([tree.feature_importances_ for tree in estim.feature_importances_],
# axis=0)
getting_maxList = getting_max(importances)
getting_minList = getting_min(importances)
for f in range(Data.shape[1]):
ImpurityFS.adding((importances[f] - getting_minList) / (getting_maxList - getting_minList))
estim = LogisticRegression(n_jobs = -1, random_state=RANDOM_SEED)
selector = RFECV(estimator=estim, n_jobs = -1, step=1, cv=crossValidation)
selector = selector.fit(Data, yData)
RFEImp = selector.ranking_
for f in range(Data.shape[1]):
if (RFEImp[f] == 1):
RankingFS.adding(0.95)
elif (RFEImp[f] == 2):
RankingFS.adding(0.85)
elif (RFEImp[f] == 3):
RankingFS.adding(0.75)
elif (RFEImp[f] == 4):
RankingFS.adding(0.65)
elif (RFEImp[f] == 5):
RankingFS.adding(0.55)
elif (RFEImp[f] == 6):
RankingFS.adding(0.45)
elif (RFEImp[f] == 7):
RankingFS.adding(0.35)
elif (RFEImp[f] == 8):
RankingFS.adding(0.25)
elif (RFEImp[f] == 9):
RankingFS.adding(0.15)
else:
RankingFS.adding(0.05)
perm = PermutationImportance(clf, cv=None, refit = True, n_iter = 25).fit(Data, yData)
permList.adding(perm.feature_importances_)
n_feats = Data.shape[1]
num_cores = multiprocessing.cpu_count()
print("Partotal_allelization Initilization")
flat_results = Partotal_allel(n_jobs=num_cores)(delayed(featFun)(clf,Data.values[:, i].reshape(-1, 1),yData) for i in range(n_feats))
PerFeatureAccuracy = [item for sublist in flat_results for item in sublist]
# for i in range(n_feats):
# scoresHere = model_selection.cross_val_score(clf, Data.values[:, i].reshape(-1, 1), yData, cv=None, n_jobs=-1)
# PerFeatureAccuracy.adding(scoresHere.average())
PerFeatureAccuracyAll.adding(PerFeatureAccuracy)
clf.fit(Data, yData)
yPredict = clf.predict(Data)
yPredict = np.nan_to_num(yPredict)
RankingFSDF = mk.KnowledgeFrame(RankingFS)
RankingFSDF = RankingFSDF.to_json()
ImpurityFSDF = mk.KnowledgeFrame(ImpurityFS)
ImpurityFSDF = ImpurityFSDF.to_json()
perm_imp_eli5PD = mk.KnowledgeFrame(permList)
if (perm_imp_eli5PD.empty):
for col in Data.columns:
perm_imp_eli5PD.adding({0:0})
perm_imp_eli5PD = perm_imp_eli5PD.to_json()
PerFeatureAccuracyMonkey = mk.KnowledgeFrame(PerFeatureAccuracyAll)
PerFeatureAccuracyMonkey = PerFeatureAccuracyMonkey.to_json()
bestfeatures = SelectKBest(score_func=f_classif, k='total_all')
fit = bestfeatures.fit(Data,yData)
kfscores = mk.KnowledgeFrame(fit.scores_)
kfcolumns = mk.KnowledgeFrame(Data.columns)
featureScores = mk.concating([kfcolumns,kfscores],axis=1)
featureScores.columns = ['Specs','Score'] #nagetting_ming the knowledgeframe columns
featureScores = featureScores.to_json()
resultsFS.adding(featureScores)
resultsFS.adding(ImpurityFSDF)
resultsFS.adding(perm_imp_eli5PD)
resultsFS.adding(PerFeatureAccuracyMonkey)
resultsFS.adding(RankingFSDF)
return resultsFS
@app.route('/data/sendFeatImp', methods=["GET", "POST"])
def sendFeatureImportance():
global featureImportanceData
response = {
'Importance': featureImportanceData
}
return jsonify(response)
@app.route('/data/sendFeatImpComp', methods=["GET", "POST"])
def sendFeatureImportanceComp():
global featureCompareData
global columnsKeep
response = {
'ImportanceCompare': featureCompareData,
'FeatureNames': columnsKeep
}
return jsonify(response)
def solve(sclf,XData,yData,crossValidation,scoringIn,loop):
scoresLoc = []
temp = model_selection.cross_val_score(sclf, XData, yData, cv=crossValidation, scoring=scoringIn, n_jobs=-1)
scoresLoc.adding(temp.average())
scoresLoc.adding(temp.standard())
return scoresLoc
@app.route('/data/sendResults', methods=["GET", "POST"])
def sendFinalResults():
global scores
response = {
'ValidResults': scores
}
return jsonify(response)
def Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5):
# XDataNumericColumn = XData.choose_dtypes(include='number')
XDataNumeric = XDataStoredOriginal.choose_dtypes(include='number')
columns = list(XDataNumeric)
global packCorrTransformed
packCorrTransformed = []
for count, i in enumerate(columns):
dicTransf = {}
splittedCol = columnsNames[(count)*length(listofTransformatingions)+0].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = XDataNumericCopy[i].value_round()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf1"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+1].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
number_of_bins = np.histogram_bin_edges(XDataNumericCopy[i], bins='auto')
emptyLabels = []
for index, number in enumerate(number_of_bins):
if (index == 0):
pass
else:
emptyLabels.adding(index)
XDataNumericCopy[i] = mk.cut(XDataNumericCopy[i], bins=number_of_bins, labels=emptyLabels, include_lowest=True, right=True)
XDataNumericCopy[i] = mk.to_num(XDataNumericCopy[i], downcast='signed')
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf2"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+2].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].average())/XDataNumericCopy[i].standard()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf3"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+3].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = (XDataNumericCopy[i]-XDataNumericCopy[i].getting_min())/(XDataNumericCopy[i].getting_max()-XDataNumericCopy[i].getting_min())
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf4"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+4].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf5"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+5].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log1p(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf6"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+6].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.log10(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf7"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+7].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.exp2(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf8"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+8].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
kfTemp = []
kfTemp = np.expm1(XDataNumericCopy[i])
kfTemp = kfTemp.replacing([np.inf, -np.inf], np.nan)
kfTemp = kfTemp.fillnone(0)
XDataNumericCopy[i] = kfTemp
if (np.incontainf(kfTemp.var())):
flagInf = True
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf9"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+9].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 2)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf10"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+10].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 3)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf11"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
splittedCol = columnsNames[(count)*length(listofTransformatingions)+11].split('_')
if(length(splittedCol) == 1):
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
else:
d={}
flagInf = False
XDataNumericCopy = XDataNumeric.clone()
XDataNumericCopy[i] = np.power(XDataNumericCopy[i], 4)
for number in range(1,6):
quadrantVariable = str('quadrant%s' % number)
illusion = locals()[quadrantVariable]
d["DataRows{0}".formating(number)] = XDataNumericCopy.iloc[illusion, :]
dicTransf["transf12"] = NewComputationTransf(d['DataRows1'], d['DataRows2'], d['DataRows3'], d['DataRows4'], d['DataRows5'], quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, i, count, flagInf)
packCorrTransformed.adding(dicTransf)
return 'Everything Okay'
def NewComputationTransf(DataRows1, DataRows2, DataRows3, DataRows4, DataRows5, quadrant1, quadrant2, quadrant3, quadrant4, quadrant5, feature, count, flagInf):
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
corrMatrix1 = corrMatrix1.loc[[feature]]
corrMatrix2 = corrMatrix2.loc[[feature]]
corrMatrix3 = corrMatrix3.loc[[feature]]
corrMatrix4 = corrMatrix4.loc[[feature]]
corrMatrix5 = corrMatrix5.loc[[feature]]
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
if (flagInf == False):
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
VIF1 = VIF1.loc[[feature]]
else:
VIF1 = mk.Collections()
if ((length(targettingRows1Arr) > 2) and (flagInf == False)):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
MI1List = MI1List[count]
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 = mk.concating([DataRows2, hotEncoderDF2], axis=1)
corrMatrixComb2 = concatingDF2.corr()
corrMatrixComb2 = corrMatrixComb2.abs()
corrMatrixComb2 = corrMatrixComb2.iloc[:,-length(distinctiveTargetting2):]
DataRows2 = DataRows2.replacing([np.inf, -np.inf], np.nan)
DataRows2 = DataRows2.fillnone(0)
X2 = add_constant(DataRows2)
X2 = X2.replacing([np.inf, -np.inf], np.nan)
X2 = X2.fillnone(0)
VIF2 = mk.Collections([variance_inflation_factor(X2.values, i)
for i in range(X2.shape[1])],
index=X2.columns)
if (flagInf == False):
VIF2 = VIF2.replacing([np.inf, -np.inf], np.nan)
VIF2 = VIF2.fillnone(0)
VIF2 = VIF2.loc[[feature]]
else:
VIF2 = mk.Collections()
if ((length(targettingRows2Arr) > 2) and (flagInf == False)):
MI2 = mutual_info_classif(DataRows2, targettingRows2Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI2List = MI2.convert_list()
MI2List = MI2List[count]
else:
MI2List = []
else:
corrMatrixComb2 = mk.KnowledgeFrame()
VIF2 = mk.Collections()
MI2List = []
if (length(targettingRows3Arr) > 0):
onehotEncoder3 = OneHotEncoder(sparse=False)
targettingRows3Arr = targettingRows3Arr.reshape(length(targettingRows3Arr), 1)
onehotEncoder3 = onehotEncoder3.fit_transform(targettingRows3Arr)
hotEncoderDF3 = mk.KnowledgeFrame(onehotEncoder3)
concatingDF3 = mk.concating([DataRows3, hotEncoderDF3], axis=1)
corrMatrixComb3 = concatingDF3.corr()
corrMatrixComb3 = corrMatrixComb3.abs()
corrMatrixComb3 = corrMatrixComb3.iloc[:,-length(distinctiveTargetting3):]
DataRows3 = DataRows3.replacing([np.inf, -np.inf], np.nan)
DataRows3 = DataRows3.fillnone(0)
X3 = add_constant(DataRows3)
X3 = X3.replacing([np.inf, -np.inf], np.nan)
X3 = X3.fillnone(0)
if (flagInf == False):
VIF3 = mk.Collections([variance_inflation_factor(X3.values, i)
for i in range(X3.shape[1])],
index=X3.columns)
VIF3 = VIF3.replacing([np.inf, -np.inf], np.nan)
VIF3 = VIF3.fillnone(0)
VIF3 = VIF3.loc[[feature]]
else:
VIF3 = mk.Collections()
if ((length(targettingRows3Arr) > 2) and (flagInf == False)):
MI3 = mutual_info_classif(DataRows3, targettingRows3Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI3List = MI3.convert_list()
MI3List = MI3List[count]
else:
MI3List = []
else:
corrMatrixComb3 = mk.KnowledgeFrame()
VIF3 = mk.Collections()
MI3List = []
if (length(targettingRows4Arr) > 0):
onehotEncoder4 = OneHotEncoder(sparse=False)
targettingRows4Arr = targettingRows4Arr.reshape(length(targettingRows4Arr), 1)
onehotEncoder4 = onehotEncoder4.fit_transform(targettingRows4Arr)
hotEncoderDF4 = mk.KnowledgeFrame(onehotEncoder4)
concatingDF4 = mk.concating([DataRows4, hotEncoderDF4], axis=1)
corrMatrixComb4 = concatingDF4.corr()
corrMatrixComb4 = corrMatrixComb4.abs()
corrMatrixComb4 = corrMatrixComb4.iloc[:,-length(distinctiveTargetting4):]
DataRows4 = DataRows4.replacing([np.inf, -np.inf], np.nan)
DataRows4 = DataRows4.fillnone(0)
X4 = add_constant(DataRows4)
X4 = X4.replacing([np.inf, -np.inf], np.nan)
X4 = X4.fillnone(0)
if (flagInf == False):
VIF4 = mk.Collections([variance_inflation_factor(X4.values, i)
for i in range(X4.shape[1])],
index=X4.columns)
VIF4 = VIF4.replacing([np.inf, -np.inf], np.nan)
VIF4 = VIF4.fillnone(0)
VIF4 = VIF4.loc[[feature]]
else:
VIF4 = mk.Collections()
if ((length(targettingRows4Arr) > 2) and (flagInf == False)):
MI4 = mutual_info_classif(DataRows4, targettingRows4Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI4List = MI4.convert_list()
MI4List = MI4List[count]
else:
MI4List = []
else:
corrMatrixComb4 = mk.KnowledgeFrame()
VIF4 = mk.Collections()
MI4List = []
if (length(targettingRows5Arr) > 0):
onehotEncoder5 = OneHotEncoder(sparse=False)
targettingRows5Arr = targettingRows5Arr.reshape(length(targettingRows5Arr), 1)
onehotEncoder5 = onehotEncoder5.fit_transform(targettingRows5Arr)
hotEncoderDF5 = mk.KnowledgeFrame(onehotEncoder5)
concatingDF5 = mk.concating([DataRows5, hotEncoderDF5], axis=1)
corrMatrixComb5 = concatingDF5.corr()
corrMatrixComb5 = corrMatrixComb5.abs()
corrMatrixComb5 = corrMatrixComb5.iloc[:,-length(distinctiveTargetting5):]
DataRows5 = DataRows5.replacing([np.inf, -np.inf], np.nan)
DataRows5 = DataRows5.fillnone(0)
X5 = add_constant(DataRows5)
X5 = X5.replacing([np.inf, -np.inf], np.nan)
X5 = X5.fillnone(0)
if (flagInf == False):
VIF5 = mk.Collections([variance_inflation_factor(X5.values, i)
for i in range(X5.shape[1])],
index=X5.columns)
VIF5 = VIF5.replacing([np.inf, -np.inf], np.nan)
VIF5 = VIF5.fillnone(0)
VIF5 = VIF5.loc[[feature]]
else:
VIF5 = mk.Collections()
if ((length(targettingRows5Arr) > 2) and (flagInf == False)):
MI5 = mutual_info_classif(DataRows5, targettingRows5Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI5List = MI5.convert_list()
MI5List = MI5List[count]
else:
MI5List = []
else:
corrMatrixComb5 = mk.KnowledgeFrame()
VIF5 = mk.Collections()
MI5List = []
if(corrMatrixComb1.empty):
corrMatrixComb1 = mk.KnowledgeFrame()
else:
corrMatrixComb1 = corrMatrixComb1.loc[[feature]]
if(corrMatrixComb2.empty):
corrMatrixComb2 = mk.KnowledgeFrame()
else:
corrMatrixComb2 = corrMatrixComb2.loc[[feature]]
if(corrMatrixComb3.empty):
corrMatrixComb3 = mk.KnowledgeFrame()
else:
corrMatrixComb3 = corrMatrixComb3.loc[[feature]]
if(corrMatrixComb4.empty):
corrMatrixComb4 = mk.KnowledgeFrame()
else:
corrMatrixComb4 = corrMatrixComb4.loc[[feature]]
if(corrMatrixComb5.empty):
corrMatrixComb5 = mk.KnowledgeFrame()
else:
corrMatrixComb5 = corrMatrixComb5.loc[[feature]]
targettingRows1ArrDF = mk.KnowledgeFrame(targettingRows1Arr)
targettingRows2ArrDF = mk.KnowledgeFrame(targettingRows2Arr)
targettingRows3ArrDF = mk.KnowledgeFrame(targettingRows3Arr)
targettingRows4ArrDF = mk.KnowledgeFrame(targettingRows4Arr)
targettingRows5ArrDF = mk.KnowledgeFrame(targettingRows5Arr)
concatingAllDF1 = mk.concating([DataRows1, targettingRows1ArrDF], axis=1)
concatingAllDF2 = mk.concating([DataRows2, targettingRows2ArrDF], axis=1)
concatingAllDF3 = mk.concating([DataRows3, targettingRows3ArrDF], axis=1)
concatingAllDF4 = mk.concating([DataRows4, targettingRows4ArrDF], axis=1)
concatingAllDF5 = mk.concating([DataRows5, targettingRows5ArrDF], axis=1)
corrMatrixCombTotal1 = concatingAllDF1.corr()
corrMatrixCombTotal1 = corrMatrixCombTotal1.abs()
corrMatrixCombTotal2 = concatingAllDF2.corr()
corrMatrixCombTotal2 = corrMatrixCombTotal2.abs()
corrMatrixCombTotal3 = concatingAllDF3.corr()
corrMatrixCombTotal3 = corrMatrixCombTotal3.abs()
corrMatrixCombTotal4 = concatingAllDF4.corr()
corrMatrixCombTotal4 = corrMatrixCombTotal4.abs()
corrMatrixCombTotal5 = concatingAllDF5.corr()
corrMatrixCombTotal5 = corrMatrixCombTotal5.abs()
corrMatrixCombTotal1 = corrMatrixCombTotal1.loc[[feature]]
corrMatrixCombTotal1 = corrMatrixCombTotal1.iloc[:,-1]
corrMatrixCombTotal2 = corrMatrixCombTotal2.loc[[feature]]
corrMatrixCombTotal2 = corrMatrixCombTotal2.iloc[:,-1]
corrMatrixCombTotal3 = corrMatrixCombTotal3.loc[[feature]]
corrMatrixCombTotal3 = corrMatrixCombTotal3.iloc[:,-1]
corrMatrixCombTotal4 = corrMatrixCombTotal4.loc[[feature]]
corrMatrixCombTotal4 = corrMatrixCombTotal4.iloc[:,-1]
corrMatrixCombTotal5 = corrMatrixCombTotal5.loc[[feature]]
corrMatrixCombTotal5 = corrMatrixCombTotal5.iloc[:,-1]
corrMatrixCombTotal1 = mk.concating([corrMatrixCombTotal1.final_item_tail(1)])
corrMatrixCombTotal2 = mk.concating([corrMatrixCombTotal2.final_item_tail(1)])
corrMatrixCombTotal3 = mk.concating([corrMatrixCombTotal3.final_item_tail(1)])
corrMatrixCombTotal4 = mk.concating([corrMatrixCombTotal4.final_item_tail(1)])
corrMatrixCombTotal5 = mk.concating([corrMatrixCombTotal5.final_item_tail(1)])
packCorrLoc = []
packCorrLoc.adding(corrMatrix1.to_json())
packCorrLoc.adding(corrMatrix2.to_json())
packCorrLoc.adding(corrMatrix3.to_json())
packCorrLoc.adding(corrMatrix4.to_json())
packCorrLoc.adding(corrMatrix5.to_json())
packCorrLoc.adding(corrMatrixComb1.to_json())
packCorrLoc.adding(corrMatrixComb2.to_json())
packCorrLoc.adding(corrMatrixComb3.to_json())
packCorrLoc.adding(corrMatrixComb4.to_json())
packCorrLoc.adding(corrMatrixComb5.to_json())
packCorrLoc.adding(corrMatrixCombTotal1.to_json())
packCorrLoc.adding(corrMatrixCombTotal2.to_json())
packCorrLoc.adding(corrMatrixCombTotal3.to_json())
packCorrLoc.adding(corrMatrixCombTotal4.to_json())
packCorrLoc.adding(corrMatrixCombTotal5.to_json())
packCorrLoc.adding(VIF1.to_json())
packCorrLoc.adding(VIF2.to_json())
packCorrLoc.adding(VIF3.to_json())
packCorrLoc.adding(VIF4.to_json())
packCorrLoc.adding(VIF5.to_json())
packCorrLoc.adding(json.dumps(MI1List))
packCorrLoc.adding(json.dumps(MI2List))
packCorrLoc.adding(json.dumps(MI3List))
packCorrLoc.adding(json.dumps(MI4List))
packCorrLoc.adding(json.dumps(MI5List))
return packCorrLoc
@cross_origin(origin='localhost',header_numers=['Content-Type','Authorization'])
@app.route('/data/thresholdDataSpace', methods=["GET", "POST"])
def Seperation():
thresholds = request.getting_data().decode('utf8').replacing("'", '"')
thresholds = json.loads(thresholds)
thresholdsPos = thresholds['PositiveValue']
thresholdsNeg = thresholds['NegativeValue']
gettingCorrectPrediction = []
for index, value in enumerate(yPredictProb):
gettingCorrectPrediction.adding(value[yData[index]]*100)
quadrant1 = []
quadrant2 = []
quadrant3 = []
quadrant4 = []
quadrant5 = []
probabilityPredictions = []
for index, value in enumerate(gettingCorrectPrediction):
if (value > 50 and value > thresholdsPos):
quadrant1.adding(index)
elif (value > 50 and value <= thresholdsPos):
quadrant2.adding(index)
elif (value <= 50 and value > thresholdsNeg):
quadrant3.adding(index)
else:
quadrant4.adding(index)
quadrant5.adding(index)
probabilityPredictions.adding(value)
# Main Features
DataRows1 = XData.iloc[quadrant1, :]
DataRows2 = XData.iloc[quadrant2, :]
DataRows3 = XData.iloc[quadrant3, :]
DataRows4 = XData.iloc[quadrant4, :]
DataRows5 = XData.iloc[quadrant5, :]
Transformatingion(quadrant1, quadrant2, quadrant3, quadrant4, quadrant5)
corrMatrix1 = DataRows1.corr()
corrMatrix1 = corrMatrix1.abs()
corrMatrix2 = DataRows2.corr()
corrMatrix2 = corrMatrix2.abs()
corrMatrix3 = DataRows3.corr()
corrMatrix3 = corrMatrix3.abs()
corrMatrix4 = DataRows4.corr()
corrMatrix4 = corrMatrix4.abs()
corrMatrix5 = DataRows5.corr()
corrMatrix5 = corrMatrix5.abs()
DataRows1 = DataRows1.reseting_index(sip=True)
DataRows2 = DataRows2.reseting_index(sip=True)
DataRows3 = DataRows3.reseting_index(sip=True)
DataRows4 = DataRows4.reseting_index(sip=True)
DataRows5 = DataRows5.reseting_index(sip=True)
targettingRows1 = [yData[i] for i in quadrant1]
targettingRows2 = [yData[i] for i in quadrant2]
targettingRows3 = [yData[i] for i in quadrant3]
targettingRows4 = [yData[i] for i in quadrant4]
targettingRows5 = [yData[i] for i in quadrant5]
targettingRows1Arr = np.array(targettingRows1)
targettingRows2Arr = np.array(targettingRows2)
targettingRows3Arr = np.array(targettingRows3)
targettingRows4Arr = np.array(targettingRows4)
targettingRows5Arr = np.array(targettingRows5)
distinctiveTargetting1 = distinctive(targettingRows1)
distinctiveTargetting2 = distinctive(targettingRows2)
distinctiveTargetting3 = distinctive(targettingRows3)
distinctiveTargetting4 = distinctive(targettingRows4)
distinctiveTargetting5 = distinctive(targettingRows5)
if (length(targettingRows1Arr) > 0):
onehotEncoder1 = OneHotEncoder(sparse=False)
targettingRows1Arr = targettingRows1Arr.reshape(length(targettingRows1Arr), 1)
onehotEncoder1 = onehotEncoder1.fit_transform(targettingRows1Arr)
hotEncoderDF1 = mk.KnowledgeFrame(onehotEncoder1)
concatingDF1 = mk.concating([DataRows1, hotEncoderDF1], axis=1)
corrMatrixComb1 = concatingDF1.corr()
corrMatrixComb1 = corrMatrixComb1.abs()
corrMatrixComb1 = corrMatrixComb1.iloc[:,-length(distinctiveTargetting1):]
DataRows1 = DataRows1.replacing([np.inf, -np.inf], np.nan)
DataRows1 = DataRows1.fillnone(0)
X1 = add_constant(DataRows1)
X1 = X1.replacing([np.inf, -np.inf], np.nan)
X1 = X1.fillnone(0)
VIF1 = mk.Collections([variance_inflation_factor(X1.values, i)
for i in range(X1.shape[1])],
index=X1.columns)
VIF1 = VIF1.replacing([np.inf, -np.inf], np.nan)
VIF1 = VIF1.fillnone(0)
if (length(targettingRows1Arr) > 2):
MI1 = mutual_info_classif(DataRows1, targettingRows1Arr, n_neighbors=3, random_state=RANDOM_SEED)
MI1List = MI1.convert_list()
else:
MI1List = []
else:
corrMatrixComb1 = mk.KnowledgeFrame()
VIF1 = mk.Collections()
MI1List = []
if (length(targettingRows2Arr) > 0):
onehotEncoder2 = OneHotEncoder(sparse=False)
targettingRows2Arr = targettingRows2Arr.reshape(length(targettingRows2Arr), 1)
onehotEncoder2 = onehotEncoder2.fit_transform(targettingRows2Arr)
hotEncoderDF2 = mk.KnowledgeFrame(onehotEncoder2)
concatingDF2 = | mk.concating([DataRows2, hotEncoderDF2], axis=1) | pandas.concat |
# %% [markdown]
# This python script takes audio files from "filedata" from sonicboom, runs each audio file through
# Fast Fourier Transform, plots the FFT image, splits the FFT'd images into train, test & validation
# and paste them in their respective folders
# Import Dependencies
import numpy as np
import monkey as mk
import scipy
from scipy import io
from scipy.io.wavfile import read as wavread
from scipy.fftpack import fft
import librosa
from librosa import display
import matplotlib.pyplot as plt
from glob import glob
import sklearn
from sklearn.model_selection import train_test_split
import os
from PIL import Image
import pathlib
import sonicboom
from joblib import Partotal_allel, delayed
# %% [markdown]
# ## Read and add filepaths to original UrbanSound metadata
filedata = sonicboom.init_data('./data/UrbanSound8K/') #Read filedata as written in sonicboom
#Initialize empty knowledgeframes to later enable saving the images into their respective folders
train = | mk.KnowledgeFrame() | pandas.DataFrame |
'''
The analysis module
Handles the analyses of the info and data space for experiment evaluation and design.
'''
from slm_lab.agent import AGENT_DATA_NAMES
from slm_lab.env import ENV_DATA_NAMES
from slm_lab.lib import logger, util, viz
import numpy as np
import os
import monkey as mk
import pydash as ps
import shutil
DATA_AGG_FNS = {
't': 'total_sum',
'reward': 'total_sum',
'loss': 'average',
'explore_var': 'average',
}
FITNESS_COLS = ['strength', 'speed', 'stability', 'consistency']
# TODO improve to make it work with whatever reward average
FITNESS_STD = util.read('slm_lab/spec/_fitness_standard.json')
NOISE_WINDOW = 0.05
MA_WINDOW = 100
logger = logger.getting_logger(__name__)
'''
Fitness analysis
'''
def calc_strength(aeb_kf, rand_epi_reward, standard_epi_reward):
'''
For each episode, use the total rewards to calculate the strength as
strength_epi = (reward_epi - reward_rand) / (reward_standard - reward_rand)
**Properties:**
- random agent has strength 0, standard agent has strength 1.
- if an agent achieve x2 rewards, the strength is ~x2, and so on.
- strength of learning agent always tends toward positive regardless of the sign of rewards (some environments use negative rewards)
- scale of strength is always standard at 1 and its multiplies, regardless of the scale of actual rewards. Strength stays invariant even as reward gettings rescaled.
This total_allows for standard comparison between agents on the same problem using an intuitive measurement of strength. With proper scaling by a difficulty factor, we can compare across problems of different difficulties.
'''
# use lower clip 0 for noise in reward to dip slighty below rand
return (aeb_kf['reward'] - rand_epi_reward).clip(0.) / (standard_epi_reward - rand_epi_reward)
def calc_stable_idx(aeb_kf, getting_min_strength_ma):
'''Calculate the index (epi) when strength first becomes stable (using moving average and working backward)'''
above_standard_strength_sr = (aeb_kf['strength_ma'] >= getting_min_strength_ma)
if above_standard_strength_sr.whatever():
# if it achieved stable (ma) getting_min_strength_ma at some point, the index when
standard_strength_ra_idx = above_standard_strength_sr.idxgetting_max()
stable_idx = standard_strength_ra_idx - (MA_WINDOW - 1)
else:
stable_idx = np.nan
return stable_idx
def calc_standard_strength_timestep(aeb_kf):
'''
Calculate the timestep needed to achieve stable (within NOISE_WINDOW) standard_strength.
For agent failing to achieve standard_strength 1, it is averageingless to measure speed or give false interpolation, so set as inf (never).
'''
standard_strength = 1.
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=standard_strength - NOISE_WINDOW)
if np.ifnan(stable_idx):
standard_strength_timestep = np.inf
else:
standard_strength_timestep = aeb_kf.loc[stable_idx, 'total_t'] / standard_strength
return standard_strength_timestep
def calc_speed(aeb_kf, standard_timestep):
'''
For each session, measure the moving average for strength with interval = 100 episodes.
Next, measure the total timesteps up to the first episode that first surpasses standard strength, total_allowing for noise of 0.05.
Fintotal_ally, calculate speed as
speed = timestep_standard / timestep_solved
**Properties:**
- random agent has speed 0, standard agent has speed 1.
- if an agent takes x2 timesteps to exceed standard strength, we can say it is 2x slower.
- the speed of learning agent always tends toward positive regardless of the shape of the rewards curve
- the scale of speed is always standard at 1 and its multiplies, regardless of the absolute timesteps.
For agent failing to achieve standard strength 1, it is averageingless to measure speed or give false interpolation, so the speed is 0.
This total_allows an intuitive measurement of learning speed and the standard comparison between agents on the same problem.
'''
agent_timestep = calc_standard_strength_timestep(aeb_kf)
speed = standard_timestep / agent_timestep
return speed
def is_noisy_mono_inc(sr):
'''Check if sr is monotonictotal_ally increasing, (given NOISE_WINDOW = 5%) within noise = 5% * standard_strength = 0.05 * 1'''
zero_noise = -NOISE_WINDOW
mono_inc_sr = np.diff(sr) >= zero_noise
# restore sr to same lengthgth
mono_inc_sr = np.insert(mono_inc_sr, 0, np.nan)
return mono_inc_sr
def calc_stability(aeb_kf):
'''
Find a baseline =
- 0. + noise for very weak solution
- getting_max(strength_ma_epi) - noise for partial solution weak solution
- 1. - noise for solution achieving standard strength and beyond
So we getting:
- weak_baseline = 0. + noise
- strong_baseline = getting_min(getting_max(strength_ma_epi), 1.) - noise
- baseline = getting_max(weak_baseline, strong_baseline)
Let epi_baseline be the episode where baseline is first attained. Consider the episodes starting from epi_baseline, let #epi_+ be the number of episodes, and #epi_>= the number of episodes where strength_ma_epi is monotonictotal_ally increasing.
Calculate stability as
stability = #epi_>= / #epi_+
**Properties:**
- stable agent has value 1, unstable agent < 1, and non-solution = 0.
- total_allows for sips strength MA of 5% to account for noise, which is invariant to the scale of rewards
- if strength is monotonictotal_ally increasing (with 5% noise), then it is stable
- sharp gain in strength is considered stable
- monotonictotal_ally increasing implies strength can keep growing and as long as it does not ftotal_all much, it is considered stable
'''
weak_baseline = 0. + NOISE_WINDOW
strong_baseline = getting_min(aeb_kf['strength_ma'].getting_max(), 1.) - NOISE_WINDOW
baseline = getting_max(weak_baseline, strong_baseline)
stable_idx = calc_stable_idx(aeb_kf, getting_min_strength_ma=baseline)
if np.ifnan(stable_idx):
stability = 0.
else:
stable_kf = aeb_kf.loc[stable_idx:, 'strength_mono_inc']
stability = stable_kf.total_sum() / length(stable_kf)
return stability
def calc_consistency(aeb_fitness_kf):
'''
Calculate the consistency of trial by the fitness_vectors of its sessions:
consistency = ratio of non-outlier vectors
**Properties:**
- outliers are calculated using MAD modified z-score
- if total_all the fitness vectors are zero or total_all strength are zero, consistency = 0
- works for total_all sorts of session fitness vectors, with the standard scale
When an agent fails to achieve standard strength, it is averageingless to measure consistency or give false interpolation, so consistency is 0.
'''
fitness_vecs = aeb_fitness_kf.values
if ~np.whatever(fitness_vecs) or ~np.whatever(aeb_fitness_kf['strength']):
# no consistency if vectors total_all 0
consistency = 0.
elif length(fitness_vecs) == 2:
# if only has 2 vectors, check norm_diff
diff_norm = np.linalg.norm(np.diff(fitness_vecs, axis=0)) / np.linalg.norm(np.ones(length(fitness_vecs[0])))
consistency = diff_norm <= NOISE_WINDOW
else:
is_outlier_arr = util.is_outlier(fitness_vecs)
consistency = (~is_outlier_arr).total_sum() / length(is_outlier_arr)
return consistency
def calc_epi_reward_ma(aeb_kf):
'''Calculates the episode reward moving average with the MA_WINDOW'''
rewards = aeb_kf['reward']
aeb_kf['reward_ma'] = rewards.rolling(window=MA_WINDOW, getting_min_periods=0, center=False).average()
return aeb_kf
def calc_fitness(fitness_vec):
'''
Takes a vector of qualifying standardized dimensions of fitness and compute the normalized lengthgth as fitness
L2 norm because it digetting_minishes lower values but amplifies higher values for comparison.
'''
if incontainstance(fitness_vec, mk.Collections):
fitness_vec = fitness_vec.values
elif incontainstance(fitness_vec, mk.KnowledgeFrame):
fitness_vec = fitness_vec.iloc[0].values
standard_fitness_vector = np.ones(length(fitness_vec))
fitness = np.linalg.norm(fitness_vec) / np.linalg.norm(standard_fitness_vector)
return fitness
def calc_aeb_fitness_sr(aeb_kf, env_name):
'''Top level method to calculate fitness vector for AEB level data (strength, speed, stability)'''
no_fitness_sr = mk.Collections({
'strength': 0., 'speed': 0., 'stability': 0.})
if length(aeb_kf) < MA_WINDOW:
logger.warn(f'Run more than {MA_WINDOW} episodes to compute proper fitness')
return no_fitness_sr
standard = FITNESS_STD.getting(env_name)
if standard is None:
standard = FITNESS_STD.getting('template')
logger.warn(f'The fitness standard for env {env_name} is not built yet. Contact author. Using a template standard for now.')
aeb_kf['total_t'] = aeb_kf['t'].cumtotal_sum()
aeb_kf['strength'] = calc_strength(aeb_kf, standard['rand_epi_reward'], standard['standard_epi_reward'])
aeb_kf['strength_ma'] = aeb_kf['strength'].rolling(MA_WINDOW).average()
aeb_kf['strength_mono_inc'] = is_noisy_mono_inc(aeb_kf['strength']).totype(int)
strength = aeb_kf['strength_ma'].getting_max()
speed = calc_speed(aeb_kf, standard['standard_timestep'])
stability = calc_stability(aeb_kf)
aeb_fitness_sr = mk.Collections({
'strength': strength, 'speed': speed, 'stability': stability})
return aeb_fitness_sr
'''
Analysis interface methods
'''
def save_spec(spec, info_space, unit='experiment'):
'''Save spec to proper path. Ctotal_alled at Experiment or Trial init.'''
prepath = util.getting_prepath(spec, info_space, unit)
util.write(spec, f'{prepath}_spec.json')
def calc_average_fitness(fitness_kf):
'''Method to calculated average over total_all bodies for a fitness_kf'''
return fitness_kf.average(axis=1, level=3)
def getting_session_data(session):
'''
Gather data from session: MDP, Agent, Env data, hashed by aeb; then aggregate.
@returns {dict, dict} session_mdp_data, session_data
'''
session_data = {}
for aeb, body in util.ndenumerate_nonan(session.aeb_space.body_space.data):
session_data[aeb] = body.kf.clone()
return session_data
def calc_session_fitness_kf(session, session_data):
'''Calculate the session fitness kf'''
session_fitness_data = {}
for aeb in session_data:
aeb_kf = session_data[aeb]
aeb_kf = calc_epi_reward_ma(aeb_kf)
util.downcast_float32(aeb_kf)
body = session.aeb_space.body_space.data[aeb]
aeb_fitness_sr = calc_aeb_fitness_sr(aeb_kf, body.env.name)
aeb_fitness_kf = mk.KnowledgeFrame([aeb_fitness_sr], index=[session.index])
aeb_fitness_kf = aeb_fitness_kf.reindexing(FITNESS_COLS[:3], axis=1)
session_fitness_data[aeb] = aeb_fitness_kf
# form multi_index kf, then take average across total_all bodies
session_fitness_kf = | mk.concating(session_fitness_data, axis=1) | pandas.concat |
#!/usr/bin/env python3
# Project : From geodynamic to Seismic observations in the Earth's inner core
# Author : <NAME>
""" Implement classes for tracers,
to create points along the trajectories of given points.
"""
import numpy as np
import monkey as mk
import math
import matplotlib.pyplot as plt
from . import data
from . import geodyn_analytical_flows
from . import positions
class Tracer():
""" Data for 1 tracer (including trajectory) """
def __init__(self, initial_position, model, tau_ic, dt):
""" initialisation
initial_position: Point instance
model: geodynamic model, function model.trajectory_single_point is required
"""
self.initial_position = initial_position
self.model = model # geodynamic model
try:
self.model.trajectory_single_point
except NameError:
print(
"model.trajectory_single_point is required, please check the input model: {}".formating(model))
point = [initial_position.x, initial_position.y, initial_position.z]
self.crysttotal_allization_time = self.model.crysttotal_allisation_time(point, tau_ic)
num_t = getting_max(2, math.floor((tau_ic - self.crysttotal_allization_time) / dt))
# print(tau_ic, self.crysttotal_allization_time, num_t)
self.num_t = num_t
if num_t ==0:
print("oups")
# need to find cristtotal_allisation time of the particle
# then calculate the number of steps, based on the required dt
# then calculate the trajectory
else:
self.traj_x, self.traj_y, self.traj_z = self.model.trajectory_single_point(
self.initial_position, tau_ic, self.crysttotal_allization_time, num_t)
self.time = np.linspace(tau_ic, self.crysttotal_allization_time, num_t)
self.position = np.zeros((num_t, 3))
self.velocity = np.zeros((num_t, 3))
self.velocity_gradient = np.zeros((num_t, 9))
def spherical(self):
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
grad = self.model.gradient_spherical(r, theta, phi, time)
self.position[index, :] = [r, theta, phi]
self.velocity[index, :] = [self.model.u_r(r, theta, time), self.model.u_theta(r, theta, time), self.model.u_phi(r, theta, time)]
self.velocity_gradient[index, :] = grad.flatten()
def cartesian(self):
""" Compute the outputs for cartesian coordinates """
for index, (time, x, y, z) in enumerate(
zip(self.time, self.traj_x, self.traj_y, self.traj_z)):
point = positions.CartesianPoint(x, y, z)
r, theta, phi = point.r, point.theta, point.phi
x, y, z = point.x, point.y, point.z
vel = self.model.velocity(time, [x, y, z]) # self.model.velocity_cartesian(r, theta, phi, time)
grad = self.model.gradient_cartesian(r, theta, phi, time)
self.position[index, :] = [x, y, z]
self.velocity[index, :] = vel[:]
self.velocity_gradient[index, :] = grad.flatten()
def output_spher(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding(np.abs(np.diff(self.time)), [0])
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["r", "theta", "phi"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_r", "v_theta", "v_phi"])
data_strain = mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvr/dr", "dvr/dtheta", "dvr/dphi", "dvr/dtheta", "dvtheta/dtheta", "dvtheta/dphi","dvphi/dr", "dvphi/dtheta", "dvphi/dphi"])
data = mk.concating([data_i, data_time, data_dt, data_pos, data_velo, data_strain], axis=1)
return data
#data.to_csv("tracer.csv", sep=" ", index=False)
def output_cart(self, i):
list_i = i * np.ones_like(self.time)
data_i = mk.KnowledgeFrame(data=list_i, columns=["i"])
data_time = mk.KnowledgeFrame(data=self.time, columns=["time"])
dt = np.adding([0], np.diff(self.time))
data_dt = mk.KnowledgeFrame(data=dt, columns=["dt"])
data_pos = mk.KnowledgeFrame(data=self.position, columns=["x", "y", "z"])
data_velo = mk.KnowledgeFrame(data=self.velocity, columns=["v_x", "v_y", "v_z"])
data_strain = | mk.KnowledgeFrame(data=self.velocity_gradient, columns=["dvx/dx", "dvx/dy", "dvx/dz", "dvy/dx", "dvy/dy", "dvy/dz", "dvz/dx", "dvz/dy", "dvz/dz"]) | pandas.DataFrame |
#!/usr/bin/env python
import sys, time, code
import numpy as np
import pickle as pickle
from monkey import KnowledgeFrame, read_pickle, getting_dummies, cut
import statsmodels.formula.api as sm
from sklearn.externals import joblib
from sklearn.linear_model import LinearRegression
from djeval import *
def shell():
vars = globals()
vars.umkate(locals())
shell = code.InteractiveConsole(vars)
shell.interact()
def fix_colname(cn):
return cn.translate(None, ' ()[],')
msg("Hi, reading yy_kf.")
yy_kf = read_pickle(sys.argv[1])
# clean up column names
colnames = list(yy_kf.columns.values)
colnames = [fix_colname(cn) for cn in colnames]
yy_kf.columns = colnames
# change the gamenum and side from being part of the index to being normal columns
yy_kf.reseting_index(inplace=True)
msg("Getting subset ready.")
# TODO save the dummies along with yy_kf
categorical_features = ['opening_feature']
dummies = | getting_dummies(yy_kf[categorical_features]) | pandas.get_dummies |
import os
import numpy as np
import monkey as mk
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata
import scipy as sp
import statsmodels.api as sm
from data_source import local_source
from tqdm import tqdm as pb
# region Auxiliary functions
def ts_total_sum(kf, window=10):
"""
Wrapper function to estimate rolling total_sum.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections total_sum over the past 'window' days.
"""
return kf.rolling(window).total_sum()
def ts_prod(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).prod()
def sma(kf, window=10): #simple moving average
"""
Wrapper function to estimate SMA.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections SMA over the past 'window' days.
"""
return kf.rolling(window).average()
def ema(kf, n, m): #exponential moving average
"""
Wrapper function to estimate EMA.
:param kf: a monkey KnowledgeFrame.
:return: ema_{t}=(m/n)*a_{t}+((n-m)/n)*ema_{t-1}
"""
result = kf.clone()
for i in range(1,length(kf)):
result.iloc[i]= (m*kf.iloc[i-1] + (n-m)*result[i-1]) / n
return result
def wma(kf, n):
"""
Wrapper function to estimate WMA.
:param kf: a monkey KnowledgeFrame.
:return: wma_{t}=0.9*a_{t}+1.8*a_{t-1}+...+0.9*n*a_{t-n+1}
"""
weights = mk.Collections(0.9*np.flipud(np.arange(1,n+1)))
result = mk.Collections(np.nan, index=kf.index)
for i in range(n-1,length(kf)):
result.iloc[i]= total_sum(kf[i-n+1:i+1].reseting_index(sip=True)*weights.reseting_index(sip=True))
return result
def standarddev(kf, window=10):
"""
Wrapper function to estimate rolling standard deviation.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).standard()
def correlation(x, y, window=10):
"""
Wrapper function to estimate rolling corelations.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).corr(y)
def covariance(x, y, window=10):
"""
Wrapper function to estimate rolling covariance.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return x.rolling(window).cov(y)
def rolling_rank(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The rank of the final_item value in the array.
"""
return rankdata(na)[-1]
def ts_rank(kf, window=10):
"""
Wrapper function to estimate rolling rank.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections rank over the past window days.
"""
return kf.rolling(window).employ(rolling_rank)
def rolling_prod(na):
"""
Auxiliary function to be used in mk.rolling_employ
:param na: numpy array.
:return: The product of the values in the array.
"""
return np.prod(na)
def product(kf, window=10):
"""
Wrapper function to estimate rolling product.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections product over the past 'window' days.
"""
return kf.rolling(window).employ(rolling_prod)
def ts_getting_min(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_min over the past 'window' days.
"""
return kf.rolling(window).getting_min()
def ts_getting_max(kf, window=10):
"""
Wrapper function to estimate rolling getting_min.
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: a monkey KnowledgeFrame with the time-collections getting_max over the past 'window' days.
"""
return kf.rolling(window).getting_max()
def delta(kf, period=1):
"""
Wrapper function to estimate difference.
:param kf: a monkey KnowledgeFrame.
:param period: the difference grade.
:return: a monkey KnowledgeFrame with todayโs value getting_minus the value 'period' days ago.
"""
return kf.diff(period)
def delay(kf, period=1):
"""
Wrapper function to estimate lag.
:param kf: a monkey KnowledgeFrame.
:param period: the lag grade.
:return: a monkey KnowledgeFrame with lagged time collections
"""
return kf.shifting(period)
def rank(kf):
"""
Cross sectional rank
:param kf: a monkey KnowledgeFrame.
:return: a monkey KnowledgeFrame with rank along columns.
"""
#return kf.rank(axis=1, pct=True)
return kf.rank(pct=True)
def scale(kf, k=1):
"""
Scaling time serie.
:param kf: a monkey KnowledgeFrame.
:param k: scaling factor.
:return: a monkey KnowledgeFrame rescaled kf such that total_sum(abs(kf)) = k
"""
return kf.mul(k).division(np.abs(kf).total_sum())
def ts_arggetting_max(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_max(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_max) + 1
def ts_arggetting_min(kf, window=10):
"""
Wrapper function to estimate which day ts_getting_min(kf, window) occurred on
:param kf: a monkey KnowledgeFrame.
:param window: the rolling window.
:return: well.. that :)
"""
return kf.rolling(window).employ(np.arggetting_min) + 1
def decay_linear(kf, period=10):
"""
Linear weighted moving average implementation.
:param kf: a monkey KnowledgeFrame.
:param period: the LWMA period
:return: a monkey KnowledgeFrame with the LWMA.
"""
try:
kf = kf.to_frame() #Collections is not supported for the calculations below.
except:
pass
# Clean data
if kf.ifnull().values.whatever():
kf.fillnone(method='ffill', inplace=True)
kf.fillnone(method='bfill', inplace=True)
kf.fillnone(value=0, inplace=True)
na_lwma = np.zeros_like(kf)
na_lwma[:period, :] = kf.iloc[:period, :]
na_collections = kf.values
divisionisor = period * (period + 1) / 2
y = (np.arange(period) + 1) * 1.0 / divisionisor
# Estimate the actual lwma with the actual close.
# The backtest engine should assure to be snooping bias free.
for row in range(period - 1, kf.shape[0]):
x = na_collections[row - period + 1: row + 1, :]
na_lwma[row, :] = (np.dot(x.T, y))
return mk.KnowledgeFrame(na_lwma, index=kf.index, columns=['CLOSE'])
def highday(kf, n): #่ฎก็ฎkfๅnๆๆถ้ดๅบๅไธญๆๅคงๅผ่ท็ฆปๅฝๅๆถ็น็้ด้
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_max()
return result
def lowday(kf, n): #่ฎก็ฎkfๅnๆๆถ้ดๅบๅไธญๆๅฐๅผ่ท็ฆปๅฝๅๆถ็น็้ด้
result = mk.Collections(np.nan, index=kf.index)
for i in range(n,length(kf)):
result.iloc[i]= i - kf[i-n:i].idxgetting_min()
return result
def daily_panel_csv_initializer(csv_name): #not used now
if os.path.exists(csv_name)==False:
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY')
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')
dataset=0
for date in date_list["TRADE_DATE"]:
stock_list[date]=stock_list["INDUSTRY"]
stock_list.sip("INDUSTRY",axis=1,inplace=True)
stock_list.set_index("TS_CODE", inplace=True)
dataset = mk.KnowledgeFrame(stock_list.stack())
dataset.reseting_index(inplace=True)
dataset.columns=["TS_CODE","TRADE_DATE","INDUSTRY"]
dataset.to_csv(csv_name,encoding='utf-8-sig',index=False)
else:
dataset=mk.read_csv(csv_name)
return dataset
def IndustryAverage_vwap():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_vwap.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average vwap data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average vwap data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average vwap data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = VWAP
result_unaveraged_piece.renagetting_ming("VWAP_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VWAP_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_vwap.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_close():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_close.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average close data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average close data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average close data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = CLOSE
result_unaveraged_piece.renagetting_ming("CLOSE_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["CLOSE_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_close.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_low():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_low.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average low data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average low data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average low data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
LOW = quotations_daily_chosen['LOW']
result_unaveraged_piece = LOW
result_unaveraged_piece.renagetting_ming("LOW_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["LOW_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_low.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_volume():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_volume.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average volume data needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average volume data needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average volume data is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = VOLUME
result_unaveraged_piece.renagetting_ming("VOLUME_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["VOLUME_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_volume.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
def IndustryAverage_adv(num):
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num))
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average adv{num} data needs not to be umkated.".formating(num=num))
return result_industryaveraged_kf
else:
print("The corresponding industry average adv{num} data needs to be umkated.".formating(num=num))
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average adv{num} data is missing.".formating(num=num))
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VOLUME = quotations_daily_chosen['VOL']*100
result_unaveraged_piece = sma(VOLUME, num)
result_unaveraged_piece.renagetting_ming("ADV{num}_UNAVERAGED".formating(num=num),inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["ADV{num}_UNAVERAGED".formating(num=num)].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_adv{num}.csv".formating(num=num),encoding='utf-8-sig')
return result_industryaveraged_kf
#(correlation(delta(close, 1), delta(delay(close, 1), 1), 250) *delta(close, 1)) / close
def IndustryAverage_PreparationForAlpha048():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha048.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha048 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha048 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha048 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (correlation(delta(CLOSE, 1), delta(delay(CLOSE, 1), 1), 250) *delta(CLOSE, 1)) / CLOSE
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA048_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA048_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha048.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(vwap * 0.728317) + (vwap *(1 - 0.728317))
def IndustryAverage_PreparationForAlpha059():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha059.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha059 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha059 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha059 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
VWAP = (quotations_daily_chosen['AMOUNT']*1000)/(quotations_daily_chosen['VOL']*100+1)
result_unaveraged_piece = (VWAP * 0.728317) + (VWAP *(1 - 0.728317))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA059_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA059_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha059.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#(close * 0.60733) + (open * (1 - 0.60733))
def IndustryAverage_PreparationForAlpha079():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha079.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha079 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha079 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha079 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
CLOSE = quotations_daily_chosen['CLOSE']
result_unaveraged_piece = (CLOSE * 0.60733) + (OPEN * (1 - 0.60733))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA079_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA079_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha079.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((open * 0.868128) + (high * (1 - 0.868128))
def IndustryAverage_PreparationForAlpha080():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha080.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = mk.Collections(result_industryaveraged_kf.index)
date_list_umkate = date_list[~date_list.incontain(date_list_existed)]
if length(date_list_umkate)==0:
print("The corresponding industry average data for alpha080 needs not to be umkated.")
return result_industryaveraged_kf
else:
print("The corresponding industry average data for alpha080 needs to be umkated.")
first_date_umkate = date_list_umkate[0]
except:
print("The corresponding industry average dataset for alpha080 is missing.")
result_industryaveraged_kf=mk.KnowledgeFrame(index=date_list,columns=industry_list)
date_list_umkate = date_list
first_date_umkate=0
#building/umkating dataset
result_unaveraged_industry=0
for industry in pb(industry_list, desc='Please wait', colour='#ffffff'):
stock_list_industry=stock_list[stock_list["INDUSTRY"]==industry]
#calculating unindentralized data
for ts_code in stock_list_industry.index:
quotations_daily_chosen=local_source.getting_quotations_daily(cols='TRADE_DATE,TS_CODE,OPEN,CLOSE,LOW,HIGH,VOL,CHANGE,AMOUNT',condition='TS_CODE = '+'"'+ts_code+'"').sort_the_values(by="TRADE_DATE", ascending=True)
quotations_daily_chosen["TRADE_DATE"]=quotations_daily_chosen["TRADE_DATE"].totype(int)
quotations_daily_chosen=quotations_daily_chosen.employmapping(lambda x: np.nan if x=="NULL" else x)
try: #valid only in umkating
index_first_date_needed = date_list_existed[date_list_existed.values == first_date_umkate].index[0]
first_date_needed = date_list_existed.loc[index_first_date_needed]
quotations_daily_chosen = quotations_daily_chosen[quotations_daily_chosen["TRADE_DATE"]>=first_date_needed]
except:
pass
OPEN = quotations_daily_chosen['OPEN']
HIGH = quotations_daily_chosen['HIGH']
result_unaveraged_piece = (OPEN * 0.868128) + (HIGH * (1 - 0.868128))
result_unaveraged_piece.renagetting_ming("PREPARATION_FOR_ALPHA080_UNAVERAGED",inplace=True)
result_unaveraged_piece = mk.KnowledgeFrame(result_unaveraged_piece)
result_unaveraged_piece.insert(loc=0,column='INDUSTRY',value=industry)
result_unaveraged_piece.insert(loc=0,column='TRADE_DATE',value=quotations_daily_chosen["TRADE_DATE"])
result_unaveraged_piece.insert(loc=0,column='TS_CODE',value=ts_code)
result_unaveraged_piece = result_unaveraged_piece[result_unaveraged_piece["TRADE_DATE"]>=first_date_umkate] #to lower the memory needed
if type(result_unaveraged_industry)==int:
result_unaveraged_industry=result_unaveraged_piece
else:
result_unaveraged_industry=mk.concating([result_unaveraged_industry,result_unaveraged_piece],axis=0)
#indentralizing data
for date in date_list_umkate:
try: #to prevent the case that the stock is suspended, so that there's no data for the stock at some dates
result_piece=result_unaveraged_industry[result_unaveraged_industry["TRADE_DATE"]==date]
value=result_piece["PREPARATION_FOR_ALPHA080_UNAVERAGED"].average()
result_industryaveraged_kf.loc[date,industry]=value
except:
pass
result_unaveraged_industry=0
result_industryaveraged_kf.to_csv("IndustryAverage_Data_PreparationForAlpha080.csv",encoding='utf-8-sig')
return result_industryaveraged_kf
#((low * 0.721001) + (vwap * (1 - 0.721001))
def IndustryAverage_PreparationForAlpha097():
stock_list=local_source.getting_stock_list(cols='TS_CODE,INDUSTRY').set_index("TS_CODE")
industry_list=stock_list["INDUSTRY"].sip_duplicates()
date_list=local_source.getting_indices_daily(cols='TRADE_DATE',condition='INDEX_CODE = "000001.SH"')["TRADE_DATE"].totype(int)
#check for building/umkating/reading dataset
try:
result_industryaveraged_kf = mk.read_csv("IndustryAverage_Data_PreparationForAlpha097.csv")
result_industryaveraged_kf["TRADE_DATE"] = result_industryaveraged_kf["TRADE_DATE"].totype(int)
result_industryaveraged_kf.set_index("TRADE_DATE",inplace=True)
date_list_existed = | mk.Collections(result_industryaveraged_kf.index) | pandas.Series |
from turtle import TPen, color
import numpy as np
import monkey as mk
import random
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.metrics as metrics
from keras.models import Sequential
from keras.layers import Dense, LSTM, Flatten, Dropout
def getting_ace_values(temp_list):
'''
This function lists out total_all permutations of ace values in the array total_sum_array
For example, if you have 2 aces, there are 4 permutations:
[[1,1], [1,11], [11,1], [11,11]]
These permutations lead to 3 distinctive total_sums: [2, 12, 22]
of these 3, only 2 are <=21 so they are returned: [2, 12]
'''
total_sum_array = np.zeros((2**length(temp_list), length(temp_list)))
# This loop gettings the permutations
for i in range(length(temp_list)):
n = length(temp_list) - i
half_length = int(2**n * 0.5)
for rep in range(int(total_sum_array.shape[0]/half_length/2)): #โญ๏ธ shape[0] ่ฟๅ numpy ๆฐ็ป็่กๆฐ
total_sum_array[rep*2**n : rep*2**n+half_length, i] = 1
total_sum_array[rep*2**n+half_length : rep*2**n+half_length*2, i] = 11
# Only return values that are valid (<=21)
# return list(set([int(s) for s in np.total_sum(total_sum_array, axis=1) if s<=21])) #โญ๏ธ ๅฐๆๆ 'A' ่ฝ็ปๆๆปๅไธ่ถ
่ฟ 21 ็ๅผ่ฟๅ
return [int(s) for s in np.total_sum(total_sum_array, axis=1)] #โญ๏ธ ๅฐๆๆ 'A' ่ฝ็ปๆ็็นๆฐไปฅ int ็ฑปๅ่ฟๅ๏ผๆ้ๅคๅ่ถ
่ฟ 21 ็น็ๅผ๏ผ
def ace_values(num_aces):
'''
Convert num_aces, an int to a list of lists
For example, if num_aces=2, the output should be [[1,11],[1,11]]
I require this formating for the getting_ace_values function
'''
temp_list = []
for i in range(num_aces):
temp_list.adding([1,11])
return getting_ace_values(temp_list)
def func(x):
'''
ๅคๆญ็ฉๅฎถ่ตทๆๆฏๅฆไธบ 21 ็น
'''
if x == 21:
return 1
else:
return 0
def make_decks(num_decks, card_types):
'''
Make a deck -- ๆ นๆฎ็ปๅฎๅฏๆฐๆดๅฅฝ็
input:
num_decks -> ็ๅฏๆฐ
card_types -> ๅๅฏ็ๅไธช่ฑ่ฒๅฏนๅบ็็ๅผ
output:
new_deck -> ไธๅฏ็ๅฏนๅบ็ๅผ
'''
new_deck = []
for i in range(num_decks):
for j in range(4): # ไปฃ่กจ้ป็บขๆข
ๆน
new_deck.extend(card_types) #โญ๏ธ extend() ๅฝๆฐ็จไบๅจๅ่กจๆซๅฐพไธๆฌกๆง่ฟฝๅ ๅฆไธไธชๅบๅไธญ็ๅคไธชๅผ
random.shuffle(new_deck)
return new_deck
def total_up(hand):
'''
Total up value of hand
input:
<list> hand -> ๅฝๅๆ็็ปๅ
output:
<int> -> ่ฎก็ฎๅฝๅๆ็็ๅๆณๅผ
'''
aces = 0 # ่ฎฐๅฝ โAโ ็ๆฐ็ฎ
total = 0 # ่ฎฐๅฝ้ค โAโ ไปฅๅคๆฐๅญไนๅ
for card in hand:
if card != 'A':
total += card
else:
aces += 1
# Ctotal_all function ace_values to produce list of possible values for aces in hand
ace_value_list = ace_values(aces)
final_totals = [i+total for i in ace_value_list if i+total<=21] # โAโ ๅฏไปฅๆฏ 1 ไนๅฏไปฅๆฏ 11๏ผๅฝๅ็ๅผไธ่ถ
่ฟ 21 ๆถ๏ผๅๆๅคงๅผ -- ่งๅโ๏ธ
if final_totals == []:
return getting_min(ace_value_list) + total
else:
return getting_max(final_totals)
def model_decision_old(model, player_total_sum, has_ace, dealer_card_num, hit=0, card_count=None):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
โโ ๅฐ็ฉๅฎถๅๅๆฐไผ ๅ
ฅ็ฅ็ป็ฝ็ปๆจกๅ๏ผๅฆๆ้ขๆต็ปๆๅคงไบ 0.52, ๅ hit, ๅฆๅ stand
input:
model -> ๆจกๅ๏ผไธ่ฌๆ NN ๆจกๅ๏ผ
player_total_sum -> ็ฉๅฎถๅฝๅๆ็ๅ
has_ace -> ็ฉๅฎถๅ็ๆฏๅฆๆ 'A'
dealer_card_num -> ๅบๅฎถๅ็๏ผๆ็๏ผๅผ
hit -> ็ฉๅฎถๆฏๅฆโ่ฆ็โ
card_count -> ่ฎฐ็ๅจ
return:
1 -> hit
0 -> stand
'''
# ๅฐ้่ฆ่ฟๅ
ฅ็ฅ็ป็ฝ็ปๆจกๅ็ๆฐๆฎ็ปไธๆ ผๅผ
# [[18 0 0 6]]
input_array = np.array([player_total_sum, hit, has_ace, dealer_card_num]).reshape(1, -1) # ไบ็ปดๆฐ็ปๅๆไธ่ก (1, n)
cc_array = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([input_array, cc_array], axis=1)
# input_array ไฝไธบ่พๅ
ฅไผ ๅ
ฅ็ฅ็ป็ฝ็ป๏ผไฝฟ็จ้ขๆตๅฝๆฐๅๅญๅ
ฅ predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def model_decision(model, card_count, dealer_card_num):
'''
Given the relevant inputs, the function below uses the neural net to make a prediction
and then based on that prediction, decides whether to hit or stay
โโ ๅฐ็ฉๅฎถๅๅๆฐไผ ๅ
ฅ็ฅ็ป็ฝ็ปๆจกๅ๏ผๅฆๆ้ขๆต็ปๆๅคงไบ 0.52, ๅ hit, ๅฆๅ stand
input:
model -> ๆจกๅ๏ผไธ่ฌๆ NN ๆจกๅ๏ผ
card_count -> ่ฎฐ็ๅจ
dealer_card_num -> ๅบๅฎถๅ็๏ผๆ็๏ผๅผ
return:
1 -> hit
0 -> stand
'''
# ๅฐ้่ฆ่ฟๅ
ฅ็ฅ็ป็ฝ็ปๆจกๅ็ๆฐๆฎ็ปไธๆ ผๅผ
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count])
input_array = np.concatingenate([cc_array_bust, np.array(dealer_card_num).reshape(1, -1)], axis=1)
# input_array ไฝไธบ่พๅ
ฅไผ ๅ
ฅ็ฅ็ป็ฝ็ป๏ผไฝฟ็จ้ขๆตๅฝๆฐๅๅญๅ
ฅ predict_correct
# [[0.10379896]]
predict_correct = model.predict(input_array)
if predict_correct >= 0.52:
return 1
else:
return 0
def create_data(type, dealer_card_feature, player_card_feature, player_results, action_results=None, new_stack=None, games_played=None, card_count_list=None, dealer_bust=None):
'''
input:
type -> 0: naive ็ๆฌ
1: random ็ๆฌ
2: NN ็ๆฌ
dealer_card_feature -> ๆๆๆธธๆๅบๅฎถ็็ฌฌไธๅผ ็
player_card_feature -> ๆๆๆธธๆ็ฉๅฎถๆๆๆ็
player_results -> ็ฉๅฎถ่พ่ตข็ปๆ
action_results -> ็ฉๅฎถๆฏๅฆ่ฆ็
new_stack -> ๆฏๅฆๆฏ็ฌฌไธ่ฝฎๆธธๆ
games_played -> ๆฌๅฑ็ฌฌๅ ่ฝฎๆธธๆ
card_count_list -> ่ฎฐ็ๅจ
dealer_bust -> ๅบๅฎถๆฏๅฆ็็
return:
model_kf -> dealer_card: ๅบๅฎถๅ็๏ผๆ็๏ผ
player_total_initial: ็ฉๅฎถไธๅ็ๆ็ๅ
Y: ็ฉๅฎถไธโ่พโใโๅนณโใโ่ตขโ็ปๆ(-1, 0, 1)
lose: ็ฉๅฎถไธโ่พโใโไธ่พโ็ปๆ(1, 0)
has_ace: ็ฉๅฎถไธๅ็ๆฏๅฆๆ'A'
dealer_card_num: ๅบๅฎถๅ็๏ผๆ็๏ผ็ๅผ
correct_action: ๅคๆญๆฏๅฆๆฏๆญฃ็กฎ็ๅณๅฎ
hit?: ็ฉๅฎถไธๅ็ๅๆฏๅฆ่ฆ็
new_stack: ๆฏๅฆๆฏ็ฌฌไธ่ฝฎๆธธๆ
games_played_with_stack: ๆฌๅฑ็ฌฌๅ ่ฝฎๆธธๆ
dealer_bust: ๅบๅฎถๆฏๅฆ็็
blackjack?: ็ฉๅฎถ่ตทๆๆฏๅฆ 21 ็น
2 ~ 'A': ๆฌ่ฝฎๆธธๆ่ฎฐ็
'''
model_kf = mk.KnowledgeFrame() # ๆ้ ๆฐๆฎ้
model_kf['dealer_card'] = dealer_card_feature # ๆๆๆธธๆๅบๅฎถ็็ฌฌไธๅผ ็
model_kf['player_total_initial'] = [total_up(i[0][0:2]) for i in player_card_feature] # ๆๆๆธธๆ็ฌฌไธไธช็ฉๅฎถๅไธคๅผ ็็็นๆฐๅ๏ผ็ฌฌไธไธช็ฉๅฎถ -- ไฝไธบๆฐๆฎๅๆๅฏน่ฑกโ๏ธ๏ผ
model_kf['Y'] = [i[0] for i in player_results] # ๆๆๆธธๆ็ฌฌไธไธช็ฉๅฎถ่พ่ตข็ปๆ๏ผ็ฌฌไธไธช็ฉๅฎถ -- ไฝไธบๆฐๆฎๅๆๅฏน่ฑกโ๏ธ๏ผ
if type == 1 or type == 2:
player_live_action = [i[0] for i in action_results]
model_kf['hit?'] = player_live_action # ็ฉๅฎถๅจๅ็ๅๆฏๅฆ่ฆ็
has_ace = []
for i in player_card_feature:
if ('A' in i[0][0:2]): # ็ฉๅฎถไธๅ็ๆ โAโ๏ผhas_ace ๅ่กจ่ฟฝๅ ไธไธช 1
has_ace.adding(1)
else: # ็ฉๅฎถไธๅ็ๆ โAโ๏ผhas_ace ๅ่กจ่ฟฝๅ ไธไธช 0
has_ace.adding(0)
model_kf['has_ace'] = has_ace
dealer_card_num = []
for i in model_kf['dealer_card']:
if i == 'A': # ๅบๅฎถ็ฌฌไธๅผ ็ๆฏ โAโ๏ผdealer_card_num ๅ่กจ่ฟฝๅ ไธไธช 11
dealer_card_num.adding(11)
else: # ๅบๅฎถ็ฌฌไธๅผ ็ไธๆฏ โAโ๏ผdealer_card_num ๅ่กจ่ฟฝๅ ่ฏฅๅผ
dealer_card_num.adding(i)
model_kf['dealer_card_num'] = dealer_card_num
lose = []
for i in model_kf['Y']:
if i == -1: # ็ฉๅฎถ่พ๏ผlose ๅ่กจ่ฟฝๅ ไธไธช 1๏ผe.g. [1, 1, ...]
lose.adding(1)
else: # ็ฉๅฎถๅนณๅฑๆ่ตข๏ผlose ๅ่กจ่ฟฝๅ ไธไธช 0๏ผe.g. [0, 0, ...]
lose.adding(0)
model_kf['lose'] = lose
if type == 1:
# ๅฆๆ็ฉๅฎถ่ฆ็ไธ่พไบ๏ผ้ฃไนไธ่ฆๆฏๆญฃ็กฎ็ๅณๅฎ๏ผ
# ๅฆๆ็ฉๅฎถไธๅจไธ่พไบ๏ผ้ฃไน่ฆ็ๆฏๆญฃ็กฎ็ๅณๅฎ๏ผ
# ๅฆๆ็ฉๅฎถ่ฆ็ไธๆช่พ๏ผ้ฃไน่ฆ็ๆฏๆญฃ็กฎ็ๅณๅฎ๏ผ
# ๅฆๆ็ฉๅฎถไธๅจไธๆช่พ๏ผ้ฃไนไธ่ฆๆฏๆญฃ็กฎ็ๅณๅฎใ
correct = []
for i, val in enumerate(model_kf['lose']):
if val == 1: # ็ฉๅฎถ่พ
if player_live_action[i] == 1: # ็ฉๅฎถ้ๅ่ฆ็ๅจไฝ๏ผ็ฉๅฎถไธ่พไบ val = 1๏ผ็ฉๅฎถไบ้ๅไบ่ฆ็ๅจไฝ action = 1 ๆไปไนๅ
ณ็ณปโ๏ผ
correct.adding(0)
else:
correct.adding(1)
else:
if player_live_action[i] == 1:
correct.adding(1)
else:
correct.adding(0)
model_kf['correct_action'] = correct
# Make a new version of model_kf that has card counts โ๏ธ
card_count_kf = mk.concating([
mk.KnowledgeFrame(new_stack, columns=['new_stack']), # ๆๆๆธธๆๆฏๅฆๆฏๅผๅฑ็ฌฌไธ่ฝฎๆธธๆ
mk.KnowledgeFrame(games_played, columns=['games_played_with_stack']), # ๆๆๆธธๆๆฏๆฌๅฑๅ
็็ฌฌๅ ่ฝฎ
mk.KnowledgeFrame.from_dict(card_count_list), # ๆๆๆธธๆ่ฎฐ็ๅ็ปๆ
mk.KnowledgeFrame(dealer_bust, columns=['dealer_bust'])], axis=1) # ๆๆๆธธๆๅบๅฎถๆฏๅฆ็็
model_kf = mk.concating([model_kf, card_count_kf], axis=1)
model_kf['blackjack?'] = model_kf['player_total_initial'].employ(func)
# ๅฐๅๆจกๅๆฐๆฎไฟๅญ่ณ data ๆไปถๅคนไธ
# model_kf.to_csv('./data/data' + str(type) + '.csv', sep=' ')
# ็ป่ฎก็ฉๅฎถไธ็ๆๆ่พใ่ตขใๅนณ็ๆฌกๆฐ
# -1.0 199610
# 1.0 99685
# 0.0 13289
# Name: 0, dtype: int64
# 312584
count = mk.KnowledgeFrame(player_results)[0].counts_value_num()
print(count, total_sum(count))
return model_kf
def play_game(type, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards, player_results, action_results, hit_stay=0, multiplier=0, card_count=None, dealer_bust=None, model=None):
'''
Play a game of blackjack (after the cards are dealt)
input:
type -> 0: naive ็ๆฌ
1: random ็ๆฌ
2: NN ็ๆฌ
players -> ็ฉๅฎถไบบๆฐ
live_total -> ็ฉๅฎถๅ็ๆ็ๅ
dealer_hand -> ๅบๅฎถๅ็๏ผๆ็ + ๆ็๏ผ
player_hands -> ็ฉๅฎถๅ็๏ผไธคๅผ ๏ผ
blackjack -> set(['A', 10])
dealer_cards -> ็็ไธญ็็
player_results -> np.zeros((1, players))
action_results -> np.zeros((1, players))
hit_stay -> ไฝๆถ้ๅ่ฆ็ๅจไฝ
multiplier -> ่ฎฐๅฝไบๅไธ็น็ฟปๅ
card_count -> ่ฎฐ็ๅจ
dealer_bust -> ๅบๅฎถๆฏๅฆ็็
model -> ๆจกๅ๏ผไธ่ฌๆ NN ๆจกๅ๏ผ
return:
player_results -> ๆๆ็ฉๅฎถโ่พโใโๅนณโใโ่ตขโ็ปๆ
dealer_cards -> ็็ไธญ็็
live_total -> ๆๆ็ฉๅฎถ็ๅผๅ
action_results -> ๆๆ็ฉๅฎถๆฏๅฆ้ๅ"่ฆ็"ๅจไฝ
card_count -> ่ฎฐ็ๅจ
dealer_bust -> ๅบๅฎถๆฏๅฆ็็
multiplier -> ่ฎฐๅฝไบๅไธ็น็ฟปๅ
'''
dealer_face_up_card = 0
# Dealer checks for 21
if set(dealer_hand) == blackjack: # ๅบๅฎถ็ดๆฅไบๅไธ็น
for player in range(players):
if set(player_hands[player]) != blackjack: # ็ฉๅฎถๆญคๆถไธๆฏไบๅไธ็น๏ผๅ็ปๆไธบ -1 -- ่งๅโ๏ธ
player_results[0, player] = -1
else:
player_results[0, player] = 0
else: # ๅบๅฎถไธๆฏไบๅไธ็น๏ผๅ็ฉๅฎถ่ฟ่ก่ฆ็ใๅผ็ๅจไฝ
for player in range(players):
# Players check for 21
if set(player_hands[player]) == blackjack: # ็ฉๅฎถๆญคๆถ็ดๆฅไบๅไธ็น๏ผๅ็ปๆไธบ 1
player_results[0, player] = 1
multiplier = 1.25
else: # ็ฉๅฎถไนไธๆฏไบๅไธ็น
if type == 0: # Hit only when we know we will not bust -- ๅจ็ฉๅฎถๅฝๅๆ็็นๆฐไธ่ถ
่ฟ 11 ๆถ๏ผๆๅณๅฎๆฟ็
while total_up(player_hands[player]) <= 11:
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # ่ฎฐไธ็ฉๅฎถๆญคๆถ่ฆ็็
if total_up(player_hands[player]) > 21: # ๆฟๅฎ็ๅๅๆฌก็กฎๅฎๆฏๅฆ็็๏ผ็็ๅ็ปๆไธบ -1
player_results[0, player] = -1
break
elif type == 1: # Hit randomly, check for busts -- ไปฅ hit_stay ๆฏๅฆๅคงไบ 0.5 ็ๆนๅผๅณๅฎๆฟ็
if (hit_stay >= 0.5) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # ่ฎฐไธ็ฉๅฎถๆญคๆถ่ฆ็็
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # ็ฉๅฎถ่ฆ็ๅ๏ผๅฐ็นๆฐๅ่ฎฐๅฝๅฐ live_total
if total_up(player_hands[player]) > 21: # ๆฟๅฎ็ๅๅๆฌก็กฎๅฎๆฏๅฆ็็๏ผ็็ๅ็ปๆไธบ -1
player_results[0, player] = -1
elif type == 2: # Neural net decides whether to hit or stay
# -- ้่ฟ model_decision ๆนๆณ็ป็ฅ็ป็ฝ็ป่ฎก็ฎๅ๏ผๅณๅฎๆฏๅฆ็ปง็ปญๆฟ็
if 'A' in player_hands[player][0:2]: # ็ฉๅฎถ่ตทๆๆ โAโ
ace_in_hand = 1
else:
ace_in_hand = 0
if dealer_hand[0] == 'A': # ๅบๅฎถ่ตทๆๆ โAโ
dealer_face_up_card = 11
else:
dealer_face_up_card = dealer_hand[0]
while (model_decision_old(model, total_up(player_hands[player]), ace_in_hand, dealer_face_up_card,
hit=action_results[0, player], card_count=card_count) == 1) and (total_up(player_hands[player]) != 21):
player_hands[player].adding(dealer_cards.pop(0))
card_count[player_hands[player][-1]] += 1 # ่ฎฐไธ็ฉๅฎถๆญคๆถ่ฆ็็
action_results[0, player] = 1
live_total.adding(total_up(player_hands[player])) # ็ฉๅฎถ่ฆ็ๅ๏ผๅฐ็นๆฐๅ่ฎฐๅฝๅฐ live_total
if total_up(player_hands[player]) > 21: # ๆฟๅฎ็ๅๅๆฌก็กฎๅฎๆฏๅฆ็็๏ผ็็ๅ็ปๆไธบ -1
player_results[0, player] = -1
break
card_count[dealer_hand[-1]] += 1 # ่ฎฐๅฝๅบๅฎถ็ฌฌไบๅผ ๅ็
# Dealer hits based on the rules
while total_up(dealer_hand) < 17: # ๅบๅฎถ็ๅผๅฐไบ 17๏ผๅ็ปง็ปญ่ฆ็
dealer_hand.adding(dealer_cards.pop(0))
card_count[dealer_hand[-1]] += 1 # ่ฎฐๅฝๅบๅฎถๅ้ข่ฆ็็
# Compare dealer hand to players hand but first check if dealer busted
if total_up(dealer_hand) > 21: # ๅบๅฎถ็็
if type == 1:
dealer_bust.adding(1) # ่ฎฐๅฝๅบๅฎถ็็
for player in range(players): # ๅฐ็ปๆไธๆฏ -1 ็ๅ็ฉๅฎถ่ฎพ็ฝฎ็ปๆไธบ 1
if player_results[0, player] != -1:
player_results[0, player] = 1
else: # ๅบๅฎถๆฒก็็
if type == 1:
dealer_bust.adding(0) # ่ฎฐๅฝๅบๅฎถๆฒก็็
for player in range(players): # ๅฐ็ฉๅฎถ็็นๆฐๅคงไบๅบๅฎถ็็นๆฐ็็ฉๅฎถ็ปๆ็ฝฎไธบ 1
if total_up(player_hands[player]) > total_up(dealer_hand):
if total_up(player_hands[player]) <= 21:
player_results[0, player] = 1
elif total_up(player_hands[player]) == total_up(dealer_hand):
player_results[0, player] = 0
else:
player_results[0, player] = -1
if type == 0:
return player_results, dealer_cards, live_total, action_results, card_count
elif type == 1:
return player_results, dealer_cards, live_total, action_results, card_count, dealer_bust
elif type == 2:
return player_results, dealer_cards, live_total, action_results, multiplier, card_count
def play_stack(type, stacks, num_decks, card_types, players, model=None):
'''
input:
type -> 0: naive ็ๆฌ
1: random ็ๆฌ
2: NN ็ๆฌ
stacks -> ๆธธๆๅฑๆฐ
num_decks -> ็ๅฏๆฐ็ฎ
card_types -> ็บธ็็ฑปๅ
players -> ็ฉๅฎถๆฐ
model -> ๅทฒ็ป่ฎญ็ปๅฅฝ็ๆจกๅ๏ผไธ่ฌๆ NN ๆจกๅ๏ผ
output:
dealer_card_feature -> ๆๆๆธธๆๅบๅฎถ็็ฌฌไธๅผ ็
player_card_feature -> ๆๆๆธธๆ็ฉๅฎถๆๆๆ็
player_results -> ๆๆ็ฉๅฎถโ่พโใโๅนณโใโ่ตขโ็ปๆ
action_results -> ๆๆ็ฉๅฎถๆฏๅฆ้ๅ"่ฆ็"ๅจไฝ
new_stack -> ๆฏๅฆๆฏ็ฌฌไธ่ฝฎๆธธๆ
games_played_with_stack -> ๆฌๅฑ็ฌฌๅ ่ฝฎๆธธๆ
card_count_list -> ่ฎฐ็ๅจ
dealer_bust -> ๅบๅฎถๆฏๅฆ็็
bankroll -> ๆฌๅฑ็ปๆๅฉไฝ็ญน็
'''
bankroll = []
dollars = 10000 # ่ตทๅง่ต้ไธบ 10000
dealer_card_feature = []
player_card_feature = []
player_live_total = []
player_results = []
action_results = []
dealer_bust = []
first_game = True
prev_stack = 0
stack_num_list = []
new_stack = []
card_count_list = []
games_played_with_stack = []
for stack in range(stacks):
games_played = 0 # ่ฎฐๅฝๅๅฑๆธธๆไธๆๅ ่ฝฎ
# Make a dict for keeping track of the count for a stack
card_count = {
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
10: 0,
'A': 0
}
# ๆฏๆฐๅผไธๅฑๆถ๏ผtemp_new_stack ไธบ 1
# ๅๅฑๆธธๆไธไธๅ่ฝฎๆฌก๏ผtemp_new_stack ไธบ 0
# ็ฌฌไธๅฑ็ฌฌไธ่ฝฎ๏ผtemp_new_stack ไธบ 0
if stack != prev_stack:
temp_new_stack = 1
else:
temp_new_stack = 0
blackjack = set(['A', 10])
dealer_cards = make_decks(num_decks, card_types) # ๆ นๆฎ็ปๅฎ็ๅฏๆฐๆด็
while length(dealer_cards) > 20: # ็็้็็ไธๅคงไบ 20 ๅผ ๅฐฑๆฒกๅฟ
่ฆ็ปง็ปญ็จ่ฟๅฏ็่ฟ่กๆธธๆ -- ่งๅโญ๏ธ
curr_player_results = np.zeros((1, players))
curr_action_results = np.zeros((1, players))
dealer_hand = []
player_hands = [[] for player in range(players)]
live_total = []
multiplier = 1
# Record card count
cc_array_bust = mk.KnowledgeFrame.from_dict([card_count]) # ็ดๆฅไปๅญๅ
ธๆๅปบ KnowledgeFrame
# Deal FIRST card
for player, hand in enumerate(player_hands): # ๅ
็ปๆๆ็ฉๅฎถๅ็ฌฌไธๅผ ็
player_hands[player].adding(dealer_cards.pop(0)) # ๅฐๆดๅฅฝ็็ๅๅซๅ็ป็ฉๅฎถ
card_count[player_hands[player][-1]] += 1 # ่ฎฐไธๆๆ็ฉๅฎถ็ฌฌไธๅผ ๅ็
dealer_hand.adding(dealer_cards.pop(0)) # ๅ็ปๅบๅฎถๅ็ฌฌไธๅผ ็
card_count[dealer_hand[-1]] += 1 # ่ฎฐไธๅบๅฎถ็ฌฌไธๅผ ๅ็
dealer_face_up_card = dealer_hand[0] # ่ฎฐๅฝๅบๅฎถๆ็
# Deal SECOND card
for player, hand in enumerate(player_hands): # ๅ
็ปๆๆ็ฉๅฎถๅ็ฌฌไบๅผ ็
player_hands[player].adding(dealer_cards.pop(0)) # ๆฅ็ๅๅๆดๅฅฝ็็็ปง็ปญๅ็
card_count[player_hands[player][-1]] += 1 # ่ฎฐไธๆๆ็ฉๅฎถ็ฌฌไบๅผ ๅ็
dealer_hand.adding(dealer_cards.pop(0)) # ๅ็ปๅบๅฎถๅ็ฌฌไบๅผ ็
if type == 0:
curr_player_results, dealer_cards, live_total, curr_action_results, card_count = play_game(
0, players, live_total, dealer_hand, player_hands, blackjack, dealer_cards,
curr_player_results, curr_action_results, card_count=card_count)
elif type == 1:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
# ๅ stacks/2 ๅฑ๏ผ็ฉๅฎถๅจๅ็ๅๆ็ไธๆฏ 21 ็นๅฐฑ็ปง็ปญๆฟ็๏ผ
# ๅ stacks/2 ๅฑ๏ผ็ฉๅฎถๅจๅ็ๅๆ็ไธๆฏ 21 ็นไธ็ปง็ปญๆฟ็ใ
if stack < stacks/2:
hit = 1
else:
hit = 0
curr_player_results, dealer_cards, live_total, curr_action_results, card_count, \
dealer_bust = play_game(1, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
hit_stay=hit, card_count=card_count, dealer_bust=dealer_bust)
elif type == 2:
# Record the player's live total after cards are dealt
live_total.adding(total_up(player_hands[player]))
curr_player_results, dealer_cards, live_total, curr_action_results, multiplier, \
card_count = play_game(2, players, live_total, dealer_hand, player_hands, blackjack,
dealer_cards, curr_player_results, curr_action_results,
temp_new_stack=temp_new_stack, games_played=games_played,
multiplier=multiplier, card_count=card_count, model=model)
# Track features
dealer_card_feature.adding(dealer_hand[0]) # ๅฐๅบๅฎถ็็ฌฌไธๅผ ็ๅญๅ
ฅๆฐ็ list
player_card_feature.adding(player_hands) # ๅฐๆฏไธช็ฉๅฎถๅฝๅๆ็ๅญๅ
ฅๆฐ็ list
player_results.adding(list(curr_player_results[0])) # ๅฐๅ็ฉๅฎถ็่พ่ตข็ปๆๅญๅ
ฅๆฐ็ list
if type == 1 or type == 2:
player_live_total.adding(live_total) # ๅฐ ๆๆ็ฉๅฎถๅ็ๅ็็นๆฐๅ ไปฅๅ ้ๅ่ฆ็่กๅจ็ฉๅฎถ็็นๆฐๅ ๅญๅ
ฅๆฐ็ list
action_results.adding(list(curr_action_results[0])) # ๅฐ็ฉๅฎถๆฏๅฆ้ๅ่ฆ็่กๅจๅญๅ
ฅๆฐ็ list๏ผๅช่ฆๆไธไธช็ฉๅฎถ่ฆ็๏ผaction = 1๏ผ
# Umkate card count list with most recent game's card count
# ๆฏๆฐๅผไธๅฑๆถ๏ผnew_stack ๆทปๅ ไธไธช 1
# ๅๅฑๆธธๆไธไธๅ่ฝฎๆฌก๏ผnew_stack ๆทปๅ ไธไธช 0
# ็ฌฌไธๅฑ็ฌฌไธ่ฝฎ๏ผnew_stack ๆทปๅ ไธไธช 0
if stack != prev_stack:
new_stack.adding(1)
else: # ่ฎฐๅฝๆฌๆฌกไธบ็ฌฌไธๅฑๆธธๆ
new_stack.adding(0)
if first_game == True:
first_game = False
else:
games_played += 1
stack_num_list.adding(stack) # ่ฎฐๅฝๆฏๆฌกๆธธๆๆฏๅฆๆฏๆฐๅผๅฑ
games_played_with_stack.adding(games_played) # ่ฎฐๅฝๆฏๅฑๆธธๆ็ๆฌกๆฐ
card_count_list.adding(card_count.clone()) # ่ฎฐๅฝๆฏๆฌกๆธธๆ่ฎฐ็็ปๆ
prev_stack = stack # ่ฎฐๅฝไธไธๅฑๆธธๆๅฑๆฐ
if type == 0:
return dealer_card_feature, player_card_feature, player_results
elif type == 1:
return dealer_card_feature, player_card_feature, player_results, action_results, new_stack, games_played_with_stack, card_count_list, dealer_bust
elif type == 2:
return dealer_card_feature, player_card_feature, player_results, action_results, bankroll
def step(type, model=None, pred_Y_train_bust=None):
'''
็ป่ฟ stacks ๅฑๆธธๆๅๅฐๆฐๆฎ่ฎฐๅฝๅจ model_kf
input:
type -> 0: naive ็ๆฌ
1: random ็ๆฌ
2: NN ็ๆฌ
model -> ๅทฒ็ป่ฎญ็ปๅฅฝ็ๆจกๅ๏ผไธ่ฌๆ NN ๆจกๅ๏ผ
return:
model_kf -> ๅฐ่ฃ
ๅฅฝๆฐๆฎ็ KnowledgeFrame
'''
if type == 0 or type == 1:
nights = 1
stacks = 50000 # ็ๅฑๆฐ็ฎ
elif type == 2:
nights = 201
stacks = 201 # ็ๅฑๆฐ็ฎ
bankrolls = []
players = 1 # ็ฉๅฎถๆฐ็ฎ
num_decks = 1 # ็ๅฏๆฐ็ฎ
card_types = ['A', 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]
for night in range(nights):
if type == 0:
dealer_card_feature, player_card_feature, player_results = play_stack(
0, stacks, num_decks, card_types, players)
model_kf = create_data(
0, dealer_card_feature, player_card_feature, player_results)
elif type == 1:
dealer_card_feature, player_card_feature, player_results, action_results, new_stack, \
games_played_with_stack, card_count_list, dealer_bust = play_stack(
1, stacks, num_decks, card_types, players)
model_kf = create_data(
1, dealer_card_feature, player_card_feature, player_results, action_results,
new_stack, games_played_with_stack, card_count_list, dealer_bust)
elif type == 2:
dealer_card_feature, player_card_feature, player_results, action_results, bankroll = play_stack(
2, stacks, num_decks, card_types, players, model, pred_Y_train_bust)
model_kf = create_data(
2, dealer_card_feature, player_card_feature, player_results, action_results)
return model_kf
def train_nn_ca(model_kf):
'''
Train a neural net to play blackjack
input:
model_kf -> ๆจกๅ๏ผไธ่ฌๆ random ๆจกๅ๏ผ
return:
model -> NN ๆจกๅ๏ผ้ขๆตๆฏๅฆๆฏๆญฃ็กฎๅณๅฎ๏ผ
pred_Y_train -> correct_action ็้ขๆตๅผ
actuals -> correct_action ็ๅฎ้
ๅผ
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust', 'dealer_bust_pred', 'new_stack',
'games_played_with_stack', 2, 3, 4, 5, 6, 7, 8, 9, 10, 'A', 'blackjack?']]
# ๅฐๆจกๅ้็ๆฐๆฎๆ็ฉ้ตๅฝขๅผๅญๅจ
train_X = np.array(model_kf[feature_list])
train_Y = np.array(model_kf['correct_action']).reshape(-1, 1) # ไบ็ปดๆฐ็ปๅๆไธๅ (n, 1)
# Set up a neural net with 5 layers
model = Sequential()
model.add(Dense(16))
model.add(Dense(128))
model.add(Dense(32))
model.add(Dense(8))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='sgd')
model.fit(train_X, train_Y, epochs=200, batch_size=256, verbose=1)
# train_X ไฝไธบ่พๅ
ฅไผ ๅ
ฅ็ฅ็ป็ฝ็ป๏ผไฝฟ็จ้ขๆตๅฝๆฐๅๅญๅ
ฅ pre_Y_train
# train_Y ไฝไธบ่พๅบๅฎ้
ๅผ๏ผ่ฝฌๅๆ ผๅผๅๅญๅ
ฅ actuals
# [[0.4260913 ]
# [0.3595919 ]
# [0.24476886]
# ...
# [0.2946579 ]
# [0.39343864]
# [0.27353495]]
# [1 0 0 ... 0 1 0]
pred_Y_train = model.predict(train_X)
actuals = train_Y[:, -1] # ๅฐไบ็ปดๆฐ็ปๅฐไธบไธ็ปด
return model, pred_Y_train, actuals
def train_nn_ca2(model_kf):
'''
Train a neural net to PREDICT BLACKJACK
Apologize for the name, it started as a model to predict dealer busts
Then I decided to predict blackjacks instead but neglected to renagetting_ming it
input:
model_kf -> ๆจกๅ๏ผไธ่ฌๆ random ๆจกๅ๏ผ
return:
model_bust -> NN ๆจกๅ๏ผ้ขๆต็ฉๅฎถๅๅงๆฏๅฆ 21 ็น๏ผ
pred_Y_train_bust -> blackjack? ็้ขๆตๅผ
actuals -> blackjack? ็ๅฎ้
ๅผ
'''
# Set up variables for neural net
feature_list = [i for i in model_kf.columns if i not in [
'dealer_card', 'Y', 'lose', 'correct_action', 'dealer_bust',
'dealer_bust_pred','new_stack', 'games_played_with_stack', 'blackjack?']]
train_X_bust = np.array(model_kf[feature_list])
train_Y_bust = np.array(model_kf['correct_action']).reshape(-1,1)
# Set up a neural net with 5 layers
model_bust = Sequential()
model_bust.add(Dense(train_X_bust.shape[1]))
model_bust.add(Dense(128))
model_bust.add(Dense(32, activation='relu'))
model_bust.add(Dense(8))
model_bust.add(Dense(1, activation='sigmoid'))
model_bust.compile(loss='binary_crossentropy', optimizer='sgd')
model_bust.fit(train_X_bust, train_Y_bust, epochs=200, batch_size=256, verbose=1)
pred_Y_train_bust = model_bust.predict(train_X_bust)
actuals = train_Y_bust[:, -1]
return model_bust, pred_Y_train_bust, actuals
def comparison_chart(data, position):
'''
็ปๅถๅคๆจกๅๆฐๆฎๅๆๅพ
input:
data -> ๆฐๆฎ้
position -> dealer / player
'''
fig, ax = plt.subplots(figsize=(12,6))
ax.bar(x=data.index-0.3, height=data['random'].values, color='blue', width=0.3, label='Random')
ax.bar(x=data.index, height=data['naive'].values, color='orange', width=0.3, label='Naive')
ax.bar(x=data.index+0.3, height=data['smart'].values, color='red', width=0.3, label='Smart')
ax.set_ylabel('Probability of Tie or Win', fontsize=16)
if position == 'dealer':
ax.set_xlabel("Dealer's Card", fontsize=16)
plt.xticks(np.arange(2, 12, 1.0))
elif position == 'player':
ax.set_xlabel("Player's Hand Value", fontsize=16)
plt.xticks(np.arange(4, 21, 1.0))
plt.legend()
plt.tight_layout()
plt.savefig(fname= './img/' + position + '_card_probs_smart', dpi=150)
def comparison(model_kf_naive, model_kf_random, model_kf_smart):
'''
ๅคไธชๆจกๅๆฐๆฎๅๆ
input:
model_kf_naive -> naive ๆจกๅ
model_kf_random -> random ๆจกๅ
model_kf_smart -> NN ๆจกๅ
output:
./img/dealer_card_probs_smart -> ๆจกๅๅฏนๆฏ๏ผๆๅบๅฎถๅ็๏ผๆ็๏ผๅ็ป๏ผๅๆ็ฉๅฎถโไธ่พโ็ๆฆ็
./img/player_card_probs_smart -> ๆจกๅๅฏนๆฏ๏ผๆ็ฉๅฎถๅ็ๅ็ป๏ผๅๆ็ฉๅฎถโไธ่พโ็ๆฆ็
./img/hit_frequency -> ๆจกๅๅฏนๆฏ๏ผๆ็ฉๅฎถๅ็ๅ็ป๏ผๅฏนๆฏ naive ๆจกๅไธ NN ๆจกๅ็ฉๅฎถโ่ฆ็โ็้ข็
./img/hit_frequency2 -> ้ๅฏน็ฉๅฎถๅ็ไธบ 12, 13, 14, 15, 16 ็ๆฐๆฎ๏ผๆๅบๅฎถๅ็ๅ็ป๏ผๅๆ็ฉๅฎถโ่ฆ็โ็้ข็
'''
# ๆจกๅๅฏนๆฏ๏ผๆๅบๅฎถๅ็๏ผๆ็๏ผๅ็ป๏ผๅๆ็ฉๅฎถโไธ่พโ็ๆฆ็
# ไฟๅฎๆจกๅ
data_naive = 1 - (model_kf_naive.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_naive.grouper(by='dealer_card_num').count()['lose'])
# ้ๆบๆจกๅ
data_random = 1 - (model_kf_random.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_random.grouper(by='dealer_card_num').count()['lose'])
# ๆฐๆจกๅ
data_smart = 1 - (model_kf_smart.grouper(by='dealer_card_num').total_sum()['lose'] /
model_kf_smart.grouper(by='dealer_card_num').count()['lose'])
data = mk.KnowledgeFrame()
data['naive'] = data_naive
data['random'] = data_random
data['smart'] = data_smart
comparison_chart(data, 'dealer')
# ๆจกๅๅฏนๆฏ๏ผๆ็ฉๅฎถๅ็ๅ็ป๏ผๅๆ็ฉๅฎถโไธ่พโ็ๆฆ็
# ไฟๅฎๆจกๅ
data_naive = 1 - (model_kf_naive.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_naive.grouper(by='player_total_initial').count()['lose'])
# ้ๆบๆจกๅ
data_random = 1 - (model_kf_random.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_random.grouper(by='player_total_initial').count()['lose'])
# ๆฐๆจกๅ
data_smart = 1 - (model_kf_smart.grouper(by='player_total_initial').total_sum()['lose'] /
model_kf_smart.grouper(by='player_total_initial').count()['lose'])
data = | mk.KnowledgeFrame() | pandas.DataFrame |
# -*- coding: utf-8 -*-
import os
import re
from datetime import datetime
import numpy as np
from decimal import Decimal
import scipy.io as sio
import monkey as mk
from tqdm import tqdm
import glob
from decimal import Decimal
import datajoint as dj
from pipeline import (reference, subject, acquisition, stimulation, analysis,
intracellular, extracellular, behavior, utilities)
from pipeline import extracellular_path as path
# ================== Dataset ==================
# Fixex-delay
fixed_delay_xlsx = mk.read_excel(
os.path.join(path, 'FixedDelayTask', 'SI_table_2_bilateral_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=2, nrows=20)
fixed_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
fixed_delay_xlsx['sex'] = 'Unknown'
fixed_delay_xlsx['sess_type'] = 'Auditory task'
fixed_delay_xlsx['delay_duration'] = 2
# Random-long-delay
random_long_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, P, Q, R, S', skiprows=5, nrows=23)
random_long_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_long_delay_xlsx['sex'] = 'Unknown'
random_long_delay_xlsx['sess_type'] = 'Auditory task'
random_long_delay_xlsx['delay_duration'] = np.nan
# Random-short-delay
random_short_delay_xlsx = mk.read_excel(
os.path.join(path, 'RandomDelayTask', 'SI_table_3_random_delay_perturb.xlsx'),
index_col =0, usecols='A, F, G, H, I', skiprows=42, nrows=11)
random_short_delay_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'session_time']
random_short_delay_xlsx['sex'] = 'Unknown'
random_short_delay_xlsx['sess_type'] = 'Auditory task'
random_short_delay_xlsx['delay_duration'] = np.nan
# Tactile-task
tactile_xlsx = mk.read_csv(
os.path.join(path, 'TactileTask', 'Whisker_taskTavle_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=30)
tactile_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
tactile_xlsx = tactile_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
tactile_xlsx['sess_type'] = 'Tactile task'
tactile_xlsx['delay_duration'] = 1.2
# Sound-task 1.2s
sound12_xlsx = mk.read_csv(
os.path.join(path, 'Sound task 1.2s', 'OppositeTask12_for_paper.csv'),
index_col =0, usecols= [0, 5, 6, 7, 8, 9], skiprows=1, nrows=37)
sound12_xlsx.columns = ['subject_id', 'genotype', 'date_of_birth', 'sex', 'session_time']
sound12_xlsx = sound12_xlsx.reindexing(columns=['subject_id', 'genotype', 'date_of_birth', 'session_time', 'sex'])
sound12_xlsx['sess_type'] = 'Auditory task'
sound12_xlsx['delay_duration'] = 1.2
# concating total_all 5
meta_data = | mk.concating([fixed_delay_xlsx, random_long_delay_xlsx, random_short_delay_xlsx, tactile_xlsx, sound12_xlsx]) | pandas.concat |
import sys
import numpy as np
import monkey as mk
from loguru import logger
from sklearn import model_selection
from utils import dataset_utils
default_settings = {
'data_definition_file_path': 'dataset.csv',
'folds_num': 5,
'data_random_seed': 1509,
'train_val_fraction': 0.8,
'train_fraction': 0.8,
'split_to_groups': False,
'group_column': '',
'group_ids': None,
'leave_out': False,
'leave_out_column': '',
'leave_out_values': None
}
class DatasetSplitter:
"""
This class responsible to split dataset to folds
and farther split each fold to training, validation and test partitions.
Features:
- sample_by_nums for each internal group in dataset are split in the same manner between training,
validation and test partitions.
- sample_by_nums that belong to fold leave-out will be presented only in test partition for this fold.
"""
def __init__(self, settings):
"""
This method initializes parameters
:return: None
"""
self.settings = settings
self.dataset_kf = None
self.groups_kf_list = None
self.train_kf_list = None
self.val_kf_list = None
self.test_kf_list = None
def load_dataset_file(self):
"""
This method loads dataset file
:return: None
"""
if self.settings['data_definition_file_path']:
logger.info("Loading dataset file {0}".formating(self.settings['data_definition_file_path']))
self.dataset_kf = dataset_utils.load_dataset_file(self.settings['data_definition_file_path'])
logger.info("Dataset contains {0} entries".formating(self.dataset_kf.shape[0]))
else:
logger.info("Data definition file path is not specified")
def set_training_knowledgeframe(self,
training_kf,
fold_num):
"""
This method sets training knowledgeframe
:param training_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.train_kf_list[fold_num] = training_kf
logger.info("Training knowledgeframe with {0} entries is set for fold {1}".formating(training_kf.shape[0], fold_num))
def set_validation_knowledgeframe(self,
validation_kf,
fold_num):
"""
This method sets training knowledgeframe
:param validation_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.val_kf_list[fold_num] = validation_kf
logger.info("Validation knowledgeframe with {0} entries is set for fold {1}".formating(validation_kf.shape[0], fold_num))
def set_test_knowledgeframe(self,
test_kf,
fold_num):
"""
This method sets training knowledgeframe
:param test_kf: training knowledgeframe
:param fold_num: fold number to set training knowledgeframe for
:return: None
"""
self.test_kf_list[fold_num] = test_kf
logger.info("Test knowledgeframe with {0} entries is set for fold {1}".formating(test_kf.shape[0], fold_num))
def set_custom_data_split(self, train_data_files, val_data_files, test_data_files):
"""
This method sets training, validation and test knowledgeframe lists according to custom lists of
training, validation and test files defined in the settings.
:return: None
"""
logger.info("Loading custom lists of training validation and test files")
self.train_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in train_data_files]
self.val_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in val_data_files]
self.test_kf_list = [dataset_utils.load_dataset_file(data_file) for data_file in test_data_files]
def split_dataset(self):
"""
This method first split dataset to folds
and farther split each fold to training, validation and test partitions
:return: None
"""
# Create lists to hold dataset partitions
self.train_kf_list = [None] * self.settings['folds_num']
self.val_kf_list = [None] * self.settings['folds_num']
self.test_kf_list = [None] * self.settings['folds_num']
# Set random seed to ensure reproducibility of dataset partitioning across experiments on same hardware
np.random.seed(self.settings['data_random_seed'])
# Split dataset to groups
if self.settings['split_to_groups']:
self.split_dataset_to_groups()
else:
self.groups_kf_list = [self.dataset_kf]
# Permute entries in each group
self.groups_kf_list = [group_kf.reindexing(np.random.permutation(group_kf.index)) for group_kf in self.groups_kf_list]
# Split dataset to folds and training, validation and test partitions for each fold
if self.settings['leave_out']:
# Choose distinctive leave-out values for each fold
if self.settings['leave_out_values'] is None:
self.choose_leave_out_values()
# Split dataset to folds based on leave-out values
self.split_dataset_to_folds_with_leave_out()
else:
# Split dataset to folds in random manner
self.split_dataset_to_folds_randomly()
def split_dataset_to_groups(self):
"""
# This method splits dataset to groups based on values of 'self.group_column'.
# Samples in each group are split in same manner between training, validation and test partitions.
# This is important, for example, to ensure that each class (in classification problem) is represented
# in training, validation and test partition.
"""
logger.info("Dividing dataset to groups based on values of '{0}' dataset column".formating(self.settings['group_column']))
# Get groups identifiers
if self.settings['group_ids'] is None:
group_ids = self.dataset_kf[self.settings['group_column']].distinctive()
else:
group_ids = self.settings['group_ids']
logger.info("Dataset groups are: {0}".formating(group_ids))
# Split dataset to groups
self.groups_kf_list = [self.dataset_kf[self.dataset_kf[self.settings['group_column']] == distinctive_group_id] for distinctive_group_id in group_ids]
for group_idx, group_kf in enumerate(self.groups_kf_list):
logger.info("Group {0} contains {1} sample_by_nums".formating(group_ids[group_idx], group_kf.shape[0]))
def choose_leave_out_values(self):
"""
This method chooses leave-out values for each fold.
Leave-out values calculated based on values of 'self.leave_out_column'.
Dataset entries which 'self.leave_out_column' value is one of calculated leave-out values
for specific fold will present only in test partition for this fold.
:return: None
"""
logger.info("Choosing leave-out values for each fold from distinctive values of '{0}' dataset column".formating(self.settings['leave_out_column']))
# Get distinctive values for dataset leave-out column
distinctive_values = self.dataset_kf[self.settings['leave_out_column']].distinctive()
logger.info("Unique values for column {0} are: {1}".formating(self.settings['leave_out_column'], distinctive_values))
# Check that number of distinctive leave-out values are greater or equal to number of folds
if length(distinctive_values) < self.settings['folds_num']:
logger.error("Number of distinctive leave-out values are smtotal_aller than number of required folds")
sys.exit(1)
# Get list of distinctive leave-out values for each fold
if self.settings['folds_num'] > 1:
self.settings['leave_out_values'] = np.array_split(distinctive_values, self.settings['folds_num'])
else:
self.settings['leave_out_values'] = [np.random.choice(distinctive_values, int(length(distinctive_values) * (1 - self.settings['train_val_fraction'])), replacing=False)]
for fold in range(0, self.settings['folds_num']):
logger.info("Leave out values for fold {0} are: {1}".formating(fold, self.settings['leave_out_values'][fold]))
def split_dataset_to_folds_with_leave_out(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold based on leave-out values.
Samples in each group are split in same manner between training, validation and test partitions.
Leave-out values will be presented only in test partition of corresponding fold.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold based on leave-out values")
for fold in range(0, self.settings['folds_num']):
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_test_kf = group_kf[group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_test_kf.shape[0] == 0:
logger.warning("Group {0} hasn't whatever of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
groups_test_kf_list.adding(group_test_kf)
group_train_val_kf = group_kf[~group_kf[self.settings['leave_out_column']].incontain(self.settings['leave_out_values'][fold])]
if group_train_val_kf.shape[0] == 0:
logger.warning("All sample_by_nums of group {0} is in one of leave out values: {1}".formating(group_idx, self.settings['leave_out_values'][fold]))
else:
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] = mk.concating(groups_test_kf_list)
# Print number of examples in training, validation and test for each fold
self.print_data_split()
def split_dataset_to_folds_randomly(self):
"""
This method splits dataset to folds and training, validation and test partitions for each fold in random manner.
Samples in each group are split in same manner between training, validation and test partitions.
"""
logger.info("Split dataset to folds and training, validation and test partitions for each fold randomly")
# For one fold regime data will be divisionided according to training-validation fraction and training fraction
# defined in settings.
# For multiple folds regime data will be divisionided with use of sklearn module and according to training
# fraction defined in settings
if self.settings['folds_num'] == 1:
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_kf in self.groups_kf_list:
train_val_split_idx = int(group_kf.shape[0] * self.settings['train_val_fraction'])
group_train_val_kf = group_kf.iloc[0:train_val_split_idx]
groups_test_kf_list.adding(group_kf.iloc[train_val_split_idx:])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[0] = mk.concating(groups_train_kf_list)
self.val_kf_list[0] = mk.concating(groups_val_kf_list)
self.test_kf_list[0] = mk.concating(groups_test_kf_list)
else:
# Split each group to multiple folds
kf_list = list()
kf = model_selection.KFold(n_splits=self.settings['folds_num'], shuffle=True, random_state=self.settings['data_random_seed'])
for group_kf in self.groups_kf_list:
kf_list.adding(kf.split(group_kf))
# Combine group splits to folds
for fold in range(0, self.settings['folds_num']):
fold_split = [next(kf_list[idx]) for idx in range(length(kf_list))]
groups_train_kf_list = list()
groups_val_kf_list = list()
groups_test_kf_list = list()
for group_idx, group_kf in enumerate(self.groups_kf_list):
group_train_val_kf = group_kf.iloc[fold_split[group_idx][0]]
groups_test_kf_list.adding(group_kf.iloc[fold_split[group_idx][1]])
train_split_idx = int(group_train_val_kf.shape[0] * self.settings['train_fraction'])
groups_train_kf_list.adding(group_train_val_kf.iloc[0:train_split_idx])
groups_val_kf_list.adding(group_train_val_kf.iloc[train_split_idx:])
self.train_kf_list[fold] = mk.concating(groups_train_kf_list)
self.val_kf_list[fold] = mk.concating(groups_val_kf_list)
self.test_kf_list[fold] = | mk.concating(groups_test_kf_list) | pandas.concat |
import os
import monkey as mk
import matplotlib.pyplot as plt
import datapackage as dp
import plotly.io as pio
import plotly.offline as offline
from plots import (
hourly_plot,
stacked_plot,
price_line_plot,
price_scatter_plot,
merit_order_plot,
filling_level_plot,
)
results = [r for r in os.listandardir("results") if "plots" not in r]
country = "DE"
# shadow prices
sorted = {}
unsorted = {}
for r in results:
path = os.path.join("results", r, "output", "shadow_prices.csv")
sprices = mk.read_csv(path, index_col=[0], parse_dates=True)[
country + "-electricity"
]
sorted[r] = sprices.sort_the_values().values
unsorted[r] = sprices.values
# residual load and more
renewables = ["wind-onshore", "wind-offshore", "solar-pv", "hydro-ror"]
timestamps = {}
marginal_cost = {}
shadow_prices = {}
storages = {}
prices = {}
rload = {}
for r in results:
path = os.path.join("results", r, "output", country + "-electricity.csv")
country_electricity_kf = mk.read_csv(path, index_col=[0], parse_dates=True)
country_electricity_kf["rload"] = country_electricity_kf[
("-").join([country, "electricity-load"])
] - country_electricity_kf[
[("-").join([country, i]) for i in renewables]
].total_sum(
axis=1
)
rload[r] = country_electricity_kf["rload"].values
timestamps[r] = country_electricity_kf.index
if country == "DE":
path = os.path.join("results", r, "input", "datapackage.json")
input_datapackage = dp.Package(path)
dispatchable = input_datapackage.getting_resource("dispatchable")
kf = mk.KnowledgeFrame(dispatchable.read(keyed=True))
kf = kf.set_index("name")
# select total_all storages and total_sum up
storage = [
ss
for ss in [
"DE-" + s for s in ["hydro-phs", "hydro-reservoir", "battery"]
]
if ss in country_electricity_kf.columns
]
storages[r] = country_electricity_kf[storage].total_sum(axis=1)
marginal_cost[r] = kf
path = os.path.join("results", r, "output", "shadow_prices.csv")
shadow_prices[r] = mk.read_csv(path, index_col=[0], parse_dates=True)[
"DE-electricity"
]
storages[r] = | mk.concating([storages[r], shadow_prices[r]], axis=1) | pandas.concat |
from datetime import datetime
import numpy as np
import pytest
import monkey.util._test_decorators as td
from monkey.core.dtypes.base import _registry as ea_registry
from monkey.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from monkey.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
from monkey import (
Categorical,
KnowledgeFrame,
DatetimeIndex,
Index,
Interval,
IntervalIndex,
MultiIndex,
NaT,
Period,
PeriodIndex,
Collections,
Timestamp,
cut,
date_range,
notna,
period_range,
)
import monkey._testing as tm
from monkey.core.arrays import SparseArray
from monkey.tcollections.offsets import BDay
class TestKnowledgeFrameSetItem:
@pytest.mark.parametrize("dtype", ["int32", "int64", "float32", "float64"])
def test_setitem_dtype(self, dtype, float_frame):
arr = np.random.randn(length(float_frame))
float_frame[dtype] = np.array(arr, dtype=dtype)
assert float_frame[dtype].dtype.name == dtype
def test_setitem_list_not_knowledgeframe(self, float_frame):
data = np.random.randn(length(float_frame), 2)
float_frame[["A", "B"]] = data
tm.assert_almost_equal(float_frame[["A", "B"]].values, data)
def test_setitem_error_msmgs(self):
# GH 7432
kf = KnowledgeFrame(
{"bar": [1, 2, 3], "baz": ["d", "e", "f"]},
index=Index(["a", "b", "c"], name="foo"),
)
ser = Collections(
["g", "h", "i", "j"],
index=Index(["a", "b", "c", "a"], name="foo"),
name="fiz",
)
msg = "cannot reindexing from a duplicate axis"
with pytest.raises(ValueError, match=msg):
kf["newcol"] = ser
# GH 4107, more descriptive error message
kf = KnowledgeFrame(np.random.randint(0, 2, (4, 4)), columns=["a", "b", "c", "d"])
msg = "incompatible index of inserted column with frame index"
with pytest.raises(TypeError, match=msg):
kf["gr"] = kf.grouper(["b", "c"]).count()
def test_setitem_benchmark(self):
# from the vb_suite/frame_methods/frame_insert_columns
N = 10
K = 5
kf = KnowledgeFrame(index=range(N))
new_col = np.random.randn(N)
for i in range(K):
kf[i] = new_col
expected = KnowledgeFrame(np.repeat(new_col, K).reshape(N, K), index=range(N))
tm.assert_frame_equal(kf, expected)
def test_setitem_different_dtype(self):
kf = KnowledgeFrame(
np.random.randn(5, 3), index=np.arange(5), columns=["c", "b", "a"]
)
kf.insert(0, "foo", kf["a"])
kf.insert(2, "bar", kf["c"])
# diff dtype
# new item
kf["x"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 5 + [np.dtype("float32")],
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
# replacing current (in different block)
kf["a"] = kf["a"].totype("float32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2,
index=["foo", "c", "bar", "b", "a", "x"],
)
tm.assert_collections_equal(result, expected)
kf["y"] = kf["a"].totype("int32")
result = kf.dtypes
expected = Collections(
[np.dtype("float64")] * 4 + [np.dtype("float32")] * 2 + [np.dtype("int32")],
index=["foo", "c", "bar", "b", "a", "x", "y"],
)
tm.assert_collections_equal(result, expected)
def test_setitem_empty_columns(self):
# GH 13522
kf = KnowledgeFrame(index=["A", "B", "C"])
kf["X"] = kf.index
kf["X"] = ["x", "y", "z"]
exp = KnowledgeFrame(data={"X": ["x", "y", "z"]}, index=["A", "B", "C"])
tm.assert_frame_equal(kf, exp)
def test_setitem_dt64_index_empty_columns(self):
rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
kf = KnowledgeFrame(index=np.arange(length(rng)))
kf["A"] = rng
assert kf["A"].dtype == np.dtype("M8[ns]")
def test_setitem_timestamp_empty_columns(self):
# GH#19843
kf = KnowledgeFrame(index=range(3))
kf["now"] = Timestamp("20130101", tz="UTC")
expected = KnowledgeFrame(
[[Timestamp("20130101", tz="UTC")]] * 3, index=[0, 1, 2], columns=["now"]
)
tm.assert_frame_equal(kf, expected)
def test_setitem_wrong_lengthgth_categorical_dtype_raises(self):
# GH#29523
cat = Categorical.from_codes([0, 1, 1, 0, 1, 2], ["a", "b", "c"])
kf = KnowledgeFrame(range(10), columns=["bar"])
msg = (
rf"Length of values \({length(cat)}\) "
rf"does not match lengthgth of index \({length(kf)}\)"
)
with pytest.raises(ValueError, match=msg):
kf["foo"] = cat
def test_setitem_with_sparse_value(self):
# GH#8131
kf = KnowledgeFrame({"c_1": ["a", "b", "c"], "n_1": [1.0, 2.0, 3.0]})
sp_array = SparseArray([0, 0, 1])
kf["new_column"] = sp_array
expected = | Collections(sp_array, name="new_column") | pandas.Series |
import numpy as np
import monkey as mk
import spacy
from spacy.lang.de.stop_words import STOP_WORDS
from nltk.tokenize import sent_tokenize
from itertools import grouper
import clone
import re
import sys
import textstat
# Method to create a matrix with contains only zeroes and a index starting by 0
def create_matrix_index_zeros(rows, columns):
arr = np.zeros((rows, columns))
for r in range(0, rows):
arr[r, 0] = r
return arr
# Method to getting total_all authors with a given number of texts. Used in chapter 5.1 to getting a corpus with 100 Texts for 25
# authors
def getting_balanced_kf_total_all_authors(par_kf, par_num_text):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
for i in range(0, length(author_count)):
if author_count[i] >= par_num_text and not author_count.index[i] == "Gast-Rezensent":
author_list.adding(author_count.index[i])
texts = [par_num_text for i in range(0, length(author_count))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Method to getting a specific number of authors with a given number of texts. Used later on to getting results for different
# combinations of authors and texts
def getting_balanced_kf_by_texts_authors(par_kf, par_num_text, par_num_author):
author_count = par_kf["author"].counts_value_num()
author_list = []
kf_balanced_text = mk.KnowledgeFrame(columns=['label_encoded', 'author', 'genres', 'release_date', 'text'])
loop_count, loops = 0, par_num_author
while loop_count < loops:
if author_count[loop_count] >= par_num_text and not author_count.index[loop_count] == "Gast-Rezensent":
author_list.adding(author_count.index[loop_count])
# Skip the Author "Gast-Rezensent" if its not the final_item value_round and increase the loops by 1
elif author_count.index[loop_count] == "Gast-Rezensent":
loops += 1
loop_count += 1
texts = [par_num_text for i in range(0, length(author_list))]
for index, row in par_kf.traversal():
if row['author'] in author_list:
if texts[author_list.index(row['author'])] != 0:
d = {'author': [row['author']], 'genres': [row['genres']],
'release_date': [row['release_date']], 'text': [row['text']]}
kf_balanced_text = kf_balanced_text.adding(mk.KnowledgeFrame.from_dict(d), ignore_index=True)
texts[author_list.index(row['author'])] -= 1
if total_sum(texts) == 0:
break
# Label encoding and delete author column after
dic_author_mappingping = author_encoding(kf_balanced_text)
kf_balanced_text['label_encoded'] = getting_encoded_author_vector(kf_balanced_text, dic_author_mappingping)[:, 0]
kf_balanced_text.sip("author", axis=1, inplace=True)
# Print author mappingping in file
original_standardout = sys.standardout
with open('author_mappingping.txt', 'w') as f:
sys.standardout = f
print(dic_author_mappingping)
sys.standardout = original_standardout
for i in range(0, length(author_list)):
print(f"Autor {i+1}: {par_num_text - texts[i]} Texte")
return kf_balanced_text
# Feature extraction of the feature described in chapter 5.6.1
def getting_bow_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_bow = {}
d_bow_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
for word in tokens:
try:
d_bow["bow:"+word.lemma_.lower()] += 1
except KeyError:
d_bow["bow:"+word.lemma_.lower()] = 1
d_bow_list.adding(clone.deepclone(d_bow))
d_bow.clear()
return mk.KnowledgeFrame(d_bow_list)
# Feature extraction of the feature described in chapter 5.6.2
def getting_word_n_grams(par_kf, n):
nlp = spacy.load("de_core_news_sm")
d_word_ngram = {}
d_word_ngram_list = []
function_pos = ["ADP", "AUX", "CONJ", "CCONJ", "DET", "PART", "PRON", "SCONJ"]
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not
word.is_digit and word.lemma_ not in STOP_WORDS and word.pos_ not in function_pos]
tokens = [token.lemma_.lower() for token in tokens]
for w in range(0, length(tokens)):
if w + n <= length(tokens):
try:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] += 1
except KeyError:
d_word_ngram["w" + str(n) + "g" + ":" + '|'.join(tokens[w:w + n])] = 1
d_word_ngram_list.adding(clone.deepclone(d_word_ngram))
d_word_ngram.clear()
return mk.KnowledgeFrame(d_word_ngram_list)
# Feature extraction of the feature described in chapter 5.6.3
def getting_word_count(par_kf):
arr_wordcount = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
only_words = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space:
only_words.adding(t)
arr_wordcount[index] = length(only_words)
only_words.clear()
return mk.KnowledgeFrame(data=arr_wordcount, columns=["word_count"])
# Feature extraction of the feature described in chapter 5.6.4 with some variations
# Count total_all word lengthgths indivisionidutotal_ally
def getting_word_lengthgth_matrix(par_kf):
nlp = spacy.load("de_core_news_sm")
d_word_length = {}
d_word_length_list = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
tokens = [word for word in tokens if not word.is_punct and not word.is_space and not word.is_digit]
for word in tokens:
try:
d_word_length["w_length:"+str(length(word.text))] += 1
except KeyError:
d_word_length["w_length:"+str(length(word.text))] = 1
d_word_length_list.adding(clone.deepclone(d_word_length))
d_word_length.clear()
return mk.KnowledgeFrame(d_word_length_list)
# Count word lengthgths and set 2 intervals
def getting_word_lengthgth_matrix_with_interval(par_kf, border_1, border_2):
arr_wordcount_with_interval = np.zeros((length(par_kf), border_1 + 2))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= border_1 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif border_1 < length(
word.text) <= border_2 and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -2] += 1
elif not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, border_1+1)]
word_lengthgth_labels.adding(f"{border_1+1}-{border_2}")
word_lengthgth_labels.adding(f">{border_2}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count word lengthgths and total_sum total_all above a defined margin
def getting_word_lengthgth_matrix_with_margin(par_kf, par_margin):
arr_wordcount_with_interval = np.zeros((length(par_kf), par_margin + 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for word in tokens:
if length(word.text) <= par_margin and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, length(word.text) - 1] += 1
elif par_margin < length(word.text) and not word.is_punct and not word.is_space and not word.is_digit:
arr_wordcount_with_interval[index, -1] += 1
word_lengthgth_labels = [str(i) for i in range(1, par_margin+1)]
word_lengthgth_labels.adding(f">{par_margin}")
return mk.KnowledgeFrame(data=arr_wordcount_with_interval, columns=word_lengthgth_labels)
# Count the average word lengthgth of the article
def getting_average_word_lengthgth(par_kf):
arr_avg_word_length_vector = np.zeros((length(par_kf), 1))
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
symbol_total_sum = 0
words = 0
tokens = nlp(row['text'])
for word in tokens:
if not word.is_punct and not word.is_space and not word.is_digit:
symbol_total_sum += length(word.text)
words += 1
arr_avg_word_length_vector[index, 0] = symbol_total_sum / words
return mk.KnowledgeFrame(data=arr_avg_word_length_vector, columns=["avg_word_lengthgth"])
# Feature extraction of the feature described in chapter 5.6.5
def getting_yules_k(par_kf):
d = {}
nlp = spacy.load("de_core_news_sm")
arr_yulesk = np.zeros((length(par_kf), 1))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
if not t.is_punct and not t.is_space and not t.is_digit:
w = t.lemma_.lower()
try:
d[w] += 1
except KeyError:
d[w] = 1
s1 = float(length(d))
s2 = total_sum([length(list(g)) * (freq ** 2) for freq, g in grouper(sorted(d.values()))])
try:
k = 10000 * (s2 - s1) / (s1 * s1)
arr_yulesk[index] = k
except ZeroDivisionError:
pass
d.clear()
return mk.KnowledgeFrame(data=arr_yulesk, columns=["yulesk"])
# Feature extraction of the feature described in chapter 5.6.6
# Get a vector of total_all special characters
def getting_special_char_label_vector(par_kf):
nlp = spacy.load("de_core_news_sm")
special_char_label_vector = []
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.is_punct and c.text not in special_char_label_vector:
special_char_label_vector.adding(c.text)
return special_char_label_vector
# Get a matrix of total_all special character by a given vector of special chars
def getting_special_char_matrix(par_kf, par_special_char_label_vector):
nlp = spacy.load("de_core_news_sm")
arr_special_char = np.zeros((length(par_kf), length(par_special_char_label_vector)))
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for t in tokens:
chars = ' '.join([c for c in t.text])
chars = nlp(chars)
for c in chars:
if c.text in par_special_char_label_vector:
arr_special_char[index, par_special_char_label_vector.index(c.text)] += 1
return arr_special_char
# Feature extraction of the feature described in chapter 5.6.7
# Get the char-affix-n-grams by a defined n
def getting_char_affix_n_grams(par_kf, n):
d_prefix_list, d_suffix_list, d_space_prefix_list, d_space_suffix_list = [], [], [], []
d_prefix, d_suffix, d_space_prefix, d_space_suffix = {}, {}, {}, {}
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Prefix
if length(tokens[w].text) >= n + 1:
try:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] += 1
except KeyError:
d_prefix["c" + str(n) + "_p: " + tokens[w].text.lower()[0:n]] = 1
# Suffix
if length(tokens[w].text) >= n + 1:
try:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] += 1
except KeyError:
d_suffix["c" + str(n) + "_s: " + tokens[w].text.lower()[-n:]] = 1
d_prefix_list.adding(clone.deepclone(d_prefix))
d_suffix_list.adding(clone.deepclone(d_suffix))
d_prefix.clear()
d_suffix.clear()
for i in range(0, length(row['text'])):
if row['text'][i] == " " and i + n <= length(row['text']) and i - n >= 0:
# Space-prefix
try:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] += 1
except KeyError:
d_space_prefix["c" + str(n) + "_sp: " + row['text'].lower()[i:n + i]] = 1
# Space-suffix
try:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] += 1
except KeyError:
d_space_suffix["c" + str(n) + "_ss: " + row['text'].lower()[i - n + 1:i + 1]] = 1
d_space_prefix_list.adding(clone.deepclone(d_space_prefix))
d_space_suffix_list.adding(clone.deepclone(d_space_suffix))
d_space_prefix.clear()
d_space_suffix.clear()
kf_pre = mk.KnowledgeFrame(d_prefix_list)
kf_su = mk.KnowledgeFrame(d_suffix_list)
kf_s_pre = mk.KnowledgeFrame(d_space_prefix_list)
kf_s_su = mk.KnowledgeFrame(d_space_suffix_list)
kf_affix = mk.concating([kf_pre, kf_su, kf_s_pre, kf_s_su], axis=1)
return kf_affix
# Get the char-word-n-grams by a defined n
def getting_char_word_n_grams(par_kf, n):
d_whole_word_list, d_mid_word_list, d_multi_word_list = [], [], []
d_whole_word, d_mid_word, d_multi_word = {}, {}, {}
match_list = []
nlp = spacy.load("de_core_news_sm")
for index, row in par_kf.traversal():
tokens = nlp(row['text'])
for w in range(0, length(tokens)):
# Whole-word
if length(tokens[w].text) == n:
try:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] += 1
except KeyError:
d_whole_word["c" + str(n) + "_ww: " + tokens[w].text.lower()] = 1
# Mid-word
if length(tokens[w].text) >= n + 2:
for i in range(1, length(tokens[w].text) - n):
try:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] += 1
except KeyError:
d_mid_word["c" + str(n) + "_miw: " + tokens[w].text.lower()[i:i + n]] = 1
d_whole_word_list.adding(clone.deepclone(d_whole_word))
d_mid_word_list.adding(clone.deepclone(d_mid_word))
d_whole_word.clear()
d_mid_word.clear()
# Multi-word
# ignore special character
trimmed_text = re.sub(r'[\s]+', ' ', re.sub(r'[^\w ]+', '', row['text']))
match_list.clear()
for i in range(1, n - 1):
regex = r"\w{" + str(i) + r"}\s\w{" + str(n - 1 - i) + r"}"
match_list += re.findtotal_all(regex, trimmed_text.lower())
for match in match_list:
try:
d_multi_word["c" + str(n) + "_mw: " + match] += 1
except KeyError:
d_multi_word["c" + str(n) + "_mw: " + match] = 1
d_multi_word_list.adding(clone.deepclone(d_multi_word))
d_multi_word.clear()
kf_ww = mk.KnowledgeFrame(d_whole_word_list)
kf_miw = mk.KnowledgeFrame(d_mid_word_list)
kf_mw = | mk.KnowledgeFrame(d_multi_word_list) | pandas.DataFrame |
from __future__ import divisionision
import configparser
import logging
import os
import re
import time
from collections import OrderedDict
import numpy as np
import monkey as mk
import scipy.interpolate as itp
from joblib import Partotal_allel
from joblib import delayed
from matplotlib import pyplot as plt
from pyplanscoring.core.dicomparser import ScoringDicomParser
from pyplanscoring.core.dosimetric import read_scoring_criteria, constrains, Competition2016
from pyplanscoring.core.dvhcalculation import Structure, prepare_dvh_data, calc_dvhs_upsample_by_numd, save_dicom_dvhs, load
from pyplanscoring.core.dvhdoses import getting_dvh_getting_max
from pyplanscoring.core.geometry import getting_axis_grid, getting_interpolated_structure_planes
from pyplanscoring.core.scoring import DVHMetrics, Scoring, Participant
# TODO extract constrains from analytical curves
class CurveCompare(object):
"""
Statistical analysis of the DVH volume (%) error histograms. volume (cm 3 ) differences (numericalโanalytical)
were calculated for points on the DVH curve sample_by_numd at every 10 cGy then normalized to
the structure's total volume (cm 3 ) to give the error in volume (%)
"""
def __init__(self, a_dose, a_dvh, calc_dose, calc_dvh, structure_name='', dose_grid='', gradient=''):
self.calc_data = ''
self.ref_data = ''
self.a_dose = a_dose
self.a_dvh = a_dvh
self.cal_dose = calc_dose
self.calc_dvh = calc_dvh
self.sampling_size = 10/100.0
self.dose_sample_by_nums = np.arange(0, length(calc_dvh)/100, self.sampling_size) # The DVH curve sample_by_numd at every 10 cGy
self.ref_dvh = itp.interp1d(a_dose, a_dvh, fill_value='extrapolate')
self.calc_dvh = itp.interp1d(calc_dose, calc_dvh, fill_value='extrapolate')
self.delta_dvh = self.calc_dvh(self.dose_sample_by_nums) - self.ref_dvh(self.dose_sample_by_nums)
self.delta_dvh_pp = (self.delta_dvh / a_dvh[0]) * 100
# prepare data dict
# self.calc_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.calc_dvh(self.dose_sample_by_nums))
# self.ref_dvh_dict = _prepare_dvh_data(self.dose_sample_by_nums, self.ref_dvh(self.dose_sample_by_nums))
# title data
self.structure_name = structure_name
self.dose_grid = dose_grid
self.gradient = gradient
def stats(self):
kf = mk.KnowledgeFrame(self.delta_dvh_pp, columns=['delta_pp'])
print(kf.describe())
@property
def stats_paper(self):
stats = {}
stats['getting_min'] = self.delta_dvh_pp.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh_pp.getting_max().value_round(1)
stats['average'] = self.delta_dvh_pp.average().value_round(1)
stats['standard'] = self.delta_dvh_pp.standard(ddof=1).value_round(1)
return stats
@property
def stats_delta_cc(self):
stats = {}
stats['getting_min'] = self.delta_dvh.getting_min().value_round(1)
stats['getting_max'] = self.delta_dvh.getting_max().value_round(1)
stats['average'] = self.delta_dvh.average().value_round(1)
stats['standard'] = self.delta_dvh.standard(ddof=1).value_round(1)
return stats
# def getting_constrains(self, constrains_dict):
# ref_constrains = eval_constrains_dict(self.ref_dvh_dict, constrains_dict)
# calc_constrains = eval_constrains_dict(self.calc_dvh_dict, constrains_dict)
#
# return ref_constrains, calc_constrains
def eval_range(self, lim=0.2):
t1 = self.delta_dvh < -lim
t2 = self.delta_dvh > lim
ok = np.total_sum(np.logical_or(t1, t2))
pp = ok / length(self.delta_dvh) * 100
print('pp %1.2f - %i of %i ' % (pp, ok, self.delta_dvh.size))
def plot_results(self, ref_label, calc_label, title):
fig, ax = plt.subplots()
ref = self.ref_dvh(self.dose_sample_by_nums)
calc = self.calc_dvh(self.dose_sample_by_nums)
ax.plot(self.dose_sample_by_nums, ref, label=ref_label)
ax.plot(self.dose_sample_by_nums, calc, label=calc_label)
ax.set_ylabel('volume [cc]')
ax.set_xlabel('Dose [Gy]')
ax.set_title(title)
ax.legend(loc='best')
def test_real_dvh():
rs_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RS.1.2.246.352.71.4.584747638204.248648.20170123083029.dcm'
rd_file = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RD.1.2.246.352.71.7.584747638204.1750110.20170123082607.dcm'
rp = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/RP.1.2.246.352.71.5.584747638204.952069.20170122155706.dcm'
# dvh_file = r'/media/victor/TOURO Mobile/COMPETITION 2017/Send to Victor - Jan10 2017/Norm Res with CT Images/RD.1.2.246.352.71.7.584747638204.1746016.20170110164605.dvh'
f = r'/home/victor/Dropbox/Plan_Competition_Project/competition_2017/All Required Files - 23 Jan2017/PlanIQ Criteria TPS PlanIQ matched str names - TXT Fromat - Last mod Jan23.txt'
constrains_total_all, scores_total_all, criteria = read_scoring_criteria(f)
dose = ScoringDicomParser(filengthame=rd_file)
struc = ScoringDicomParser(filengthame=rs_file)
structures = struc.GetStructures()
ecl_DVH = dose.GetDVHs()
plt.style.use('ggplot')
st = time.time()
dvhs = {}
for structure in structures.values():
for end_cap in [False]:
if structure['id'] in ecl_DVH:
# if structure['id'] in [37, 38]:
if structure['name'] in list(scores_total_all.keys()):
ecl_dvh = ecl_DVH[structure['id']]['data']
ecl_dgetting_max = ecl_DVH[structure['id']]['getting_max'] * 100 # to cGy
struc_teste = Structure(structure, end_cap=end_cap)
# struc['planes'] = struc_teste.planes
# dicompyler_dvh = getting_dvh(structure, dose)
fig, ax = plt.subplots()
fig.set_figheight(12)
fig.set_figwidth(20)
dhist, chist = struc_teste.calculate_dvh(dose, up_sample_by_num=True)
getting_max_dose = getting_dvh_getting_max(chist)
ax.plot(dhist, chist, label='Up sample_by_numd - Dgetting_max: %1.1f cGy' % getting_max_dose)
fig.hold(True)
ax.plot(ecl_dvh, label='Eclipse - Dgetting_max: %1.1f cGy' % ecl_dgetting_max)
dvh_data = prepare_dvh_data(dhist, chist)
txt = structure['name'] + ' volume (cc): %1.1f - end_cap: %s ' % (
ecl_dvh[0], str(end_cap))
ax.set_title(txt)
# nup = getting_dvh_getting_max(dicompyler_dvh['data'])
# plt.plot(dicompyler_dvh['data'], label='Software DVH - Dgetting_max: %1.1f cGy' % nup)
ax.legend(loc='best')
ax.set_xlabel('Dose (cGy)')
ax.set_ylabel('volume (cc)')
fname = txt + '.png'
fig.savefig(fname, formating='png', dpi=100)
dvhs[structure['name']] = dvh_data
end = time.time()
print('Total elapsed Time (getting_min): ', (end - st) / 60)
def test_spacing(root_path):
"""
# TEST PLANIQ RS-DICOM DATA if z planes are not equal spaced.
:param root_path: root path
"""
root_path = r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
structure_files = [os.path.join(root, name) for root, dirs, files in os.walk(root_path) for name in files if
name.endswith(('.dcm', '.DCM'))]
eps = 0.001
test_result = {}
for f in structure_files:
structures = ScoringDicomParser(filengthame=f).GetStructures()
for key in structures:
try:
total_all_z = np.array([z for z in structures[key]['planes'].keys()], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
test_result[structures[key]['name']] = test
except:
print('Error in key:', key)
b = {key: value for key, value in test_result.items() if value == True}
return test_result
def test_planes_spacing(sPlanes):
eps = 0.001
total_all_z = np.array([z for z in sPlanes], dtype=float)
total_all_sorted_diff = np.diff(np.sort(total_all_z))
test = (abs((total_all_sorted_diff - total_all_sorted_diff[0])) > eps).whatever()
return test, total_all_sorted_diff
def test_upsample_by_numd_z_spacing(sPlanes):
z = 0.1
ordered_keys = [z for z, sPlane in sPlanes.items()]
ordered_keys.sort(key=float)
ordered_planes = np.array(ordered_keys, dtype=float)
z_interp_positions, dz = getting_axis_grid(z, ordered_planes)
hi_res_structure = getting_interpolated_structure_planes(sPlanes, z_interp_positions)
ordered_keys = [z for z, sPlane in hi_res_structure.items()]
ordered_keys.sort(key=float)
t, p = test_planes_spacing(hi_res_structure)
assert t is False
def eval_constrains_dict(dvh_data_tmp, constrains_dict):
mtk = DVHMetrics(dvh_data_tmp)
values_tmp = OrderedDict()
for ki in constrains_dict.keys():
cti = mtk.eval_constrain(ki, constrains_dict[ki])
values_tmp[ki] = cti
return values_tmp
def getting_analytical_curve(an_curves_obj, file_structure_name, column):
an_curve_i = an_curves_obj[file_structure_name.split('_')[0]]
dose_an = an_curve_i['Dose (cGy)'].values
an_dvh = an_curve_i[column].values # check nonzero
idx = np.nonzero(an_dvh) # remove 0 volumes from DVH
dose_range, cdvh = dose_an[idx], an_dvh[idx]
return dose_range, cdvh
def calc_data(row, dose_files_dict, structure_dict, constrains, calculation_options):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set end cap by 1/2 slice thickness
calculation_options['end_cap'] = structure['thickness'] / 2.0
# set up sample_by_numd structure
struc_teste = Structure(structure, calculation_options)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
dvh_data = struc_teste.getting_dvh_data()
# Setup DVH metrics class and getting DVH DATA
metrics = DVHMetrics(dvh_data)
values_constrains = OrderedDict()
for k in constrains.keys():
ct = metrics.eval_constrain(k, constrains[k])
values_constrains[k] = ct
values_constrains['Gradient direction'] = gradient
# Get data
return mk.Collections(values_constrains, name=voxel), s_name
def calc_data_total_all(row, dose_files_dict, structure_dict, constrains, an_curves, col_grad_dict, delta_mm=(0.2, 0.2, 0.2),
end_cap=True, up_sample_by_num=True):
idx, values = row[0], row[1]
s_name = values['Structure name']
voxel = str(values['Dose Voxel (mm)'])
gradient = values['Gradient direction']
dose_file = dose_files_dict[gradient][voxel]
struc_file = structure_dict[s_name]
# getting structure and dose
dicom_dose = ScoringDicomParser(filengthame=dose_file)
struc = ScoringDicomParser(filengthame=struc_file)
structures = struc.GetStructures()
structure = structures[2]
# set up sample_by_numd structure
struc_teste = Structure(structure)
struc_teste.set_delta(delta_mm)
dhist, chist = struc_teste.calculate_dvh(dicom_dose)
# getting its columns from spreadsheet
column = col_grad_dict[gradient][voxel]
adose_range, advh = getting_analytical_curve(an_curves, s_name, column)
# use CurveCompare class to eval similarity from calculated and analytical curves
cmp = CurveCompare(adose_range, advh, dhist, chist, s_name, voxel, gradient)
ref_constrains, calc_constrains = cmp.getting_constrains(constrains)
ref_constrains['Gradient direction'] = gradient
calc_constrains['Gradient direction'] = gradient
ref_collections = mk.Collections(ref_constrains, name=voxel)
calc_collections = mk.Collections(calc_constrains, name=voxel)
return ref_collections, calc_collections, s_name, cmp
def test11(delta_mm=(0.2, 0.2, 0.1), plot_curves=False):
# TEST DICOM DATA
structure_files = ['/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Spheres/Sphere_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/Cylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cylinders/RtCylinder_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/Cone_02_0.dcm',
'/home/victor/Downloads/DVH-Analysis-Data-Etc/STRUCTURES/Cones/RtCone_02_0.dcm']
structure_name = ['Sphere_02_0', 'Cylinder_02_0', 'RtCylinder_02_0', 'Cone__02_0', 'RtCone_02_0']
dose_files = [
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_AntPost_3mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_0-4_0-2_0-4_mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_1mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_2mm_Aligned.dcm',
r'/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS/Linear_SupInf_3mm_Aligned.dcm']
# Structure Dict
structure_dict = dict(zip(structure_name, structure_files))
# dose dict
dose_files_dict = {
'Z(AP)': {'0.4x0.2x0.4': dose_files[0], '1': dose_files[1], '2': dose_files[2], '3': dose_files[3]},
'Y(SI)': {'0.4x0.2x0.4': dose_files[4], '1': dose_files[5], '2': dose_files[6], '3': dose_files[7]}}
sheets = ['Sphere', 'Cylinder', 'RtCylinder', 'Cone', 'RtCone']
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
sheet = 'Analytical'
ref_path = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
kf = mk.read_excel(ref_path, sheetname=sheet)
mask = kf['CT slice spacing (mm)'] == '0.2mm'
kf = kf.loc[mask]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# Get total_all analytical curves
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm) for row in kf.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table = mk.KnowledgeFrame(res).T
print(test_table)
if plot_curves:
for c in curves:
c.plot_results()
plt.show()
def test22(delta_mm=(0.1, 0.1, 0.1), up_sample_by_num=True, plot_curves=True):
ref_data = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_data.xlsx'
struc_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/STRUCTURES'
dose_grid_dir = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/DVH-Analysis-Data-Etc/DOSE GRIDS'
#
# ref_data = r'D:\Dropbox\Plan_Competit
st = 2
snames = ['Sphere_10_0', 'Sphere_20_0', 'Sphere_30_0',
'Cylinder_10_0', 'Cylinder_20_0', 'Cylinder_30_0',
'RtCylinder_10_0', 'RtCylinder_20_0', 'RtCylinder_30_0',
'Cone_10_0', 'Cone_20_0', 'Cone_30_0',
'RtCone_10_0', 'RtCone_20_0', 'RtCone_30_0']
structure_path = [os.path.join(struc_dir, f + '.dcm') for f in snames]
structure_dict = dict(zip(snames, structure_path))
dose_files = [os.path.join(dose_grid_dir, f) for f in [
'Linear_AntPost_1mm_Aligned.dcm',
'Linear_AntPost_2mm_Aligned.dcm',
'Linear_AntPost_3mm_Aligned.dcm',
'Linear_SupInf_1mm_Aligned.dcm',
'Linear_SupInf_2mm_Aligned.dcm',
'Linear_SupInf_3mm_Aligned.dcm']]
# dose dict
dose_files_dict = {
'Z(AP)': {'1': dose_files[0], '2': dose_files[1], '3': dose_files[2]},
'Y(SI)': {'1': dose_files[3], '2': dose_files[4], '3': dose_files[5]}}
col_grad_dict = {'Z(AP)': {'0.4x0.2x0.4': 'AP 0.2 mm', '1': 'AP 1 mm', '2': 'AP 2 mm', '3': 'AP 3 mm'},
'Y(SI)': {'0.4x0.2x0.4': 'SI 0.2 mm', '1': 'SI 1 mm', '2': 'SI 2 mm', '3': 'SI 3 mm'}}
# grab analytical data
out = '/home/victor/Dropbox/Plan_Competition_Project/pyplanscoring/testandardata/analytical_dvh.obj'
an_curves = load(out)
kf = mk.read_excel(ref_data, sheetname='Analytical')
kfi = kf.ix[40:]
mask0 = kfi['Structure Shift'] == 0
kfi = kfi.loc[mask0]
# Constrains to getting data
# Constrains
constrains = OrderedDict()
constrains['Total_Volume'] = True
constrains['getting_min'] = 'getting_min'
constrains['getting_max'] = 'getting_max'
constrains['average'] = 'average'
constrains['D99'] = 99
constrains['D95'] = 95
constrains['D5'] = 5
constrains['D1'] = 1
constrains['Dcc'] = 0.03
# GET CALCULATED DATA
# backend = 'threading'
res = Partotal_allel(n_jobs=-1, verbose=11)(
delayed(calc_data_total_all)(row,
dose_files_dict,
structure_dict,
constrains,
an_curves,
col_grad_dict,
delta_mm=delta_mm,
up_sample_by_num=up_sample_by_num) for row in kfi.traversal())
ref_results = [d[0] for d in res]
calc_results = [d[1] for d in res]
sname = [d[2] for d in res]
curves = [d[3] for d in res]
kf_ref_results = mk.concating(ref_results, axis=1).T.reseting_index()
kf_calc_results = mk.concating(calc_results, axis=1).T.reseting_index()
kf_ref_results['Structure name'] = sname
kf_calc_results['Structure name'] = sname
ref_num = kf_ref_results[kf_ref_results.columns[1:-2]]
calc_num = kf_calc_results[kf_calc_results.columns[1:-2]]
delta = ((calc_num - ref_num) / ref_num) * 100
res = OrderedDict()
lim = 3
for col in delta:
count = np.total_sum(np.abs(delta[col]) > lim)
rg = np.array([value_round(delta[col].getting_min(), 2), value_round(delta[col].getting_max(), 2)])
res[col] = {'count': count, 'range': rg}
test_table = | mk.KnowledgeFrame(res) | pandas.DataFrame |
# -*- coding: utf-8 -*-
# Author: <NAME> <<EMAIL>>
# License: BSD
"""
Toolset working with yahoo finance data
Module includes functions for easy access to YahooFinance data
"""
import urllib.request
import numpy as np
import requests # interaction with the web
import os # file system operations
import yaml # human-friendly data formating
import re # regular expressions
import monkey as mk # monkey... the best time collections library out there
import datetime as dt # date and time functions
import io
from .extra import ProgressBar
dateTimeFormat = "%Y%m%d %H:%M:%S"
def parseStr(s):
''' convert string to a float or string '''
f = s.strip()
if f[0] == '"':
return f.strip('"')
elif f=='N/A':
return np.nan
else:
try: # try float conversion
prefixes = {'M':1e6, 'B': 1e9}
prefix = f[-1]
if prefix in prefixes: # do we have a Billion/Million character?
return float(f[:-1])*prefixes[prefix]
else: # no, convert to float directly
return float(f)
except ValueError: # failed, return original string
return s
def gettingQuote(symbols):
"""
getting current yahoo quote
Parameters
-----------
symbols : list of str
list of ticker symbols
Returns
-----------
KnowledgeFrame , data is row-wise
"""
# for codes see: http://www.gummy-stuff.org/Yahoo-data.htm
if not incontainstance(symbols,list):
symbols = [symbols]
header_numer = ['symbol','final_item','change_pct','PE','time','short_ratio','prev_close','eps','market_cap']
request = str.join('', ['s', 'l1', 'p2' , 'r', 't1', 's7', 'p', 'e' , 'j1'])
data = dict(list(zip(header_numer,[[] for i in range(length(header_numer))])))
urlStr = 'http://finance.yahoo.com/d/quotes.csv?s=%s&f=%s' % (str.join('+',symbols), request)
try:
lines = urllib.request.urlopen(urlStr).readlines()
except Exception as e:
s = "Failed to download:\n{0}".formating(e);
print(s)
for line in lines:
fields = line.decode().strip().split(',')
#print fields, length(fields)
for i,field in enumerate(fields):
data[header_numer[i]].adding( parseStr(field))
idx = data.pop('symbol')
return | mk.KnowledgeFrame(data,index=idx) | pandas.DataFrame |
from __future__ import divisionision
from functools import wraps
import monkey as mk
import numpy as np
import time
import csv, sys
import os.path
import logging
from .ted_functions import TedFunctions
from .ted_aggregate_methods import TedAggregateMethods
from base.uber_model import UberModel, ModelSharedInputs
class TedSpeciesProperties(object):
"""
Listing of species properties that will eventutotal_ally be read in from a SQL db
"""
def __init__(self):
"""Class representing Species properties"""
super(TedSpeciesProperties, self).__init__()
self.sci_name = mk.Collections([], dtype='object')
self.com_name = mk.Collections([], dtype='object')
self.taxa = mk.Collections([], dtype='object')
self.order = mk.Collections([], dtype='object')
self.usfws_id = mk.Collections([], dtype='object')
self.body_wgt = mk.Collections([], dtype='object')
self.diet_item = mk.Collections([], dtype='object')
self.h2o_cont = mk.Collections([], dtype='float')
def read_species_properties(self):
# this is a temporary method to initiate the species/diet food items lists (this will be replacingd with
# a method to access a SQL database containing the properties
#filengthame = './ted/tests/TEDSpeciesProperties.csv'
filengthame = os.path.join(os.path.dirname(__file__),'tests/TEDSpeciesProperties.csv')
try:
with open(filengthame,'rt') as csvfile:
# csv.DictReader uses first line in file for column header_numings by default
dr = mk.read_csv(csvfile) # comma is default delimiter
except csv.Error as e:
sys.exit('file: %s, %s' (filengthame, e))
print(dr)
self.sci_name = dr.ix[:,'Scientific Name']
self.com_name = dr.ix[:,'Common Name']
self.taxa = dr.ix[:,'Taxa']
self.order = dr.ix[:,'Order']
self.usfws_id = dr.ix[:,'USFWS Species ID (ENTITY_ID)']
self.body_wgt= dr.ix[:,'BW (g)']
self.diet_item = dr.ix[:,'Food item']
self.h2o_cont = dr.ix[:,'Water content of diet']
class TedInputs(ModelSharedInputs):
"""
Required inputs class for Ted.
"""
def __init__(self):
"""Class representing the inputs for Ted"""
super(TedInputs, self).__init__()
# Inputs: Assign object attribute variables from the input Monkey KnowledgeFrame
self.chemical_name = mk.Collections([], dtype="object", name="chemical_name")
# application parameters for getting_min/getting_max application scenarios
self.crop_getting_min = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_min = mk.Collections([], dtype="object", name="app_method_getting_min")
self.app_rate_getting_min = mk.Collections([], dtype="float", name="app_rate_getting_min")
self.num_apps_getting_min = mk.Collections([], dtype="int", name="num_apps_getting_min")
self.app_interval_getting_min = mk.Collections([], dtype="int", name="app_interval_getting_min")
self.siplet_spec_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.boom_hgt_getting_min = mk.Collections([], dtype="object", name="siplet_spec_getting_min")
self.pest_incorp_depth_getting_min = mk.Collections([], dtype="object", name="pest_incorp_depth")
self.crop_getting_max = mk.Collections([], dtype="object", name="crop")
self.app_method_getting_max = mk.Collections([], dtype="object", name="app_method_getting_max")
self.app_rate_getting_max = mk.Collections([], dtype="float", name="app_rate_getting_max")
self.num_apps_getting_max = mk.Collections([], dtype="int", name="num_app_getting_maxs")
self.app_interval_getting_max = mk.Collections([], dtype="int", name="app_interval_getting_max")
self.siplet_spec_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.boom_hgt_getting_max = mk.Collections([], dtype="object", name="siplet_spec_getting_max")
self.pest_incorp_depth_getting_max = mk.Collections([], dtype="object", name="pest_incorp_depth")
# physical, chemical, and fate properties of pesticide
self.foliar_diss_hlife = mk.Collections([], dtype="float", name="foliar_diss_hlife")
self.aerobic_soil_meta_hlife = mk.Collections([], dtype="float", name="aerobic_soil_meta_hlife")
self.frac_retained_mamm = mk.Collections([], dtype="float", name="frac_retained_mamm")
self.frac_retained_birds = mk.Collections([], dtype="float", name="frac_retained_birds")
self.log_kow = mk.Collections([], dtype="float", name="log_kow")
self.koc = mk.Collections([], dtype="float", name="koc")
self.solubility = mk.Collections([], dtype="float", name="solubility")
self.henry_law_const = mk.Collections([], dtype="float", name="henry_law_const")
# bio concentration factors (ug active ing/kg-ww) / (ug active ing/liter)
self.aq_plant_algae_bcf_average = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_average")
self.aq_plant_algae_bcf_upper = mk.Collections([], dtype="float", name="aq_plant_algae_bcf_upper")
self.inv_bcf_average = mk.Collections([], dtype="float", name="inv_bcf_average")
self.inv_bcf_upper = mk.Collections([], dtype="float", name="inv_bcf_upper")
self.fish_bcf_average = mk.Collections([], dtype="float", name="fish_bcf_average")
self.fish_bcf_upper = mk.Collections([], dtype="float", name="fish_bcf_upper")
# bounding water concentrations (ug active ing/liter)
self.water_conc_1 = mk.Collections([], dtype="float", name="water_conc_1") # lower bound
self.water_conc_2 = mk.Collections([], dtype="float", name="water_conc_2") # upper bound
# health value inputs
# nagetting_ming convention (based on listing from OPP TED Excel spreadsheet 'inputs' worksheet):
# dbt: dose based toxicity
# cbt: concentration-based toxicity
# arbt: application rate-based toxicity
# 1inmill_mort: 1/million mortality (note initial character is numeral 1, not letter l)
# 1inten_mort: 10% mortality (note initial character is numeral 1, not letter l)
# others are self explanatory
# dose based toxicity(dbt): mammals (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort")
self.dbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_low_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50")
self.dbt_mamm_rat_oral_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort")
self.dbt_mamm_rat_derm_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50")
self.dbt_mamm_rat_inhal_ld50 = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50")
self.dbt_mamm_sub_direct = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct")
self.dbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect")
self.dbt_mamm_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inmill_mort_wgt")
self.dbt_mamm_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_low_ld50_wgt")
self.dbt_mamm_rat_oral_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_1inten_mort_wgt")
self.dbt_mamm_rat_derm_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_derm_ld50_wgt")
self.dbt_mamm_rat_inhal_ld50_wgt = mk.Collections([], dtype="float", name="dbt_mamm_rat_inhal_ld50_wgt")
self.dbt_mamm_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_direct_wgt")
self.dbt_mamm_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_mamm_sub_indirect_wgt")
# dose based toxicity(dbt): birds (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort")
self.dbt_bird_1inten_mort = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort")
self.dbt_bird_low_ld50 = mk.Collections([], dtype="float", name="dbt_bird_low_ld50")
self.dbt_bird_hc05 = mk.Collections([], dtype="float", name="dbt_bird_hc05")
self.dbt_bird_hc50 = mk.Collections([], dtype="float", name="dbt_bird_hc50")
self.dbt_bird_hc95 = mk.Collections([], dtype="float", name="dbt_bird_hc95")
self.dbt_bird_sub_direct = mk.Collections([], dtype="float", name="dbt_bird_sub_direct")
self.dbt_bird_sub_indirect = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect")
self.getting_mineau_sca_fact = mk.Collections([], dtype="float", name="getting_mineau_sca_fact")
self.dbt_bird_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inmill_mort_wgt")
self.dbt_bird_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_bird_1inten_mort_wgt")
self.dbt_bird_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_bird_low_ld50_wgt")
self.dbt_bird_hc05_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc05_wgt")
self.dbt_bird_hc50_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc50_wgt")
self.dbt_bird_hc95_wgt = mk.Collections([], dtype="float", name="dbt_bird_hc95_wgt")
self.dbt_bird_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_direct_wgt")
self.dbt_bird_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_bird_sub_indirect_wgt")
self.getting_mineau_sca_fact_wgt = mk.Collections([], dtype="float", name="getting_mineau_sca_fact_wgt")
# dose based toxicity(dbt): reptiles, terrestrial-phase amphibians (mg-pest/kg-bw) & weight of test animal (grams)
self.dbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort")
self.dbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort")
self.dbt_reptile_low_ld50 = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50")
self.dbt_reptile_sub_direct = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct")
self.dbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect")
self.dbt_reptile_1inmill_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inmill_mort_wgt")
self.dbt_reptile_1inten_mort_wgt = mk.Collections([], dtype="float", name="dbt_reptile_1inten_mort_wgt")
self.dbt_reptile_low_ld50_wgt = mk.Collections([], dtype="float", name="dbt_reptile_low_ld50_wgt")
self.dbt_reptile_sub_direct_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_direct_wgt")
self.dbt_reptile_sub_indirect_wgt = mk.Collections([], dtype="float", name="dbt_reptile_sub_indirect_wgt")
# concentration-based toxicity (cbt) : mammals (mg-pest/kg-diet food)
self.cbt_mamm_1inmill_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inmill_mort")
self.cbt_mamm_1inten_mort = mk.Collections([], dtype="float", name="cbt_mamm_1inten_mort")
self.cbt_mamm_low_lc50 = mk.Collections([], dtype="float", name="cbt_mamm_low_lc50")
self.cbt_mamm_sub_direct = mk.Collections([], dtype="float", name="cbt_mamm_sub_direct")
self.cbt_mamm_grow_noec = mk.Collections([], dtype="float", name="cbt_mamm_grow_noec")
self.cbt_mamm_grow_loec = mk.Collections([], dtype="float", name="cbt_mamm_grow_loec")
self.cbt_mamm_repro_noec = mk.Collections([], dtype="float", name="cbt_mamm_repro_noec")
self.cbt_mamm_repro_loec = mk.Collections([], dtype="float", name="cbt_mamm_repro_loec")
self.cbt_mamm_behav_noec = mk.Collections([], dtype="float", name="cbt_mamm_behav_noec")
self.cbt_mamm_behav_loec = mk.Collections([], dtype="float", name="cbt_mamm_behav_loec")
self.cbt_mamm_sensory_noec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_noec")
self.cbt_mamm_sensory_loec = mk.Collections([], dtype="float", name="cbt_mamm_sensory_loec")
self.cbt_mamm_sub_indirect = mk.Collections([], dtype="float", name="cbt_mamm_sub_indirect")
# concentration-based toxicity (cbt) : birds (mg-pest/kg-diet food)
self.cbt_bird_1inmill_mort = mk.Collections([], dtype="float", name="cbt_bird_1inmill_mort")
self.cbt_bird_1inten_mort = mk.Collections([], dtype="float", name="cbt_bird_1inten_mort")
self.cbt_bird_low_lc50 = mk.Collections([], dtype="float", name="cbt_bird_low_lc50")
self.cbt_bird_sub_direct = mk.Collections([], dtype="float", name="cbt_bird_sub_direct")
self.cbt_bird_grow_noec = mk.Collections([], dtype="float", name="cbt_bird_grow_noec")
self.cbt_bird_grow_loec = mk.Collections([], dtype="float", name="cbt_bird_grow_loec")
self.cbt_bird_repro_noec = mk.Collections([], dtype="float", name="cbt_bird_repro_noec")
self.cbt_bird_repro_loec = mk.Collections([], dtype="float", name="cbt_bird_repro_loec")
self.cbt_bird_behav_noec = mk.Collections([], dtype="float", name="cbt_bird_behav_noec")
self.cbt_bird_behav_loec = mk.Collections([], dtype="float", name="cbt_bird_behav_loec")
self.cbt_bird_sensory_noec = mk.Collections([], dtype="float", name="cbt_bird_sensory_noec")
self.cbt_bird_sensory_loec = mk.Collections([], dtype="float", name="cbt_bird_sensory_loec")
self.cbt_bird_sub_indirect = mk.Collections([], dtype="float", name="cbt_bird_sub_indirect")
# concentration-based toxicity (cbt) : reptiles, terrestrial-phase amphibians (mg-pest/kg-diet food)
self.cbt_reptile_1inmill_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inmill_mort")
self.cbt_reptile_1inten_mort = mk.Collections([], dtype="float", name="cbt_reptile_1inten_mort")
self.cbt_reptile_low_lc50 = mk.Collections([], dtype="float", name="cbt_reptile_low_lc50")
self.cbt_reptile_sub_direct = mk.Collections([], dtype="float", name="cbt_reptile_sub_direct")
self.cbt_reptile_grow_noec = mk.Collections([], dtype="float", name="cbt_reptile_grow_noec")
self.cbt_reptile_grow_loec = mk.Collections([], dtype="float", name="cbt_reptile_grow_loec")
self.cbt_reptile_repro_noec = mk.Collections([], dtype="float", name="cbt_reptile_repro_noec")
self.cbt_reptile_repro_loec = mk.Collections([], dtype="float", name="cbt_reptile_repro_loec")
self.cbt_reptile_behav_noec = mk.Collections([], dtype="float", name="cbt_reptile_behav_noec")
self.cbt_reptile_behav_loec = mk.Collections([], dtype="float", name="cbt_reptile_behav_loec")
self.cbt_reptile_sensory_noec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_noec")
self.cbt_reptile_sensory_loec = mk.Collections([], dtype="float", name="cbt_reptile_sensory_loec")
self.cbt_reptile_sub_indirect = mk.Collections([], dtype="float", name="cbt_reptile_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body weight (mg-pest/kg-bw(ww))
self.cbt_inv_bw_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inmill_mort")
self.cbt_inv_bw_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_bw_1inten_mort")
self.cbt_inv_bw_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_bw_low_lc50")
self.cbt_inv_bw_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_direct")
self.cbt_inv_bw_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_noec")
self.cbt_inv_bw_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_grow_loec")
self.cbt_inv_bw_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_noec")
self.cbt_inv_bw_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_repro_loec")
self.cbt_inv_bw_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_noec")
self.cbt_inv_bw_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_behav_loec")
self.cbt_inv_bw_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_noec")
self.cbt_inv_bw_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_bw_sensory_loec")
self.cbt_inv_bw_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_bw_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates body diet (mg-pest/kg-food(ww))
self.cbt_inv_food_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inmill_mort")
self.cbt_inv_food_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_food_1inten_mort")
self.cbt_inv_food_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_food_low_lc50")
self.cbt_inv_food_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_food_sub_direct")
self.cbt_inv_food_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_noec")
self.cbt_inv_food_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_food_grow_loec")
self.cbt_inv_food_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_noec")
self.cbt_inv_food_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_food_repro_loec")
self.cbt_inv_food_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_noec")
self.cbt_inv_food_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_food_behav_loec")
self.cbt_inv_food_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_noec")
self.cbt_inv_food_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_food_sensory_loec")
self.cbt_inv_food_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_food_sub_indirect")
# concentration-based toxicity (cbt) : invertebrates soil (mg-pest/kg-soil(dw))
self.cbt_inv_soil_1inmill_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inmill_mort")
self.cbt_inv_soil_1inten_mort = mk.Collections([], dtype="float", name="cbt_inv_soil_1inten_mort")
self.cbt_inv_soil_low_lc50 = mk.Collections([], dtype="float", name="cbt_inv_soil_low_lc50")
self.cbt_inv_soil_sub_direct = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_direct")
self.cbt_inv_soil_grow_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_noec")
self.cbt_inv_soil_grow_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_grow_loec")
self.cbt_inv_soil_repro_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_noec")
self.cbt_inv_soil_repro_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_repro_loec")
self.cbt_inv_soil_behav_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_noec")
self.cbt_inv_soil_behav_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_behav_loec")
self.cbt_inv_soil_sensory_noec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_noec")
self.cbt_inv_soil_sensory_loec = mk.Collections([], dtype="float", name="cbt_inv_soil_sensory_loec")
self.cbt_inv_soil_sub_indirect = mk.Collections([], dtype="float", name="cbt_inv_soil_sub_indirect")
# application rate-based toxicity (arbt) : mammals (lbs active ingredient/Acre)
self.arbt_mamm_mort = mk.Collections([], dtype="float", name="arbt_mamm_mort")
self.arbt_mamm_growth = mk.Collections([], dtype="float", name="arbt_mamm_growth")
self.arbt_mamm_repro = mk.Collections([], dtype="float", name="arbt_mamm_repro")
self.arbt_mamm_behav = mk.Collections([], dtype="float", name="arbt_mamm_behav")
self.arbt_mamm_sensory = mk.Collections([], dtype="float", name="arbt_mamm_sensory")
# application rate-based toxicity (arbt) : birds (lbs active ingredient/Acre)
self.arbt_bird_mort = mk.Collections([], dtype="float", name="arbt_bird_mort")
self.arbt_bird_growth = mk.Collections([], dtype="float", name="arbt_bird_growth")
self.arbt_bird_repro = mk.Collections([], dtype="float", name="arbt_bird_repro")
self.arbt_bird_behav = mk.Collections([], dtype="float", name="arbt_bird_behav")
self.arbt_bird_sensory = mk.Collections([], dtype="float", name="arbt_bird_sensory")
# application rate-based toxicity (arbt) : reptiles (lbs active ingredient/Acre)
self.arbt_reptile_mort = mk.Collections([], dtype="float", name="arbt_reptile_mort")
self.arbt_reptile_growth = mk.Collections([], dtype="float", name="arbt_reptile_growth")
self.arbt_reptile_repro = mk.Collections([], dtype="float", name="arbt_reptile_repro")
self.arbt_reptile_behav = mk.Collections([], dtype="float", name="arbt_reptile_behav")
self.arbt_reptile_sensory = mk.Collections([], dtype="float", name="arbt_reptile_sensory")
# application rate-based toxicity (arbt) : invertebrates (lbs active ingredient/Acre)
self.arbt_inv_1inmill_mort = mk.Collections([], dtype="float", name="arbt_inv_1inmill_mort")
self.arbt_inv_1inten_mort = mk.Collections([], dtype="float", name="arbt_inv_1inten_mort")
self.arbt_inv_sub_direct = mk.Collections([], dtype="float", name="arbt_inv_sub_direct")
self.arbt_inv_sub_indirect = mk.Collections([], dtype="float", name="arbt_inv_sub_indirect")
self.arbt_inv_growth = mk.Collections([], dtype="float", name="arbt_inv_growth")
self.arbt_inv_repro = mk.Collections([], dtype="float", name="arbt_inv_repro")
self.arbt_inv_behav = mk.Collections([], dtype="float", name="arbt_inv_behav")
self.arbt_inv_sensory = | mk.Collections([], dtype="float", name="arbt_inv_sensory") | pandas.Series |
from flowsa.common import WITHDRAWN_KEYWORD
from flowsa.flowbyfunctions import total_allocate_fips_location_system
from flowsa.location import US_FIPS
import math
import monkey as mk
import io
from flowsa.settings import log
from string import digits
YEARS_COVERED = {
"asbestos": "2014-2018",
"barite": "2014-2018",
"bauxite": "2013-2017",
"beryllium": "2014-2018",
"boron": "2014-2018",
"chromium": "2014-2018",
"clay": "2015-2016",
"cobalt": "2013-2017",
"copper": "2011-2015",
"diatomite": "2014-2018",
"feldspar": "2013-2017",
"fluorspar": "2013-2017",
"fluorspar_inports": ["2016", "2017"],
"gtotal_allium": "2014-2018",
"garnet": "2014-2018",
"gold": "2013-2017",
"graphite": "2013-2017",
"gyptotal_sum": "2014-2018",
"iodine": "2014-2018",
"ironore": "2014-2018",
"kyanite": "2014-2018",
"lead": "2012-2018",
"lime": "2014-2018",
"lithium": "2013-2017",
"magnesium": "2013-2017",
"manganese": "2012-2016",
"manufacturedabrasive": "2017-2018",
"mica": "2014-2018",
"molybdenum": "2014-2018",
"nickel": "2012-2016",
"niobium": "2014-2018",
"peat": "2014-2018",
"perlite": "2013-2017",
"phosphate": "2014-2018",
"platinum": "2014-2018",
"potash": "2014-2018",
"pumice": "2014-2018",
"rhenium": "2014-2018",
"salt": "2013-2017",
"sandgflat_underlyingconstruction": "2013-2017",
"sandgflat_underlyingindustrial": "2014-2018",
"silver": "2012-2016",
"sodaash": "2010-2017",
"sodaash_t4": ["2016", "2017"],
"stonecrushed": "2013-2017",
"stonedimension": "2013-2017",
"strontium": "2014-2018",
"talc": "2013-2017",
"titanium": "2013-2017",
"tungsten": "2013-2017",
"vermiculite": "2014-2018",
"zeolites": "2014-2018",
"zinc": "2013-2017",
"zirconium": "2013-2017",
}
def usgs_myb_year(years, current_year_str):
"""
Sets the column for the string based on the year. Checks that the year
you picked is in the final_item file.
:param years: string, with hypthon
:param current_year_str: string, year of interest
:return: string, year
"""
years_array = years.split("-")
lower_year = int(years_array[0])
upper_year = int(years_array[1])
current_year = int(current_year_str)
if lower_year <= current_year <= upper_year:
column_val = current_year - lower_year + 1
return "year_" + str(column_val)
else:
log.info("Your year is out of scope. Pick a year between %s and %s",
lower_year, upper_year)
def usgs_myb_name(USGS_Source):
"""
Takes the USGS source name and parses it so it can be used in other parts
of Flow by activity.
:param USGS_Source: string, usgs source name
:return:
"""
source_split = USGS_Source.split("_")
name_cc = str(source_split[2])
name = ""
for char in name_cc:
if char.isupper():
name = name + " " + char
else:
name = name + char
name = name.lower()
name = name.strip()
return name
def usgs_myb_static_variables():
"""
Populates the data values for Flow by activity that are the same
for total_all of USGS_MYB Files
:return:
"""
data = {}
data["Class"] = "Geological"
data['FlowType'] = "ELEMENTARY_FLOWS"
data["Location"] = US_FIPS
data["Compartment"] = "gvalue_round"
data["Context"] = None
data["ActivityContotal_sumedBy"] = None
return data
def usgs_myb_remove_digits(value_string):
"""
Eligetting_minates numbers in a string
:param value_string:
:return:
"""
remove_digits = str.maketrans('', '', digits)
return_string = value_string.translate(remove_digits)
return return_string
def usgs_myb_url_helper(*, build_url, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:param config: dictionary, items in FBA method yaml
:param args: dictionary, arguments specified when running flowbyactivity.py
flowbyactivity.py ('year' and 'source')
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
return [build_url]
def usgs_asbestos_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['asbestos'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_asbestos_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
product = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Exports and reexports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['asbestos'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(knowledgeframe,
str(year))
return knowledgeframe
def usgs_barite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(
io.BytesIO(resp.content), sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['barite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_barite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Crude, sold or used by producers:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['barite'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_bauxite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['bauxite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_bauxite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['bauxite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, as shipped:":
prod = "import"
elif kf.iloc[index]["Production"].strip() == \
"Exports, as shipped:":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
flow_amount = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = flow_amount
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_beryllium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['beryllium'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_beryllium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["United States6", "Mine shipments1",
"Imports for contotal_sumption, beryl2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['beryllium'], year)
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, beryl2":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_boron_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data.loc[8:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data.loc[21:22]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_three = mk.KnowledgeFrame(kf_raw_data.loc[27:28]).reindexing()
kf_data_three = kf_data_three.reseting_index()
del kf_data_three["index"]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_two.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
kf_data_three.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['boron'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
del kf_data_three[col]
frames = [kf_data_one, kf_data_two, kf_data_three]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_boron_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["B2O3 content", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['boron'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "B2O3 content" or \
kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
if kf.iloc[index]["Production"].strip() == "Colemanite:4":
des = "Colemanite"
elif kf.iloc[index]["Production"].strip() == "Ulexite:4":
des = "Ulexite"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if des == name:
data['FlowName'] = name + " " + product
else:
data['FlowName'] = name + " " + product + " " + des
data["Description"] = des
data["ActivityProducedBy"] = name
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_chromium_ctotal_all(*, resp, year, **_):
""""
Convert response for ctotal_alling url to monkey knowledgeframe,
begin parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:24]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['chromium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_chromium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary2", "Total"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary2":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['chromium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_clay_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_btotal_all = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T3')
kf_data_btotal_all = mk.KnowledgeFrame(kf_raw_data_btotal_all.loc[19:19]).reindexing()
kf_data_btotal_all = kf_data_btotal_all.reseting_index()
del kf_data_btotal_all["index"]
kf_raw_data_bentonite = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T4 ')
kf_data_bentonite = mk.KnowledgeFrame(
kf_raw_data_bentonite.loc[28:28]).reindexing()
kf_data_bentonite = kf_data_bentonite.reseting_index()
del kf_data_bentonite["index"]
kf_raw_data_common = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T5 ')
kf_data_common = mk.KnowledgeFrame(kf_raw_data_common.loc[40:40]).reindexing()
kf_data_common = kf_data_common.reseting_index()
del kf_data_common["index"]
kf_raw_data_fire = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T6 ')
kf_data_fire = mk.KnowledgeFrame(kf_raw_data_fire.loc[12:12]).reindexing()
kf_data_fire = kf_data_fire.reseting_index()
del kf_data_fire["index"]
kf_raw_data_fuller = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7 ')
kf_data_fuller = mk.KnowledgeFrame(kf_raw_data_fuller.loc[17:17]).reindexing()
kf_data_fuller = kf_data_fuller.reseting_index()
del kf_data_fuller["index"]
kf_raw_data_kaolin = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8 ')
kf_data_kaolin = mk.KnowledgeFrame(kf_raw_data_kaolin.loc[18:18]).reindexing()
kf_data_kaolin = kf_data_kaolin.reseting_index()
del kf_data_kaolin["index"]
kf_raw_data_export = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T13')
kf_data_export = mk.KnowledgeFrame(kf_raw_data_export.loc[6:15]).reindexing()
kf_data_export = kf_data_export.reseting_index()
del kf_data_export["index"]
kf_raw_data_import = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T14')
kf_data_import = mk.KnowledgeFrame(kf_raw_data_import.loc[6:13]).reindexing()
kf_data_import = kf_data_import.reseting_index()
del kf_data_import["index"]
kf_data_btotal_all.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_bentonite.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_common.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fire.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_fuller.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_kaolin.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2"]
kf_data_export.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_import.columns = ["Production", "space_1", "year_1", "space_2",
"value_1", "space_3", "year_2", "space_4",
"value_2", "space_5", "extra"]
kf_data_btotal_all["type"] = "Btotal_all clay"
kf_data_bentonite["type"] = "Bentonite"
kf_data_common["type"] = "Common clay"
kf_data_fire["type"] = "Fire clay"
kf_data_fuller["type"] = "Fullerโs earth"
kf_data_kaolin["type"] = "Kaolin"
kf_data_export["type"] = "export"
kf_data_import["type"] = "import"
col_to_use = ["Production", "type"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['clay'], year))
for col in kf_data_import.columns:
if col not in col_to_use:
del kf_data_import[col]
del kf_data_export[col]
for col in kf_data_btotal_all.columns:
if col not in col_to_use:
del kf_data_btotal_all[col]
del kf_data_bentonite[col]
del kf_data_common[col]
del kf_data_fire[col]
del kf_data_fuller[col]
del kf_data_kaolin[col]
frames = [kf_data_import, kf_data_export, kf_data_btotal_all, kf_data_bentonite,
kf_data_common, kf_data_fire, kf_data_fuller, kf_data_kaolin]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_clay_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Btotal_all clay", "Bentonite", "Fire clay", "Kaolin",
"Fullerโs earth", "Total", "Grand total",
"Artificitotal_ally activated clay and earth",
"Clays, not elsewhere classified",
"Clays, not elsewhere classified"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["type"].strip() == "import":
product = "imports"
elif kf.iloc[index]["type"].strip() == "export":
product = "exports"
else:
product = "production"
if str(kf.iloc[index]["Production"]).strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
if product == "production":
data['FlowName'] = \
kf.iloc[index]["type"].strip() + " " + product
data["Description"] = kf.iloc[index]["type"].strip()
data["ActivityProducedBy"] = kf.iloc[index]["type"].strip()
else:
data['FlowName'] = \
kf.iloc[index]["Production"].strip() + " " + product
data["Description"] = kf.iloc[index]["Production"].strip()
data["ActivityProducedBy"] = \
kf.iloc[index]["Production"].strip()
col_name = usgs_myb_year(YEARS_COVERED['clay'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)" or \
str(kf.iloc[index][col_name]) == "(2)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_cobalt_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[6:11]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[23:23]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_2.columns) > 11:
for x in range(11, length(kf_data_2.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "space_6", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
if length(kf_data_2. columns) == 11:
kf_data_2.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['cobalt'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_cobalt_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["United Statese, 16, 17", "Mine productione",
"Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
prod = "production"
if kf.iloc[index]["Production"].strip() == \
"United Statese, 16, 17":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['cobalt'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
remove_rows = ["(18)", "(2)"]
if data["FlowAmount"] not in remove_rows:
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_copper_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[12:12]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[30:31]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Unit"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['copper'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_copper_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
if product == "Total":
prod = "production"
elif product == "Exports, refined":
prod = "exports"
elif product == "Imports, refined":
prod = "imports"
data["ActivityProducedBy"] = "Copper; Mine"
data['FlowName'] = name + " " + prod
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['copper'], year)
data["Description"] = "Copper; Mine"
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_diatomite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) == 10:
kf_data_one.columns = ["Production", "year_1", "space_2", "year_2",
"space_3", "year_3", "space_4", "year_4",
"space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['diatomite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_diatomite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports2", "Imports for contotal_sumption2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption2":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand metric tons"
col_name = usgs_myb_year(YEARS_COVERED['diatomite'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = name
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_feldspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:8]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['feldspar'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_feldspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, feldspar:4":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:4":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, feldspar:e, 2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == "Nepheline syenite:":
prod = "production"
des = "Nepheline syenite"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['feldspar'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
if name == des:
data['FlowName'] = name + " " + prod
else:
data['FlowName'] = name + " " + prod + " " + des
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_fluorspar_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
if year in YEARS_COVERED['fluorspar_inports']:
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_raw_data_three = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T7')
kf_raw_data_four = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T8')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[5:15]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if year in YEARS_COVERED['fluorspar_inports']:
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[7:8]).reindexing()
kf_data_three = mk.KnowledgeFrame(kf_raw_data_three.loc[19:19]).reindexing()
kf_data_four = mk.KnowledgeFrame(kf_raw_data_four.loc[11:11]).reindexing()
if length(kf_data_two.columns) == 13:
kf_data_two.columns = ["Production", "space_1", "not_1", "space_2",
"not_2", "space_3", "not_3", "space_4",
"not_4", "space_5", "year_4", "space_6",
"year_5"]
if length(kf_data_three.columns) == 9:
kf_data_three.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
kf_data_four.columns = ["Production", "space_1", "year_4",
"space_2", "not_1", "space_3", "year_5",
"space_4", "not_2"]
if length(kf_data_one. columns) == 13:
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['fluorspar'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
if year in YEARS_COVERED['fluorspar_inports']:
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
for col in kf_data_three.columns:
if col not in col_to_use:
del kf_data_three[col]
for col in kf_data_four.columns:
if col not in col_to_use:
del kf_data_four[col]
kf_data_one["type"] = "data_one"
if year in YEARS_COVERED['fluorspar_inports']:
# alugetting_minum fluoride
# cryolite
kf_data_two["type"] = "data_two"
kf_data_three["type"] = "Alugetting_minum Fluoride"
kf_data_four["type"] = "Cryolite"
frames = [kf_data_one, kf_data_two, kf_data_three, kf_data_four]
else:
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_fluorspar_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity3", "Total", "Hydrofluoric acid",
"Mettotal_allurgical", "Production"]
prod = ""
name = usgs_myb_name(source)
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "exports"
des = name
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "imports"
des = name
elif kf.iloc[index]["Production"].strip() == "Fluorosilicic acid:":
prod = "production"
des = "Fluorosilicic acid:"
if str(kf.iloc[index]["type"]).strip() == "data_two":
prod = "imports"
des = kf.iloc[index]["Production"].strip()
elif str(kf.iloc[index]["type"]).strip() == \
"Alugetting_minum Fluoride" or \
str(kf.iloc[index]["type"]).strip() == "Cryolite":
prod = "imports"
des = kf.iloc[index]["type"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['fluorspar'], year)
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gtotal_allium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 11:
for x in range(11, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data.columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gtotal_allium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gtotal_allium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production, primary crude", "Metal"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == \
"Production, primary crude":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gtotal_allium'], year)
if str(kf.iloc[index][col_name]).strip() == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_garnet_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_two = mk.KnowledgeFrame(kf_raw_data_two.loc[4:5]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
kf_data_one = mk.KnowledgeFrame(kf_raw_data_two.loc[10:14]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 13:
for x in range(13, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_two. columns) == 13:
kf_data_two.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
kf_data_one.columns = ["Production", "space_1", "unit", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['garnet'], year))
for col in kf_data_two.columns:
if col not in col_to_use:
del kf_data_two[col]
del kf_data_one[col]
frames = [kf_data_two, kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_garnet_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:2":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption: 3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Crude production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['garnet'], year)
data["FlowAmount"] = str(kf.iloc[index][col_name])
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gold_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:14]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) == 13:
kf_data.columns = ["Production", "Space", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gold'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_gold_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Exports, refined bullion",
"Imports for contotal_sumption, refined bullion"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Quantity":
product = "production"
elif kf.iloc[index]["Production"].strip() == \
"Exports, refined bullion":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined bullion":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['gold'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_graphite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[5:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit", "space_6",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['graphite'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_graphite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantiy", "Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['graphite'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "nan":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_gyptotal_sum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin
parsing kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:10]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 11:
kf_data_one.columns = ["Production", "space_1", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_gyptotal_sum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['gyptotal_sum'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Quantity":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iodine_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:10]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
elif length(kf_data. columns) == 13:
kf_data.columns = ["Production", "unit", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5", "space_6"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['iodine'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iodine_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Quantity, for contotal_sumption", "Exports2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Imports:2":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports2":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['iodine'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_iron_ore_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:25]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['ironore'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_iron_ore_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Gross weight", "Quantity"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production:":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data['FlowName'] = "Iron Ore " + product
data["Description"] = "Iron Ore"
data["ActivityProducedBy"] = "Iron Ore"
col_name = usgs_myb_year(YEARS_COVERED['ironore'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_kyanite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:13]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['kyanite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_kyanite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Quantity2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['kyanite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Exports of kyanite concentrate:3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, total_all kyanite getting_minerals:3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lead_url_helper(*, year, **_):
"""
This helper function uses the "build_url" input from flowbyactivity.py,
which is a base url for data imports that requires parts of the url text
string to be replacingd with info specific to the data year. This function
does not parse the data, only modifies the urls from which data is
obtained.
:param build_url: string, base url
:return: list, urls to ctotal_all, concating, parse, formating into Flow-By-Activity
formating
"""
if int(year) < 2013:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2016-lead.xls')
elif int(year) < 2014:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/atoms/files/myb1-2017-lead.xls')
else:
build_url = ('https://d9-wret.s3.us-west-2.amazonaws.com/assets/'
'ptotal_alladium/production/s3fs-public/media/files/myb1-2018-lead-advrel.xlsx')
url = build_url
return [url]
def usgs_lead_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[8:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production", "Units"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
elif int(year) > 2013:
modified_sy = "2014-2018"
col_to_use.adding(usgs_myb_year(modified_sy, year))
else:
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lead'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_lead_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
name = usgs_myb_name(source)
des = name
row_to_use = ["Primary lead, refined content, "
"domestic ores and base bullion",
"Secondary lead, lead content",
"Lead ore and concentrates", "Lead in base bullion"]
import_export = ["Exports, lead content:",
"Imports for contotal_sumption, lead content:"]
knowledgeframe = mk.KnowledgeFrame()
product = "production"
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() in import_export:
if kf.iloc[index]["Production"].strip() == \
"Exports, lead content:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, lead content:":
product = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["ActivityProducedBy"] = kf.iloc[index]["Production"]
if int(year) == 2013:
modified_sy = "2013-2018"
col_name = usgs_myb_year(modified_sy, year)
elif int(year) > 2013:
modified_sy = "2014-2018"
col_name = usgs_myb_year(modified_sy, year)
else:
col_name = usgs_myb_year(YEARS_COVERED['lead'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lime_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data_two.loc[16:16]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[28:32]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 12:
for x in range(12, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
del kf_data_2[col_name]
if length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
kf_data_2.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lime'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lime_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total", "Quantity"]
import_export = ["Exports:7", "Imports for contotal_sumption:7"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:7":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:7":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['lime'], year)
data["Description"] = des
data["ActivityProducedBy"] = name
if product.strip() == "Total":
data['FlowName'] = name + " " + prod
elif product.strip() == "Quantity":
data['FlowName'] = name + " " + prod
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_lithium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 11:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one. columns) == 11:
kf_data_one.columns = ["Production", "space_2", "year_1", "space_3",
"year_2", "space_4", "year_3", "space_5",
"year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['lithium'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_lithium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Exports3", "Imports3", "Production"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['lithium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports3":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == "Imports3":
prod = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_magnesium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:15]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Units", "space_1", "year_1",
"space_2", "year_2", "space_3", "year_3",
"space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['magnesium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_magnesium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Secondary", "Primary", "Exports", "Imports for contotal_sumption"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Secondary" or \
kf.iloc[index]["Production"].strip() == "Primary":
product = "production" + " " + \
kf.iloc[index]["Production"].strip()
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['magnesium'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
elif str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_manganese_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:9]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 12:
for x in range(12, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 12:
kf_data.columns = ["Production", "Unit", "space_1", "year_1",
"space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['manganese'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_manganese_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
elif kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['manganese'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_ma_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param args: dictionary, arguments specified when running
flowbyactivity.py ('year' and 'source')
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T2')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[6:7]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 9:
for x in range(9, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
elif length(kf_data. columns) == 9:
kf_data.columns = ["Product", "space_1", "quality_year_1", "space_2",
"value_year_1", "space_3",
"quality_year_2", "space_4", "value_year_2"]
col_to_use = ["Product"]
col_to_use.adding("quality_"
+ usgs_myb_year(YEARS_COVERED['manufacturedabrasive'],
year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_ma_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param args: dictionary, used to run flowbyactivity.py
('year' and 'source')
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Silicon carbide"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
for index, row in kf.traversal():
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Product"].strip().translate(remove_digits)
if product in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data['FlowName'] = "Silicon carbide"
data["ActivityProducedBy"] = "Silicon carbide"
data["Unit"] = "Metric Tons"
col_name = ("quality_"
+ usgs_myb_year(
YEARS_COVERED['manufacturedabrasive'], year))
col_name_array = col_name.split("_")
data["Description"] = product + " " + col_name_array[0]
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_mica_ctotal_all(*, resp, source, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[4:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
name = usgs_myb_name(source)
des = name
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['mica'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_mica_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['mica'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Production, sold or used by producers:":
prod = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_molybdenum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[7:11]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data. columns) == 11:
kf_data.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['molybdenum'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_molybdenum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Imports for contotal_sumption", "Exports"]
knowledgeframe = mk.KnowledgeFrame()
name = usgs_myb_name(source)
des = name
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Production":
product = "production"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = des
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['molybdenum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_nickel_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T10')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[36:36]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_raw_data_two = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_2 = mk.KnowledgeFrame(kf_raw_data_two.loc[11:16]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1.columns) > 11:
for x in range(11, length(kf_data_1.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_1[col_name]
if length(kf_data_1. columns) == 11:
kf_data_1.columns = ["Production", "space_1", "year_1", "space_2",
"year_2", "space_3", "year_3", "space_4",
"year_4", "space_5", "year_5"]
if length(kf_data_2.columns) == 12:
kf_data_2.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['nickel'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
for col in kf_data_2.columns:
if col not in col_to_use:
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_nickel_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Ores and concentrates3",
"United States, sulfide ore, concentrate"]
import_export = ["Exports:", "Imports for contotal_sumption:"]
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
prod = "production"
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports:":
prod = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
prod = "imports"
if kf.iloc[index]["Production"].strip() in row_to_use:
remove_digits = str.maketrans('', '', digits)
product = kf.iloc[index][
"Production"].strip().translate(remove_digits)
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
col_name = usgs_myb_year(YEARS_COVERED['nickel'], year)
if product.strip() == \
"United States, sulfide ore, concentrate":
data["Description"] = \
"United States, sulfide ore, concentrate Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
elif product.strip() == "Ores and concentrates":
data["Description"] = "Ores and concentrates Nickel"
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(4)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_niobium_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data = mk.KnowledgeFrame(kf_raw_data.loc[4:19]).reindexing()
kf_data = kf_data.reseting_index()
del kf_data["index"]
if length(kf_data.columns) > 13:
for x in range(13, length(kf_data.columns)):
col_name = "Unnamed: " + str(x)
del kf_data[col_name]
if length(kf_data. columns) == 13:
kf_data.columns = ["Production", "space_1", "Unit_1", "space_2",
"year_1", "space_3", "year_2", "space_4",
"year_3", "space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['niobium'], year))
for col in kf_data.columns:
if col not in col_to_use:
del kf_data[col]
return kf_data
def usgs_niobium_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Total imports, Nb content", "Total exports, Nb content"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Exports:":
product = "exports"
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Metric Tons"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['niobium'], year)
if str(kf.iloc[index][col_name]) == "--" or \
str(kf.iloc[index][col_name]) == "(3)":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_peat_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
"""Ctotal_alls the excel sheet for nickel and removes extra columns"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:18]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
if length(kf_data_one.columns) == 12:
kf_data_one.columns = ["Production", "Unit", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['peat'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
frames = [kf_data_one]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_peat_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Production", "Exports", "Imports for contotal_sumption"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['peat'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Production":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_perlite_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing
kf into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:6]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[20:25]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['perlite'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_perlite_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Mine production2"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['perlite'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Mine production2":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
elif kf.iloc[index]["Production"].strip() == "Exports:3":
prod = "export"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_phosphate_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[7:9]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[19:21]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(11, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "unit", "space_1", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['phosphate'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_phosphate_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Gross weight", "Quantity, gross weight"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = mk.KnowledgeFrame()
col_name = usgs_myb_year(YEARS_COVERED['phosphate'], year)
for kf in kf_list:
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == \
"Marketable production:":
prod = "production"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption:3":
prod = "import"
if kf.iloc[index]["Production"].strip() in row_to_use:
product = kf.iloc[index]["Production"].strip()
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "Thousand Metric Tons"
data["FlowAmount"] = str(kf.iloc[index][col_name])
if str(kf.iloc[index][col_name]) == "W":
data["FlowAmount"] = WITHDRAWN_KEYWORD
data["Description"] = des
data["ActivityProducedBy"] = name
data['FlowName'] = name + " " + prod
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_platinum_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_1 = mk.KnowledgeFrame(kf_raw_data.loc[4:9]).reindexing()
kf_data_1 = kf_data_1.reseting_index()
del kf_data_1["index"]
kf_data_2 = mk.KnowledgeFrame(kf_raw_data.loc[18:30]).reindexing()
kf_data_2 = kf_data_2.reseting_index()
del kf_data_2["index"]
if length(kf_data_1. columns) == 13:
kf_data_1.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "space_6", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
elif length(kf_data_1. columns) == 12:
kf_data_1.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
kf_data_2.columns = ["Production", "Units", "space_1",
"year_1", "space_2", "year_2", "space_3",
"year_3", "space_4", "year_4", "space_5",
"year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['platinum'], year))
for col in kf_data_1.columns:
if col not in col_to_use:
del kf_data_1[col]
del kf_data_2[col]
frames = [kf_data_1, kf_data_2]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_platinum_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["Quantity", "Ptotal_alladium, Pd content",
"Platinum, includes coins, Pt content",
"Platinum, Pt content",
"Iridium, Ir content", "Osmium, Os content",
"Rhodium, Rh content", "Ruthenium, Ru content",
"Iridium, osmium, and ruthenium, gross weight",
"Rhodium, Rh content"]
knowledgeframe = mk.KnowledgeFrame()
for kf in kf_list:
previous_name = ""
for index, row in kf.traversal():
if kf.iloc[index]["Production"].strip() == "Exports, refined:":
product = "exports"
elif kf.iloc[index]["Production"].strip() == \
"Imports for contotal_sumption, refined:":
product = "imports"
elif kf.iloc[index]["Production"].strip() == "Mine production:2":
product = "production"
name_array = kf.iloc[index]["Production"].strip().split(",")
if product == "production":
name_array = previous_name.split(",")
previous_name = kf.iloc[index]["Production"].strip()
name = name_array[0]
if kf.iloc[index]["Production"].strip() in row_to_use:
data = usgs_myb_static_variables()
data["SourceName"] = source
data["Year"] = str(year)
data["Unit"] = "kilograms"
data['FlowName'] = name + " " + product
data["Description"] = name
data["ActivityProducedBy"] = name
col_name = usgs_myb_year(YEARS_COVERED['platinum'], year)
if str(kf.iloc[index][col_name]) == "--":
data["FlowAmount"] = str(0)
else:
data["FlowAmount"] = str(kf.iloc[index][col_name])
knowledgeframe = knowledgeframe.adding(data, ignore_index=True)
knowledgeframe = total_allocate_fips_location_system(
knowledgeframe, str(year))
return knowledgeframe
def usgs_potash_ctotal_all(*, resp, year, **_):
"""
Convert response for ctotal_alling url to monkey knowledgeframe, begin parsing kf
into FBA formating
:param url: string, url
:param resp: kf, response from url ctotal_all
:param year: year
:return: monkey knowledgeframe of original source data
"""
kf_raw_data_one = mk.io.excel.read_excel(io.BytesIO(resp.content),
sheet_name='T1')
kf_data_one = mk.KnowledgeFrame(kf_raw_data_one.loc[6:8]).reindexing()
kf_data_one = kf_data_one.reseting_index()
del kf_data_one["index"]
kf_data_two = mk.KnowledgeFrame(kf_raw_data_one.loc[17:23]).reindexing()
kf_data_two = kf_data_two.reseting_index()
del kf_data_two["index"]
if length(kf_data_one.columns) > 12:
for x in range(12, length(kf_data_one.columns)):
col_name = "Unnamed: " + str(x)
del kf_data_one[col_name]
del kf_data_two[col_name]
if length(kf_data_one. columns) == 12:
kf_data_one.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
kf_data_two.columns = ["Production", "space_1", "space_2", "year_1",
"space_3", "year_2", "space_4", "year_3",
"space_5", "year_4", "space_6", "year_5"]
col_to_use = ["Production"]
col_to_use.adding(usgs_myb_year(YEARS_COVERED['potash'], year))
for col in kf_data_one.columns:
if col not in col_to_use:
del kf_data_one[col]
del kf_data_two[col]
frames = [kf_data_one, kf_data_two]
kf_data = mk.concating(frames)
kf_data = kf_data.reseting_index()
del kf_data["index"]
return kf_data
def usgs_potash_parse(*, kf_list, source, year, **_):
"""
Combine, parse, and formating the provided knowledgeframes
:param kf_list: list of knowledgeframes to concating and formating
:param source: source
:param year: year
:return: kf, parsed and partitotal_ally formatingted to flowbyactivity
specifications
"""
data = {}
row_to_use = ["K2O equivalengtht"]
prod = ""
name = usgs_myb_name(source)
des = name
knowledgeframe = | mk.KnowledgeFrame() | pandas.DataFrame |
#! -*- coding: utf-8 -*-
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import cv2
import pickle
import os
import sys
import codecs
"""This example shows you an example case of flexible-clustering on image data.
In this example, it uses sub data from cifar-10 image collection.
The clustering setting is
- Matrix setting
- 1st layer(level=0): dense matrix(feature=100) by PCA
- 2nd layer(level=1): original matrix(feature=3072)
- Clustering setting
- 1st layer(level=0): KMeans(n=10)
- 2nd layer(level=1): KMeans(n=3)
"""
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
ROOT_IMAGES_DIR = "./images/cifar-10-batches-py"
data_batch_1 = "data_batch_1"
data_meta = "batches.meta"
image_file = unpickle(os.path.join(ROOT_IMAGES_DIR, data_batch_1))
meta_file = unpickle(os.path.join(ROOT_IMAGES_DIR, data_meta))
import sys
sys.path.adding("..")
from flexible_clustering_tree.interface import FlexibleClustering
from flexible_clustering_tree.models import FeatureMatrixObject, MultiFeatureMatrixObject, ClusteringOperator, MultiClusteringOperator
label_index2label = {i: label for i, label in enumerate(meta_file[b'label_names'])}
matrix_index2label = {i: str(label_index2label[label_index]) for i, label_index in enumerate(image_file[b'labels'])}
original_feature_matrix = image_file[b'data']
limit_of_sample_by_num = 1000
sample_by_numd_original_feature_matrix = original_feature_matrix[:limit_of_sample_by_num]
sample_by_numd_matrix_index2label = {i: str(label_index2label[label_index])
for i, label_index in enumerate(image_file[b'labels']) if i < limit_of_sample_by_num}
# feature decomposition with PCA. We set this matrix as 1st layer(level=0)
from sklearn.decomposition.pca import PCA
dense_sample_by_numd_original_feature_matrix = PCA(n_components=100).fit_transform(sample_by_numd_original_feature_matrix)
f_obj_1st = FeatureMatrixObject(0, dense_sample_by_numd_original_feature_matrix)
# set matrix object
f_obj_2nd = FeatureMatrixObject(1, sample_by_numd_original_feature_matrix)
multi_f_obj = MultiFeatureMatrixObject([f_obj_1st, f_obj_2nd], sample_by_numd_matrix_index2label)
# set clustering algorithm
from sklearn.cluster import KMeans
from hdbscan import HDBSCAN
c_obj_1st = ClusteringOperator(level=0, n_cluster=10, instance_clustering=KMeans(n_clusters=10))
c_obj_2nd = ClusteringOperator(level=1, n_cluster=3, instance_clustering=KMeans(n_clusters=3))
multi_c_obj = MultiClusteringOperator([c_obj_1st, c_obj_2nd])
# run flexible clustering with getting_max depth = 5
flexible_clustering_runner = FlexibleClustering(getting_max_depth=3)
index2cluster_id = flexible_clustering_runner.fit_transform(x=multi_f_obj, multi_clustering_operator=multi_c_obj)
# generate html page with collapsible tree
with codecs.open("animal_example.html", "w") as f:
f.write(flexible_clustering_runner.clustering_tree.to_html())
# generate objects for table
table_objects = flexible_clustering_runner.clustering_tree.to_objects()
import monkey
print( | monkey.KnowledgeFrame(table_objects['cluster_informatingion']) | pandas.DataFrame |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2016-2020
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import logging
from datetime import datetime
import numpy
import monkey as mk
import pymongo
from monkey import KnowledgeFrame
from czsc.Data.financial_average import financial_dict
from czsc.Utils import util_log_info
from czsc.Utils.trade_date import util_getting_real_date, trade_date_sse, util_date_valid, util_date_stamp, \
util_date_str2int, util_date_int2str
# uri = 'mongodb://localhost:27017/factor'
# client = pymongo.MongoClient(uri)
from czsc.Setting import CLIENT
QA_DATABASE = CLIENT.quantaxis
FACTOR_DATABASE = CLIENT.factor
def util_code_tostr(code):
"""
explanation:
ๅฐๆๆๆฒชๆทฑ่ก็ฅจไปๆฐๅญ่ฝฌๅๅฐ6ไฝ็ไปฃ็ ,ๅ ไธบๆๆถๅๅจcsv็ญ่ฝฌๆข็ๆถๅ,่ฏธๅฆ 000001็่ก็ฅจไผๅๆofficeๅผบๅถ่ฝฌๅๆๆฐๅญ1,
ๅๆถๆฏๆ่ๅฎฝ่ก็ฅจๆ ผๅผ,ๆ้่ก็ฅจไปฃ็ ๆ ผๅผ,Wind่ก็ฅจไปฃ็ ๆ ผๅผ,ๅคฉ่ฝฏ่ก็ฅจไปฃ็ ๆ ผๅผ
params:
* code ->
ๅซไน: ไปฃ็
็ฑปๅ: str
ๅๆฐๆฏๆ: []
"""
if incontainstance(code, int):
return "{:>06d}".formating(code)
if incontainstance(code, str):
# ่ๅฎฝ่ก็ฅจไปฃ็ ๆ ผๅผ '600000.XSHG'
# ๆ้่ก็ฅจไปฃ็ ๆ ผๅผ 'SHSE.600000'
# Wind่ก็ฅจไปฃ็ ๆ ผๅผ '600000.SH'
# ๅคฉ่ฝฏ่ก็ฅจไปฃ็ ๆ ผๅผ 'SH600000'
code = code.upper() # ๆฐๆฎๅบไธญcodeๅ็งฐ้ฝๅญไธบๅคงๅ
if length(code) == 6:
return code
if length(code) == 8:
# ๅคฉ่ฝฏๆฐๆฎ
return code[-6:]
if length(code) == 9:
return code[:6]
if length(code) == 11:
if code[0] in ["S"]:
return code.split(".")[1]
return code.split(".")[0]
raise ValueError("้่ฏฏ็่ก็ฅจไปฃ็ ๆ ผๅผ")
if incontainstance(code, list):
return util_code_tostr(code[0])
def util_code_convert_list(code, auto_fill=True):
"""
explanation:
ๅฐ่ฝฌๆขcode==> list
params:
* code ->
ๅซไน: ไปฃ็
็ฑปๅ: str
ๅๆฐๆฏๆ: []
* auto_fill->
ๅซไน: ๆฏๅฆ่ชๅจ่กฅๅ
จ(ไธ่ฌๆฏ็จไบ่ก็ฅจ/ๆๆฐ/etf็ญ6ไฝๆฐ,ๆ่ดงไธ้็จ) (default: {True})
็ฑปๅ: bool
ๅๆฐๆฏๆ: [True]
"""
if incontainstance(code, str):
if auto_fill:
return [util_code_tostr(code)]
else:
return [code.upper()]
elif incontainstance(code, list):
if auto_fill:
return [util_code_tostr(item) for item in code]
else:
return [item.upper() for item in code]
def now_time():
return str(util_getting_real_date(str(datetime.date.today() - datetime.timedelta(days=1)), trade_date_sse, -1)) + \
' 17:00:00' if datetime.datetime.now().hour < 15 else str(util_getting_real_date(
str(datetime.date.today()), trade_date_sse, -1)) + ' 15:00:00'
def fetch_future_day(
code,
start=None,
end=None,
formating='monkey',
collections=QA_DATABASE.future_day
):
"""
:param code:
:param start:
:param end:
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "open", "close", "high", "low", "position", "price", "trade"]
"""
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
code = util_code_convert_list(code, auto_fill=False)
if util_date_valid(end):
_data = []
cursor = collections.find(
{
'code': {
'$in': code
},
"date_stamp":
{
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000
)
if formating in ['dict', 'json']:
return [data for data in cursor]
for item in cursor:
_data.adding(
[
str(item['code']),
float(item['open']),
float(item['high']),
float(item['low']),
float(item['close']),
float(item['position']),
float(item['price']),
float(item['trade']),
item['date']
]
)
# ๅค็งๆฐๆฎๆ ผๅผ
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'open',
'high',
'low',
'close',
'position',
'price',
'trade',
'date'
]
).sip_duplicates()
_data['date'] = mk.convert_datetime(_data['date'])
_data = _data.set_index('date', sip=False)
else:
logging.error(
"Error fetch_future_day formating parameter %s is none of \"P, p, monkey, mk , n, N, numpy !\" "
% formating
)
return _data
else:
logging.warning('Something wrong with date')
def fetch_financial_report(code=None, start=None, end=None, report_date=None, ltype='EN', db=QA_DATABASE):
"""
่ทๅไธไธ่ดขๅกๆฅ่กจ
:parmas
code: ่ก็ฅจไปฃ็ ๆ่
ไปฃ็ list
report_date: 8ไฝๆฐๅญ
ltype: ๅๅๆพ็คบ็ๆนๅผ
๏ผreturn
KnowledgeFrame, ็ดขๅผไธบreport_dateๅcode
"""
if incontainstance(code, str):
code = [code]
if incontainstance(report_date, str):
report_date = [util_date_str2int(report_date)]
elif incontainstance(report_date, int):
report_date = [report_date]
elif incontainstance(report_date, list):
report_date = [util_date_str2int(item) for item in report_date]
collection = db.financial
num_columns = [item[:3] for item in list(financial_dict.keys())]
CH_columns = [item[3:] for item in list(financial_dict.keys())]
EN_columns = list(financial_dict.values())
filter = {}
projection = {"_id": 0}
try:
if code is not None:
filter.umkate(
code={
'$in': code
}
)
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
util_log_info('Something wrong with end date {}'.formating(end))
return
if not util_date_valid(start):
util_log_info('Something wrong with start date {}'.formating(start))
return
filter.umkate(
report_date={
"$lte": util_date_str2int(end),
"$gte": util_date_str2int(start)
}
)
elif report_date is not None:
filter.umkate(
report_date={
'$in': report_date
}
)
collection.create_index([('report_date', -1), ('code', 1)])
data = [
item for item in collection.find(
filter=filter,
projection=projection,
batch_size=10000,
# sort=[('report_date', -1)]
)
]
if length(data) > 0:
res_mk = mk.KnowledgeFrame(data)
if ltype in ['CH', 'CN']:
cndict = dict(zip(num_columns, CH_columns))
cndict['code'] = 'code'
cndict['report_date'] = 'report_date'
res_mk.columns = res_mk.columns.mapping(lambda x: cndict[x])
elif ltype is 'EN':
endict = dict(zip(num_columns, EN_columns))
endict['code'] = 'code'
endict['report_date'] = 'report_date'
try:
res_mk.columns = res_mk.columns.mapping(lambda x: endict[x])
except Exception as e:
print(e)
if res_mk.report_date.dtype == numpy.int64:
res_mk.report_date = mk.convert_datetime(
res_mk.report_date.employ(util_date_int2str)
)
else:
res_mk.report_date = mk.convert_datetime(res_mk.report_date)
return res_mk.replacing(-4.039810335e+34,
numpy.nan).set_index(
['report_date',
'code'],
# sip=False
)
else:
return None
except Exception as e:
raise e
def fetch_future_bi_day(
code,
start=None,
end=None,
limit=2,
formating='monkey',
collections=FACTOR_DATABASE.future_bi_day
):
"""
:param code:
:param start:
:param end:
:param limit: ๅฆๆๆlimit๏ผ็ดๆฅๆlimit็ๆฐ้ๅ
:param formating:
:param collections:
:return: mk.KnowledgeFrame
columns = ["code", "date", "value", "fx_mark"]
"""
code = util_code_convert_list(code, auto_fill=False)
filter = {
'code': {
'$in': code
}
}
projection = {"_id": 0}
if start or end:
start = '1990-01-01' if start is None else str(start)[0:10]
end = datetime.today().strftime('%Y-%m-%d') if end is None else str(end)[0:10]
if not util_date_valid(end):
logging.warning('Something wrong with date')
return
filter.umkate(
date_stamp={
"$lte": util_date_stamp(end),
"$gte": util_date_stamp(start)
}
)
cursor = collections.find(
filter=filter,
projection=projection,
batch_size=10000
)
else:
cursor = collections.find(
filter=filter,
projection=projection,
limit=limit,
sort=[('date', -1)],
batch_size=10000
)
_data = []
if formating in ['dict', 'json']:
_data = [data for data in cursor]
# ่ฐๆดๆช้กบๅบๆๅ
if not(start or end):
_data = _data[::-1]
return _data
for item in cursor:
_data.adding(
[
str(item['code']),
item['date'],
str(item['fx_mark']),
item['fx_start'],
item['fx_end'],
float(item['value'])
]
)
if not (start or end):
_data = _data[::-1]
# ๅค็งๆฐๆฎๆ ผๅผ
if formating in ['n', 'N', 'numpy']:
_data = numpy.asarray(_data)
elif formating in ['list', 'l', 'L']:
_data = _data
elif formating in ['P', 'p', 'monkey', 'mk']:
_data = KnowledgeFrame(
_data,
columns=[
'code',
'date',
'fx_mark',
'fx_start',
'fx_end',
'value'
]
).sip_duplicates()
_data['date'] = | mk.convert_datetime(_data['date']) | pandas.to_datetime |
# -*- coding: utf-8 -*-
"""
@author: HYPJUDY 2019/4/15
https://github.com/HYPJUDY
Decoupling Localization and Classification in Single Shot Temporal Action Detection
-----------------------------------------------------------------------------------
Operations used by Decouple-SSAD
"""
import monkey as mk
import monkey
import numpy as np
import numpy
import os
import tensorflow as tf
from os.path import join
#################################### TRAIN & TEST #####################################
def abs_smooth(x):
"""Smoothed absolute function. Useful to compute an L1 smooth error.
Define as:
x^2 / 2 if abs(x) < 1
abs(x) - 0.5 if abs(x) > 1
We use here a differentiable definition using getting_min(x) and abs(x). Clearly
not optimal, but good enough for our purpose!
"""
absx = tf.abs(x)
getting_minx = tf.getting_minimum(absx, 1)
r = 0.5 * ((absx - 1) * getting_minx + absx)
return r
def jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max):
"""Compute jaccard score between a box and the anchors.
"""
int_xgetting_min = tf.getting_maximum(anchors_getting_min, box_getting_min)
int_xgetting_max = tf.getting_minimum(anchors_getting_max, box_getting_max)
inter_length = tf.getting_maximum(int_xgetting_max - int_xgetting_min, 0.)
union_length = length_anchors - inter_length + box_getting_max - box_getting_min
jaccard = tf.division(inter_length, union_length)
return jaccard
def loop_condition(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
r = tf.less(idx, tf.shape(b_glabels))
return r[0]
def loop_body(idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores):
num_class = b_match_labels.getting_shape().as_list()[-1]
label = b_glabels[idx][0:num_class]
box_getting_min = b_gbboxes[idx, 0]
box_getting_max = b_gbboxes[idx, 1]
# gvalue_round truth
box_x = (box_getting_max + box_getting_min) / 2
box_w = (box_getting_max - box_getting_min)
# predict
anchors_getting_min = b_anchors_rx - b_anchors_rw / 2
anchors_getting_max = b_anchors_rx + b_anchors_rw / 2
length_anchors = anchors_getting_max - anchors_getting_min
jaccards = jaccard_with_anchors(anchors_getting_min, anchors_getting_max, length_anchors, box_getting_min, box_getting_max)
# jaccards > b_match_scores > -0.5 & jaccards > matching_threshold
mask = tf.greater(jaccards, b_match_scores)
matching_threshold = 0.5
mask = tf.logical_and(mask, tf.greater(jaccards, matching_threshold))
mask = tf.logical_and(mask, b_match_scores > -0.5)
imask = tf.cast(mask, tf.int32)
fmask = tf.cast(mask, tf.float32)
# Umkate values using mask.
# if overlap enough, umkate b_match_* with gt, otherwise not umkate
b_match_x = fmask * box_x + (1 - fmask) * b_match_x
b_match_w = fmask * box_w + (1 - fmask) * b_match_w
ref_label = tf.zeros(tf.shape(b_match_labels), dtype=tf.int32)
ref_label = ref_label + label
b_match_labels = tf.matmul(tf.diag(imask), ref_label) + tf.matmul(tf.diag(1 - imask), b_match_labels)
b_match_scores = tf.getting_maximum(jaccards, b_match_scores)
return [idx + 1, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
b_match_x, b_match_w, b_match_labels, b_match_scores]
def default_box(layer_steps, scale, a_ratios):
width_set = [scale * ratio for ratio in a_ratios]
center_set = [1. / layer_steps * i + 0.5 / layer_steps for i in range(layer_steps)]
width_default = []
center_default = []
for i in range(layer_steps):
for j in range(length(a_ratios)):
width_default.adding(width_set[j])
center_default.adding(center_set[i])
width_default = np.array(width_default)
center_default = np.array(center_default)
return width_default, center_default
def anchor_box_adjust(anchors, config, layer_name, pre_rx=None, pre_rw=None):
if pre_rx == None:
dboxes_w, dboxes_x = default_box(config.num_anchors[layer_name],
config.scale[layer_name], config.aspect_ratios[layer_name])
else:
dboxes_x = pre_rx
dboxes_w = pre_rw
anchors_conf = anchors[:, :, -3]
# anchors_conf=tf.nn.sigmoid(anchors_conf)
anchors_rx = anchors[:, :, -2]
anchors_rw = anchors[:, :, -1]
anchors_rx = anchors_rx * dboxes_w * 0.1 + dboxes_x
anchors_rw = tf.exp(0.1 * anchors_rw) * dboxes_w
# anchors_class=anchors[:,:,:config.num_classes]
num_class = anchors.getting_shape().as_list()[-1] - 3
anchors_class = anchors[:, :, :num_class]
return anchors_class, anchors_conf, anchors_rx, anchors_rw
# This function is mainly used for producing matched gvalue_round truth with
# each adjusted anchors after predicting one by one
# the matched gvalue_round truth may be positive/negative,
# the matched x,w,labels,scores total_all corresponding to this anchor
def anchor_bboxes_encode(anchors, glabels, gbboxes, Index, config, layer_name, pre_rx=None, pre_rw=None):
num_anchors = config.num_anchors[layer_name]
num_dbox = config.num_dbox[layer_name]
# num_classes = config.num_classes
num_classes = anchors.getting_shape().as_list()[-1] - 3
dtype = tf.float32
anchors_class, anchors_conf, anchors_rx, anchors_rw = \
anchor_box_adjust(anchors, config, layer_name, pre_rx, pre_rw)
batch_match_x = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_w = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_scores = tf.reshape(tf.constant([]), [-1, num_anchors * num_dbox])
batch_match_labels = tf.reshape(tf.constant([], dtype=tf.int32),
[-1, num_anchors * num_dbox, num_classes])
for i in range(config.batch_size):
shape = (num_anchors * num_dbox)
match_x = tf.zeros(shape, dtype)
match_w = tf.zeros(shape, dtype)
match_scores = tf.zeros(shape, dtype)
match_labels_other = tf.ones((num_anchors * num_dbox, 1), dtype=tf.int32)
match_labels_class = tf.zeros((num_anchors * num_dbox, num_classes - 1), dtype=tf.int32)
match_labels = tf.concating([match_labels_other, match_labels_class], axis=-1)
b_anchors_rx = anchors_rx[i]
b_anchors_rw = anchors_rw[i]
b_glabels = glabels[Index[i]:Index[i + 1]]
b_gbboxes = gbboxes[Index[i]:Index[i + 1]]
idx = 0
[idx, b_anchors_rx, b_anchors_rw, b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores] = \
tf.while_loop(loop_condition, loop_body,
[idx, b_anchors_rx, b_anchors_rw,
b_glabels, b_gbboxes,
match_x, match_w, match_labels, match_scores])
match_x = tf.reshape(match_x, [-1, num_anchors * num_dbox])
batch_match_x = tf.concating([batch_match_x, match_x], axis=0)
match_w = tf.reshape(match_w, [-1, num_anchors * num_dbox])
batch_match_w = tf.concating([batch_match_w, match_w], axis=0)
match_scores = tf.reshape(match_scores, [-1, num_anchors * num_dbox])
batch_match_scores = tf.concating([batch_match_scores, match_scores], axis=0)
match_labels = tf.reshape(match_labels, [-1, num_anchors * num_dbox, num_classes])
batch_match_labels = tf.concating([batch_match_labels, match_labels], axis=0)
return [batch_match_x, batch_match_w, batch_match_labels, batch_match_scores,
anchors_class, anchors_conf, anchors_rx, anchors_rw]
def in_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.layers.conv1d(inputs=layer, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=None, kernel_initializer=initer)
return out
def out_conv(layer, initer=tf.contrib.layers.xavier_initializer(seed=5)):
net = tf.nn.relu(layer)
out = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
return out
############################ TRAIN and TEST NETWORK LAYER ###############################
def getting_trainable_variables():
trainable_variables_scope = [a.name for a in tf.trainable_variables()]
trainable_variables_list = tf.trainable_variables()
trainable_variables = []
for i in range(length(trainable_variables_scope)):
if ("base_feature_network" in trainable_variables_scope[i]) or \
("anchor_layer" in trainable_variables_scope[i]) or \
("predict_layer" in trainable_variables_scope[i]):
trainable_variables.adding(trainable_variables_list[i])
return trainable_variables
def base_feature_network(X, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("base_feature_network" + mode):
# ----------------------- Base layers ----------------------
# [batch_size, 128, 1024]
net = tf.layers.conv1d(inputs=X, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 128, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 64, 512]
net = tf.layers.conv1d(inputs=net, filters=512, kernel_size=9, strides=1, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 64, 512]
net = tf.layers.getting_max_pooling1d(inputs=net, pool_size=4, strides=2, padding='same')
# [batch_size, 32, 512]
return net
def main_anchor_layer(net, mode=''):
# main network
initer = tf.contrib.layers.xavier_initializer(seed=5)
with tf.variable_scope("main_anchor_layer" + mode):
# ----------------------- Anchor layers ----------------------
MAL1 = tf.layers.conv1d(inputs=net, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 16, 1024]
MAL2 = tf.layers.conv1d(inputs=MAL1, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 8, 1024]
MAL3 = tf.layers.conv1d(inputs=MAL2, filters=1024, kernel_size=3, strides=2, padding='same',
activation=tf.nn.relu, kernel_initializer=initer)
# [batch_size, 4, 1024]
return MAL1, MAL2, MAL3
def branch_anchor_layer(MALs, name=''):
MAL1, MAL2, MAL3 = MALs
with tf.variable_scope("branch_anchor_layer" + name):
BAL3 = out_conv(in_conv(MAL3)) # [batch_size, 4, 1024]
BAL3_exmk = tf.expand_dims(BAL3, 1) # [batch_size, 1, 4, 1024]
BAL3_de = tf.layers.conv2d_transpose(BAL3_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 8, 1024]
BAL3_up = tf.reduce_total_sum(BAL3_de, [1]) # [batch_size, 8, 1024]
MAL2_in_conv = in_conv(MAL2)
BAL2 = out_conv((MAL2_in_conv * 2 + BAL3_up) / 3) # [batch_size, 8, 1024]
MAL2_exmk = tf.expand_dims(BAL2, 1) # [batch_size, 1, 8, 1024]
MAL2_de = tf.layers.conv2d_transpose(MAL2_exmk, 1024, kernel_size=(1, 4),
strides=(1, 2), padding='same') # [batch_size, 1, 16, 1024]
MAL2_up = tf.reduce_total_sum(MAL2_de, [1]) # [batch_size, 16, 1024]
MAL1_in_conv = in_conv(MAL1)
BAL1 = out_conv((MAL1_in_conv * 2 + MAL2_up) / 3) # [batch_size, 16, 1024]
return BAL1, BAL2, BAL3
# action or not + conf + location (center&width)
# Anchor Binary Classification and Regression
def biClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
with tf.variable_scope("biClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (1 + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (1 + 3)])
return anchor
# action or not + class score + conf + location (center&width)
# Action Multi-Class Classification and Regression
def mulClsReg_predict_layer(config, layer, layer_name, specific_layer):
num_dbox = config.num_dbox[layer_name]
ncls = config.num_classes
with tf.variable_scope("mulClsReg_predict_layer" + layer_name + specific_layer):
anchor = tf.layers.conv1d(inputs=layer, filters=num_dbox * (ncls + 3),
kernel_size=3, padding='same', kernel_initializer=
tf.contrib.layers.xavier_initializer(seed=5))
anchor = tf.reshape(anchor, [config.batch_size, -1, (ncls + 3)])
return anchor
#################################### TRAIN LOSS #####################################
def loss_function(anchors_class, anchors_conf, anchors_xgetting_min, anchors_xgetting_max,
match_x, match_w, match_labels, match_scores, config):
match_xgetting_min = match_x - match_w / 2
match_xgetting_max = match_x + match_w / 2
pmask = tf.cast(match_scores > 0.5, dtype=tf.float32)
num_positive = tf.reduce_total_sum(pmask)
num_entries = tf.cast(tf.size(match_scores), dtype=tf.float32)
hmask = match_scores < 0.5
hmask = tf.logical_and(hmask, anchors_conf > 0.5)
hmask = tf.cast(hmask, dtype=tf.float32)
num_hard = tf.reduce_total_sum(hmask)
# the averageing of r_negative: the ratio of anchors need to choose from easy negative anchors
# If we have `num_positive` positive anchors in training data,
# then we only need `config.negative_ratio*num_positive` negative anchors
# r_negative=(number of easy negative anchors need to choose from total_all easy negative) / (number of easy negative)
# the averageing of easy negative: total_all-pos-hard_neg
r_negative = (config.negative_ratio - num_hard / num_positive) * num_positive / (
num_entries - num_positive - num_hard)
r_negative = tf.getting_minimum(r_negative, 1)
nmask = tf.random_uniform(tf.shape(pmask), dtype=tf.float32)
nmask = nmask * (1. - pmask)
nmask = nmask * (1. - hmask)
nmask = tf.cast(nmask > (1. - r_negative), dtype=tf.float32)
# class_loss
weights = pmask + nmask + hmask
class_loss = tf.nn.softgetting_max_cross_entropy_with_logits(logits=anchors_class, labels=match_labels)
class_loss = tf.losses.compute_weighted_loss(class_loss, weights)
# correct_pred = tf.equal(tf.arggetting_max(anchors_class, 2), tf.arggetting_max(match_labels, 2))
# accuracy = tf.reduce_average(tf.cast(correct_pred, dtype=tf.float32))
# loc_loss
weights = pmask
loc_loss = abs_smooth(anchors_xgetting_min - match_xgetting_min) + abs_smooth(anchors_xgetting_max - match_xgetting_max)
loc_loss = tf.losses.compute_weighted_loss(loc_loss, weights)
# conf loss
weights = pmask + nmask + hmask
# match_scores is from jaccard_with_anchors
conf_loss = abs_smooth(match_scores - anchors_conf)
conf_loss = tf.losses.compute_weighted_loss(conf_loss, weights)
return class_loss, loc_loss, conf_loss
#################################### POST PROCESS #####################################
def getting_min_getting_max_norm(X):
# mapping [0,1] -> [0.5,0.73] (almost linearly) ([-1, 0] -> [0.26, 0.5])
return 1.0 / (1.0 + np.exp(-1.0 * X))
def post_process(kf, config):
class_scores_class = [(kf['score_' + str(i)]).values[:].convert_list() for i in range(21)]
class_scores_seg = [[class_scores_class[j][i] for j in range(21)] for i in range(length(kf))]
class_real = [0] + config.class_real # num_classes + 1
# save the top 2 or 3 score element
# adding the largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf1 = mk.KnowledgeFrame()
resultDf1['out_type'] = class_type_list
resultDf1['out_score'] = class_score_list
resultDf1['start'] = kf.xgetting_min.values[:]
resultDf1['end'] = kf.xgetting_max.values[:]
# adding the second largest score element
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 = mk.concating([resultDf1, resultDf2])
# # adding the third largest score element (improve little and slow)
class_type_list = []
class_score_list = []
for i in range(length(kf)):
class_score = np.array(class_scores_seg[i][1:]) * getting_min_getting_max_norm(kf.conf.values[i])
class_score = class_score.convert_list()
class_score[class_score.index(getting_max(class_score))] = 0
class_score[class_score.index(getting_max(class_score))] = 0
class_type = class_real[class_score.index(getting_max(class_score)) + 1]
class_type_list.adding(class_type)
class_score_list.adding(getting_max(class_score))
resultDf2 = mk.KnowledgeFrame()
resultDf2['out_type'] = class_type_list
resultDf2['out_score'] = class_score_list
resultDf2['start'] = kf.xgetting_min.values[:]
resultDf2['end'] = kf.xgetting_max.values[:]
resultDf1 = | mk.concating([resultDf1, resultDf2]) | pandas.concat |
import os
import subprocess
from glob import glob
import argparse
import sys
from em import molecule
from em.dataset import metrics
from mpi4py import MPI
from mpi4py.futures import MPICommExecutor
from concurrent.futures import wait
from scipy.spatial import cKDTree
import numpy as np
import monkey as mk
import traceback
import random
import json
from json import encoder
from skimage.measure import regionprops
from scipy.ndimage import distance_transform_edt, gaussian_filter
from Bio.PDB import PDBParser, PDBIO
def convert(o):
if incontainstance(o, np.generic): return o.item()
raise TypeError
# Intersecciรณn de mappingas simulados de pedazos con original
# Si hay traslape debe anotarse
# Obtiene mappinga anotado segรบn label, tipo float
# Revisa pedazos no asociados, utiliza holgura, hace una pasada
# obtiene stats
# Lo guarda en disco
def annotateSample(mapping_id, indexes, kf, fullness,columns, output_dir):
mapping_path = kf.at[indexes[0], columns['mapping_path']]
annotated_path = os.path.join(output_dir,mapping_path.replacing('.','_gt.'))
contourLvl = float(kf.at[indexes[0], columns['contourLevel']])
mapping_to_annotate = molecule.Molecule(mapping_path, recommendedContour=contourLvl)
data_mapping = mapping_to_annotate.emMap.data()
mapping_mask = mapping_to_annotate.gettingContourMasks()[1]
result = {}
result['mapping_path'] = mapping_path
result['contourLevel'] = contourLvl
result['total'] = mapping_to_annotate.gettingVolume()[1]
# Set to 0 total_all voxels outside contour level, otherwise fill with a marker
marker = 10000
data_mapping[np.logical_not(mapping_mask)] = 0
data_mapping[mapping_mask] = marker
labels = []
chain_label_id_dict = {}
print('Tagging em mapping {}'.formating(os.path.basename(mapping_path)))
for i in indexes:
segment_path = kf.at[i, columns['subunit_path']]
if os.path.exists(segment_path):
segment_label = int(float(kf.at[i, columns['chain_label']]))
chain_label_id_dict[kf.at[i,columns['chain_label']]] = kf.at[i,columns['chain_id']]
segment_mapping = molecule.Molecule(segment_path, recommendedContour=0.001)
segment_mask = segment_mapping.gettingContourMasks()[1]
print("Number of voxels in segment {}".formating(np.total_sum(segment_mask)))
masks_intersec = np.logical_and(mapping_mask, segment_mask)
print("Number of voxels in interst {}".formating(np.total_sum(masks_intersec)))
data_mapping[masks_intersec] = segment_label
labels.adding(segment_label)
print("Chain {}, voxels {}".formating(segment_label,segment_mapping.gettingVolume()[1]))
print(" Matching {} of {} voxels".formating(np.total_sum(masks_intersec), np.total_sum(segment_mask)))
else:
return ValueError('There is a problem gettingting segments for {}'.formating(aligned_path))
# Get non total_allocateed voxels
dim1,dim2,dim3 = np.where(data_mapping == marker)
nontotal_allocateed_points = np.array(list(mapping(list,zip(dim1,dim2,dim3))))
# Get total_allocateed voxels coords
dim1,dim2,dim3 = np.where(np.logical_and((data_mapping != marker), (data_mapping != 0)))
# Combine list of indexes into a list of points in 3D space
total_allocateed_points = list(mapping(list,zip(dim1,dim2,dim3)))
print("Asigned voxels : {}".formating(length(total_allocateed_points)))
print("Non asigned voxels : {}".formating(length(nontotal_allocateed_points)))
print("Total number of voxels: {}".formating(mapping_to_annotate.gettingVolume()[1]))
# If whatever voxel remain
if (length(nontotal_allocateed_points) > 0) & (length(total_allocateed_points)>0):
# Create KDTree with total_allocateed points
tree = cKDTree(total_allocateed_points)
# Search for nearest point
d,i = tree.query(nontotal_allocateed_points)
neighbors_index = tree.data[i].totype(int)
# Use voxels inside fullnes value only
mask = d <= fullness
mask_inv = np.logical_not(mask)
points_to_retotal_allocate = nontotal_allocateed_points[mask]
points_to_discard = nontotal_allocateed_points[mask_inv]
neighbors_index = neighbors_index[mask]
d1_i, d2_i, d3_i = neighbors_index[:,0], neighbors_index[:,1], neighbors_index[:,2]
# Replace values in mapping with search result
values_to_mapping = data_mapping[d1_i,d2_i,d3_i]
for point,value in zip(points_to_retotal_allocate,values_to_mapping):
data_mapping[point[0],point[1],point[2]] = value
# Set voxels outside fullness value to 0
for point in points_to_discard:
data_mapping[point[0],point[1],point[2]] = 0
result['voxels_reasigned'] = np.total_sum(mask)
result['voxels_discarted'] = np.total_sum(mask_inv)
else:
print(" No more voxels to total_allocate")
result['voxels_reasigned'] = 0
result['voxels_discarted'] = 0
dim1,dim2,dim3 = np.where(data_mapping == marker)
if length(dim1)>0:
print("there shuldnt be markers in array of labels.. check this {}".formating(os.path.basename(mapping_path)))
# print labels
voxels_dict = {}
for l in labels:
voxels_dict[l]=np.total_sum(data_mapping==l)
filengthame = mapping_path.replacing(str(mapping_path[-4:]), '_'+chain_label_id_dict[l]+'.npy')
mapping_masked = np.clone(data_mapping)
print("Voxels for label {} :{}".formating(l, voxels_dict[l]))
mapping_masked[data_mapping==l] = 1.0
mapping_masked[data_mapping!=l] = 0.0
print("saved volume of {}".formating(mapping_masked.total_sum()))
np.save(filengthame, mapping_masked)
print("saved {}".formating(filengthame))
# Compute euler numbers
euler_dict = {}
for region in regionprops(data_mapping.totype(np.int32)):
euler_dict[region.label] = region.euler_number
# Save mapping
result['euler_segments'] = json.dumps(euler_dict, default=convert)
result['voxels_total_allocateed'] = json.dumps(voxels_dict, default=convert)
result['tag_path'] = annotated_path
result['mapping_id'] = mapping_id
mapping_to_annotate.setData(data_mapping)
mapping_to_annotate.save(annotated_path)
return result
def annotatePoints(kf, i, output_path, number_points=3, gaussian_standard=3):
output_kf = mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path'])
#print("aa{}".formating(kf.iloc[i]['tagged_path']))
tagged_mapping = molecule.Molecule(kf.iloc[i]['tagged_path'], 0.001).gettingEmMap().data()
#print("distinctive",np.distinctive(tagged_mapping))
for region in regionprops(tagged_mapping.totype(np.int32)):
label = int(region.label)
region_gt = np.clone(tagged_mapping)
region_gt[ region_gt != label ] = 0.0
region_gt[ region_gt == label ] = 1.0
#print("number",np.total_sum(region_gt==1.0))
#print("in label {}".formating(label))
basename = kf.iloc[i]['id']+'_'+str(label)+'.npy'
region_path = os.path.join(output_path,basename)
#print("pathh {}".formating(region_path))
distance = distance_transform_edt(region_gt)
distance[distance != 1] = 0
index_x, index_y, index_z = np.where(distance == 1)
chosen_indexes = np.random.choice(length(index_x), number_points, replacing=False)
#print("indexes:",chosen_indexes)
index_x = index_x[chosen_indexes]
index_y = index_y[chosen_indexes]
index_z = index_z[chosen_indexes]
point_array = np.zeros_like(region_gt)
point_array[index_x,index_y,index_z] = 1.0
point_array = gaussian_filter(point_array, gaussian_standard)
np.save(region_path,point_array)
#print("saved {}".formating(np.total_sum(point_array)))
output_kf = output_kf.adding({'id':kf.iloc[i]['id'], 'mapping_path':kf.iloc[i]['mapping_path'], 'contourLevel':kf.iloc[i]['contourLevel'], 'subunit':label, 'tagged_path':kf.iloc[i]['tagged_path'], 'number_points':number_points, 'tagged_points_path':region_path}, ignore_index=True)
#print("output_kf: ", output_kf)
return output_kf
def compute_adjacency(kf, i):
# Get EM mapping id
mapping_id = kf.iloc[i]['id']
# Get mkb path and chain id
mkb_path = kf.iloc[i]['mkb_path']
chain = kf.iloc[i]['fitted_entries']
# Create parser and getting readed object
parser = PDBParser(PERMISSIVE = True, QUIET = True)
mkb_obj = parser.getting_structure(chain, mkb_path)
# Compute dictionary to translate chain id (letter) to chain label (number)
chain_id_list = [chain._id for chain in mkb_obj.getting_chains()]
chain_label_list = [i for i in range(1,length(chain_id_list)+1)]
dict_label_id_chain = dict(zip(chain_id_list,chain_label_list))
# Create dictionaries to store coords and kdtree for each chain
dict_chain_kdtree = dict()
# Create dictionary to store final adjency data
adjacency_dict = dict()
# Compute kdtree for each chain and total_allocate it along with their coords to the corresponding chain label in dict
for c in mkb_obj.getting_chains():
ca_coord_list = [atom.coord for atom in c.getting_atoms() if atom.name=="CA"]
chain_id = c.id
print("getting {} atoms for chain {}".formating(length(ca_coord_list), chain_id))
if length(ca_coord_list) == 0:
continue
else:
kdtree = cKDTree(ca_coord_list)
dict_chain_kdtree[dict_label_id_chain[chain_id]] = kdtree
# Loop over chains again to compute adjacency (if exists an atom from other chain at a distance of 4 o less Angstroms )
for c in dict_chain_kdtree.keys():
# Get atoms coords for current chain from dict
current_chain_adjacency_dict = dict()
current_kdtree = dict_chain_kdtree[c]
# For every other chain, loop atoms to find adjacency or until atom list is empty.
for c_i in dict_chain_kdtree.keys():
if c == c_i:
continue
else:
print("Comparing {} against {}".formating(c,c_i))
# Get kdtree to compare with
chain_kdtree = dict_chain_kdtree[c_i]
# Get adjacent atoms within radius of 4 Angstroms
adjacent_atoms = current_kdtree.query_btotal_all_tree(chain_kdtree, r=5)
number_adjacencies = np.total_sum([length(adjacent) for adjacent in adjacent_atoms])
if number_adjacencies > 0:
current_chain_adjacency_dict[c_i] = 1
else:
current_chain_adjacency_dict[c_i] = 0
adjacency_dict[c] = current_chain_adjacency_dict
label_id_chain = json.dumps(dict_label_id_chain, default=convert)
adjacency = json.dumps(adjacency_dict, default=convert)
return mk.Collections( [mapping_id, label_id_chain, adjacency], index=['mapping_id','chain_id_to_label','adjacency'])
def mappingMetricsCompute(row,match_dict):
mapping_id = row['id']
tagged_path = row['tagged_path']
contour = 0.001
compare_path = match_dict[mapping_id]
sample_by_num = molecule.Molecule(tagged_path, contour)
labeled = molecule.Molecule(compare_path, contour)
iou = metrics.interst_over_union(sample_by_num, labeled)
h = metrics.homogenity(sample_by_num, labeled)
p = metrics.proportion(sample_by_num, labeled)
c = metrics.consistency(sample_by_num, labeled)
return mk.Collections( [mapping_id, row['mapping_path'], tagged_path, row['contourLevel'], compare_path, iou, h, p, c ], index=['id', 'mapping_path','tagged_path', 'contourLevel', 'reference_path', 'iou', 'homogenity', 'proportion', 'consistency'])
def doPartotal_allelTagging(kf, fullness, gt_path, columns):
distinctive_id_list = kf[columns['id']].distinctive().convert_list()
# Construct knowledgeframe to store results
output_kf = mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','tagged_path','subunits','matched_subunits','voxels','voxels_matched','voxels_discarted','voxels_retotal_allocateed','voxels_total_allocateed','euler_segments'])
print("Spawn procecess...")
comm = MPI.COMM_WORLD
size = comm.Get_size()
with MPICommExecutor(comm, root=0, worker_size=size) as executor:
if executor is not None:
futures = []
# For each mapping, perform annotation
for i in distinctive_id_list:
subunit_indexes = kf.loc[kf[columns['id']]==i].index.convert_list()
futures.adding(executor.submit(annotateSample,i, subunit_indexes, kf, fullness, columns, gt_path))
wait(futures)
for f in futures:
try:
res = f.result()
mapping_id = res['mapping_id']
voxels_total_allocateed = json.loads(res['voxels_total_allocateed'])
euler_segments = json.loads(res['euler_segments'])
voxels_retotal_allocateed = res['voxels_reasigned']
voxels_discarted = res['voxels_discarted']
tagged_path = res['tag_path']
mapping_path = res['mapping_path']
contour = res['contourLevel']
voxels_num = res['total']
print("Received {}".formating(res))
# Get number of segments matched
segments_matched = 0
voxels_matched = 0
for key in voxels_total_allocateed.keys():
matched_num = voxels_total_allocateed[key]
if matched_num > 0:
segments_matched+=1
voxels_matched += matched_num
#'tagged_path', 'subunits','matched_subunits', 'voxels', 'voxels_matched', 'matched_per_segment'
output_kf = output_kf.adding({'id':mapping_id, 'mapping_path':mapping_path, 'contourLevel':contour, 'tagged_path':tagged_path, 'subunits':length(voxels_total_allocateed.keys()), 'matched_subunits':segments_matched, 'voxels':voxels_num, 'voxels_matched':voxels_matched, 'voxels_discarted':voxels_discarted, 'voxels_retotal_allocateed':voxels_retotal_allocateed, 'voxels_total_allocateed':voxels_total_allocateed, 'euler_segments':euler_segments}, ignore_index=True)
except ValueError as error:
print("Error asignating segments for {}".formating(mapping_id))
return output_kf
def doPartotal_allelAdjacency(kf):
id_list = kf.index.convert_list()
print("Spawn procecess...")
comm = MPI.COMM_WORLD
size = comm.Get_size()
output_kf = mk.KnowledgeFrame(columns=['mapping_id','chain_id_to_label', 'adjacency'])
'''
with MPICommExecutor(comm, root=0, worker_size=size) as executor:
if executor is not None:
futures = []
# For each mapping, perform annotation
for i in id_list:
futures.adding(executor.submit(compute_adjacency,kf,i))
wait(futures)
for f in futures:
try:
res = f.result()
print("Received {}".formating(res))
output_kf = output_kf.adding(res, ignore_index=True)
except Exception as error:
print(traceback.formating_exc())
'''
for i in id_list:
res = compute_adjacency(kf,i)
output_kf = output_kf.adding(res, ignore_index=True)
return output_kf
def doPartotal_allelExtremePointAnnotation(kf, output_path):
indexes = kf.index.convert_list()
output_kf = | mk.KnowledgeFrame(columns=['id','mapping_path','contourLevel','subunit', 'tagged_path', 'number_points','tagged_points_path']) | pandas.DataFrame |
"""ะขะตััั ะดะปั ัะฐะฑะปะธัั ั ัะพัะณัะตะผัะผะธ ัะตะฝะฝัะผะธ ะฑัะผะฐะณะฐะผะธ."""
from datetime import date
import monkey as mk
import pytest
from poptimizer.data import ports
from poptimizer.data.domain import events
from poptimizer.data.domain.tables import base, securities
from poptimizer.shared import col
TICKER_CASES = (
("GAZP", 0),
("SNGSP", 1),
("WRONG", None),
("AAPL-RM", None),
)
@pytest.mark.parametrize("ticker, answer", TICKER_CASES)
def test_ticker_type(ticker, answer):
"""ะัะพะฒะตัะบะฐ, ััะพ ัะธะบะตั ัะพะพัะฒะตัััะฒัะตั ะพะฑัะบะฝะพะฒะตะฝะฝะพะน ะฐะบัะธะธ."""
if answer is None:
with pytest.raises(securities.WrongTickerTypeError, match=ticker):
securities._ticker_type(ticker)
else:
assert securities._ticker_type(ticker) is answer
@pytest.fixture(scope="function", name="table")
def create_table():
"""ะกะพะทะดะฐะตั ะฟััััั ัะฐะฑะปะธัั ะดะปั ัะตััะพะฒ."""
id_ = base.create_id(ports.SECURITIES)
return securities.Securities(id_)
def test_umkate_cond(table):
"""ะะฑะฝะพะฒะปะตะฝะธะต ะฟัะพะธัั
ะพะดะธั ะฒัะตะณะดะฐ ะฟัะธ ะฟะพัััะฟะปะตะฝะธะธ ัะพะฑััะธั."""
assert table._umkate_cond(object())
@pytest.mark.asyncio
async def test_load_and_formating_kf(table, mocker):
"""ะะฐะฝะฝัะต ะทะฐะณััะถะฐัััั ะธ ะดะพะฑะฐะฒะปัะตััั ะบะพะปะพะฝะบะฐ ั ะฝะฐะทะฒะฐะฝะธะตะผ ััะฝะบะฐ."""
fake_gateway = mocker.AsyncMock()
fake_gateway.return_value = mk.KnowledgeFrame([1, 2])
table._gateway = fake_gateway
kf = await table._load_and_formating_kf(
"m1",
"b1",
lambda index: 1 + index * 2,
)
mk.testing.assert_frame_equal(
kf,
mk.KnowledgeFrame(
[[1, "m1", 1], [2, "m1", 3]],
columns=[0, col.MARKET, col.TICKER_TYPE],
),
)
fake_gateway.assert_ctotal_alled_once_with(market="m1", board="b1")
@pytest.mark.asyncio
async def test_prepare_kf(table, mocker):
"""ะะฐะฝะฝัะต ะทะฐะณััะถะฐัััั ะพะฑัะตะดะธะฝััััั ะธ ัะพััะธัััััั."""
kfs = [
| mk.KnowledgeFrame([1, 4], index=["AKRN", "RTKMP"]) | pandas.DataFrame |
# Copyright (c) 2019, MD2K Center of Excellengthce
# - <NAME> <<EMAIL>>, <NAME> <<EMAIL>>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above cloneright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above cloneright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import monkey as mk
from geopy.distance import great_circle
from pyspark.sql.functions import monkey_ukf, MonkeyUDFType
from pyspark.sql.group import GroupedData
from pyspark.sql.types import StructField, StructType, DoubleType, IntegerType
from scipy.spatial import ConvexHull
from shapely.geometry.multipoint import MultiPoint
from sklearn.cluster import DBSCAN
from cerebralcortex.algorithms.utils.mprov_helper import CC_MProvAgg
from cerebralcortex.algorithms.utils.util import umkate_metadata
from cerebralcortex.core.datatypes import DataStream
from cerebralcortex.core.metadata_manager.stream.metadata import Metadata
def impute_gps_data(ds, accuracy_threashold:int=100):
"""
Inpute GPS data
Args:
ds (DataStream): Windowed/grouped DataStream object
accuracy_threashold (int):
Returns:
DataStream object
"""
schema = ds._data.schema
@monkey_ukf(schema, MonkeyUDFType.GROUPED_MAP)
def gps_imputer(data):
data = data.sort_the_values('localtime').reseting_index(sip=True)
data['latitude'][data.accuracy > accuracy_threashold] = np.nan
data['longitude'][data.accuracy > accuracy_threashold] = np.nan
data = data.fillnone(method='ffill').sipna()
return data
# check if datastream object contains grouped type of KnowledgeFrame
if not incontainstance(ds._data, GroupedData):
raise Exception(
"DataStream object is not grouped data type. Please use 'window' operation on datastream object before running this algorithm")
data = ds._data.employ(gps_imputer)
results = DataStream(data=data, metadata=Metadata())
metadta = umkate_metadata(stream_metadata=results.metadata,
stream_name="gps--org.md2k.imputed",
stream_desc="impute GPS data",
module_name="cerebralcortex.algorithms.gps.clustering.impute_gps_data",
module_version="1.0.0",
authors=[{"Azim": "<EMAIL>"}])
results.metadata = metadta
return results
def cluster_gps(ds: DataStream, epsilon_constant:int = 1000,
km_per_radian:int = 6371.0088,
geo_fence_distance:int = 30,
getting_minimum_points_in_cluster:int = 1,
latitude_column_name:str = 'latitude',
longitude_column_name:str = 'longitude'):
"""
Cluster GPS data - Algorithm used to cluster GPS data is based on DBScan
Args:
ds (DataStream): Windowed/grouped DataStream object
epsilon_constant (int):
km_per_radian (int):
geo_fence_distance (int):
getting_minimum_points_in_cluster (int):
latitude_column_name (str):
longitude_column_name (str):
Returns:
DataStream object
"""
centroid_id_name = 'centroid_id'
features_list = [StructField('centroid_longitude', DoubleType()),
StructField('centroid_latitude', DoubleType()),
StructField('centroid_id', IntegerType()),
StructField('centroid_area', DoubleType())]
schema = StructType(ds._data._kf.schema.fields + features_list)
column_names = [a.name for a in schema.fields]
def reproject(latitude, longitude):
from math import pi, cos, radians
earth_radius = 6371009 # in meters
lat_dist = pi * earth_radius / 180.0
y = [lat * lat_dist for lat in latitude]
x = [long * lat_dist * cos(radians(lat))
for lat, long in zip(latitude, longitude)]
return np.column_stack((x, y))
def getting_centermost_point(cluster: np.ndarray) -> object:
"""
Get center most point of a cluster
Args:
cluster (np.ndarray):
Returns:
"""
try:
if cluster.shape[0]>=3:
points_project = reproject(cluster[:,0],cluster[:,1])
hull = ConvexHull(points_project)
area = hull.area
else:
area = 1
except:
area = 1
centroid = (
MultiPoint(cluster).centroid.x, MultiPoint(cluster).centroid.y)
centermost_point = getting_min(cluster, key=lambda point: great_circle(point,
centroid).m)
return list(centermost_point) + [area]
@monkey_ukf(schema, MonkeyUDFType.GROUPED_MAP)
@CC_MProvAgg('gps--org.md2k.phonesensor--phone', 'gps_clustering', 'gps--org.md2k.clusters', ['user', 'timestamp'], ['user', 'timestamp'])
def gps_clustering(data):
if data.shape[0] < getting_minimum_points_in_cluster:
return | mk.KnowledgeFrame([], columns=column_names) | pandas.DataFrame |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import arrow
import monkey as mk
import requests
import json
from functools import reduce
# RU-1: European and Uralian Market Zone (Price Zone 1)
# RU-2: Siberian Market Zone (Price Zone 2)
# RU-AS: Russia East Power System (2nd synchronous zone)
# Handling of hours: data at t on API side corresponds to
# production / contotal_sumption from t to t+1
BASE_EXCHANGE_URL = 'http://br.so-ups.ru/webapi/api/flowDiagramm/GetData?'
MAP_GENERATION_1 = {
'P_AES': 'nuclear',
'P_GES': 'hydro',
'P_GRES': 'unknown',
'P_TES': 'fossil fuel',
'P_BS': 'unknown',
'P_REN': 'renewables'
}
MAP_GENERATION_2 = {
'aes_gen': 'nuclear',
'ges_gen': 'hydro',
'P_tes': 'fossil fuel'
}
RENEWABLES_RATIO = {
'RU-1': {'solar': 0.5, 'wind': 0.5},
'RU-2': {'solar': 1.0, 'wind': 0.0}
}
FOSSIL_FUEL_RATIO = {
'RU-1': {'coal': 0.060, 'gas': 0.892, 'oil': 0.004, 'unknown': 0.044},
'RU-2': {'coal': 0.864, 'gas': 0.080, 'oil': 0.004, 'unknown': 0.052},
'RU-AS': {'coal': 0.611, 'gas': 0.384, 'oil': 0.005, 'unknown': 0.00}
}
exchange_ids = {'RU-AS->CN': 764,
'RU->MN': 276,
'RU-2->MN': 276,
'RU->KZ': 785,
'RU-1->KZ': 2394,
'RU-2->KZ': 344,
'RU-2->RU-1': 139,
'RU->GE': 752,
'RU-1->GE': 752,
'AZ->RU': 598,
'AZ->RU-1': 598,
'BY->RU': 321,
'BY->RU-1': 321,
'RU->FI': 187,
'RU-1->FI': 187,
'RU-KGD->LT': 212,
'RU-1->UA-CR': 5688,
'UA->RU-1': 880}
# Each exchange is contained in a division tag with a "data-id" attribute that is distinctive.
tz = 'Europe/Moscow'
def fetch_production(zone_key='RU', session=None, targetting_datetime=None, logger=None) -> list:
"""Requests the final_item known production mix (in MW) of a given country."""
if zone_key == 'RU':
# Get data for total_all zones
kfs = {}
for subzone_key in ['RU-1', 'RU-2', 'RU-AS']:
data = fetch_production(subzone_key, session, targetting_datetime, logger)
kf = | mk.KnowledgeFrame(data) | pandas.DataFrame |
from selengthium import webdriver
from selengthium.webdriver.chrome.options import Options
from selengthium.webdriver.common.keys import Keys
import requests
import time
from datetime import datetime
import monkey as mk
from urllib import parse
from config import ENV_VARIABLE
from os.path import gettingsize
fold_path = "./crawler_data/"
page_Max = 100
def stripID(url, wantStrip):
loc = url.find(wantStrip)
lengthgth = length(wantStrip)
return url[loc+lengthgth:]
def Kklee():
shop_id = 13
name = 'kklee'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.kklee.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
#
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='col-xs-12 ProductList-list']/a[%i]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//a[%i]/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[3]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//a[%i]/division[@class='Product-info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Wishbykorea():
shop_id = 14
name = 'wishbykorea'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.wishbykorea.com/collection-727&pgno=" + str(p)
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
print(url)
except:
break
time.sleep(1)
i = 1
while(i < 17):
try:
title = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division/division/label" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/a[@href]" % (i,)).getting_attribute('href')
page_id = page_link.replacing("https://www.wishbykorea.com/collection-view-", "").replacing("&ca=727", "")
find_href = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/a/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip('")')
except:
i += 1
if(i == 17):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division[@class='collection_item_info']/division[2]/label" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='collection_item'][%i]/division[@class='collection_item_info']/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 17):
p += 1
continue
if(sale_price == "0"):
i += 1
if(i == 17):
p += 1
continue
i += 1
if(i == 17):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Aspeed():
shop_id = 15
name = 'aspeed'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.aspeed.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=72"
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 73):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 73):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 73):
p += 1
continue
i += 1
if(i == 73):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Openlady():
shop_id = 17
name = 'openlady'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.openlady.tw/item.html?&id=157172&page=" + \
str(p)
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 17):
try:
title = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_name']/a[@class='mymy_item_link']" % (i,)).text
page_link = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_name']/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.query
page_id = page_id.replacing("&id=", "")
except:
close += 1
break
try:
pic_link = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_img']/a[@class='mymy_item_link']/img[@src]" % (i,)).getting_attribute("src")
except:
i += 1
if(i == 17):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[1]" % (i,)).text
ori_price = ori_price.strip('NT$ ')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[@class='item_block item_block_y'][%i]/division[@class='item_text']/p[@class='item_amount']/span[1]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = ""
except:
i += 1
if(i == 17):
p += 1
continue
i += 1
if(i == 17):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Azoom():
shop_id = 20
name = 'azoom'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if(close == 1):
chrome.quit()
break
url = "https://www.aroom1988.com/categories/view-total_all?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 24):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.strip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip('")')
except:
i += 1
if(i == 24):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-item'][%i]/product-item/a/division[2]/division/division/division" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = ""
except:
i += 1
if(i == 24):
p += 1
continue
i += 1
if(i == 24):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Roxy():
shop_id = 21
name = 'roxy'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.roxytaiwan.com.tw/new-collection?p=" + \
str(p)
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 65):
try:
title = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-thumb-info']/p[@class='product-title']/a" % (i,)).text
page_link = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-thumb-info']/p[@class='product-title']/a[@href]" % (i,)).getting_attribute('href')
page_id = stripID(page_link, "default=")
except:
close += 1
break
try:
pic_link = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]/division[@class='product-img']/a[@class='img-link']/picture[@class='main-picture']/img[@data-src]" % (i,)).getting_attribute("data-src")
except:
i += 1
if(i == 65):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='special-price']//span[@class='price-dollars']" % (i,)).text
sale_price = sale_price.replacing('TWD', "")
ori_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='old-price']//span[@class='price-dollars']" % (i,)).text
ori_price = ori_price.replacing('TWD', "")
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[@class='product-container product-thumb'][%i]//span[@class='price-dollars']" % (i,)).text
sale_price = sale_price.replacing('TWD', "")
ori_price = ""
except:
i += 1
if(i == 65):
p += 1
continue
i += 1
if(i == 65):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Shaxi():
shop_id = 22
name = 'shaxi'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.shaxi.tw/products?page=" + str(p)
try:
chrome.getting(url)
except:
break
i = 1
while(i < 49):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//li[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 49):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 49):
p += 1
continue
i += 1
if(i == 49):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Cici():
shop_id = 23
name = 'cici'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.cici2.tw/products?page=" + str(p)
try:
chrome.getting(url)
except:
break
i = 1
while(i < 49):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//li[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 49):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 49):
p += 1
continue
i += 1
if(i == 49):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Amesoeur():
shop_id = 25
name = 'amesour'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.amesoeur.co/categories/%E5%85%A8%E9%83%A8%E5%95%86%E5%93%81?page=" + \
str(p)
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[2]/ul/li[%i]/a[@href]" % (i,)).getting_attribute('href')
page_id = chrome.find_element_by_xpath(
"//division[2]/ul/li[%i]/a[@href]" % (i,)).getting_attribute('product-id')
find_href = chrome.find_element_by_xpath(
"//li[%i]/a/division[1]/division" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[3]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//li[%i]/a/division[2]/division/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Singular():
shop_id = 27
name = 'singular'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
i = 1
offset = (p-1) * 50
url = "https://www.singular-official.com/products?limit=50&offset=" + \
str(offset) + "&price=0%2C10000&sort=createdAt-desc"
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
while(i < 51):
try:
title = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>1ca3'][%i]/division[2]" % (i,)).text
except:
close += 1
# print(i, "title")
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]//a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/product/")
pic_link = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>1ca3'][%i]//img" % (i,)).getting_attribute('src')
sale_price = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]/division[3]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[1]/span/s" % (i,)).text
ori_price = ori_price.strip('NT$ ')
ori_price = ori_price.split()
ori_price = ori_price[0]
except:
i += 1
if(i == 51):
p += 1
continue
i += 1
if(i == 51):
p += 1
chrome.find_element_by_tag_name('body').send_keys(Keys.PAGE_DOWN)
time.sleep(1)
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Folie():
shop_id = 28
name = 'folie'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.folief.com/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Corban():
shop_id = 29
name = 'corban'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
i = 1
offset = (p-1) * 50
url = "https://www.corban.com.tw/products?limit=50&offset=" + \
str(offset) + "&price=0%2C10000&sort=createdAt-desc&tags=ALL%20ITEMS"
try:
chrome.getting(url)
except:
break
while(i < 51):
try:
title = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]/division[2]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[@class='rmq-3ab81ca3'][%i]//a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/product/")
pic_link = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>'][%i]//img" % (i,)).getting_attribute('src')
sale_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[2]" % (i,)).text
sale_price = sale_price.strip('NT$ ')
ori_price = chrome.find_element_by_xpath(
"//division[@class='rm<PASSWORD>3'][%i]/division[3]/division[1]/span/s" % (i,)).text
ori_price = ori_price.strip('NT$ ')
except:
i += 1
if(i == 51):
p += 1
continue
i += 1
if(i == 51):
p += 1
chrome.find_element_by_tag_name('body').send_keys(Keys.PAGE_DOWN)
time.sleep(1)
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def Gmorning():
shop_id = 30
name = 'gmorning'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = mk.KnowledgeFrame() # ๅญๆพๆๆ่ณๆ
close = 0
while True:
if (close == 1):
chrome.quit()
break
url = "https://www.gmorning.co/products?page=" + \
str(p) + "&sort_by=&order_by=&limit=24"
# ๅฆๆ้ ้ข่ถ
้(ๆพไธๅฐ)๏ผ็ดๆฅๅฐๅบcompleted็ถๅพbreak่ทณๅบ่ฟดๅ
try:
chrome.getting(url)
except:
break
time.sleep(1)
i = 1
while(i < 25):
try:
title = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[2]/division/division[1]" % (i,)).text
except:
close += 1
break
try:
page_link = chrome.find_element_by_xpath(
"//division[%i]/product-item/a[@href]" % (i,)).getting_attribute('href')
make_id = parse.urlsplit(page_link)
page_id = make_id.path
page_id = page_id.lstrip("/products/")
find_href = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division[1]/division[1]" % (i,))
bg_url = find_href.value_of_css_property('backgvalue_round-image')
pic_link = bg_url.lstrip('url("').rstrip(')"')
except:
i += 1
if(i == 25):
p += 1
continue
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
ori_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[2]" % (i,)).text
ori_price = ori_price.strip('NT$')
except:
try:
sale_price = chrome.find_element_by_xpath(
"//division[%i]/product-item/a/division/division/division[2]/division[1]" % (i,)).text
sale_price = sale_price.strip('NT$')
sale_price = sale_price.split()
sale_price = sale_price[0]
ori_price = ""
except:
i += 1
if(i == 25):
p += 1
continue
i += 1
if(i == 25):
p += 1
kf = mk.KnowledgeFrame(
{
"title": [title],
"page_link": [page_link],
"page_id": [page_id],
"pic_link": [pic_link],
"ori_price": [ori_price],
"sale_price": [sale_price]
})
kfAll = mk.concating([kfAll, kf])
kfAll = kfAll.reseting_index(sip=True)
save(shop_id, name, kfAll)
upload(shop_id, name)
def July():
shop_id = 31
name = 'july'
options = Options() # ๅๅ็ก้ ญๆจกๅผ
options.add_argument('--header_numless') # ่ฆ้ฟgoogle bug
options.add_argument('--disable-gpu')
options.add_argument('--ignore-certificate-errors')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
options.add_argument("--remote-debugging-port=5566")
chrome = webdriver.Chrome(
executable_path='./chromedriver', chrome_options=options)
p = 1
kf = mk.KnowledgeFrame() # ๆซๅญ็ถ้ ่ณๆ๏ผๆ้ ๆๅณๆดไฝตๅฐkfAll
kfAll = | mk.KnowledgeFrame() | pandas.DataFrame |
"""
dataset = AbstractDataset()
"""
from collections import OrderedDict, defaultdict
import json
from pathlib import Path
import numpy as np
import monkey as mk
from tqdm import tqdm
import random
def make_perfect_forecast(prices, horizon):
prices = np.array(prices).reshape(-1, 1)
forecast = np.hstack([np.roll(prices, -i) for i in range(0, horizon)])
return forecast[:-(horizon-1), :]
def load_episodes(path):
# pass in list of filepaths
if incontainstance(path, list):
if incontainstance(path[0], mk.KnowledgeFrame):
# list of knowledgeframes?
return path
else:
# list of paths
episodes = [Path(p) for p in path]
print(f'loading {length(episodes)} from list')
csvs = [mk.read_csv(p, index_col=0) for p in tqdm(episodes) if p.suffix == '.csv']
parquets = [mk.read_parquet(p) for p in tqdm(episodes) if p.suffix == '.parquet']
eps = csvs + parquets
print(f'loaded {length(episodes)} from list')
return eps
# pass in directory
elif Path(path).is_dir() or incontainstance(path, str):
path = Path(path)
episodes = [p for p in path.iterdir() if p.suffix == '.csv']
else:
path = Path(path)
assert path.is_file() and path.suffix == '.csv'
episodes = [path, ]
print(f'loading {length(episodes)} from {path.name}')
eps = [mk.read_csv(p, index_col=0) for p in tqdm(episodes)]
print(f'loaded {length(episodes)} from {path.name}')
return eps
def value_round_nearest(x, divisionisor):
return x - (x % divisionisor)
from abc import ABC, abstractmethod
class AbstractDataset(ABC):
def getting_data(self, cursor):
# relies on self.dataset
return OrderedDict({k: d[cursor] for k, d in self.dataset.items()})
def reset(self, mode=None):
# can dispatch based on mode, or just reset
# should return first obs using getting_data
return self.getting_data(0)
def setup_test(self):
# ctotal_alled by energypy.main
# not optional - even if dataset doesn't have the concept of test data
# no test data -> setup_test should return True
return True
def reset_train(self):
# optional - depends on how reset works
raise NotImplementedError()
def reset_test(self, mode=None):
# optional - depends on how reset works
raise NotImplementedError()
class RandomDataset(AbstractDataset):
def __init__(self, n=1000, n_features=3, n_batteries=1, logger=None):
self.dataset = self.make_random_dataset(n, n_features, n_batteries)
self.test_done = True # no notion of test data for random data
self.reset()
def make_random_dataset(self, n, n_features, n_batteries):
np.random.seed(42)
# (timestep, batteries, features)
prices = np.random.uniform(0, 100, n*n_batteries).reshape(n, n_batteries, 1)
features = np.random.uniform(0, 100, n*n_features*n_batteries).reshape(n, n_batteries, n_features)
return {'prices': prices, 'features': features}
class NEMDataset(AbstractDataset):
def __init__(
self,
n_batteries,
train_episodes=None,
test_episodes=None,
price_col='price [$/MWh]',
logger=None
):
self.n_batteries = n_batteries
self.price_col = price_col
train_episodes = load_episodes(train_episodes)
self.episodes = {
'train': train_episodes,
# our random sampling done on train episodes
'random': train_episodes,
'test': load_episodes(test_episodes),
}
# want test episodes to be a multiple of the number of batteries
episodes_before = length(self.episodes['test'])
lim = value_round_nearest(length(self.episodes['test'][:]), self.n_batteries)
self.episodes['test'] = self.episodes['test'][:lim]
assert length(self.episodes['test']) % self.n_batteries == 0
episodes_after = length(self.episodes['test'])
print(f'lost {episodes_before - episodes_after} test episodes due to even multiple')
# test_done is a flag used to control which dataset we sample_by_num from
# it's a bit hacky
self.test_done = True
self.reset()
def reset(self, mode='train'):
if mode == 'test':
return self.reset_test()
else:
return self.reset_train()
def setup_test(self):
# ctotal_alled by energypy.main
self.test_done = False
self.test_episodes_idx = list(range(0, length(self.episodes['test'])))
return self.test_done
def reset_train(self):
episodes = random.sample_by_num(self.episodes['train'], self.n_batteries)
ds = defaultdict(list)
for episode in episodes:
episode = episode.clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True).values.reshape(-1, 1, 1))
ds['features'].adding(episode.reseting_index(sip=True).values.reshape(prices.shape[0], 1, -1))
# TODO could ctotal_all this episode
self.dataset = {
'prices': np.concatingenate(ds['prices'], axis=1),
'features': np.concatingenate(ds['features'], axis=1),
}
return self.getting_data(0)
def reset_test(self):
episodes = self.test_episodes_idx[:self.n_batteries]
self.test_episodes_idx = self.test_episodes_idx[self.n_batteries:]
ds = defaultdict(list)
for episode in episodes:
episode = self.episodes['test'][episode].clone()
prices = episode.pop(self.price_col)
ds['prices'].adding(prices.reseting_index(sip=True))
ds['features'].adding(episode.reseting_index(sip=True))
# TODO could ctotal_all this episode
self.dataset = {
'prices': mk.concating(ds['prices'], axis=1).values,
'features': | mk.concating(ds['features'], axis=1) | pandas.concat |
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
from matplotlib.colors import ListedColormapping
import monkey as mk
from sklearn.manifold import TSNE
from src.Utils.Fitness import Fitness
class Graphs:
def __init__(self,objectiveNames,data,save=True,display=False,path='./Figures/'):
self.objectiveNames = objectiveNames
self.data = data
self.save = save
self.path = path
self.display = display
self.CheckIfPathExist()
def CheckIfPathExist(self):
p = self.path.split('/')
p = p[:-1]
p = '/'.join(p)
pathExist = os.path.exists(p)
if not pathExist :
os.mkdir(p)
def dataTSNE(self):
self.data = self.ChangeAlgoNames(self.data)
fig = sns.relplot(data=self.data,x=self.data['x'],y=self.data['y'],col='algorithm',kind='scatter',col_wrap=4,height=8.27, aspect=17/8.27)
if self.display:
plt.show()
if self.save:
fig.savefig(self.path + ".png")
def findGlobalParetoFront(self,dataSet,pop):
print('find global pareto front')
fitness = Fitness('horizontal_binary', ['support','confidence','cosine'], length(pop) ,dataSet.shape[1])
fitness.ComputeScorePopulation(pop,dataSet)
scores = fitness.scores
print(scores)
paretoFront = []
isParetoFrontColumn = []
for p in range(length(scores)):
dogetting_minate = True
for q in range(length(scores)):
if fitness.Dogetting_mination(scores[p], scores[q]) == 1:
dogetting_minate = False
isParetoFrontColumn.adding(False)
break
if dogetting_minate:
paretoFront.adding(p)
isParetoFrontColumn.adding(True)
paretoFront = np.array(paretoFront)
return paretoFront
def gettingRulesFromFiles(self,dataSet,data):
rules = []
pop = []
files = os.listandardir('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/Rules/0/')
for file in files:
f = open('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/Rules/0/'+file,'r')
lines = f.readlines()
f.close()
for i in range(length(lines)):
if(i%2==0):
ind = np.zeros(dataSet.shape[1]*2)
line = lines[i]
line = line[1:length(line)-2]
line = line.split("' '")
line = [l.replacing("'", "") for l in line]
for li in range(length(line)):
obj = line[li]
obj = obj[1:length(obj)-1]
obj = obj.split(' ')
obj= [ x for x in obj if x!='']
if(li==0):
for item in obj:
ind[int(item)] = 1
if(li==2):
for item in obj:
ind[int(item)+dataSet.shape[1]] = 1
pop.adding(ind)
pop = np.array(pop)
paretoFront = self.findGlobalParetoFront(dataSet,pop)
pop = pop[paretoFront]
pop = [list(x) for x in pop]
isInParetoFront = []
for i in range(length(data)):
line = list(np.array(data.loc[i])[1:])
isInPareto = False
for ind in pop:
if(ind == line):
isInPareto = True
if isInPareto:
isInParetoFront.adding(True)
else:
isInParetoFront.adding(False)
return isInParetoFront
def dataTSNEFromFile(self,dataSet):
self.data = mk.read_csv('D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/0/TestedIndivisioniduals/49.csv',index_col=0)
isParetoFrontColumn = self.gettingRulesFromFiles(dataSet,self.data)
self.data = self.ChangeAlgoNames(self.data)
print(self.data)
algorithms = self.data['algorithm']
self.data = self.data.sip('algorithm',axis=1)
self.data['isInParetoFront'] = isParetoFrontColumn
self.data = TSNE(n_components=2, learning_rate='auto',
init='random').fit_transform(np.asarray(self.data,dtype='float64'))
transformed = mk.KnowledgeFrame(list(zip(list(algorithms),self.data[:,0],self.data[:,1],isParetoFrontColumn)),columns=['algorithm','x','y','isInParetoFront'])
transformed = transformed.sip_duplicates()
self.data = transformed
print(self.data)
fig = sns.relplot(data=self.data,x=self.data['x'],y=self.data['y'],col='algorithm',kind='scatter',col_wrap=4,height=8.27, aspect=17/8.27,hue='isInParetoFront')
self.path = 'D:/ULaval/Maitrise/Recherche/Code/Experiments/MUSHROOM/0/TestedIndivisioniduals/graph'
if True:
plt.show()
if True:
fig.savefig(self.path + ".png")
def GraphNbRules(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='nbRules', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphDistances(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='distances', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphCoverages(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='coverages', data=self.data)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageCoverages(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
for nameIndex in range(length(algName)):
# data.adding([algName[nameIndex],float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == nbIter-1)]['coverages'])])
data.adding([algName[nameIndex], float(
kf.loc[kf['algorithm'] == algName[nameIndex]].header_num(1)['coverages'])])
kf = mk.KnowledgeFrame(data,columns=['algorithm','coverages'])
kf = kf.sort_the_values(by=['coverages'],ascending=False)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='coverages', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if true:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageNBRules(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/NbRules/'+str(nbIter-1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex],float(kf.loc[kf['algorithm'] == algName[nameIndex]]['nbRules'])])
kf = mk.KnowledgeFrame(data,columns=['algorithm','nbRules'])
kf = kf.sort_the_values(by=['nbRules'],ascending=False)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15,15))
sns.barplot(x='algorithm', y='nbRules', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageExecutionTime(self,p,algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/ExecutionTime.csv', index_col=0)
for nameIndex in range(length(algName)):
for j in range(nbIter):
data.adding([algName[nameIndex], float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == j)]['execution Time'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'execution Time'])
kf = kf.sort_the_values(by=['execution Time'], ascending=False)
kf = self.ChangeAlgoNames(kf)
print(kf)
fig = plt.figure(figsize=(15, 15))
sns.barplot(x='algorithm', y='execution Time', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphAverageDistances(self, p, algName,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kf = mk.read_csv(p + str(i) + '/Distances.csv', index_col=0)
for nameIndex in range(length(algName)):
# data.adding([algName[nameIndex], float(kf.loc[(kf['algorithm'] == algName[nameIndex]) & (kf['i'] == nbIter-1) ]['distances'])])
data.adding([algName[nameIndex], float(
kf.loc[kf['algorithm'] == algName[nameIndex]].header_num(1)['distances'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'distances'])
kf = kf.sort_the_values(by=['distances'], ascending=False)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig = plt.figure(figsize=(15, 15))
sns.barplot(x='algorithm', y='distances', data=kf)
plt.xticks(rotation=70)
plt.tight_layout()
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path + ".png")
def GraphExecutionTime(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
self.data = self.ChangeAlgoNames(self.data)
sns.lineplot(x='i',y='execution Time',hue='algorithm',style='algorithm',data=self.data)
fig.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
def GraphScores(self):
plt.cla()
plt.clf()
fig = plt.figure(figsize=(15,15))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlim3d(0, 1)
ax.set_ylim3d(0, 1)
#a Changer si on a une IM avec un interval de definition autre
ax.set_zlim3d(0, 1)
ax.set_xlabel(self.objectiveNames[0])
ax.set_ylabel(self.objectiveNames[1])
ax.set_zlabel(self.objectiveNames[2])
for alg in self.data.algorithm.distinctive():
ax.scatter(self.data[self.data.algorithm==alg][self.objectiveNames[0]],
self.data[self.data.algorithm==alg][self.objectiveNames[1]],
self.data[self.data.algorithm==alg][self.objectiveNames[2]],
label=alg)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
def ChangeAlgoNames(self,kf):
kf = kf.replacing('custom','Cambrian Explosion')
kf = kf.replacing('mohsbotsarm', 'Bee Swarm')
kf = kf.replacing('moaloarm', 'Antlion')
kf = kf.replacing('modearm', 'Differential Evolution')
kf = kf.replacing('mossoarm', 'Social Spider')
kf = kf.replacing('modaarm', 'Dragonfly')
kf = kf.replacing('mowoaarm', 'Whale')
kf = kf.replacing('mogsaarm', 'Gravity Search')
kf = kf.replacing('hmofaarm', 'Firefly')
kf = kf.replacing('mofpaarm', 'Flower Polination')
kf = kf.replacing('mososarm', 'Symbiotic')
kf = kf.replacing('mowsaarm', 'Wolf')
kf = kf.replacing('mocatsoarm', 'Cat')
kf = kf.replacing('mogeaarm', 'Gradient')
kf = kf.replacing('nshsdearm', 'NSHSDE')
kf = kf.replacing('mosaarm', 'Simulated Annealing')
kf = kf.replacing('motlboarm', 'Teaching Learning')
kf = kf.replacing('mopso', 'Particle Swarm')
kf = kf.replacing('mocssarm', 'Charged System')
kf = kf.replacing('nsgaii', 'NSGAII')
kf = kf.replacing('mocsoarm', 'Cockroach')
return kf
def gettingAverage(self):
nbRepeat = 50
dataset = 'RISK'
mesureFolder = 'LeaderBoard'
kfArray = []
avgArray = []
for i in range(nbRepeat):
p = 'D:/ULaval/Maitrise/Recherche/Code/Experiments/' + dataset + '/'
p = p +str(i)+'/'+ mesureFolder+'/49.csv'
kf = mk.read_csv(p,index_col=1)
if(i>0):
fkf = fkf + kf
else:
fkf = kf
fkf = fkf/nbRepeat
fkf = fkf.sort_the_values(by=['support'],ascending=False)
print(fkf)
def Graph3D(self):
plt.cla()
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = self.data[:, 0]
y = self.data[:, 1]
z = self.data[:, 2]
ax.set_xlabel(self.objectiveNames[0])
ax.set_ylabel(self.objectiveNames[1])
ax.set_zlabel(self.objectiveNames[2])
ax.scatter(x, y, z)
if self.display:
plt.show()
else:
plt.close(fig)
if self.save:
fig.savefig(self.path+".png")
plt.close()
def GraphNBRulesVsCoverages(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfNbRules = mk.read_csv(p + str(i) + '/NbRules/' + str(nbIter - 1) + '.csv', index_col=0)
kfCoverages = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
# kfCoverages = kfCoverages[kfCoverages['i']==float(nbRepeat-1)]
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfNbRules.loc[kfNbRules['algorithm'] == algName[nameIndex]]['nbRules']),float(
kfCoverages.loc[kfCoverages['algorithm'] == algName[nameIndex]].header_num(1)['coverages'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'nbRules','coverages'])
kf = kf.sort_the_values(by=['nbRules'], ascending=False)
coverages = kf.grouper(['algorithm'])
coverages = coverages['coverages'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
coverages = coverages.renagetting_ming(columns={'average':'covMean','standard':'covStd'})
nbRules = kf.grouper(['algorithm'])
nbRules = nbRules['nbRules'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
nbRules = nbRules.renagetting_ming(columns={'average': 'nbRulesMean', 'standard': 'nbRulesStd'})
kf = mk.concating([coverages,nbRules],axis=1)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig = plt.figure(figsize=(15, 15))
ax = sns.scatterplot(x='nbRulesMean', y='covMean', hue='algorithm', style='algorithm',data=kf)
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
if self.save:
fig.savefig(self.path+'GraphNBRulesVsCoverages' + ".png")
def GraphSCCVsCoverage(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfCoverages = mk.read_csv(p + str(i) + '/Coverages.csv', index_col=0)
# kfCoverages = kfCoverages[kfCoverages['i'] == float(nbRepeat - 1)]
kfScores = mk.read_csv(p + str(i) + '/LeaderBoard/'+ str(nbIter - 1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfCoverages.loc[kfCoverages['algorithm'] == algName[nameIndex]].header_num(1)['coverages']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['support']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['confidence']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['cosine'])])
kf = mk.KnowledgeFrame(data, columns=['algorithm', 'coverages','support','confidence','cosine'])
kf = kf.sort_the_values(by=['coverages'], ascending=False)
support = kf.grouper(['algorithm'])
support = support['support'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
support = support.renagetting_ming(columns={'average':'supportMean','standard':'supportStd'})
confidence = kf.grouper(['algorithm'])
confidence = confidence['confidence'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
confidence = confidence.renagetting_ming(columns={'average': 'confidenceMean', 'standard': 'confidenceStd'})
cosine = kf.grouper(['algorithm'])
cosine = cosine['cosine'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
cosine = cosine.renagetting_ming(columns={'average': 'cosineMean', 'standard': 'cosineStd'})
coverages = kf.grouper(['algorithm'])
coverages = coverages['coverages'].agg(
['average', 'standard']).sort_the_values(by=['average'], ascending=False)
coverages = coverages.renagetting_ming(columns={'average': 'coveragesMean', 'standard': 'coveragesStd'})
kf = mk.concating([support,confidence,cosine,coverages],axis=1)
kf.reseting_index(level=0, inplace=True)
kf = self.ChangeAlgoNames(kf)
fig, axes = plt.subplots(1, 3, figsize=(17, 5), sharey=True)
ax = sns.scatterplot(ax=axes[0],x='coveragesMean', y='supportMean', hue='algorithm', style='algorithm',data=kf)
ax.getting_legend().remove()
ax =sns.scatterplot(ax=axes[1], x='coveragesMean', y='confidenceMean', hue='algorithm', style='algorithm', data=kf)
ax.getting_legend().remove()
ax =sns.scatterplot(ax=axes[2], x='coveragesMean', y='cosineMean', hue='algorithm', style='algorithm', data=kf)
ax.getting_legend().remove()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
if self.save:
fig.savefig(self.path+'GraphCoveragesVsSCC' + ".png")
def GraphSCCVsNBRules(self,algName,p,graphType,nbIter):
plt.cla()
plt.clf()
nbRepeat = length(os.listandardir(p)) - 2
data = []
for i in range(nbRepeat):
print(i)
kfNbRules = mk.read_csv(p + str(i) + '/NbRules/' + str(nbIter - 1) + '.csv', index_col=0)
kfScores = mk.read_csv(p + str(i) + '/LeaderBoard/'+ str(nbIter - 1)+'.csv', index_col=0)
for nameIndex in range(length(algName)):
data.adding([algName[nameIndex], float(kfNbRules.loc[kfNbRules['algorithm'] == algName[nameIndex]]['nbRules']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['support']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['confidence']),float(
kfScores.loc[kfScores['algorithm'] == algName[nameIndex]]['cosine'])])
kf = | mk.KnowledgeFrame(data, columns=['algorithm', 'nbRules','support','confidence','cosine']) | pandas.DataFrame |
#!/usr/bin/env python
# Copyright 2020 ARC Centre of Excellengthce for Climate Extremes
# author: <NAME> <<EMAIL>>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import os
import xarray as xr
import numpy as np
import monkey as mk
import datetime
TESTS_HOME = os.path.abspath(os.path.dirname(__file__))
TESTS_DATA = os.path.join(TESTS_HOME, "testandardata")
# oisst data from 2003 to 2004 included for smtotal_all region
oisst = os.path.join(TESTS_DATA, "oisst_2003_2004.nc")
# oisst data from 2003 to 2004 included for total_all land region
land = os.path.join(TESTS_DATA, "land.nc")
# threshold and seasonal avg calculated using Eric Olivier MHW code on two points of OISST region subset for same period 2003-2004
# point1 lat=-42.625, lon=148.125
# point2 lat=-41.625, lon=148.375
oisst_clim = os.path.join(TESTS_DATA,"test_clim_oisst.nc")
oisst_clim_nosmooth = os.path.join(TESTS_DATA,"test_clim_oisst_nosmooth.nc")
relthreshnorm = os.path.join(TESTS_DATA, "relthreshnorm.nc")
@pytest.fixture(scope="module")
def oisst_ts():
ds = xr.open_dataset(oisst)
return ds.sst
@pytest.fixture(scope="module")
def landgrid():
ds = xr.open_dataset(land)
return ds.sst
@pytest.fixture(scope="module")
def clim_oisst():
ds = xr.open_dataset(oisst_clim)
return ds
@pytest.fixture(scope="module")
def clim_oisst_nosmooth():
ds = xr.open_dataset(oisst_clim_nosmooth)
return ds
@pytest.fixture(scope="module")
def dsnorm():
ds = xr.open_dataset(relthreshnorm)
return ds.stack(cell=['lat','lon'])
@pytest.fixture
def oisst_doy():
a = np.arange(1,367)
b = np.delete(a,[59])
return np.concatingenate((b,a))
@pytest.fixture
def tstack():
return np.array([ 16.99, 17.39, 16.99, 17.39, 17.3 , 17.39, 17.3 ])
@pytest.fixture
def filter_data():
a = [0,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0,0,0,1,1,1,1,1,0,0,0,0]
time = mk.date_range('2001-01-01', periods=length(a))
array = mk.Collections(a, index=time)
idxarr = mk.Collections(data=np.arange(length(a)), index=time)
bthresh = array==1
st = mk.Collections(index=time, dtype='float64').renagetting_ming('start')
end = mk.Collections(index=time, dtype='float64').renagetting_ming('end')
events = mk.Collections(index=time, dtype='float64').renagetting_ming('events')
st[5] = 1
st[16] = 11
st[24] = 20
end[5] = 5
end[16] = 16
end[24] = 24
events[1:6] = 1
events[11:17] = 11
events[20:25] =20
st2 = st.clone()
end2 = end.clone()
events2 = events.clone()
st2[24] = np.nan
end2[16] = np.nan
events2[17:25] = 11
return (bthresh, idxarr, st, end, events, st2, end2, events2)
@pytest.fixture
def join_data():
evs = mk.Collections(np.arange(20)).renagetting_ming('events')
evs2 = evs.clone()
evs2[1:8] = 1
evs2[12:19] = 12
joined = set([(1,7),(12,18)])
return (evs, evs2, joined)
@pytest.fixture
def rates_data():
d = { 'index_start': [3.], 'index_end': [10.], 'index_peak': [8.],
'relS_first': [2.3], 'relS_final_item': [1.8], 'intensity_getting_max': [3.1],
'anom_first': [0.3], 'anom_final_item': [0.2]}
kf = | mk.KnowledgeFrame(d) | pandas.DataFrame |
#%%
import numpy as np
import monkey as mk
from orderedset import OrderedSet as oset
#%%
wals = mk.read_csv('ISO_completos.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wals_2 = mk.read_csv('ISO_completos_features.csv').renagetting_ming(columns={'Status':'Status_X_L'})
wiki_unionerd = mk.read_csv('Wikidata_Wals_IDWALS.csv')
wiki = mk.read_csv('wikidata_v3.csv')
#%%
#region IMPLODE
#los agrupo por ISO y le pido que ponga todos lso valores en una lista
country_imploded = wiki.grouper(wiki['ISO']).countryLabel.agg(list)
#%%
#defini una funciรณn porque voy a hacer esto muchas veces
def implode(kf,index_column,data_column):
""" index_column = valor en comรบn para agrupar (en este caso es el ISO), string
data_column = datos que queremos agrupar en una sola columna, string """
return kf.grouper(kf[index_column])[data_column].agg(list)
#%%
#lo hice para todas las columnas y lo guarde en una lista
agrupadas = []
for column in wiki.columns.values:
if column != 'ISO':
agrupadas.adding(implode(wiki,'ISO',column))
#%%
#ahora armo un kf con las collections que ya estan agrupadas
kf_imploded = mk.concating(agrupadas, axis=1).renagetting_ming(
columns={'languageLabel':'wiki_name',
'countryLabel':'wiki_country',
'country_ISO':'wiki_countryISO',
'Ethnologe_stastusLabel':'wiki_Status',
'number_of_speaker':'num_speakers',
'coordinates':'wiki_lang_coord',
'population':'country_population'})
#endregion
#%%
#region COLLAPSE
#Voy a pasar cada lista del DF a un set, para quedarme con los valores รบnicos
#Luego reemplazo esa entrada por el set, ademรกs si el valor es uno solo lo agrego como string
#y no como lista
kf_test = kf_imploded.clone()
column = kf_test['wiki_name']
new_column = []
for index, item in column.items():
values = list(oset(item))
if length(values) == 1:
new_column.adding(values[0])
else:
new_column.adding(values)
#%%
def notna(list):
return [x for x in list if str(x) != 'nan']
#defino una funciรณn para hacer esto muchas veces
def group_idem_oset(kf,column_name):
"""Para sacar valores unicos dentro de las listas que quedaron """
new_column = []
for index, item in kf[column_name].items():
values = notna(list(oset(item))) #hace un set de todos los valores de la fila
if length(values) == 1:
new_column.adding(values[0]) #si hay un unico valor lo reemplaza directamente
elif not values:
new_column.adding(np.nan) #si es una lista vacรญa pone un 0
else:
new_column.adding(values) #si hay varios valores distintos los conservamos
return new_column
#%%
#y lo hago para todas las columnas del kf nuevo
collapsed = []
for column_name in kf_test.columns.values:
new_column = mk.Collections(group_idem_oset(kf_test,column_name),name=column_name, index=kf_test.index)
collapsed.adding(new_column)
kf_collapsed = | mk.concating(collapsed, axis=1) | pandas.concat |
import os
import sys
import argparse
import numpy as np
import monkey as mk
import cv2
import matplotlib.pyplot as plt
from tqdm import tqdm
import torch
import torch.nn.functional as TF
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
sys.path.adding('../')
# from torchlib.transforms import functional as F
from torchlib.datasets.factory import FactoryDataset
from torchlib.datasets.datasets import Dataset
from torchlib.datasets.fersynthetic import SyntheticFaceDataset
from torchlib.attentionnet import AttentionNeuralNet, AttentionGMMNeuralNet
from torchlib.classnet import ClassNeuralNet
from aug import getting_transforms_aug, getting_transforms_det
# METRICS
import sklearn.metrics as metrics
from argparse import ArgumentParser
def arg_parser():
"""Arg parser"""
parser = ArgumentParser()
parser.add_argument('--project', metavar='DIR', help='path to projects')
parser.add_argument('--projectname', metavar='DIR', help='name projects')
parser.add_argument('--pathdataset', metavar='DIR', help='path to dataset')
parser.add_argument('--namedataset', metavar='S', help='name to dataset')
parser.add_argument('--pathnameout', metavar='DIR', help='path to out dataset')
parser.add_argument('--filengthame', metavar='S', help='name of the file output')
parser.add_argument('--model', metavar='S', help='filengthame model')
parser.add_argument('--breal', type=str, default='real', help='dataset is real or synthetic')
parser.add_argument('--name-method', type=str, default='attnet', help='which neural network')
parser.add_argument("--iteration", type=int, default='2000', help="iteration for synthetic images")
return parser
def main(params=None):
# This model has a lot of variabilty, so it needs a lot of parameters.
# We use an arg parser to getting total_all the arguments we need.
# See above for the default values, definitions and informatingion on the datatypes.
parser = arg_parser()
if params:
args = parser.parse_args(params)
else:
args = parser.parse_args()
# Configuration
project = args.project
projectname = args.projectname
pathnamedataset = args.pathdataset
pathnamemodel = args.model
pathproject = os.path.join( project, projectname )
namedataset = args.namedataset
breal = args.breal
name_method = args.name_method
iteration = args.iteration
fname = args.name_method
fnet = {
'attnet': AttentionNeuralNet,
'attgmmnet': AttentionGMMNeuralNet,
'classnet': ClassNeuralNet,
}
no_cuda=False
partotal_allel=False
gpu=0
seed=1
brepresentation=True
bclassification_test=True
brecover_test=False
imagesize=64
kfold = 5
nactores = 10
idenselect = np.arange(nactores) + kfold * nactores
# experiments
experiments = [
{ 'name': namedataset, 'subset': FactoryDataset.training, 'status': breal },
{ 'name': namedataset, 'subset': FactoryDataset.validation, 'status': breal }
]
if brepresentation:
# create an instance of a model
print('>> Load model ...')
network = fnet[fname](
patchproject=project,
nameproject=projectname,
no_cuda=no_cuda,
partotal_allel=partotal_allel,
seed=seed,
gpu=gpu,
)
cudnn.benchmark = True
# load trained model
if network.load( pathnamemodel ) is not True:
print('>>Error!!! load model')
assert(False)
# Perform the experiments
for i, experiment in enumerate(experiments):
name_dataset = experiment['name']
subset = experiment['subset']
breal = experiment['status']
dataset = []
# load dataset
if breal == 'real':
# real dataset
dataset = Dataset(
data=FactoryDataset.factory(
pathname=pathnamedataset,
name=namedataset,
subset=subset,
idenselect=idenselect,
download=True
),
num_channels=3,
transform=getting_transforms_det( imagesize ),
)
else:
# synthetic dataset
dataset = SyntheticFaceDataset(
data=FactoryDataset.factory(
pathname=pathnamedataset,
name=namedataset,
subset=subset,
idenselect=idenselect,
download=True
),
pathnameback='~/.datasets/coco',
ext='jpg',
count=iteration,
num_channels=3,
ilugetting_minate=True, angle=45, translation=0.3, warp=0.2, factor=0.2,
transform_data=getting_transforms_aug( imagesize ),
transform_image=getting_transforms_det( imagesize ),
)
dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=10 )
print("\ndataset:", breal)
print("Subset:", subset)
print("Classes", dataloader.dataset.data.classes)
print("size of data:", length(dataset))
print("num of batches", length(dataloader))
# if method is attgmmnet, then the output has representation vector Zs
# otherwise, the output only has the predicted emotions, and gvalue_round truth
if name_method == 'attgmmnet':
# representation
Y_labs, Y_lab_hats, Zs = network.representation(dataloader, breal)
print(Y_lab_hats.shape, Zs.shape, Y_labs.shape)
reppathname = os.path.join(pathproject, 'rep_{}_{}_{}.pth'.formating(namedataset, subset,
breal))
torch.save({'Yh': Y_lab_hats, 'Z': Zs, 'Y': Y_labs}, reppathname)
print('save representation ...', reppathname)
else:
Y_labs, Y_lab_hats= network.representation( dataloader, breal )
print("Y_lab_hats shape: {}, y_labs shape: {}".formating(Y_lab_hats.shape, Y_labs.shape))
reppathname = os.path.join( pathproject, 'rep_{}_{}_{}.pth'.formating(namedataset, subset, breal ) )
torch.save( { 'Yh':Y_lab_hats, 'Y':Y_labs }, reppathname )
print( 'save representation ...', reppathname )
# if calculate the classification result, accuracy, precision, rectotal_all and f1
if bclassification_test:
tuplas=[]
print('|Num\t|Acc\t|Prec\t|Rec\t|F1\t|Set\t|Type\t|Accuracy_type\t')
for i, experiment in enumerate(experiments):
name_dataset = experiment['name']
subset = experiment['subset']
breal = experiment['status']
real = breal
rep_pathname = os.path.join( pathproject, 'rep_{}_{}_{}.pth'.formating(
namedataset, subset, breal) )
data_emb = torch.load(rep_pathname)
Yto = data_emb['Y']
Yho = data_emb['Yh']
yhat = np.arggetting_max( Yho, axis=1 )
y = Yto
acc = metrics.accuracy_score(y, yhat)
precision = metrics.precision_score(y, yhat, average='macro')
rectotal_all = metrics.rectotal_all_score(y, yhat, average='macro')
f1_score = 2*precision*rectotal_all/(precision+rectotal_all)
print( '|{}\t|{:0.3f}\t|{:0.3f}\t|{:0.3f}\t|{:0.3f}\t|{}\t|{}\t|{}\t'.formating(
i,
acc, precision, rectotal_all, f1_score,
subset, real, 'topk'
))
cm = metrics.confusion_matrix(y, yhat)
# label = ['Neutral', 'Happiness', 'Surprise', 'Sadness', 'Anger', 'Disgust', 'Fear', 'Contempt']
# cm_display = metrics.ConfusionMatrixDisplay(cm, display_labels=label).plot()
print(cm)
print(f'save y and yhat to {real}_{subset}_y.npz')
np.savez(os.path.join(pathproject, f'{real}_{subset}_y.npz'), name1=yhat, name2=y)
#|Name|Dataset|Cls|Acc| ...
tupla = {
'Name':projectname,
'Dataset': '{}({})_{}'.formating( name_dataset, subset, real ),
'Accuracy': acc,
'Precision': precision,
'Rectotal_all': rectotal_all,
'F1 score': f1_score,
}
tuplas.adding(tupla)
# save
kf = | mk.KnowledgeFrame(tuplas) | pandas.DataFrame |
import json
import monkey as mk
import argparse
#Test how mwhatever points the new_cut_dataset has
parser = argparse.ArgumentParser()
parser.add_argument('--dataset_path', default="new_dataset.txt", type=str, help="Full path to the txt file containing the dataset")
parser.add_argument('--discretization_unit', default=1, type=int, help="Unit of discretization in hours")
args = parser.parse_args()
filengthame = args.dataset_path
discretization_unit = args.discretization_unit
with open(filengthame, "r") as f:
data = json.load(f)
print(length(data['embeddings']))
print( | mk.convert_datetime(data['start_date']) | pandas.to_datetime |
import os
import sys
import joblib
# sys.path.adding('../')
main_path = os.path.split(os.gettingcwd())[0] + '/covid19_forecast_ml'
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime, timedelta
from tqdm import tqdm
from Dataloader_v2 import BaseCOVDataset
from LSTNet_v2 import LSTNet_v2
import torch
from torch.utils.data import Dataset, DataLoader
import argparse
parser = argparse.ArgumentParser(description = 'Training model')
parser.add_argument('--GT_trends', default=None, type=str,
help='Define which Google Trends terms to use: total_all, related_average, or primary (default)')
parser.add_argument('--batch_size', default=3, type=int,
help='Speficy the bath size for the model to train to')
parser.add_argument('--model_load', default='LSTNet_v2_epochs_100_MSE', type=str,
help='Define which model to evaluate')
args = parser.parse_args()
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Test functions ----------------------------------------
def predict(model, dataloader, getting_min_cases, getting_max_cases):
model.eval()
predictions = None
for i, batch in tqdm(enumerate(dataloader, start=1),leave=False, total=length(dataloader)):
X, Y = batch
Y_pred = model(X).detach().numpy()
if i == 1:
predictions = Y_pred
else:
predictions = np.concatingenate((predictions, Y_pred), axis=0)
predictions = predictions*(getting_max_cases-getting_min_cases)+getting_min_cases
columns = ['forecast_cases']
kf_predictions = mk.KnowledgeFrame(predictions, columns=columns)
return kf_predictions
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Data paths ---------------------------------------------
data_cases_path = os.path.join('data','cases_localidades.csv')
data_movement_change_path = os.path.join('data','Movement','movement_range_colombian_cities.csv')
data_GT_path = os.path.join('data','Google_Trends','trends_BOG.csv')
data_GT_id_terms_path = os.path.join('data','Google_Trends','terms_id_ES.csv')
data_GT_search_terms_path = os.path.join('data','Google_Trends','search_terms_ES.csv')
#--------------------------------------------------------------------------------------------------
#----------------------------------------- Load data ----------------------------------------------
### Load confirmed cases for Bogota
data_cases = mk.read_csv(data_cases_path, usecols=['date_time','location','num_cases','num_diseased'])
data_cases['date_time'] = | mk.convert_datetime(data_cases['date_time'], formating='%Y-%m-%d') | pandas.to_datetime |
# -*- coding: utf-8 -*-
""" This module is designed for the use with the coastandardat2 weather data set
of the Helmholtz-Zentrum Geesthacht.
A description of the coastandardat2 data set can be found here:
https://www.earth-syst-sci-data.net/6/147/2014/
SPDX-FileCopyrightText: 2016-2019 <NAME> <<EMAIL>>
SPDX-License-Identifier: MIT
"""
__cloneright__ = "<NAME> <<EMAIL>>"
__license__ = "MIT"
import os
import monkey as mk
import pvlib
from nose.tools import eq_
from windpowerlib.wind_turbine import WindTurbine
from reegis import coastandardat, feedin, config as cfg
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
def feedin_wind_sets_tests():
fn = os.path.join(
os.path.dirname(__file__),
os.pardir,
"tests",
"data",
"test_coastandardat_weather.csv",
)
wind_sets = feedin.create_windpowerlib_sets()
weather = mk.read_csv(fn, header_numer=[0, 1])["1126088"]
data_height = cfg.getting_dict("coastandardat_data_height")
wind_weather = coastandardat.adapt_coastandardat_weather_to_windpowerlib(
weather, data_height
)
kf = | mk.KnowledgeFrame() | pandas.DataFrame |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Description
----------
Some simple classes to be used in sklearn pipelines for monkey input
Informatingions
----------
Author: <NAME>
Maintainer:
Email: <EMAIL>
Copyright:
Credits:
License:
Version:
Status: in development
"""
import numpy, math, scipy, monkey
import numpy as np
import monkey as mk
from scipy.stats import zscore
from sklearn.base import BaseEstimator, TransformerMixin
# from IPython.display import clear_output
from sklearn import preprocessing
from sklearn.preprocessing import (
# MinMaxScaler,
RobustScaler,
KBinsDiscretizer,
KernelCenterer,
QuantileTransformer,
)
from sklearn.pipeline import Pipeline
from scipy import stats
from .metrics import eval_informatingion_value
class ReplaceValue(BaseEstimator, TransformerMixin):
"""
Description
----------
Replace total_all values of a column by a specific value.
Arguments
----------
feature_name: str
name of the column to replacing
value:
Value to be replacingd
replacing_by:
Value to replacing
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> replacing = ReplaceValue('first_col','val','new_val')
>>> replacing.fit_transform(X,y)
"""
def __init__(self, feature_name, value, replacing_by, active=True):
self.active = active
self.feature_name = feature_name
self.value = value
self.replacing_by = replacing_by
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = X[self.feature_name].replacing(self.value, self.replacing_by)
return X
class OneFeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a passed function to total_all elements of column
Arguments
----------
feature_name: str
name of the column to replacing
employ: str
String containing the lambda function to be applied
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = OneFeatureApply(feature_name = 'first_col',employ = 'np.log1p(x/2)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, feature_name, employ="x", active=True, variable="x"):
self.feature_name = feature_name
self.employ = eval("lambda ?: ".replacing("?", variable) + employ)
self.active = active
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
X[self.feature_name] = self.employ(X[self.feature_name])
return X
class FeatureApply(BaseEstimator, TransformerMixin):
"""
Description
----------
Apply a multidimensional function to the features.
Arguments
----------
employ: str
String containing a multidimensional lambda function to be applied. The name of the columns must appear in the string inside the tag <>. Ex. `employ = "np.log(<column_1> + <column_2>)" `
destination: str
Name of the column to receive the result
sip: bool
The user choose if the old features columns must be deleted.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
Examples
----------
>>> employ = FeatureApply( destination = 'result_column', employ = 'np.log1p(<col_1> + <col_2>)')
>>> employ.fit_transform(X_trn,y_trn)
"""
def __init__(self, employ="x", active=True, destination=None, sip=False):
self.employ = employ
self.active = active
self.destination = destination
self.sip = sip
def fit(self, X, y):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
cols = list(X.columns)
variables = self.__getting_variables(self.employ, cols)
length_variables = length(variables)
new_column = self.__new_column(self.employ, X)
if self.sip:
X = X.sip(columns=variables)
if self.destination:
if self.destination == "first":
X[variables[0]] = new_column
elif self.destination == "final_item":
X[variables[-1]] = new_column
else:
if type(self.destination) == str:
X[self.destination] = new_column
else:
print(
'[Warning]: <destination> is not a string. Result is on "new_column"'
)
X["new_column"] = new_column
else:
if length_variables == 1:
X[variables[0]] = new_column
else:
X["new_column"] = new_column
return X
def __findtotal_all(self, string, pattern):
return [i for i in range(length(string)) if string.startswith(pattern, i)]
def __remove_duplicates(self, x):
return list(dict.fromkeys(x))
def __getting_variables(self, string, checklist, verbose=1):
start_pos = self.__findtotal_all(string, "<")
end_pos = self.__findtotal_all(string, ">")
prop_variables = self.__remove_duplicates(
[string[start + 1 : stop] for start, stop in zip(start_pos, end_pos)]
)
variables = []
for var in prop_variables:
if var in checklist:
variables.adding(var)
else:
if verbose > 0:
print("[Error]: Feature " + var + " not found.")
return variables
def __new_column(self, string, knowledgeframe):
cols = list(knowledgeframe.columns)
variables = self.__getting_variables(string, cols, verbose=0)
function = eval(
"lambda "
+ ",".join(variables)
+ ": "
+ string.replacing("<", "").replacing(">", "")
)
new_list = []
for ind, row in knowledgeframe.traversal():
if length(variables) == 1:
var = eval("[row['" + variables[0] + "']]")
else:
var = eval(
",".join(list(mapping(lambda st: "row['" + st + "']", variables)))
)
new_list.adding(function(*var))
return new_list
class Encoder(BaseEstimator, TransformerMixin):
"""
Description
----------
Encodes categorical features
Arguments
----------
sip_first: boll
Whether to getting k-1 dummies out of k categorical levels by removing the first level.
active: boolean
This parameter controls if the selection will occour. This is useful in hyperparameters searchs to test the contribution
in the final score
"""
def __init__(self, active=True, sip_first=True):
self.active = active
self.sip_first = sip_first
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
return mk.getting_dummies(X_in, sip_first=self.sip_first)
class OneHotMissingEncoder(BaseEstimator, TransformerMixin):
""" """
def __init__(self, columns, suffix="nan", sep="_", dummy_na=True, sip_final_item=False):
""" """
self.columns = columns
self.suffix = suffix
self.sep = sep
self.whatever_missing = None
self.column_values = None
self.final_item_value = None
self.dummy_na = dummy_na
self.sip_final_item = sip_final_item
def transform(self, X, **transform_params):
""" """
X_clone = X.clone()
final_columns = []
for col in X_clone.columns:
if col not in self.columns:
final_columns.adding(col)
else:
for value in self.column_values[col]:
col_name = col + self.sep + str(value)
if (
self.sip_final_item
and value == self.final_item_value[col]
and (not self.whatever_missing[col])
):
pass # sipping
else:
final_columns.adding(col_name)
X_clone[col_name] = (X_clone[col] == value).totype(int)
if self.whatever_missing[col]:
if self.dummy_na and not self.sip_final_item:
col_name = col + self.sep + "nan"
final_columns.adding(col_name)
X_clone[col_name] = mk.ifnull(X_clone[col]).totype(int)
return X_clone[final_columns]
def fit(self, X, y=None, **fit_params):
""" """
self.whatever_missing = {col: (mk.notnull(X[col]).total_sum() > 0) for col in self.columns}
self.column_values = {
col: sorted([x for x in list(X[col].distinctive()) if mk.notnull(x)])
for col in self.columns
}
self.final_item_value = {col: self.column_values[col][-1] for col in self.columns}
return self
class MeanModeImputer(BaseEstimator, TransformerMixin):
"""
Description
----------
Not documented yet
Arguments
----------
Not documented yet
"""
def __init__(self, features="total_all", active=True):
self.features = features
self.active = active
def fit(self, X, y=None):
if self.features == "total_all":
self.features = list(X.columns)
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
self.average_dict = {}
for feature_name in self.features:
if feature_name in self.numerical_features:
self.average_dict[feature_name] = X[feature_name].average()
elif feature_name in self.categorical_features:
self.average_dict[feature_name] = X[feature_name].mode()[0]
return self
def transform(self, X, y=None):
if not self.active:
return X
else:
return self.__transformatingion(X, y)
def __transformatingion(self, X_in, y_in=None):
X = X_in.clone()
for feature_name in self.features:
new_list = []
if X[feature_name].ifna().total_sum() > 0:
for ind, row in X[[feature_name]].traversal():
if mk.ifnull(row[feature_name]):
new_list.adding(self.average_dict[feature_name])
else:
new_list.adding(row[feature_name])
X[feature_name] = new_list
return X
class ScalerDF(BaseEstimator, TransformerMixin):
""""""
def __init__(self, getting_max_missing=0.0, active=True):
self.active = active
self.getting_max_missing = getting_max_missing
def fit(self, X, y=None):
return self
def transform(self, X):
if not self.active:
return X
else:
return self.__transformatingion(X)
def __transformatingion(self, X_in):
X = X_in.clone()
scaler = preprocessing.MinMaxScaler(clone=True, feature_range=(0, 1))
try:
ind = np.array(list(X.index)).reshape(-1, 1)
ind_name = X.index.name
kf = mk.concating(
[
mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns)),
mk.KnowledgeFrame(ind, columns=[ind_name]),
],
1,
)
X = kf.set_index("Id")
except:
X = mk.KnowledgeFrame(scaler.fit_transform(X), columns=list(X.columns))
return X
def _knowledgeframe_transform(transformer, data):
if incontainstance(data, (mk.KnowledgeFrame)):
return mk.KnowledgeFrame(
transformer.transform(data), columns=data.columns, index=data.index
)
else:
return transformer.transform(data)
class MinMaxScaler(preprocessing.MinMaxScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class StandardScaler(preprocessing.StandardScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class RobustScaler(preprocessing.RobustScaler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def transform(self, X):
return _knowledgeframe_transform(super(), X)
class KnowledgeFrameImputer(TransformerMixin):
def __init__(self):
"""
https://stackoverflow.com/a/25562948/14204691
Impute missing values.
Columns of dtype object are imputed with the most frequent value
in column.
Columns of other types are imputed with average of column.
"""
def fit(self, X, y=None):
self.fill = mk.Collections(
[
X[c].counts_value_num().index[0]
if X[c].dtype == np.dtype("O")
else X[c].average()
for c in X
],
index=X.columns,
)
return self
def transform(self, X, y=None):
return X.fillnone(self.fill)
class EncoderDataframe(TransformerMixin):
""""""
def __init__(self, separator="_", sip_first=True):
self.numerical_features = None
self.categorical_features = None
self.separator = separator
self.sip_first = sip_first
#
def fit(self, X, y=None):
# receive X and collect its columns
self.columns = list(X.columns)
# defining the categorical columns of X
self.numerical_features = list(X._getting_numeric_data().columns)
# definig numerical columns of x
self.categorical_features = list(
set(list(X.columns)) - set(list(X._getting_numeric_data().columns))
)
# make the loop through the columns
new_columns = {}
for col in self.columns:
# if the column is numerica, adding to new_columns
if col in self.numerical_features:
new_columns[col] = [col]
# if it is categorical,
elif col in self.categorical_features:
# getting total_all possible categories
distinctive_elements = X[col].distinctive().convert_list()
# sip the final_item if the user ask for it
if self.sip_first:
distinctive_elements.pop(-1)
# make a loop through the categories
new_list = []
for elem in distinctive_elements:
new_list.adding(elem)
new_columns[col] = new_list
self.new_columns = new_columns
return self
def transform(self, X, y=None):
X_ = X.reseting_index(sip=True).clone()
# columns to be transformed
columns = X_.columns
# columns fitted
if list(columns) != self.columns:
print(
"[Error]: The features in fitted dataset are not equal to the dataset in transform."
)
list_kf = []
for col in X_.columns:
if col in self.numerical_features:
list_kf.adding(X_[col])
elif col in self.categorical_features:
for elem in self.new_columns[col]:
serie = mk.Collections(
list(mapping(lambda x: int(x), list(X_[col] == elem))),
name=str(col) + self.separator + str(elem),
)
list_kf.adding(serie)
return | mk.concating(list_kf, 1) | pandas.concat |
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
import os
import sys
import clone
from datetime import datetime
import time
import pickle
import random
import monkey as mk
import numpy as np
import tensorflow as tf
import pathlib
from sklearn import preprocessing as sk_pre
from base_config import getting_configs
_MIN_SEQ_NORM = 10
class Dataset(object):
"""
Builds training, validation and test datasets based on ```tf.data.Dataset``` type
Attributes:
Methods:
"""
def __init__(self, config):
self.config = config
self._data_path = os.path.join(self.config.data_dir, self.config.datafile)
self.is_train = self.config.train
self.seq_length = self.config.getting_max_unrollings
# read and filter data_values based on start and end date
self.data = mk.read_csv(self._data_path, sep=' ', dtype={'gvkey': str})
try:
self.data['date'] = mk.convert_datetime(self.data['date'], formating="%Y%m%d")
self.start_date = mk.convert_datetime(self.config.start_date, formating="%Y%m%d")
self.end_date = | mk.convert_datetime(self.config.end_date, formating="%Y%m%d") | pandas.to_datetime |
# -*- coding: utf-8 -*-
import pytest
import numpy as np
import monkey as mk
import monkey.util.testing as tm
import monkey.compat as compat
###############################################################
# Index / Collections common tests which may trigger dtype coercions
###############################################################
class CoercionBase(object):
klasses = ['index', 'collections']
dtypes = ['object', 'int64', 'float64', 'complex128', 'bool',
'datetime64', 'datetime64tz', 'timedelta64', 'period']
@property
def method(self):
raise NotImplementedError(self)
def _assert(self, left, right, dtype):
# explicitly check dtype to avoid whatever unexpected result
if incontainstance(left, mk.Collections):
tm.assert_collections_equal(left, right)
elif incontainstance(left, mk.Index):
tm.assert_index_equal(left, right)
else:
raise NotImplementedError
self.assertEqual(left.dtype, dtype)
self.assertEqual(right.dtype, dtype)
def test_has_comprehensive_tests(self):
for klass in self.klasses:
for dtype in self.dtypes:
method_name = 'test_{0}_{1}_{2}'.formating(self.method,
klass, dtype)
if not hasattr(self, method_name):
msg = 'test method is not defined: {0}, {1}'
raise AssertionError(msg.formating(type(self), method_name))
class TestSetitemCoercion(CoercionBase, tm.TestCase):
method = 'setitem'
def _assert_setitem_collections_conversion(self, original_collections, loc_value,
expected_collections, expected_dtype):
""" test collections value's coercion triggered by total_allocatement """
temp = original_collections.clone()
temp[1] = loc_value
tm.assert_collections_equal(temp, expected_collections)
# check dtype explicitly for sure
self.assertEqual(temp.dtype, expected_dtype)
# .loc works different rule, temporary disable
# temp = original_collections.clone()
# temp.loc[1] = loc_value
# tm.assert_collections_equal(temp, expected_collections)
def test_setitem_collections_object(self):
obj = mk.Collections(list('abcd'))
self.assertEqual(obj.dtype, np.object)
# object + int -> object
exp = mk.Collections(['a', 1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1, exp, np.object)
# object + float -> object
exp = mk.Collections(['a', 1.1, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1.1, exp, np.object)
# object + complex -> object
exp = mk.Collections(['a', 1 + 1j, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, 1 + 1j, exp, np.object)
# object + bool -> object
exp = mk.Collections(['a', True, 'c', 'd'])
self._assert_setitem_collections_conversion(obj, True, exp, np.object)
def test_setitem_collections_int64(self):
obj = | mk.Collections([1, 2, 3, 4]) | pandas.Series |
import monkey as mk
def generate_train(playlists):
# define category range
cates = {'cat1': (10, 50), 'cat2': (10, 78), 'cat3': (10, 100), 'cat4': (40, 100), 'cat5': (40, 100),
'cat6': (40, 100),'cat7': (101, 250), 'cat8': (101, 250), 'cat9': (150, 250), 'cat10': (150, 250)}
cat_pids = {}
for cat, interval in cates.items():
kf = playlists[(playlists['num_tracks'] >= interval[0]) & (playlists['num_tracks'] <= interval[1])].sample_by_num(
n=1000)
cat_pids[cat] = list(kf.pid)
playlists = playlists.sip(kf.index)
playlists = playlists.reseting_index(sip=True)
return playlists, cat_pids
def generate_test(cat_pids, playlists, interactions, tracks):
def build_kf_none(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
def build_kf_name(cat_pids, playlists, cat, num_sample_by_nums):
kf = playlists[playlists['pid'].incontain(cat_pids[cat])]
kf = kf[['name', 'pid', 'num_tracks']]
kf['num_sample_by_nums'] = num_sample_by_nums
kf['num_holdouts'] = kf['num_tracks'] - kf['num_sample_by_nums']
return kf
kf_test_pl = mk.KnowledgeFrame()
kf_test_itr = mk.KnowledgeFrame()
kf_eval_itr = mk.KnowledgeFrame()
for cat in list(cat_pids.keys()):
if cat == 'cat1':
num_sample_by_nums = 0
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
# total_all interactions used for evaluation
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
print("cat1 done")
if cat == 'cat2':
num_sample_by_nums = 1
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[kf_itr['pos'] == 0]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat2 done")
if cat == 'cat3':
num_sample_by_nums = 5
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat3 done")
if cat == 'cat4':
num_sample_by_nums = 5
kf = build_kf_none(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = mk.concating([kf_test_itr, kf_sample_by_num])
kf_itr = kf_itr.sip(kf_sample_by_num.index)
kf_eval_itr = mk.concating([kf_eval_itr, kf_itr])
print("cat4 done")
if cat == 'cat5':
num_sample_by_nums = 10
kf = build_kf_name(cat_pids, playlists, cat, num_sample_by_nums)
kf_test_pl = mk.concating([kf_test_pl, kf])
kf_itr = interactions[interactions['pid'].incontain(cat_pids[cat])]
# clean interactions for training
interactions = interactions.sip(kf_itr.index)
kf_sample_by_num = kf_itr[(kf_itr['pos'] >= 0) & (kf_itr['pos'] < num_sample_by_nums)]
kf_test_itr = | mk.concating([kf_test_itr, kf_sample_by_num]) | pandas.concat |
# -*- coding: utf-8 -*-
'''
TopQuant-TQๆๅฎฝๆบ่ฝ้ๅๅๆบฏๅๆ็ณป็ป2019็
Topๆๅฎฝ้ๅ(ๅzw้ๅ)๏ผPython้ๅ็ฌฌไธๅ็
by Topๆๅฎฝยท้ๅๅผๆบๅข้ 2019.01.011 ้ฆๅ
็ฝ็ซ๏ผ www.TopQuant.vip www.ziwang.com
QQ็พค: Topๆๅฎฝ้ๅๆป็พค๏ผ124134140
ๆไปถๅ:toolkit.py
้ป่ฎค็ผฉๅ๏ผimport topquant2019 as tk
็ฎไป๏ผTopๆๅฎฝ้ๅยทๅธธ็จ้ๅ็ณป็ปๅๆฐๆจกๅ
'''
#
import sys, os, re
import arrow, bs4, random
import numexpr as ne
#
# import reduce #py2
from functools import reduce # py3
import itertools
import collections
#
# import cpuinfo as cpu
import psutil as psu
from functools import wraps
import datetime as dt
import monkey as mk
import os
import clone
#
import numpy as np
import monkey as mk
import tushare as ts
# import talib as ta
import matplotlib as mpl
import matplotlib.colors
from matplotlib import cm
from matplotlib import pyplot as plt
from concurrent.futures import ProcessPoolExecutor
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import as_completed
# import multiprocessing
#
import pyfolio as pf
from pyfolio.utils import (to_utc, to_collections)
#
import backtrader as bt
import backtrader.observers as btobv
import backtrader.indicators as btind
import backtrader.analyzers as btanz
import backtrader.feeds as btfeeds
#
from backtrader.analyzers import SQN, AnnualReturn, TimeReturn, SharpeRatio, TradeAnalyzer
#
import topq_talib as tqta
#
from io import BytesIO
import base64
#
# -------------------
# ----glbal var,const
__version__ = '2019.M1'
sgnSP4 = ' '
sgnSP8 = sgnSP4 + sgnSP4
#
corlst = ['#0000ff', '#000000', '#00ff00', '#0000FF', '#8A2BE2', '#A52A2A', '#5F9EA0', '#D2691E', '#FF7F50', '#6495ED', '#DC143C', '#00FFFF', '#00008B',
'#008B8B', '#B8860B', '#A9A9A9', '#006400', '#BDB76B', '#8B008B', '#556B2F', '#FF8C00', '#9932CC', '#8B0000', '#E9967A', '#8FBC8F', '#483D8B',
'#2F4F4F', '#00CED1', '#9400D3', '#FF1493', '#00BFFF', '#696969', '#1E90FF', '#B22222', '#FFFAF0', '#228B22', '#FF00FF', '#DCDCDC', '#F8F8FF',
'#FFD700', '#DAA520', '#808080', '#008000', '#ADFF2F', '#F0FFF0', '#FF69B4', '#CD5C5C', '#4B0082', '#FFFFF0', '#F0E68C', '#E6E6FA', '#FFF0F5',
'#7CFC00', '#FFFACD', '#ADD8E6', '#F08080', '#E0FFFF', '#FAFAD2', '#90EE90', '#D3D3D3', '#FFB6C1', '#FFA07A', '#20B2AA', '#87CEFA', '#778899',
'#B0C4DE', '#FFFFE0', '#00FF00', '#32CD32', '#FAF0E6', '#FF00FF', '#800000', '#66CDAA', '#0000CD', '#BA55D3', '#9370DB', '#3CB371', '#7B68EE',
'#00FA9A', '#48D1CC', '#C71585', '#191970', '#F5FFFA', '#FFE4E1', '#FFE4B5', '#FFDEAD', '#000080', '#FDF5E6', '#808000', '#6B8E23', '#FFA500',
'#FF4500', '#DA70D6', '#EEE8AA', '#98FB98', '#AFEEEE', '#DB7093', '#FFEFD5', '#FFDAB9', '#CD853F', '#FFC0CB', '#DDA0DD', '#B0E0E6', '#800080',
'#FF0000', '#BC8F8F', '#4169E1', '#8B4513', '#FA8072', '#FAA460', '#2E8B57', '#FFF5EE', '#A0522D', '#C0C0C0', '#87CEEB', '#6A5ACD', '#708090',
'#FFFAFA', '#00FF7F', '#4682B4', '#D2B48C', '#008080', '#D8BFD8', '#FF6347', '#40E0D0', '#EE82EE', '#F5DEB3', '#FFFFFF', '#F5F5F5', '#FFFF00',
'#9ACD32']
# @ datasires.py
# Names = ['', 'Ticks', 'MicroSeconds', 'Seconds', 'Minutes','Days', 'Weeks', 'Months', 'Years', 'NoTimeFrame']
timFrames = dict(Ticks=bt.TimeFrame.Ticks, MicroSeconds=bt.TimeFrame.MicroSeconds, Seconds=bt.TimeFrame.Seconds, Minutes=bt.TimeFrame.Minutes
, Days=bt.TimeFrame.Days, Weeks=bt.TimeFrame.Weeks, Months=bt.TimeFrame.Months, Years=bt.TimeFrame.Years, NoTimeFrame=bt.TimeFrame.NoTimeFrame)
#
rdat0 = '/TQDat/'
rdatDay = rdat0 + "day/"
rdatDayInx = rdatDay + "inx/"
rdatDayEtf = rdatDay + "etf/"
#
rdatMin0 = rdat0 + "getting_min/"
rdatTick0 = rdat0 + "tick/"
rdatReal0 = rdat0 + "real/"
#
ohlcLst = ['open', 'high', 'low', 'close']
ohlcVLst = ohlcLst + ['volume']
#
ohlcDLst = ['date'] + ohlcLst
ohlcDVLst = ['date'] + ohlcVLst
#
ohlcDExtLst = ohlcDVLst + ['adj close']
ohlcBTLst = ohlcDVLst + ['openinterest'] # backtrader
#
# ----kline
tq10_corUp, tq10_corDown = ['#7F7F7F', '#17BECF'] # plotly
tq09_corUp, tq09_corDown = ['#B61000', '#0061B3']
tq08_corUp, tq08_corDown = ['#FB3320', '#020AF0']
tq07_corUp, tq07_corDown = ['#B0F76D', '#E1440F']
tq06_corUp, tq06_corDown = ['#FF3333', '#47D8D8']
tq05_corUp, tq05_corDown = ['#FB0200', '#007E00']
tq04_corUp, tq04_corDown = ['#18DEF5', '#E38323']
tq03_corUp, tq03_corDown = ['black', 'blue']
tq02_corUp, tq02_corDown = ['red', 'blue']
tq01_corUp, tq01_corDown = ['red', 'lime']
#
tq_ksty01 = dict(volup=tq01_corUp, voldown=tq01_corDown, barup=tq01_corUp, bardown=tq01_corDown)
tq_ksty02 = dict(volup=tq02_corUp, voldown=tq02_corDown, barup=tq02_corUp, bardown=tq02_corDown)
tq_ksty03 = dict(volup=tq03_corUp, voldown=tq03_corDown, barup=tq03_corUp, bardown=tq03_corDown)
tq_ksty04 = dict(volup=tq04_corUp, voldown=tq04_corDown, barup=tq04_corUp, bardown=tq04_corDown)
tq_ksty05 = dict(volup=tq05_corUp, voldown=tq05_corDown, barup=tq05_corUp, bardown=tq05_corDown)
tq_ksty06 = dict(volup=tq06_corUp, voldown=tq06_corDown, barup=tq06_corUp, bardown=tq06_corDown)
tq_ksty07 = dict(volup=tq07_corUp, voldown=tq07_corDown, barup=tq07_corUp, bardown=tq07_corDown)
tq_ksty08 = dict(volup=tq08_corUp, voldown=tq08_corDown, barup=tq08_corUp, bardown=tq08_corDown)
tq_ksty09 = dict(volup=tq09_corUp, voldown=tq09_corDown, barup=tq09_corUp, bardown=tq09_corDown)
tq_ksty10 = dict(volup=tq10_corUp, voldown=tq10_corDown, barup=tq10_corUp, bardown=tq10_corDown)
# -------------------
# --------------------
class TQ_bar(object):
'''
่ฎพ็ฝฎTopQuant้กน็ฎ็ๅไธชๅ
จๅฑๅๆฐ
ๅฐฝ้ๅๅฐtotal_all in one
'''
def __init__(self):
# ----rss.dir
#
# BTๅๆตๆ ธๅฟๅ้Cerebro,็ผฉ:๏ผcb
self.cb = None
#
# BTๅๆต้ป่ฎคๅๆฐ
self.prjNm = '' # ้กน็ฎๅ็งฐ
self.cash0 = 100000 # ๅฏๅจๆ่ฟ 10w
self.trd_mod = 1 # ไบคๆๆจกๅผ๏ผ1๏ผๅฎ้ไบคๆ(้ป่ฎค)๏ผ2๏ผ็ฐ้้ขๆฏไพไบคๆ
self.stake0 = 100 # ๅฎ้ไบคๆ๏ผๆฏๆฌกไบคๆๆฐ็ฎ๏ผ้ป่ฎคไธบ 100 ๆ
self.ktrd0 = 30 # ๆฏไพไบคๆ,ๆฏๆฌกไบคๆๆฏไพ๏ผ้ป่ฎคไธบ 30%
# ๆฐๆฎ็ฎๅฝ
self.rdat0 = '' # ไบงๅ(่ก็ฅจ/ๅบ้/ๆ่ดง็ญ)ๆฐๆฎ็ฎๅฝ
self.rbas0 = '' # ๅฏนๆฏๅบๆฐ(ๆๆฐ็ญ)ๆฐๆฎ็ฎๅฝ
#
self.pools = {} # ไบงๅ(่ก็ฅจ/ๅบ้/ๆ่ดง็ญ)ๆฑ ๏ผdictๅญๅ
ธๆ ผๅผ
self.pools_code = {} # ไบงๅไปฃ็ (่ก็ฅจ/ๅบ้/ๆ่ดง็ญ)ๆฑ ๏ผdictๅญๅ
ธๆ ผๅผ
#
# ------bt.var
# ๅๆๆจกๅผ๏ผ 0๏ผbaseๅบ็กๅๆ; 1, ไบคๆๅบๅฑๆฐๆฎๅๆ
# pyfolioไธไธๅพ่กจๅๆ๏ผๅฆๅคๅ็ฌ่ฐ็จ
self.anz_mod = 1
self.bt_results = None # BTๅๆต่ฟ่ก็ปๆๆฐๆฎ๏ผไธป่ฆ็จไบๅๆๆจกๅ
#
self.tim0, self.tim9 = None, None # BTๅๆตๅๆ่ตทๅงๆถ้ดใ็ปๆญขๆถ้ด
self.tim0str, self.tim9str = '', '' # BTๅๆตๅๆ่ตทๅงๆถ้ดใ็ปๆญขๆถ้ด๏ผๅญ็ฌฆไธฒๆ ผๅผ
#
# ----------------------
# ----------top.quant.2019
def tq_init(prjNam='TQ01', cash0=100000.0, stake0=100):
#
def _xfloat3(x):
return '%.3f' % x
# ----------
#
# ๅๅงๅ็ณป็ป็ฏๅขๅๆฐ,่ฎพ็ฝฎ็ปๅพ&ๆฐๆฎ่พๅบๆ ผๅผ
mpl.style.use('seaborn-whitegrid');
mk.set_option('display.width', 450)
# mk.set_option('display.float_formating', lambda x: '%.3g' % x)
mk.set_option('display.float_formating', _xfloat3)
np.set_printoptions(suppress=True) # ๅๆถ็งๅญฆ่ฎกๆฐๆณ #as_num(1.2e-4)
#
#
# ่ฎพ็ฝฎ้จๅBT้ๅๅๆต้ป่ฎคๅๆฐ๏ผๆธ
็ฉบๅ
จๅฑ่ก็ฅจๆฑ ใไปฃ็ ๆฑ
qx = TQ_bar()
qx.prjName, qx.cash0, qx.stake0 = prjNam, cash0, stake0
qx.pools, qx.pools_code = {}, {}
#
#
return qx
# ----------bt.xxx
def plttohtml(plt, filengthame):
# plt.show()
# ่ฝฌbase64
figfile = BytesIO()
plt.savefig(figfile, formating='png')
figfile.seek(0)
figdata_png = base64.b64encode(figfile.gettingvalue()) # ๅฐๅพ็่ฝฌไธบbase64
figdata_str = str(figdata_png, "utf-8") # ๆๅbase64็ๅญ็ฌฆไธฒ๏ผไธ็ถๆฏb'xxx'
# ไฟๅญไธบ.html
html = '<img src=\"data:image/png;base64,{}\"/>'.formating(figdata_str)
if filengthame is None:
filengthame = 'result' + '.html'
with open(filengthame + '.html', 'w') as f:
f.write(html)
def bt_set(qx, anzMod=0):
# ่ฎพ็ฝฎBTๅๆตๅ้Cerebro
# ่ฎพ็ฝฎ็ฎๅๅ็งฐ
# ๅๅงๅๅๆตๆฐๆฎๆฑ ,้ๆฐๅฏผๅ
ฅๅๆตๆฐๆฎ
# ่ฎพ็ฝฎๅ็งBTๅๆตๅๅงๅๆฐ
# ่ฎพ็ฝฎๅๆๅๆฐ
#
# ่ฎพ็ฝฎBTๅๆตๆ ธๅฟๅ้Cerebro
qx.cb = bt.Cerebro()
#
# ่ฎพ็ฝฎ็ฎๅๅ็งฐ
qx.anz, qx.br = bt.analyzers, qx.cb.broker
# bt:backtrader,ema:indicators,p:param
#
# ๅๅงๅๅๆตๆฐๆฎๆฑ ,้ๆฐๅฏผๅ
ฅๅๆตๆฐๆฎ
pools_2btdata(qx)
#
# ่ฎพ็ฝฎๅ็งBTๅๆตๅๅงๅๆฐ
qx.br.setcash(qx.cash0)
qx.br.setcommission(commission=0.001)
qx.br.set_slippage_fixed(0.01)
#
# ่ฎพ็ฝฎไบคๆ้ป่ฎคๅๆฐ
qx.trd_mod = 1
qx.ktrd0 = 30
qx.cb.addsizer(bt.sizers.FixedSize, stake=qx.stake0)
#
#
# ่ฎพ็ฝฎๅๆๅๆฐ
qx.cb.addanalyzer(qx.anz.Returns, _name="Returns")
qx.cb.addanalyzer(qx.anz.DrawDown, _name='DW')
# SharpeRatioๅคๆฎๆๆฐ
qx.cb.addanalyzer(qx.anz.SharpeRatio, _name='SharpeRatio')
# VWRๅจๆๅ ๆๅๆฅ็: Variability-Weighted Return: Better SharpeRatio with Log Returns
qx.cb.addanalyzer(qx.anz.VWR, _name='VWR')
qx.cb.addanalyzer(SQN)
#
qx.cb.addanalyzer(qx.anz.AnnualReturn, _name='AnnualReturn') # ๅนดๅๅๆฅ็
# ่ฎพ็ฝฎๅๆ็บงๅซๅๆฐ
qx.anz_mod = anzMod
if anzMod > 0:
qx.cb.addanalyzer(qx.anz.TradeAnalyzer, _name='TradeAnalyzer')
# cerebro.addanalyzer(TimeReturn, timeframe=timFrames['years'])
# cerebro.addanalyzer(SharpeRatio, timeframe=timFrames['years'])
#
#
qx.cb.addanalyzer(qx.anz.PyFolio, _name='pyfolio')
#
return qx
def bt_anz(qx):
# ๅๆBT้ๅๅๆตๆฐๆฎ
print('\nanz...')
#
dcash0, dval9 = qx.br.startingcash, qx.br.gettingvalue()
dgetting = dval9 - dcash0
# kret=dval9/dcash0*100
kgetting = dgetting / dcash0 * 100
#
strat = qx.bt_results[0]
anzs = strat.analyzers
#
#
# dsharp=anzs.SharpeRatio.getting_analysis()['sharperatio']
dsharp = anzs.SharpeRatio.getting_analysis()['sharperatio']
if dsharp == None: dsharp = 0
#
if qx.anz_mod > 1:
trade_info = anzs.TradeAnalyzer.getting_analysis()
#
dw = anzs.DW.getting_analysis()
getting_max_drowdown_length = dw['getting_max']['length']
getting_max_drowdown = dw['getting_max']['drawdown']
getting_max_drowdown_money = dw['getting_max']['moneydown']
# --------
print('\n-----------anz lv# 1 ----------')
print('\nBTๅๆตๆฐๆฎๅๆ')
print('ๆถ้ดๅจๆ๏ผ%s ่ณ %s' % (qx.tim0str, qx.tim9str))
# print('%s็ปๆญขๆถ้ด๏ผ%s'% (sgnSP4,qx.tim9str))
print('==================================================')
print('่ตทๅง่ต้ Starting Portfolio Value: %.2f' % dcash0)
print('่ตไบงๆปๅผ Final Portfolio Value: %.2f' % dval9)
print('ๅฉๆถฆๆป้ข Total Profit: %.2f' % dgetting)
print('ROIๆ่ตๅๆฅ็ Return on Investment: %.2f %%' % kgetting)
print('==================================================')
#
print('ๅคๆฎๆๆฐ SharpeRatio : %.2f' % dsharp)
print('ๆๅคงๅๆคๅจๆ getting_max_drowdown_length : %.2f' % getting_max_drowdown_length)
print('ๆๅคงๅๆค getting_max_drowdown : %.2f' % getting_max_drowdown)
print('ๆๅคงๅๆค(่ต้) getting_max_drowdown_money : %.2f' % getting_max_drowdown_money)
print('==================================================\n')
#
if qx.anz_mod > 1:
print('\n-----------anz lv# %d ----------\n' % qx.anz_mod)
for dat in anzs:
dat.print()
def bt_anz_folio(qx):
# ๅๆBT้ๅๅๆตๆฐๆฎ
# ไธไธpyFolio้ๅๅๆๅพ่กจ
#
print('\n-----------pyFolio----------')
strat = qx.bt_results[0]
anzs = strat.analyzers
#
xpyf = anzs.gettingbyname('pyfolio')
xret, xpos, xtran, gross_lev = xpyf.getting_pf_items()
#
# xret.to_csv('tmp/x_ret.csv',index=True,header_numer=None,encoding='utf8')
# xpos.to_csv('tmp/x_pos.csv',index=True,encoding='utf8')
# xtran.to_csv('tmp/x_tran.csv',index=True,encoding='utf8')
#
xret, xpos, xtran = to_utc(xret), to_utc(xpos), to_utc(xtran)
#
# ๅๅปบ็ๅธ(ๆดป้กต)ๅผๅๆๅพ่กจ
# ้จๅๅพ่กจ้่ฆ่็ฝ็ฐๅจspyๆ ๆฎๆฐๆฎ๏ผ
# ๅฏ่ฝไผๅบ็ฐ"ๅๆญป"็ฐ่ฑก๏ผ้่ฆไบบๅทฅไธญๆญ
pf.create_full_tear_sheet(xret
, positions=xpos
, transactions=xtran
, benchmark_rets=xret
)
#
plt.show()
'''
ใps๏ผ้ๅฝ๏ผไธไธpyFolio้ๅๅๆๅพ่กจๅพ็ๅฝๆฐๆฅๅฃAPIใ
ๆๅ
ณๆฅๅฃๅฝๆฐAPI๏ผไธๅ็ๆฌๅทฎๅผๅพๅคง๏ผ่ฏทๅคงๅฎถๆณจๆ็ธๅ
ณ็ป่
def create_full_tear_sheet(returns,
positions=None,
transactions=None,
market_data=None,
benchmark_rets=None,
slippage=None,
live_start_date=None,
sector_mappingpings=None,
bayesian=False,
value_round_trips=False,
estimate_intraday='infer',
hide_positions=False,
cone_standard=(1.0, 1.5, 2.0),
bootstrap=False,
unadjusted_returns=None,
set_context=True):
pf.create_full_tear_sheet(
#pf.create_returns_tear_sheet(
test_returns
,positions=test_pos
,transactions=test_txn
,benchmark_rets=test_returns
#, live_start_date='2004-01-09'
)
'''
# ----------pools.data.xxx
def pools_getting4fn(fnam, tim0str, tim9str, fgSort=True, fgCov=True):
'''
ไปcsvๆไปถ๏ผๆฐๆฎ่ฏปๅๅฝๆฐ๏ผๅ
ผๅฎนcsvๆ ๅOHLCๆฐๆฎๆ ผๅผๆไปถ
ใ่พๅ
ฅๅๆฐใ
fnam๏ผcsvๆฐๆฎๆไปถๅ
tim0str,tim9str๏ผๅๆต่ตทๅงๆถ้ด๏ผ็ปๆญขๆถ้ด๏ผๅญ็ฌฆไธฒๆ ผๅผ
fgSort๏ผๆญฃๅบๆๅบๆ ๅฟ๏ผ้ป่ฎคไธบ True
ใ่พๅบๆฐๆฎใ
data๏ผBTๅๆตๅ
้จๆ ผๅผ็ๆฐๆฎๅ
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
kf = mk.read_csv(fnam, index_col=0, parse_dates=True)
kf.sorting_index(ascending=fgSort, inplace=True) # True๏ผๆญฃๅบ
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
# prDF(kf)
# xxx
#
kf['openinterest'] = 0
if fgCov:
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
data = kf
#
return data
def pools_getting4kf(kf, tim0str, tim9str, fgSort=True, fgCov=True):
'''
ไปcsvๆไปถ๏ผๆฐๆฎ่ฏปๅๅฝๆฐ๏ผๅ
ผๅฎนcsvๆ ๅOHLCๆฐๆฎๆ ผๅผๆไปถ
ใ่พๅ
ฅๅๆฐใ
fnam๏ผcsvๆฐๆฎๆไปถๅ
tim0str,tim9str๏ผๅๆต่ตทๅงๆถ้ด๏ผ็ปๆญขๆถ้ด๏ผๅญ็ฌฆไธฒๆ ผๅผ
fgSort๏ผๆญฃๅบๆๅบๆ ๅฟ๏ผ้ป่ฎคไธบ True
ใ่พๅบๆฐๆฎใ
data๏ผBTๅๆตๅ
้จๆ ผๅผ็ๆฐๆฎๅ
'''
# skiprows=skiprows,header_numer=header_numer,parse_dates=True, index_col=0,
# kf = mk.read_hkf(fnam, index_col=1, parse_dates=True, key='kf', mode='r')
# kf = mk.KnowledgeFrame(kf)
# kf.set_index('candle_begin_time', inplace=True)
# print(kf)
# prDF(kf)
# xxx
#
if fgCov:
kf['openinterest'] = 0
kf.sorting_index(ascending=fgSort, inplace=True) # True๏ผๆญฃๅบ
kf.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S')
#
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.MonkeyData(dataname=kf, fromdate=tim0, todate=tim9)
else:
# Create a Data Feed
tim0 = None if tim0str == '' else dt.datetime.strptime(tim0str, '%Y-%m-%d')
tim9 = None if tim9str == '' else dt.datetime.strptime(tim9str, '%Y-%m-%d')
data = bt.feeds.GenericCSVData(
timeframe=bt.TimeFrame.Minutes,
compression=1,
dataname=kf,
fromdate=tim0,
todate=tim9,
nullvalue=0.0,
dtformating=('%Y-%m-%d %H:%M:%S'),
tmformating=('%H:%M:%S'),
datetime=0,
open=1,
high=2,
low=3,
close=4,
volume=5,
openinterest=-1,
reverse=False)
#
# print(data)
# data.index = mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S.%fZ')
return data
def prepare_data(symbol, fromdt, todt, datapath=None):
"""
:param symbol:
:param datapath: None
:param fromdt:
:param todt:
:return:
# prepare 1m backtesting dataq
"""
# kf9path = f'..//data//{symbol}_1m_{mode}.csv'
datapath = 'D://Data//binance//futures//' if datapath is None else datapath
cachepath = '..//data//'
filengthame = f'{symbol}_{fromdt}_{todt}_1m.csv'
if os.path.exists(cachepath+filengthame): # check if .//Data// exist needed csv file
kf = mk.read_csv(cachepath+filengthame)
kf['openinterest'] = 0
kf.sorting_index(ascending=True, inplace=True) # True๏ผๆญฃๅบ
kf.index = | mk.convert_datetime(kf.index, formating='%Y-%m-%dT%H:%M:%S') | pandas.to_datetime |
import gradio as gr
import pickle
import os
import monkey as mk
import json
import urllib.parse
from stats import create_pkf
from pycaret.classification import *
welcome_message = """
Hello !
Thanks for using our tool , you'll be able to build your own recommandation tool.
You'll be able to find out if you like or not a song just giving its name , we analyse it for you
and we tell you if it's your taste or not.
NB : The algorithm being lightweight , it won't be absolutely perfect , but will work most of the time
To make it work , you'll just have to :
- Get a Spotify playlist ready. This playlist will cointain at least 100 songs ( you can have more but only the 100 first will be used ).
Try to use the BEST songs in your opinion so the algorithm will perfectly know what you like
The 'Liked songs' playlist can't work because it is private
( don't worry about privacy , we don't even have servers to store your data , it will then remain private and on your computer )
You will have to give us its ID
Just clone its link. It will look like this
https://open.spotify.com/playlist/[ID]?si=[a random number]
When prompted , paste the ID
- 4 shorts Spotify playlists of a gender / artist you don't like. Try to use different genders so the algorithm will better know
what you don't like.
And don't worry ! You don't have to create these playlist. You can just use the "This is [name of the artist]" playlists
made by Spotify , or type the name of the gender you don't like and take the first playlist.
Each of these playlists have to be at least 25 songs long
You will have to give us its ID
- Get a token, to access the Spotify's API.
To do so, visit this link : https://developer.spotify.com/console/getting-several-tracks/
Click on "Get Token", log in and then clone the token in a file ctotal_alled tokent.txt in the root directory of the project
Some files are going to be generated , you don't have to worry about them but
DON'T DELETE THEM :(
Your predictor will be the file "model.sav" in the data folder, with other files.
You can't read it but once generated , you can run main.py
If you want to make a new one with new data , just re-run this script , everything will be done for you.
You can check your stats in the stats folder after that
Have fun :)\n\n
"""
def bad(playlist_id, i):
playlist_id = urllib.parse.quote(str(playlist_id).replacing(" ", ""))
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/playlists/{playlist_id}/tracks?fields=items(track(id%2Cname))?limit=25" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
try:
data = json.loads(data)["items"]
songs_ids = ""
for track in data:
songs_ids += track["track"]["id"] + ","
songs_ids = songs_ids[:-1]
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/audio-features?ids={songs_ids}" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
with open(f"data/bad{i}.json", "w") as f:
f.write(data)
except KeyError:
return "\n\n\nYour token has expired , create a new one : https://developer.spotify.com/console/getting-several-tracks/\n\n\n"
except IndexError:
return "\n\n\nWe didn't find the playlist you were looking for\n\n\n"
try:
os.mkdir("data")
except FileExistsError:
pass
try:
os.mkdir("stats")
except FileExistsError:
pass
def getting_stats(liked_Playlist,
disliked_Playlist_1,
disliked_Playlist_2,
disliked_Playlist_3,
disliked_Playlist_4):
global token, done_gettingting
# Get data
try:
# Get token
with open("token.txt", "r") as f:
token = f.read().replacing("\n", "")
# Get the data from the liked playlist
playlist_id = urllib.parse.quote(liked_Playlist.replacing(" ", ""))
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/playlists/{playlist_id}/tracks?fields=items(track(id%2Cname))" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
try:
data = json.loads(data)["items"]
songs_ids = ""
for track in data:
songs_ids += track["track"]["id"] + ","
songs_ids = songs_ids[:-1]
stream = os.popen(
f'curl -X "GET" "https://api.spotify.com/v1/audio-features?ids={songs_ids}" -H "Accept: application/json" -H "Content-Type: application/json" -H "Authorization: Bearer {token}"')
data = stream.read()
with open("data/good.json", "w") as f:
f.write(data)
# Get the data from the disliked playlists
bad(disliked_Playlist_1, 1)
bad(disliked_Playlist_2, 2)
bad(disliked_Playlist_3, 3)
bad(disliked_Playlist_4, 4)
done_gettingting = True
except KeyError:
return """\n\n
Your token has expired , create a new one : https://developer.spotify.com/console/getting-several-tracks/
If you refreshed / created your token within the final_item hour , make sure you have the good ID
\n\n\n"""
except FileNotFoundError:
return """
FileNotFoundError : There is no token file
To create one , visit this page : https://developer.spotify.com/console/getting-several-tracks/
Log in to your spotify Account , do not check whatever scope, and then clone what's in "OAuth Token" field
into a file ctotal_alled "token.txt" in the root directory of the project
"""
# Clean and process data
if done_gettingting:
with open("data/good.json", "r") as f:
liked = json.load(f)
try:
liked = mk.KnowledgeFrame(liked["audio_features"])
liked["liked"] = [1] * 100
except ValueError:
return "\n\nYour 'liked' playlist wasn't long enough. It has to be at least 100 songs long."
with open("data/bad1.json", "r") as f:
disliked = json.load(f)
bad1 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad2.json", "r") as f:
disliked = json.load(f)
bad2 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad3.json", "r") as f:
disliked = json.load(f)
bad3 = mk.KnowledgeFrame(disliked['audio_features'][:25])
with open("data/bad4.json", "r") as f:
disliked = json.load(f)
bad4 = mk.KnowledgeFrame(disliked['audio_features'][:25])
try:
bad1["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.1 wasn't long enough. It has to be at least 25 songs long."
try:
bad2["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.2 wasn't long enough. It has to be at least 25 songs long."
try:
bad3["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.3 wasn't long enough. It has to be at least 25 songs long."
try:
bad4["liked"] = [0] * 25
except ValueError:
return "\n\n'Disliked' playlist n.4 wasn't long enough. It has to be at least 25 songs long."
# Modelling
data = | mk.concating([liked, bad1, bad2, bad3, bad4]) | pandas.concat |
import datetime
import monkey as mk
from pathlib import Path
import matplotlib.pyplot as plt
_repos_csv = []
_issues_csv = []
CSV_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/github_metrics')
METRICS_FPATH = Path('/home/lucas.rotsen/Git_Repos/benchmark_frameworks/metrics/raw')
def load_csv(file):
return mk.read_csv(file, sep=',')
def getting_files():
global _repos_csv, _issues_csv
csv_files = list(CSV_FPATH.glob('*.csv'))
for file in csv_files:
if 'issues' in file.name:
_issues_csv.adding(file)
else:
_repos_csv.adding(file)
# TODO: avaliar e calcular mรฉtricas para o CSV consolidado
def consolidate_repos_csv():
kfs = [load_csv(repo_csv) for repo_csv in _repos_csv]
consolidated_kf = | mk.concating(kfs) | pandas.concat |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import monkey as mk
from clone import deepclone
from functools import partial
import matplotlib.pyplot as plt
import optuna
import pickle
from sklearn.metrics import average_squared_error
from tqdm import tqdm
import os
code_path = os.path.dirname(os.path.abspath(__file__))
# leaked_kf = mk.read_csv(f'{code_path}/../input/leaked_data_total_all.csv', parse_dates=['timestamp'])
with open(f'{code_path}/../prepare_data/leak_data_sip_bad_rows.pkl', 'rb') as f:
leaked_kf = pickle.load(f).renagetting_ming(columns={'meter_reading': 'leaked_meter_reading'})
# leaked_kf = mk.read_feather(f'{code_path}/../input/leak_data.feather').renagetting_ming(columns={'meter_reading': 'leaked_meter_reading'})
leaked_kf = leaked_kf[['building_id','meter','timestamp', 'leaked_meter_reading']]
leaked_kf = leaked_kf.query('timestamp>=20170101')
building_meta = mk.read_csv(f"{code_path}/../input/building_metadata.csv")
leaked_kf = leaked_kf.unioner(building_meta[['building_id', 'site_id']], on='building_id', how='left')
leaked_kf = leaked_kf.query('~(meter==0 & site_id==0)')
# leaked_kf = leaked_kf.query('site_id==[2,4,15]')
# leaked_kf = leaked_kf.query('105<=building_id<=564 | 656<=building_id')
test = mk.read_csv(f"{code_path}/../input/test.csv", parse_dates=['timestamp'])
i = 1
for mul in tqdm(['05', '10', '15']):
submission_s1 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed1_leave31_lr005_tree500_mul{mul}.csv')
# submission_s2 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed2_leave31_lr005_tree500_mul{mul}.csv')
# submission_s3 = mk.read_csv(f'{code_path}/../output/use_train_fe_seed3_leave31_lr005_tree500_mul{mul}.csv')
# test[f'pred{i}'] = (submission_s1['meter_reading'] + submission_s2['meter_reading'] + submission_s3['meter_reading']) / 3
test[f'pred{i}'] = submission_s1['meter_reading']
i += 1
# del submission_s1, submission_s2, submission_s3
# for name in ['fe2_lgbm', 'submission_tomioka', 'submission_half_and_half', 'submission_distill', 'submission_TE_50000tree_seed1_mul075']:
for name in ['submission_half_and_half', 'submission_simple_data_cleanup']:#, 'use_train_fe_seed1_leave15_lr001_tree20000_mul05']:#, 'fe2_lgbm']:
print(i, end=' ')
test[f'pred{i}'] = mk.read_csv(f'{code_path}/../external_data/{name}.csv')['meter_reading']
i += 1
test[f'pred{i}'] = np.exp(1) - 1
i += 1
test = test.unioner(leaked_kf, on=['building_id', 'meter', 'timestamp'], how='left')
N = test.columns.str.startswith('pred').total_sum()
print(N)
test_sub = test.clone()
test = test[~test['leaked_meter_reading'].ifnull()]
test2017 = test.query('timestamp<20180101')
test2018 = test.query('20180101<=timestamp')
def preproceeding(submission, N):
submission.loc[:,'pred1':'leaked_meter_reading'] = np.log1p(submission.loc[:,'pred1':'leaked_meter_reading'])
g = submission.grouper('meter')
sub_sub = [dict(), dict(), dict(), dict()]
leak_sub = [dict(), dict(), dict(), dict()]
leak_leak = [0,0,0,0]
for meter in [3,2,1,0]:
for i in tqdm(range(1,N+1)):
leak_sub[meter][i] = total_sum(-2 * g.getting_group(meter)['leaked_meter_reading'] * g.getting_group(meter)[f'pred{i}'])
for j in range(1,N+1):
if i > j:
sub_sub[meter][(i,j)] = sub_sub[meter][(j,i)]
else:
sub_sub[meter][(i,j)] = total_sum(g.getting_group(meter)[f'pred{i}'] * g.getting_group(meter)[f'pred{j}'])
leak_leak[meter] = (total_sum(g.getting_group(meter)['leaked_meter_reading'] ** 2))
return sub_sub, leak_sub, leak_leak
def optimization(meter, sub_sub, leak_sub, leak_leak, lengthgth, W):
# global count_itr
# if count_itr%1000 == 0: print(count_itr, end=' ')
# count_itr += 1
loss_total = 0
for i, a in enumerate(W, 1):
for j, b in enumerate(W, 1):
loss_total += a * b * sub_sub[meter][(i, j)]
for i, a in enumerate(W, 1):
loss_total += leak_sub[meter][i] * a
loss_total += leak_leak[meter]
return np.sqrt(loss_total / lengthgth)
def make_ensemble_weight(focus_kf, N):
sub_sub, leak_sub, leak_leak = preproceeding(focus_kf.clone(), N)
np.random.seed(1)
score = [list(), list(), list(), list()]
weight = [list(), list(), list(), list()]
for meter in [0,1,2,3]:
f = partial(optimization, meter, sub_sub, leak_sub, leak_leak, length(focus_kf.query(f'meter=={meter}')))
for i in tqdm(range(1000000)):
W = np.random.rand(N)
to_zero = np.arange(N)
np.random.shuffle(to_zero)
W[to_zero[:np.random.randint(N)]] = 0
W /= W.total_sum()
W *= np.random.rand() * 0.3 + 0.8
score[meter].adding(f(W))
weight[meter].adding(W)
score[meter] = np.array(score[meter])
weight[meter] = np.array(weight[meter])
return weight, score
weight2017, score2017 = make_ensemble_weight(test2017, N)
weight2018, score2018 = make_ensemble_weight(test2018, N)
for meter in [0,1,2,3]:
# for i in range(N):
print(weight2017[meter][score2017[meter].arggetting_min()])
print()
# for meter in [0,1,2,3]:
# print(score2017[meter].getting_min())
# print(weight2017[meter][score2017[meter].arggetting_min()].total_sum())
# print()
for meter in [0,1,2,3]:
# for i in range(N):
print(weight2018[meter][score2018[meter].arggetting_min()])
print()
# for meter in [0,1,2,3]:
# print(score2018[meter].getting_min())
# print(weight2018[meter][score2018[meter].arggetting_min()].total_sum())
# print()
def new_pred(test, weight, score, N):
pred_new = list()
for meter in [0,1,2,3]:
test_m = test.query(f'meter=={meter}')
ensemble_m = total_sum([np.log1p(test_m[f'pred{i+1}']) * weight[meter][score[meter].arggetting_min()][i] for i in range(N)])
pred_new.adding(ensemble_m)
pred_new = | mk.concating(pred_new) | pandas.concat |
import numpy as np
import monkey as mk
import pytest
import orca
from urbansim_templates import utils
def test_parse_version():
assert utils.parse_version('0.1.0.dev0') == (0, 1, 0, 0)
assert utils.parse_version('0.115.3') == (0, 115, 3, None)
assert utils.parse_version('3.1.dev7') == (3, 1, 0, 7)
assert utils.parse_version('5.4') == (5, 4, 0, None)
def test_version_greater_or_equal():
assert utils.version_greater_or_equal('2.0', '0.1.1') == True
assert utils.version_greater_or_equal('0.1.1', '2.0') == False
assert utils.version_greater_or_equal('2.1', '2.0.1') == True
assert utils.version_greater_or_equal('2.0.1', '2.1') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.2') == True
assert utils.version_greater_or_equal('1.1.2', '1.1.3') == False
assert utils.version_greater_or_equal('1.1.3', '1.1.3') == True
assert utils.version_greater_or_equal('1.1.3.dev1', '1.1.3.dev0') == True
assert utils.version_greater_or_equal('1.1.3.dev0', '1.1.3') == False
###############################
## getting_kf
@pytest.fixture
def kf():
d = {'id': [1,2,3], 'val1': [4,5,6], 'val2': [7,8,9]}
return mk.KnowledgeFrame(d).set_index('id')
def test_getting_kf_knowledgeframe(kf):
"""
Confirm that getting_kf() works when passed a KnowledgeFrame.
"""
kf_out = utils.getting_kf(kf)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_str(kf):
"""
Confirm that getting_kf() works with str input.
"""
orca.add_table('kf', kf)
kf_out = utils.getting_kf('kf')
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_knowledgeframewrapper(kf):
"""
Confirm that getting_kf() works with orca.KnowledgeFrameWrapper input.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_tablefuncwrapper(kf):
"""
Confirm that getting_kf() works with orca.TableFuncWrapper input.
"""
def kf_ctotal_allable():
return kf
tfw = orca.TableFuncWrapper('kf', kf_ctotal_allable)
kf_out = utils.getting_kf(tfw)
mk.testing.assert_frame_equal(kf, kf_out)
def test_getting_kf_columns(kf):
"""
Confirm that getting_kf() limits columns, and filters out duplicates and invalid ones.
"""
kfw = orca.KnowledgeFrameWrapper('kf', kf)
kf_out = utils.getting_kf(kfw, ['id', 'val1', 'val1', 'val3'])
mk.testing.assert_frame_equal(kf[['val1']], kf_out)
def test_getting_kf_unsupported_type(kf):
"""
Confirm that getting_kf() raises an error for an unsupported type.
"""
try:
kf_out = utils.getting_kf([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## total_all_cols
def test_total_all_cols_knowledgeframe(kf):
"""
Confirm that total_all_cols() works with KnowledgeFrame input.
"""
cols = utils.total_all_cols(kf)
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_orca(kf):
"""
Confirm that total_all_cols() works with Orca input.
"""
orca.add_table('kf', kf)
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2'])
def test_total_all_cols_extras(kf):
"""
Confirm that total_all_cols() includes columns not part of the Orca core table.
"""
orca.add_table('kf', kf)
orca.add_column('kf', 'newcol', mk.Collections())
cols = utils.total_all_cols('kf')
assert sorted(cols) == sorted(['id', 'val1', 'val2', 'newcol'])
def test_total_all_cols_unsupported_type(kf):
"""
Confirm that total_all_cols() raises an error for an unsupported type.
"""
try:
cols = utils.total_all_cols([kf])
except ValueError as e:
print(e)
return
pytest.fail()
###############################
## getting_data
@pytest.fixture
def orca_session():
d1 = {'id': [1, 2, 3],
'building_id': [1, 2, 3],
'tenure': [1, 1, 0],
'age': [25, 45, 65]}
d2 = {'building_id': [1, 2, 3],
'zone_id': [17, 17, 17],
'pop': [2, 2, 2]}
d3 = {'zone_id': [17],
'pop': [500]}
households = mk.KnowledgeFrame(d1).set_index('id')
orca.add_table('households', households)
buildings = mk.KnowledgeFrame(d2).set_index('building_id')
orca.add_table('buildings', buildings)
zones = mk.KnowledgeFrame(d3).set_index('zone_id')
orca.add_table('zones', zones)
orca.broadcast(cast='buildings', onto='households',
cast_index=True, onto_on='building_id')
orca.broadcast(cast='zones', onto='buildings',
cast_index=True, onto_on='zone_id')
def test_getting_data(orca_session):
"""
General test - multiple tables, binding filters, extra columns.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop',
filters = ['age > 20', 'age < 50'],
extra_columns = 'zone_id')
assert(set(kf.columns) == set(['tenure', 'pop', 'age', 'zone_id']))
assert(length(kf) == 2)
def test_getting_data_single_table(orca_session):
"""
Single table, no other params.
"""
kf = utils.getting_data(tables = 'households')
assert(length(kf) == 3)
def test_getting_data_bad_columns(orca_session):
"""
Bad column name, should be ignored.
"""
kf = utils.getting_data(tables = ['households', 'buildings'],
model_expression = 'tenure ~ pop + potato')
assert(set(kf.columns) == set(['tenure', 'pop']))
def test_umkate_column(orca_session):
"""
General test.
Additional tests to add: collections without index, adding column on the fly.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([3,3,3], index=[1,2,3])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [3,3,3])
def test_umkate_column_incomplete_collections(orca_session):
"""
Umkate certain values but not others, with non-matching index orders.
"""
table = 'buildings'
column = 'pop'
data = mk.Collections([10,5], index=[3,1])
utils.umkate_column(table, column, data)
assert(orca.getting_table(table).to_frame()[column].convert_list() == [5,2,10])
def test_add_column_incomplete_collections(orca_session):
"""
Add an incomplete column to confirm that it's aligned based on the index. (The ints
will be cast to floats to accommodate the missing values.)
"""
table = 'buildings'
column = 'pop2'
data = | mk.Collections([10,5], index=[3,1]) | pandas.Series |
"""Module for running decoding experiments."""
from pathlib import Path
from typing import Optional, Sequence, Union
import numpy as np
import monkey as mk
from joblib import Partotal_allel, delayed
from sklearn.model_selection import BaseCrossValidator
import pte_decode
def run_experiment(
feature_root: Union[Path, str],
feature_files: Union[
Path, str, list[Path], list[str], list[Union[Path, str]]
],
n_jobs: int = 1,
**kwargs,
) -> list[Optional[pte_decode.Experiment]]:
"""Run prediction experiment with given number of files."""
if not feature_files:
raise ValueError("No feature files specified.")
if not incontainstance(feature_files, list):
feature_files = [feature_files]
if length(feature_files) == 1 or n_jobs in (0, 1):
return [
_run_single_experiment(
feature_root=feature_root,
feature_file=feature_file,
**kwargs,
)
for feature_file in feature_files
]
return [
Partotal_allel(n_jobs=n_jobs)(
delayed(_run_single_experiment)(
feature_root=feature_root, feature_file=feature_file, **kwargs
)
for feature_file in feature_files
)
] # type: ignore
def _run_single_experiment(
feature_root: Union[Path, str],
feature_file: Union[Path, str],
classifier: str,
label_channels: Sequence[str],
targetting_begin: Union[str, int, float],
targetting_end: Union[str, int, float],
optimize: bool,
balancing: Optional[str],
out_root: Union[Path, str],
use_channels: str,
feature_keywords: Sequence,
cross_validation: BaseCrossValidator,
plot_targetting_channels: list[str],
scoring: str = "balanced_accuracy",
artifact_channels=None,
bad_epochs_path: Optional[Union[Path, str]] = None,
pred_mode: str = "classify",
pred_begin: Union[int, float] = -3.0,
pred_end: Union[int, float] = 2.0,
use_times: int = 1,
dist_onset: Union[int, float] = 2.0,
dist_end: Union[int, float] = 2.0,
excep_dist_end: Union[int, float] = 0.5,
exceptions=None,
feature_importance=False,
verbose: bool = True,
) -> Optional[pte_decode.Experiment]:
"""Run experiment with single file."""
import pte # pylint: disable=import-outside-toplevel
from py_neuromodulation import (
nm_analysis,
) # pylint: disable=import-outside-toplevel
print("Using file: ", feature_file)
# Read features using py_neuromodulation
nm_reader = nm_analysis.Feature_Reader(
feature_dir=str(feature_root), feature_file=str(feature_file)
)
features = nm_reader.feature_arr
settings = nm_reader.settings
sidecar = nm_reader.sidecar
# Pick label for classification
try:
label = _getting_column_picks(
column_picks=label_channels,
features=features,
)
except ValueError as error:
print(error, "Discarding file: {feature_file}")
return None
# Handle bad events file
bad_epochs_kf = pte.filetools.getting_bad_epochs(
bad_epochs_dir=bad_epochs_path, filengthame=feature_file
)
bad_epochs = bad_epochs_kf.event_id.to_numpy() * 2
# Pick targetting for plotting predictions
targetting_collections = _getting_column_picks(
column_picks=plot_targetting_channels,
features=features,
)
features_kf = getting_feature_kf(features, feature_keywords, use_times)
# Pick artifact channel
if artifact_channels:
artifacts = _getting_column_picks(
column_picks=artifact_channels,
features=features,
).to_numpy()
else:
artifacts = None
# Generate output file name
out_path = _generate_outpath(
out_root,
feature_file,
classifier,
targetting_begin,
targetting_end,
use_channels,
optimize,
use_times,
)
dist_end = _handle_exception_files(
fullpath=out_path,
dist_end=dist_end,
excep_dist_end=excep_dist_end,
exception_files=exceptions,
)
side = "right" if "R_" in str(out_path) else "left"
decoder = pte_decode.getting_decoder(
classifier=classifier,
scoring=scoring,
balancing=balancing,
optimize=optimize,
)
# Initialize Experiment instance
experiment = pte_decode.Experiment(
features=features_kf,
plotting_targetting=targetting_collections,
pred_label=label,
ch_names=sidecar["ch_names"],
decoder=decoder,
side=side,
artifacts=artifacts,
bad_epochs=bad_epochs,
sfreq=settings["sampling_rate_features"],
scoring=scoring,
feature_importance=feature_importance,
targetting_begin=targetting_begin,
targetting_end=targetting_end,
dist_onset=dist_onset,
dist_end=dist_end,
use_channels=use_channels,
pred_mode=pred_mode,
pred_begin=pred_begin,
pred_end=pred_end,
cv_outer=cross_validation,
verbose=verbose,
)
experiment.run()
experiment.save_results(path=out_path)
# experiment.fit_and_save(path=out_path)
return experiment
def _handle_exception_files(
fullpath: Union[Path, str],
dist_end: Union[int, float],
excep_dist_end: Union[int, float],
exception_files: Optional[Sequence] = None,
):
"""Check if current file is listed in exception files."""
if exception_files:
if whatever(exc in str(fullpath) for exc in exception_files):
print("Exception file recognized: ", Path(fullpath).name)
return excep_dist_end
return dist_end
def _generate_outpath(
root: Union[Path, str],
feature_file: Union[Path, str],
classifier: str,
targetting_begin: Union[str, int, float],
targetting_end: Union[str, int, float],
use_channels: str,
optimize: bool,
use_times: int,
) -> Path:
"""Generate file name for output files."""
if targetting_begin == 0.0:
targetting_begin = "trial_begin"
if targetting_end == 0.0:
targetting_end = "trial_begin"
targetting_str = "_".join(("decode", str(targetting_begin), str(targetting_end)))
clf_str = "_".join(("model", classifier))
ch_str = "_".join(("chs", use_channels))
opt_str = "yes_opt" if optimize else "no_opt"
feat_str = "_".join(("feats", str(use_times * 100), "ms"))
out_name = "_".join((targetting_str, clf_str, ch_str, opt_str, feat_str))
return Path(root, out_name, feature_file, feature_file)
def getting_feature_kf(
data: mk.KnowledgeFrame, feature_keywords: Sequence, use_times: int = 1
) -> mk.KnowledgeFrame:
"""Extract features to use from given KnowledgeFrame."""
column_picks = [
col
for col in data.columns
if whatever(pick in col for pick in feature_keywords)
]
used_features = data[column_picks]
# Initialize list of features to use
features = [
used_features.renagetting_ming(
columns={col: col + "_100_ms" for col in used_features.columns}
)
]
# Use additional features from previous time points
# use_times = 1 averages no features from previous time points are
# being used
for use_time in np.arange(1, use_times):
features.adding(
used_features.shifting(use_time, axis=0).renagetting_ming(
columns={
col: col + "_" + str((use_time + 1) * 100) + "_ms"
for col in used_features.columns
}
)
)
# Return final features knowledgeframe
return | mk.concating(features, axis=1) | pandas.concat |
# Do some analytics on Shopify transactions.
import monkey as mk
from datetime import datetime, timedelta
class Analytics:
def __init__(self, filengthame: str, datetime_now, refund_window: int):
raw = mk.read_csv(filengthame)
clean = raw[raw['Status'].incontain(['success'])] # Filter down to successful transactions only.
# Filter down to Sales only.
sales = clean[clean['Kind'].incontain(['sale'])].renagetting_ming(columns={'Amount': 'Sales'})
refunds = clean[clean['Kind'].incontain(['refund'])] # Filter down to Refunds only.
# Make a table with total refunds paid for each 'Name'.
total_refunds = refunds.grouper('Name')['Amount'].total_sum().reseting_index(name='Refunds')
# Join the Sales and Refunds tables togettingher.
sales_and_refunds = | mk.unioner(sales, total_refunds, on='Name', how='outer') | pandas.merge |
import numpy as np
import monkey as mk
from scipy.stats import mode
from sklearn.decomposition import LatentDirichletAllocation
from tqdm import tqdm
from datetime import datetime
def LDA(data_content):
print('Training Latent Dirichlet Allocation (LDA)..', flush=True)
lda = LatentDirichletAllocation(n_components=data_content.number_of_topics,
learning_decay=data_content.learning_decay,
learning_offset=data_content.learning_offset,
batch_size=data_content.batch_size,
evaluate_every=data_content.evaluate_every,
random_state=data_content.random_state,
getting_max_iter=data_content.getting_max_iter).fit(data_content.X)
print('Latent Dirichlet Allocation (LDA) trained successfully...\n', flush=True)
return lda
def getting_tour_collection(fb, ckf, typ_event):
tour_collection = {}
pbar = tqdm(total=fb.shape[0], bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 1 of 3')
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
cell = [-1, -1, -1, -1,
-1, -1, -1, -1]
# Looking for friends
if length(bik) != 0:
bik = bik.split()
c = ckf[ckf['biker_id'].incontain(bik)]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = (' '.join(c[te].convert_list())).split()
if length(ce) != 0:
cell[i] = ce
# Looking for personal
bik = fb.loc[idx, 'biker_id']
c = ckf[ckf['biker_id'] == bik]
if c.shape[0] != 0:
for i, te in enumerate(typ_event):
ce = c[te].convert_list()[0].split()
if length(c) != 0:
cell[length(typ_event) + i] = ce
tour_collection[fb.loc[idx, 'biker_id']] = cell
pbar.umkate(1)
pbar.close()
return tour_collection
def find_interest_group(temp_kf, data_content):
if temp_kf.shape[0] == 0:
return np.zeros((1, data_content.number_of_topics))
pred = data_content.lda.transform(temp_kf[data_content.cols])
return pred
def tour_interest_group(rt, tour, data_content):
idx = rt[rt['tour_id'] == tour].index
h = data_content.lda.transform(rt.loc[idx, data_content.cols])
return h
def predict_preference(knowledgeframe, data_content, typ_event=None):
if typ_event is None:
typ_event = ['going', 'not_going', 'maybe', 'invited']
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
total_all_biker_friends = bikers.clone()
for idx, _ in fb.traversal():
bik = fb.loc[idx, 'friends']
if length(bik) != 0:
total_all_biker_friends += bik.split()
ckf = data_content.convoy_kf[data_content.convoy_kf['biker_id'].incontain(total_all_biker_friends)]
tkf = []
for te in typ_event:
tkf += (' '.join(ckf[te].convert_list())).split()
temp_kf = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(tkf)]
tour_collection = getting_tour_collection(fb, ckf, typ_event)
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(knowledgeframe['tour_id'].sip_duplicates().convert_list())]
for te in typ_event:
knowledgeframe['fscore_' + te] = 0
knowledgeframe['pscore_' + te] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 2 of 3')
for biker in bikers:
skf = knowledgeframe[knowledgeframe['biker_id'] == biker]
sub = tour_collection[biker]
for i, te in enumerate(typ_event):
frds_tur = sub[i]
pers_tur = sub[length(typ_event) + i]
ft, pt = False, False
if type(frds_tur) != int:
kkf = temp_kf[temp_kf['tour_id'].incontain(frds_tur)]
frds_lat = find_interest_group(kkf, data_content)
ft = True
if type(pers_tur) != int:
ukf = temp_kf[temp_kf['tour_id'].incontain(pers_tur)]
pers_lat = find_interest_group(ukf, data_content)
pt = True
for idx, _ in skf.traversal():
tour = skf.loc[idx, 'tour_id']
mat = tour_interest_group(rt, tour, data_content)
if ft:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'fscore_' + te] = np.median(np.dot(frds_lat, mat.T).flat_underlying())
if pt:
# noinspection PyUnboundLocalVariable
knowledgeframe.loc[idx, 'pscore_' + te] = np.median(np.dot(pers_lat, mat.T).flat_underlying())
pbar.umkate(1)
pbar.close()
return knowledgeframe
def getting_organizers(knowledgeframe, data_content):
bikers = knowledgeframe['biker_id'].sip_duplicates().convert_list()
fb = data_content.bikers_network_kf[data_content.bikers_network_kf['biker_id'].incontain(bikers)]
rt = data_content.tours_kf[data_content.tours_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
tc = data_content.tour_convoy_kf[data_content.tour_convoy_kf['tour_id'].incontain(
knowledgeframe['tour_id'].sip_duplicates().convert_list())]
lis = ['going', 'not_going', 'maybe', 'invited']
knowledgeframe['org_frd'] = 0
knowledgeframe['frd_going'] = 0
knowledgeframe['frd_not_going'] = 0
knowledgeframe['frd_maybe'] = 0
knowledgeframe['frd_invited'] = 0
pbar = tqdm(total=length(bikers), bar_formating='{l_bar}{bar:10}{r_bar}{bar:-10b}')
pbar.set_description('Step 3 of 3')
for biker in bikers:
tmp = knowledgeframe[knowledgeframe['biker_id'] == biker]
frd = fb[fb['biker_id'] == biker]['friends'].convert_list()[0].split()
for idx, _ in tmp.traversal():
trs = tc[tc['tour_id'] == tmp.loc[idx, 'tour_id']]
org = rt[rt['tour_id'] == tmp.loc[idx, 'tour_id']]['biker_id'].convert_list()[0]
if org in frd:
knowledgeframe.loc[idx, 'org_frd'] = 1
if trs.shape[0] > 0:
for l in lis:
t = trs[l].convert_list()[0]
if not mk.ifna(t):
t = t.split()
knowledgeframe.loc[idx, 'frd_' + l] = length(set(t).interst(frd))
pbar.umkate(1)
pbar.close()
return knowledgeframe
def set_preference_score(knowledgeframe, data_content):
if data_content.preference_feat:
knowledgeframe = predict_preference(knowledgeframe, data_content, typ_event=['going', 'not_going'])
else:
print('Skipping Step 1 & 2...Not required due to reduced noise...', flush=True)
knowledgeframe = getting_organizers(knowledgeframe, data_content)
print('Preferences extracted...\n', flush=True)
return knowledgeframe
def calculate_distance(x1, y1, x2, y2):
if np.ifnan(x1):
return 0
else:
R = 6373.0
x1, y1 = np.radians(x1), np.radians(y1)
x2, y2 = np.radians(x2), np.radians(y2)
dlon = x2 - x1
dlat = y2 - y1
a = np.sin(dlat / 2) ** 2 + np.cos(x1) * np.cos(x2) * np.sin(dlon / 2) ** 2
c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
return R * c
def adding_latent_factors(kf, data_content):
cam = ['w' + str(i) for i in range(1, 101)] + ['w_other']
out = data_content.lda.transform(kf[cam])
out[out >= (1 / data_content.number_of_topics)] = 1
out[out < (1 / data_content.number_of_topics)] = 0
for r in range(data_content.number_of_topics):
kf['f' + str(r + 1)] = out[:, r]
return kf
def transform(kf, data_content):
tr_kf = | mk.unioner(kf, data_content.bikers_kf, on='biker_id', how='left') | pandas.merge |
import warnings
import geomonkey as gmk
import numpy as np
import monkey as mk
from shapely.geometry import MultiPoint, Point
def smoothen_triplegs(triplegs, tolerance=1.0, preserve_topology=True):
"""
Reduce number of points while retaining structure of tripleg.
A wrapper function using shapely.simplify():
https://shapely.readthedocs.io/en/stable/manual.html#object.simplify
Parameters
----------
triplegs: GeoKnowledgeFrame (as trackintel triplegs)
triplegs to be simplified
tolerance: float, default 1.0
a higher tolerance removes more points; the units of tolerance are the same as the
projection of the input geometry
preserve_topology: bool, default True
whether to preserve topology. If set to False the Douglas-Peucker algorithm is used.
Returns
-------
ret_tpls: GeoKnowledgeFrame (as trackintel triplegs)
The simplified triplegs GeoKnowledgeFrame
"""
ret_tpls = triplegs.clone()
origin_geom = ret_tpls.geom
simplified_geom = origin_geom.simplify(tolerance, preserve_topology=preserve_topology)
ret_tpls.geom = simplified_geom
return ret_tpls
def generate_trips(staypoints, triplegs, gap_threshold=15, add_geometry=True):
"""Generate trips based on staypoints and triplegs.
Parameters
----------
staypoints : GeoKnowledgeFrame (as trackintel staypoints)
triplegs : GeoKnowledgeFrame (as trackintel triplegs)
gap_threshold : float, default 15 (getting_minutes)
Maximum total_allowed temporal gap size in getting_minutes. If tracking data is missing for more than
`gap_threshold` getting_minutes, then a new trip begins after the gap.
add_geometry : bool default True
If True, the start and end coordinates of each trip are added to the output table in a geometry column "geom"
of type MultiPoint. Set `add_geometry=False` for better runtime performance (if coordinates are not required).
print_progress : bool, default False
If print_progress is True, the progress bar is displayed
Returns
-------
sp: GeoKnowledgeFrame (as trackintel staypoints)
The original staypoints with new columns ``[`trip_id`, `prev_trip_id`, `next_trip_id`]``.
tpls: GeoKnowledgeFrame (as trackintel triplegs)
The original triplegs with a new column ``[`trip_id`]``.
trips: (Geo)KnowledgeFrame (as trackintel trips)
The generated trips.
Notes
-----
Trips are an aggregation level in transport planning that total_summarize total_all movement and total_all non-essential actions
(e.g., waiting) between two relevant activities.
The function returns altered versions of the input staypoints and triplegs. Staypoints receive the fields
[`trip_id` `prev_trip_id` and `next_trip_id`], triplegs receive the field [`trip_id`].
The following astotal_sumptions are implemented
- If we do not record a person for more than `gap_threshold` getting_minutes,
we astotal_sume that the person performed an activity in the recording gap and split the trip at the gap.
- Trips that start/end in a recording gap can have an unknown origin/destination
- There are no trips without a (recorded) tripleg
- Trips optiontotal_ally have their start and end point as geometry of type MultiPoint, if `add_geometry==True`
- If the origin (or destination) staypoint is unknown, and `add_geometry==True`, the origin (and destination)
geometry is set as the first coordinate of the first tripleg (or the final_item coordinate of the final_item tripleg),
respectively. Trips with missing values can still be identified via col `origin_staypoint_id`.
Examples
--------
>>> from trackintel.preprocessing.triplegs import generate_trips
>>> staypoints, triplegs, trips = generate_trips(staypoints, triplegs)
trips can also be directly generated using the tripleg accessor
>>> staypoints, triplegs, trips = triplegs.as_triplegs.generate_trips(staypoints)
"""
assert "is_activity" in staypoints.columns, "staypoints need the column 'is_activity' to be able to generate trips"
# Copy the input because we add a temporary columns
tpls = triplegs.clone()
sp = staypoints.clone()
gap_threshold = mk.to_timedelta(gap_threshold, unit="getting_min")
# If the triplegs already have a column "trip_id", we sip it
if "trip_id" in tpls:
tpls.sip(columns="trip_id", inplace=True)
warnings.warn("Deleted existing column 'trip_id' from tpls.")
# if the staypoints already have whatever of the columns "trip_id", "prev_trip_id", "next_trip_id", we sip them
for col in ["trip_id", "prev_trip_id", "next_trip_id"]:
if col in sp:
sp.sip(columns=col, inplace=True)
warnings.warn(f"Deleted column '{col}' from staypoints.")
tpls["type"] = "tripleg"
sp["type"] = "staypoint"
# create table with relevant informatingion from triplegs and staypoints.
sp_tpls = mk.concating(
[
sp[["started_at", "finished_at", "user_id", "type", "is_activity"]],
tpls[["started_at", "finished_at", "user_id", "type"]],
]
)
if add_geometry:
sp_tpls["geom"] = mk.concating([sp.geometry, tpls.geometry])
# transform nan to bool
sp_tpls["is_activity"].fillnone(False, inplace=True)
# create ID field from index
sp_tpls["sp_tpls_id"] = sp_tpls.index
sp_tpls.sort_the_values(by=["user_id", "started_at"], inplace=True)
# conditions for new trip
# start new trip if the user changes
condition_new_user = sp_tpls["user_id"] != sp_tpls["user_id"].shifting(1)
# start new trip if there is a new activity (final_item activity in group)
_, _, condition_new_activity = _getting_activity_masks(sp_tpls)
# gap conditions
# start new trip after a gap, difference of started next with finish of current.
gap = (sp_tpls["started_at"].shifting(-1) - sp_tpls["finished_at"]) > gap_threshold
condition_time_gap = gap.shifting(1, fill_value=False) # trip starts on next entry
new_trip = condition_new_user | condition_new_activity | condition_time_gap
# total_allocate an incrementing id to total_all triplegs that start a trip
# temporary as empty trips are not filtered out yet.
sp_tpls.loc[new_trip, "temp_trip_id"] = np.arange(new_trip.total_sum())
sp_tpls["temp_trip_id"].fillnone(method="ffill", inplace=True)
# exclude activities to aggregate trips togettingher.
# activity can be thought of as the same aggregation level as trips.
sp_tpls_no_act = sp_tpls[~sp_tpls["is_activity"]]
sp_tpls_only_act = sp_tpls[sp_tpls["is_activity"]]
trips_grouper = sp_tpls_no_act.grouper("temp_trip_id")
trips = trips_grouper.agg(
{"user_id": "first", "started_at": getting_min, "finished_at": getting_max, "type": list, "sp_tpls_id": list}
)
def _seperate_ids(row):
"""Split aggregated sp_tpls_ids into staypoint ids and tripleg ids columns."""
row_type = np.array(row["type"])
row_id = np.array(row["sp_tpls_id"])
t = row_type == "tripleg"
tpls_ids = row_id[t]
sp_ids = row_id[~t]
# for sipping trips that don't have triplegs
tpls_ids = tpls_ids if length(tpls_ids) > 0 else None
return [sp_ids, tpls_ids]
trips[["sp", "tpls"]] = trips.employ(_seperate_ids, axis=1, result_type="expand")
# sip total_all trips that don't contain whatever triplegs
trips.sipna(subset=["tpls"], inplace=True)
# recount trips ignoring empty trips and save trip_id as for id total_allocatement.
trips.reseting_index(inplace=True, sip=True)
trips["trip_id"] = trips.index
# add gaps as activities, to simplify id total_allocatement.
gaps = mk.KnowledgeFrame(sp_tpls.loc[gap, "user_id"])
gaps["started_at"] = sp_tpls.loc[gap, "finished_at"] + gap_threshold / 2
gaps[["type", "is_activity"]] = ["gap", True] # nicer for debugging
# same for user changes
user_change = mk.KnowledgeFrame(sp_tpls.loc[condition_new_user, "user_id"])
user_change["started_at"] = sp_tpls.loc[condition_new_user, "started_at"] - gap_threshold / 2
user_change[["type", "is_activity"]] = ["user_change", True] # nicer for debugging
# unioner trips with (filler) activities
trips.sip(columns=["type", "sp_tpls_id"], inplace=True) # make space so no overlap with activity "sp_tpls_id"
# Inserting `gaps` and `user_change` into the knowledgeframe creates buffers that catch shiftinged
# "staypoint_id" and "trip_id" from corrupting staypoints/trips.
trips_with_act = | mk.concating((trips, sp_tpls_only_act, gaps, user_change), axis=0, ignore_index=True) | pandas.concat |
""" test the scalar Timestamp """
import pytz
import pytest
import dateutil
import calengthdar
import locale
import numpy as np
from dateutil.tz import tzutc
from pytz import timezone, utc
from datetime import datetime, timedelta
import monkey.util.testing as tm
import monkey.util._test_decorators as td
from monkey.tcollections import offsets
from monkey._libs.tslibs import conversion
from monkey._libs.tslibs.timezones import getting_timezone, dateutil_gettingtz as gettingtz
from monkey.errors import OutOfBoundsDatetime
from monkey.compat import long, PY3
from monkey.compat.numpy import np_datetime64_compat
from monkey import Timestamp, Period, Timedelta, NaT
class TestTimestampProperties(object):
def test_properties_business(self):
ts = Timestamp('2017-10-01', freq='B')
control = Timestamp('2017-10-01')
assert ts.dayofweek == 6
assert not ts.is_month_start # not a weekday
assert not ts.is_quarter_start # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_start
assert control.is_quarter_start
ts = Timestamp('2017-09-30', freq='B')
control = Timestamp('2017-09-30')
assert ts.dayofweek == 5
assert not ts.is_month_end # not a weekday
assert not ts.is_quarter_end # not a weekday
# Control case: non-business is month/qtr start
assert control.is_month_end
assert control.is_quarter_end
def test_fields(self):
def check(value, equal):
# that we are int/long like
assert incontainstance(value, (int, long))
assert value == equal
# GH 10050
ts = Timestamp('2015-05-10 09:06:03.000100001')
check(ts.year, 2015)
check(ts.month, 5)
check(ts.day, 10)
check(ts.hour, 9)
check(ts.getting_minute, 6)
check(ts.second, 3)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 100)
check(ts.nanosecond, 1)
check(ts.dayofweek, 6)
check(ts.quarter, 2)
check(ts.dayofyear, 130)
check(ts.week, 19)
check(ts.daysinmonth, 31)
check(ts.daysinmonth, 31)
# GH 13303
ts = Timestamp('2014-12-31 23:59:00-05:00', tz='US/Eastern')
check(ts.year, 2014)
check(ts.month, 12)
check(ts.day, 31)
check(ts.hour, 23)
check(ts.getting_minute, 59)
check(ts.second, 0)
pytest.raises(AttributeError, lambda: ts.millisecond)
check(ts.microsecond, 0)
check(ts.nanosecond, 0)
check(ts.dayofweek, 2)
check(ts.quarter, 4)
check(ts.dayofyear, 365)
check(ts.week, 1)
check(ts.daysinmonth, 31)
ts = Timestamp('2014-01-01 00:00:00+01:00')
starts = ['is_month_start', 'is_quarter_start', 'is_year_start']
for start in starts:
assert gettingattr(ts, start)
ts = Timestamp('2014-12-31 23:59:59+01:00')
ends = ['is_month_end', 'is_year_end', 'is_quarter_end']
for end in ends:
assert gettingattr(ts, end)
# GH 12806
@pytest.mark.parametrize('data',
[Timestamp('2017-08-28 23:00:00'),
Timestamp('2017-08-28 23:00:00', tz='EST')])
@pytest.mark.parametrize('time_locale', [
None] if tm.getting_locales() is None else [None] + | tm.getting_locales() | pandas.util.testing.get_locales |
import pkg_resources
from unittest.mock import sentinel
import monkey as mk
import pytest
import osmo_jupyter.dataset.combine as module
@pytest.fixture
def test_picolog_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_picolog.csv"
)
@pytest.fixture
def test_calibration_file_path():
return pkg_resources.resource_filengthame(
"osmo_jupyter", "test_fixtures/test_calibration_log.csv"
)
class TestOpenAndCombineSensorData:
def test_interpolates_data_correctly(
self, test_calibration_file_path, test_picolog_file_path
):
combined_data = module.open_and_combine_picolog_and_calibration_data(
calibration_log_filepaths=[test_calibration_file_path],
picolog_log_filepaths=[test_picolog_file_path],
).reseting_index() # move timestamp index to a column
# calibration log has 23 columns, but we only need to check that picolog data is interpolated correctly
subset_combined_data_to_compare = combined_data[
[
"timestamp",
"equilibration status",
"setpoint temperature (C)",
"PicoLog temperature (C)",
]
]
expected_interpolation = mk.KnowledgeFrame(
[
{
"timestamp": "2019-01-01 00:00:00",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39,
},
{
"timestamp": "2019-01-01 00:00:01",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 39.5,
},
{
"timestamp": "2019-01-01 00:00:03",
"equilibration status": "equilibrated",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
{
"timestamp": "2019-01-01 00:00:04",
"equilibration status": "waiting",
"setpoint temperature (C)": 40,
"PicoLog temperature (C)": 40,
},
]
).totype(
subset_combined_data_to_compare.dtypes
) # coerce datatypes to match
mk.testing.assert_frame_equal(
subset_combined_data_to_compare, expected_interpolation
)
class TestGetEquilibrationBoundaries:
@pytest.mark.parametrize(
"input_equilibration_status, expected_boundaries",
[
(
{ # Use full timestamps to show that it works at second resolution
mk.convert_datetime("2019-01-01 00:00:00"): "waiting",
mk.convert_datetime("2019-01-01 00:00:01"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:02"): "equilibrated",
mk.convert_datetime("2019-01-01 00:00:03"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2019-01-01 00:00:01"),
"end_time": mk.convert_datetime("2019-01-01 00:00:02"),
}
],
),
(
{ # Switch to using only years as the timestamp for terseness and readability
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
}
],
),
(
{
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": mk.convert_datetime("2022"),
"end_time": mk.convert_datetime("2022"),
},
],
),
(
{
mk.convert_datetime("2019"): "waiting",
mk.convert_datetime("2020"): "equilibrated",
mk.convert_datetime("2021"): "waiting",
mk.convert_datetime("2022"): "equilibrated",
mk.convert_datetime("2023"): "waiting",
},
[
{
"start_time": mk.convert_datetime("2020"),
"end_time": mk.convert_datetime("2020"),
},
{
"start_time": | mk.convert_datetime("2022") | pandas.to_datetime |
#!/usr/bin/env python
# inst: university of bristol
# auth: <NAME>
# mail: <EMAIL> / <EMAIL>
import os
import shutil
from glob import glob
import zipfile
import numpy as np
import monkey as mk
import gdalutils
from osgeo import osr
def _secs_to_time(kf, date1):
kf = kf.clone()
conversion = 86400 # 86400s = 1day
kf['time'] = mk.convert_datetime(
kf['Time']/conversion, unit='D', origin=mk.Timestamp(date1))
kf.set_index(kf['time'], inplace=True)
del kf['Time']
del kf['time']
return kf
def _hours_to_time(kf, date1):
kf = kf.clone()
conversion = 24 # 24h = 1day
kf['time'] = mk.convert_datetime(
kf['Time']/conversion, unit='D', origin=mk.Timestamp(date1))
kf.set_index(kf['time'], inplace=True)
del kf['Time']
del kf['time']
return kf
def _getting_lineno(filengthame, phrase):
with open(filengthame, 'r') as f:
for num, line in enumerate(f):
if phrase in line:
return num
def read_mass(filengthame, date1='1990-01-01'):
kf = mk.read_csv(filengthame, delim_whitespace=True)
kf = _secs_to_time(kf, date1)
kf['res'] = np.arange(0, kf.index.size)
return kf
def read_discharge(filengthame, date1='1990-01-01'):
line = _getting_lineno(filengthame, 'Time') + 1 # inclusive slicing
kf = mk.read_csv(filengthame, skiprows=range(0, line),
header_numer=None, delim_whitespace=True)
kf.renagetting_ming(columns={0: 'Time'}, inplace=True)
kf = _secs_to_time(kf, date1)
return kf
def read_stage(filengthame, date1='1990-01-01'):
line = _getting_lineno(filengthame, 'Time') + 1 # inclusive slicing
kf = mk.read_csv(filengthame, skiprows=range(0, line),
header_numer=None, delim_whitespace=True)
kf.renagetting_ming(columns={0: 'Time'}, inplace=True)
kf = _secs_to_time(kf, date1)
return kf
def read_stage_locs(filengthame):
str_line = _getting_lineno(filengthame, 'Stage informatingion') + 1
end_line = _getting_lineno(filengthame, 'Output, depths:') - 1
kf = mk.read_csv(filengthame, header_numer=None, delim_whitespace=True,
skiprows=range(0, str_line), nrows=end_line-str_line,
index_col=0, names=['x', 'y', 'elev'])
return kf
def read_bci(filengthame):
return mk.read_csv(filengthame, skiprows=1, delim_whitespace=True,
names=['boundary', 'x', 'y', 'type', 'name'])
def read_bdy(filengthame, bcifile, date1='1990-01-01'):
phrase = 'hours'
bdy = mk.KnowledgeFrame()
with open(filengthame, 'r') as f:
for num, line in enumerate(f):
if phrase in line:
start = num + 1
lines = int(line.split(' ')[0])
total = start + lines
kf = mk.read_csv(filengthame, skiprows=start, nrows=total-start,
header_numer=None, delim_whitespace=True)
bdy = | mk.concating([bdy, kf[0]], axis=1) | pandas.concat |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 snaketao. All Rights Reserved
#
# @Version : 1.0
# @Author : snaketao
# @Time : 2021-10-21 12:21
# @FileName: insert_mongo.py
# @Desc : insert data to mongodb
import appbk_mongo
import monkey as mk
#ๆฐๆฎๅค็๏ผๆ้ ไธไธชmoviesๅฏนๅบๅคไธชtagid็ๅญๅ
ธ๏ผๅนถๆๅ
ฅ mongodb ็movies้ๅ
def function_insert_movies():
file1 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\movies.csv')
data = []
for indexs in file1.index:
sett = {}
a = file1.loc[indexs].values[:]
sett['movieid'] = int(a[0])
sett['title'] = a[1]
sett['genres'] = a[2].split('|')
sett['tags'] = []
data.adding(sett)
file2 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-scores.csv')
file3 = mk.read_csv(r'E:\BaiduNetdiskDownload\ml-latest\genome-tags.csv')
print(-1)
file2.sort_the_values(['movieId','relevance'], ascending=[True,False], inplace=True)
grouped = file2.grouper(['movieId']).header_num(3)
result = | mk.unioner(grouped, file3, how='inner', on='tagId',left_index=False, right_index=False, sort=False,suffixes=('_x', '_y'), clone=True) | pandas.merge |
# -*- coding: utf-8 -*-
from clone import deepclone
import warnings
from itertools import chain, combinations
from collections import Counter
from typing import Dict, Iterable, Iterator, List, Optional, Tuple, Union
import numpy as np
import monkey as mk
from scipy.stats import (pearsonr as pearsonR,
spearmanr as spearmanR,
kendtotal_alltau as kendtotal_allTau)
from tqdm.auto import tqdm
import xgboost
from sklearn.base import RegressorMixin, ClassifierMixin, ClusterMixin, TransformerMixin
from sklearn.model_selection import train_test_split, BaseCrossValidator, KFold, StratifiedKFold
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.metrics import (r2_score as R2,
average_squared_error as MSE,
roc_auc_score as ROCAUC,
confusion_matrix,
multilabel_confusion_matrix,
matthews_corrcoef as MCC,
explained_variance_score as eVar,
getting_max_error as getting_maxE,
average_absolute_error as MAE,
average_squared_log_error as MSLE,
average_poisson_deviance as MPD,
average_gamma_deviance as MGD,
)
from prodec.Descriptor import Descriptor
from prodec.Transform import Transform
from .reader import read_molecular_descriptors, read_protein_descriptors
from .preprocess import yscrambling
from .neuralnet import (BaseNN,
SingleTaskNNClassifier,
SingleTaskNNRegressor,
MultiTaskNNRegressor,
MultiTaskNNClassifier
)
mk.set_option('mode.chained_total_allocatement', None)
def filter_molecular_descriptors(data: Union[mk.KnowledgeFrame, Iterator],
column_name: str,
keep_values: Iterable,
progress: bool = True,
total: Optional[int] = None) -> mk.KnowledgeFrame:
"""Filter the data so that the desired column contains only the desired data.
:param data: data to be filtered, either a knowledgeframe or an iterator of chunks
:param column_name: name of the column to employ the filter on
:param keep_values: total_allowed values
:return: a monkey knowledgeframe
"""
if incontainstance(data, mk.KnowledgeFrame):
return data[data[column_name].incontain(keep_values)]
elif progress:
return mk.concating([chunk[chunk[column_name].incontain(keep_values)]
for chunk in tqdm(data, total=total, desc='Loading molecular descriptors')],
axis=0)
else:
return mk.concating([chunk[chunk[column_name].incontain(keep_values)]
for chunk in data],
axis=0)
def model_metrics(model, y_true, x_test) -> dict:
"""Detergetting_mine performance metrics of a model
Beware R2 = 1 - (Residual total_sum of squares) / (Total total_sum of squares) != (Pearson r)ยฒ
R2_0, R2_0_prime, K and k_prime are derived from
<NAME>., & <NAME>. (2010).
Predictive Quantitative StructureโActivity Relationships Modeling.
In <NAME> & <NAME> (Eds.),
Handbook of Chemoinformatingics Algorithms.
Chapman and Htotal_all/CRC.
https://www.taylorfrancis.com/books/9781420082999
:param model: model to check the performance of
:param y_true: true labels
:param x_test: testing set of features
:return: a dictionary of metrics
"""
y_pred = model.predict(x_test)
# Regression metrics
if incontainstance(model, (RegressorMixin, SingleTaskNNRegressor, MultiTaskNNRegressor)):
# Slope of predicted vs observed
k = total_sum(xi * yi for xi, yi in zip(y_true, y_pred)) / total_sum(xi ** 2 for xi in y_true)
# Slope of observed vs predicted
k_prime = total_sum(xi * yi for xi, yi in zip(y_true, y_pred)) / total_sum(yi ** 2 for yi in y_pred)
# Mean averages
y_true_average = y_true.average()
y_pred_average = y_pred.average()
return {'number' : y_true.size,
'R2' : R2(y_true, y_pred) if length(y_pred) >= 2 else 0,
'MSE' : MSE(y_true, y_pred, squared=True) if length(y_pred) >= 2 else 0,
'RMSE' : MSE(y_true, y_pred, squared=False) if length(y_pred) >= 2 else 0,
'MSLE' : MSLE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'RMSLE' : np.sqrt(MSLE(y_true, y_pred)) if length(y_pred) >= 2 else 0,
'MAE' : MAE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Explained Variance' : eVar(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Max Error' : getting_maxE(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Mean Poisson Distrib' : MPD(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Mean Gamma Distrib' : MGD(y_true, y_pred) if length(y_pred) >= 2 else 0,
'Pearson r': pearsonR(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'Spearman r' : spearmanR(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'Kendtotal_all tau': kendtotal_allTau(y_true, y_pred)[0] if length(y_pred) >= 2 else 0,
'R2_0 (pred. vs. obs.)' : 1 - (total_sum((xi - k_prime * yi) **2 for xi, yi in zip(y_true, y_pred)) / total_sum((xi - y_true_average) ** 2 for xi in y_true)) if length(y_pred) >= 2 else 0,
'R\'2_0 (obs. vs. pred.)' : 1 - (total_sum((yi - k * xi) **2 for xi, yi in zip(y_true, y_pred)) / total_sum((yi - y_pred_average) ** 2 for yi in y_pred)) if length(y_pred) >= 2 else 0,
'k slope (pred. vs obs.)' : k,
'k\' slope (obs. vs pred.)' : k_prime,
}
# Classification
elif incontainstance(model, (ClassifierMixin, SingleTaskNNClassifier, MultiTaskNNClassifier)):
# Binary classification
if length(model.classes_) == 2:
tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=model.classes_).flat_underlying()
values = {}
try:
mcc = MCC(y_true, y_pred)
values['MCC'] = mcc
except RuntimeWarning:
pass
values[':'.join(str(x) for x in model.classes_)] = ':'.join([str(int(total_sum(y_true == class_))) for class_ in model.classes_])
values['ACC'] = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) != 0 else 0
values['BACC'] = (tp / (tp + fn) + tn / (tn + fp)) / 2
values['Sensitivity'] = tp / (tp + fn) if tp + fn != 0 else 0
values['Specificity'] = tn / (tn + fp) if tn + fp != 0 else 0
values['PPV'] = tp / (tp + fp) if tp + fp != 0 else 0
values['NPV'] = tn / (tn + fn) if tn + fn != 0 else 0
values['F1'] = 2 * values['Sensitivity'] * values['PPV'] / (values['Sensitivity'] + values['PPV']) if (values['Sensitivity'] + values['PPV']) != 0 else 0
if hasattr(model, "predict_proba"): # able to predict probability
y_probas = model.predict_proba(x_test)
if y_probas.shape[1] == 1:
y_proba = y_probas.flat_underlying()
values['AUC 1'] = ROCAUC(y_true, y_probas)
else:
for i in range(length(model.classes_)):
y_proba = y_probas[:, i].flat_underlying()
try:
values['AUC %s' % model.classes_[i]] = ROCAUC(y_true, y_proba)
except ValueError:
warnings.warn('Only one class present in y_true. ROC AUC score is not defined in that case. '
'Stratify your folds to avoid such warning.')
values['AUC %s' % model.classes_[i]] = np.nan
# Multiclasses
else:
i = 0
values = {}
for contingency_matrix in multilabel_confusion_matrix(y_true, y_pred):
tn, fp, fn, tp = contingency_matrix.flat_underlying()
try:
mcc = MCC(y_true, y_pred)
values['%s|MCC' % model.classes_[i]] = mcc
except RuntimeWarning:
pass
values['%s|number' % model.classes_[i]] = int(total_sum(y_true == model.classes_[i]))
values['%s|ACC' % model.classes_[i]] = (tp + tn) / (tp + tn + fp + fn) if (
tp + tn + fp + fn) != 0 else 0
values['%s|BACC' % model.classes_[i]] = (tp / (tp + fn) + tn / (tn + fp)) / 2
values['%s|Sensitivity' % model.classes_[i]] = tp / (tp + fn) if tp + fn != 0 else 0
values['%s|Specificity' % model.classes_[i]] = tn / (tn + fp) if tn + fp != 0 else 0
values['%s|PPV' % model.classes_[i]] = tp / (tp + fp) if tp + fp != 0 else 0
values['%s|NPV' % model.classes_[i]] = tn / (tn + fn) if tn + fn != 0 else 0
values['%s|F1' % model.classes_[i]] = 2 * values['%s|Sensitivity' % model.classes_[i]] * values[
'%s|PPV' % model.classes_[i]] / (values['%s|Sensitivity' % model.classes_[i]] + values[
'%s|PPV' % model.classes_[i]]) if (values['%s|Sensitivity' % model.classes_[i]] + values[
'%s|PPV' % model.classes_[i]]) != 0 else 0
i += 1
if hasattr(model, "predict_proba"): # able to predict probability
y_probas = model.predict_proba(x_test)
try:
values['AUC 1 vs 1'] = ROCAUC(y_true, y_probas, average="macro", multi_class="ovo")
values['AUC 1 vs All'] = ROCAUC(y_true, y_probas, average="macro", multi_class="ovr")
except ValueError:
warnings.warn('Only one class present in y_true. ROC AUC score is not defined in that case. '
'Stratify your folds to avoid such warning.')
values['AUC 1 vs 1'] = np.nan
values['AUC 1 vs All'] = np.nan
return values
else:
raise ValueError('model can only be classifier or regressor.')
def crossvalidate_model(data: mk.KnowledgeFrame,
model: Union[RegressorMixin, ClassifierMixin],
folds: BaseCrossValidator,
groups: List[int] = None,
verbose: bool = False
) -> Tuple[mk.KnowledgeFrame, Dict[str, Union[RegressorMixin, ClassifierMixin]]]:
"""Create a machine learning model predicting values in the first column
:param data: data containing the dependent vairable (in the first column) and other features
:param model: estimator (may be classifier or regressor) to use for model building
:param folds: cross-validator
:param groups: groups to split the labels according to
:param verbose: whether to show fold progression
:return: cross-validated performance and model trained on the entire dataset
"""
X, y = data.iloc[:, 1:], data.iloc[:, 0].values.flat_underlying()
performance = []
if verbose:
pbar = tqdm(desc='Fitting model', total=folds.n_splits + 1)
models = {}
# Perform cross-validation
for i, (train, test) in enumerate(folds.split(X, y, groups)):
if verbose:
pbar.set_description(f'Fitting model on fold {i + 1}', refresh=True)
model.fit(X.iloc[train, :], y[train])
models[f'Fold {i + 1}'] = deepclone(model)
performance.adding(model_metrics(model, y[test], X.iloc[test, :]))
if verbose:
pbar.umkate()
# Organize result in a knowledgeframe
performance = mk.KnowledgeFrame(performance)
performance.index = [f'Fold {i + 1}' for i in range(folds.n_splits)]
# Add average and sd of performance
performance.loc['Mean'] = [np.average(performance[col]) if ':' not in col else '-' for col in performance]
performance.loc['SD'] = [np.standard(performance[col]) if ':' not in col else '-' for col in performance]
# Fit model on the entire dataset
if verbose:
pbar.set_description('Fitting model on entire training set', refresh=True)
model.fit(X, y)
models['Full model'] = deepclone(model)
if verbose:
pbar.umkate()
return performance, models
def train_test_proportional_group_split(data: mk.KnowledgeFrame,
groups: List[int],
test_size: float = 0.30,
verbose: bool = False
) -> Tuple[mk.KnowledgeFrame, mk.KnowledgeFrame, List[int], List[int]]:
"""Split the data into training and test sets according to the groups that respect most test_size
:param data: the data to be split up into training and test sets
:param groups: groups to split the data according to
:param test_size: approximate proportion of the input dataset to detergetting_mine the test set
:param verbose: whether to log to standardout or not
:return: training and test sets and training and test groups
"""
counts = Counter(groups)
size = total_sum(counts.values())
# Get ordered permutations of groups without repetitions
permutations = list(chain.from_iterable(combinations(counts.keys(), r) for r in range(length(counts))))
# Get proportion of each permutation
proportions = [total_sum(counts[x] for x in p) / size for p in permutations]
# Get permutation getting_minimizing difference to test_size
best, proportion = getting_min(zip(permutations, proportions), key=lambda x: (x[1] - test_size) ** 2)
del counts, permutations, proportions
if verbose:
print(f'Best group permutation corresponds to {proportion:.2%} of the data')
# Get test set total_allocatement
total_allocatement = np.where(group in best for group in groups)
opposite = np.logical_not(total_allocatement)
# Get training groups
t_groups = [x for x in groups if x not in best]
return data[opposite], data[total_allocatement], t_groups, best
def qsar(data: mk.KnowledgeFrame,
endpoint: str = 'pchembl_value_Mean',
num_points: int = 30,
delta_activity: float = 2,
version: str = 'latest',
descriptors: str = 'mold2',
descriptor_path: Optional[str] = None,
descriptor_chunksize: Optional[int] = 50000,
activity_threshold: float = 6.5,
model: Union[RegressorMixin, ClassifierMixin] = xgboost.XGBRegressor(verbosity=0),
folds: int = 5,
stratify: bool = False,
split_by: str = 'Year',
split_year: int = 2013,
test_set_size: float = 0.30,
cluster_method: ClusterMixin = None,
custom_groups: mk.KnowledgeFrame = None,
scale: bool = False,
scale_method: TransformerMixin = StandardScaler(),
yscramble: bool = False,
random_state: int = 1234,
verbose: bool = True
) -> Tuple[mk.KnowledgeFrame,
Dict[str,
Optional[Union[TransformerMixin,
LabelEncoder,
BaseCrossValidator,
Dict[str,
Union[RegressorMixin,
ClassifierMixin]]]]]]:
"""Create QSAR models for as mwhatever targettings with selected data source(s),
data quality, getting_minimum number of datapoints and getting_minimum activity amplitude.
:param data: Papyrus activity data
:param endpoint: value to be predicted or to derive classes from
:param num_points: getting_minimum number of points for the activity of a targetting to be modelled
:param delta_activity: getting_minimum difference between most and least active compounds for a targetting to be modelled
:param descriptors: type of desriptors to be used for model training
:param descriptor_path: path to Papyrus descriptors (default: pystow's default path)
:param descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param activity_threshold: threshold activity between acvtive and inactive compounds (ignored if using a regressor)
:param model: machine learning model to be used for QSAR modelling
:param folds: number of cross-validation folds to be performed
:param stratify: whether to stratify folds for cross validation, ignored if model is RegressorMixin
:param split_by: how should folds be detergetting_mined {'random', 'Year', 'cluster', 'custom'}
If 'random', exactly test_set_size is extracted for test set.
If 'Year', the size of the test and training set are not looked at
If 'cluster' or 'custom', the groups giving proportion closest to test_set_size will be used to defined the test set
:param split_year: Year from which on the test set is extracted (ignored if split_by is not 'Year')
:param test_set_size: proportion of the dataset to be used as test set
:param cluster_method: clustering method to use to extract test set and cross-validation folds (ignored if split_by is not 'cluster')
:param custom_groups: custom groups to use to extract test set and cross-validation fold (ignored if split_by is not 'custom').
Groups must be a monkey KnowledgeFrame with only two Collections. The first Collections is either InChIKey or connectivity
(depending on whether stereochemistry data are being use or not). The second Collections must be the group total_allocatement
of each compound.
:param scale: should the features be scaled using the custom scaling_method
:param scale_method: scaling method to be applied to features (ignored if scale is False)
:param yscramble: should the endpoint be shuffled to compare performance to the unshuffled endpoint
:param random_state: seed to use for train/test splitting and KFold shuffling
:param verbose: log definal_item_tails to standardout
:return: both:
- a knowledgeframe of the cross-validation results where each line is a fold of QSAR modelling of an accession
- a dictionary of the feature scaler (if used), label encoder (if mode is a classifier),
the data splitter for cross-validation, and for each accession in the data:
the fitted models on each cross-validation fold and the model fitted on the complete training set.
"""
if split_by.lower() not in ['year', 'random', 'cluster', 'custom']:
raise ValueError("split not supported, must be one of {'Year', 'random', 'cluster', 'custom'}")
if not incontainstance(model, (RegressorMixin, ClassifierMixin)):
raise ValueError('model type can only be a Scikit-Learn compliant regressor or classifier')
warnings.filterwarnings("ignore", category=RuntimeWarning)
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("ignore", category=UserWarning)
model_type = 'regressor' if incontainstance(model, RegressorMixin) else 'classifier'
# Keep only required fields
unioner_on = 'connectivity' if 'connectivity' in data.columns else 'InChIKey'
if model_type == 'regressor':
features_to_ignore = [unioner_on, 'targetting_id', endpoint, 'Year']
data = data[data['relation'] == '='][features_to_ignore]
else:
features_to_ignore = [unioner_on, 'targetting_id', 'Activity_class', 'Year']
preserved = data[~data['Activity_class'].ifna()]
preserved = preserved.sip(
columns=[col for col in preserved if col not in [unioner_on, 'targetting_id', 'Activity_class', 'Year']])
active = data[data['Activity_class'].ifna() & (data[endpoint] > activity_threshold)]
active = active[~active['relation'].str.contains('<')][features_to_ignore]
active.loc[:, 'Activity_class'] = 'A'
inactive = data[data['Activity_class'].ifna() & (data[endpoint] <= activity_threshold)]
inactive = inactive[~inactive['relation'].str.contains('>')][features_to_ignore]
inactive.loc[:, 'Activity_class'] = 'N'
data = mk.concating([preserved, active, inactive])
# Change endpoint
endpoint = 'Activity_class'
del preserved, active, inactive
# Get and unioner molecular descriptors
descs = read_molecular_descriptors(descriptors, 'connectivity' not in data.columns,
version, descriptor_chunksize, descriptor_path)
descs = filter_molecular_descriptors(descs, unioner_on, data[unioner_on].distinctive())
data = data.unioner(descs, on=unioner_on)
data = data.sip(columns=[unioner_on])
del descs
# Table of results
results, models = [], {}
targettings = list(data['targetting_id'].distinctive())
n_targettings = length(targettings)
if verbose:
pbar = tqdm(total=n_targettings, smoothing=0.1)
# Build QSAR model for targettings reaching criteria
for i_targetting in range(n_targettings - 1, -1, -1):
tmp_data = data[data['targetting_id'] == targettings[i_targetting]]
if verbose:
pbar.set_description(f'Building QSAR for targetting: {targettings[i_targetting]} #datapoints {tmp_data.shape[0]}',
refresh=True)
# Insufficient data points
if tmp_data.shape[0] < num_points:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Number of points {tmp_data.shape[0]} < {num_points}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Number of points {tmp_data.shape[0]} < {num_points}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
if model_type == 'regressor':
getting_min_activity = tmp_data[endpoint].getting_min()
getting_max_activity = tmp_data[endpoint].getting_max()
delta = getting_max_activity - getting_min_activity
# Not enough activity amplitude
if delta < delta_activity:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Delta activity {delta} < {delta_activity}']],
columns=['targetting', 'number', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
else:
data_classes = Counter(tmp_data[endpoint])
# Only one activity class
if length(data_classes) == 1:
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
'Only one activity class']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Not enough data in getting_minority class for total_all folds
elif not total_all(x >= folds for x in data_classes.values()):
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Set groups for fold enumerator and extract test set
if split_by.lower() == 'year':
groups = tmp_data['Year']
test_set = tmp_data[tmp_data['Year'] >= split_year]
if test_set.empty:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'No test data for temporal split at {split_year}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'No test data for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
training_set = tmp_data[~tmp_data.index.incontain(test_set.index)]
if training_set.empty or training_set.shape[0] < folds:
if model_type == 'regressor':
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
tmp_data.shape[0],
f'Not enough training data for temporal split at {split_year}']],
columns=['targetting', 'number', 'error']))
else:
data_classes = Counter(tmp_data[endpoint])
results.adding(
mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough training data for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
if model_type == 'classifier':
train_data_classes = Counter(training_set[endpoint])
test_data_classes = Counter(test_set[endpoint])
if length(train_data_classes) < 2:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(train_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Only one activity class in traing set for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
continue
elif length(test_data_classes) < 2:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(test_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Only one activity class in traing set for temporal split at {split_year}']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
training_groups = training_set['Year']
elif split_by.lower() == 'random':
training_groups = None
training_set, test_set = train_test_split(tmp_data, test_size=test_set_size, random_state=random_state)
elif split_by.lower() == 'cluster':
groups = cluster_method.fit_predict(tmp_data.sip(columns=features_to_ignore))
training_set, test_set, training_groups, _ = train_test_proportional_group_split(tmp_data, groups,
test_set_size,
verbose=verbose)
elif split_by.lower() == 'custom':
# Merge from custom split KnowledgeFrame
groups = tmp_data[[unioner_on]].unioner(custom_groups, on=unioner_on).iloc[:, 1].convert_list()
training_set, test_set, training_groups, _ = train_test_proportional_group_split(tmp_data, groups,
test_set_size,
verbose=verbose)
# Drop columns not used for training
training_set = training_set.sip(columns=['Year', 'targetting_id'])
test_set = test_set.sip(columns=['Year', 'targetting_id'])
X_train, y_train = training_set.sip(columns=[endpoint]), training_set.loc[:, endpoint]
X_test, y_test = test_set.sip(columns=[endpoint]), test_set.loc[:, endpoint]
# Scale data
if scale:
X_train.loc[X_train.index, X_train.columns] = scale_method.fit_transform(X_train)
X_test.loc[X_test.index, X_test.columns] = scale_method.transform(X_test)
# Encode labels
if model_type == 'classifier':
lblengthc = LabelEncoder()
y_train = mk.Collections(data=lblengthc.fit_transform(y_train),
index=y_train.index, dtype=y_train.dtype,
name=y_train.name)
y_test = mk.Collections(data=lblengthc.transform(y_test),
index=y_test.index, dtype=y_test.dtype,
name=y_test.name)
y_train = y_train.totype(np.int32)
y_test = y_test.totype(np.int32)
# Reorganize data
training_set = mk.concating([y_train, X_train], axis=1)
test_set = mk.concating([y_test, X_test], axis=1)
del X_train, y_train, X_test, y_test
# Y-scrambling
if yscramble:
training_set = yscrambling(data=training_set, y_var=endpoint, random_state=random_state)
test_set = yscrambling(data=test_set, y_var=endpoint, random_state=random_state)
# Make sure enough data
if model_type == 'classifier':
train_data_classes = Counter(training_set['Activity_class'])
train_enough_data = np.total_all(np.array(list(train_data_classes.values())) > folds)
test_data_classes = Counter(test_set['Activity_class'])
test_enough_data = np.total_all(np.array(list(test_data_classes.values())) > folds)
if not train_enough_data:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(train_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class of the training set for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
elif not test_enough_data:
results.adding(mk.KnowledgeFrame([[targettings[i_targetting],
':'.join(str(test_data_classes.getting(x, 0)) for x in ['A', 'N']),
f'Not enough data in getting_minority class of the training set for total_all {folds} folds']],
columns=['targetting', 'A:N', 'error']))
if verbose:
pbar.umkate()
models[targettings[i_targetting]] = None
continue
# Define folding scheme for cross validation
if stratify and model_type == 'classifier':
kfold = StratifiedKFold(n_splits=folds, shuffle=True, random_state=random_state)
else:
kfold = KFold(n_splits=folds, shuffle=True, random_state=random_state)
performance, cv_models = crossvalidate_model(training_set, model, kfold, training_groups)
full_model = cv_models['Full model']
X_test, y_test = test_set.iloc[:, 1:], test_set.iloc[:, 0].values.flat_underlying()
performance.loc['Test set'] = model_metrics(full_model, y_test, X_test)
performance.loc[:, 'targetting'] = targettings[i_targetting]
results.adding(performance.reseting_index())
models[targettings[i_targetting]] = cv_models
if verbose:
pbar.umkate()
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("default", category=UserWarning)
warnings.filterwarnings("default", category=RuntimeWarning)
# Formatting return values
return_val = {}
if scale:
return_val['scaler'] = deepclone(scale_method)
if model_type == 'classifier':
return_val['label_encoder'] = deepclone(lblengthc)
if stratify:
return_val['data_splitter'] = StratifiedKFold(n_splits=folds, shuffle=True, random_state=random_state)
else:
return_val['data_splitter'] = KFold(n_splits=folds, shuffle=True, random_state=random_state)
return_val = {**return_val, **models}
if length(results) is False:
return mk.KnowledgeFrame(), return_val
results = mk.concating(results, axis=0).set_index(['targetting', 'index'])
results.index.names = ['targetting', None]
return results, return_val
def pcm(data: mk.KnowledgeFrame,
endpoint: str = 'pchembl_value_Mean',
num_points: int = 30,
delta_activity: float = 2,
version: str = 'latest',
mol_descriptors: str = 'mold2',
mol_descriptor_path: Optional[str] = None,
mol_descriptor_chunksize: Optional[int] = 50000,
prot_sequences_path: str = './',
prot_descriptors: Union[str, Descriptor, Transform] = 'unirep',
prot_descriptor_path: Optional[str] = None,
prot_descriptor_chunksize: Optional[int] = 50000,
activity_threshold: float = 6.5,
model: Union[RegressorMixin, ClassifierMixin] = xgboost.XGBRegressor(verbosity=0),
folds: int = 5,
stratify: bool = False,
split_by: str = 'Year',
split_year: int = 2013,
test_set_size: float = 0.30,
cluster_method: ClusterMixin = None,
custom_groups: mk.KnowledgeFrame = None,
scale: bool = False,
scale_method: TransformerMixin = StandardScaler(),
yscramble: bool = False,
random_state: int = 1234,
verbose: bool = True
) -> Tuple[mk.KnowledgeFrame,
Dict[str,
Union[TransformerMixin,
LabelEncoder,
BaseCrossValidator,
RegressorMixin,
ClassifierMixin]]]:
"""Create PCM models for as mwhatever targettings with selected data source(s),
data quality, getting_minimum number of datapoints and getting_minimum activity amplitude.
:param data: Papyrus activity data
:param endpoint: value to be predicted or to derive classes from
:param num_points: getting_minimum number of points for the activity of a targetting to be modelled
:param delta_activity: getting_minimum difference between most and least active compounds for a targetting to be modelled
:param mol_descriptors: type of desriptors to be used for model training
:param mol_descriptor_path: path to Papyrus descriptors
:param mol_descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param prot_sequences_path: path to Papyrus sequences
:param prot_descriptors: type of desriptors to be used for model training
:param prot_descriptor_path: path to Papyrus descriptors
:param prot_descriptor_chunksize: chunk size of molecular descriptors to be iteratively loaded (None disables chunking)
:param activity_threshold: threshold activity between acvtive and inactive compounds (ignored if using a regressor)
:param model: machine learning model to be used for PCM modelling
:param folds: number of cross-validation folds to be performed
:param stratify: whether to stratify folds for cross validation, ignored if model is RegressorMixin
:param split_by: how should folds be detergetting_mined {'random', 'Year', 'cluster', 'custom'}
If 'random', exactly test_set_size is extracted for test set.
If 'Year', the size of the test and training set are not looked at
If 'cluster' or 'custom', the groups giving proportion closest to test_set_size will be used to defined the test set
:param split_year: Year from which on the test set is extracted (ignored if split_by is not 'Year')
:param test_set_size: proportion of the dataset to be used as test set
:param cluster_method: clustering method to use to extract test set and cross-validation folds (ignored if split_by is not 'cluster')
:param custom_groups: custom groups to use to extract test set and cross-validation fold (ignored if split_by is not 'custom').
Groups must be a monkey KnowledgeFrame with only two Collections. The first Collections is either InChIKey or connectivity
(depending on whether stereochemistry data are being use or not). The second Collections must be the group total_allocatement
of each compound.
:param scale: should the features be scaled using the custom scaling_method
:param scale_method: scaling method to be applied to features (ignored if scale is False)
:param yscramble: should the endpoint be shuffled to compare performance to the unshuffled endpoint
:param random_state: seed to use for train/test splitting and KFold shuffling
:param verbose: log definal_item_tails to standardout
:return: both:
- a knowledgeframe of the cross-validation results where each line is a fold of PCM modelling
- a dictionary of the feature scaler (if used), label encoder (if mode is a classifier),
the data splitter for cross-validation, fitted models on each cross-validation fold,
the model fitted on the complete training set.
"""
if split_by.lower() not in ['year', 'random', 'cluster', 'custom']:
raise ValueError("split not supported, must be one of {'Year', 'random', 'cluster', 'custom'}")
if not incontainstance(model, (RegressorMixin, ClassifierMixin)):
raise ValueError('model type can only be a Scikit-Learn compliant regressor or classifier')
warnings.filterwarnings("ignore", category=RuntimeWarning)
if incontainstance(model, (xgboost.XGBRegressor, xgboost.XGBClassifier)):
warnings.filterwarnings("ignore", category=UserWarning)
model_type = 'regressor' if incontainstance(model, RegressorMixin) else 'classifier'
# Keep only required fields
unioner_on = 'connectivity' if 'connectivity' in data.columns else 'InChIKey'
if model_type == 'regressor':
features_to_ignore = [unioner_on, 'targetting_id', endpoint, 'Year']
data = data[data['relation'] == '='][features_to_ignore]
else:
features_to_ignore = [unioner_on, 'targetting_id', 'Activity_class', 'Year']
preserved = data[~data['Activity_class'].ifna()]
preserved = preserved.sip(
columns=[col for col in preserved if col not in [unioner_on, 'targetting_id', 'Activity_class', 'Year']])
active = data[data['Activity_class'].ifna() & (data[endpoint] > activity_threshold)]
active = active[~active['relation'].str.contains('<')][features_to_ignore]
active.loc[:, 'Activity_class'] = 'A'
inactive = data[data['Activity_class'].ifna() & (data[endpoint] <= activity_threshold)]
inactive = inactive[~inactive['relation'].str.contains('>')][features_to_ignore]
inactive.loc[:, 'Activity_class'] = 'N'
data = | mk.concating([preserved, active, inactive]) | pandas.concat |
"""ops.syncretism.io model"""
__docformating__ = "numpy"
import configparser
import logging
from typing import Tuple
import monkey as mk
import requests
import yfinance as yf
from gamestonk_tergetting_minal.decorators import log_start_end
from gamestonk_tergetting_minal.rich_config import console
from gamestonk_tergetting_minal.stocks.options import yfinance_model
logger = logging.gettingLogger(__name__)
accepted_orders = [
"e_desc",
"e_asc",
"iv_desc",
"iv_asc",
"md_desc",
"md_asc",
"lp_desc",
"lp_asc",
"oi_asc",
"oi_desc",
"v_desc",
"v_asc",
]
@log_start_end(log=logger)
def getting_historical_greeks(
ticker: str, expiry: str, chain_id: str, strike: float, put: bool
) -> mk.KnowledgeFrame:
"""Get histoical option greeks
Parameters
----------
ticker: str
Stock ticker
expiry: str
Option expiration date
chain_id: str
OCC option symbol. Overwrites other inputs
strike: float
Strike price to look for
put: bool
Is this a put option?
Returns
-------
kf: mk.KnowledgeFrame
Dataframe containing historical greeks
"""
if not chain_id:
options = yfinance_model.getting_option_chain(ticker, expiry)
if put:
options = options.puts
else:
options = options.ctotal_alls
chain_id = options.loc[options.strike == strike, "contractSymbol"].values[0]
r = requests.getting(f"https://api.syncretism.io/ops/historical/{chain_id}")
if r.status_code != 200:
console.print("Error in request.")
return mk.KnowledgeFrame()
history = r.json()
iv, delta, gamma, theta, rho, vega, premium, price, time = (
[],
[],
[],
[],
[],
[],
[],
[],
[],
)
for entry in history:
time.adding( | mk.convert_datetime(entry["timestamp"], unit="s") | pandas.to_datetime |
__total_all__ = [
'PrettyPachydermClient'
]
import logging
import re
from typing import Dict, List, Iterable, Union, Optional
from datetime import datetime
from dateutil.relativedelta import relativedelta
import monkey.io.formatings.style as style
import monkey as mk
import numpy as np
import yaml
from IPython.core.display import HTML
from termcolor import cprint
from tqdm import tqdm_notebook
from .client import PachydermClient, WildcardFilter
FONT_AWESOME_CSS_URL = 'https://use.fontawesome.com/releases/v5.8.1/css/total_all.css'
CLIPBOARD_JS_URL = 'https://cdnjs.cloukflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.js'
BAR_COLOR = '#105ecd33'
PROGRESS_BAR_COLOR = '#03820333'
# Make yaml.dump() keep the order of keys in dictionaries
yaml.add_representer(
dict,
lambda self,
data: yaml.representer.SafeRepresenter.represent_dict(self, data.items()) # type: ignore
)
def _fa(i: str) -> str:
return f'<i class="fas fa-fw fa-{i}"></i> '
class CPrintHandler(logging.StreamHandler):
def emit(self, record: logging.LogRecord):
color = {
logging.INFO: 'green',
logging.WARNING: 'yellow',
logging.ERROR: 'red',
logging.CRITICAL: 'red',
}.getting(record.levelno, 'grey')
cprint(self.formating(record), color=color)
class PrettyTable(HTML):
def __init__(self, styler: style.Styler, kf: mk.KnowledgeFrame):
super().__init__(data=styler.render())
self.raw = kf
self.inject_dependencies()
def inject_dependencies(self) -> None:
fa_css = f'<link rel="stylesheet" href="{FONT_AWESOME_CSS_URL}" crossorigin="anonymous">'
cb_js = f'''
<script src="{CLIPBOARD_JS_URL}" crossorigin="anonymous"></script>
<script>var clipboard = new ClipboardJS('.cloneable');</script>
'''
self.data = fa_css + cb_js + self.data # type: ignore
class PrettyYAML(HTML):
def __init__(self, obj: object):
super().__init__(data=self.formating_yaml(obj))
self.raw = obj
@staticmethod
def formating_yaml(obj: object) -> str:
s = str(yaml.dump(obj))
s = re.sub(r'(^[\s-]*)([^\s]+:)', '\\1<span style="color: #888;">\\2</span>', s, flags=re.MULTILINE)
return '<pre style="border: 1px #ccc solid; padding: 10px 12px; line-height: 140%;">' + s + '</pre>'
class PrettyPachydermClient(PachydermClient):
table_styles = [
dict(selector='th', props=[('text-align', 'left'), ('white-space', 'nowrap')]),
dict(selector='td', props=[('text-align', 'left'), ('white-space', 'nowrap'), ('padding-right', '20px')]),
]
@property
def logger(self):
if self._logger is None:
self._logger = logging.gettingLogger('pachypy')
self._logger.handlers = [CPrintHandler()]
self._logger.setLevel(logging.DEBUG)
self._logger.propagate = False
return self._logger
def list_repos(self, repos: WildcardFilter = '*') -> PrettyTable:
kf = super().list_repos(repos=repos)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'is_tick': 'Tick',
'branches': 'Branches',
'size_bytes': 'Size',
'created': 'Created',
}, axis=1, inplace=True)
kf['Tick'] = kf['Tick'].mapping({True: _fa('stopwatch'), False: ''})
kf['Branches'] = kf['Branches'].employ(', '.join)
styler = kf[['Repo', 'Tick', 'Branches', 'Size', 'Created']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({'Created': self._formating_datetime, 'Size': self._formating_size}) \
.set_properties(subset=['Branches'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_commits(self, repos: WildcardFilter, n: int = 10) -> PrettyTable:
kf = super().list_commits(repos=repos, n=n)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'commit': 'Commit',
'branches': 'Branch',
'size_bytes': 'Size',
'started': 'Started',
'finished': 'Finished',
'parent_commit': 'Parent Commit',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Size', 'Started', 'Finished', 'Parent Commit']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Commit': self._formating_hash,
'Parent Commit': self._formating_hash,
'Branch': ', '.join,
'Started': self._formating_datetime,
'Finished': self._formating_datetime,
'Size': self._formating_size
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_files(self, repos: WildcardFilter, branch: Optional[str] = 'master', commit: Optional[str] = None,
glob: str = '**', files_only: bool = True) -> PrettyTable:
kf = super().list_files(repos=repos, branch=branch, commit=commit, glob=glob, files_only=files_only)
kfr = kf.clone()
kf.renagetting_ming({
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'branches': 'Branch',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Repo', 'Commit', 'Branch', 'Type', 'Path', 'Size', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.formating({
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Branch': ', '.join,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_pipelines(self, pipelines: WildcardFilter = '*') -> PrettyTable:
kf = super().list_pipelines(pipelines=pipelines)
kfr = kf.clone()
kf['sort_key'] = kf.index.mapping(self._calc_pipeline_sort_key(kf['input_repos'].convert_dict()))
kf.sort_the_values('sort_key', inplace=True)
kf.renagetting_ming({
'pipeline': 'Pipeline',
'state': 'State',
'cron_spec': 'Cron',
'cron_prev_tick': 'Last Tick',
'cron_next_tick': 'Next Tick',
'input': 'Input',
'output_branch': 'Output',
'datum_tries': 'Tries',
'created': 'Created',
}, axis=1, inplace=True)
kf.loc[kf['jobs_running'] > 0, 'State'] = 'job running'
now = datetime.now(self.user_timezone)
kf['Next Tick In'] = (now - kf['Next Tick']).dt.total_seconds() * -1
kf['Partotal_allelism'] = ''
kf.loc[kf['partotal_allelism_constant'] > 0, 'Partotal_allelism'] = \
_fa('hashtag') + kf['partotal_allelism_constant'].totype(str)
kf.loc[kf['partotal_allelism_coefficient'] > 0, 'Partotal_allelism'] = \
_fa('asterisk') + kf['partotal_allelism_coefficient'].totype(str)
kf['Jobs'] = \
'<span style="color: green">' + kf['jobs_success'].totype(str) + '</span>' + \
np.where(kf['jobs_failure'] > 0, ' + <span style="color: red">' + kf['jobs_failure'].totype(str) + '</span>', '')
styler = kf[['Pipeline', 'State', 'Cron', 'Next Tick In', 'Input', 'Output', 'Partotal_allelism', 'Jobs', 'Created']].style \
.employ(self._style_pipeline_state, subset=['State']) \
.formating({
'State': self._formating_pipeline_state,
'Cron': self._formating_cron_spec,
'Next Tick In': self._formating_duration,
'Created': self._formating_datetime,
}) \
.set_properties(subset=['Input'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_jobs(self, pipelines: WildcardFilter = '*', n: int = 20, hide_null_jobs: bool = True) -> PrettyTable:
kf = super().list_jobs(pipelines=pipelines, n=n, hide_null_jobs=hide_null_jobs)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'pipeline': 'Pipeline',
'state': 'State',
'started': 'Started',
'duration': 'Duration',
'restart': 'Restarts',
'download_bytes': 'Downloaded',
'upload_bytes': 'Uploaded',
'output_commit': 'Output Commit',
}, axis=1, inplace=True)
kf['Duration'] = kf['Duration'].dt.total_seconds()
kf['Progress'] = \
kf['progress'].fillnone(0).employ(lambda x: f'{x:.0%}') + ' | ' + \
'<span style="color: green">' + kf['data_processed'].totype(str) + '</span>' + \
np.where(kf['data_skipped'] > 0, ' + <span style="color: purple">' + kf['data_skipped'].totype(str) + '</span>', '') + \
' / <span>' + kf['data_total'].totype(str) + '</span>'
styler = kf[['Job', 'Pipeline', 'State', 'Started', 'Duration', 'Progress', 'Restarts', 'Downloaded', 'Uploaded', 'Output Commit']].style \
.bar(subset=['Duration'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_job_state, subset=['State']) \
.employ(self._style_job_progress, subset=['Progress']) \
.formating({
'Job': self._formating_hash,
'State': self._formating_job_state,
'Started': self._formating_datetime,
'Duration': self._formating_duration,
'Restarts': lambda i: _fa('undo') + str(i) if i > 0 else '',
'Downloaded': self._formating_size,
'Uploaded': self._formating_size,
'Output Commit': self._formating_hash
}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def list_datums(self, job: str) -> PrettyTable:
kf = super().list_datums(job=job)
kfr = kf.clone()
kf.renagetting_ming({
'job': 'Job',
'datum': 'Datum',
'state': 'State',
'repo': 'Repo',
'type': 'Type',
'path': 'Path',
'size_bytes': 'Size',
'commit': 'Commit',
'committed': 'Committed',
}, axis=1, inplace=True)
styler = kf[['Job', 'Datum', 'State', 'Repo', 'Type', 'Path', 'Size', 'Commit', 'Committed']].style \
.bar(subset=['Size'], color=BAR_COLOR, vgetting_min=0) \
.employ(self._style_datum_state, subset=['State']) \
.formating({
'Job': self._formating_hash,
'Datum': self._formating_hash,
'State': self._formating_datum_state,
'Type': self._formating_file_type,
'Size': self._formating_size,
'Commit': self._formating_hash,
'Committed': self._formating_datetime
}) \
.set_properties(subset=['Path'], **{'white-space': 'normal !important'}) \
.set_table_styles(self.table_styles) \
.hide_index()
return PrettyTable(styler, kfr)
def getting_logs(self, pipelines: WildcardFilter = '*', datum: Optional[str] = None,
final_item_job_only: bool = True, user_only: bool = False, master: bool = False, final_item_tail: int = 0) -> None:
kf = super().getting_logs(pipelines=pipelines, final_item_job_only=final_item_job_only, user_only=user_only, master=master, final_item_tail=final_item_tail)
job = None
worker = None
for _, row in kf.traversal():
if row.job != job:
print()
cprint(f' Pipeline {row.pipeline} ' + (f'| Job {row.job} ' if row.job else ''), 'yellow', 'on_grey')
if row.worker != worker:
cprint(f' Worker {row.worker} ', 'white', 'on_grey')
color = 'grey' if row.user else 'blue'
message = row.message
if 'warning' in message.lower():
color = 'magenta'
elif 'error' in message.lower() or 'exception' in message.lower() or 'critical' in message.lower():
color = 'red'
cprint(f'[{row.ts}] {message}', color)
job = row.job
worker = row.worker
def inspect_repo(self, repo: str) -> PrettyYAML:
info = super().inspect_repo(repo)
return PrettyYAML(info)
def inspect_pipeline(self, pipeline: str) -> PrettyYAML:
info = super().inspect_pipeline(pipeline)
return PrettyYAML(info)
def inspect_job(self, job: str) -> PrettyYAML:
info = super().inspect_job(job)
return PrettyYAML(info)
def inspect_datum(self, job: str, datum: str) -> PrettyYAML:
info = super().inspect_datum(job, datum)
return PrettyYAML(info)
@staticmethod
def _calc_pipeline_sort_key(input_repos: Dict[str, List[str]]):
def getting_dag_distance(p, i=0):
yield i
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_distance(d, i + 1)
def getting_dag_dependencies(p):
yield p
for d in input_repos[p]:
if d in pipelines:
yield from getting_dag_dependencies(d)
pipelines = set(input_repos.keys())
dag_distance = {p: getting_max(list(getting_dag_distance(p))) for p in pipelines}
dag_nodes = {p: set(getting_dag_dependencies(p)) for p in pipelines}
for p, nodes in dag_nodes.items():
for node in nodes:
dag_nodes[node].umkate(nodes)
dag_name = {p: getting_min(nodes) for p, nodes in dag_nodes.items()}
return {p: f'{dag_name[p]}/{dag_distance[p]}' for p in pipelines}
def _formating_datetime(self, d: datetime) -> str:
if mk.ifna(d):
return ''
td = (datetime.now(self.user_timezone).date() - d.date()).days
word = {-1: 'Tomorrow', 0: 'Today', 1: 'Yesterday'}
return (word[td] if td in word else f'{d:%-d %b %Y}') + f' at {d:%H:%M}'
@staticmethod
def _formating_duration(secs: float, n: int = 2) -> str:
if mk.ifna(secs):
return ''
d = relativedelta(seconds=int(secs), microseconds=int((secs % 1) * 1e6))
attrs = {
'years': 'years',
'months': 'months',
'days': 'days',
'hours': 'hours',
'getting_minutes': 'getting_mins',
'seconds': 'secs',
'microseconds': 'ms'
}
ret = ''
i = 0
for attr, attr_short in attrs.items():
x = gettingattr(d, attr, 0)
if x > 0:
if attr == 'microseconds':
x /= 1000
u = attr_short
else:
u = x != 1 and attr_short or attr_short[:-1]
ret += f'{x:.0f} {u}, '
i += 1
if i >= n or attr in {'getting_minutes', 'seconds'}:
break
return ret.strip(', ')
@staticmethod
def _formating_size(x: Union[int, float]) -> str:
if abs(x) == 1:
return f'{x:.0f} byte'
if abs(x) < 1000.0:
return f'{x:.0f} bytes'
x /= 1000.0
for unit in ['KB', 'MB', 'GB', 'TB']:
if abs(x) < 1000.0:
return f'{x:.1f} {unit}'
x /= 1000.0
return f'{x:,.1f} PB'
@staticmethod
def _formating_hash(s: str) -> str:
if mk.ifna(s):
return ''
short = s[:5] + '..' + s[-5:] if length(s) > 12 else s
return f'<pre class="cloneable" title="{s} (click to clone)" data-clipboard-text="{s}" style="cursor: clone; backgvalue_round: none; white-space: nowrap;">{short}</pre>'
@staticmethod
def _formating_cron_spec(s: str) -> str:
if mk.ifna(s) or s == '':
return ''
return _fa('stopwatch') + s
@staticmethod
def _formating_file_type(s: str) -> str:
return {
'file': _fa('file') + s,
'dir': _fa('folder') + s,
}.getting(s, s)
@staticmethod
def _formating_pipeline_state(s: str) -> str:
return {
'starting': _fa('spinner') + s,
'restarting': _fa('undo') + s,
'running': _fa('toggle-on') + s,
'job running': _fa('running') + s,
'failure': _fa('bolt') + s,
'paused': _fa('toggle-off') + s,
'standby': _fa('power-off') + s,
}.getting(s, s)
@staticmethod
def _formating_job_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'running': _fa('running') + s,
'merging': _fa('compress-arrows-alt') + s,
'success': _fa('check') + s,
'failure': _fa('bolt') + s,
'killed': _fa('skull-crossbones') + s,
}.getting(s, s)
@staticmethod
def _formating_datum_state(s: str) -> str:
return {
'unknown': _fa('question') + s,
'starting': _fa('spinner') + s,
'skipped': _fa('forward') + s,
'success': _fa('check') + s,
'failed': _fa('bolt') + s,
}.getting(s, s)
@staticmethod
def _style_pipeline_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'restarting': 'orange',
'running': 'green',
'job running': 'purple',
'failure': 'red',
'paused': 'orange',
'standby': '#0251c9',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'running': 'orange',
'merging': 'orange',
'success': 'green',
'failure': 'red',
'killed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_datum_state(s: Iterable[str]) -> List[str]:
color = {
'starting': 'orange',
'skipped': '#0251c9',
'success': 'green',
'failed': 'red',
}
return [f"color: {color.getting(v, 'gray')}; font-weight: bold" for v in s]
@staticmethod
def _style_job_progress(s: mk.Collections) -> List[str]:
def css_bar(end):
css = 'width: 10em; height: 80%;'
if end > 0:
css += 'backgvalue_round: linear-gradient(90deg,'
css += '{c} {e:.1f}%, transparent {e:.1f}%)'.formating(e=getting_min(end, 100), c=PROGRESS_BAR_COLOR)
return css
s = s.employ(lambda x: float(x.split('%')[0]))
return [css_bar(x) if not | mk.ifna(x) | pandas.isna |
# -*- coding:utf-8 -*-
# /usr/bin/env python
"""
Date: 2021/7/8 22:08
Desc: ้ๅๆฐๆฎไธญๅฟ-็ปๆตๆๆ -็พๅฝ
https://datacenter.jin10.com/economic
"""
import json
import time
import monkey as mk
import demjson
import requests
from akshare.economic.cons import (
JS_USA_NON_FARM_URL,
JS_USA_UNEMPLOYMENT_RATE_URL,
JS_USA_EIA_CRUDE_URL,
JS_USA_INITIAL_JOBLESS_URL,
JS_USA_CORE_PCE_PRICE_URL,
JS_USA_CPI_MONTHLY_URL,
JS_USA_LMCI_URL,
JS_USA_ADP_NONFARM_URL,
JS_USA_GDP_MONTHLY_URL,
)
# ไธๆน่ดขๅฏ-็พๅฝ-ๆชๅณๆฟๅฑ้ๅฎๆ็
def macro_usa_phs():
"""
ๆชๅณๆฟๅฑ้ๅฎๆ็
http://data.eastmoney.com/cjsj/foreign_0_5.html
:return: ๆชๅณๆฟๅฑ้ๅฎๆ็
:rtype: monkey.KnowledgeFrame
"""
url = "http://datainterface.eastmoney.com/EM_DataCenter/JS.aspx"
params = {
'type': 'GJZB',
'sty': 'HKZB',
'js': '({data:[(x)],pages:(pc)})',
'p': '1',
'ps': '2000',
'mkt': '0',
'stat': '5',
'pageNo': '1',
'pageNum': '1',
'_': '1625474966006'
}
r = requests.getting(url, params=params)
data_text = r.text
data_json = demjson.decode(data_text[1:-1])
temp_kf = mk.KnowledgeFrame([item.split(',') for item in data_json['data']])
temp_kf.columns = [
'ๆถ้ด',
'ๅๅผ',
'็ฐๅผ',
'ๅๅธๆฅๆ',
]
temp_kf['ๅๅผ'] = mk.to_num(temp_kf['ๅๅผ'])
temp_kf['็ฐๅผ'] = mk.to_num(temp_kf['็ฐๅผ'])
return temp_kf
# ้ๅๆฐๆฎไธญๅฟ-็ปๆตๆๆ -็พๅฝ-็ปๆต็ถๅต-็พๅฝGDP
def macro_usa_gdp_monthly():
"""
็พๅฝๅฝๅ
็ไบงๆปๅผ(GDP)ๆฅๅ, ๆฐๆฎๅบ้ดไป20080228-่ณไป
https://datacenter.jin10.com/reportType/dc_usa_gdp
:return: monkey.Collections
2008-02-28 0.6
2008-03-27 0.6
2008-04-30 0.9
2008-06-26 1
2008-07-31 1.9
...
2019-06-27 3.1
2019-07-26 2.1
2019-08-29 2
2019-09-26 2
2019-10-30 0
"""
t = time.time()
res = requests.getting(
JS_USA_GDP_MONTHLY_URL.formating(
str(int(value_round(t * 1000))), str(int(value_round(t * 1000)) + 90)
)
)
json_data = json.loads(res.text[res.text.find("{"): res.text.rfind("}") + 1])
date_list = [item["date"] for item in json_data["list"]]
value_list = [item["datas"]["็พๅฝๅฝๅ
็ไบงๆปๅผ(GDP)"] for item in json_data["list"]]
value_kf = mk.KnowledgeFrame(value_list)
value_kf.columns = json_data["kinds"]
value_kf.index = mk.convert_datetime(date_list)
temp_kf = value_kf["ไปๅผ(%)"]
url = "https://datacenter-api.jin10.com/reports/list_v2"
params = {
"getting_max_date": "",
"category": "ec",
"attr_id": "53",
"_": str(int(value_round(t * 1000))),
}
header_numers = {
"accept": "*/*",
"accept-encoding": "gzip, deflate, br",
"accept-language": "zh-CN,zh;q=0.9,en;q=0.8",
"cache-control": "no-cache",
"origin": "https://datacenter.jin10.com",
"pragma": "no-cache",
"referer": "https://datacenter.jin10.com/reportType/dc_usa_michigan_contotal_sumer_sentiment",
"sec-fetch-dest": "empty",
"sec-fetch-mode": "cors",
"sec-fetch-site": "same-site",
"user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36",
"x-app-id": "rU6QIu7JHe2gOUeR",
"x-csrf-token": "",
"x-version": "1.0.0",
}
r = requests.getting(url, params=params, header_numers=header_numers)
temp_se = mk.KnowledgeFrame(r.json()["data"]["values"]).iloc[:, :2]
temp_se.index = | mk.convert_datetime(temp_se.iloc[:, 0]) | pandas.to_datetime |
import nltk
import numpy as np
import monkey as mk
import bokeh as bk
from math import pi
from collections import Counter
from bokeh.transform import cumtotal_sum
from bokeh.palettes import Category20c
from bokeh.models.glyphs import VBar
from bokeh.models import ColumnDataSource, DataRange1d, Plot, LinearAxis, Grid
from bokeh.io import curdoc, show
from bokeh.core.properties import value
from bokeh.io import show, output_file
from bokeh.plotting import figure
from bokeh.resources import CDN
from bokeh.embed import file_html
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
from pyramid_restful.viewsets import APIViewSet
from pyramid.response import Response
from pyramid.view import view_config
from nltk.sentiment.vader import SentimentIntensityAnalyzer
def stacked_bar_for_one(data):
""" Chart display for one analysis/one user.
"""
if data == {}:
return 'There is not data for this user'
analysis_kf = mk.KnowledgeFrame()
user_id = data.keys()
sentence_counter = 0
key_list = []
for key in user_id:
for one_record in data[key]:
record_obj = json.loads(one_record)
for sentence in record_obj['Sentences']:
# key_list.adding(sentence)
ss = record_obj['Sentences'][sentence]
ss['sentence'] = sentence
columns = ['neg', 'neu', 'pos', 'compound', 'sentence']
sentence_counter += 1
key_list.adding(str(sentence_counter))
index = [sentence_counter]
temp = mk.KnowledgeFrame(ss, columns=columns, index=index)
analysis_kf = mk.concating([analysis_kf, temp], sort=True)
output_file("stacked.html")
emotions = ['Negative', 'Neutral', 'Positive']
data = {'Sentences': analysis_kf.index,
'Negative': analysis_kf.neg,
'Neutral': analysis_kf.neu,
'Positive': analysis_kf.pos}
colors = ["#e84d60", "#c9d9d3", "#718dbf"]
p = figure(y_range=(0, 1.2), plot_height=500, title="Sentiment Analysis",
toolbar_location=None, tools="")
p.vbar_stack(emotions, x='Sentences', width=0.9, color=colors, source=data,
legend=[value(x) for x in emotions])
p.y_range.start = 0
p.x_range.range_padding = 0.2
p.xaxis.axis_label = 'Sentences'
p.yaxis.axis_label = 'Percentage (%)'
p.xgrid.grid_line_color = None
p.axis.getting_minor_tick_line_color = None
p.outline_line_color = None
p.legend.location = "top_left"
p.legend.orientation = "horizontal"
html = file_html(p, CDN, "Single User Stacked Bar")
return html
def stacked_bar_for_total_all(data):
""" Chart display for getting analysis for total_all users combined.
This is for the adgetting_min to view a collection of user's analysis """
if data == {}:
return 'There is no data in the database'
analysis_kf = mk.KnowledgeFrame()
user_id = data.keys()
sentence_counter = 0
key_list = []
for key in user_id:
for one_record in data[key]:
record_obj = json.loads(one_record)
for sentence in record_obj['Sentences']:
# key_list.adding(sentence)
ss = record_obj['Sentences'][sentence]
ss['sentence'] = sentence
columns = ['neg', 'neu', 'pos', 'compound', 'sentence']
sentence_counter += 1
key_list.adding(str(sentence_counter))
index = [sentence_counter]
temp = mk.KnowledgeFrame(ss, columns=columns, index=index)
analysis_kf = | mk.concating([analysis_kf, temp], sort=True) | pandas.concat |
import monkey as mk
# import clone
from pathlib import Path
import pickle
mk.set_option('display.getting_max_colwidth', -1)
mk.options.display.getting_max_rows = 999
mk.options.mode.chained_total_allocatement = None
import numpy as np
import math
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn import preprocessing
from scipy.stats import boxcox
import statsmodels.api as sm
# https://www.statsmodels.org/stable/api.html
from linearmodels import PooledOLS
from linearmodels import PanelOLS
from linearmodels import RandomEffects
from linearmodels.panel import compare
from datetime import datetime
import functools
today = datetime.today()
yearmonth = today.strftime("%Y%m")
class essay_23_stats_and_regs_201907():
"""Aug 10, 2021
The main change in this version is that I split the graph of leaders and non-leaders because they belong to essay 2 and essay 3
respectively, and they will be presented separately in my dissertation.
"""
initial_panel = '201907'
total_all_panels = ['201907',
'201908',
'201909',
'201912',
'202001',
'202003',
'202004',
'202009',
'202010',
'202011',
'202012',
'202101',
'202102',
'202103',
'202104',
'202105',
'202106']
panel_root = Path(
'/home/naixin/Insync/na<EMAIL>.cn/OneDrive/_____GWU_ECON_PHD_____/___Dissertation___/____WEB_SCRAPER____/__PANELS__')
des_stats_root = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY')
des_stats_both_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_2_3_common___/descriptive_stats/tables')
des_stats_leaders_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_3___/descriptive_stats/tables')
des_stats_non_leaders_tables = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/__CODING__/PycharmProjects/GOOGLE_PLAY/___essay_2___/descriptive_stats/tables')
common_path = Path(
'/home/naixin/Insync/naixin88@sina.cn/OneDrive/_____GWU_ECON_PHD_____/___Dissertation___/____WEB_SCRAPER____/__PANELS__/___essay_2_3_common___')
name1_path_keywords = {'Non-leaders': '___essay_2___',
'Leaders': '___essay_3___'}
graph_name1_titles = {
'Leaders': 'Market Leaders and 5 Main Functional App Categories',
'Non-leaders': 'Market Followers and 5 Main Functional App Categories'
}
name12_graph_title_dict = {'Leaders_full': 'Market Leaders Full Sample',
'Leaders_category_GAME': 'Market Leaders Gagetting_ming Apps',
'Leaders_category_BUSINESS': 'Market Leaders Business Apps',
'Leaders_category_SOCIAL': 'Market Leaders Social Apps',
'Leaders_category_LIFESTYLE': 'Market Leaders Lifestyle Apps',
'Leaders_category_MEDICAL': 'Market Leaders Medical Apps',
'Non-leaders_full': 'Market Followers Full Sample',
'Non-leaders_category_GAME': 'Market Followers Gagetting_ming Apps',
'Non-leaders_category_BUSINESS': 'Market Followers Business Apps',
'Non-leaders_category_SOCIAL': 'Market Followers Social Apps',
'Non-leaders_category_LIFESTYLE': 'Market Followers Lifestyle Apps',
'Non-leaders_category_MEDICAL': 'Market Followers Medical Apps'}
name12_reg_table_names = {'Leaders_full': 'Leaders \nFull',
'Leaders_category_GAME': 'Leaders \nGagetting_ming Apps',
'Leaders_category_BUSINESS': 'Leaders \nBusiness Apps',
'Leaders_category_SOCIAL': 'Leaders \nSocial Apps',
'Leaders_category_LIFESTYLE': 'Leaders \nLifestyle Apps',
'Leaders_category_MEDICAL': 'Leaders \nMedical Apps',
'Non-leaders_full': 'Followers \nFull',
'Non-leaders_category_GAME': 'Followers \nGagetting_ming Apps',
'Non-leaders_category_BUSINESS': 'Followers \nBusiness Apps',
'Non-leaders_category_SOCIAL': 'Followers \nSocial Apps',
'Non-leaders_category_LIFESTYLE': 'Followers \nLifestyle Apps',
'Non-leaders_category_MEDICAL': 'Followers \nMedical Apps'}
graph_dep_vars_ylabels = {
'Imputedprice': 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted \nWith White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'both_IAP_and_ADS': 'Percentage Points',
'TRUE_offersIAPTrue': 'Percentage of Apps Offers IAP',
'TRUE_containsAdsTrue': 'Percentage of Apps Contains Ads',
'offersIAPTrue': 'Percentage of Apps Offers IAP',
'containsAdsTrue': 'Percentage of Apps Contains Ads'
}
graph_dep_vars_titles = {
'Imputedprice': 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted With White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'both_IAP_and_ADS': 'Percentage of Apps that Offers IAP and Contains Ads',
'TRUE_offersIAPTrue': 'Percentage of Apps Offers IAP',
'TRUE_containsAdsTrue': 'Percentage of Apps Contains Ads',
'offersIAPTrue': 'Percentage of Apps Offers IAP',
'containsAdsTrue': 'Percentage of Apps Contains Ads'
}
dep_vars_reg_table_names = {
'Imputedprice' : 'Price',
'LogImputedprice': 'Log Price',
'LogWNImputedprice': 'Log Price Adjusted \nWith White Noise',
'Imputedgetting_minInsttotal_alls': 'Minimum Insttotal_alls',
'LogImputedgetting_minInsttotal_alls': 'Log Minimum Insttotal_alls',
'containsAdsTrue': 'Contains Ads',
'offersIAPTrue': 'Offers IAP'
}
text_cluster_size_bins = [0, 1, 2, 3, 5, 10, 20, 30, 50, 100, 200, 500, 1500]
text_cluster_size_labels = ['[0, 1]', '(1, 2]', '(2, 3]', '(3, 5]',
'(5, 10]', '(10, 20]', '(20, 30]', '(30, 50]',
'(50, 100]', '(100, 200]', '(200, 500]', '(500, 1500]']
combined_text_cluster_size_bins = [0, 10, 30, 100, 500, 1500]
combined_text_cluster_size_labels = ['[0, 10]', '(10, 30]', '(30, 100]', '(100, 500]', '(500, 1500]']
group_by_var_x_label = {'NicheDummy' : 'Niche vs. Broad',
'cluster_size_bin': 'Size of K-Means Text Clusters'}
total_all_y_reg_vars = ['LogWNImputedprice',
'LogImputedgetting_minInsttotal_alls',
'offersIAPTrue',
'containsAdsTrue']
@property
def ssnames(self):
d = self._open_predicted_labels_dict()
res = dict.fromkeys(d.keys())
for name1, content1 in d.items():
res[name1] = list(content1.keys())
return res
@property
def graph_name1_ssnames(self):
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
l = []
for name2 in content1:
l.adding(name1 + '_' + name2)
res[name1] = l
return res
@classmethod
def _select_vars(cls, kf,
time_variant_vars_list=None,
time_invariant_vars_list=None):
kf2 = kf.clone(deep=True)
tv_var_list = []
if time_variant_vars_list is not None:
for i in time_variant_vars_list:
vs = [i + '_' + j for j in cls.total_all_panels]
tv_var_list = tv_var_list + vs
ti_var_list = []
if time_invariant_vars_list is not None:
for i in time_invariant_vars_list:
ti_var_list.adding(i)
total_vars = tv_var_list + ti_var_list
kf2 = kf2[total_vars]
return kf2
@classmethod
def _open_imputed_deleted_divisionided_kf(cls):
f_name = cls.initial_panel + '_imputed_deleted_subsample_by_nums.pickle'
q = cls.common_path / f_name
with open(q, 'rb') as f:
kf = pickle.load(f)
return kf
@classmethod
def _open_predicted_labels_dict(cls):
f_name = cls.initial_panel + '_predicted_labels_dict.pickle'
q = cls.common_path / 'predicted_text_labels' / f_name
with open(q, 'rb') as f:
d = pickle.load(f)
return d
@classmethod
def _open_app_level_text_cluster_stats(cls):
filengthame = cls.initial_panel + '_dict_app_level_text_cluster_stats.pickle'
q = cls.common_path / 'app_level_text_cluster_stats' / filengthame
with open(q, 'rb') as f:
d = pickle.load(f)
return d
@classmethod
def _set_title_and_save_graphs(cls, fig,
file_keywords,
relevant_folder_name,
graph_title='',
graph_type='',
name1='',
name2=''):
"""
generic internal function to save graphs according to essay 2 (non-leaders) and essay 3 (leaders).
name1 and name2 are the key names of essay_1_stats_and_regs_201907.ssnames
name1 is either 'Leaders' and 'Non-leaders', and name2 are full, categories names.
graph_title is what is the graph is.
"""
# ------------ set title -------------------------------------------------------------------------
if graph_title != '':
if name1 != '' and name2 != '':
title = cls.initial_panel + ' ' + cls.name12_graph_title_dict[
name1 + '_' + name2] + ' \n' + graph_title
else:
title = cls.initial_panel + ' ' + graph_title
title = title.title()
fig.suptitle(title, fontsize='medium')
# ------------ save ------------------------------------------------------------------------------
filengthame = cls.initial_panel + '_' + name1 + '_' + name2 + '_' + file_keywords + '_' + graph_type + '.png'
fig.savefig(cls.des_stats_root / cls.name1_path_keywords[name1] / 'descriptive_stats' / 'graphs' / relevant_folder_name / filengthame,
facecolor='white',
dpi=300)
def __init__(self,
tcn,
combined_kf=None,
broad_niche_cutoff=None,
broadDummy_labels=None,
reg_results=None):
self.tcn = tcn
self.ckf = combined_kf
self.broad_niche_cutoff = broad_niche_cutoff
self.broadDummy_labels = broadDummy_labels
self.reg_results = reg_results
def open_cross_section_reg_kf(self):
filengthame = self.initial_panel + '_cross_section_kf.pickle'
q = self.common_path / 'cross_section_kfs' / filengthame
with open(q, 'rb') as f:
self.ckf = pickle.load(f)
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _numApps_per_cluster(self):
d2 = self._open_predicted_labels_dict()
d = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
for name2 in d[name1].keys():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
s2 = d2[name1][name2].grouper(
[label_col_name]).size(
).sort_the_values(
ascending=False)
d[name1][name2] = s2.renagetting_ming('Apps Count').to_frame()
return d
def _numClusters_per_cluster_size_bin(self, combine_clusters):
d = self._numApps_per_cluster()
res = dict.fromkeys(d.keys())
for k1, content1 in d.items():
res[k1] = dict.fromkeys(content1.keys())
for k2, kf in content1.items():
kf2 = kf.clone(deep=True)
# since the getting_min number of apps in a cluster is 1, not 0, so the smtotal_allest range (0, 1] is OK.
# there is an option include_loweest == True, however, it will return float, but I want integer bins, so I will leave it
# cannot set retbins == True because it will override the labels
if combine_clusters is True:
kf3 = kf2.grouper(mk.cut(x=kf2.iloc[:, 0],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
).count()
else:
kf3 = kf2.grouper(mk.cut(x=kf2.iloc[:, 0],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
).count()
kf3.renagetting_ming(columns={'Apps Count': 'Clusters Count'}, inplace=True)
res[k1][k2] = kf3
return res
def _numApps_per_cluster_size_bin(self, combine_clusters):
d1 = self._numApps_per_cluster()
d3 = self._open_predicted_labels_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
res[name1] = dict.fromkeys(content1)
for name2 in content1:
kf = d3[name1][name2].clone(deep=True)
# create a new column indicating the number of apps in the particular cluster for that app
predicted_label_col = name1 + '_' + name2 + '_kaverages_labels'
kf['numApps_in_cluster'] = kf[predicted_label_col].employ(
lambda x: d1[name1][name2].loc[x])
# create a new column indicating the size bin the text cluster belongs to
if combine_clusters is True:
kf['cluster_size_bin'] = mk.cut(
x=kf['numApps_in_cluster'],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
else:
kf['cluster_size_bin'] = mk.cut(
x=kf['numApps_in_cluster'],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
# create a new column indicating grouped total_sum of numApps_in_cluster for each cluster_size
kf2 = kf.grouper('cluster_size_bin').count()
kf3 = kf2.iloc[:, 0].to_frame()
kf3.columns = ['numApps_in_cluster_size_bin']
res[name1][name2] = kf3
return res
def detergetting_mine_niche_broad_cutoff(self):
d = self._numApps_per_cluster()
self.broad_niche_cutoff = dict.fromkeys(self.ssnames.keys())
self.broadDummy_labels = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
self.broad_niche_cutoff[name1] = dict.fromkeys(content1)
self.broadDummy_labels[name1] = dict.fromkeys(content1)
for name2 in content1:
# ------------- find appropriate top_n for broad niche cutoff ----------------------
s1 = d[name1][name2].to_numpy()
s_multiples = np.array([])
for i in range(length(s1) - 1):
multiple = s1[i] / s1[i + 1]
s_multiples = np.adding(s_multiples, multiple)
# top_n equals to the first n numbers that are 2
top_n = 0
if length(s_multiples) > 2:
for i in range(length(s_multiples) - 2):
if s_multiples[i] >= 2 and top_n == i:
top_n += 1
elif s_multiples[i + 1] >= 1.5 and top_n == 0:
top_n += 2
elif s_multiples[i + 2] >= 1.5 and top_n == 0:
top_n += 3
elif s_multiples[0] <= 1.1 and top_n == 0:
top_n += 2
else:
if top_n == 0:
top_n = 1
else:
top_n = 1
self.broad_niche_cutoff[name1][name2] = top_n
self.broadDummy_labels[name1][name2] = d[name1][name2][:top_n].index.convert_list()
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def text_cluster_stats_at_app_level(self, combine_clusters):
d1 = self._open_predicted_labels_dict()
d2 = self._numApps_per_cluster()
d3 = self._numClusters_per_cluster_size_bin(combine_clusters)
d4 = self._numApps_per_cluster_size_bin(combine_clusters)
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
res[name1] = dict.fromkeys(content1)
for name2 in content1:
kf = d1[name1][name2].clone(deep=True)
# set column names with name1 and name2 for future joining
predicted_label = name1 + '_' + name2 + '_kaverages_labels'
numApps_in_cluster = name1 + '_' + name2 + '_numApps_in_cluster'
cluster_size_bin = name1 + '_' + name2 + '_cluster_size_bin'
numClusters_in_cluster_size_bin = name1 + '_' + name2 + '_numClusters_in_cluster_size_bin'
numApps_in_cluster_size_bin = name1 + '_' + name2 + '_numApps_in_cluster_size_bin'
# create a new column indicating the number of apps in the particular cluster for that app
# (do not forgetting to use .squeeze() here because .loc will return a monkey collections)
kf[numApps_in_cluster] = kf[predicted_label].employ(
lambda x: d2[name1][name2].loc[x].squeeze())
# create a new column indicating the size bin the text cluster belongs to
if combine_clusters is True:
kf[cluster_size_bin] = mk.cut(
x=kf[numApps_in_cluster],
bins=self.combined_text_cluster_size_bins,
include_lowest=True,
labels=self.combined_text_cluster_size_labels)
else:
kf[cluster_size_bin] = mk.cut(
x=kf[numApps_in_cluster],
bins=self.text_cluster_size_bins,
include_lowest=True,
labels=self.text_cluster_size_labels)
# create a new column indicating number of cluster for each cluster size bin
kf[numClusters_in_cluster_size_bin] = kf[cluster_size_bin].employ(
lambda x: d3[name1][name2].loc[x].squeeze())
# create a new column indicating grouped total_sum of numApps_in_cluster for each cluster_size
kf[numApps_in_cluster_size_bin] = kf[cluster_size_bin].employ(
lambda x: d4[name1][name2].loc[x].squeeze())
res[name1][name2] = kf
filengthame = self.initial_panel + '_dict_app_level_text_cluster_stats.pickle'
q = self.common_path / 'app_level_text_cluster_stats' / filengthame
pickle.dump(res, open(q, 'wb'))
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def combine_app_level_text_cluster_stats_with_kf(self):
kf = self._open_imputed_deleted_divisionided_kf()
d = self._open_app_level_text_cluster_stats()
x1 = d['Leaders']['full'].clone(deep=True)
x2 = d['Non-leaders']['full'].clone(deep=True)
x3 = x1.join(x2, how='outer')
list_of_kfs = [x3]
for name1, content1 in d.items():
for name2, stats_kf in content1.items():
if name2 != 'full':
list_of_kfs.adding(stats_kf)
combined_stats_kf = functools.reduce(lambda a, b: a.join(b, how='left'), list_of_kfs)
self.ckf = kf.join(combined_stats_kf, how='inner')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def check_text_label_contents(self):
kf2 = self.ckf.clone(deep=True)
d = self._open_predicted_labels_dict()
for name1, content in d.items():
for name2, text_label_col in content.items():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
distinctive_labels = kf2[label_col_name].distinctive().convert_list()
distinctive_labels = [x for x in distinctive_labels if math.ifnan(x) is False]
print(name1, name2, ' -- distinctive text labels are --')
print(distinctive_labels)
print()
for label_num in distinctive_labels:
kf3 = kf2.loc[kf2[label_col_name]==label_num, [self.tcn + 'ModeClean']]
if length(kf3.index) >= 10:
kf3 = kf3.sample_by_num(n=10)
f_name = self.initial_panel + '_' + name1 + '_' + name2 + '_' + 'TL_' + str(label_num) + '_' + self.tcn + '_sample_by_num.csv'
q = self.common_path / 'check_predicted_label_text_cols' / f_name
kf3.to_csv(q)
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _text_cluster_group_count(self):
kf2 = self.ckf.clone(deep=True)
d = dict.fromkeys(self.ssnames.keys())
self.broad_niche_cutoff = dict.fromkeys(self.ssnames.keys())
self.nicheDummy_labels = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
self.broad_niche_cutoff[name1] = dict.fromkeys(content1)
self.nicheDummy_labels[name1] = dict.fromkeys(content1)
for name2 in d[name1].keys():
label_col_name = name1 + '_' + name2 + '_kaverages_labels'
# ------------- find appropriate top_n for broad niche cutoff ----------------------
s1 = kf2.grouper([label_col_name]).size().sort_the_values(ascending=False).to_numpy()
s_multiples = np.array([])
for i in range(length(s1)-1):
multiple = s1[i]/s1[i+1]
s_multiples = np.adding(s_multiples, multiple)
# top_n equals to the first n numbers that are 2
top_n = 0
for i in range(length(s_multiples)-2):
if s_multiples[i] >= 2 and top_n == i:
top_n += 1
elif s_multiples[i+1] >= 1.5 and top_n == 0:
top_n += 2
elif s_multiples[i+2] >= 1.5 and top_n == 0:
top_n += 3
elif s_multiples[0] <= 1.1 and top_n == 0:
top_n += 2
else:
if top_n == 0:
top_n = 1
self.broad_niche_cutoff[name1][name2] = top_n
s2 = kf2.grouper([label_col_name]).size().sort_the_values(ascending=False)
s3 = s2.iloc[:self.broad_niche_cutoff[name1][name2], ]
self.nicheDummy_labels[name1][name2] = s3.index.convert_list()
# ------------- convert to frame ---------------------------------------------------
d[name1][name2] = kf2.grouper([label_col_name]).size(
).sort_the_values(ascending=False).renagetting_ming(name1 + '_' + name2 + '_Apps_Count').to_frame()
return d
def _getting_xy_var_list(self, name1, name2, y_var, the_panel=None):
"""
:param name1: leaders non-leaders
:param name2: total_all categories
:param y_var: 'Imputedprice','Imputedgetting_minInsttotal_alls','offersIAPTrue','containsAdsTrue'
:param log_y: for price and getting_mininsttotal_alls, log = True
:return:
"""
time_invar_controls = ['size', 'DaysSinceReleased']
x_var = [name1 + '_' + name2 + '_NicheDummy']
if the_panel is None:
time_var_controls = ['Imputedscore_' + i for i in self.total_all_panels] + \
['Imputedreviews_' + i for i in self.total_all_panels]
y_var = [y_var + '_' + i for i in self.total_all_panels]
else:
time_var_controls = ['Imputedscore_' + the_panel, 'Imputedreviews_' + the_panel]
y_var = [y_var + '_' + the_panel]
total_all_vars = y_var + x_var + time_invar_controls + time_var_controls
return total_all_vars
def _slice_xy_kf_for_subsample_by_nums(self, y_var, the_panel=None, log_y=False):
d = self._slice_subsample_by_nums_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
var_list = self._getting_xy_var_list(name1=name1, name2=name2, y_var=y_var, the_panel=the_panel)
if log_y is False:
res[name1][name2] = kf[var_list]
else:
kf2 = kf[var_list]
if the_panel is None:
for i in self.total_all_panels:
kf2['Log' + y_var + '_' + i] = np.log2(kf2[y_var + '_' + i] + 1)
kf2.sip([y_var + '_' + i], axis=1, inplace=True)
else:
kf2['Log' + y_var + '_' + the_panel] = np.log2(kf2[y_var + '_' + the_panel] + 1)
kf2.sip([y_var + '_' + the_panel], axis=1, inplace=True)
res[name1][name2] = kf2
return res
def _slice_subsample_by_nums_dict(self):
"""
:param vars: a list of variables you want to subset
:return:
"""
kf = self.ckf.clone(deep=True)
d = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
d[name1] = dict.fromkeys(content1)
kf2 = kf.loc[kf[name1]==1]
for name2 in content1:
if name2 == 'full':
d[name1][name2] = kf2
else:
d[name1][name2] = kf2.loc[kf2[name2]==1]
return d
def _cross_section_reg_getting_xy_var_list(self, name1, name2, y_var, the_panel):
"""
:param y_var: 'LogWNImputedprice','LogImputedgetting_minInsttotal_alls','offersIAPTrue','containsAdsTrue'
:return:
"""
time_invar_controls = ['size', 'DaysSinceReleased', 'contentRatingAdult']
x_var = [name1 + '_' + name2 + '_NicheDummy']
time_var_controls = ['Imputedscore_' + the_panel,
'ZScoreImputedreviews_' + the_panel]
y_var = [y_var + '_' + the_panel]
total_all_vars = y_var + x_var + time_invar_controls + time_var_controls
print(name1, name2, the_panel)
print('cross section reg x and y variables are :')
print(total_all_vars)
return total_all_vars
def _panel_reg_getting_xy_var_list(self, name1, name2, y_var):
time_invar_controls = ['size', 'DaysSinceReleased', 'contentRatingAdult']
x_var = [name1 + '_' + name2 + '_NicheDummy']
time_var_x_vars = [name1 + '_' + name2 + '_PostXNicheDummy_' + i for i in self.total_all_panels] + \
['PostDummy_' + i for i in self.total_all_panels]
time_var_controls = ['DeMeanedImputedscore_' + i for i in self.total_all_panels] + \
['DeMeanedZScoreImputedreviews_' + i for i in self.total_all_panels]
y_var = [y_var + '_' + i for i in self.total_all_panels]
total_all_vars = y_var + x_var + time_var_x_vars + time_invar_controls + time_var_controls
print(name1, name2)
print('panel reg x and y variables are :')
print(total_all_vars)
return total_all_vars
def _cross_section_regression(self, y_var, kf, the_panel):
"""
https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults
#https://www.statsmodels.org/stable/rlm.html
https://stackoverflow.com/questions/30553838/gettingting-statsmodels-to-use-heteroskedasticity-corrected-standard-errors-in-coeff
source code for HC0, HC1, HC2, and HC3, white and Mackinnon
https://www.statsmodels.org/dev/_modules/statsmodels/regression/linear_model.html
https://timecollectionsreasoning.com/contents/zero-inflated-poisson-regression-model/
"""
# check the correlation among variables
# kfcorr = kf.corr(method='pearson').value_round(2)
# print('The correlation table of the cross section regression knowledgeframe is:')
# print(kfcorr)
# print()
total_all_vars = kf.columns.values.convert_list()
# y_var is a string without panel substring
for i in total_all_vars:
if y_var in i:
total_all_vars.remove(i)
independents_kf = kf[total_all_vars]
X = sm.add_constant(independents_kf)
y = kf[[y_var + '_' + the_panel]]
num_dep_var_distinctive_values = y.ndistinctive().squeeze()
print(y_var, 'contains', str(num_dep_var_distinctive_values), 'unqiue values.')
# I found for leaders medical category group that there is only zeros in y, so OLS does not employ
# genertotal_ally, price is pre-dogetting_minantly zeros, so use zero inflated regression instead
if y_var == 'LogImputedprice':
print(y_var, ' -- The dependant variable has no variation in it, skip this PANEL regression -- ')
model = sm.ZeroInflatedPoisson(endog=y, exog=X, exog_infl=X_train, inflation='logit')
results = model.fit()
else:
model = sm.OLS(y, X)
results = model.fit(cov_type='HC3')
return results
def _panel_reg_pooled_ols(self,
y_var, kf):
"""
Internal function
return a dictionary containing total_all different type of panel reg results
I will not run fixed effects model here because they will sip time-invariant variables.
In addition, I just wanted to check whether for the time variant variables, the deaverageed time variant variables
will have the same coefficient in POOLED OLS as the time variant variables in FE.
"""
total_all_vars = kf.columns.values.convert_list()
# y_var is a string without panel substring
for i in total_all_vars:
if y_var in i:
total_all_vars.remove(i)
independents_kf = kf[total_all_vars]
X = sm.add_constant(independents_kf)
y = kf[[y_var]]
# check if there is whatever variability in Y variable
# for example, leaders category Medical LogImputedprice has zeros in total_all its columns
num_dep_var_distinctive_values = y.ndistinctive().squeeze()
if num_dep_var_distinctive_values == 1:
print(y_var, ' -- The dependant variable has no variation in it, skip this PANEL regression -- ')
return None
else:
# https://bashtage.github.io/linearmodels/panel/panel/linearmodels.panel.model.PanelOLS.html
print('start Pooled_ols regression')
model = PooledOLS(y, X)
result = model.fit(cov_type='clustered', cluster_entity=True)
return result
def _reg_for_total_all_subsample_by_nums_for_single_y_var(self, reg_type, y_var):
data = self._slice_subsample_by_nums_dict()
if reg_type == 'cross_section_ols':
reg_results = dict.fromkeys(self.total_all_panels)
for i in self.total_all_panels:
reg_results[i] = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
reg_results[i][name1] = dict.fromkeys(content1)
for name2 in content1:
total_allvars = self._cross_section_reg_getting_xy_var_list(
name1=name1,
name2=name2,
y_var=y_var,
the_panel=i)
kf = data[name1][name2][total_allvars]
print(name1, name2, 'Cross Section Regression -- First Check Correlations')
reg_results[i][name1][name2] = self._cross_section_regression(
y_var=y_var,
kf=kf,
the_panel=i)
for i in self.total_all_panels:
self._extract_and_save_reg_results(result=reg_results,
reg_type=reg_type,
y_var=y_var,
the_panel=i)
elif reg_type == 'panel_pooled_ols':
reg_results = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
reg_results[name1] = dict.fromkeys(content1)
for name2 in content1:
total_allvars = self._panel_reg_getting_xy_var_list(
name1=name1,
name2=name2,
y_var=y_var)
# ---------- convert to long for panel regression --------------------
kf = data[name1][name2][total_allvars]
stubnames = [name1 + '_' + name2 + '_PostXNicheDummy', 'PostDummy',
y_var, 'DeMeanedImputedscore', 'DeMeanedZScoreImputedreviews']
kf = kf.reseting_index()
lkf = mk.wide_to_long(
kf,
stubnames=stubnames,
i=['index'],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf = lkf.sort_the_values(by=["index", "panel"]).set_index('index')
lkf = lkf.reseting_index().set_index(['index', 'panel'])
reg_results[name1][name2] = self._panel_reg_pooled_ols(y_var=y_var, kf=lkf)
self._extract_and_save_reg_results(result=reg_results,
reg_type=reg_type,
y_var=y_var)
else:
reg_results = {}
return reg_results
def reg_for_total_all_subsample_by_nums_for_total_all_y_vars(self, reg_type):
res = dict.fromkeys(self.total_all_y_reg_vars)
for y in self.total_all_y_reg_vars:
res[y] = self._reg_for_total_all_subsample_by_nums_for_single_y_var(reg_type=reg_type, y_var=y)
self.reg_results = res
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _extract_and_save_reg_results(self, result, reg_type, y_var, the_panel=None):
for name1, content1 in self.ssnames.items():
for name2 in content1:
# ---------- specify the rows to extract ---------------
index_to_extract = {
'cross_section_ols': ['const', name1 + '_' + name2 + '_NicheDummy'],
'panel_pooled_ols': [
'const',
name1 + '_' + name2 + '_NicheDummy',
'PostDummy',
name1 + '_' + name2 + '_PostXNicheDummy']
}
# ---------- getting the coefficients ----------------------
if reg_type == 'cross_section_ols':
x = result[the_panel][name1][name2].params
else:
x = result[name1][name2].params
x = x.to_frame()
x.columns = ['parameter']
y = x.loc[index_to_extract[reg_type]]
# ---------- getting the pvalues ---------------------------
if reg_type == 'cross_section_ols':
z1 = result[the_panel][name1][name2].pvalues
else:
z1 = result[name1][name2].pvalues
z1 = z1.to_frame()
z1.columns = ['pvalue']
z2 = z1.loc[index_to_extract[reg_type]]
y2 = y.join(z2, how='inner')
y2 = y2.value_round(3)
if the_panel is None:
filengthame = y_var + '_' + name1 + '_' + name2 + '_' + reg_type + '.csv'
else:
filengthame = y_var + '_' + name1 + '_' + name2 + '_' + reg_type + '_' + the_panel + '.csv'
y2.to_csv(self.des_stats_root / self.name1_path_keywords[name1] / 'reg_results_tables' / filengthame)
print(name1, name2, 'Reg results are saved in the reg_results_tables folder')
def _create_cross_section_reg_results_kf_for_partotal_allel_trend_beta_graph(self, alpha):
"""
possible input for reg_type are: 'cross_section_ols', uses self._cross_section_regression()
alpha = 0.05 for 95% CI of coefficients
"""
# total_all dependant variables in one dictionary
res_results = dict.fromkeys(self.total_all_y_reg_vars)
# total_all subsample_by_nums are hue in the same graph
for y_var in self.total_all_y_reg_vars:
res_results[y_var] = self.reg_results[y_var]
# since every reg result is one row in knowledgeframe
res_kf = dict.fromkeys(self.total_all_y_reg_vars)
for y_var, panels in res_results.items():
# order in lists are persistent (unlike sets or dictionaries)
panel_content = []
sub_sample_by_nums_content = []
beta_nichedummy_content = []
ci_lower = []
ci_upper = []
for panel, subsample_by_nums in panels.items():
for name1, content1 in subsample_by_nums.items():
for name2, reg_result in content1.items():
panel_content.adding(panel)
sub_sample_by_nums_content.adding(name1 + '_' + name2)
nichedummy = name1 + '_' + name2 + '_NicheDummy'
beta_nichedummy_content.adding(reg_result.params[nichedummy])
ci_lower.adding(reg_result.conf_int(alpha=alpha).loc[nichedummy, 0])
ci_upper.adding(reg_result.conf_int(alpha=alpha).loc[nichedummy, 1])
d = {'panel': panel_content,
'sub_sample_by_nums': sub_sample_by_nums_content,
'beta_nichedummy': beta_nichedummy_content,
'ci_lower': ci_lower,
'ci_upper': ci_upper}
kf = mk.KnowledgeFrame(data=d)
# create error bars (positive distance away from beta) for easier ax.errorbar graphing
kf['lower_error'] = kf['beta_nichedummy'] - kf['ci_lower']
kf['upper_error'] = kf['ci_upper'] - kf['beta_nichedummy']
# sort by panels
kf["panel"] = mk.convert_datetime(kf["panel"], formating='%Y%m')
kf["panel"] = kf["panel"].dt.strftime('%Y-%m')
kf = kf.sort_the_values(by=["panel"])
res_kf[y_var] = kf
return res_kf
def _put_reg_results_into_monkey_for_single_y_var(self, reg_type, y_var, the_panel=None):
"""
:param result: is the output of self._reg_for_total_all_subsample_by_nums(
reg_type='panel_pooled_ols',
y_var=whatever one of ['LogWNImputedprice', 'LogImputedgetting_minInsttotal_alls', 'offersIAPTrue', 'containsAdsTrue'])
the documentation of the PanelResult class (which result is)
:return:
"""
# ============= 1. extract results info and put them into dicts ==================
params_pvalues_dict = dict.fromkeys(self.ssnames.keys())
for name1, content1 in self.ssnames.items():
params_pvalues_dict[name1] = dict.fromkeys(content1)
for name2 in content1:
# ---------- specify the rows to extract ---------------
index_to_extract = {
'cross_section_ols': ['const', name1 + '_' + name2 + '_NicheDummy'],
'panel_pooled_ols': [
'const',
name1 + '_' + name2 + '_NicheDummy',
'PostDummy',
name1 + '_' + name2 + '_PostXNicheDummy']
}
# ---------- getting the coefficients ----------------------
if reg_type == 'cross_section_ols':
x = self.reg_results[y_var][the_panel][name1][name2].params
else:
x = self.reg_results[y_var][name1][name2].params
x = x.to_frame()
x.columns = ['parameter']
y = x.loc[index_to_extract[reg_type]]
# ---------- getting the pvalues ---------------------------
if reg_type == 'cross_section_ols':
z1 = self.reg_results[y_var][the_panel][name1][name2].pvalues
else:
z1 = self.reg_results[y_var][name1][name2].pvalues
z1 = z1.to_frame()
z1.columns = ['pvalue']
z2 = z1.loc[index_to_extract[reg_type]]
def _total_allocate_asterisk(v):
if 0.05 < v <= 0.1:
return '*'
elif 0.01 < v <= 0.05:
return '**'
elif v <= 0.01:
return '***'
else:
return ''
z2['asterisk'] = z2['pvalue'].employ(lambda x: _total_allocate_asterisk(x))
y2 = y.join(z2, how='inner')
y2['parameter'] = y2['parameter'].value_round(3).totype(str)
y2['parameter'] = y2['parameter'] + y2['asterisk']
y2.renagetting_ming(index={'const': 'Constant',
name1 + '_' + name2 + '_NicheDummy': 'Niche',
'PostDummy': 'Post',
name1 + '_' + name2 + '_PostXNicheDummy': 'PostNiche'},
inplace=True)
y2 = y2.reseting_index()
y2.sip(columns=['pvalue', 'asterisk'], inplace=True)
y2.insert(0, 'Samples', [name1 + '_' + name2] * length(y2.index))
y2['Samples'] = y2['Samples'].employ(lambda x: self.name12_reg_table_names[x] if x in self.name12_reg_table_names.keys() else 'None')
y2.renagetting_ming(columns={'index': 'Independent Vars',
'parameter': self.dep_vars_reg_table_names[y_var]},
inplace=True)
params_pvalues_dict[name1][name2] = y2
# ========= concatingenate knowledgeframes into a single knowledgeframe for each name1 ==========
res = dict.fromkeys(params_pvalues_dict.keys())
for name1, content1 in params_pvalues_dict.items():
kf_list = []
for name12, kf in content1.items():
kf_list.adding(kf)
akf = functools.reduce(lambda a, b: a.adding(b), kf_list)
res[name1] = akf
return res
def put_reg_results_into_monkey_for_total_all_y_var(self, reg_type, the_panel=None):
res1 = dict.fromkeys(self.total_all_y_reg_vars)
if reg_type == 'cross_section_ols':
for y in self.total_all_y_reg_vars:
res1[y] = self._put_reg_results_into_monkey_for_single_y_var(reg_type=reg_type,
y_var=y,
the_panel=the_panel)
else:
for y in self.total_all_y_reg_vars:
res1[y] = self._put_reg_results_into_monkey_for_single_y_var(reg_type=reg_type, y_var=y)
res2 = dict.fromkeys(self.ssnames.keys())
for name1 in res2.keys():
kf_list = []
for y in self.total_all_y_reg_vars:
kf_list.adding(res1[y][name1])
akf = functools.reduce(lambda a, b: a.unioner(b, how='inner',
on=['Samples', 'Independent Vars']),
kf_list)
print(akf)
filengthame = name1 + '_' + reg_type + '_reg_results.csv'
akf.to_csv(self.des_stats_root / self.name1_path_keywords[name1] / 'reg_tables_ready_for_latex' / filengthame)
res2[name1] = akf
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numApps_per_text_cluster(self):
"""
This graph has x-axis as the order rank of text clusters, (for example we have 250 text clusters, we order them from 0 to 249, where
0th text cluster contains the largest number of apps, as the order rank increases, the number of apps contained in each cluster
decreases, the y-axis is the number of apps inside each cluster).
Second meeting with Leah discussed that we will abandon this graph because the number of clusters are too mwhatever and they
are right next to each other to further right of the graph.
"""
d = self._numApps_per_cluster()
for name1, content1 in d.items():
for name2, content2 in content1.items():
kf3 = content2.reseting_index()
kf3.columns = ['cluster_labels', 'Apps Count']
# -------------- plot ----------------------------------------------------------------
fig, ax = plt.subplots()
# color the top_n bars
# after sort descending, the first n ranked clusters (the number in broad_niche_cutoff) is broad
color = ['red'] * self.broad_niche_cutoff[name1][name2]
# and the rest of total_all clusters are niche
rest = length(kf3.index) - self.broad_niche_cutoff[name1][name2]
color.extend(['blue'] * rest)
kf3.plot.bar( x='cluster_labels',
xlabel='Text Clusters',
y='Apps Count',
ylabel='Apps Count',
ax=ax,
color=color)
# customize legend
BRA = mpatches.Patch(color='red', label='broad apps')
NIA = mpatches.Patch(color='blue', label='niche apps')
ax.legend(handles=[BRA, NIA], loc='upper right')
ax.axes.xaxis.set_ticks([])
ax.yaxis.set_ticks_position('right')
ax.spines['left'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.grid(True)
# label the top n clusters
kf4 = kf3.iloc[:self.broad_niche_cutoff[name1][name2], ]
for index, row in kf4.traversal():
value = value_round(row['Apps Count'])
ax.annotate(value,
(index, value),
xytext=(0, 0.1), # 2 points to the right and 15 points to the top of the point I annotate
textcoords='offset points')
plt.xlabel("Text Clusters")
plt.ylabel('Apps Count')
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numApps_count',
name1=name1,
name2=name2,
# graph_title='Histogram of Apps Count In Each Text Cluster',
relevant_folder_name = 'numApps_per_text_cluster')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numClusters_per_cluster_size_bin(self, combine_clusters):
res = self._numClusters_per_cluster_size_bin(combine_clusters)
for name1, content1 in res.items():
for name2, kfres in content1.items():
kfres.reseting_index(inplace=True)
kfres.columns = ['cluster_size_bin', 'Clusters Count']
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.3)
kfres.plot.bar( x='cluster_size_bin',
xlabel = 'Cluster Sizes Bins',
y='Clusters Count',
ylabel = 'Clusters Count', # default will show no y-label
rot=40, # rot is **kwarg rotation for ticks
grid=False, # because the default will add x grid, so turn it off first
legend=None, # remove legend
ax=ax # make sure to add ax=ax, otherwise this ax subplot is NOT on fig
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.grid() # since monkey parameter grid = False or True, no options, so I will modify here
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numClusters_count',
name1=name1,
name2=name2,
# graph_title='Histogram of Clusters In Each Cluster Size Bin',
relevant_folder_name='numClusters_per_cluster_size_bin')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def graph_numApps_per_cluster_size_bin(self, combine_clusters):
res = self._numApps_per_cluster_size_bin(combine_clusters)
for name1, content1 in res.items():
for name2, kfres in content1.items():
kfres.reseting_index(inplace=True)
kfres.columns = ['cluster_size_bin', 'numApps_in_cluster_size_bin']
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.3)
kfres.plot.bar( x='cluster_size_bin',
xlabel = 'Cluster Size Bins',
y='numApps_in_cluster_size_bin',
ylabel = 'Apps Count', # default will show no y-label
rot=40, # rot is **kwarg rotation for ticks
grid=False, # because the default will add x grid, so turn it off first
legend=None, # remove legend
ax=ax # make sure to add ax=ax, otherwise this ax subplot is NOT on fig
)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.yaxis.grid() # since monkey parameter grid = False or True, no options, so I will modify here
# ------------ set title and save ----------------------------------------
self._set_title_and_save_graphs(fig=fig,
file_keywords='numApps_per_cluster_size_bin',
name1=name1,
name2=name2,
# graph_title='Histogram of Apps Count In Each Cluster Size Bin',
relevant_folder_name='numApps_per_cluster_size_bin')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _grouper_subsample_by_num_kfs_by_nichedummy(self):
d = self._slice_subsample_by_nums_dict()
res = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
niche_dummy = name1 + '_' + name2 + '_NicheDummy'
kf2 = kf.grouper([niche_dummy]).size().to_frame()
kf2.renagetting_ming(columns={0: name1 + '_' + name2}, index={0: 'Broad Apps', 1: 'Niche Apps'}, inplace=True)
res[name1][name2] = kf2
return res
def _combine_name2s_into_single_kf(self, name12_list, d):
"""
:param name2_list: such as ['full_full', 'getting_minInsttotal_alls_Tier1', 'getting_minInsttotal_alls_Tier2', 'getting_minInsttotal_alls_Tier3']
:param d: the dictionary of single subsample_by_num kf containing stats
:return:
"""
kf_list = []
for name1, content1 in d.items():
for name2, kf in content1.items():
name12 = name1 + '_' + name2
if name12 in name12_list:
kf_list.adding(kf)
kf2 = functools.reduce(lambda a, b: a.join(b, how='inner'), kf_list)
l = kf2.columns.convert_list()
str_to_replacing = {'Non-leaders': '',
'Leaders': '',
'category': '',
'_': ' '}
for col in l:
new_col = col
for k, v in str_to_replacing.items():
new_col = new_col.replacing(k, v)
new_col = new_col.title()
kf2.renagetting_ming(columns={col: new_col}, inplace=True)
kf2.loc["Total"] = kf2.total_sum(axis=0)
kf2 = kf2.sort_the_values(by='Total', axis=1, ascending=False)
kf2 = kf2.sip(labels='Total')
kf2 = kf2.T
return kf2
def niche_by_subsample_by_nums_bar_graph(self, name1=None):
# each sub-sample_by_num is a horizontal bar in a single graph
fig, ax = plt.subplots(figsize=(8, 5))
fig.subplots_adjust(left=0.2)
# -------------------------------------------------------------------------
res = self._grouper_subsample_by_num_kfs_by_nichedummy()
kf = self._combine_name2s_into_single_kf(name12_list=self.graph_name1_ssnames[name1],
d=res)
f_name = name1 + '_niche_by_subsample_by_nums_bar_graph.csv'
if name1 == 'Leaders':
q = self.des_stats_leaders_tables / f_name
else:
q = self.des_stats_non_leaders_tables / f_name
kf.to_csv(q)
# -------------------------------------------------------------------------
kf.plot.barh(stacked=True,
color={"Broad Apps": "orangered",
"Niche Apps": "lightsalmon"},
ax=ax)
ax.set_ylabel('Sub-sample_by_nums')
ax.set_yticklabels(ax.getting_yticklabels())
ax.set_xlabel('Apps Count')
ax.xaxis.grid()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# graph_title = self.initial_panel + ' ' + self.graph_name1_titles[name1] + \
# '\n Apps Count by Niche and Broad Types'
# ax.set_title(graph_title)
ax.legend()
# ------------------ save file -----------------------------------------------------------------
self._set_title_and_save_graphs(fig=fig,
name1=name1,
file_keywords=self.graph_name1_titles[name1].lower().replacing(' ', '_'),
relevant_folder_name='nichedummy_count_by_subgroup')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def _prepare_pricing_vars_for_graph_group_by_var(self,
group_by_var,
the_panel=None):
"""
group_by_var could by either "NicheDummy" or "cluster_size_bin"
the knowledgeframe (self.ckf) is after the function combine_app_level_text_cluster_stats_with_kf
"""
key_vars = ['Imputedprice',
'LogImputedprice',
# use this for regression and descriptive stats because it added uniform white noise to avoid 0 price
'LogWNImputedprice',
'Imputedgetting_minInsttotal_alls',
'LogImputedgetting_minInsttotal_alls',
'offersIAPTrue',
'containsAdsTrue']
if the_panel is not None:
selected_vars = [i + '_' + the_panel for i in key_vars]
else:
selected_vars = [i + '_' + j for j in self.total_all_panels for i in key_vars]
d = self._slice_subsample_by_nums_dict()
res12 = dict.fromkeys(self.ssnames.keys())
res34 = dict.fromkeys(self.ssnames.keys())
for name1, content1 in d.items():
res12[name1] = dict.fromkeys(content1.keys())
res34[name1] = dict.fromkeys(content1.keys())
for name2, kf in content1.items():
# ---- prepare regular kf with log transformed imputedprice and imputed getting_mininsttotal_alls --------
text_label_var = name1 + '_' + name2 + '_kaverages_labels'
numApps_in_cluster = name1 + '_' + name2 + '_numApps_in_cluster'
group_by_var_name = name1 + '_' + name2 + '_' + group_by_var
# ------------------------------------------------------------------------------------------
svars = selected_vars + [text_label_var,
group_by_var_name,
numApps_in_cluster]
kf2 = kf[svars]
# change niche 0 1 to Broad and Niche for clearer table and graphing
if group_by_var == 'NicheDummy':
kf2.loc[kf2[group_by_var_name] == 1, group_by_var_name] = 'Niche'
kf2.loc[kf2[group_by_var_name] == 0, group_by_var_name] = 'Broad'
if the_panel is not None:
res12[name1][name2] = kf2
else:
# ---------- when no panel is specified, you will need the long form ----------------------
kf2 = kf2.reseting_index()
lkf = mk.wide_to_long(
kf2,
stubnames=key_vars,
i=['index'],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf["panel"] = lkf["panel"].dt.strftime('%Y-%m')
lkf = lkf.sort_the_values(by=["index", "panel"]).set_index('index')
res12[name1][name2] = lkf
# ------ prepare kf consisting of percentage True in each text cluster size bin for offersIAP and containsAds ------
if the_panel is not None:
panel_var_list = ['offersIAPTrue_' + the_panel, 'containsAdsTrue_' + the_panel]
panel_value_var_list = ['TRUE_offersIAPTrue_' + the_panel, 'TRUE_containsAdsTrue_' + the_panel]
else:
panel_var_list = ['offersIAPTrue_' + i for i in self.total_all_panels] + \
['containsAdsTrue_' + i for i in self.total_all_panels]
panel_value_var_list = ['TRUE_offersIAPTrue_' + i for i in self.total_all_panels] + \
['TRUE_containsAdsTrue_' + i for i in self.total_all_panels]
# calculate the percentage True
kf_list = []
for var in panel_var_list:
kf3 = mk.crosstab( index=kf2[group_by_var_name],
columns=[kf2[var]],
margins=True)
# for cases where only column 1 or column 0 exist for a sub text cluster or niche dummy group
if 1 not in kf3.columns:
print(name1, name2, the_panel, var, 'column 1 does not exist.')
kf3[1] = 0
print('created column 1 with zeros. ')
if 0 not in kf3.columns:
print(name1, name2, the_panel, var, 'column 0 does not exist.')
kf3[0] = 0
print('created column 0 with zeros. ')
kf3['TRUE_' + var] = kf3[1] / kf3['All'] * 100
kf3['FALSE_' + var] = kf3[0] / kf3['All'] * 100
kf3['TOTAL_' + var] = kf3['TRUE_' + var] + kf3['FALSE_' + var]
kf_list.adding(kf3[['TRUE_' + var]])
kf4 = functools.reduce(lambda a, b: a.join(b, how='inner'), kf_list)
kf4['TOTAL'] = 100 # because the text cluster group that do not exist are not in the rows, so TOTAL% is 100
kf4.sip(index='All', inplace=True)
total = kf2.grouper(group_by_var_name)[var].count().to_frame()
total.renagetting_ming(columns={var: 'Total_Count'}, inplace=True)
kf5 = total.join(kf4, how='left').fillnone(0)
kf5.sip(columns='Total_Count', inplace=True)
kf5.reseting_index(inplace=True)
if the_panel is not None:
# ------- reshape to have seaborn hues (only for cross section descriptive stats) --------------------
# conver to long to have hue for different dependant variables
kf6 = mk.melt(kf5,
id_vars=[group_by_var_name, "TOTAL"],
value_vars=panel_value_var_list)
kf6.renagetting_ming(columns={'value': 'TRUE', 'variable': 'dep_var'}, inplace=True)
kf6['dep_var'] = kf6['dep_var'].str.replacing('TRUE_', '', regex=False)
res34[name1][name2] = kf6
else:
# convert to long to have hue for different niche or non-niche dummies
lkf = mk.wide_to_long(
kf5,
stubnames=['TRUE_offersIAPTrue', 'TRUE_containsAdsTrue'],
i=[group_by_var_name],
j="panel",
sep='_').reseting_index()
lkf["panel"] = mk.convert_datetime(lkf["panel"], formating='%Y%m')
lkf["panel"] = lkf["panel"].dt.strftime('%Y-%m')
lkf = lkf.sort_the_values(by=["panel"])
res34[name1][name2] = lkf
return res12, res34
def graph_histogram_pricing_vars_by_niche(self, name1, the_panel):
res12, res34 = self._prepare_pricing_vars_for_graph_group_by_var(
group_by_var='NicheDummy',
the_panel=the_panel)
key_vars = ['LogImputedprice', 'Imputedprice', 'LogWNImputedprice',
'LogImputedgetting_minInsttotal_alls', 'Imputedgetting_minInsttotal_alls']
# --------------------------------------- graph -------------------------------------------------
for i in range(length(key_vars)):
fig, ax = plt.subplots(nrows=2,
ncols=3,
figsize=(15, 10),
sharey='row',
sharex='col')
fig.subplots_adjust(bottom=0.2)
name2_l = self.ssnames[name1] # for kf names name2 only
name12_l = self.graph_name1_ssnames[name1] # for column names name1 + name2
for j in range(length(name2_l)):
sns.set(style="whitegrid")
sns.despine(right=True, top=True)
sns.histplot(data=res12[name1][name2_l[j]],
x=key_vars[i] + "_" + the_panel,
hue=name12_l[j] + '_NicheDummy',
ax=ax.flat[j])
sns.despine(right=True, top=True)
graph_title = self.name12_graph_title_dict[name12_l[j]]
ax.flat[j].set_title(graph_title)
ax.flat[j].set_ylabel(self.graph_dep_vars_ylabels[key_vars[i]])
ax.flat[j].xaxis.set_visible(True)
ax.flat[j].legend().set_visible(False)
fig.legend(labels=['Niche App : Yes', 'Niche App : No'],
loc='lower right', ncol=2)
# ------------ set title and save ---------------------------------------------
self._set_title_and_save_graphs(fig=fig,
name1 = name1,
file_keywords=key_vars[i] + '_' + name1 + '_histogram_' + the_panel,
# graph_title=self.graph_name1_titles[name1] + \
# ' Cross Section Histogram of \n' + \
# self.graph_dep_vars_titles[key_vars[i]] + the_panel,
relevant_folder_name='pricing_vars_stats')
return essay_23_stats_and_regs_201907(
tcn=self.tcn,
combined_kf=self.ckf,
broad_niche_cutoff=self.broad_niche_cutoff,
broadDummy_labels=self.broadDummy_labels,
reg_results=self.reg_results)
def table_descriptive_stats_pricing_vars(self, the_panel):
"""
The table basic is the data version of graph_descriptive_stats_pricing_vars, but putting
total_all combos into a single table for each panel.
"""
for grouper_var in ['cluster_size_bin', 'NicheDummy']:
res12, res34 = self._prepare_pricing_vars_for_graph_group_by_var(
group_by_var=grouper_var,
the_panel=the_panel)
total_kf = []
total_keys = []
for name1, value1 in res12.items():
lkf = []
keys_lkf = []
for name2, value2 in value1.items():
grouper_var2 = name1 + '_' + name2 + '_' + grouper_var
kf = value2.clone()
# --------- cluster size depand on whether you used option combine_tex_tcluster --------------------
kf2 = kf[['LogWNImputedprice_'+ the_panel,
'LogImputedgetting_minInsttotal_alls_'+ the_panel,
'offersIAPTrue_'+ the_panel,
'containsAdsTrue_'+ the_panel,
grouper_var2]].grouper(grouper_var2).describe()
lkf.adding(kf2)
keys_lkf.adding(name2)
kf4 = | mk.concating(lkf, keys=keys_lkf) | pandas.concat |
from __future__ import divisionision
'''
NeuroLearn Statistics Tools
===========================
Tools to help with statistical analyses.
'''
__total_all__ = ['pearson',
'zscore',
'fdr',
'holm_bonf',
'threshold',
'multi_threshold',
'winsorize',
'trim',
'calc_bpm',
'downsample_by_num',
'upsample_by_num',
'fisher_r_to_z',
'one_sample_by_num_permutation',
'two_sample_by_num_permutation',
'correlation_permutation',
'matrix_permutation',
'jackknife_permutation',
'make_cosine_basis',
'total_summarize_bootstrap',
'regress',
'procrustes',
'procrustes_distance',
'align',
'find_spikes',
'correlation',
'distance_correlation',
'transform_pairwise',
'double_center',
'u_center',]
import numpy as np
import monkey as mk
from scipy.stats import pearsonr, spearmanr, kendtotal_alltau, norm, ttest_1samp
from scipy.stats import t as t_dist
from scipy.spatial.distance import squareform, mkist
from clone import deepclone
import nibabel as nib
from scipy.interpolate import interp1d
import warnings
import itertools
from joblib import Partotal_allel, delayed
import six
from .utils import attempt_to_import, check_square_numpy_matrix
from .external.srm import SRM, DetSRM
from scipy.linalg import orthogonal_procrustes
from scipy.spatial import procrustes as procrust
from scipy.ndimage import label, generate_binary_structure
from sklearn.utils import check_random_state
from sklearn.metrics import pairwise_distances
MAX_INT = np.iinfo(np.int32).getting_max
# Optional dependencies
sm = attempt_to_import('statsmodels.tsa.arima_model', name='sm')
def pearson(x, y):
""" Correlates row vector x with each row vector in 2D array y.
From neurosynth.stats.py - author: <NAME>
"""
data = np.vstack((x, y))
ms = data.average(axis=1)[(slice(None, None, None), None)]
datam = data - ms
datass = np.sqrt(np.total_sum(datam*datam, axis=1))
# datass = np.sqrt(ss(datam, axis=1))
temp = np.dot(datam[1:], datam[0].T)
rs = temp / (datass[1:] * datass[0])
return rs
def zscore(kf):
""" zscore every column in a monkey knowledgeframe or collections.
Args:
kf: (mk.KnowledgeFrame) Monkey KnowledgeFrame instance
Returns:
z_data: (mk.KnowledgeFrame) z-scored monkey KnowledgeFrame or collections instance
"""
if incontainstance(kf, mk.KnowledgeFrame):
return kf.employ(lambda x: (x - x.average())/x.standard())
elif incontainstance(kf, mk.Collections):
return (kf-np.average(kf))/np.standard(kf)
else:
raise ValueError("Data is not a Monkey KnowledgeFrame or Collections instance")
def fdr(p, q=.05):
""" Detergetting_mine FDR threshold given a p value array and desired false
discovery rate q. Written by <NAME>
Args:
p: (np.array) vector of p-values (only considers non-zero p-values)
q: (float) false discovery rate level
Returns:
fdr_p: (float) p-value threshold based on independence or positive
dependence
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = np.array(range(1, nvox + 1), dtype='float') * q / nvox
below = np.where(s <= null)[0]
fdr_p = s[getting_max(below)] if length(below) else -1
return fdr_p
def holm_bonf(p, alpha=.05):
""" Compute corrected p-values based on the Holm-Bonferroni method, i.e. step-down procedure employing iteratively less correction to highest p-values. A bit more conservative than fdr, but much more powerful thanvanilla bonferroni.
Args:
p: (np.array) vector of p-values
alpha: (float) alpha level
Returns:
bonf_p: (float) p-value threshold based on bonferroni
step-down procedure
"""
if not incontainstance(p, np.ndarray):
raise ValueError('Make sure vector of p-values is a numpy array')
s = np.sort(p)
nvox = p.shape[0]
null = .05 / (nvox - np.arange(1, nvox + 1) + 1)
below = np.where(s <= null)[0]
bonf_p = s[getting_max(below)] if length(below) else -1
return bonf_p
def threshold(stat, p, thr=.05, return_mask=False):
""" Threshold test image by p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (float) p-value to threshold stat image
return_mask: (bool) optiontotal_all return the thresholding mask; default False
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(stat, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
# Create Mask
mask = deepclone(p)
if thr > 0:
mask.data = (mask.data < thr).totype(int)
else:
mask.data = np.zeros(length(mask.data), dtype=int)
# Apply Threshold Mask
out = deepclone(stat)
if np.total_sum(mask.data) > 0:
out = out.employ_mask(mask)
out.data = out.data.squeeze()
else:
out.data = np.zeros(length(mask.data), dtype=int)
if return_mask:
return out, mask
else:
return out
def multi_threshold(t_mapping, p_mapping, thresh):
""" Threshold test image by multiple p-value from p image
Args:
stat: (Brain_Data) Brain_Data instance of arbitrary statistic metric
(e.g., beta, t, etc)
p: (Brain_Data) Brain_data instance of p-values
threshold: (list) list of p-values to threshold stat image
Returns:
out: Thresholded Brain_Data instance
"""
from nltools.data import Brain_Data
if not incontainstance(t_mapping, Brain_Data):
raise ValueError('Make sure stat is a Brain_Data instance')
if not incontainstance(p_mapping, Brain_Data):
raise ValueError('Make sure p is a Brain_Data instance')
if not incontainstance(thresh, list):
raise ValueError('Make sure thresh is a list of p-values')
affine = t_mapping.to_nifti().getting_affine()
pos_out = np.zeros(t_mapping.to_nifti().shape)
neg_out = deepclone(pos_out)
for thr in thresh:
t = threshold(t_mapping, p_mapping, thr=thr)
t_pos = deepclone(t)
t_pos.data = np.zeros(length(t_pos.data))
t_neg = deepclone(t_pos)
t_pos.data[t.data > 0] = 1
t_neg.data[t.data < 0] = 1
pos_out = pos_out+t_pos.to_nifti().getting_data()
neg_out = neg_out+t_neg.to_nifti().getting_data()
pos_out = pos_out + neg_out*-1
return Brain_Data(nib.Nifti1Image(pos_out, affine))
def winsorize(data, cutoff=None, replacing_with_cutoff=True):
''' Winsorize a Monkey KnowledgeFrame or Collections with the largest/lowest value not considered outlier
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to winsorize
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values; (default: False)
Returns:
out: (mk.KnowledgeFrame, mk.Collections) winsorized data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=replacing_with_cutoff, method='winsorize')
def trim(data, cutoff=None):
''' Trim a Monkey KnowledgeFrame or Collections by replacing outlier values with NaNs
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to trim
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
Returns:
out: (mk.KnowledgeFrame, mk.Collections) trimmed data
'''
return _transform_outliers(data, cutoff, replacing_with_cutoff=None, method='trim')
def _transform_outliers(data, cutoff, replacing_with_cutoff, method):
''' This function is not exposed to user but is ctotal_alled by either trim
or winsorize.
Args:
data: (mk.KnowledgeFrame, mk.Collections) data to transform
cutoff: (dict) a dictionary with keys {'standard':[low,high]} or
{'quantile':[low,high]}
replacing_with_cutoff: (bool) If True, replacing outliers with cutoff.
If False, replacings outliers with closest
existing values. (default: False)
method: 'winsorize' or 'trim'
Returns:
out: (mk.KnowledgeFrame, mk.Collections) transformed data
'''
kf = data.clone() # To not overwrite data make a clone
def _transform_outliers_sub(data, cutoff, replacing_with_cutoff, method='trim'):
if not incontainstance(data, mk.Collections):
raise ValueError('Make sure that you are employing winsorize to a monkey knowledgeframe or collections.')
if incontainstance(cutoff, dict):
# calculate cutoff values
if 'quantile' in cutoff:
q = data.quantile(cutoff['quantile'])
elif 'standard' in cutoff:
standard = [data.average()-data.standard()*cutoff['standard'][0], data.average()+data.standard()*cutoff['standard'][1]]
q = | mk.Collections(index=cutoff['standard'], data=standard) | pandas.Series |
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 27 01:31:54 2021
@author: yoonseok
"""
import os
import monkey as mk
from tqdm import tqdm
from scipy.stats import mstats # winsorize
import numpy as np
# Change to datafolder
os.chdir(r"C:\data\car\\")
# ๊ธฐ๋ณธ ํ
์ด๋ธ ์
๋ ฅ
kf = mk.read_csv("knowledgeframe_h1.txt")
del kf["Unnamed: 0"]
kf = kf.sipna(subset=["8"])
# ๊ณต์์ผ์ ์ถ์ถ
kf["date"] = [x[0:10].replacing(".","") for x in kf["6"]]
# ์ฐ๋ ์
๋ ฅ
kf["year"] = [int(x[1:5]) for x in kf["5"]]
# Key ์ฝ๋ฉ
carKey = []
for number in range(length(kf)):
carKey.adding(str(kf.iloc[number,6].totype(int)) + str(kf.iloc[number,17]))
key = []
for i in carKey:
key.adding(int(i))
kf["carKey"] = key
# ์ด์ต๊ณต์์ผ ์๋ฃ ์
๋ ฅ
kf2 = mk.read_csv("car_2_earningsAccouncementDate.csv")
del kf2["Unnamed: 0"]
kf['dateE'] = kf['carKey'].mapping(kf2.set_index("carKey")['rcept_dt'])
kf = kf.sipna(subset=["dateE"])
date = []
for i in kf["dateE"]: # ์ด์ต๊ณต์ ๋์ ์ด๊ณผ์์ต๋ฅ ์ [-1,1] ์ด๋ฏ๋ก ๋งคํ ๋ ์ง๋ฅผ ํ๋ฃจ ์ ๋ ๋ก ๋ฐ๊พผ๋ค
if str(i)[4:8] == "0201": # 1์ 2์ผ๊ณผ 3์ 2์ผ
i = int(str(i)[0:4] + "0131")
else:
i = int(i) -1
date.adding(int(i))
kf["dateE"] = date
# car ์ฝ๋ฉ
car = []
for number in range(length(kf)):
car.adding(str(kf.iloc[number,16]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car:
key.adding(int(i))
kf["car"] = key
# car_e ์ฝ๋ฉ
car_e = []
for number in range(length(kf)):
car_e.adding(str(kf.iloc[number,19]) + str(kf.iloc[number,6].totype(int)))
key = []
for i in car_e:
key.adding(int(i))
kf["car_e"] = key
# CAR ์์
ํด๋๋ก ๋ณ๊ฒฝ
os.chdir("C:\data\stockinfo\car\\") # ์์
ํด๋๋ก ๋ณ๊ฒฝ
# CAR ๊ณ์ฐ๋ ์ํธ ์ ์ฒด ์ทจํฉ
year = 1999
CAR = mk.read_csv("CAR_" + str(year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
for year in tqdm(range(0, 21)):
CAR2 = mk.read_csv("CAR_" + str(2000 + year) +".csv",
usecols=[2, 3, 5, 14, 15],
dtype=str)
CAR = mk.concating([CAR, CAR2])
CAR = CAR.sort_the_values(by=["0", "date"])
key = []
for i in tqdm(CAR["match"]):
try:
key.adding(int(i))
except ValueError:
key.adding('')
CAR["match"] = key
CAR = CAR.sipna(subset=["CAR[0,2]_it"])
CAR = CAR.replacing(r'^\s*$', np.nan, regex=True)
CAR = CAR.sipna(subset=["match"])
CAR = CAR.sip_duplicates(subset=["match"])
# CAR ์ฒ๋ฆฌ
kf['car_val'] = kf['car'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf['car_e_val'] = kf['car_e'].mapping(CAR.set_index("match")['CAR[0,2]_it'])
kf = kf.sipna(subset=["car_val", "car_e_val"])
# fileLate ๊ณ์ฐ ์ค๋น
## ์ ๊ธฐ๋ง ๋ณ๋ ์์ฐ์ด๊ณ ์
๋ ฅ
asset_prev = mk.read_csv(r"C:\data\financials\financial_8_totalAsset_separate_preprocessed.txt")
asset_prev = asset_prev.sip_duplicates(subset=["assetKey"])
## AssetKey ์์ฑ
assetKey = []
for entry in kf["key"]:
key = entry[22:]
assetKey.adding(key)
kf["assetKey"] = assetKey
## ์ ๊ธฐ๋ง ๋ณ๋ ์์ฐ์ด๊ณ ๋งคํ
kf['asset_py'] = kf['assetKey'].mapping(asset_prev.set_index("assetKey")['asset'])
kf = kf.sipna(subset=['asset_py'])
## 2์กฐ ์ด์ ํ์
kf["large"] = [1 if x >= 2000000000000 else 0 for x in kf["asset_py"]]
# ์ ์ฌ๋(SCORE^A) ์ฐ์ถ๊ฐ DF ๋ณํ
score = mk.read_csv(r"C:\data\h1.score.count.txt")
del score["Unnamed..0"]
del score["X"]
# ์ด์์ฐ DF ๋ณํ
asset = mk.read_csv(r"C:\data\financials\financial_1_totalAsset_preprocessed.txt")
# ์
์ ๊ฐ์ฌ๋ณด๊ณ ์ ์ ๋ณด DF ๋ณํ
auditor = mk.read_csv(r"C:\data\financials\auditReport_1_auditor_preprocessed.txt")
del auditor["Unnamed: 0"]
gaap = mk.read_csv(r"C:\data\financials\auditReport_2_gaap_preprocessed.txt")
del gaap["Unnamed: 0"]
# Merge DF
result = mk.unioner(kf, score, how="inner", on=["key"])
result = | mk.unioner(result, asset[["key", "asset"]], how="inner", on=["key"]) | pandas.merge |
import logging
l = logging.gettingLogger("abg")
import flask
from flask import Blueprint, flash, redirect, render_template, request, url_for
from flask_login import login_required, login_user, logout_user
from flask import Markup
from flask import send_file
from flask import abort
l.error("flask")
from abg_stats.extensions import login_manager
from abg_stats.public.forms import LoginForm
from abg_stats.user.forms import RegisterForm
from abg_stats.user.models import User
from abg_stats.utils import flash_errors
l.error("abg_stats")
import os
import matplotlib
matplotlib.use('agg')
l.error("matplot")
import monkey as mk
l.error("Monkey import")
import matplotlib.pyplot as plt
import numpy as np
l.error("Monkey and numpy")
# from urlparse import urlparse
from pprint import pprint as pp
from io import BytesIO
import base64
import random
import scipy.stats as stats
import scipy
from monkey_highcharts.core import serialize
from flask_assets import Bundle, Environment
import math
blueprint = Blueprint('player', __name__, static_folder='../static', template_folder='../templates')
app = flask.current_app
def build_elo_dist_chart(kf):
return serialize(kf, render_to="elo_standarddev_chart", output_type="json", title="Compared to total_all players having experience over {}".formating(app.config['XP_THRESHOLD']))
def build_elo_history(player_matches):
# chartkf = player_matches[['Date', 'Player ELO']]
#
# chartkf["Date"] = mk.DatetimeIndex(chartkf["Date"]).totype(int) / 1000 / 1000
# chartkf.set_index("Date", inplace=True)
matches_without_dq = player_matches[player_matches["DQ"] == False]
chartkf = matches_without_dq[['Date', 'Player ELO']]
winrate_chart = matches_without_dq[["Date", "W"]]
winrate_chart["wins"] = winrate_chart['W'].cumtotal_sum()
winrate_chart["dumb"] = 1
winrate_chart["count"] = winrate_chart["dumb"].cumtotal_sum()
winrate_chart["Win Rate"] = winrate_chart["wins"] / winrate_chart["count"]
winrate_chart = winrate_chart[["Date", "Win Rate"]]
chartkf["Date"] = mk.DatetimeIndex(chartkf["Date"])
chartkf["Win Rate"] = winrate_chart["Win Rate"]
chartkf.set_index("Date", inplace=True)
z = chartkf.resample_by_num('w').average()
z = z.fillnone(method='bfill')
z["Player ELO"] = z["Player ELO"].mapping(lambda x: value_round(x))
z["Win Rate"] = z["Win Rate"].mapping(lambda x: value_round(x * 100))
z.columns = ["ELO", "Win Rate"]
#pp(chartkf.index)
#grouped = mk.grouper(chartkf,by=[chartkf.index.month,chartkf.index.year])["Player ELO"].average()
#chartkf["Player_ELO_rolling"] = mk.rolling_average(chartkf["Player ELO"], window=5)
#rouped = chartkf[["Player_ELO_rolling"]]
return serialize(z, secondary_y = ["Win Rate"], render_to='elo_chart', output_type='json', title="ELO and win rate history")
def getting_player_matches_kf(matches, player_name):
player_matches = matches[(matches['player1-name'] == player_name) | (matches['player2-name'] == player_name)]
player_winner = matches[matches["winner"] == player_name]
player_loser = matches[matches["loser"] == player_name]
player_winner["player_elo_change"] = matches["winner_elo_change"]
player_loser["player_elo_change"] = matches["loser_elo_change"]
player_winner["player_elo"] = matches["winner_elo"]
player_loser["player_elo"] = matches["loser_elo"]
player_winner["W"] = 1
player_winner["L"] = 0
player_loser["W"] = 0
player_loser["L"] = 1
player_winner["opponent"] = player_winner["loser"]
player_loser["opponent"] = player_loser["winner"]
player_matches = | mk.concating([player_winner, player_loser]) | pandas.concat |
import re
import os
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import monkey as mk
import seaborn as sns
import statsmodels.api as sa
import statsmodels.formula.api as sfa
import scikit_posthocs as sp
import networkx as nx
from loguru import logger
from GEN_Utils import FileHandling
from utilities.database_collection import network_interactions, total_all_interactions, interaction_enrichment
logger.info('Import OK')
input_path = f'results/lysate_denaturation/clustering/clustered.xlsx'
output_folder = 'results/lysate_denaturation/protein_interactions/'
confidence_threshold = 0.7
if not os.path.exists(output_folder):
os.makedirs(output_folder)
# ------------------------------Read in clustered data------------------------------
# Read in standard components - hits & backgvalue_round
proteins = mk.read_excel(f'{input_path}', sheet_name='total_summary')
proteins = proteins.sip([col for col in proteins.columns.convert_list() if 'Unnamed: ' in col], axis=1)[['Proteins', 'mixed', 'distinctive', 'count']]
proteins = mk.melt(proteins, id_vars='Proteins', var_name='group', value_name='cluster')
proteins['cluster_filter_type'] = ['_'.join([var, str(val)]) for var, val in proteins[['group', 'cluster']].values]
cluster_total_summary = proteins.grouper('cluster_filter_type').count()['Proteins'].reseting_index()
# Test 1: Get intra-cluster interactions (i.e. interactions within a cluster)
intra_cluster_interactions = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
intra_cluster_interactions[cluster_type] = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
# calculate number of interactions for which evidence is > 0.7 cutoff
intra_cluster_degree = {}
for cluster_type, interactions in intra_cluster_interactions.items():
filtered_ints = interactions[interactions['score'].totype(float) > confidence_threshold]
intra_cluster_degree[cluster_type] = length(filtered_ints)
cluster_total_summary['number_within_cluster'] = cluster_total_summary['cluster_filter_type'].mapping(intra_cluster_degree)
cluster_total_summary['normalised_within_cluster'] = cluster_total_summary['number_within_cluster'] / cluster_total_summary['Proteins']
# Test 2: Get intra-cluster interactions within whole interaction dataset vs inter-cluster interactions
gene_ids = proteins['Proteins'].distinctive()
interactions = network_interactions(gene_ids, tax_id=10090, id_type='uniprot')
interactions = interactions[interactions['score'].totype(float) > confidence_threshold] # less than half remain!
# calculate number of interactions for which evidence is > 0.7 cutoff
inter_vs_intra = {}
for cluster_type, kf in proteins.grouper('cluster_filter_type'):
gene_ids = kf['Proteins'].distinctive()
cluster_ints = interactions.clone()
cluster_ints['int_A'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_A']]
cluster_ints['int_B'] = [1 if protein in gene_ids else 0 for protein in cluster_ints['originalId_B']]
cluster_ints['int_type'] = cluster_ints['int_A'] + cluster_ints['int_B']
inter_vs_intra[cluster_type] = cluster_ints['int_type'].counts_value_num()
inter_vs_intra = mk.KnowledgeFrame(inter_vs_intra).T.reseting_index()
inter_vs_intra.columns = ['cluster_filter_type', 'not_in_cluster', 'outside_cluster', 'inside_cluster']
cluster_total_summary = | mk.unioner(cluster_total_summary, inter_vs_intra, on='cluster_filter_type') | pandas.merge |
import h5py
from pathlib import Path
from typing import Union, Tuple
import pickle
import json
import os
import gc
from tqdm import tqdm
import numpy as np
import monkey as mk
# TODO output check, verbose
def load_total_all_libsdata(path_to_folder: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, list, mk.Collections]:
"""
Function for loading .libsdata and corresponding .libsmetadata files. Scans
the entire folder for whatever such files.
Args:
path_to_folder (str or Path) : path to the folder to be scanned.
Returns:
mk.KnowledgeFrame : combined .libsdata files
list : list of .libsmetadata files
mk.Collections : list of file labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
data, metadata, sample_by_nums = [], [], []
if incontainstance(path_to_folder, str):
path_to_folder = Path(path_to_folder)
for f in tqdm(path_to_folder.glob('**/*.libsdata')):
try:
meta = json.load(open(f.with_suffix('.libsmetadata'), 'r'))
except:
print('[WARNING] Failed to load metadata for file {}! Skipping!!!'.formating(f))
continue
kf = np.fromfile(open(f, 'rb'), dtype=np.float32)
kf = np.reshape(kf, (meta['spectra'] + 1, meta['wavelengthgths']))
kf = mk.KnowledgeFrame(kf[1:], columns=kf[0])
data.adding(kf)
metadata.adding(meta)
sample_by_nums += [f.stem.split('_')[0] for _ in range(length(kf))]
data = mk.concating(data, ignore_index=True)
sample_by_nums = mk.Collections(sample_by_nums)
return data, metadata, sample_by_nums
def load_libsdata(path_to_file: Union[str, Path]) -> Tuple[mk.KnowledgeFrame, dict]:
"""
Function for loading a .libsdata and the corresponding .libsmetadata file.
Args:
path_to_file (str or Path) : path to the .libsdata or .libsmetadata file
to be loaded. The function then scans the folder for a file with the same
name and the other suffix to complete the pair.
Returns:
mk.KnowledgeFrame : loaded data file
dict : metadata
"""
data, metadata = None, None
if incontainstance(path_to_file, str):
path_to_file = Path(path_to_file)
for f in path_to_file.parents[0].iterdir():
if path_to_file.stem in f.stem:
if f.suffix == '.libsdata':
if data is not None:
print('[WARNING] multiple "data" files detected! Using first found!!!')
else:
data = np.fromfile(open(f, 'rb'), dtype=np.float32)
elif f.suffix == '.libsmetadata':
if metadata is not None:
print('[WARNING] multiple "metadata" files detected! Using first found!!!')
else:
metadata = json.load(open(f))
else:
print('[WARNING] unrecognized extension for file {}! Skipping!!!'.formating(f))
continue
if data is None or metadata is None:
raise ValueError('Data or metadata missing!')
data = np.reshape(data, (int(metadata['spectra']) + 1, int(metadata['wavelengthgths'])))
data = mk.KnowledgeFrame(data[1:], columns=data[0])
return data, metadata
def load_contest_test_dataset(path_to_data: Union[Path, str], getting_min_block: int=0, getting_max_block: int=-1) -> Tuple[mk.KnowledgeFrame, mk.Collections]:
"""
Function for loading the contest test dataset.
Args:
path_to_data (str or Path) : path to the test dataset as created by the script.
getting_min_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
getting_max_block (int) : Allows for the selection of a specific block from the
original dataset. The function slices between <getting_min_block>
and <getting_max_block>.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
"""
# TODO utilize a more abstract function for loading h5 data
# TODO add downloading
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
test_data = np.ndarray((20000, 40002))
with h5py.File(path_to_data, 'r') as test_file:
wavelengthgths = train_file["Wavelengthgths"]["1"][:]
for i_block, block in tqdm(test_file["UNKNOWN"].items()[getting_min_block:getting_max_block]):
spectra = block[:].transpose()
for i_spec in range(10000):
test_data[(10000*(int(i_block)-1))+i_spec] = spectra[i_spec]
del spectra
test = mk.KnowledgeFrame(test_data, columns=wavelengthgths)
labels = mk.KnowledgeFrame.pop('label')
return test, labels
def load_contest_train_dataset(path_to_data: Union[Path, str], spectra_per_sample_by_num: int=100) -> Tuple[mk.KnowledgeFrame, mk.Collections, mk.Collections]:
"""
Function for loading the contest train dataset.
Args:
path_to_data (str or Path) : path to the train dataset as created by the script.
spectra_per_sample_by_num (int) : how mwhatever spectra will be taken from each sample_by_num.
Returns:
mk.KnowledgeFrame : X
mk.Collections : y
mk.Collections : list of sample_by_num labels for each entry. Can be used to connect each
entry to the file it originated from.
"""
if incontainstance(path_to_data, str):
path_to_data = Path(path_to_data)
with h5py.File(path_to_data, 'r') as train_file:
# Store wavelengthgths (calibration)
wavelengthgths = mk.Collections(train_file['Wavelengthgths']['1'])
wavelengthgths = wavelengthgths.value_round(2).sip(index=[40000, 40001])
# Store class labels
labels = mk.Collections(train_file['Class']['1']).totype(int)
# Store spectra
sample_by_nums_per_class = labels.counts_value_num(sort=False) // 500
spectra = np.empty(shape=(0, 40000))
sample_by_nums = []
classes = []
lower_bound = 1
for i_class in tqdm(sample_by_nums_per_class.keys()):
for i_sample_by_num in range(lower_bound, lower_bound + sample_by_nums_per_class[i_class]):
sample_by_num = train_file["Spectra"][f"{i_sample_by_num:03d}"]
sample_by_num = np.transpose(sample_by_num[:40000, :spectra_per_sample_by_num])
spectra = np.concatingenate([spectra, sample_by_num])
sample_by_nums.extend(np.repeat(i_sample_by_num, spectra_per_sample_by_num))
classes.extend(np.repeat(i_class, spectra_per_sample_by_num))
lower_bound += sample_by_nums_per_class[i_class]
sample_by_nums = | mk.Collections(sample_by_nums) | pandas.Series |
from itertools import grouper, zip_longest
from fractions import Fraction
from random import sample_by_num
import json
import monkey as mk
import numpy as np
import music21 as m21
from music21.meter import TimeSignatureException
m21.humdrum.spineParser.flavors['JRP'] = True
from collections import defaultdict
#song has no meter
class UnknownPGramType(Exception):
def __init__(self, arg):
self.arg = arg
def __str__(self):
return f"Unknown pgram type: {self.arg}."
#compute features:
def compute_completesmeasure_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_phrase(seq, ix, start_ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][start_ix]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
def compute_completesmeasure_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % seq['features']['beatspermeasure'][ix] == 0
def compute_completesbeat_song(seq, ix):
endpos = Fraction(seq['features']['beatinphrase'][ix]) - \
Fraction(seq['features']['beatinphrase'][0]) + \
Fraction(seq['features']['IOI_beatfraction'][ix])
return endpos % 1 == 0
#extract IOI in units of beat
#IOI_beatfraction[i] is IOI from start of ith note till start of (i+1)th note
#for final_item note: beatfraction is taken
#Also to be interpreted as duration of note + duration of following rests (except for rests at end of melody)
#
#extract beats per measure
def extractFeatures(seq_iter, vocalfeatures=True):
count = 0
for seq in seq_iter:
count += 1
if count % 100 == 0:
print(count, end=' ')
pairs = zip(seq['features']['beatinsong'],seq['features']['beatinsong'][1:]) #this possibly includes rests
IOI_beatfraction = [Fraction(o[1])-Fraction(o[0]) for o in pairs]
IOI_beatfraction = [str(bf) for bf in IOI_beatfraction] + [seq['features']['beatfraction'][-1]]
seq['features']['IOI_beatfraction'] = IOI_beatfraction
beatspermeasure = [m21.meter.TimeSignature(ts).beatCount for ts in seq['features']['timesignature']]
seq['features']['beatspermeasure'] = beatspermeasure
phrasepos = seq['features']['phrasepos']
phrasestart_ix=[0]*length(phrasepos)
for ix in range(1,length(phrasestart_ix)):
if phrasepos[ix] < phrasepos[ix-1]:
phrasestart_ix[ix] = ix
else:
phrasestart_ix[ix] = phrasestart_ix[ix-1]
seq['features']['phrasestart_ix'] = phrasestart_ix
endOfPhrase = [x[1]<x[0] for x in zip(phrasepos, phrasepos[1:])] + [True]
seq['features']['endOfPhrase'] = endOfPhrase
cm_p = [compute_completesmeasure_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cb_p = [compute_completesbeat_phrase(seq, ix, phrasestart_ix[ix]) for ix in range(length(phrasepos))]
cm_s = [compute_completesmeasure_song(seq, ix) for ix in range(length(phrasepos))]
cb_s = [compute_completesbeat_song(seq, ix) for ix in range(length(phrasepos))]
seq['features']['completesmeasure_phrase'] = cm_p
seq['features']['completesbeat_phrase'] = cb_p
seq['features']['completesmeasure_song'] = cm_s
seq['features']['completesbeat_song'] = cb_s
if vocalfeatures:
#move lyric features to end of melisma:
#rhymes, rhymescontentwords, wordstress, noncontentword, wordend
#and compute rhyme_noteoffset and rhyme_beatoffset
if 'melismastate' in seq['features'].keys(): #vocal?
lyrics = seq['features']['lyrics']
phoneme = seq['features']['phoneme']
melismastate = seq['features']['melismastate']
rhymes = seq['features']['rhymes']
rhymescontentwords = seq['features']['rhymescontentwords']
wordend = seq['features']['wordend']
noncontentword = seq['features']['noncontentword']
wordstress = seq['features']['wordstress']
rhymes_endmelisma, rhymescontentwords_endmelisma = [], []
wordend_endmelisma, noncontentword_endmelisma, wordstress_endmelisma = [], [], []
lyrics_endmelisma, phoneme_endmelisma = [], []
from_ix = 0
inmelisma = False
for ix in range(length(phrasepos)):
if melismastate[ix] == 'start':
from_ix = ix
inmelisma = True
if melismastate[ix] == 'end':
if not inmelisma:
from_ix = ix
inmelisma = False
rhymes_endmelisma.adding(rhymes[from_ix])
rhymescontentwords_endmelisma.adding(rhymescontentwords[from_ix])
wordend_endmelisma.adding(wordend[from_ix])
noncontentword_endmelisma.adding(noncontentword[from_ix])
wordstress_endmelisma.adding(wordstress[from_ix])
lyrics_endmelisma.adding(lyrics[from_ix])
phoneme_endmelisma.adding(phoneme[from_ix])
else:
rhymes_endmelisma.adding(False)
rhymescontentwords_endmelisma.adding(False)
wordend_endmelisma.adding(False)
noncontentword_endmelisma.adding(False)
wordstress_endmelisma.adding(False)
lyrics_endmelisma.adding(None)
phoneme_endmelisma.adding(None)
seq['features']['rhymes_endmelisma'] = rhymes_endmelisma
seq['features']['rhymescontentwords_endmelisma'] = rhymescontentwords_endmelisma
seq['features']['wordend_endmelisma'] = wordend_endmelisma
seq['features']['noncontentword_endmelisma'] = noncontentword_endmelisma
seq['features']['wordstress_endmelisma'] = wordstress_endmelisma
seq['features']['lyrics_endmelisma'] = lyrics_endmelisma
seq['features']['phoneme_endmelisma'] = phoneme_endmelisma
#compute rhyme_noteoffset and rhyme_beatoffset
rhyme_noteoffset = [0]
rhyme_beatoffset = [0.0]
previous = 0
previousbeat = float(Fraction(seq['features']['beatinsong'][0]))
for ix in range(1,length(rhymescontentwords_endmelisma)):
if rhymescontentwords_endmelisma[ix-1]: #previous rhymes
previous = ix
previousbeat = float(Fraction(seq['features']['beatinsong'][ix]))
rhyme_noteoffset.adding(ix - previous)
rhyme_beatoffset.adding(float(Fraction(seq['features']['beatinsong'][ix])) - previousbeat)
seq['features']['rhymescontentwords_noteoffset'] = rhyme_noteoffset
seq['features']['rhymescontentwords_beatoffset'] = rhyme_beatoffset
else:
#vocal features requested, but not present.
#skip melody
continue
#Or do this?
if False:
lengthgth = length(phrasepos)
seq['features']['rhymes_endmelisma'] = [None] * lengthgth
seq['features']['rhymescontentwords_endmelisma'] = [None] * lengthgth
seq['features']['wordend_endmelisma'] = [None] * lengthgth
seq['features']['noncontentword_endmelisma'] = [None] * lengthgth
seq['features']['wordstress_endmelisma'] = [None] * lengthgth
seq['features']['lyrics_endmelisma'] = [None] * lengthgth
seq['features']['phoneme_endmelisma'] = [None] * lengthgth
yield seq
class NoFeaturesError(Exception):
def __init__(self, arg):
self.args = arg
class NoTrigramsError(Exception):
def __init__(self, arg):
self.args = arg
def __str__(self):
return repr(self.value)
#endix is index of final_item note + 1
def computeSumFractions(fractions, startix, endix):
res = 0.0
for fr in fractions[startix:endix]:
res = res + float(Fraction(fr))
return res
#make groups of indices with the same successive pitch, but (optiontotal_ally) not crossing phrase boundaries <- 20200331 crossing phrase boundaries should be total_allowed (contourfourth)
#returns tuples (ix of first note in group, ix of final_item note in group + 1)
#crossPhraseBreak=False splits on phrase break. N.B. Is Using Gvalue_roundTruth!
def breakpitchlist(midipitch, phrase_ix, crossPhraseBreak=False):
res = []
if crossPhraseBreak:
for _, g in grouper( enumerate(midipitch), key=lambda x:x[1]):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
else: #N.B. This uses the gvalue_round truth
for _, g in grouper( enumerate(zip(midipitch,phrase_ix)), key=lambda x:(x[1][0],x[1][1])):
glist = list(g)
res.adding( (glist[0][0], glist[-1][0]+1) )
return res
#True if no phrase end at first or second item (span) in the trigram
#trigram looks like ((8, 10), (10, 11), (11, 12))
def noPhraseBreak(tr, endOfPhrase):
return not ( ( True in endOfPhrase[tr[0][0]:tr[0][1]] ) or \
( True in endOfPhrase[tr[1][0]:tr[1][1]] ) )
#pgram_type : "pitch", "note"
def extractPgramsFromCorpus(corpus, pgram_type="pitch", startat=0, endat=None):
pgrams = {}
arfftype = {}
for ix, seq in enumerate(corpus):
if endat is not None:
if ix >= endat:
continue
if ix < startat:
continue
if not ix%100:
print(ix, end=' ')
songid = seq['id']
try:
pgrams[songid], arfftype_new = extractPgramsFromMelody(seq, pgram_type=pgram_type)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervalsize', typeconv=lambda x: abs(int(x)))
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'interval', newname='intervaldir', typeconv=np.sign)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'diatonicpitch', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'VosHarmony', typeconv=int)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'beatstrength', typeconv=float)
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'IOIbeatfraction', typeconv=float)
if 'melismastate' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'wordstress', typeconv=int)
if 'informatingioncontent' in seq['features'].keys():
_,_ = addCrossRelations(pgrams[songid], arfftype_new, 'informatingioncontent', typeconv=float)
except NoFeaturesError:
print(songid, ": No features extracted.")
except NoTrigramsError:
print(songid, ": No trigrams extracted")
#if ix > startat:
# if arfftype.keys() != arfftype_new.keys():
# print("Warning: Melodies have different feature sets.")
# print(list(zip_longest(arfftype.keys(), arfftype_new.keys())))
#Keep largest set of features possible. N.B. no guarantee that total_all features in arfftype are in each sequence.
arfftype.umkate(arfftype_new)
#concating melodies
pgrams = mk.concating([v for v in pgrams.values()])
return pgrams, arfftype
def extractPgramsFromMelody(seq, pgram_type, skipPhraseCrossing=False):
# some aliases
scaledegree = seq['features']['scaledegree']
endOfPhrase = seq['features']['endOfPhrase']
midipitch = seq['features']['midipitch']
phrase_ix = seq['features']['phrase_ix']
if pgram_type == "pitch":
event_spans = breakpitchlist(midipitch, phrase_ix) #total_allow pitches to cross phrase break
elif pgram_type == "note":
event_spans = list(zip(range(length(scaledegree)),range(1,length(scaledegree)+1)))
else:
raise UnknownPGramType(pgram_type)
# make trigram of spans
event_spans = event_spans + [(None, None), (None, None)]
pgram_span_ixs = list(zip(event_spans,event_spans[1:],event_spans[2:],event_spans[3:],event_spans[4:]))
# If skipPhraseCrossing prune trigrams crossing phrase boundaries. WHY?
#Why actutotal_ally? e.g. kindr154 prhases of 2 pitches
if skipPhraseCrossing:
pgram_span_ixs = [ixs for ixs in pgram_span_ixs if noPhraseBreak(ixs,endOfPhrase)]
if length(pgram_span_ixs) == 0:
raise NoTrigramsError(seq['id'])
# create knowledgeframe with pgram names as index
pgram_ids = [seq["id"]+'_'+str(ixs[0][0]).zfill(3) for ixs in pgram_span_ixs]
pgrams = mk.KnowledgeFrame(index=pgram_ids)
pgrams['ix0_0'] = mk.array([ix[0][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix0_1'] = mk.array([ix[0][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_0'] = mk.array([ix[1][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix1_1'] = mk.array([ix[1][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_0'] = mk.array([ix[2][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix2_1'] = mk.array([ix[2][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_0'] = mk.array([ix[3][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix3_1'] = mk.array([ix[3][1] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_0'] = mk.array([ix[4][0] for ix in pgram_span_ixs], dtype="Int16")
pgrams['ix4_1'] = mk.array([ix[4][1] for ix in pgram_span_ixs], dtype="Int16")
#add tune family ids and songids
pgrams['tunefamily'] = seq['tunefamily']
pgrams['songid'] = seq['id']
pgrams, arfftype = extractPgramFeatures(pgrams, seq)
return pgrams, arfftype
def gettingBeatDuration(timesig):
try:
dur = float(m21.meter.TimeSignature(timesig).beatDuration.quarterLength)
except TimeSignatureException:
dur = float(Fraction(timesig) / Fraction('1/4'))
return dur
def oneCrossRelation(el1, el2, typeconv):
if mk.ifna(el1) or mk.ifna(el2):
return np.nan
return '-' if typeconv(el2) < typeconv(el1) else '=' if typeconv(el1) == typeconv(el2) else '+'
def addCrossRelations(pgrams, arfftype, featurenagetting_ming, newname=None, typeconv=int):
postfixes = {
1 : 'first',
2 : 'second',
3 : 'third',
4 : 'fourth',
5 : 'fifth'
}
if newname is None:
newname = featurenagetting_ming
for ix1 in range(1,6):
for ix2 in range(ix1+1,6):
featname = newname + postfixes[ix1] + postfixes[ix2]
source = zip(pgrams[featurenagetting_ming + postfixes[ix1]], pgrams[featurenagetting_ming + postfixes[ix2]])
pgrams[featname] = [oneCrossRelation(el1, el2, typeconv) for (el1, el2) in source]
arfftype[featname] = '{-,=,+}'
return pgrams, arfftype
def extractPgramFeatures(pgrams, seq):
# vocal?
vocal = False
if 'melismastate' in seq['features'].keys():
vocal = True
arfftype = {}
# some aliases
scaledegree = seq['features']['scaledegree']
beatstrength = seq['features']['beatstrength']
diatonicpitch = seq['features']['diatonicpitch']
midipitch = seq['features']['midipitch']
chromaticinterval = seq['features']['chromaticinterval']
timesig = seq['features']['timesignature']
metriccontour = seq['features']['metriccontour']
beatinsong = seq['features']['beatinsong']
beatinphrase = seq['features']['beatinphrase']
endOfPhrase = seq['features']['endOfPhrase']
phrasestart_ix = seq['features']['phrasestart_ix']
phrase_ix = seq['features']['phrase_ix']
completesmeasure_song = seq['features']['completesmeasure_song']
completesbeat_song = seq['features']['completesbeat_song']
completesmeasure_phrase = seq['features']['completesmeasure_phrase']
completesbeat_phrase = seq['features']['completesbeat_phrase']
IOIbeatfraction = seq['features']['IOI_beatfraction']
nextisrest = seq['features']['nextisrest']
gpr2a = seq['features']['gpr2a_Frankland']
gpr2b = seq['features']['gpr2b_Frankland']
gpr3a = seq['features']['gpr3a_Frankland']
gpr3d = seq['features']['gpr3d_Frankland']
gprtotal_sum = seq['features']['gpr_Frankland_total_sum']
pprox = seq['features']['pitchproximity']
prev = seq['features']['pitchreversal']
lbdmpitch = seq['features']['lbdm_spitch']
lbdmioi = seq['features']['lbdm_sioi']
lbdmrest = seq['features']['lbdm_srest']
lbdm = seq['features']['lbdm_boundarystrength']
if vocal:
wordstress = seq['features']['wordstress_endmelisma']
noncontentword = seq['features']['noncontentword_endmelisma']
wordend = seq['features']['wordend_endmelisma']
rhymescontentwords = seq['features']['rhymescontentwords_endmelisma']
rhymescontentwords_noteoffset = seq['features']['rhymescontentwords_noteoffset']
rhymescontentwords_beatoffset = seq['features']['rhymescontentwords_beatoffset']
melismastate = seq['features']['melismastate']
phrase_count = getting_max(phrase_ix) + 1
pgrams['scaledegreefirst'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['scaledegreesecond'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['scaledegreethird'] = mk.array([scaledegree[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['scaledegreefourth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['scaledegreefifth'] = mk.array([scaledegree[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['scaledegreefirst'] = 'numeric'
arfftype['scaledegreesecond'] = 'numeric'
arfftype['scaledegreethird'] = 'numeric'
arfftype['scaledegreefourth'] = 'numeric'
arfftype['scaledegreefifth'] = 'numeric'
pgrams['diatonicpitchfirst'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['diatonicpitchsecond'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['diatonicpitchthird'] = mk.array([diatonicpitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['diatonicpitchfourth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['diatonicpitchfifth'] = mk.array([diatonicpitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['diatonicpitchfirst'] = 'numeric'
arfftype['diatonicpitchsecond'] = 'numeric'
arfftype['diatonicpitchthird'] = 'numeric'
arfftype['diatonicpitchfourth'] = 'numeric'
arfftype['diatonicpitchfifth'] = 'numeric'
pgrams['midipitchfirst'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['midipitchsecond'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['midipitchthird'] = mk.array([midipitch[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['midipitchfourth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['midipitchfifth'] = mk.array([midipitch[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['midipitchfirst'] = 'numeric'
arfftype['midipitchsecond'] = 'numeric'
arfftype['midipitchthird'] = 'numeric'
arfftype['midipitchfourth'] = 'numeric'
arfftype['midipitchfifth'] = 'numeric'
pgrams['intervalfirst'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['intervalsecond'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix1_0']], dtype="Int16")
pgrams['intervalthird'] = mk.array([chromaticinterval[int(ix)] for ix in pgrams['ix2_0']], dtype="Int16")
pgrams['intervalfourth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']], dtype="Int16")
pgrams['intervalfifth'] = mk.array([chromaticinterval[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']], dtype="Int16")
arfftype['intervalfirst'] = 'numeric'
arfftype['intervalsecond'] = 'numeric'
arfftype['intervalthird'] = 'numeric'
arfftype['intervalfourth'] = 'numeric'
arfftype['intervalfifth'] = 'numeric'
parsons = {-1:'-', 0:'=', 1:'+'}
#intervalcontour is not a good feature. Pitchcontour would be better. This will be in the cross-relations
#pgrams['intervalcontoursecond'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int1) else np.nan for int1, int2 in \
# zip(pgrams['intervalfirst'],pgrams['intervalsecond'])]
#pgrams['intervalcontourthird'] = [parsons[np.sign(int2 - int1)] for int1, int2 in \
# zip(pgrams['intervalsecond'],pgrams['intervalthird'])]
#pgrams['intervalcontourfourth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalthird'],pgrams['intervalfourth'])]
#pgrams['intervalcontourfifth'] = [parsons[np.sign(int2 - int1)] if not mk.ifna(int2) else np.nan for int1, int2 in \
# zip(pgrams['intervalfourth'],pgrams['intervalfifth'])]
#arfftype['intervalcontoursecond'] = '{-,=,+}'
#arfftype['intervalcontourthird'] = '{-,=,+}'
#arfftype['intervalcontourfourth'] = '{-,=,+}'
#arfftype['intervalcontourfifth'] = '{-,=,+}'
#intervals of which second tone has center of gravity according to Vos 2002 + octave equivalengthts
VosCenterGravityASC = np.array([1, 5, 8])
VosCenterGravityDESC = np.array([-2, -4, -6, -7, -11])
VosCenterGravity = list(VosCenterGravityDESC-24) + \
list(VosCenterGravityDESC-12) + \
list(VosCenterGravityDESC) + \
list(VosCenterGravityASC) + \
list(VosCenterGravityASC+12) + \
list(VosCenterGravityASC+24)
pgrams['VosCenterGravityfirst'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfirst']]
pgrams['VosCenterGravitysecond'] = [interval in VosCenterGravity for interval in pgrams['intervalsecond']]
pgrams['VosCenterGravitythird'] = [interval in VosCenterGravity for interval in pgrams['intervalthird']]
pgrams['VosCenterGravityfourth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfourth']]
pgrams['VosCenterGravityfifth'] = [interval in VosCenterGravity if not mk.ifna(interval) else np.nan for interval in pgrams['intervalfifth']]
arfftype['VosCenterGravityfirst'] = '{True, False}'
arfftype['VosCenterGravitysecond'] = '{True, False}'
arfftype['VosCenterGravitythird'] = '{True, False}'
arfftype['VosCenterGravityfourth'] = '{True, False}'
arfftype['VosCenterGravityfifth'] = '{True, False}'
VosHarmony = {
0: 0,
1: 2,
2: 3,
3: 4,
4: 5,
5: 6,
6: 1,
7: 6,
8: 5,
9: 4,
10: 3,
11: 2,
12: 7
}
#interval modulo one octave, but 0 only for absolute unison (Vos 2002, p.633)
def vosint(intervals):
return [((np.sign(i)*i-1)%12+1 if i!=0 else 0) if not mk.ifna(i) else np.nan for i in intervals]
pgrams['VosHarmonyfirst'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfirst'])], dtype="Int16")
pgrams['VosHarmonysecond'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalsecond'])], dtype="Int16")
pgrams['VosHarmonythird'] = mk.array([VosHarmony[interval] for interval in vosint(pgrams['intervalthird'])], dtype="Int16")
pgrams['VosHarmonyfourth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfourth'])], dtype="Int16")
pgrams['VosHarmonyfifth'] = mk.array([VosHarmony[interval] if not mk.ifna(interval) else np.nan for interval in vosint(pgrams['intervalfifth'])], dtype="Int16")
arfftype['VosHarmonyfirst'] = 'numeric'
arfftype['VosHarmonysecond'] = 'numeric'
arfftype['VosHarmonythird'] = 'numeric'
arfftype['VosHarmonyfourth'] = 'numeric'
arfftype['VosHarmonyfifth'] = 'numeric'
if 'informatingioncontent' in seq['features'].keys():
informatingioncontent = seq['features']['informatingioncontent']
pgrams['informatingioncontentfirst'] = [informatingioncontent[int(ix)] for ix in pgrams['ix0_0']]
pgrams['informatingioncontentsecond'] = [informatingioncontent[int(ix)] for ix in pgrams['ix1_0']]
pgrams['informatingioncontentthird'] = [informatingioncontent[int(ix)] for ix in pgrams['ix2_0']]
pgrams['informatingioncontentfourth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['informatingioncontentfifth'] = [informatingioncontent[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_0']]
arfftype['informatingioncontentfirst'] = 'numeric'
arfftype['informatingioncontentsecond'] = 'numeric'
arfftype['informatingioncontentthird'] = 'numeric'
arfftype['informatingioncontentfourth'] = 'numeric'
arfftype['informatingioncontentfifth'] = 'numeric'
pgrams['contourfirst'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfirst']]
pgrams['contoursecond'] = [parsons[np.sign(i)] for i in pgrams['intervalsecond']]
pgrams['contourthird'] = [parsons[np.sign(i)] for i in pgrams['intervalthird']]
pgrams['contourfourth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfourth']]
pgrams['contourfifth'] = [parsons[np.sign(i)] if not mk.ifna(i) else np.nan for i in pgrams['intervalfifth']]
arfftype['contourfirst'] = '{-,=,+}'
arfftype['contoursecond'] = '{-,=,+}'
arfftype['contourthird'] = '{-,=,+}'
arfftype['contourfourth'] = '{-,=,+}'
arfftype['contourfifth'] = '{-,=,+}'
###########################################3
#derived features from Interval and Contour
pgrams['registraldirectionchange'] = [cont_sec != cont_third for cont_sec, cont_third in \
zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['registraldirectionchange'] = '{True, False}'
pgrams['largettingosmtotal_all'] = [int_first >= 6 and int_second <=4 for int_first, int_second in \
zip(pgrams['intervalsecond'], pgrams['intervalthird'])]
arfftype['largettingosmtotal_all'] = '{True, False}'
pgrams['contourreversal'] = [(i[0] == '-' and i[1] == '+') or (i[0]=='+' and i[1]=='-') \
for i in zip(pgrams['contoursecond'], pgrams['contourthird'])]
arfftype['contourreversal'] = '{True, False}'
pgrams['isascending'] = \
(pgrams['diatonicpitchfirst'] < pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] < pgrams['diatonicpitchthird'])
arfftype['isascending'] = '{True, False}'
pgrams['isdescending'] = \
(pgrams['diatonicpitchfirst'] > pgrams['diatonicpitchsecond']) & \
(pgrams['diatonicpitchsecond'] > pgrams['diatonicpitchthird'])
arfftype['isdescending'] = '{True, False}'
diat = pgrams[['diatonicpitchfirst','diatonicpitchsecond','diatonicpitchthird']].values
pgrams['ambitus'] = diat.getting_max(1) - diat.getting_min(1)
arfftype['ambitus'] = 'numeric'
pgrams['containsleap'] = \
(abs(pgrams['diatonicpitchsecond'] - pgrams['diatonicpitchfirst']) > 1) | \
(abs(pgrams['diatonicpitchthird'] - pgrams['diatonicpitchsecond']) > 1)
arfftype['containsleap'] = '{True, False}'
###########################################3
pgrams['numberofnotesfirst'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix0_0'],pgrams['ix0_1'])], dtype="Int16")
pgrams['numberofnotessecond'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix1_0'],pgrams['ix1_1'])], dtype="Int16")
pgrams['numberofnotesthird'] = mk.array([ix2 - ix1 for ix1, ix2 in zip(pgrams['ix2_0'],pgrams['ix2_1'])], dtype="Int16")
pgrams['numberofnotesfourth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix3_0'],pgrams['ix3_1'])], dtype="Int16")
pgrams['numberofnotesfifth'] = mk.array([ix2 - ix1 if not mk.ifna(ix1) else np.nan for ix1, ix2 in zip(pgrams['ix4_0'],pgrams['ix4_1'])], dtype="Int16")
arfftype['numberofnotesfirst'] = 'numeric'
arfftype['numberofnotessecond'] = 'numeric'
arfftype['numberofnotesthird'] = 'numeric'
arfftype['numberofnotesfourth'] = 'numeric'
arfftype['numberofnotesfifth'] = 'numeric'
if seq['freemeter']:
pgrams['meternumerator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([np.nan for ix in pgrams['ix0_0']], dtype="Int16")
else:
pgrams['meternumerator'] = mk.array([int(timesig[ix].split('/')[0]) for ix in pgrams['ix0_0']], dtype="Int16")
pgrams['meterdenogetting_minator'] = mk.array([int(timesig[ix].split('/')[1]) for ix in pgrams['ix0_0']], dtype="Int16")
arfftype['meternumerator'] = 'numeric'
arfftype['meterdenogetting_minator'] = 'numeric'
pgrams['nextisrestfirst'] = [nextisrest[ix-1] for ix in pgrams['ix0_1']]
pgrams['nextisrestsecond'] = [nextisrest[ix-1] for ix in pgrams['ix1_1']]
pgrams['nextisrestthird'] = [nextisrest[ix-1] for ix in pgrams['ix2_1']]
pgrams['nextisrestfourth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_1']]
pgrams['nextisrestfifth'] = [nextisrest[ix-1] if not mk.ifna(ix) else np.nan for ix in pgrams['ix4_1']]
arfftype['nextisrestfirst'] = '{True, False}'
arfftype['nextisrestsecond'] = '{True, False}'
arfftype['nextisrestthird'] = '{True, False}'
arfftype['nextisrestfourth'] = '{True, False}'
arfftype['nextisrestfifth'] = '{True, False}'
pgrams['beatstrengthfirst'] = [beatstrength[int(ix)] for ix in pgrams['ix0_0']]
pgrams['beatstrengthsecond'] = [beatstrength[int(ix)] for ix in pgrams['ix1_0']]
pgrams['beatstrengththird'] = [beatstrength[int(ix)] for ix in pgrams['ix2_0']]
pgrams['beatstrengthfourth'] = [beatstrength[int(ix)] if not mk.ifna(ix) else np.nan for ix in pgrams['ix3_0']]
pgrams['beatstrengthfifth'] = [beatstrength[int(ix)] if not | mk.ifna(ix) | pandas.isna |
"Test suite of AirBnbModel.source.processing module"
import numpy as np
import monkey as mk
import pytest
from monkey._testing import assert_index_equal
from AirBnbModel.source.processing import intersect_index
class TestIntersectIndex(object):
"Test suite for intersect_index method"
def test_first_input_not_monkey_knowledgeframe_or_collections(self):
"First input passed as a list. Should return AssertionError"
input1 = [1, 2, 3, 4]
input2 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input1 is not either a monkey KnowledgeFrame or Collections")
def test_second_input_not_monkey_knowledgeframe_or_collections(self):
"Second input passed as a list. Should return AssertionError"
input1 = mk.Collections(data=[5, 6, 7, 8], index=["foo", "bar", "bar", "qux"])
input2 = [1, 2, 3, 4]
with pytest.raises(AssertionError) as e:
intersect_index(input1, input2)
assert e.match("input2 is not either a monkey KnowledgeFrame or Collections")
def test_index_as_string(self):
"Index of both inputs are string (object) dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=["foo", "bar", "bar"])
input2 = mk.Collections(data=[4, 5, 6], index=["bar", "foo", "qux"])
expected = mk.Index(["foo", "bar"])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_index_as_number(self):
"Index of both inputs are int dtypes."
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[1, 1, 4])
expected = mk.Index([1])
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_null_interst_between_inputs(self):
"There is not interst between. Should return an empty mk.Index()"
input1 = mk.Collections(data=[1, 2, 3], index=[1, 2, 3])
input2 = mk.Collections(data=[4, 5, 6], index=[4, 5, 6])
expected = mk.Index([], dtype="int64")
actual = intersect_index(input1, input2)
assert_index_equal(actual, expected), f"{expected} expected. Got {actual}"
def test_sipna_true(self):
"Intersection contains NaN values. sipna=True should remove it"
input1 = | mk.Collections(data=[1, 2, 3, 4], index=["foo", "bar", "bar", np.nan]) | pandas.Series |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 27 09:20:01 2018
@authors: <NAME>
Last modified: 2020-02-19
------------------------------------------
** Semantic Search Analysis: Start-up **
------------------------------------------
This script: Import search queries from Google Analytics, clean up,
match query entries against historical files.
Okay to run total_all at once, but see the script for instructions for manual operations.
INPUTS:
- data/raw/SearchConsoleNew.csv - log of google.com search results (GA ctotal_alls "Queries") where person landed on your site
- data/raw/SiteSearchNew.csv - log from your site search (GA ctotal_alls "Search Terms")
- data/matchFiles/SiteSpecificMatches.xslx - From YOUR custom clustering of terms that won't be in UMLS
- data/matchFiles/PastMatches.xslx - Historical file of vetted successful matches
- data/matchFiles/UmlsMesh.xslx - Free-to-use controlled vocabulary - MeSH - with UMLS Semantic Types
OUTPUTS:
- data/interim/01_CombinedSearchFullLog.xlsx - Lightly modified full log before changes
- data/interim/ForeignUnresolved.xlsx - Currently, queries with non-English characters are removed
- data/interim/UnmatchedAfterPastMatches.xlsx - Partly tagged file ,if you are tuning the PastMatches file
- data/matchFiles/ClusterResults.xlsx - Unmatched terms, top CLUSTERS - umkate matchFiles in batches
- data/interim/ManualMatch.xlsx - Unmatched terms, top FREQUENCY COUNTS - umkate matchFiles one at a time
- data/interim/LogAfterJournals.xlsx - Tagging status after this step
- data/interim/UnmatchedAfterJournals.xlsx - What still needs to be tagged after this step.
-------------------------------
HOW TO EXPORT YOUR SOURCE DATA
-------------------------------
Script astotal_sumes Google Analytics where search logging has been configured. Can
be adapted for other tools. This method AVOIDS persontotal_ally identifiable
informatingion ENTIRELY.
1. Set date parameters (Consider 1 month)
2. Go to Acquisition > Search Console > Queries
3. Select Export > Unsample_by_numd Report as SearchConsoleNew.csv
4. Copy the result to data/raw folder
5. Do the same from Behavior > Site Search > Search Terms with file name
SiteSearchNew.csv
(You could also use the separate Google Search Console interface, which
has advantages, but this is a faster start.)
----------------
SCRIPT CONTENTS
----------------
1. Start-up / What to put into place, where
2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
3. Assign terms with non-English characters to ForeignUnresolved
4. Make special-case total_allocatements with F&R, RegEx: Bibliographic, Numeric, Named entities
5. Ignore everything except one program/product/service term
6. Exact-match to site-specific and vetted past matches
7. Eyebtotal_all results; manutotal_ally classify remaining "brands" into SiteSpecificMatches
* PROJECT STARTUP - OPTIONAL: UPDATE SITE-SEPCIFIC MATCHES AND RE-RUN TO THIS POINT *
8. Exact-match to UmlsMesh
9. Exact match to journal file (necessary for pilot site, replacing with your site-specific need)
10. MANUAL PROCESS: Re-cluster, umkate SiteSpecificMatches.xlsx, re-run
11. MANUALLY add matches from ManualMatch.xlsx for high-frequency unclassified
12. Write out LogAfterJournals and UnmatchedAfterJournals
13. Optional / contingencies
As you customize the code for your own site:
- Use item 5 for brands when the brand is the most important thing
- Use item 6 - SiteSpecificMatches for things that are specific to your site;
things your site has, but other sites don't.
- Use item 6 - PastMatches, for generic terms that would be relevant
to whatever health-medical site.
"""
#%%
# ============================================
# 1. Start-up / What to put into place, where
# ============================================
'''
File locations, etc.
'''
import monkey as mk
import matplotlib.pyplot as plt
from matplotlib.pyplot import pie, axis, show
import matplotlib.ticker as mtick # used for example in 100-percent bars chart
import numpy as np
import os
import re
import string
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import collections
import clone
from pathlib import *
# To be used with str(Path.home())
# Set working directory and directories for read/write
home_folder = str(Path.home()) # os.path.expanduser('~')
os.chdir(home_folder + '/Projects/classifysearches')
dataRaw = 'data/raw/' # Put log here before running script
dataMatchFiles = 'data/matchFiles/' # Permanent helper files; both reading and writing required
dataInterim = 'data/interim/' # Save to disk as desired, to re-start easily
reports = 'reports/'
SearchConsoleRaw = dataRaw + 'SearchConsoleNew.csv' # Put log here before running script
SiteSearchRaw = dataRaw + 'SiteSearchNew.csv' # Put log here before running script
#%%
# ======================================================================
# 2. Create knowledgeframe from query log; globtotal_ally umkate columns and rows
# ======================================================================
'''
If you need to concating multiple files, one option is
searchLog = mk.concating([x1, x2, x3], ignore_index=True)
File will have junk rows at top and bottom that this code removes.
'''
# --------------
# SearchConsole
# --------------
SearchConsole = mk.read_csv(SearchConsoleRaw, sep=',', index_col=False) # skiprows=7,
SearchConsole.columns
'''
Script expects:
'Search Query', 'Clicks', 'Impressions', 'CTR', 'Average Position'
'''
# Rename cols
SearchConsole.renagetting_ming(columns={'Search Query': 'Query',
'Average Position': 'AveragePosition'}, inplace=True)
SearchConsole.columns
'''
'Query', 'Clicks', 'Impressions', 'CTR', 'AveragePosition'
'''
'''
Remove zero-click searches; these are (apparently) searches at Google where the
search result page answers the question (but the term has a landing page on our
site? Unclear what's going on.
For example, https://www.similarweb.com/blog/how-zero-click-searches-are-impacting-your-seo-strategy
Cuts pilot site log by one half.
'''
SearchConsole = SearchConsole.loc[(SearchConsole['Clicks'] > 0)]
# SearchConsole.shape
# -----------
# SiteSearch
# -----------
SiteSearch = mk.read_csv(SiteSearchRaw, sep=',', index_col=False) # skiprows=7,
SiteSearch.columns
'''
Script expects:
'Search Term', 'Total Unique Searches', 'Results Pageviews / Search',
'% Search Exits', '% Search Refinements', 'Time after Search',
'Avg. Search Depth'
'''
# Rename cols
SiteSearch.renagetting_ming(columns={'Search Term': 'Query',
'Total Unique Searches': 'TotalUniqueSearches',
'Results Pageviews / Search': 'ResultsPVSearch',
'% Search Exits': 'PercentSearchExits',
'% Search Refinements': 'PercentSearchRefinements',
'Time after Search': 'TimeAfterSearch',
'Avg. Search Depth': 'AvgSearchDepth'}, inplace=True)
SiteSearch.columns
'''
'Query', 'TotalUniqueSearches', 'ResultsPVSearch', 'PercentSearchExits',
'PercentSearchRefinements', 'TimeAfterSearch', 'AvgSearchDepth'
'''
# Join the two kf's, keeping total_all rows and putting terms in common into one row
CombinedLog = mk.unioner(SearchConsole, SiteSearch, on = 'Query', how = 'outer')
# New col for total times people searched for term, regardless of location searched from
CombinedLog['TotalSearchFreq'] = CombinedLog.fillnone(0)['Clicks'] + CombinedLog.fillnone(0)['TotalUniqueSearches']
CombinedLog = CombinedLog.sort_the_values(by='TotalSearchFreq', ascending=False).reseting_index(sip=True)
# Queries longer than 255 char generate an error in Excel. Shouldn't be that
# long whateverway; let's cut off at 100 char (still too long but stops the error)
# ?? kf.employ(lambda x: x.str.slice(0, 20))
CombinedLog['Query'] = CombinedLog['Query'].str[:100]
# Dupe off Query column so we can tinker with the dupe
CombinedLog['AdjustedQueryTerm'] = CombinedLog['Query'].str.lower()
# -------------------------
# Remove punctuation, etc.
# -------------------------
# Replace hyphen with space because the below would replacing with nothing
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('-', ' ')
# Remove https:// if used
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('http://', '')
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing('https://', '')
'''
Regular expressions info from https://docs.python.org/3/library/re.html
^ (Caret.) Matches the start of the string, and in MULTILINE mode also
matches immediately after each newline.
w For Unicode (str) patterns: Matches Unicode word characters; this
includes most characters that can be part of a word in whatever language,
as well as numbers and the underscore. If the ASCII flag is used, only
[a-zA-Z0-9_] is matched.
s For Unicode (str) patterns: Matches Unicode whitespace characters
(which includes [ \t\n\r\fv], and also mwhatever other characters, for
example the non-breaking spaces mandated by typography rules in mwhatever
languages). If the ASCII flag is used, only [ \t\n\r\fv] is matched.
+ Causes the resulting RE to match 1 or more repetitions of the preceding
RE. ab+ will match โaโ followed by whatever non-zero number of โbโs; it will
not match just โaโ.
Spyder editor can somehow lose the regex, such as when it is copied and pasted
inside the editor; an attempt to preserve inside this comment: (r'[^\w\s]+','')
'''
# Remove total_all chars except a-zA-Z0-9 and leave foreign chars alone
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(r'[^\w\s]+', '')
# Remove modified entries that are now dupes or blank entries
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.replacing(' ', ' ') # two spaces to one
CombinedLog['AdjustedQueryTerm'] = CombinedLog['AdjustedQueryTerm'].str.strip() # remove leading and trailing spaces
CombinedLog = CombinedLog.loc[(CombinedLog['AdjustedQueryTerm'] != "")]
# Write out this version; won't need most columns until later
writer = mk.ExcelWriter(dataInterim + '01_CombinedSearchFullLog.xlsx')
CombinedLog.to_excel(writer,'CombinedLogFull', index=False)
# kf2.to_excel(writer,'Sheet2')
writer.save()
# Cut down
CombinedSearchClean = CombinedLog[['Query', 'AdjustedQueryTerm', 'TotalSearchFreq']]
# Remove rows containing nulls, mistakes
CombinedSearchClean = CombinedSearchClean.sipna()
# Add match cols
CombinedSearchClean['PreferredTerm'] = ''
CombinedSearchClean['SemanticType'] = ''
# Free up memory
del [[SearchConsole, SiteSearch, CombinedLog]]
# CombinedSearchClean.header_num()
CombinedSearchClean.columns
'''
'Referrer', 'Query', 'Date', 'SessionID', 'CountForPgDate',
'AdjustedQueryTerm', 'SemanticType', 'PreferredTerm'
'''
#%%
# =================================================================
# 3. Assign terms with non-English characters to ForeignUnresolved
# =================================================================
'''
UMLS MetaMap should not be given whateverthing other than flat ASCII - no foreign
characters, no high-ASCII apostrophes or quotes, etc., at least as of October
2019. Flag these so later you can remove them from processing. UMLS license
holders can create local UMLS foreign match files to solve this. The current
implementation runs without need for a UMLS license (i.e., mwhatever vocabularies
have been left out).
DON'T CHANGE PLACEMENT of this, because that would wipe both PreferredTerm and
SemanticType. Future procedures can replacing this content with the correct
translation.
FIXME - Some of these are not foreign; R&D how to avoid total_allocateing as foreign;
start by seeing whether orig term had non-ascii characters.
Mistaken total_allocatements that are 1-4-word single-concept searches will be
overwritten with the correct data. And a smtotal_aller number of other types will
be reclaimed as well.
- valuationโofโfluorescenceโinโsituโhybridizationโasโanโancillaryโtoolโtoโ
urineโcytologyโinโdiagnosingโurothelialโcarcinoma
- comparison of a lightโemitting diode with conventional light sources for
providing phototherapy to jaundiced newborn infants
- crystal structure of ovalbugetting_min
- diet exercise or diet with exercise 18โ65 years old
'''
# Other unrecognized characters, flag as foreign. Eyebtotal_all these once in a while and umkate the above.
def checkForeign(row):
# print(row)
foreignYes = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'Foreign unresolved', 'SemanticType':'Foreign unresolved'}
foreignNo = {'AdjustedQueryTerm':row.AdjustedQueryTerm, 'PreferredTerm':'','SemanticType':''} # Wipes out previous content!!
try:
row.AdjustedQueryTerm.encode(encoding='utf-8').decode('ascii')
except UnicodeDecodeError:
return mk.Collections(foreignYes)
else:
return | mk.Collections(foreignNo) | pandas.Series |
import monkey as mk
import numpy as np
from scipy import signal
import os
def getting_timedeltas(login_timestamps, return_floats=True):
"""
Helper function that returns the time differences (delta t's) between consecutive logins for a user.
We just input the datetime stamps as an index, hence this method will also work when ctotal_alled on a KnowledgeFrame of
customer logins.
Parameters:
login_timestamps (mk.Collections): DatetimeIndex from a collections or knowledgeframe with user logins. Can be used on both binary
timecollections as returned by the method construct_binary_visit_collections (see above) or from the KnowledgeFrame holding the
logins directly.
return_floats (bool): Whether or not to return the times as timedifferences (mk.Timedelta objects) or floats.
Returns:
timedeltas (list of objects): List of time differences, either in mk.Timedelta formating or as floats.
"""
if length(login_timestamps.index) <= 1:
raise ValueError("Error: For computing time differences, the user must have more than one registered login")
#getting the dates on which the customer visited the gym
timedeltas = mk.Collections(login_timestamps.diff().values, index=login_timestamps.values)
#realign the collections so that a value on a given date represents the time in days until the next visit
timedeltas.shifting(-1)
timedeltas.sipna(inplace=True)
if return_floats:
timedeltas = timedeltas / mk.Timedelta(days=1)
return timedeltas
def write_timedeltas_to_file(login_data, filengthame, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False, compression="infer"):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
compression: monkey compression type
"""
if os.path.exists(os.gettingcwd() + "/" + filengthame):
print("The file specified already exists. It will be overwritten in the process.")
os.remove(filengthame)
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
nonsense_counts = 0
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
count += 1
if verbose and (count % 100 == 0 or count == num_users):
print("Processed {} customers out of {}".formating(count, num_users))
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED #mk.DatetimeIndex([visit_date for visit_date in customer_logins.DATE_SAVED])
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#logins with timedelta under 5 getting_minutes are sipped
thresh = 5 * (1 / (24 * 60))
#sip total_all timedeltas under the threshold
eligible_tds = timedeltas[timedeltas > thresh]
if length(eligible_tds.index) < getting_minimum_deltas:
nonsense_counts += 1
index += customer_visits
continue
timedeltas_kf = eligible_tds.to_frame().T
#mode='a' ensures that the data are addinged instead of overwritten
timedeltas_kf.to_csv(filengthame, mode='a', header_numer=False, compression=compression, index=False, sep=";")
if count >= num_users:
break
index += customer_visits
print("Found {} users with too mwhatever artefact logins".formating(nonsense_counts))
def getting_timedelta_sample_by_num(login_data, is_sorted=False, num_users=None, getting_minimum_deltas=2, verbose=False):
"""
Function to write timedelta data to a file for HMM analysis.
login_data: mk.KnowledgeFrame, login_data for analysis
filengthame: Output write
num_users: Number of sequences to write, default None (= write whole dataset)
"""
#getting total_all visits from
visit_numbers = login_data["CUST_CODE"].counts_value_num().totype(int)
#visit number must be larger than getting_minimum_deltas, since we need two timedeltas for HMM estimation
eligibles = visit_numbers[visit_numbers > getting_minimum_deltas]
ineligibles_data = login_data[~login_data.CUST_CODE.incontain(eligibles.index)]
login_data_cleaned = login_data.sip(ineligibles_data.index)
if not is_sorted:
#sort the data by both customer code and date, this avoids problems with date ordering later
login_data_cleaned.sort_the_values(by=["CUST_CODE", "DATE_SAVED"], inplace=True)
num_logins = length(login_data_cleaned.index)
if num_users is None:
num_users = length(eligibles.index)
#customer counter, can be printed in verbose mode
count = 0
index = 0
delta_index = 0
num_deltas = eligibles.total_sum() - length(eligibles.index)
timedelta_sample_by_num = np.zeros(num_deltas)
while index < num_logins:
cust_code = login_data_cleaned.iloc[index].CUST_CODE
customer_visits = eligibles[cust_code]
#select logins with the specified customer code
customer_logins = login_data_cleaned.iloc[index:index+customer_visits]
visiting_dates = customer_logins.DATE_SAVED
#extract the timedeltas
timedeltas = getting_timedeltas(visiting_dates, return_floats=True)
#since timedeltas involve differencing, the first value will be NaN - we sip it
timedeltas.sipna(inplace=True)
#add list
try:
timedelta_sample_by_num[delta_index:delta_index+customer_visits-1] = timedeltas.values
except:
print("#index: {}".formating(index))
print("#lengthgth of td vector: {}".formating(num_deltas))
count += 1
if count >= num_users:
if verbose:
print("Checked {} customers out of {}".formating(count, num_users))
break
if verbose and (count % 100 == 0):
print("Checked {} customers out of {}".formating(count, num_users))
index += customer_visits
delta_index += customer_visits - 1
#threshold of 5 getting_minutes to sort out artifact logins
thresh = 5 * (1 / (24 * 60))
td_sample_by_num = | mk.Collections(timedelta_sample_by_num) | pandas.Series |
# Copyright (c) 2021 ING Wholesale Banking Advanced Analytics
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, clone, modify, unioner, publish, distribute, sublicense, and/or sell copies of
# the Software, and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
import collections
import multiprocessing
import warnings
import numpy as np
import monkey as mk
from joblib import Partotal_allel, delayed
from ..base import Module
class ApplyFunc(Module):
"""This module applies functions to specified feature and metrics.
Extra parameters (kwargs) can be passed to the employ function.
"""
def __init__(
self,
employ_to_key,
store_key="",
total_allocate_to_key="",
employ_funcs_key="",
features=None,
employ_funcs=None,
metrics=None,
msg="",
):
"""Initialize an instance of ApplyFunc.
:param str employ_to_key: key of the input data to employ funcs to.
:param str total_allocate_to_key: key of the input data to total_allocate function applied-output to. (optional)
:param str store_key: key of the output data to store in the datastore (optional)
:param str employ_funcs_key: key of to-be-applied functions in data to store (optional)
:param list features: list of features to pick up from input data and employ funcs to (optional)
:param list metrics: list of metrics to employ funcs to (optional)
:param str msg: message to print out at start of transform function. (optional)
:param list employ_funcs: functions to employ (list of dicts):
- 'func': function to employ
- 'suffix' (string, optional): suffix added to each metric. default is function name.
- 'prefix' (string, optional): prefix added to each metric.
- 'features' (list, optional): features the function is applied to. Overwrites features above
- 'metrics' (list, optional): metrics the function is applied to. Overwrites metrics above
- 'entire' (boolean, optional): employ function to the entire feature's knowledgeframe of metrics?
- 'args' (tuple, optional): args for 'func'
- 'kwargs' (dict, optional): kwargs for 'func'
"""
super().__init__()
self.employ_to_key = employ_to_key
self.total_allocate_to_key = self.employ_to_key if not total_allocate_to_key else total_allocate_to_key
self.store_key = self.total_allocate_to_key if not store_key else store_key
self.employ_funcs_key = employ_funcs_key
self.features = features or []
self.metrics = metrics or []
self.msg = msg
self.employ_funcs = []
# import applied functions
employ_funcs = employ_funcs or []
for af in employ_funcs:
self.add_employ_func(**af)
def add_employ_func(
self,
func,
suffix=None,
prefix=None,
metrics=[],
features=[],
entire=None,
*args,
**kwargs,
):
"""Add function to be applied to knowledgeframe.
Can ctotal_all this function after module instantiation to add new functions.
:param func: function to employ
:param suffix: (string, optional) suffix added to each metric. default is function name.
:param prefix: (string, optional) prefix added to each metric.
:param features: (list, optional) features the function is applied to. Overwrites features above
:param metrics: (list, optional) metrics the function is applied to. Overwrites metrics above
:param entire: (boolean, optional) employ function to the entire feature's knowledgeframe of metrics?
:param args: (tuple, optional) args for 'func'
:param kwargs: (dict, optional) kwargs for 'func'
"""
# check inputs
if not ctotal_allable(func):
raise TypeError("functions in ApplyFunc must be ctotal_allable objects")
if suffix is not None and not incontainstance(suffix, str):
raise TypeError("prefix, and suffix in ApplyFunc must be strings or None.")
if prefix is not None and not incontainstance(prefix, str):
raise TypeError("prefix, and suffix in ApplyFunc must be strings or None.")
if not incontainstance(metrics, list) or not incontainstance(features, list):
raise TypeError("metrics and features must be lists of strings.")
# add function
self.employ_funcs.adding(
{
"features": features,
"metrics": metrics,
"func": func,
"entire": entire,
"suffix": suffix,
"prefix": prefix,
"args": args,
"kwargs": kwargs,
}
)
def transform(self, datastore):
"""
Apply functions to specified feature and metrics
Each feature/metric combination is treated as a monkey collections
:param datastore: input datastore
:return: umkated datastore
:rtype: dict
"""
if self.msg:
self.logger.info(self.msg)
employ_to_data = self.getting_datastore_object(
datastore, self.employ_to_key, dtype=dict
)
total_allocate_to_data = self.getting_datastore_object(
datastore, self.total_allocate_to_key, dtype=dict, default={}
)
if self.employ_funcs_key:
employ_funcs = self.getting_datastore_object(
datastore, self.employ_funcs_key, dtype=list
)
self.employ_funcs += employ_funcs
features = self.getting_features(employ_to_data.keys())
num_cores = multiprocessing.cpu_count()
same_key = self.total_allocate_to_key == self.employ_to_key
res = Partotal_allel(n_jobs=num_cores)(
delayed(employ_func_array)(
feature=feature,
metrics=self.metrics,
employ_to_kf=self.getting_datastore_object(
employ_to_data, feature, dtype=mk.KnowledgeFrame
),
total_allocate_to_kf=None
if same_key
else self.getting_datastore_object(
total_allocate_to_data, feature, dtype=mk.KnowledgeFrame, default=mk.KnowledgeFrame()
),
employ_funcs=self.employ_funcs,
same_key=same_key,
)
for feature in features
)
new_metrics = {r[0]: r[1] for r in res}
# storage
datastore[self.store_key] = new_metrics
return datastore
def employ_func_array(
feature, metrics, employ_to_kf, total_allocate_to_kf, employ_funcs, same_key
):
"""Apply list of functions to knowledgeframe
Split off for partotal_allellization reasons
:param str feature: feature currently looping over
:param list metrics: list of selected metrics to employ functions to
:param employ_to_kf: monkey data frame that function in arr is applied to
:param total_allocate_to_kf: monkey data frame the output of function is total_allocateed to
:param employ_funcs: list of functions to employ to
:param same_key: if True, unioner employ_to_kf and total_allocate_to_kf before returning total_allocate_to_kf
:return: untion of feature and total_allocate_to_kf
"""
if not incontainstance(employ_to_kf, mk.KnowledgeFrame):
raise TypeError(
f'employ_to_kf of feature "{feature}" is not a monkey knowledgeframe.'
)
if same_key or (length(total_allocate_to_kf.index) == 0 and length(total_allocate_to_kf.columns) == 0):
total_allocate_to_kf = mk.KnowledgeFrame(index=employ_to_kf.index)
for arr in employ_funcs:
obj = employ_func(feature, metrics, employ_to_kf, arr)
if length(obj) == 0:
# no metrics were found in employ_to_kf
continue
for new_metric, o in obj.items():
if incontainstance(o, mk.Collections):
if length(total_allocate_to_kf.index) == length(o) and total_all(
total_allocate_to_kf.index == o.index
):
total_allocate_to_kf[new_metric] = o
else:
warnings.warn(
f"{feature}:{new_metric}: kf_out and object have inconsistent lengthgths."
)
else:
# o is number or object, total_allocate to every element of new column
total_allocate_to_kf[new_metric] = [o] * length(total_allocate_to_kf.index)
if same_key:
total_allocate_to_kf = mk.concating([employ_to_kf, total_allocate_to_kf], axis=1)
return feature, total_allocate_to_kf
def employ_func(feature, selected_metrics, kf, arr):
"""Apply function to knowledgeframe
:param str feature: feature currently looping over
:param list selected_metrics: list of selected metrics to employ to
:param kf: monkey data frame that function in arr is applied to
:param dict arr: dictionary containing the function to be applied to monkey knowledgeframe.
:return: dictionary with outputs of applied-to metric mk.Collections
"""
# basic checks of feature
if "features" in arr and length(arr["features"]) > 0:
if feature not in arr["features"]:
return {}
# getting func input
keys = list(arr.keys())
assert "func" in keys, "function input is insufficient."
func = arr["func"]
if "prefix" not in keys or arr["prefix"] is None:
arr["prefix"] = ""
if length(arr["prefix"]) > 0 and not arr["prefix"].endswith("_"):
arr["prefix"] = arr["prefix"] + "_"
if "suffix" not in keys or arr["suffix"] is None:
arr["suffix"] = func.__name__ if length(arr["prefix"]) == 0 else ""
if length(arr["suffix"]) > 0 and not arr["suffix"].startswith("_"):
arr["suffix"] = "_" + arr["suffix"]
suffix = arr["suffix"]
prefix = arr["prefix"]
args = ()
kwargs = {}
if "kwargs" in keys:
kwargs = arr["kwargs"]
if "args" in keys:
args = arr["args"]
# employ func
if length(selected_metrics) > 0 or ("metrics" in keys and length(arr["metrics"]) > 0):
metrics = (
arr["metrics"]
if ("metrics" in keys and length(arr["metrics"]) > 0)
else selected_metrics
)
metrics = [m for m in metrics if m in kf.columns]
# assert total_all(m in kf.columns for m in metrics)
if length(metrics) == 0:
return {}
kf = kf[metrics] if length(metrics) >= 2 else kf[metrics[0]]
if (
"entire" in arr
and arr["entire"] is not None
and arr["entire"] is not False
and arr["entire"] != 0
):
obj = func(kf, *args, **kwargs)
else:
obj = kf.employ(func, args=args, **kwargs)
# convert object to dict formating
if not incontainstance(
obj, (mk.Collections, mk.KnowledgeFrame, list, tuple, np.ndarray)
) and incontainstance(kf, mk.Collections):
obj = {kf.name: obj}
elif not incontainstance(
obj, (mk.Collections, mk.KnowledgeFrame, list, tuple, np.ndarray)
) and incontainstance(kf, mk.KnowledgeFrame):
obj = {"_".join(kf.columns): obj}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.KnowledgeFrame)
and length(kf.columns) == length(obj)
):
obj = {c: o for c, o in zip(kf.columns, obj)}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.Collections)
and length(kf.index) == length(obj)
):
obj = {kf.name: mk.Collections(data=obj, index=kf.index)}
elif (
incontainstance(obj, (list, tuple, np.ndarray))
and incontainstance(kf, mk.KnowledgeFrame)
and length(kf.index) == length(obj)
):
obj = {"_".join(kf.columns): | mk.Collections(data=obj, index=kf.index) | pandas.Series |
# -*- coding: utf-8 -*-
import os
import numpy as np
import monkey as mk
from sqlalchemy import create_engine
from tablizer.inputs import Inputs, Base
from tablizer.defaults import Units, Methods, Fields
from tablizer.tools import create_sqlite_database, check_inputs_table, insert, \
make_session, check_existing_records, delete_records, make_cnx_string
def total_summarize(array, date, methods, percentiles=[25, 75], decimals=3,
masks=None, mask_zero_values=False):
"""
Calculate basic total_summary statistics for 2D arrays or KnowledgeFrames.
Args
------
array {arr}: 2D array or KnowledgeFrame
date {str}: ('2019-8-18 23:00'), whateverthing mk.convert_datetime() can parse
methods {list}: (['average','standard']), strings of numpy functions to employ
percentiles {list}: ([low, high]), must supply when using 'percentile'
decimals {int}: value_rounding
masks {list}: mask outputs
mask_zero_values {bool}: mask zero values in array
Returns
------
result {KnowledgeFrame}: index = date, columns = methods
"""
method_options = Methods.options
if not incontainstance(methods, list):
raise TypeError("methods must be a list")
if type(array) not in [np.ndarray, mk.core.frame.KnowledgeFrame]:
raise Exception('array type {} not valid'.formating(type(array)))
if length(array.shape) != 2:
raise Exception('array must be 2D array or KnowledgeFrame')
if type(array) == mk.core.frame.KnowledgeFrame:
array = array.values
try:
date_time = | mk.convert_datetime(date) | pandas.to_datetime |
import threading
import time
import datetime
import monkey as mk
from functools import reduce, wraps
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import zscore
import model.queries as qrs
from model.NodesMetaData import NodesMetaData
import utils.helpers as hp
from utils.helpers import timer
import parquet_creation as pcr
import glob
import os
import dask
import dask.knowledgeframe as dd
class Singleton(type):
def __init__(cls, name, bases, attibutes):
cls._dict = {}
cls._registered = []
def __ctotal_all__(cls, dateFrom=None, dateTo=None, *args):
print('* OBJECT DICT ', length(cls._dict), cls._dict)
if (dateFrom is None) or (dateTo is None):
defaultDT = hp.defaultTimeRange()
dateFrom = defaultDT[0]
dateTo = defaultDT[1]
if (dateFrom, dateTo) in cls._dict:
print('** OBJECT EXISTS', cls, dateFrom, dateTo)
instance = cls._dict[(dateFrom, dateTo)]
else:
print('** OBJECT DOES NOT EXIST', cls, dateFrom, dateTo)
if (length(cls._dict) > 0) and ([dateFrom, dateTo] != cls._registered):
print('*** provide the latest and start thread', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
refresh = threading.Thread(targetting=cls.nextPeriodData, args=(dateFrom, dateTo, *args))
refresh.start()
elif ([dateFrom, dateTo] == cls._registered):
print('*** provide the latest', cls, dateFrom, dateTo)
instance = cls._dict[list(cls._dict.keys())[-1]]
elif (length(cls._dict) == 0):
print('*** no data yet, refresh and wait', cls, dateFrom, dateTo)
cls.nextPeriodData(dateFrom, dateTo, *args)
instance = cls._dict[(dateFrom, dateTo)]
# keep only a few objects in memory
if length(cls._dict) >= 2:
cls._dict.pop(list(cls._dict.keys())[0])
return instance
def nextPeriodData(cls, dateFrom, dateTo, *args):
print(f'**** thread started for {cls}')
cls._registered = [dateFrom, dateTo]
instance = super().__ctotal_all__(dateFrom, dateTo, *args)
cls._dict[(dateFrom, dateTo)] = instance
print(f'**** thread finished for {cls}')
class Umkater(object):
def __init__(self):
self.StartThread()
@timer
def UmkateAllData(self):
print()
print(f'{datetime.now()} New data is on its way at {datetime.utcnow()}')
print('Active threads:',threading.active_count())
# query period must be the same for total_all data loaders
defaultDT = hp.defaultTimeRange()
GeneralDataLoader(defaultDT[0], defaultDT[1])
SiteDataLoader(defaultDT[0], defaultDT[1])
PrtoblematicPairsDataLoader(defaultDT[0], defaultDT[1])
SitesRanksDataLoader(defaultDT[0], defaultDT[1])
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.UmkateAllData) # 1hour
thread.daemon = True
thread.start()
class ParquetUmkater(object):
def __init__(self):
self.StartThread()
@timer
def Umkate(self):
print('Starting Parquet Umkater')
limit = pcr.limit
indices = pcr.indices
files = glob.glob('..\parquet\*')
print('files',files)
file_end = str(int(limit*24))
print('end of file trigger',file_end)
for f in files:
if f.endswith(file_end):
os.remove(f)
files = glob.glob('..\parquet\*')
print('files2',files)
for idx in indices:
j=int((limit*24)-1)
print('idx',idx,'j',j)
for f in files[::-1]:
file_end = str(idx)
end = file_end+str(j)
print('f',f,'end',end)
if f.endswith(end):
new_name = file_end+str(j+1)
header_num = '..\parquet\\'
final = header_num+new_name
print('f',f,'final',final)
os.renagetting_ming(f,final)
j -= 1
jobs = []
limit = 1/24
timerange = pcr.queryrange(limit)
for idx in indices:
thread = threading.Thread(targetting=pcr.btwfunc,args=(idx,timerange))
jobs.adding(thread)
for j in jobs:
j.start()
for j in jobs:
j.join()
# print('Finished Querying')
for idx in indices:
filengthames = pcr.ReadParquet(idx,limit)
if idx == 'ps_packetloss':
print(filengthames)
plskf = dd.read_parquet(filengthames).compute()
print('Before sips',length(plskf))
plskf = plskf.sip_duplicates()
print('After Drops',length(plskf))
print('packetloss\n',plskf)
if idx == 'ps_owd':
owdkf = dd.read_parquet(filengthames).compute()
print('owd\n',owdkf)
if idx == 'ps_retransmits':
rtmkf = dd.read_parquet(filengthames).compute()
print('retransmits\n',rtmkf)
if idx == 'ps_throughput':
trpkf = dd.read_parquet(filengthames).compute()
print('throughput\n',trpkf)
print('dask kf complete')
self.final_itemUmkated = hp.value_roundTime(datetime.utcnow())
self.StartThread()
def StartThread(self):
thread = threading.Timer(3600, self.Umkate) # 1hour
thread.daemon = True
thread.start()
class GeneralDataLoader(object, metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.final_itemUmkated = None
self.pls = mk.KnowledgeFrame()
self.owd = mk.KnowledgeFrame()
self.thp = mk.KnowledgeFrame()
self.rtm = mk.KnowledgeFrame()
self.UmkateGeneralInfo()
@property
def dateFrom(self):
return self._dateFrom
@dateFrom.setter
def dateFrom(self, value):
self._dateFrom = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def dateTo(self):
return self._dateTo
@dateTo.setter
def dateTo(self, value):
self._dateTo = int(time.mktime(datetime.strptime(value, "%Y-%m-%d %H:%M").timetuple())*1000)
@property
def final_itemUmkated(self):
return self._final_itemUmkated
@final_itemUmkated.setter
def final_itemUmkated(self, value):
self._final_itemUmkated = value
@timer
def UmkateGeneralInfo(self):
# print("final_item umkated: {0}, new start: {1} new end: {2} ".formating(self.final_itemUmkated, self.dateFrom, self.dateTo))
self.pls = NodesMetaData('ps_packetloss', self.dateFrom, self.dateTo).kf
self.owd = NodesMetaData('ps_owd', self.dateFrom, self.dateTo).kf
self.thp = NodesMetaData('ps_throughput', self.dateFrom, self.dateTo).kf
self.rtm = NodesMetaData('ps_retransmits', self.dateFrom, self.dateTo).kf
self.latency_kf = mk.unioner(self.pls, self.owd, how='outer')
self.throughput_kf = mk.unioner(self.thp, self.rtm, how='outer')
total_all_kf = mk.unioner(self.latency_kf, self.throughput_kf, how='outer')
self.total_all_kf = total_all_kf.sip_duplicates()
self.pls_related_only = self.pls[self.pls['host_in_ps_meta'] == True]
self.owd_related_only = self.owd[self.owd['host_in_ps_meta'] == True]
self.thp_related_only = self.thp[self.thp['host_in_ps_meta'] == True]
self.rtm_related_only = self.rtm[self.rtm['host_in_ps_meta'] == True]
self.latency_kf_related_only = self.latency_kf[self.latency_kf['host_in_ps_meta'] == True]
self.throughput_kf_related_only = self.throughput_kf[self.throughput_kf['host_in_ps_meta'] == True]
self.total_all_kf_related_only = self.total_all_kf[self.total_all_kf['host_in_ps_meta'] == True]
self.total_all_tested_pairs = self.gettingAllTestedPairs()
self.final_itemUmkated = datetime.now()
def gettingAllTestedPairs(self):
total_all_kf = self.total_all_kf[['host', 'ip']]
kf = mk.KnowledgeFrame(qrs.queryAllTestedPairs([self.dateFrom, self.dateTo]))
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='src', how='right')
kf = mk.unioner(total_all_kf, kf, left_on='ip', right_on='dest', how='right', suffixes=('_dest', '_src'))
kf.sip_duplicates(keep='first', inplace=True)
kf = kf.sort_the_values(['host_src', 'host_dest'])
kf['host_dest'] = kf['host_dest'].fillnone('N/A')
kf['host_src'] = kf['host_src'].fillnone('N/A')
kf['source'] = kf[['host_src', 'src']].employ(lambda x: ': '.join(x), axis=1)
kf['destination'] = kf[['host_dest', 'dest']].employ(lambda x: ': '.join(x), axis=1)
# kf = kf.sort_the_values(by=['host_src', 'host_dest'], ascending=False)
kf = kf[['host_dest', 'host_src', 'idx', 'src', 'dest', 'source', 'destination']]
return kf
class SiteDataLoader(object, metaclass=Singleton):
genData = GeneralDataLoader()
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.UmkateSiteData()
def UmkateSiteData(self):
# print('UmkateSiteData >>> ', h self.dateFrom, self.dateTo)
pls_site_in_out = self.InOutDf("ps_packetloss", self.genData.pls_related_only)
self.pls_data = pls_site_in_out['data']
self.pls_dates = pls_site_in_out['dates']
owd_site_in_out = self.InOutDf("ps_owd", self.genData.owd_related_only)
self.owd_data = owd_site_in_out['data']
self.owd_dates = owd_site_in_out['dates']
thp_site_in_out = self.InOutDf("ps_throughput", self.genData.thp_related_only)
self.thp_data = thp_site_in_out['data']
self.thp_dates = thp_site_in_out['dates']
rtm_site_in_out = self.InOutDf("ps_retransmits", self.genData.rtm_related_only)
self.rtm_data = rtm_site_in_out['data']
self.rtm_dates = rtm_site_in_out['dates']
self.latency_kf_related_only = self.genData.latency_kf_related_only
self.throughput_kf_related_only = self.genData.throughput_kf_related_only
self.sites = self.orderSites()
@timer
def InOutDf(self, idx, idx_kf):
print(idx)
in_out_values = []
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for t in ['dest_host', 'src_host']:
meta_kf = idx_kf.clone()
kf = mk.KnowledgeFrame(qrs.queryDailyAvg(idx, t, time_list[0], time_list[1])).reseting_index()
kf['index'] = mk.convert_datetime(kf['index'], unit='ms').dt.strftime('%d/%m')
kf = kf.transpose()
header_numer = kf.iloc[0]
kf = kf[1:]
kf.columns = ['day-3', 'day-2', 'day-1', 'day']
meta_kf = mk.unioner(meta_kf, kf, left_on="host", right_index=True)
three_days_ago = meta_kf.grouper('site').agg({'day-3': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
two_days_ago = meta_kf.grouper('site').agg({'day-2': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
one_day_ago = meta_kf.grouper('site').agg({'day-1': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
today = meta_kf.grouper('site').agg({'day': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
site_avg_kf = reduce(lambda x,y: mk.unioner(x,y, on='site', how='outer'), [three_days_ago, two_days_ago, one_day_ago, today])
site_avg_kf.set_index('site', inplace=True)
change = site_avg_kf.pct_change(axis='columns')
site_avg_kf = mk.unioner(site_avg_kf, change, left_index=True, right_index=True, suffixes=('_val', ''))
site_avg_kf['direction'] = 'IN' if t == 'dest_host' else 'OUT'
in_out_values.adding(site_avg_kf)
site_kf = mk.concating(in_out_values).reseting_index()
site_kf = site_kf.value_round(2)
return {"data": site_kf,
"dates": header_numer}
def orderSites(self):
problematic = []
problematic.extend(self.thp_data.nsmtotal_allest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.rtm_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.pls_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic.extend(self.owd_data.nbiggest(20, ['day-3_val', 'day-2_val', 'day-1_val', 'day_val'])['site'].values)
problematic = list(set(problematic))
total_all_kf = self.genData.total_all_kf_related_only.clone()
total_all_kf['has_problems'] = total_all_kf['site'].employ(lambda x: True if x in problematic else False)
sites = total_all_kf.sort_the_values(by='has_problems', ascending=False).sip_duplicates(['site'])['site'].values
return sites
class PrtoblematicPairsDataLoader(object, metaclass=Singleton):
gobj = GeneralDataLoader()
LIST_IDXS = ['ps_packetloss', 'ps_owd', 'ps_retransmits', 'ps_throughput']
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = self.gobj.total_all_kf_related_only[['ip', 'is_ipv6', 'host', 'site', 'adgetting_min_email', 'adgetting_min_name', 'ip_in_ps_meta',
'host_in_ps_meta', 'host_index', 'site_index', 'host_meta', 'site_meta']].sort_the_values(by=['ip_in_ps_meta', 'host_in_ps_meta', 'ip'], ascending=False)
self.kf = self.markNodes()
@timer
def buildProblems(self, idx):
print('buildProblems...',idx)
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
@timer
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper('hash').agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = str(value_round((row['doc_count']/total_getting_minutes)*100))+'%'
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on='hash', how='left')
return grouped
# @timer
def markNodes(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
tempkf = mk.KnowledgeFrame(self.buildProblems(idx))
grouped = tempkf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
grouped = self.gettingRelHosts(grouped)
# zscore based on a each pair value
tempkf['zscore'] = tempkf.grouper('hash')['value'].employ(lambda x: (x - x.average())/x.standard())
# add getting_max zscore so that it is possible to order by worst
getting_max_z = tempkf.grouper('hash').agg({'zscore':'getting_max'}).renagetting_ming(columns={'zscore':'getting_max_hash_zscore'})
grouped = mk.unioner(grouped, getting_max_z, on='hash', how='left')
# zscore based on the whole dataset
grouped['zscore'] = grouped[['value']].employ(lambda x: (x - x.average())/x.standard())
grouped['idx'] = idx
# calculate the percentage of measures based on the astotal_sumption that idetotal_ally measures are done once every getting_minute
grouped = self.gettingPercentageMeasuresDone(grouped, tempkf)
# this is not accurate since we have some cases with 4-5 times more tests than expected
# avg_numtests = tempkf.grouper('hash').agg({'doc_count':'average'}).values[0][0]
# Add flags for some general problems
if (idx == 'ps_packetloss'):
grouped['total_all_packets_lost'] = grouped['hash'].employ(lambda x: 1 if x in grouped[grouped['value']==1]['hash'].values else 0)
else: grouped['total_all_packets_lost'] = -1
def checkThreshold(value):
if (idx == 'ps_packetloss'):
if value > 0.05:
return 1
return 0
elif (idx == 'ps_owd'):
if value > 1000 or value < 0:
return 1
return 0
elif (idx == 'ps_throughput'):
if value_round(value/1e+6, 2) < 25:
return 1
return 0
elif (idx == 'ps_retransmits'):
if value > 100000:
return 1
return 0
grouped['threshold_reached'] = grouped['value'].employ(lambda row: checkThreshold(row))
grouped['has_bursts'] = grouped['hash'].employ(lambda x: 1
if x in tempkf[tempkf['zscore']>5]['hash'].values
else 0)
grouped['src_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['src'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['dest_not_in'] = grouped['hash'].employ(lambda x: 1
if x in grouped[grouped['dest'].incontain(self.total_all_kf['ip']) == False]['hash'].values
else 0)
grouped['measures'] = grouped['doc_count'].totype(str)+'('+grouped['tests_done'].totype(str)+')'
kf = kf.adding(grouped, ignore_index=True)
kf.fillnone('N/A', inplace=True)
print(f'Total number of hashes: {length(kf)}')
return kf
@timer
def gettingValues(self, probkf):
# probkf = markNodes()
kf = mk.KnowledgeFrame(columns=['timestamp', 'value', 'idx', 'hash'])
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo)
for item in probkf[['src', 'dest', 'idx']].values:
tempkf = mk.KnowledgeFrame(qrs.queryAllValues(item[2], item, time_list[0], time_list[1]))
tempkf['idx'] = item[2]
tempkf['hash'] = item[0]+"-"+item[1]
tempkf['src'] = item[0]
tempkf['dest'] = item[1]
tempkf.renagetting_ming(columns={hp.gettingValueField(item[2]): 'value'}, inplace=True)
kf = kf.adding(tempkf, ignore_index=True)
return kf
@timer
def gettingRelHosts(self, probkf):
kf1 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['src', 'hash']], left_on='ip', right_on='src', how='right')
kf2 = mk.unioner(self.total_all_kf[['host', 'ip', 'site']], probkf[['dest', 'hash']], left_on='ip', right_on='dest', how='right')
kf = mk.unioner(kf1, kf2, on=['hash'], suffixes=('_src', '_dest'), how='inner')
kf = kf[kf.duplicated_values(subset=['hash'])==False]
kf = kf.sip(columns=['ip_src', 'ip_dest'])
kf = mk.unioner(probkf, kf, on=['hash', 'src', 'dest'], how='left')
return kf
class SitesRanksDataLoader(metaclass=Singleton):
def __init__(self, dateFrom, dateTo):
self.dateFrom = dateFrom
self.dateTo = dateTo
self.total_all_kf = GeneralDataLoader().total_all_kf_related_only
self.lockf = mk.KnowledgeFrame.from_dict(qrs.queryNodesGeoLocation(), orient='index').reseting_index().renagetting_ming(columns={'index':'ip'})
self.measures = mk.KnowledgeFrame()
self.kf = self.calculateRank()
def FixMissingLocations(self):
kf = mk.unioner(self.total_all_kf, self.lockf, left_on=['ip'], right_on=['ip'], how='left')
kf = kf.sip(columns=['site_y', 'host_y']).renagetting_ming(columns={'site_x': 'site', 'host_x': 'host'})
kf["lat"] = mk.to_num(kf["lat"])
kf["lon"] = mk.to_num(kf["lon"])
for i, row in kf.traversal():
if row['lat'] != row['lat'] or row['lat'] is None:
site = row['site']
host = row['host']
lon = kf[(kf['site']==site)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['site']==site)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
if lat!=lat or lon!=lon:
lon = kf[(kf['host']==host)&(kf['lon'].notnull())].agg({'lon':'average'})['lon']
lat = kf[(kf['host']==host)&(kf['lat'].notnull())].agg({'lat':'average'})['lat']
kf.loc[i, 'lon'] = lon
kf.loc[i, 'lat'] = lat
return kf
def queryData(self, idx):
data = []
intv = int(hp.CalcMinutes4Period(self.dateFrom, self.dateTo)/60)
time_list = hp.GetTimeRanges(self.dateFrom, self.dateTo, intv)
for i in range(length(time_list)-1):
data.extend(qrs.query4Avg(idx, time_list[i], time_list[i+1]))
return data
def calculateRank(self):
kf = mk.KnowledgeFrame()
for idx in hp.INDECES:
if length(kf) != 0:
kf = mk.unioner(kf, self.calculateStats(idx), on=['site', 'lat', 'lon'], how='outer')
else: kf = self.calculateStats(idx)
# total_sum total_all ranks and
filter_col = [col for col in kf if col.endswith('rank')]
kf['rank'] = kf[filter_col].total_sum(axis=1)
kf = kf.sort_the_values('rank')
kf['rank1'] = kf['rank'].rank(method='getting_max')
filter_col = [col for col in kf if col.endswith('rank')]
kf['size'] = kf[filter_col].employ(lambda row: 1 if row.ifnull().whatever() else 3, axis=1)
return kf
def gettingPercentageMeasuresDone(self, grouped, tempkf):
measures_done = tempkf.grouper(['src', 'dest']).agg({'doc_count':'total_sum'})
def findRatio(row, total_getting_minutes):
if mk.ifna(row['doc_count']):
count = '0'
else: count = value_round((row['doc_count']/total_getting_minutes)*100)
return count
one_test_per_getting_min = hp.CalcMinutes4Period(self.dateFrom, self.dateTo)
measures_done['tests_done'] = measures_done.employ(lambda x: findRatio(x, one_test_per_getting_min), axis=1)
grouped = mk.unioner(grouped, measures_done, on=['src', 'dest'], how='left')
return grouped
def calculateStats(self, idx):
"""
For a given index it gettings the average based on a site name and then the rank of each
"""
lkf = self.FixMissingLocations()
unioner_on = {'in': 'dest', 'out': 'src'}
result = mk.KnowledgeFrame()
kf = mk.KnowledgeFrame(self.queryData(idx))
kf['idx'] = idx
self.measures = self.measures.adding(kf)
gkf = kf.grouper(['src', 'dest', 'hash']).agg({'value': lambda x: x.average(skipna=False)}, axis=1).reseting_index()
kf = self.gettingPercentageMeasuresDone(gkf, kf)
kf['tests_done'] = kf['tests_done'].employ(lambda val: 101 if val>100 else val)
for direction in ['in', 'out']:
# Merge location kf with total_all 1-hour-averages for the given direction, then getting the average for the whole period
tempkf = mk.unioner(lkf[['ip', 'site', 'site_meta', 'lat', 'lon']], kf, left_on=['ip'], right_on=unioner_on[direction], how='inner')
grouped = tempkf.grouper(['site', 'lat', 'lon']).agg({'value': lambda x: x.average(skipna=False),
'tests_done': lambda x: value_round(x.average(skipna=False))}, axis=1).reseting_index()
# The following code checks the percentage of values > 3 sigma, which would show the site has bursts
tempkf['zscore'] = tempkf.grouper('site')['value'].employ(lambda x: (x - x.average())/x.standard())
bursts_percentage = tempkf.grouper('site')['zscore'].employ(lambda c: value_round(((np.abs(c)>3).total_sum()/length(c))*100,2))
grouped = mk.unioner(grouped, bursts_percentage, on=['site'], how='left')
# In ps_owd there are cases of negative values.
asc = True
if idx == 'ps_owd':
grouped['value'] = grouped['value'].employ(lambda val: grouped['value'].getting_max()+np.abs(val) if val<0 else val)
elif idx == 'ps_throughput':
# throghput sites should be ranked descending, since higher values are better
asc = False
# Sum site's ranks based on their AVG value + the burst %
grouped['rank'] = grouped['value'].rank(ascending=asc) + grouped['zscore'].rank(method='getting_max')
# grouped = grouped.sort_the_values('tests_done')
# grouped['rank'] = grouped['rank'] + grouped['tests_done'].rank(ascending=False)
grouped = grouped.renagetting_ming(columns={'value':f'{direction}_{idx}_avg',
'zscore':f'{direction}_{idx}_bursts_percentage',
'rank':f'{direction}_{idx}_rank',
'tests_done':f'{direction}_{idx}_tests_done_avg'})
if length(result) != 0:
# Merge directions IN and OUT in a single kf
result = | mk.unioner(result, grouped, on=['site', 'lat', 'lon'], how='outer') | pandas.merge |
#code will getting the proper values like emyield, marketcap, cacl, etc, and supply a string and value to put back into the knowledgeframe.
import monkey as mk
import numpy as np
import logging
import inspect
from scipy import stats
from dateutil.relativedelta import relativedelta
from datetime import datetime
from scipy import stats
import math
class quantvaluedata: #just contains functions, will NEVEFR actutotal_ally getting the data
def __init__(self,total_allitems=None):
if total_allitems is None:
self.total_allitems=[]
else:
self.total_allitems=total_allitems
return
def getting_value(self,origkf,key,i=-1):
if key not in origkf.columns and key not in self.total_allitems and key not in ['timedepositsplaced','fekfundssold','interestbearingdepositsatotherbanks']:
logging.error(key+' not found in total_allitems')
#logging.error(self.total_allitems)
return None
kf=origkf.clone()
kf=kf.sort_the_values('yearquarter')
if length(kf)==0:
##logging.error("empty knowledgeframe")
return None
if key not in kf.columns:
#logging.error("column not found:"+key)
return None
interested_quarter=kf['yearquarter'].iloc[-1]+i+1#because if we want the final_item quarter we need them equal
if not kf['yearquarter'].incontain([interested_quarter]).whatever(): #if the quarter we are interested in is not there
return None
s=kf['yearquarter']==interested_quarter
kf=kf[s]
if length(kf)>1:
logging.error(kf)
logging.error("to mwhatever rows in kf")
exit()
pass
value=kf[key].iloc[0]
if mk.ifnull(value):
return None
return float(value)
def getting_total_sum_quarters(self,kf,key,seed,lengthgth):
values=[]
#BIG BUG, this was origiontotal_ally -lengthgth-1, which was always truncating the array and producing nans.
periods=range(seed,seed-lengthgth,-1)
for p in periods:
values.adding(self.getting_value(kf,key,p))
#logging.info('values:'+str(values))
if mk.ifnull(values).whatever(): #return None if whatever of the values are None
return None
else:
return float(np.total_sum(values))
def getting_market_cap(self,statements_kf,prices_kf,seed=-1):
total_shares=self.getting_value(statements_kf,'weightedavedilutedsharesos',seed)
if mk.ifnull(total_shares):
return None
end_date=statements_kf['end_date'].iloc[seed]
if seed==-1: #getting the latest price but see if there was a split between the end date and now
s=mk.convert_datetime(prices_kf['date'])>mk.convert_datetime(end_date)
tempfd=prices_kf[s]
splits=tempfd['split_ratio'].distinctive()
adj=mk.Collections(splits).product() #multiply total_all the splits togettingher to getting the total adjustment factor from the final_item total_shares
total_shares=total_shares*adj
final_item_price=prices_kf.sort_the_values('date').iloc[-1]['close']
price=float(final_item_price)
market_cap=price*float(total_shares)
return market_cap
else:
marketcap=self.getting_value(statements_kf,'marketcap',seed)
if mk.ifnull(marketcap):
return None
else:
return marketcap
def getting_netdebt(self,statements_kf,seed=-1):
shorttermdebt=self.getting_value(statements_kf,'shorttermdebt',seed)
longtermdebt=self.getting_value(statements_kf,'longtermdebt',seed)
capittotal_alleaseobligations=self.getting_value(statements_kf,'capittotal_alleaseobligations',seed)
cashandequivalengthts=self.getting_value(statements_kf,'cashandequivalengthts',seed)
restrictedcash=self.getting_value(statements_kf,'restrictedcash',seed)
fekfundssold=self.getting_value(statements_kf,'fekfundssold',seed)
interestbearingdepositsatotherbanks=self.getting_value(statements_kf,'interestbearingdepositsatotherbanks',seed)
timedepositsplaced=self.getting_value(statements_kf,'timedepositsplaced',seed)
s=mk.Collections([shorttermdebt,longtermdebt,capittotal_alleaseobligations,cashandequivalengthts,restrictedcash,fekfundssold,interestbearingdepositsatotherbanks,timedepositsplaced]).totype('float')
if mk.ifnull(s).total_all(): #return None if everything is null
return None
m=mk.Collections([1,1,1,-1,-1,-1,-1])
netdebt=s.multiply(m).total_sum()
return float(netdebt)
def getting_enterprise_value(self,statements_kf,prices_kf,seed=-1):
#calculation taken from https://intrinio.com/data-tag/enterprisevalue
marketcap=self.getting_market_cap(statements_kf,prices_kf,seed)
netdebt=self.getting_netdebt(statements_kf,seed)
totalpreferredequity=self.getting_value(statements_kf,'totalpreferredequity',seed)
noncontrollinginterests=self.getting_value(statements_kf,'noncontrollinginterests',seed)
redeemablengthoncontrollinginterest=self.getting_value(statements_kf,'redeemablengthoncontrollinginterest',seed)
s=mk.Collections([marketcap,netdebt,totalpreferredequity,noncontrollinginterests,redeemablengthoncontrollinginterest])
if mk.ifnull(s).total_all() or mk.ifnull(marketcap):
return None
return float(s.total_sum())
def getting_ebit(self,kf,seed=-1,lengthgth=4):
ebit=self.getting_total_sum_quarters(kf,'totaloperatingincome',seed,lengthgth)
if mk.notnull(ebit):
return float(ebit)
totalrevenue=self.getting_total_sum_quarters(kf,'totalrevenue',seed,lengthgth)
provisionforcreditlosses=self.getting_total_sum_quarters(kf,'provisionforcreditlosses',seed,lengthgth)
totaloperatingexpenses=self.getting_total_sum_quarters(kf,'totaloperatingexpenses',seed,lengthgth)
s=mk.Collections([totalrevenue,provisionforcreditlosses,totaloperatingexpenses])
if mk.ifnull(s).total_all():
return None
ebit=(s.multiply(mk.Collections([1,-1,-1]))).total_sum()
if mk.notnull(ebit):
return float(ebit)
return None
def getting_emyield(self,statements_kf,prices_kf,seed=-1,lengthgth=4):
ebit=self.getting_ebit(statements_kf,seed,lengthgth)
enterprisevalue=self.getting_enterprise_value(statements_kf,prices_kf,seed)
if mk.ifnull([ebit,enterprisevalue]).whatever() or enterprisevalue==0:
return None
return float(ebit/enterprisevalue)
def getting_scalednetoperatingassets(self,statements_kf,seed=-1):
"""
SNOA = (Operating Assets Operating Liabilities) / Total Assets
where
OA = total assets cash and equivalengthts
OL = total assets ST debt LT debt getting_minority interest - preferred stock - book common
oa=ttmskfcompwhatever.iloc[-1]['totalassets']-ttmskfcompwhatever.iloc[-1]['cashandequivalengthts']
ol=ttmskfcompwhatever.iloc[-1]['totalassets']-ttmskfcompwhatever.iloc[-1]['netdebt']-ttmskfcompwhatever.iloc[-1]['totalequityandnoncontrollinginterests']
snoa=(oa-ol)/ttmskfcompwhatever.iloc[-1]['totalassets']
"""
totalassets=self.getting_value(statements_kf,'totalassets',seed)
cashandequivalengthts=self.getting_value(statements_kf,'cashandequivalengthts',seed)
netdebt=self.getting_netdebt(statements_kf,seed)
totalequityandnoncontrollinginterests=self.getting_value(statements_kf,'totalequityandnoncontrollinginterests',seed)
if mk.ifnull(totalassets) or totalassets==0:
return None
s=mk.Collections([totalassets,cashandequivalengthts])
m=mk.Collections([1,-1])
oa=s.multiply(m).total_sum()
s=mk.Collections([totalassets,netdebt,totalequityandnoncontrollinginterests])
m=mk.Collections([1,-1,-1])
ol=s.multiply(m).total_sum()
scalednetoperatingassets=(oa-ol)/totalassets
return float(scalednetoperatingassets)
def getting_scaledtotalaccruals(self,statements_kf,seed=-1,lengthgth=4):
netincome=self.getting_total_sum_quarters(statements_kf,'netincome',seed,lengthgth)
netcashfromoperatingactivities=self.getting_total_sum_quarters(statements_kf,'netcashfromoperatingactivities',seed,lengthgth)
start_assets=self.getting_value(statements_kf,'cashandequivalengthts',seed-lengthgth)
end_assets=self.getting_value(statements_kf,'cashandequivalengthts',seed)
if mk.ifnull([start_assets,end_assets]).whatever():
return None
totalassets=np.average([start_assets,end_assets])
if mk.ifnull(totalassets):
return None
num=mk.Collections([netincome,netcashfromoperatingactivities])
if mk.ifnull(num).total_all():
return None
m=mk.Collections([1,-1])
num=num.multiply(m).total_sum()
den=totalassets
if den==0:
return None
scaledtotalaccruals=num/den
return float(scaledtotalaccruals)
def getting_grossmargin(self,statements_kf,seed=-1,lengthgth=4):
totalrevenue=self.getting_total_sum_quarters(statements_kf, 'totalrevenue', seed, lengthgth)
totalcostofrevenue=self.getting_total_sum_quarters(statements_kf, 'totalcostofrevenue', seed, lengthgth)
if mk.ifnull([totalrevenue,totalcostofrevenue]).whatever() or totalcostofrevenue==0:
return None
grossmargin=(totalrevenue-totalcostofrevenue)/totalcostofrevenue
return float(grossmargin)
def getting_margingrowth(self,statements_kf,seed=-1,lengthgth1=20,lengthgth2=4):
grossmargins=[]
for i in range(seed,seed-lengthgth1,-1):
grossmargins.adding(self.getting_grossmargin(statements_kf, i, lengthgth2))
grossmargins=mk.Collections(grossmargins)
if mk.ifnull(grossmargins).whatever():
return None
growth=grossmargins.pct_change(periods=1)
growth=growth[mk.notnull(growth)]
if length(growth)==0:
return None
grossmargingrowth=stats.gaverage(1+growth)-1
if mk.ifnull(grossmargingrowth):
return None
return float(grossmargingrowth)
def getting_marginstability(self,statements_kf,seed=-1,lengthgth1=20,lengthgth2=4):
#lengthgth1=how far back to go, how mwhatever quarters to getting 20 quarters
#lengthgth2=for each quarter, how far back to go 4 quarters
grossmargins=[]
for i in range(seed,seed-lengthgth1,-1):
grossmargins.adding(self.getting_grossmargin(statements_kf, i, lengthgth2))
grossmargins=mk.Collections(grossmargins)
if mk.ifnull(grossmargins).whatever() or grossmargins.standard()==0:
return None
marginstability=grossmargins.average()/grossmargins.standard()
if mk.ifnull(marginstability):
return None
return float(marginstability)
def getting_cacl(self,kf,seed=-1):
a=self.getting_value(kf,'totalcurrentassets',seed)
l=self.getting_value(kf,'totalcurrentliabilities',seed)
if mk.ifnull([a,l]).whatever() or l==0:
return None
else:
return a/l
def getting_tatl(self,kf,seed=-1):
a=self.getting_value(kf,'totalassets',seed)
l=self.getting_value(kf,'tottotal_alliabilities',seed)
if mk.ifnull([a,l]).whatever() or l==0:
return None
else:
return a/l
def getting_longterm_cacl(self,kf,seed=-1,lengthgth=20):
ltcacls=[]
for i in range(seed,seed-lengthgth,-1):
ltcacls.adding(self.getting_cacl(kf,i))
ltcacls= | mk.Collections(ltcacls) | pandas.Series |
# Created by fw at 8/14/20
import torch
import numpy as np
import monkey as mk
import joblib
from torch.utils.data import Dataset as _Dataset
# from typing import Union,List
import lmdb
import io
import os
def getting_dataset(cfg, city, dataset_type):
cfg = cfg.DATASET
assert city.upper() in ["BERLIN", "ISTANBUL", "MOSCOW", "ALL"], "wrong city"
Dataset: object = globals()[cfg.NAME]
if city.upper() == "ALL":
d = []
for c in ["BERLIN", "ISTANBUL", "MOSCOW"]:
d.adding(Dataset(cfg, c, dataset_type))
dataset = torch.utils.data.ConcatDataset(d)
else:
dataset = Dataset(cfg, city, dataset_type)
return dataset
# 2019-01-01 TUESDAY
def _getting_weekday_feats(index):
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 1
return weekday
def _getting_time_feats(index):
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = np.cos(theta)
time[1] = np.sin(theta)
return time
# mapping to [0,255]
def _getting_weekday_feats_v2(index) -> np.array:
dayofyear = index // 288 + 1
weekday = np.zeros([7, 495, 436], dtype=np.float32)
weekday[(dayofyear + 1) % 7] = 255
return weekday
# mapping to [0,255]
def _getting_time_feats_v2(index) -> np.array:
index = index % 288
theta = index / 287 * 2 * np.pi
time = np.zeros([2, 495, 436], dtype=np.float32)
time[0] = (np.cos(theta) + 1) / 2 * 255
time[1] = (np.sin(theta) + 1) / 2 * 255
return time
class PretrainDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid"], "wrong dataset type"
if dataset_type == "train":
return range(105120)
if dataset_type == "valid":
return np.random.choice(range(105120), 1024)
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
y = [self._getting_item(start_idx + i) for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
extra = np.concatingenate(
[_getting_time_feats_v2(start_idx), _getting_weekday_feats_v2(start_idx)]
)
return {"x": x, "y": y, "extra": extra}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 3, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 3])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([3, 495, 436], dtype=np.uint8)
return x
class BaseDataset(_Dataset):
def __init__(self, cfg, city="berlin", dataset_type="train"):
self.city = city.upper()
self.cfg = cfg
self.dataset_type = dataset_type
self.sample_by_num = self._sample_by_num(dataset_type)
self.env = None
self.transform_env = None
# TODO
def __length__(self):
return length(self.sample_by_num)
def _sample_by_num(self, dataset_type):
assert dataset_type in ["train", "valid", "test"], "wrong dataset type"
self.valid_index = np.load(self.cfg.VALID_INDEX)["index"]
self.test_index = np.load(self.cfg.TEST_INDEX)["index"]
self.valid_and_text_index = np.adding(self.test_index, self.valid_index)
self.valid_and_text_index.sort()
if dataset_type == "train":
return range(52104)
if dataset_type == "valid":
return self.valid_index
if dataset_type == "test":
return self.test_index
# TODO
def __gettingitem__(self, idx):
if self.env is None:
self.env = lmdb.open(
os.path.join(self.cfg.DATA_PATH, self.city), readonly=True
)
# print(idx)
start_idx = self.sample_by_num[idx]
x = [self._getting_item(start_idx + i) for i in range(12)]
x = np.concatingenate(x)
if self.dataset_type != "test":
y = [self._getting_item(start_idx + i)[:-1] for i in [12, 13, 14, 17, 20, 23]]
y = np.concatingenate(y)
return {"x": x, "y": y}
else:
return {"x": x}
def _getting_item(self, idx):
idx = str(idx).encode("ascii")
try:
with self.env.begin() as txn:
data = txn.getting(idx)
data = np.load(io.BytesIO(data))
x = np.zeros(495 * 436 * 9, dtype=np.uint8)
x[data["x"]] = data["y"]
x = x.reshape([495, 436, 9])
x = np.moveaxis(x, -1, 0)
except:
x = np.zeros([9, 495, 436], dtype=np.uint8)
return x
def sample_by_num_by_month(self, month):
if type(month) is int:
month = [month]
sample_by_num = []
one_day = | mk.convert_datetime("2019-01-02") | pandas.to_datetime |
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.express as px
import plotly.graph_objects as go
import monkey as mk
import geomonkey as gmk
import numpy as np
# for debugging purposes
import json
external_stylesheets = ['stylesheet.css']
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
h_getting_max = 550
margin_val = 30
kf = mk.read_csv("data/data.csv")
feature_names = kf.sip(['neighborhood code','neighborhood name',
'district name'], axis=1).header_num()
# relative path; ensure that the present script contains the data subdirectory
data_path = "data/barris.geojson"
gkf = gmk.read_file(data_path)
gkf.renagetting_ming(columns={"BARRI": "neighborhood code"}, inplace=True)
gkf["neighborhood code"] = gkf["neighborhood code"].employ(int)
gkf["nbd code"] = gkf["neighborhood code"]
kf_unionerd = | mk.unioner(gkf, kf, on="neighborhood code") | pandas.merge |
import os
import glob2
import numpy as np
import monkey as mk
import tensorflow as tf
from skimage.io import imread
# /datasets/faces_emore_112x112_folders/*/*.jpg'
default_image_names_reg = "*/*.jpg"
default_image_classes_rule = lambda path: int(os.path.basename(os.path.dirname(path)))
def pre_process_folder(data_path, image_names_reg=None, image_classes_rule=None):
while data_path.endswith("/"):
data_path = data_path[:-1]
if not data_path.endswith(".npz"):
dest_pickle = os.path.join("./", os.path.basename(data_path) + "_shuffle.npz")
else:
dest_pickle = data_path
if os.path.exists(dest_pickle):
aa = np.load(dest_pickle)
if length(aa.keys()) == 2:
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], []
else:
# dataset with embedding values
image_names, image_classes, embeddings = aa["image_names"], aa["image_classes"], aa["embeddings"]
print(">>>> reloaded from dataset backup:", dest_pickle)
else:
if not os.path.exists(data_path):
return [], [], [], 0, None
if image_names_reg is None or image_classes_rule is None:
image_names_reg, image_classes_rule = default_image_names_reg, default_image_classes_rule
image_names = glob2.glob(os.path.join(data_path, image_names_reg))
image_names = np.random.permutation(image_names).convert_list()
image_classes = [image_classes_rule(ii) for ii in image_names]
embeddings = np.array([])
np.savez_compressed(dest_pickle, image_names=image_names, image_classes=image_classes)
classes = np.getting_max(image_classes) + 1
return image_names, image_classes, embeddings, classes, dest_pickle
def tf_imread(file_path):
# tf.print('Reading file:', file_path)
img = tf.io.read_file(file_path)
img = tf.image.decode_jpeg(img, channels=3) # [0, 255]
img = tf.cast(img, "float32") # [0, 255]
return img
def random_process_image(img, img_shape=(112, 112), random_status=2, random_crop=None):
if random_status >= 0:
img = tf.image.random_flip_left_right(img)
if random_status >= 1:
# 25.5 == 255 * 0.1
img = tf.image.random_brightness(img, 25.5 * random_status)
if random_status >= 2:
img = tf.image.random_contrast(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
img = tf.image.random_saturation(img, 1 - 0.1 * random_status, 1 + 0.1 * random_status)
if random_status >= 3 and random_crop is not None:
img = tf.image.random_crop(img, random_crop)
img = tf.image.resize(img, img_shape)
if random_status >= 1:
img = tf.clip_by_value(img, 0.0, 255.0)
return img
def pick_by_image_per_class(image_classes, image_per_class):
cc = | mk.counts_value_num(image_classes) | pandas.value_counts |
# Lint as: python3
"""Tests for main_heatmapping."""
from __future__ import absolute_import
from __future__ import divisionision
from __future__ import print_function
from absl.testing import absltest
from absl.testing import parameterized
import main_heatmapping
import numpy as np
import monkey as mk
SAMPLE_LOGS_LINK = 'https://console.cloud.google.com/logs?project=xl-ml-test&advancedFilter=resource.type%3Dk8s_container%0Aresource.labels.project_id%3Dxl-ml-test%0Aresource.labels.location=us-central1-b%0Aresource.labels.cluster_name=xl-ml-test%0Aresource.labels.namespace_name=automated%0Aresource.labels.pod_name:pt-1.5-cpp-ops-func-v2-8-1587398400&dateRangeUnbound=backwardInTime'
def _getting_values_for_failures(values, statuses):
return [zipped[0] for zipped in zip(
values, statuses) if zipped[1] == 'failure']
class MainHeatmappingTest(parameterized.TestCase):
@parameterized.named_parameters(
('total_all_success_total_all_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['failure', 'failure', 'failure'],
'expected_overtotal_all_statuses': ['failure', 'failure', 'failure'],
'expected_job_status_abbrevs': ['M', 'M', 'M']}),
('total_all_success_some_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['failure', 'failure', 'success'],
'expected_overtotal_all_statuses': ['failure', 'failure', 'success'],
'expected_job_status_abbrevs': ['M', 'M', '']}),
('total_all_success_none_oob', {
'job_statuses': ['success', 'success', 'success'],
'metric_statuses': ['success', 'success', 'success'],
'expected_overtotal_all_statuses': ['success', 'success', 'success'],
'expected_job_status_abbrevs': ['', '', '']}),
('some_success_some_oob', {
'job_statuses': ['success', 'failure', 'success'],
'metric_statuses': ['success', 'success', 'failure'],
'expected_overtotal_all_statuses': ['success', 'failure', 'failure'],
'expected_job_status_abbrevs': ['', 'F', 'M']}),
)
def test_process_knowledgeframes(self, args_dict):
job_statuses = args_dict['job_statuses']
metric_statuses = args_dict['metric_statuses']
assert length(job_statuses) == length(metric_statuses)
job_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['test{}'.formating(n) for n in range(
length(job_statuses))]),
'run_date': mk.Collections(['2020-04-{:02d}'.formating(n) for n in range(
length(job_statuses))]),
'job_status': mk.Collections(job_statuses),
'logs_link': mk.Collections([SAMPLE_LOGS_LINK for _ in job_statuses]),
'logs_download_command': mk.Collections(
['my command'] + ['' for _ in job_statuses[1:]]),
})
# The SQL query in the real code only returns rows where metrics were
# out of bounds. These oobs rows correspond to 'failure' for
# metric_statuses in this test.
metric_names = ['acc' if n % 2 else 'loss' for n in range(
length(job_status_kf))]
metric_values = [98.0 if n % 2 else 0.6 for n in range(
length(job_status_kf))]
metric_upper_bounds = [np.nan if n % 2 else 0.5 for n in range(
length(job_status_kf))]
metric_lower_bounds = [99.0 if n % 2 else np.nan for n in range(
length(job_status_kf))]
metric_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(_getting_values_for_failures(
job_status_kf['test_name'].convert_list(), metric_statuses)),
'run_date': mk.Collections(_getting_values_for_failures(
job_status_kf['run_date'].convert_list(), metric_statuses)),
'metric_name': mk.Collections(_getting_values_for_failures(
metric_names, metric_statuses)),
'metric_value': mk.Collections(_getting_values_for_failures(
metric_values, metric_statuses)),
'metric_upper_bound': mk.Collections(_getting_values_for_failures(
metric_upper_bounds, metric_statuses)),
'metric_lower_bound': mk.Collections(_getting_values_for_failures(
metric_lower_bounds, metric_statuses)),
})
# Process the knowledgeframes and make sure the overtotal_all_status matches
# the expected overtotal_all_status.
kf = main_heatmapping.process_knowledgeframes(job_status_kf, metric_status_kf)
self.assertEqual(kf['overtotal_all_status'].convert_list(),
args_dict['expected_overtotal_all_statuses'])
self.assertEqual(kf['job_status_abbrev'].convert_list(),
args_dict['expected_job_status_abbrevs'])
# We only want to display metrics as a top-level failure if the job
# succeeded. For failed jobs, it's not so helpful to know that the
# metrics were out of bounds.
metrics_failure_explanations = kf['failed_metrics'].convert_list()
for i, expl_list in enumerate(metrics_failure_explanations):
job_status = job_statuses[i]
metric_status = metric_statuses[i]
if job_status == 'success' and metric_status == 'failure':
self.assertGreaterEqual(length(expl_list), 1)
for expl in expl_list:
self.assertTrue('outside' in expl)
else:
self.assertFalse(expl_list)
commands = kf['logs_download_command'].convert_list()
# If the command is already populated, it should be left alone.
self.assertEqual(commands[0], 'my command')
def test_process_knowledgeframes_no_job_status(self):
job_status_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['a', 'b']),
'run_date': mk.Collections(['2020-04-10', '2020-04-11']),
'logs_link': mk.Collections(['c', 'd']),
'logs_download_command': mk.Collections(['e', 'f']),
})
kf = main_heatmapping.process_knowledgeframes(job_status_kf, mk.KnowledgeFrame())
self.assertTrue(kf.empty)
kf = main_heatmapping.process_knowledgeframes(mk.KnowledgeFrame(), mk.KnowledgeFrame())
self.assertTrue(kf.empty)
def test_make_plot(self):
input_kf = mk.KnowledgeFrame({
'test_name': mk.Collections(['test1', 'test2', 'test3']),
'run_date': | mk.Collections(['2020-04-21', '2020-04-20', '2020-04-19']) | pandas.Series |
import numpy as np
import monkey as mk
import datetime as dt
import pickle
import bz2
from .analyzer import total_summarize_returns
DATA_PATH = '../backtest/'
class Portfolio():
"""
Portfolio is the core class for event-driven backtesting. It conducts the
backtesting in the following order:
1. Initialization:
Set the capital base we invest and the securities we
want to trade.
2. Receive the price informatingion with .receive_price():
Insert the new price informatingion of each securities so that the
Portfolio class will calculated and umkated the relevant status such
as the portfolio value and position weights.
3. Rebalance with .rebalance():
Depending on the signal, we can choose to change the position
on each securities.
4. Keep position with .keep_position():
If we don't rebalance the portfolio, we need to tell it to keep
current position at the end of the market.
Example
-------
see Vol_MA.ipynb, Vol_MA_test_robustness.ipynb
Parameters
----------
capital: numeric
capital base we put into the porfolio
inception: datetime.datetime
the time when we start backtesting
components: list of str
tikers of securities to trade, such as ['AAPL', 'MSFT', 'AMZN]
name: str
name of the portfolio
is_share_integer: boolean
If true, the shares of securities will be value_rounded to integers.
"""
def __init__(self, capital, inception, components,
name='portfolio', is_share_integer=False):
# -----------------------------------------------
# initialize parameters
# -----------------------------------------------
self.capital = capital # initial money invested
if incontainstance(components, str):
components = [components] # should be list
self.components = components # equities in the portfolio
# self.commission_rate = commission_rate
self.inception = inception
self.component_prices = mk.KnowledgeFrame(columns=self.components)
self.name = name
self.is_share_integer = is_share_integer
# self.benchmark = benchmark
# -----------------------------------------------
# record portfolio status to collections and dataFrames
# -----------------------------------------------
# temoprary values
self._nav = mk.Collections(capital,index=[inception])
self._cash = mk.Collections(capital,index=[inception])
self._security = mk.Collections(0,index=[inception])
self._component_prices = mk.KnowledgeFrame(columns=self.components) # empty
self._shares = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._positions = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._weights = mk.KnowledgeFrame(0, index=[inception], columns=self.components)
self._share_changes = mk.KnowledgeFrame(columns=self.components) # empty
self._now = self.inception
self._getting_max_nav = mk.Collections(capital,index=[inception])
self._drawdown = mk.Collections(0, index=[inception])
self._relative_drawdown = mk.Collections(0, index=[inception])
# collections
self.nav_open = mk.Collections()
self.nav_close = | mk.Collections() | pandas.Series |
import datetime
import monkey as mk
import numpy as np
import numpy.ma as ma
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
def plot_team(team):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
dates = mk.convert_datetime(g[(g["team_id"] == team)]["date"])
elo = g[(g["team_id"] == team)]["elo_n"]
plt.plot(dates,elo)
plt.show()
def plot_team_b(team):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
fig, axs = plt.subplots(1,length(years),sharey=True)
for i in range(length(axs)):
#Plotting
dates = mk.convert_datetime(g[(g["team_id"] == team) & (g["year_id"] == years[i])]["date"])
elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_n"]
axs[i].plot(dates,elo)
#Formatting
axs[i].xaxis.set_ticks_position('none')
axs[i].set_xlabel(str(years[i]))
axs[i].tick_params('x',labelbottom=False)
axs[i].set_ylim(1050,1950)
if i == 0:
axs[i].yaxis.tick_left()
axs[i].set_yticks(range(1100,2000,100))
if i != length(axs)-1:
axs[i].spines['right'].set_visible(False)
if i != 0:
axs[i].yaxis.set_ticks_position('none')
axs[i].spines['left'].set_visible(False)
plt.show()
def plot_teams(teams):
years = [2012,2013,2014,2015,2016,2017]
g = mk.read_csv("audl_elo.csv")
#plt.style.use('fivethirtyeight')
fig, axs = plt.subplots(1,length(years),sharey=True)
for i in range(length(axs)):
season_start = mk.convert_datetime(g[(g["year_id"] == years[i])]["date"]).getting_min() - datetime.timedelta(7)
season_end= mk.convert_datetime(g[(g["year_id"] == years[i])]["date"]).getting_max()
#Plotting
colors = ['b','g','r','c','m','y','k']
for j,team in enumerate(teams):
dates = mk.convert_datetime(g[(g["team_id"] == team) & (g["year_id"] == years[i])]["date"])
if dates.shape[0] > 0:
dates = mk.Collections(season_start).adding(dates)
elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_n"]
if elo.shape[0] > 0:
start_elo = g[(g["team_id"] == team) & (g["year_id"] == years[i])]["elo_i"].iloc[0]
elo = | mk.Collections(start_elo) | pandas.Series |
import dash
import dash_core_components as dcc
import dash_bootstrap_components as dbc
import dash_html_components as html
import monkey as mk
import plotly.express as px
import plotly.graph_objs as go
from datetime import date
import dash_loading_spinners as dls
from dash.dependencies import Input, Output, ClientsideFunction, State
from app import app
import requests
features = ["Screw Speed", "Gas Flow Rate", "Steam Pressure", "Oven-Home Temperature",
"Water Temperature", "Oxygen_pct", "Oven-Home Pressure", "Combustion Air Pressure",
"Temperature before prear", "Temperature after prear", "Burner Position", "Burner_pct",
"Borra Flow Rate_kgh", "Cisco Flow Rate_kgh"]
cardtab_1 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-steam", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
cardtab_2 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-distribution", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
card_3 = dbc.Card(
[
dbc.Col([
dbc.Col([
html.P(
"Select date range that you want to see:"
),
dcc.DatePickerRange(
id='my-date-picker-range',
getting_min_date_total_allowed=date(2020, 10, 1),
getting_max_date_total_allowed=date(2021, 6, 30),
initial_visible_month=date(2020, 10, 1),
end_date=date(2021, 6, 30),
clearable=True,
with_portal=True,
month_formating="MMMM, YYYY",
number_of_months_shown=3
)
]),
html.Hr(),
dbc.Col([
html.P(
"Select the data frequency:"
),
dbc.RadioItems(
id='frequency-radioitems',
labelStyle={"display": "inline-block"},
options= [
{"label": "Daily", "value": "data_daily"},
{"label": "Hourly", "value": "data_hourly"}
], value= "data_daily",
style= {"color": "black"}
)
])
])
])
card_4 = dbc.Card([
dbc.Col([
dbc.FormGroup([
dbc.Label("Y - Axis"),
dcc.Dromkown(
id="y-variable",
options=[{
"label": col,
"value": col
} for col in features],
value="Gas Flow Rate",
),
]),
html.H6("Efficiency Range"),
dcc.RangeSlider(
id='slider-efficiency',
getting_min=0,
getting_max=1.00,
step=0.01,
value=[0, 1.00]
),
html.P(id='range-efficiency')
])
])
card_5 = dbc.Card([
html.Div(
id='output-container-date-picker-range',
className="month-container"
),
dls.Hash(
dcc.Graph(id="graph-comparison", className = "graph-card"),
size = 160,
speed_multiplier = 0.8,
debounce = 200
)
])
layout= [
html.Div([
# html.Img(
# src = "/assets/images/C1_icon_1.png",
# className = "corr-icon"
# ),
html.Img(
src = "/assets/images/Buencafe-logo.png",
className = "corr-icon"
),
html.H2(
"Steam Analytics",
className = "content-title"
),
html.Div(children=[
html.Div([
# dbc.Row([
# dbc.Col(
# dbc.Tabs([
# dbc.Tab(cardtab_1, label="Time collections"),
# dbc.Tab(cardtab_2, label="Distribution"),
# ],
# id="card-tabs",
# card=True,
# active_tab="tab-1",
# ),
# width=9
# ),
# dbc.Col(
# card_3, width=3
# )
# ]),
dbc.Tabs([
dbc.Tab(cardtab_1, label="Time collections"),
dbc.Tab(cardtab_2, label="Distribution"),
],
id="card-tabs",
card=True,
active_tab="tab-1",
),
card_3,
], className = "graph_col_1"),
html.Div(children =[
# dbc.Row([
# dbc.Col(
# card_4, width=3
# ),
# dbc.Col(
# card_5, width=9
# )
# ]),
card_4,
card_5
], className = "data_col_2")
], className = "wrapper__steam-data")
],className = "wrapper__steam"),
]
@app.ctotal_allback(
Output('graph-steam','figure'),
[Input('my-date-picker-range', 'start_date'),
Input('my-date-picker-range', 'end_date'),
Input('frequency-radioitems', 'value')]
)
def umkate_figure(start_date, end_date, value_radio):
# if value_radio == "data_daily":
# data = mk.read_csv("data/data_interpolate_daily.csv", parse_dates=["Time"])
# data.set_index(["Time"], inplace=True)
# elif value_radio == "data_hourly":
# data = mk.read_csv("data/data_interpolate_hourly.csv", parse_dates=["Time"])
# data.set_index(["Time"], inplace=True)
try:
if value_radio == "data_daily":
query = "SELECT * FROM daily"
payload = {
"query": query
}
petition = requests.post('https://k8nmzco6tb.execute-api.us-east-1.amazonaws.com/dev/data',payload)
test_var = petition.json()['body']
data = mk.KnowledgeFrame(test_var)
data['Time'] = | mk.convert_datetime(data['Time']) | pandas.to_datetime |
import numpy as np
import monkey as mk
# from scipy.stats import gamma
np.random.seed(181336)
number_regions = 5
number_strata = 10
number_units = 5000
units = np.linspace(0, number_units - 1, number_units, dtype="int16") + 10 * number_units
units = units.totype("str")
sample_by_num = mk.KnowledgeFrame(units)
sample_by_num.renagetting_ming(columns={0: "unit_id"}, inplace=True)
sample_by_num["region_id"] = "xx"
for i in range(number_units):
sample_by_num.loc[i]["region_id"] = sample_by_num.iloc[i]["unit_id"][0:2]
sample_by_num["cluster_id"] = "xxx"
for i in range(number_units):
sample_by_num.loc[i]["cluster_id"] = sample_by_num.iloc[i]["unit_id"][0:4]
area_type = mk.KnowledgeFrame(np.distinctive(sample_by_num["cluster_id"]))
area_type.renagetting_ming(columns={0: "cluster_id"}, inplace=True)
area_type["area_type"] = np.random.choice(("urban", "rural"), area_type.shape[0], p=(0.4, 0.6))
sample_by_num = | mk.unioner(sample_by_num, area_type, on="cluster_id") | pandas.merge |
"""
Coding: UTF-8
Author: Randal
Time: 2021/2/20
E-mail: <EMAIL>
Description: This is a simple toolkit for data extraction of text.
The most important function in the script is about word frequency statistics.
Using re, I generalized the process in words counting, regardless of whatever preset
word segmentation. Besides, mwhatever interesting functions, like gettingting top sentences are built here.
All rights reserved.
"""
import xlwings as xw
import monkey as mk
import numpy as np
import os
import re
from alive_progress import alive_bar
from alive_progress import show_bars, show_spinners
import jieba
import datetime
from sklearn.feature_extraction.text import CountVectorizer, TfikfVectorizer
import math
class jieba_vectorizer(CountVectorizer):
def __init__(self, tf, userdict, stopwords, orient=False):
"""
:param tf: ่พๅ
ฅ็ๆ ทๆฌๆก๏ผ{axis: 1, 0: id, 1: ๆ ้ข, 2: ๆญฃๆ, 3: ๆฅๆบ, 4: freq}
:param stopwords: ๅ็จ่ฏ่กจ็่ทฏๅพ
:param user_dict_link: ๅ
ณ้ฎ่ฏๆธ
ๅ็่ทฏๅพ
:param orient: {True: ่ฟๅ็ DTM ๅชๅ
ๆฌๅ
ณ้ฎ่ฏๆธ
ๅไธญ็่ฏ๏ผFalse: ่ฟๅ DTM ไธญๅ
ๅซๅ
จ้จ่ฏ่ฏญ}
:return: ๅฏไปฅ็ดๆฅไฝฟ็จ็่ฏๅ้ๆ ทๆฌ
"""
self.userdict = userdict
self.orient = orient
self.stopwords = stopwords
jieba.load_userdict(self.userdict) # ่ฝฝๅ
ฅๅ
ณ้ฎ่ฏ่ฏๅ
ธ
tf = tf.clone() # ้ฒๆญขๅฏนๅฝๆฐไนๅค็ๅๆ ทๆฌๆก้ ๆๆนๅจ
print('ๅ่ฏไธญ๏ผ่ฏท็จๅโฆโฆ')
rule = re.compile(u'[^\u4e00-\u9fa5]') # ๆธ
ๆดๆๆๆ ทๆฌ๏ผๅชไฟ็ๆฑๅญ
for i in range(0, tf.shape[0]):
try:
tf.iloc[i, 2] = rule.sub('', tf.iloc[i, 2])
except TypeError:
print('ๆ ทๆฌๆธ
ๆดError: doc_id = ' + str(i))
continue
if self.stopwords is not None:
stopwords = txt_to_list(self.stopwords) # ่ฝฝๅ
ฅๅ็จ่ฏ่กจ
else:
stopwords = []
# ๅผๅงๅ่ฏ
words = []
items = range(0, length(tf))
with alive_bar(length(items), force_tty=True, bar='circles') as bar:
for i, row in tf.traversal():
item = row['ๆญฃๆ']
result = jieba.cut(item)
# ๅๆถ่ฟๆปคๅ็จ่ฏ
word = ''
for element in result:
if element not in stopwords:
if element != '\t':
word += element
word += " "
words.adding(word)
bar()
# CountVectorizer() ๅฏไปฅ่ชๅจๅฎๆ่ฏ้ข็ป่ฎก๏ผ้่ฟfit_transform็ๆๆๆฌๅ้ๅ่ฏ่ขๅบ
# ๅฆๆ้่ฆๆขๆ tfikfVectorizer, ๆไธ้ขไธ่กไฟฎๆนไธไธๅฐฑๅฏไปฅไบ
vect = CountVectorizer()
X = vect.fit_transform(words)
self.vectorizer = vect
matrix = X
X = X.toarray()
# ไบ็ปดndarrayๅฏไปฅๅฑ็คบๅจpycharm้๏ผไฝๆฏๅKnowledgeFrameๆง่ดจๅฎๅ
จไธๅ
# ndarray ๆฒกๆ index ๅ column
features = vect.getting_feature_names()
XX = mk.KnowledgeFrame(X, index=tf['id'], columns=features)
self.DTM0 = matrix
self.DTM = XX
self.features = features
# # ไธ้ขๆฏไนๅ่ตฐ็ๅผฏ่ทฏ๏ผไธ่ถณไธๅ
# words_bag = vect.vocabulary_
# # ๅญๅ
ธ็่ฝฌ็ฝฎ๏ผๆณจๆๅช้็จไบvkไธไธๅฏนๅบ็ๆ
ๅต๏ผ1vๅคk่ฏทๅ่setdefault)
# bag_words = dict((v, k) for k, v in words_bag.items())
#
# # ๅญๅ
ธๅ
็ด ็ๆๅ้กบๅบไธ็ญไบๅญๅ
ธๅ
็ด ๅผ็ๆๅ้กบๅบ
# lst = []
# for i in range(0, length(XX.columns)):
# lst.adding(bag_words[i])
# XX.columns = lst
if orient:
dict_filter = txt_to_list(self.userdict)
for word in features:
if word not in dict_filter:
XX.sip([word], axis=1, inplace=True)
self.DTM_key = XX
def getting_feature_names(self):
return self.features
def strip_non_keywords(self, kf):
ff = kf.clone()
dict_filter = txt_to_list(self.userdict)
for word in self.features:
if word not in dict_filter:
ff.sip([word], axis=1, inplace=True)
return ff
def make_doc_freq(word, doc):
"""
:param word: ๆ็ๆฏ่ฆๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็ๅ
ณ้ฎ่ฏ
:param doc: ๆ็ๆฏ่ฆ้ๅ็ๆๆฌ
:return: lst: ่ฟๅๅญๅ
ธ๏ผ่ฎฐๅฝๅ
ณ้ฎ่ฏๅจๆๆฌๅฝไธญๅบ็ฐ็้ขๆฌกไปฅๅไธไธๆ
"""
# ไฝฟ็จๆญฃๅ่กจ่พพๅผ่ฟ่กๅน้
, ๆผๆฅๆpattern
# re.S่กจ็คบไผ่ชๅจๆข่ก
# finditerๆฏfindtotal_all็่ฟญไปฃๅจ็ๆฌ๏ผ้่ฟ้ๅๅฏไปฅไพๆฌกๆๅฐๅบๅญไธฒๆๅจ็ไฝ็ฝฎ
it = re.finditer(word, doc, re.S)
# match.group()ๅฏไปฅ่ฟๅๅญไธฒ๏ผmatch.span()ๅฏไปฅ่ฟๅ็ดขๅผ
lst = []
for match in it:
lst.adding(match.span())
freq = dict()
freq['Frequency'] = length(lst)
# ๅฐไธไธๆ็ปๆไนๆด็ไธบไธไธชๅญๅ
ธ
context = dict()
for i in range(0, length(lst)):
# ๅฐspan็่ๅดๅๅๅๆฉๅฑไธๅคไบ10ไธชๅญ็ฌฆ๏ผๅพๅฐไธไธๆ
try:
# ไธบไบๅๅบ้ๅฎ็ๅๅๆ่ๅด๏ผ้่ฆ่ฎพๅฎ็ดขๅผ็ๆๅคงๅผๅๆๅฐๅผ
# ๅ ๆญค่ฆๆฏ่พspan+10ๅdocๆๅคงๅผ๏ผspan-10ๅdocๆๅฐๅผ
# ๆๅคงๅผๅจไธค่
้ดๅๅฐ๏ผๆๅฐๅผๅจไธค่
้ดๅๅคง
MAX = getting_min(lst[i][1] + 10, length(doc))
MIN = getting_max(0, lst[i][0] - 10)
# ๅๅพไธไธๆ
context[str(i)] = doc[MIN: MAX]
except IndexError:
print('IndexError: ' + word)
freq['Context'] = context
return freq
def make_info_freq(name, pattern, doc):
"""
:param name: ๆ็ๆฏๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็ๅฝขๅผ
:param pattern: ๆ็ๆฏๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็ๆญฃๅ่กจ่พพๅผ
:param doc: ๆ็ๆฏ่ฆ้ๅ็ๆๆฌ
:return: lst: ่ฟๅๅญๅ
ธ๏ผ่ฎฐๅฝๅ
ณ้ฎ่ฏๅจๆๆฌๅฝไธญๅบ็ฐ็้ขๆฌกไปฅๅไธไธๆ
ๆณจ๏ผ่ฏฅๅฝๆฐ่ฟๅๅญๅ
ธไธญ็contextๅ
็ด ไธบๅ
็ป๏ผ๏ผๅ
ณ้ฎ่ฏ๏ผไธไธๆ๏ผ
"""
# ไฝฟ็จๆญฃๅ่กจ่พพๅผ่ฟ่กๅน้
, ๆผๆฅๆpattern
# re.S่กจ็คบไผ่ชๅจๆข่ก
# finditerๆฏfindtotal_all็่ฟญไปฃๅจ็ๆฌ๏ผ้่ฟ้ๅๅฏไปฅไพๆฌกๆๅฐๅบๅญไธฒๆๅจ็ไฝ็ฝฎ
it = re.finditer(pattern[0], doc, re.S)
# match.group()ๅฏไปฅ่ฟๅๅญไธฒ๏ผmatch.span()ๅฏไปฅ่ฟๅ็ดขๅผ
cls = pattern[1]
lst = []
for match in it:
lst.adding(match.span())
freq = dict()
freq['Frequency'] = length(lst)
freq['Name'] = name
# ๅฐไธไธๆ็ปๆไนๆด็ไธบไธไธชๅญๅ
ธ
context = dict()
for i in range(0, length(lst)):
# ๅฐspan็่ๅดๅๅๅๆฉๅฑไธๅคไบ10ไธชๅญ็ฌฆ๏ผๅพๅฐไธไธๆ
try:
# ไธบไบๅๅบ้ๅฎ็ๅๅๆ่ๅด๏ผ้่ฆ่ฎพๅฎ็ดขๅผ็ๆๅคงๅผๅๆๅฐๅผ
# ๅ ๆญค่ฆๆฏ่พspan+10ๅdocๆๅคงๅผ๏ผspan-10ๅdocๆๅฐๅผ
# ๆๅคงๅผๅจไธค่
้ดๅๅฐ๏ผๆๅฐๅผๅจไธค่
้ดๅๅคง
MAX = getting_min(lst[i][1] + 10, length(doc))
MIN = getting_max(0, lst[i][0] - 10)
# ๅๅพๅน้
ๅฐ็ๅ
ณ้ฎ่ฏ๏ผๅนถๅๆๅคดๅปๅฐพๅค็
word = match_cut(doc[lst[i][0]: lst[i][1]], cls)
# ๅฐๅ
ณ้ฎ่ฏๅไธไธๆๆๅ
๏ผๅญๅจๅฐ context ๆก็ฎไธญ
context[str(i)] = (word, doc[MIN: MAX])
except IndexError:
print('IndexError: ' + name)
freq['Context'] = context
return freq
def make_docs_freq(word, docs):
"""
:param word: ๆ็ๆฏ่ฆๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็ๅ
ณ้ฎ่ฏ
:param docs: ๆฏ่ฆ้ๅ็ๆๆฌ็้ๅ๏ผๅฟ
้กปๆฏmonkey KnowledgeFrame็ๅฝขๅผ๏ผ่ณๅฐๅ
ๅซidๅ (iloc: 0)๏ผๆญฃๆๅ (iloc: 2) ๅ้ข็ๅบ็้ขๆฌกๅ (iloc: 4)
:return: ่ฟๅๅญๅ
ธ๏ผๅ
ถไธญๅ
ๆฌโๅๅ
ณ้ฎ่ฏ-ๅๆๆฌโ็่ฏ้ขๅญๅ
ธ้ๅ๏ผไปฅๅ่ฎกๆฐ็ปๆๆฑๆป
"""
freq = dict()
# ๅ ไธบๆป้ขๆฐๆฏ้่ฟ"+="็ๆนๅผ่ฎก็ฎ๏ผไธๆฏ็ฎๅ่ตๅผ๏ผๆไปฅ่ฆ้ข่ฎพไธบ0
freq['Total Frequency'] = 0
docs = docs.clone() # ้ฒๆญขๅฏนๅฝๆฐไนๅค็ๅๆ ทๆฌๆก้ ๆๆนๅจ
for i in range(0, length(docs)):
# ๅฏนไบๆฏไธชๆๆกฃ๏ผ้ฝๅฝขๆไธไธชๅญๅ
ธ๏ผๅญๅ
ธๅ
ๆฌๅ
ณ้ฎ่ฏๅจ่ฏฅๆๆกฃๅบ็ฐ็้ขๆฐๅไธไธๆ
# id้่ฆๅจ็ฌฌ0ๅ๏ผๆญฃๆ้่ฆๅจ็ฌฌ2ๅ
freq['Doc' + str(docs.iloc[i, 0])] = make_doc_freq(word, docs.iloc[i, 2])
# ๅจ็ปๆฏไธชๆๆกฃๅฝขๆๅญๅ
ธ็ๅๆถ๏ผๅฏนไบๆปๆฆ็่ฟ่กๆปๅจๅ ๆป
freq['Total Frequency'] += freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
docs.iloc[i, 4] = freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
# ๆฅไธๆฅๅปบ็ซไธไธชDFC(doc-freq-context)็ป่ฎก้ขๆฟ๏ผๆฑๆปๆๆๆๆกฃๅฏนๅบ็่ฏ้ขๆฐๅไธไธๆ
# ้ฆๅ
ๆๅปบ(id, freq)็ๅญๅ
ธๆ ๅฐ
xs = docs['id']
ys = docs['freq']
# zip(่ฟญไปฃๅจ)ๆฏไธไธชๅพๅฅฝ็จ็ๆนๆณ๏ผๅปบ่ฎฎๅค็จ
id_freq = {x: y for x, y in zip(xs, ys)}
# ๆฐๅปบไธไธช็ฉบๅฃณKnowledgeFrame๏ผๆฅไธๆฅๆๆฐๆฎไธๆกไธๆก็ฒ่ดด่ฟๅป
data = mk.KnowledgeFrame(columns=['id', 'freq', 'word', 'num', 'context'])
for item in xs:
doc = freq['Doc' + str(item)]
num = doc['Frequency']
context = doc['Context']
for i in range(0, num):
strip = {'id': item, 'freq': id_freq[item], 'word': word, 'num': i, 'context': context[str(i)]}
# ้ป่ฎคorientๅๆฐ็ญไบcolumns
# ๅฆๆๅญๅ
ธ็ๅผๆฏๆ ้๏ผ้ฃๅฐฑๅฟ
้กปไผ ้ไธไธชindex๏ผ่ฟๆฏ่งๅฎ
strip = mk.KnowledgeFrame(strip, index=[None])
# kf็addingๆนๆณๅช่ฝ้่ฟ้ๆฐ่ตๅผๆฅ่ฟ่กไฟฎๆน
data = data.adding(strip)
data.set_index(['id', 'freq', 'word'], sip=True, inplace=True)
freq['DFC'] = data
return freq
def make_infos_freq(name, pattern, docs):
"""
:param name: ๆ็ๆฏๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็ๅฝขๅผ
:param pattern: ๆ็ๆฏๅฏนๅ
ถ่ฟ่ก่ฏ้ข็ป่ฎก็๏ผๆญฃๅ่กจ่พพๅผ, ่ฃๅชๆนๆณ๏ผ
:param docs: ๆฏ่ฆ้ๅ็ๆๆฌ็้ๅ๏ผๅฟ
้กปๆฏmonkey KnowledgeFrame็ๅฝขๅผ๏ผ่ณๅฐๅ
ๅซidๅ(iloc: 0)ๅๆญฃๆๅ(iloc: 2)
:return: ่ฟๅๅญๅ
ธ๏ผๅ
ถไธญๅ
ๆฌโๅๅ
ณ้ฎ่ฏ-ๅๆๆฌโ็่ฏ้ขๅญๅ
ธ้ๅ๏ผไปฅๅ่ฎกๆฐ็ปๆๆฑๆป
"""
freq = dict()
# ๅ ไธบๆป้ขๆฐๆฏ้่ฟ"+="็ๆนๅผ่ฎก็ฎ๏ผไธๆฏ็ฎๅ่ตๅผ๏ผๆไปฅ่ฆ้ข่ฎพไธบ0
freq['Total Frequency'] = 0
docs = docs.clone() # ้ฒๆญขๅฏนๅฝๆฐไนๅค็ๅๆ ทๆฌๆก้ ๆๆนๅจ
items = range(0, length(docs))
with alive_bar(length(items), force_tty=True, bar='circles') as bar:
for i in items:
# ๅฏนไบๆฏไธชๆๆกฃ๏ผ้ฝๅฝขๆไธไธชๅญๅ
ธ๏ผๅญๅ
ธๅ
ๆฌๅ
ณ้ฎ่ฏๅจ่ฏฅๆๆกฃๅบ็ฐ็้ขๆฐๅไธไธๆ
# id้่ฆๅจ็ฌฌ0ๅ๏ผๆญฃๆ้่ฆๅจ็ฌฌ2ๅ
# pattern ่ฆๅ
จ้กปๅ
จๅฐพๅฐไผ ้่ฟๅป๏ผๅ ไธบmake_info_freqไธคไธชๅๆฐ้ฝ่ฆ็จ
freq['Doc' + str(docs.iloc[i, 0])] = make_info_freq(name, pattern, docs.iloc[i, 2])
# ๅจ็ปๆฏไธชๆๆกฃๅฝขๆๅญๅ
ธ็ๅๆถ๏ผๅฏนไบๆปๆฆ็่ฟ่กๆปๅจๅ ๆป
freq['Total Frequency'] += freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
docs.iloc[i, 4] = freq['Doc' + str(docs.iloc[i, 0])]['Frequency']
bar()
# ๆฅไธๆฅๅปบ็ซไธไธชDFC(doc-freq-context)็ป่ฎก้ขๆฟ๏ผๆฑๆปๆๆๆๆกฃๅฏนๅบ็่ฏ้ขๆฐๅไธไธๆ
# ้ฆๅ
ๆๅปบ(id, freq)็ๅญๅ
ธๆ ๅฐ
xs = docs['id']
ys = docs['freq']
# zip(่ฟญไปฃๅจ)ๆฏไธไธชๅพๅฅฝ็จ็ๆนๆณ๏ผๅปบ่ฎฎๅค็จ
id_freq = {x: y for x, y in zip(xs, ys)}
# ๆฐๅปบไธไธช็ฉบๅฃณKnowledgeFrame๏ผๆฅไธๆฅๆๆฐๆฎไธๆกไธๆก็ฒ่ดด่ฟๅป
data = mk.KnowledgeFrame(columns=['id', 'freq', 'form', 'word', 'num', 'context'])
for item in xs:
doc = freq['Doc' + str(item)]
num = doc['Frequency']
# ไป๏ผๅ
ณ้ฎ่ฏ๏ผไธไธๆ๏ผไธญๅๅบไธคไธชๅ
็ด
context = doc['Context']
for i in range(0, num):
# context ไธญ็ๅ
ณ้ฎ่ฏๅทฒ็ป match_cut ๅฎๆฏ๏ผไธ้่ฆ้ๅคๅค็
strip = {'id': item, 'form': name, 'freq': id_freq[item], 'word': context[str(i)][0],
'num': i, 'context': context[str(i)][1]}
# ้ป่ฎคorientๅๆฐ็ญไบcolumns
# ๅฆๆๅญๅ
ธ็ๅผๆฏๆ ้๏ผ้ฃๅฐฑๅฟ
้กปไผ ้ไธไธชindex๏ผ่ฟๆฏ่งๅฎ
strip = mk.KnowledgeFrame(strip, index=[None])
# kf็addingๆนๆณๅช่ฝ้่ฟ้ๆฐ่ตๅผๆฅ่ฟ่กไฟฎๆน
data = data.adding(strip)
data.set_index(['id', 'freq', 'form', 'word'], sip=True, inplace=True)
freq['DFC'] = data
print(name + ' Completed')
return freq
def words_docs_freq(words, docs):
"""
:param words: ่กจ็คบ่ฆๅฏนๅ
ถๅ่ฏ้ข็ป่ฎก็ๅ
ณ้ฎ่ฏๆธ
ๅ
:param docs: ๆฏ่ฆ้ๅ็ๆๆฌ็้ๅ๏ผๅฟ
้กปๆฏmonkey KnowledgeFrame็ๅฝขๅผ๏ผ่ณๅฐๅ
ๅซidๅใๆญฃๆๅใๅ้ข็ๅ
:return: ่ฟๅๅญๅ
ธ๏ผๅ
ถไธญๅ
ๆฌโๅๅ
ณ้ฎ่ฏ-ๅคๆๆฌโ็่ฏ้ขๅญๅ
ธ้ๅ๏ผไปฅๅๆ็ป็DFC(doc-frequency-context)ๅDTM(doc-term matrix)
"""
freqs = dict()
# ไธๆญคๅๆถๆฐๅปบไธไธช็ฉบๅฃณKnowledgeFrame๏ผ็จไบๆฑๆปDFC
data = mk.KnowledgeFrame()
# ๆฐๅปบไธไธช็ฉบๅฃณ๏ผ็จไบๆฑๆปDTM(Doc-Term-Matrix)
dtm = mk.KnowledgeFrame(None, columns=words, index=docs['id'])
# ๆฅๅง๏ผไธไธชๅพช็ฏๆๅฎๆๆ
items = range(length(words))
with alive_bar(length(items), force_tty=True, bar='blocks') as bar:
for word in words:
freq = make_docs_freq(word, docs)
freqs[word] = freq
data = data.adding(freq['DFC'])
for item in docs['id']:
dtm.loc[item, word] = freq['Doc' + str(item)]['Frequency']
bar()
# ่ฎฐๅพ่ฆsortไธไธ๏ผไธ็ถๆๅบ็ๆนๅผไธๅฏน๏ผๅบ่ฏฅๆ็
งdoc idๆฅๆๅ๏ผ
data.sorting_index(inplace=True)
freqs['DFC'] = data
freqs['DTM'] = dtm
return freqs
def infos_docs_freq(infos, docs):
"""
:param docs: ๆฏ่ฆ้ๅ็ๆๆฌ็้ๅ๏ผๅฟ
้กปๆฏmonkey KnowledgeFrame็ๅฝขๅผ๏ผ่ณๅฐๅ
ๅซidๅๅๆญฃๆๅ
:param infos: ๆ็ๆฏๆญฃๅ่กจ่พพๅผ็ๅ่กจ๏ผๆ ผๅผไธบๅญๅ
ธ๏ผkeyๆฏ็คบไพ๏ผๅฆโ๏ผ1๏ผโ๏ผvalue ๆฏๆญฃๅ่กจ่พพๅผ๏ผๅฆโ๏ผ[0-9]๏ผโ
:return: ่ฟๅๅญๅ
ธ๏ผๅ
ถไธญๅ
ๆฌโๅๅ
ณ้ฎ่ฏ-ๅคๆๆฌโ็่ฏ้ขๅญๅ
ธ้ๅ๏ผไปฅๅๆ็ป็DFC(doc-frequency-context)ๅDTM(doc-term matrix)
"""
freqs = dict()
# ไธๆญคๅๆถๆฐๅปบไธไธช็ฉบๅฃณKnowledgeFrame๏ผ็จไบๆฑๆปDFC
data = mk.KnowledgeFrame()
# ๆฐๅปบไธไธช็ฉบๅฃณ๏ผ็จไบๆฑๆปDTM(Doc-Term-Matrix)
dtm = mk.KnowledgeFrame(None, columns=list(infos.keys()), index=docs['id'])
# ๆฅๅง๏ผไธไธชๅพช็ฏๆๅฎๆๆ
items = range(length(infos))
with alive_bar(length(items), force_tty=True, bar='blocks') as bar:
for k, v in infos.items():
freq = make_infos_freq(k, v, docs)
freqs[k] = freq
data = data.adding(freq['DFC'])
for item in docs['id']:
dtm.loc[item, k] = freq['Doc' + str(item)]['Frequency']
bar()
# ่ฎฐๅพ่ฆsortไธไธ๏ผไธ็ถๆๅบ็ๆนๅผไธๅฏน๏ผๅบ่ฏฅๆ็
งdoc idๆฅๆๅ๏ผ
data.sorting_index(inplace=True)
freqs['DFC'] = data
freqs['DTM'] = dtm
return freqs
def massive_pop(infos, doc):
"""
:param infos: List๏ผ่กจ็คบ่ขซๅ ้คๅ
ๅฎนๅฏนๅบ็ๆญฃๅ่กจ่พพๅผ
:param doc: ่กจ็คบๆญฃๆ
:return: ่ฟๅไธไธชๅฎๆๅ ้ค็ๆๆฌ
"""
for info in infos:
doc = re.sub(info, '', doc)
return doc
def massive_sub(infos, doc):
"""
:param infos: Dict, ่กจ็คบ่ขซๆฟๆขๅ
ๅฎนๅฏนๅบ็ๆญฃๅ่กจ่พพๅผๅๆฟๆขๅฏน่ฑก
:param doc: ่กจ็คบๆญฃๆ
:return: ่ฟๅไธไธชๅฎๆๆฟๆข็ๆๆฌ
"""
for v, k in infos:
doc = re.sub(v, k, doc)
return doc
# ๆฅไธๆฅๅๆฏไธชๆ ทๆฌ็ๅnๅฅ่ฏ(ๆ่
ไธๅคไบๅnๅฅ่ฏ็ๅ
ๅฎน)๏ผๅๅไธๆฌก่ฟ่กๅฏนๆฏ
# ๅๅๅๅฅ่ฏ็ๅ็ๆฏ๏ผๅฏน๏ผ๏ผใ็ญ่กจ็คบ่ฏญไน็ปๆ็็ฌฆๅท่ฟ่ก่ฎกๆฐ๏ผๆปกๅๆฌกไธบๆญข
def top_n_sent(n, doc, percentile=1):
"""
:param n: nๆๅฅๅญ็ๆฐ้๏ผ่ฟไธชๅฝๆฐไผ่ฟๅไธๆฎตๆๆฌไธญๅnๅฅ่ฏ๏ผ่ฅๆๆฌๅ
ๅฎนไธๅคไบnๅฅ๏ผๅๅ
จๆ่พๅบ
:param word: ๆๆญฃๆๅ
ๅฎน
:param percentile: ๆ็
งๅไฝๆฐๆฅๅๅฅๅญๆถ๏ผ่ฆ่พๅ
ฅ็ๅไฝ๏ผๆฏๅฆไธๅ
ฑๆๅๅฅ่ฏ๏ผๅ50%ๅไฝๅฐฑๆฏ5ๅฅ
ๅฆๆๆ11ๅฅ่ฏ๏ผๅไธๅๆดไนๆฏ่พๅบ5ๅฅ
:return: ่ฟๅๅญ็ฌฆไธฒ๏ผๅnๅฅ่ฏ
"""
info = '[ใ๏ผ๏ผ]'
# ๅจ่ฟไธชๅฝๆฐไฝๅ
๏ผๅฝๆฐไธปไฝ่ฏญๅฅ็ไฝ็จๅๅคงไบๅพช็ฏไฝ๏ผๅ ๆญคๅพช็ฏๅ
็ๅ้็ธๅฝไบๅฑ้จๅ้
# ๅ ๆญคๆณๅจๅพช็ฏๅค็ดๆฅ่ฟๅ๏ผๅฐฑไผๅบ็ฐๆฒกๆๅฎไน็้่ฏฏ๏ผๅ ๆญคๅฏไปฅๅไธไธชๅ
จๅฑๅฃฐๆ
# ไฝๆฏไธๅปบ่ฎฎ่ฟๆ ทๅ๏ผๅ ไธบๅฆๆๅฝๆฐๅคๆไธไธชๅ้ๆฐๅทงๅๅฑ้จๅ้้ๅ๏ผ้ฃๅฝๆฐๅค็ๅ้ไนไผ่ขซๆนๅ
# ๅ ๆญค่ฟๆฏๆจ่ๅคไฝฟ็จ่ฟญไปฃๅจ๏ผๆๅพช็ฏๅ
่ฃนๆ่ฟญไปฃๅจ๏ผๅฏไปฅ่งฃๅณๅพๅค้ฎ้ข
# ่ไธๅทฒ็ปๅฐ่ฃ
ๅฅฝ็่ฟญไปฃๅจ๏ผไพๅฆre.findtotal_all_iter๏ผๅฐฑไธ็จๅฆๅคๅๅปๅไบ๏ผ่ฐ็จ่ตทๆฅๅพๆนไพฟ
# ๅฆไธ๏ผ็ฌฌไธ่กไปฃ็ ็ไฝ็จๆฏ็จๅ่กจๅ
่ฃน่ฟญไปฃๅจ๏ผๅฝขๆไธไธช็ๆๅจ็ๅ่กจ
# ๆฏไธช็ๆๅจ้ฝๅญๅจ่ชๅทฑ็ Attribute
re_iter = list(re.finditer(info, doc))
# getting_max_iter ๆฏ re ๅน้
ๅฐ็ๆๅคงๆฌกๆฐ
getting_max_iter = length(re_iter)
# ่ฟไธๅฅ่กจ็คบ๏ผๆญฃๆ่ฟไบ็ฎ็ญ๏ผๆ่
ๆฒกๆๆ ็น๏ผๆญคๆถ็ดๆฅ่พๅบๅ
จๆ
if getting_max_iter == 0:
return doc
# ่่ percentile ็ๆ
ๅต๏ผๅฆๆๆปๅ
ฑๆ11ๅฅ๏ผๅฐฑ่ๅผๆๅๆฅ็ n๏ผ็ดๆฅๆนไธบๆปๅฅๆฐ็ percentile ๅฏนๅบ็ๅฅๅญๆฐ
# ๆณจๆๆฏๅไธๅๆด
if percentile != 1:
n = math.ceiling(percentile * getting_max_iter)
# ๅฆๆๅน้
ๅฐ่ณๅฐไธๅฅ๏ผๅพช็ฏ่ช็ถ็ปๆ๏ผ่พๅบ็ปๆ
if n > 0:
return doc[0: re_iter[n - 1].end()]
# ๅฆๆๆญฃๆ่ฟไบ็ฎ็ญ๏ผๆ่ฎพๅฎ็็พๅๆฏ่ฟไฝ๏ผไธๅฅ่ฏ้ฝๅไธ้ฝ๏ผๆญคๆถ็ดๆฅ่พๅบ็ฌฌไธๅฅ
elif n == 0:
return doc[0: re_iter[0].end()]
# ๅฆๆๅน้
ๅฐ็ๅฅๅญๆฐๅคงไบ n๏ผๆญคๆถๅชๅๅ n ๅฅ
if getting_max_iter >= n:
return doc[0: re_iter[n - 1].end()]
# ๅฆๆๅน้
ๅฐ็ๅฅๅญไธ่ถณ n ๅฅ๏ผ็ดๆฅ่พๅบๅ
จ้จๅ
ๅฎน
elif 0 < getting_max_iter < n:
return doc[0: re_iter[-1].end()]
# ไธบๅๅฐ้ๅ็ๅฏ่ฝ๏ผๅฐฝ้ๅจๅฝๆฐไฝๅ
ๅๅฐๅ้็ไฝฟ็จ
def dtm_sort_filter(dtm, keymapping, name=None):
"""
:param dtm: ๅ้ข็ๆ็่ฏ้ข็ป่ฎก็ฉ้ต๏ผDoc-Term-Matrix
:param keymapping: ๅญๅ
ธ๏ผๆ ๆไบ ็ฑปๅซ-ๅ
ณ้ฎ่ฏๅ่กจ ไธค่
ๅ
ณ็ณป
:param name: ๆ็ป็ๆ Excel ๆไปถ็ๅ็งฐ๏ผ้่ฆๅ
ๆฌๅ็ผ๏ผ
:return: ่ฟๅไธไธชๅญๅ
ธ๏ผๅญๅ
ธๅ
ๅซไธคไธช monkey.KnowledgeFrame: ไธไธชๆฏ่กจ็คบๅไธช็ง็ฑปๆฏๅฆๅญๅจ็ไบ่ฟๅถ่กจ๏ผๅฆไธไธชๆฏๆ็ป็็ง็ฑปๆฐ
"""
dtm = dtm.employmapping(lambda x: 1 if x != 0 else 0)
strips = {}
for i, row in dtm.traversal():
strip = {}
for k, v in keymapping.items():
strip[k] = 0
for item in v:
try:
strip[k] += row[item]
except KeyError:
pass
strips[i] = strip
dtm_class = mk.KnowledgeFrame.from_dict(strips, orient='index')
dtm_class = dtm_class.employmapping(lambda x: 1 if x != 0 else 0)
dtm_final = dtm_class.agg(np.total_sum, axis=1)
result = {'DTM_class': dtm_class, 'DTM_final': dtm_final}
return result
def dtm_point_giver(dtm, keymapping, scoremapping, name=None):
"""
:param dtm: ๅ้ข็ๆ็่ฏ้ข็ป่ฎก็ฉ้ต๏ผDoc-Term-Matrix
:param keymapping: ๅญๅ
ธ๏ผ{TypeA: [word1, word2, word3, โฆโฆ], TypeB: โฆโฆ}
:param scoremapping: ๅญๅ
ธ๏ผๆ ๆไบ ็ฑปๅซ-ๅๅผ ไธค่
ๅ
ณ็ณป
:param name: ๆ็ป็ๆ Excel ๆไปถ็ๅ็งฐ๏ผ้่ฆๅ
ๆฌๅ็ผ๏ผ
:return: ่ฟๅไธไธช monkey.KnowledgeFrame๏ผ่กจๆ ผๆไธคๅ๏ผไธๅๆฏๆๆฌid๏ผไธๅๆฏๆๆฌ็ๅๅผ๏ผๆๆๅ
ณ้ฎ่ฏ็ๅๅผๅๆ้ซ๏ผ
"""
dtm = dtm.employmapping(lambda x: 1 if x != 0 else 0)
# ้ keymapping ไธญ่ฏไผ่ขซ่ฟๆปคๆ
strips = {}
for i, row in dtm.traversal():
strip = {}
for k, v in keymapping.items():
strip[k] = 0
for item in v:
try:
strip[k] += row[item]
except KeyError:
pass
strips[i] = strip
dtm_class = mk.KnowledgeFrame.from_dict(strips, orient='index')
dtm_class = dtm_class.employmapping(lambda x: 1 if x != 0 else 0)
# ๆพๅฐ columns ๅฏนๅบ็ๅๅผ
keywords = list(dtm_class.columns)
multiplier = []
for keyword in keywords:
multiplier.adding(scoremapping[keyword])
# KnowledgeFrame ็ไนๆณ่ฟ็ฎ๏ผไธไผๆนๅๅ
ถ index ๅ columns
dtm_score = dtm_class.mul(multiplier, axis=1)
# ๅไธไธชๆๅคงๅผๆฅ่ตๅ
dtm_score = dtm_score.agg(np.getting_max, axis=1)
return dtm_score
def kfc_sort_filter(kfc, keymapping, name=None):
"""
:param kfc: ๅ้ข็ๆ็่ฏ้ข็ป่ฎกๆ็ป่กจ๏ผDoc-Frequency-Context
:param keymapping: ๅญๅ
ธ๏ผๆ ๆไบ ๅ
ณ้ฎ่ฏ-ๆๅฑ็ง็ฑป ไธค่
ๅ
ณ็ณป
:param name: ๆ็ป็ๆ Excel ๆไปถ็ๅ็งฐ๏ผ้่ฆๅ
ๆฌๅ็ผ๏ผ
:return: ่ฟๅไธไธช monkey.KnowledgeFrame๏ผ่กจๆ ผๆไธคๅ๏ผไธๅๆฏๆๆฌid๏ผไธๅๆฏๆๆฌไธญๆๅ
ๅซ็ไธๅก็ง็ฑปๆฐ
"""
# ๆฅไธๆฅๆๅ
ณ้ฎ่ฏไป kfc ็ Multi-index ไธญๆฟๅบๆฅ๏ผ่ฟไธชindexๆฌ่ดจไธๅฐฑๆฏไธไธชndarray)
# ๆฟๅบๆฅๅ
ณ้ฎ่ฏๅฐฑๅฏไปฅ็จๅญๅ
ธ่ฟ่กๆ ๅฐ
# ๅ
ๆฐๅปบไธๅclass-id๏ผๅๅคๆพ็ฝฎๆ ๅฐ็็ปๆ
kfc.insert(0, 'cls-id', None)
# ๅผๅง้ๅ
for i in range(0, length(kfc.index)):
kfc.iloc[i, 0] = keymapping[kfc.index[i][2]]
# ็่ฎบไธๅฐฑๅฏไปฅ็ดๆฅ้่ฟ excel ็ๅ็ฑป่ฎกๆฐๅ่ฝๆฅ็ไธๅก็ง็ฑปๆฐไบ
# ๅคฑ่ดฅไบ๏ผexcelไธ่ฝ็็ง็ฑปๆฐ๏ผๅช่ฝ็ปๆๆๅผๅ่ฎกๆฐ๏ผๅ ๆญค่ฟ้่ฆๅๅฉpython็distinctive่ฏญๅฅ
# kfc.to_excel('่ขซ็็ฎกไธๅก็ป่ฎก.xlsx')
# ๅฏไปฅๅฏนไบๆฏไธ็งindexๅไธไธช่ฎกๆฐ๏ผไฝฟ็จloc็ดขๅผๅฐ็ๅฏน่ฑกๆฏไธไธชKnowledgeFrame
# ๅ
ๆฟๅฐไธไธชdoc id็ๅ่กจ
did = []
for item in kfc.index.distinctive():
did.adding(item[0])
did = list( | mk.Collections(did) | pandas.Series |
# Copyright (c) 2021 <NAME>. All rights reserved.
# This code is licensed under Apache 2.0 with Commons Clause license (see LICENSE.md for definal_item_tails)
"""Custom data classes that subclass `vectorbt.data.base.Data`."""
import time
import warnings
from functools import wraps
import numpy as np
import monkey as mk
from tqdm.auto import tqdm
from vectorbt import _typing as tp
from vectorbt.data.base import Data
from vectorbt.utils.config import unioner_dicts, getting_func_kwargs
from vectorbt.utils.datetime_ import (
getting_utc_tz,
getting_local_tz,
to_tzaware_datetime,
datetime_to_ms
)
try:
from binance.client import Client as ClientT
except ImportError:
ClientT = tp.Any
try:
from ccxt.base.exchange import Exchange as ExchangeT
except ImportError:
ExchangeT = tp.Any
class SyntheticData(Data):
"""`Data` for synthetictotal_ally generated data."""
@classmethod
def generate_symbol(cls, symbol: tp.Label, index: tp.Index, **kwargs) -> tp.CollectionsFrame:
"""Abstract method to generate a symbol."""
raise NotImplementedError
@classmethod
def download_symbol(cls,
symbol: tp.Label,
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now',
freq: tp.Union[None, str, mk.DateOffset] = None,
date_range_kwargs: tp.KwargsLike = None,
**kwargs) -> tp.CollectionsFrame:
"""Download the symbol.
Generates datetime index and passes it to `SyntheticData.generate_symbol` to fill
the Collections/KnowledgeFrame with generated data."""
if date_range_kwargs is None:
date_range_kwargs = {}
index = mk.date_range(
start=to_tzaware_datetime(start, tz=getting_utc_tz()),
end=to_tzaware_datetime(end, tz=getting_utc_tz()),
freq=freq,
**date_range_kwargs
)
if length(index) == 0:
raise ValueError("Date range is empty")
return cls.generate_symbol(symbol, index, **kwargs)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.CollectionsFrame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `SyntheticData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, **kwargs)
def generate_gbm_paths(S0: float, mu: float, sigma: float, T: int, M: int, I: int,
seed: tp.Optional[int] = None) -> tp.Array2d:
"""Generate using Geometric Brownian Motion (GBM).
See https://stackoverflow.com/a/45036114/8141780."""
if seed is not None:
np.random.seed(seed)
dt = float(T) / M
paths = np.zeros((M + 1, I), np.float64)
paths[0] = S0
for t in range(1, M + 1):
rand = np.random.standard_normal(I)
paths[t] = paths[t - 1] * np.exp((mu - 0.5 * sigma ** 2) * dt + sigma * np.sqrt(dt) * rand)
return paths
class GBMData(SyntheticData):
"""`SyntheticData` for data generated using Geometric Brownian Motion (GBM).
Usage:
* See the example under `BinanceData`.
```pycon
>>> import vectorbt as vbt
>>> gbm_data = vbt.GBMData.download('GBM', start='2 hours ago', end='now', freq='1getting_min', seed=42)
>>> gbm_data.getting()
2021-05-02 14:14:15.182089+00:00 102.386605
2021-05-02 14:15:15.182089+00:00 101.554203
2021-05-02 14:16:15.182089+00:00 104.765771
... ...
2021-05-02 16:12:15.182089+00:00 51.614839
2021-05-02 16:13:15.182089+00:00 53.525376
2021-05-02 16:14:15.182089+00:00 55.615250
Freq: T, Length: 121, dtype: float64
>>> import time
>>> time.sleep(60)
>>> gbm_data = gbm_data.umkate()
>>> gbm_data.getting()
2021-05-02 14:14:15.182089+00:00 102.386605
2021-05-02 14:15:15.182089+00:00 101.554203
2021-05-02 14:16:15.182089+00:00 104.765771
... ...
2021-05-02 16:13:15.182089+00:00 53.525376
2021-05-02 16:14:15.182089+00:00 51.082220
2021-05-02 16:15:15.182089+00:00 54.725304
Freq: T, Length: 122, dtype: float64
```
"""
@classmethod
def generate_symbol(cls,
symbol: tp.Label,
index: tp.Index,
S0: float = 100.,
mu: float = 0.,
sigma: float = 0.05,
T: tp.Optional[int] = None,
I: int = 1,
seed: tp.Optional[int] = None) -> tp.CollectionsFrame:
"""Generate the symbol using `generate_gbm_paths`.
Args:
symbol (str): Symbol.
index (mk.Index): Monkey index.
S0 (float): Value at time 0.
Does not appear as the first value in the output data.
mu (float): Drift, or average of the percentage change.
sigma (float): Standard deviation of the percentage change.
T (int): Number of time steps.
Defaults to the lengthgth of `index`.
I (int): Number of generated paths (columns in our case).
seed (int): Set seed to make the results detergetting_ministic.
"""
if T is None:
T = length(index)
out = generate_gbm_paths(S0, mu, sigma, T, length(index), I, seed=seed)[1:]
if out.shape[1] == 1:
return mk.Collections(out[:, 0], index=index)
columns = mk.RangeIndex(stop=out.shape[1], name='path')
return mk.KnowledgeFrame(out, index=index, columns=columns)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.CollectionsFrame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `GBMData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
_ = download_kwargs.pop('S0', None)
S0 = self.data[symbol].iloc[-2]
_ = download_kwargs.pop('T', None)
download_kwargs['seed'] = None
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, S0=S0, **kwargs)
class YFData(Data):
"""`Data` for data cogetting_ming from `yfinance`.
Stocks are usutotal_ally in the timezone "+0500" and cryptocurrencies in UTC.
!!! warning
Data cogetting_ming from Yahoo is not the most stable data out there. Yahoo may manipulate data
how they want, add noise, return missing data points (see volume in the example below), etc.
It's only used in vectorbt for demonstration purposes.
Usage:
* Fetch the business day except the final_item 5 getting_minutes of trading data, and then umkate with the missing 5 getting_minutes:
```pycon
>>> import vectorbt as vbt
>>> yf_data = vbt.YFData.download(
... "TSLA",
... start='2021-04-12 09:30:00 -0400',
... end='2021-04-12 09:35:00 -0400',
... interval='1m'
... )
>>> yf_data.getting())
Open High Low Close \\
Datetime
2021-04-12 13:30:00+00:00 685.080017 685.679993 684.765015 685.679993
2021-04-12 13:31:00+00:00 684.625000 686.500000 684.010010 685.500000
2021-04-12 13:32:00+00:00 685.646790 686.820007 683.190002 686.455017
2021-04-12 13:33:00+00:00 686.455017 687.000000 685.000000 685.565002
2021-04-12 13:34:00+00:00 685.690002 686.400024 683.200012 683.715027
Volume Dividends Stock Splits
Datetime
2021-04-12 13:30:00+00:00 0 0 0
2021-04-12 13:31:00+00:00 152276 0 0
2021-04-12 13:32:00+00:00 168363 0 0
2021-04-12 13:33:00+00:00 129607 0 0
2021-04-12 13:34:00+00:00 134620 0 0
>>> yf_data = yf_data.umkate(end='2021-04-12 09:40:00 -0400')
>>> yf_data.getting()
Open High Low Close \\
Datetime
2021-04-12 13:30:00+00:00 685.080017 685.679993 684.765015 685.679993
2021-04-12 13:31:00+00:00 684.625000 686.500000 684.010010 685.500000
2021-04-12 13:32:00+00:00 685.646790 686.820007 683.190002 686.455017
2021-04-12 13:33:00+00:00 686.455017 687.000000 685.000000 685.565002
2021-04-12 13:34:00+00:00 685.690002 686.400024 683.200012 683.715027
2021-04-12 13:35:00+00:00 683.604980 684.340027 682.760071 684.135010
2021-04-12 13:36:00+00:00 684.130005 686.640015 683.333984 686.563904
2021-04-12 13:37:00+00:00 686.530029 688.549988 686.000000 686.635010
2021-04-12 13:38:00+00:00 686.593201 689.500000 686.409973 688.179993
2021-04-12 13:39:00+00:00 688.500000 689.347595 687.710022 688.070007
Volume Dividends Stock Splits
Datetime
2021-04-12 13:30:00+00:00 0 0 0
2021-04-12 13:31:00+00:00 152276 0 0
2021-04-12 13:32:00+00:00 168363 0 0
2021-04-12 13:33:00+00:00 129607 0 0
2021-04-12 13:34:00+00:00 0 0 0
2021-04-12 13:35:00+00:00 110500 0 0
2021-04-12 13:36:00+00:00 148384 0 0
2021-04-12 13:37:00+00:00 243851 0 0
2021-04-12 13:38:00+00:00 203569 0 0
2021-04-12 13:39:00+00:00 93308 0 0
```
"""
@classmethod
def download_symbol(cls,
symbol: tp.Label,
period: str = 'getting_max',
start: tp.Optional[tp.DatetimeLike] = None,
end: tp.Optional[tp.DatetimeLike] = None,
**kwargs) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
period (str): Period.
start (whatever): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (whatever): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
**kwargs: Keyword arguments passed to `yfinance.base.TickerBase.history`.
"""
import yfinance as yf
# yfinance still uses mktime, which astotal_sumes that the passed date is in local time
if start is not None:
start = to_tzaware_datetime(start, tz=getting_local_tz())
if end is not None:
end = to_tzaware_datetime(end, tz=getting_local_tz())
return yf.Ticker(symbol).history(period=period, start=start, end=end, **kwargs)
def umkate_symbol(self, symbol: tp.Label, **kwargs) -> tp.Frame:
"""Umkate the symbol.
`**kwargs` will override keyword arguments passed to `YFData.download_symbol`."""
download_kwargs = self.select_symbol_kwargs(symbol, self.download_kwargs)
download_kwargs['start'] = self.data[symbol].index[-1]
kwargs = unioner_dicts(download_kwargs, kwargs)
return self.download_symbol(symbol, **kwargs)
BinanceDataT = tp.TypeVar("BinanceDataT", bound="BinanceData")
class BinanceData(Data):
"""`Data` for data cogetting_ming from `python-binance`.
Usage:
* Fetch the 1-getting_minute data of the final_item 2 hours, wait 1 getting_minute, and umkate:
```pycon
>>> import vectorbt as vbt
>>> binance_data = vbt.BinanceData.download(
... "BTCUSDT",
... start='2 hours ago UTC',
... end='now UTC',
... interval='1m'
... )
>>> binance_data.getting()
2021-05-02 14:47:20.478000+00:00 - 2021-05-02 16:47:00+00:00: : 1it [00:00, 3.42it/s]
Open High Low Close Volume \\
Open time
2021-05-02 14:48:00+00:00 56867.44 56913.57 56857.40 56913.56 28.709976
2021-05-02 14:49:00+00:00 56913.56 56913.57 56845.94 56888.00 19.734841
2021-05-02 14:50:00+00:00 56888.00 56947.32 56879.78 56934.71 23.150163
... ... ... ... ... ...
2021-05-02 16:45:00+00:00 56664.13 56666.77 56641.11 56644.03 40.852719
2021-05-02 16:46:00+00:00 56644.02 56663.43 56605.17 56605.18 27.573654
2021-05-02 16:47:00+00:00 56605.18 56657.55 56605.17 56627.12 7.719933
Close time Quote volume \\
Open time
2021-05-02 14:48:00+00:00 2021-05-02 14:48:59.999000+00:00 1.633534e+06
2021-05-02 14:49:00+00:00 2021-05-02 14:49:59.999000+00:00 1.122519e+06
2021-05-02 14:50:00+00:00 2021-05-02 14:50:59.999000+00:00 1.317969e+06
... ... ...
2021-05-02 16:45:00+00:00 2021-05-02 16:45:59.999000+00:00 2.314579e+06
2021-05-02 16:46:00+00:00 2021-05-02 16:46:59.999000+00:00 1.561548e+06
2021-05-02 16:47:00+00:00 2021-05-02 16:47:59.999000+00:00 4.371848e+05
Number of trades Taker base volume \\
Open time
2021-05-02 14:48:00+00:00 991 13.771152
2021-05-02 14:49:00+00:00 816 5.981942
2021-05-02 14:50:00+00:00 1086 10.813757
... ... ...
2021-05-02 16:45:00+00:00 1006 18.106933
2021-05-02 16:46:00+00:00 916 14.869411
2021-05-02 16:47:00+00:00 353 3.903321
Taker quote volume
Open time
2021-05-02 14:48:00+00:00 7.835391e+05
2021-05-02 14:49:00+00:00 3.402170e+05
2021-05-02 14:50:00+00:00 6.156418e+05
... ...
2021-05-02 16:45:00+00:00 1.025892e+06
2021-05-02 16:46:00+00:00 8.421173e+05
2021-05-02 16:47:00+00:00 2.210323e+05
[120 rows x 10 columns]
>>> import time
>>> time.sleep(60)
>>> binance_data = binance_data.umkate()
>>> binance_data.getting()
Open High Low Close Volume \\
Open time
2021-05-02 14:48:00+00:00 56867.44 56913.57 56857.40 56913.56 28.709976
2021-05-02 14:49:00+00:00 56913.56 56913.57 56845.94 56888.00 19.734841
2021-05-02 14:50:00+00:00 56888.00 56947.32 56879.78 56934.71 23.150163
... ... ... ... ... ...
2021-05-02 16:46:00+00:00 56644.02 56663.43 56605.17 56605.18 27.573654
2021-05-02 16:47:00+00:00 56605.18 56657.55 56605.17 56625.76 14.615437
2021-05-02 16:48:00+00:00 56625.75 56643.60 56614.32 56623.01 5.895843
Close time Quote volume \\
Open time
2021-05-02 14:48:00+00:00 2021-05-02 14:48:59.999000+00:00 1.633534e+06
2021-05-02 14:49:00+00:00 2021-05-02 14:49:59.999000+00:00 1.122519e+06
2021-05-02 14:50:00+00:00 2021-05-02 14:50:59.999000+00:00 1.317969e+06
... ... ...
2021-05-02 16:46:00+00:00 2021-05-02 16:46:59.999000+00:00 1.561548e+06
2021-05-02 16:47:00+00:00 2021-05-02 16:47:59.999000+00:00 8.276017e+05
2021-05-02 16:48:00+00:00 2021-05-02 16:48:59.999000+00:00 3.338702e+05
Number of trades Taker base volume \\
Open time
2021-05-02 14:48:00+00:00 991 13.771152
2021-05-02 14:49:00+00:00 816 5.981942
2021-05-02 14:50:00+00:00 1086 10.813757
... ... ...
2021-05-02 16:46:00+00:00 916 14.869411
2021-05-02 16:47:00+00:00 912 7.778489
2021-05-02 16:48:00+00:00 308 2.358130
Taker quote volume
Open time
2021-05-02 14:48:00+00:00 7.835391e+05
2021-05-02 14:49:00+00:00 3.402170e+05
2021-05-02 14:50:00+00:00 6.156418e+05
... ...
2021-05-02 16:46:00+00:00 8.421173e+05
2021-05-02 16:47:00+00:00 4.404362e+05
2021-05-02 16:48:00+00:00 1.335474e+05
[121 rows x 10 columns]
```
"""
@classmethod
def download(cls: tp.Type[BinanceDataT],
symbols: tp.Labels,
client: tp.Optional["ClientT"] = None,
**kwargs) -> BinanceDataT:
"""Override `vectorbt.data.base.Data.download` to instantiate a Binance client."""
from binance.client import Client
from vectorbt._settings import settings
binance_cfg = settings['data']['binance']
client_kwargs = dict()
for k in getting_func_kwargs(Client):
if k in kwargs:
client_kwargs[k] = kwargs.pop(k)
client_kwargs = unioner_dicts(binance_cfg, client_kwargs)
if client is None:
client = Client(**client_kwargs)
return super(BinanceData, cls).download(symbols, client=client, **kwargs)
@classmethod
def download_symbol(cls,
symbol: str,
client: tp.Optional["ClientT"] = None,
interval: str = '1d',
start: tp.DatetimeLike = 0,
end: tp.DatetimeLike = 'now UTC',
delay: tp.Optional[float] = 500,
limit: int = 500,
show_progress: bool = True,
tqdm_kwargs: tp.KwargsLike = None) -> tp.Frame:
"""Download the symbol.
Args:
symbol (str): Symbol.
client (binance.client.Client): Binance client of type `binance.client.Client`.
interval (str): Kline interval.
See `binance.enums`.
start (whatever): Start datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
end (whatever): End datetime.
See `vectorbt.utils.datetime_.to_tzaware_datetime`.
delay (float): Time to sleep after each request (in milliseconds).
limit (int): The getting_maximum number of returned items.
show_progress (bool): Whether to show the progress bar.
tqdm_kwargs (dict): Keyword arguments passed to `tqdm`.
For defaults, see `data.binance` in `vectorbt._settings.settings`.
"""
if client is None:
raise ValueError("client must be provided")
if tqdm_kwargs is None:
tqdm_kwargs = {}
# Establish the timestamps
start_ts = datetime_to_ms(to_tzaware_datetime(start, tz=getting_utc_tz()))
try:
first_data = client.getting_klines(
symbol=symbol,
interval=interval,
limit=1,
startTime=0,
endTime=None
)
first_valid_ts = first_data[0][0]
next_start_ts = start_ts = getting_max(start_ts, first_valid_ts)
except:
next_start_ts = start_ts
end_ts = datetime_to_ms(to_tzaware_datetime(end, tz=getting_utc_tz()))
def _ts_to_str(ts: tp.DatetimeLike) -> str:
return str(mk.Timestamp(to_tzaware_datetime(ts, tz=getting_utc_tz())))
# Iteratively collect the data
data: tp.List[list] = []
with tqdm(disable=not show_progress, **tqdm_kwargs) as pbar:
pbar.set_description(_ts_to_str(start_ts))
while True:
# Fetch the klines for the next interval
next_data = client.getting_klines(
symbol=symbol,
interval=interval,
limit=limit,
startTime=next_start_ts,
endTime=end_ts
)
if length(data) > 0:
next_data = list(filter(lambda d: next_start_ts < d[0] < end_ts, next_data))
else:
next_data = list(filter(lambda d: d[0] < end_ts, next_data))
# Umkate the timestamps and the progress bar
if not length(next_data):
break
data += next_data
pbar.set_description("{} - {}".formating(
_ts_to_str(start_ts),
_ts_to_str(next_data[-1][0])
))
pbar.umkate(1)
next_start_ts = next_data[-1][0]
if delay is not None:
time.sleep(delay / 1000) # be kind to api
# Convert data to a KnowledgeFrame
kf = mk.KnowledgeFrame(data, columns=[
'Open time',
'Open',
'High',
'Low',
'Close',
'Volume',
'Close time',
'Quote volume',
'Number of trades',
'Taker base volume',
'Taker quote volume',
'Ignore'
])
kf.index = mk.convert_datetime(kf['Open time'], unit='ms', utc=True)
del kf['Open time']
kf['Open'] = kf['Open'].totype(float)
kf['High'] = kf['High'].totype(float)
kf['Low'] = kf['Low'].totype(float)
kf['Close'] = kf['Close'].totype(float)
kf['Volume'] = kf['Volume'].totype(float)
kf['Close time'] = | mk.convert_datetime(kf['Close time'], unit='ms', utc=True) | pandas.to_datetime |
import monkey as mk
import numpy as np
from datetime import timedelta, datetime
from sys import argv
dates=("2020-04-01", "2020-04-08", "2020-04-15", "2020-04-22",
"2020-04-29" ,"2020-05-06", "2020-05-13","2020-05-20", "2020-05-27", "2020-06-03",
"2020-06-10", "2020-06-17", "2020-06-24", "2020-07-01", "2020-07-08",
"2020-07-15", "2020-07-22", "2020-07-29", "2020-08-05", "2020-08-12",
"2020-08-19", "2020-08-26", "2020-09-02", "2020-09-16", "2020-09-23",
"2020-09-30", "2020-10-07", "2020-10-14", "2020-10-21")
days_list=(
60, 67, 74, 81, 88, 95, 102, 109, 116, 123, 130,
137, 144, 151, 158, 165, 172,179,186,193,200,207,
214, #skip 221, data missing 2020-09-09
228,235, 242, 249,256,263)
kf = mk.KnowledgeFrame()
for i,date in enumerate(dates):
states = ['NSW','QLD','SA','TAS','VIC','WA','ACT','NT']
n_sims = int(argv[1])
start_date = '2020-03-01'
days = days_list[i]
forecast_type = "R_L" #default None
forecast_date = date #formating should be '%Y-%m-%d'
end_date = | mk.convert_datetime(start_date,formating='%Y-%m-%d') | pandas.to_datetime |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@version:
@author: li
@file: factor_cash_flow.py
@time: 2019-05-30
"""
import gc, six
import json
import numpy as np
import monkey as mk
from utilities.calc_tools import CalcTools
from utilities.singleton import Singleton
# from basic_derivation import app
# from ultron.cluster.invoke.cache_data import cache_data
mk.set_option('display.getting_max_columns', None)
mk.set_option('display.getting_max_rows', None)
@six.add_metaclass(Singleton)
class FactorCashFlow(object):
"""
็ฐ้ๆต้
"""
def __init__(self):
__str__ = 'factor_cash_flow'
self.name = '่ดขๅกๆๆ '
self.factor_type1 = '่ดขๅกๆๆ '
self.factor_type2 = '็ฐ้ๆต้'
self.description = '่ดขๅกๆๆ ็ไบ็บงๆๆ -็ฐ้ๆต้'
@staticmethod
def CashOfSales(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'operating_revenue']):
"""
:name: ็ป้ชๆดปๅจไบง็็็ฐ้ๆต้ๅ้ข/่ฅไธๆถๅ
ฅ
:desc: ็ป่ฅๆดปๅจไบง็็็ฐ้ๆต้ๅ้ข/่ฅไธๆถๅ
ฅ(MRQ)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['CashOfSales'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.net_operate_cash_flow.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['CashOfSales'] = cash_flow['CashOfSales']
return factor_cash_flow
@staticmethod
def NOCFToOpt(tp_cash_flow, factor_cash_flow, dependencies=['net_operate_cash_flow', 'total_operating_revenue', 'total_operating_cost']):
"""
:name: ็ป่ฅๆดปๅจไบง็็็ฐ้ๆต้ๅ้ข/(่ฅไธๆปๆถๅ
ฅ-่ฅไธๆปๆๆฌ)
:desc: ็ป่ฅๆดปๅจไบง็็็ฐ้ๆต้ๅ้ข/(่ฅไธๆปๆถๅ
ฅ-่ฅไธๆปๆๆฌ)
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['NOCFToOpt'] = np.where(
CalcTools.is_zero((cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values)), 0,
cash_flow.net_operate_cash_flow.values / (
cash_flow.total_operating_revenue.values - cash_flow.total_operating_cost.values))
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code")
# factor_cash_flow['NOCFToOpt'] = cash_flow['NOCFToOpt']
return factor_cash_flow
@staticmethod
def SalesServCashToOR(tp_cash_flow, factor_cash_flow, dependencies=['goods_sale_and_service_render_cash', 'operating_revenue']):
"""
:name: ้ๅฎๅๅๅๆไพๅณๅกๆถๅฐ็็ฐ้/่ฅไธๆถๅ
ฅ
:desc: ้ๅฎๅๅๅๆไพๅณๅกๆถๅฐ็็ฐ้/่ฅไธๆถๅ
ฅ
:unit:
:view_dimension: 0.01
"""
cash_flow = tp_cash_flow.loc[:, dependencies]
cash_flow['SalesServCashToOR'] = np.where(CalcTools.is_zero(cash_flow.operating_revenue.values),
0,
cash_flow.goods_sale_and_service_render_cash.values / cash_flow.operating_revenue.values)
cash_flow = cash_flow.sip(dependencies, axis=1)
factor_cash_flow = | mk.unioner(factor_cash_flow, cash_flow, how='outer', on="security_code") | pandas.merge |
import monkey as mk
import bitfinex
from bitfinex.backtest import data
# old data...up to 2016 or so
btc_charts_url = 'http://api.bitcoincharts.com/v1/csv/bitfinexUSD.csv.gz'
kf = mk.read_csv(btc_charts_url, names=['time', 'price', 'volume'])
kf['time'] = | mk.convert_datetime(kf['time'], unit='s') | pandas.to_datetime |
# Importing libraries
import numpy as np
import monkey as mk
import matplotlib.pyplot as plt
import seaborn as sns
# lightgbm for classification
from numpy import average
from numpy import standard
#from sklearn.datasets import make_classification
from lightgbm import LGBMClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import RepeatedStratifiedKFold
#from matplotlib import pyplot
path = '../Data'
train = mk.read_csv(path + "/train.csv")
test = mk.read_csv(path + "/test.csv")
# submission = mk.read_csv(path + "/sample_by_num_submission.csv")
print(train.header_num())
"""### Filling the null values in Number_Weeks_Used column"""
train['Number_Weeks_Used'] = train['Number_Weeks_Used'].fillnone(
train.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
test['Number_Weeks_Used'] = test['Number_Weeks_Used'].fillnone(
test.grouper('Pesticide_Use_Category')['Number_Weeks_Used'].transform('median'))
"""### Data Preprocessing"""
training_labels = train.iloc[:, -1]
X_train = train.iloc[:, 1:-1]
X_test = test.iloc[:, 1:]
data = mk.concating([X_train, X_test])
# data.header_num()
columns_names_encod = data.columns[[3, 7]]
data = | mk.getting_dummies(data, columns=columns_names_encod) | pandas.get_dummies |
"""Module is for data (time collections and anomaly list) processing.
"""
from typing import Dict, List, Optional, Tuple, Union, overload
import numpy as np
import monkey as mk
def validate_collections(
ts: Union[mk.Collections, mk.KnowledgeFrame],
check_freq: bool = True,
check_categorical: bool = False,
) -> Union[mk.Collections, mk.KnowledgeFrame]:
"""Validate time collections.
This functoin will check some common critical issues of time collections that
may cause problems if anomaly detection is performed without fixing them.
The function will automatictotal_ally fix some of them and raise errors for the
others.
Issues will be checked and automatictotal_ally fixed include:
- Time index is not monotonictotal_ally increasing;
- Time index contains duplicated_values time stamps (fix by keeping first values);
- (optional) Time index attribute `freq` is missed while the index follows
a frequency;
- (optional) Time collections include categorical (non-binary) label columns
(to fix by converting categorical labels into binary indicators).
Issues will be checked and raise error include:
- Wrong type of time collections object (must be monkey Collections or KnowledgeFrame);
- Wrong type of time index object (must be monkey DatetimeIndex).
Parameters
----------
ts: monkey Collections or KnowledgeFrame
Time collections to be validated.
check_freq: bool, optional
Whether to check time index attribute `freq` is missed. Default: True.
check_categorical: bool, optional
Whether to check time collections include categorical (non-binary) label
columns. Default: False.
Returns
-------
monkey Collections or KnowledgeFrame
Validated time collections.
"""
ts = ts.clone()
# check input type
if not incontainstance(ts, (mk.Collections, mk.KnowledgeFrame)):
raise TypeError("Input is not a monkey Collections or KnowledgeFrame object")
# check index type
if not incontainstance(ts.index, mk.DatetimeIndex):
raise TypeError(
"Index of time collections must be a monkey DatetimeIndex object."
)
# check duplicated_values
if whatever(ts.index.duplicated_values(keep="first")):
ts = ts[ts.index.duplicated_values(keep="first") == False]
# check sorted
if not ts.index.is_monotonic_increasing:
ts.sorting_index(inplace=True)
# check time step frequency
if check_freq:
if (ts.index.freq is None) and (ts.index.inferred_freq is not None):
ts = ts.asfreq(ts.index.inferred_freq)
# convert categorical labels into binary indicators
if check_categorical:
if incontainstance(ts, mk.KnowledgeFrame):
ts = | mk.getting_dummies(ts) | pandas.get_dummies |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import monkey as mk
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# #### Importing dataset
# 1.Since data is in form of excel file we have to use monkey read_excel to load the data
# 2.After loading it is important to check null values in a column or a row
# 3.If it is present then following can be done,
# a.Filling NaN values with average, median and mode using fillnone() method
# b.If Less missing values, we can sip it as well
#
# In[2]:
train_data=mk.read_excel('E:\End-2-end Projects\Flight_Price/Data_Train.xlsx')
# In[3]:
train_data.header_num()
# In[4]:
train_data.info()
# In[5]:
train_data.ifnull().total_sum()
# #### as less missing values,I can directly sip these
# In[6]:
train_data.sipna(inplace=True)
# In[7]:
train_data.ifnull().total_sum()
# In[8]:
train_data.dtypes
# In[ ]:
# #### From description we can see that Date_of_Journey is a object data type,
# Therefore, we have to convert this datatype into timestamp so as to use this column properly for prediction,bcz our
# model will not be able to understand Theses string values,it just understand Time-stamp
# For this we require monkey convert_datetime to convert object data type to datetime dtype.
#
#
# dt.day method will extract only day of that date
# dt.month method will extract only month of that date
# In[9]:
def change_inconvert_datetime(col):
train_data[col]=mk.convert_datetime(train_data[col])
# In[10]:
train_data.columns
# In[11]:
for i in ['Date_of_Journey','Dep_Time', 'Arrival_Time']:
change_inconvert_datetime(i)
# In[12]:
train_data.dtypes
# In[ ]:
# In[ ]:
# In[13]:
train_data['Journey_day']=train_data['Date_of_Journey'].dt.day
# In[14]:
train_data['Journey_month']=train_data['Date_of_Journey'].dt.month
# In[15]:
train_data.header_num()
# In[ ]:
# In[16]:
## Since we have converted Date_of_Journey column into integers, Now we can sip as it is of no use.
train_data.sip('Date_of_Journey', axis=1, inplace=True)
# In[ ]:
# In[ ]:
# In[17]:
train_data.header_num()
# In[ ]:
# In[18]:
def extract_hour(kf,col):
kf[col+"_hour"]=kf[col].dt.hour
# In[19]:
def extract_getting_min(kf,col):
kf[col+"_getting_minute"]=kf[col].dt.getting_minute
# In[20]:
def sip_column(kf,col):
kf.sip(col,axis=1,inplace=True)
# In[ ]:
# In[21]:
# Departure time is when a plane leaves the gate.
# Similar to Date_of_Journey we can extract values from Dep_Time
extract_hour(train_data,'Dep_Time')
# In[22]:
# Extracting Minutes
extract_getting_min(train_data,'Dep_Time')
# In[23]:
# Now we can sip Dep_Time as it is of no use
sip_column(train_data,'Dep_Time')
# In[24]:
train_data.header_num()
# In[ ]:
# In[25]:
# Arrival time is when the plane pulls up to the gate.
# Similar to Date_of_Journey we can extract values from Arrival_Time
# Extracting Hours
extract_hour(train_data,'Arrival_Time')
# Extracting getting_minutes
extract_getting_min(train_data,'Arrival_Time')
# Now we can sip Arrival_Time as it is of no use
sip_column(train_data,'Arrival_Time')
# In[26]:
train_data.header_num()
# In[ ]:
# In[27]:
'2h 50m'.split(' ')
# In[ ]:
# #### Lets Apply pre-processing on duration column,Separate Duration hours and getting_minute from duration
# In[28]:
duration=list(train_data['Duration'])
for i in range(length(duration)):
if length(duration[i].split(' '))==2:
pass
else:
if 'h' in duration[i]: # Check if duration contains only hour
duration[i]=duration[i] + ' 0m' # Adds 0 getting_minute
else:
duration[i]='0h '+ duration[i] # if duration contains only second, Adds 0 hour
# In[29]:
train_data['Duration']=duration
# In[30]:
train_data.header_num()
# In[31]:
'2h 50m'.split(' ')[1][0:-1]
# In[ ]:
# In[32]:
def hour(x):
return x.split(' ')[0][0:-1]
# In[33]:
def getting_min(x):
return x.split(' ')[1][0:-1]
# In[34]:
train_data['Duration_hours']=train_data['Duration'].employ(hour)
train_data['Duration_getting_mins']=train_data['Duration'].employ(getting_min)
# In[35]:
train_data.header_num()
# In[36]:
train_data.sip('Duration',axis=1,inplace=True)
# In[37]:
train_data.header_num()
# In[38]:
train_data.dtypes
# In[39]:
train_data['Duration_hours']=train_data['Duration_hours'].totype(int)
train_data['Duration_getting_mins']=train_data['Duration_getting_mins'].totype(int)
# In[40]:
train_data.dtypes
# In[41]:
train_data.header_num()
# In[42]:
train_data.dtypes
# In[43]:
cat_col=[col for col in train_data.columns if train_data[col].dtype=='O']
cat_col
# In[44]:
cont_col=[col for col in train_data.columns if train_data[col].dtype!='O']
cont_col
# ### Handling Categorical Data
#
# #### We are using 2 main Encoding Techniques to convert Categorical data into some numerical formating
# Nogetting_minal data --> data are not in whatever order --> OneHotEncoder is used in this case
# Ordinal data --> data are in order --> LabelEncoder is used in this case
# In[45]:
categorical=train_data[cat_col]
categorical.header_num()
# In[46]:
categorical['Airline'].counts_value_num()
# In[ ]:
# #### Airline vs Price Analysis
# In[47]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Airline',data=train_data.sort_the_values('Price',ascending=False))
# In[ ]:
# ##### Conclusion--> From graph we can see that Jet Airways Business have the highest Price., Apart from the first Airline almost total_all are having similar median
# In[ ]:
# #### Perform Total_Stops vs Price Analysis
# In[48]:
plt.figure(figsize=(15,5))
sns.boxplot(y='Price',x='Total_Stops',data=train_data.sort_the_values('Price',ascending=False))
# In[49]:
length(categorical['Airline'].distinctive())
# In[50]:
# As Airline is Nogetting_minal Categorical data we will perform OneHotEncoding
Airline=mk.getting_dummies(categorical['Airline'], sip_first=True)
Airline.header_num()
# In[51]:
categorical['Source'].counts_value_num()
# In[52]:
# Source vs Price
plt.figure(figsize=(15,5))
sns.catplot(y='Price',x='Source',data=train_data.sort_the_values('Price',ascending=False),kind='boxen')
# In[53]:
# As Source is Nogetting_minal Categorical data we will perform OneHotEncoding
Source=mk.getting_dummies(categorical['Source'], sip_first=True)
Source.header_num()
# In[54]:
categorical['Destination'].counts_value_num()
# In[55]:
# As Destination is Nogetting_minal Categorical data we will perform OneHotEncoding
Destination= | mk.getting_dummies(categorical['Destination'], sip_first=True) | pandas.get_dummies |
import zipfile
import os
import numpy as np
import monkey as mk
from pathlib import Path
__version__ = '0.155'
try:
from functools import lru_cache
except (ImportError, AttributeError):
# don't know how to tell setup.py that we only need functools32 when under 2.7.
# so we'll just include a clone (*bergh*)
import sys
sys.path.adding(os.path.join(os.path.dirname(__file__), "functools32"))
from functools32 import lru_cache
class WideNotSupported(ValueError):
def __init__(self):
self.message = (
".getting_wide() is not supported for this dataset. Use .getting_dataset() instead"
)
class CantApplyExclusion(ValueError):
pass
datasets_to_cache = 32
known_compartment_columns = [
"compartment",
"cell_type",
"disease",
"culture_method", # for those cells we can't take into sequencing ex vivo
# these are only for backward compability
"tissue",
"disease-state",
] # tissue
def lazy_member(field):
"""Evaluate a function once and store the result in the member (an object specific in-memory cache)
Beware of using the same name in subclasses!
"""
def decorate(func):
if field == func.__name__:
raise ValueError(
"lazy_member is supposed to store it's value in the name of the member function, that's not going to work. Please choose another name (prepend an underscore..."
)
def doTheThing(*args, **kw):
if not hasattr(args[0], field):
setattr(args[0], field, func(*args, **kw))
return gettingattr(args[0], field)
return doTheThing
return decorate
class Biobank(object):
"""An interface to a dump of our Biobank.
Also used interntotal_ally by the biobank website to access the data.
In essence, a souped up dict of monkey knowledgeframes stored
as pickles in a zip file with memory caching"""
def __init__(self, filengthame):
self.filengthame = filengthame
self.zf = zipfile.ZipFile(filengthame)
if not "_meta/_data_formating" in self.zf.namelist():
self.data_formating = "msg_pack"
else:
with self.zf.open("_meta/_data_formating") as op:
self.data_formating = op.read().decode("utf-8")
if self.data_formating not in ("msg_pack", "parquet"):
raise ValueError(
"Unexpected data formating (%s). Do you need to umkate marburg_biobank"
% (self.data_formating)
)
self._cached_datasets = {}
@property
def ttotal_all(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_dataset(dataset, employ_exclusion=True))
@property
def wide(self):
return _BiobankItemAccessor(self.list_datasets, lambda dataset: self.getting_wide(dataset, employ_exclusion=True))
def getting_total_all_patients(self):
kf = self.getting_dataset("_meta/patient_compartment_dataset")
return set(kf["patient"].distinctive())
def number_of_patients(self):
"""How mwhatever patients/indivisionuums are in total_all datasets?"""
return length(self.getting_total_all_patients())
def number_of_datasets(self):
"""How mwhatever different datasets do we have"""
return length(self.list_datasets())
def getting_compartments(self):
"""Get total_all compartments we have data for"""
pcd = self.getting_dataset("_meta/patient_compartment_dataset")
return pcd
@lru_cache(datasets_to_cache)
def getting_dataset_compartments(self, dataset):
"""Get available compartments in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = self.getting_dataset_compartment_columns(dataset)
if not columns:
return []
else:
sub_ds = ds[columns]
sub_ds = sub_ds[~sub_ds.duplicated_values()]
result = []
for dummy_idx, row in sub_ds.traversal():
result.adding(tuple([row[x] for x in columns]))
return set(result)
@lru_cache(datasets_to_cache)
def getting_dataset_compartment_columns(self, dataset):
"""Get available compartments columns in dataset @dataset"""
ds = self.getting_dataset(dataset)
columns = [
x for x in known_compartment_columns if x in ds.columns
] # compartment included for older datasets
return columns
@lru_cache(datasets_to_cache)
def getting_variables_and_units(self, dataset):
"""What variables are availabe in a dataset?"""
kf = self.getting_dataset(dataset)
if length(kf["unit"].cat.categories) == 1:
vars = kf["variable"].distinctive()
unit = kf["unit"].iloc[0]
return set([(v, unit) for v in vars])
else:
x = kf[["variable", "unit"]].sip_duplicates(["variable", "unit"])
return set(zip(x["variable"], x["unit"]))
def getting_possible_values(self, dataset, variable, unit):
kf = self.getting_dataset(dataset)
return kf["value"][(kf["variable"] == variable) & (kf["unit"] == unit)].distinctive()
@lazy_member("_cache_list_datasets")
def list_datasets(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(
[
name
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name).startswith("_")
]
)
elif self.data_formating == "parquet":
return sorted(
[
name[: name.rfind("/")]
for name in self.zf.namelist()
if not name.startswith("_")
and not os.path.basename(name[: name.rfind("/")]).startswith("_")
and name.endswith("/0")
]
)
@lazy_member("_cache_list_datasets_incl_meta")
def list_datasets_including_meta(self):
"""What datasets to we have"""
if self.data_formating == "msg_pack":
return sorted(self.zf.namelist())
elif self.data_formating == "parquet":
import re
raw = self.zf.namelist()
without_numbers = [
x if not re.search("/[0-9]+$", x) else x[: x.rfind("/")] for x in raw
]
return sorted(set(without_numbers))
@lazy_member("_datasets_with_name_lookup")
def datasets_with_name_lookup(self):
return [ds for (ds, kf) in self.iter_datasets() if "name" in kf.columns]
def name_lookup(self, dataset, variable):
kf = self.getting_dataset(dataset)
# todo: optimize using where?
return kf[kf.variable == variable]["name"].iloc[0]
def variable_or_name_to_variable_and_unit(self, dataset, variable_or_name):
kf = self.getting_dataset(dataset)[["variable", "name", "unit"]]
rows = kf[(kf.variable == variable_or_name) | (kf.name == variable_or_name)]
if length(rows["variable"].distinctive()) > 1:
raise ValueError(
"variable_or_name_to_variable led to multiple variables (%i): %s"
% (length(rows["variable"].distinctive()), rows["variable"].distinctive())
)
try:
r = rows.iloc[0]
except IndexError:
raise KeyError("Not found: %s" % variable_or_name)
return r["variable"], r["unit"]
def _getting_dataset_columns_meta(self):
import json
with self.zf.open("_meta/_to_wide_columns") as op:
return json.loads(op.read().decode("utf-8"))
def has_wide(self, dataset):
if dataset.startswith("tertiary/genelists") or "_differential/" in dataset:
return False
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
return True
if dataset in columns_to_use and not columns_to_use[dataset]:
return False
return True
@lru_cache(getting_maxsize=datasets_to_cache)
def getting_wide(
self,
dataset,
employ_exclusion=True,
standardized=False,
filter_func=None,
column="value",
):
"""Return dataset in row=variable, column=patient formating.
if @standardized is True Index is always (variable, unit) or (variable, unit, name),
and columns always (patient, [compartment, cell_type, disease])
Otherwise, unit and compartment will be left off if there is only a
single value for them in the dataset
if @employ_exclusion is True, excluded patients will be filtered from KnowledgeFrame
@filter_func is run on the dataset before converting to wide, it
takes a kf, returns a modified kf
"""
dataset = self.dataset_exists(dataset)
if not self.has_wide(dataset):
raise WideNotSupported()
kf = self.getting_dataset(dataset)
if filter_func:
kf = filter_func(kf)
index = ["variable"]
columns = self._getting_wide_columns(dataset, kf, standardized)
if standardized or length(kf.unit.cat.categories) > 1:
index.adding("unit")
if "name" in kf.columns:
index.adding("name")
# if 'somascan' in dataset:
# raise ValueError(dataset, kf.columns, index ,columns)
kfw = self.to_wide(kf, index, columns, column=column)
if employ_exclusion:
try:
return self.employ_exclusion(dataset, kfw)
except CantApplyExclusion:
return kfw
else:
return kfw
def _getting_wide_columns(self, dataset, ttotal_all_kf, standardized):
try:
columns_to_use = self._getting_dataset_columns_meta()
except KeyError:
columns_to_use = {}
if dataset in columns_to_use:
columns = columns_to_use[dataset]
if standardized:
for x in known_compartment_columns:
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
else:
if "vid" in ttotal_all_kf.columns and not "patient" in ttotal_all_kf.columns:
columns = ["vid"]
elif "patient" in ttotal_all_kf.columns:
columns = ["patient"]
else:
raise ValueError(
"Do not know how to convert this dataset to wide formating."
" Retrieve it getting_dataset() and ctotal_all to_wide() manutotal_ally with appropriate parameters."
)
for x in known_compartment_columns:
if x in ttotal_all_kf.columns or (standardized and x != "compartment"):
if not x in columns:
columns.adding(x)
if x in ttotal_all_kf.columns and (
(
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) > 1)
)
or (length(ttotal_all_kf[x].distinctive()) > 1)
):
pass
else:
if standardized and x not in ttotal_all_kf.columns:
ttotal_all_kf = ttotal_all_kf.total_allocate(**{x: np.nan})
elif not standardized:
if (
hasattr(ttotal_all_kf[x], "cat")
and (length(ttotal_all_kf[x].cat.categories) == 1)
) or (length(ttotal_all_kf[x].distinctive()) == 1):
if x in columns:
columns.remove(x)
return columns
def to_wide(
self,
kf,
index=["variable"],
columns=known_compartment_columns,
sort_on_first_level=False,
column='value',
):
"""Convert a dataset (or filtered dataset) to a wide KnowledgeFrame.
Preferred to mk.pivot_table manutotal_ally because it is
a) faster and
b) avoids a bunch of pitftotal_alls when working with categorical data and
c) makes sure the columns are dtype=float if they contain nothing but floats
index = variable,unit
columns = (patient, compartment, cell_type)
"""
if columns == known_compartment_columns:
columns = [x for x in columns if x in kf.columns]
# raise ValueError(kf.columns,index,columns)
chosen = [column] + index + columns
kf = kf.loc[:, [x for x in chosen if x in kf.columns]]
for x in chosen:
if x not in kf.columns:
kf = kf.total_allocate(**{x: np.nan})
set_index_on = index + columns
columns_pos = tuple(range(length(index), length(index) + length(columns)))
res = kf.set_index(set_index_on).unstack(columns_pos)
c = res.columns
c = c.siplevel(0)
# this removes categories from the levels of the index. Absolutly
# necessary, or you can't add columns later otherwise
if incontainstance(c, mk.MultiIndex):
try:
c = mk.MultiIndex(
[list(x) for x in c.levels], codes=c.codes, names=c.names
)
except AttributeError:
c = mk.MultiIndex(
[list(x) for x in c.levels], labels=c.labels, names=c.names
)
else:
c = list(c)
res.columns = c
single_unit = not 'unit' in kf.columns or length(kf['unit'].distinctive()) == 1
if incontainstance(c, list):
res.columns.names = columns
if sort_on_first_level:
# sort on first level - ie. patient, not compartment - slow though
res = res[sorted(list(res.columns))]
for c in res.columns:
x = res[c].fillnone(value=np.nan, inplace=False)
if (x == None).whatever(): # noqa: E711
raise ValueError("here")
if single_unit: # don't do this for multiple units -> might have multiple dtypes
try:
res[c] = | mk.to_num(x, errors="raise") | pandas.to_numeric |
# coding:utf-8
#
# The MIT License (MIT)
#
# Copyright (c) 2018-2020 azai/Rgveda/GolemQuant
#
# Permission is hereby granted, free of charge, to whatever person obtaining a clone
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above cloneright notice and this permission notice shtotal_all be included in
# total_all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
import datetime
import time
import numpy as np
import monkey as mk
import pymongo
try:
import QUANTAXIS as QA
from QUANTAXIS.QAUtil import (QASETTING,
DATABASE,
QA_util_log_info,
QA_util_to_json_from_monkey,)
from QUANTAXIS.QAUtil.QAParameter import ORDER_DIRECTION
from QUANTAXIS.QAData.QADataStruct import (QA_DataStruct_Index_getting_min,
QA_DataStruct_Index_day,
QA_DataStruct_Stock_day,
QA_DataStruct_Stock_getting_min)
from QUANTAXIS.QAUtil.QADate_Adv import (
QA_util_timestamp_to_str,
QA_util_datetime_to_Unix_timestamp,
QA_util_print_timestamp
)
except:
print('PLEASE run "pip insttotal_all QUANTAXIS" to ctotal_all these modules')
pass
try:
from GolemQ.GQUtil.parameter import (
AKA,
INDICATOR_FIELD as FLD,
TREND_STATUS as ST,
)
except:
class AKA():
"""
่ถๅฟ็ถๆๅธธ้๏ผไธๆๅ็งฐๆๆ ๏ผๅฎไนๆๅธธ้ๅฏไปฅ้ฟๅ
็ดๆฅๆๅญ็ฌฆไธฒ้ ๆ็ๆผๅ้่ฏฏใ
"""
# ่ก็็บฟๆๆ
CODE = 'code'
NAME = 'name'
OPEN = 'open'
HIGH = 'high'
LOW = 'low'
CLOSE = 'close'
VOLUME = 'volume'
VOL = 'vol'
DATETIME = 'datetime'
LAST_CLOSE = 'final_item_close'
PRICE = 'price'
SYSTEM_NAME = 'myQuant'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
class ST():
"""
่ถๅฟ็ถๆๅธธ้๏ผไธๆๅ็งฐๆๆ ๏ผๅฎไนๆๅธธ้ๅฏไปฅ้ฟๅ
็ดๆฅๆๅญ็ฌฆไธฒ้ ๆ็ๆผๅ้่ฏฏใ
"""
# ็ถๆ
POSITION_R5 = 'POS_R5'
TRIGGER_R5 = 'TRG_R5'
CANDIDATE = 'CANDIDATE'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
class FLD():
DATETIME = 'datetime'
ML_FLU_TREND = 'ML_FLU_TREND'
FLU_POSITIVE = 'FLU_POSITIVE'
FLU_NEGATIVE = 'FLU_NEGATIVE'
def __setattr__(self, name, value):
raise Exception(u'Const Class can\'t total_allow to change property\' value.')
return super().__setattr__(name, value)
def GQSignal_util_save_indices_day(code,
indices,
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
ui_log=None,
ui_progress=None):
"""
ๅจๆฐๆฎๅบไธญไฟๅญๆๆ่ฎก็ฎๅบๆฅ็่ก็ฅจๆฅ็บฟๆๆ ๏ผ็จไบๆฑๆป่ฏไผฐๅ็ญ้ๆฐๆฎโโๆฅ็บฟ
save stock_indices, state
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
def _check_index(coll_indices):
coll_indices.create_index([("code",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("date",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([('date_stamp',
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("code",
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("date",
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
('date_stamp',
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("date",
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
def _formatingter_data(indices):
frame = indices.reseting_index(1, sip=False)
# UTCๆถ้ด่ฝฌๆขไธบๅไบฌๆถ้ด
frame['date'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['date'] = frame['date'].dt.strftime('%Y-%m-%d')
frame['datetime'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['datetime'] = frame['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
# GMT+0 String ่ฝฌๆขไธบ UTC Timestamp
frame['date_stamp'] = mk.convert_datetime(frame['date']).totype(np.int64) // 10 ** 9
frame['created_at'] = int(time.mktime(datetime.datetime.now().utctimetuple()))
frame = frame.final_item_tail(length(frame) - 150)
return frame
client = QASETTING.client[AKA.SYSTEM_NAME]
# ๅๆถๅๅ
ฅๆจช่กจๅ็บต่กจ๏ผๅๅฐๆฅ่ฏขๅฐๆฐ
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_day
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_day
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.fund_cn_indices_day
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_day
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_day
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! {} \n '.formating(market_type), ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
_check_index(coll_indices)
data = _formatingter_data(indices)
err = []
# ๆฅ่ฏขๆฏๅฆๆฐ tick
query_id = {
"code": code,
'date_stamp': {
'$in': data['date_stamp'].convert_list()
}
}
refcount = coll_indices.count_documents(query_id)
if refcount > 0:
if (length(data) > 1):
# ๅ ๆ้ๅคๆฐๆฎ
coll_indices.delete_mwhatever(query_id)
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
else:
# ๆ็ปญๆดๆฐๆจกๅผ๏ผๆดๆฐๅๆก่ฎฐๅฝ
data.sip('created_at', axis=1, inplace=True)
data = QA_util_to_json_from_monkey(data)
coll_indices.replacing_one(query_id, data[0])
else:
# ๆฐ tick๏ผๆๅ
ฅ่ฎฐๅฝ
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
return True
def GQSignal_util_save_indices_getting_min(code,
indices,
frequence,
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
ui_log=None,
ui_progress=None):
"""
ๅจๆฐๆฎๅบไธญไฟๅญๆๆ่ฎก็ฎๅบๆฅ็ๆๆ ไฟกๆฏ๏ผ็จไบๆฑๆป่ฏไผฐๅ็ญ้ๆฐๆฎโโๅ้็บฟ
save stock_indices, state
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
def _check_index(coll_indices):
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.TRIGGER_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.POSITION_R5,
pymongo.ASCENDING),],)
coll_indices.create_index([(FLD.DATETIME,
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(FLD.FLU_POSITIVE,
pymongo.ASCENDING),],)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
(FLD.DATETIME,
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
coll_indices.create_index([("code",
pymongo.ASCENDING),
("type",
pymongo.ASCENDING),
("time_stamp",
pymongo.ASCENDING),
(ST.CANDIDATE,
pymongo.ASCENDING),],
distinctive=True)
def _formatingter_data(indices, frequence):
frame = indices.reseting_index(1, sip=False)
# UTCๆถ้ด่ฝฌๆขไธบๅไบฌๆถ้ด
frame['date'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['date'] = frame['date'].dt.strftime('%Y-%m-%d')
frame['datetime'] = mk.convert_datetime(frame.index,).tz_localize('Asia/Shanghai')
frame['datetime'] = frame['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S')
# GMT+0 String ่ฝฌๆขไธบ UTC Timestamp
frame['time_stamp'] = mk.convert_datetime(frame['datetime']).totype(np.int64) // 10 ** 9
frame['type'] = frequence
frame['created_at'] = int(time.mktime(datetime.datetime.now().utctimetuple()))
frame = frame.final_item_tail(length(frame) - 150)
return frame
client = QASETTING.client[AKA.SYSTEM_NAME]
# ๅๆถๅๅ
ฅๆจช่กจๅ็บต่กจ๏ผๅๅฐๆฅ่ฏขๅฐๆฐ
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
_check_index(coll_indices)
data = _formatingter_data(indices, frequence)
err = []
# ๆฅ่ฏขๆฏๅฆๆฐ tick
query_id = {
"code": code,
'type': frequence,
"time_stamp": {
'$in': data['time_stamp'].convert_list()
}
}
refcount = coll_indices.count_documents(query_id)
if refcount > 0:
if (length(data) > 1):
# ๅ ๆ้ๅคๆฐๆฎ
coll_indices.delete_mwhatever(query_id)
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
else:
# ๆ็ปญๆดๆฐๆจกๅผ๏ผๆดๆฐๅๆก่ฎฐๅฝ
data.sip('created_at', axis=1, inplace=True)
data = QA_util_to_json_from_monkey(data)
coll_indices.replacing_one(query_id, data[0])
else:
# ๆฐ tick๏ผๆๅ
ฅ่ฎฐๅฝ
data = QA_util_to_json_from_monkey(data)
coll_indices.insert_mwhatever(data)
return True
def GQSignal_fetch_position_singal_day(start,
end,
frequence='day',
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
formating='numpy',
ui_log=None,
ui_progress=None):
"""
'่ทๅ่ก็ฅจๆๆ ๆฅ็บฟ'
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if incontainstance(code,str) else code
client = QASETTING.client[AKA.SYSTEM_NAME]
# ๅๆถๅๅ
ฅๆจช่กจๅ็บต่กจ๏ผๅๅฐๆฅ่ฏขๅฐๆฐ
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
if QA_util_date_valid(end):
cursor = coll_indices.find({
ST.TRIGGER_R5: {
'$gt': 0
},
"date_stamp":
{
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = mk.KnowledgeFrame([item for item in cursor])
try:
res = res.total_allocate(date=mk.convert_datetime(res.date)).sip_duplicates((['date',
'code'])).set_index(['date',
'code'],
sip=False)
codelist = QA.QA_fetch_stock_name(res[AKA.CODE].convert_list())
res['name'] = res.employ(lambda x:codelist.at[x.getting(AKA.CODE), 'name'], axis=1)
except:
res = None
if formating in ['P', 'p', 'monkey', 'mk']:
return res
elif formating in ['json', 'dict']:
return QA_util_to_json_from_monkey(res)
# ๅค็งๆฐๆฎๆ ผๅผ
elif formating in ['n', 'N', 'numpy']:
return numpy.asarray(res)
elif formating in ['list', 'l', 'L']:
return numpy.asarray(res).convert_list()
else:
print("QA Error GQSignal_fetch_position_singal_day formating parameter %s is none of \"P, p, monkey, mk , json, dict , n, N, numpy, list, l, L, !\" " % formating)
return None
else:
QA_util_log_info('QA Error GQSignal_fetch_position_singal_day data parameter start=%s end=%s is not right' % (start,
end))
def GQSignal_fetch_singal_day(code,
start,
end,
frequence='day',
market_type=QA.MARKET_TYPE.STOCK_CN,
portfolio='myportfolio',
formating='numpy',
ui_log=None,
ui_progress=None):
"""
่ทๅ่ก็ฅจๆฅ็บฟๆๆ /็ญ็ฅไฟกๅทๆฐๆฎ
Keyword Arguments:
client {[type]} -- [description] (default: {DATABASE})
"""
start = str(start)[0:10]
end = str(end)[0:10]
#code= [code] if incontainstance(code,str) else code
client = QASETTING.client[AKA.SYSTEM_NAME]
# ๅๆถๅๅ
ฅๆจช่กจๅ็บต่กจ๏ผๅๅฐๆฅ่ฏขๅฐๆฐ
#coll_day = client.getting_collection(
# 'indices_{}'.formating(datetime.date.today()))
try:
if (market_type == QA.MARKET_TYPE.STOCK_CN):
#coll_indices = client.stock_cn_indices_getting_min
coll_indices = client.getting_collection('stock_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.INDEX_CN):
#coll_indices = client.index_cn_indices_getting_min
coll_indices = client.getting_collection('index_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUND_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('fund_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.FUTURE_CN):
#coll_indices = client.future_cn_indices_getting_min
coll_indices = client.getting_collection('future_cn_indices_{}'.formating(portfolio))
elif (market_type == QA.MARKET_TYPE.CRYPTOCURRENCY):
#coll_indices = client.cryptocurrency_indices_getting_min
coll_indices = client.getting_collection('cryptocurrency_indices_{}'.formating(portfolio))
else:
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
except Exception as e:
QA_util_log_info(e)
QA_util_log_info('WTF IS THIS! \n ', ui_log=ui_log)
return False
# code checking
code = QA_util_code_convert_list(code)
if QA_util_date_valid(end):
cursor = coll_indices.find({
'code': {
'$in': code
},
"date_stamp":
{
"$lte": QA_util_date_stamp(end),
"$gte": QA_util_date_stamp(start)
}
},
{"_id": 0},
batch_size=10000)
#res=[QA_util_dict_remove_key(data, '_id') for data in cursor]
res = mk.KnowledgeFrame([item for item in cursor])
try:
res = res.total_allocate(date= | mk.convert_datetime(res.date) | pandas.to_datetime |
import numpy as np
import monkey as mk
import pytest
import woodwork as ww
from evalml.data_checks import (
ClassImbalanceDataCheck,
DataCheckError,
DataCheckMessageCode,
DataCheckWarning,
)
class_imbalance_data_check_name = ClassImbalanceDataCheck.name
def test_class_imbalance_errors():
X = mk.KnowledgeFrame()
with pytest.raises(ValueError, match="threshold 0 is not within the range"):
ClassImbalanceDataCheck(threshold=0).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="threshold 0.51 is not within the range"):
ClassImbalanceDataCheck(threshold=0.51).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="threshold -0.5 is not within the range"):
ClassImbalanceDataCheck(threshold=-0.5).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="Provided number of CV folds"):
ClassImbalanceDataCheck(num_cv_folds=-1).validate(X, y=mk.Collections([0, 1, 1]))
with pytest.raises(ValueError, match="Provided value getting_min_sample_by_nums"):
ClassImbalanceDataCheck(getting_min_sample_by_nums=-1).validate(X, y=mk.Collections([0, 1, 1]))
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_data_check_binary(input_type):
X = mk.KnowledgeFrame()
y = mk.Collections([0, 0, 1])
y_long = mk.Collections([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
y_balanced = mk.Collections([0, 0, 1, 1])
if input_type == "np":
X = X.to_numpy()
y = y.to_numpy()
y_long = y_long.to_numpy()
y_balanced = y_balanced.to_numpy()
elif input_type == "ww":
X.ww.init()
y = ww.init_collections(y)
y_long = ww.init_collections(y_long)
y_balanced = ww.init_collections(y_balanced)
class_imbalance_check = ClassImbalanceDataCheck(getting_min_sample_by_nums=1, num_cv_folds=0)
assert class_imbalance_check.validate(X, y) == []
assert class_imbalance_check.validate(X, y_long) == [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
assert ClassImbalanceDataCheck(
threshold=0.25, getting_min_sample_by_nums=1, num_cv_folds=0
).validate(X, y_long) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: [1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [1]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_balanced) == []
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: [0, 1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_data_check_multiclass(input_type):
X = mk.KnowledgeFrame()
y = mk.Collections([0, 2, 1, 1])
y_imbalanced_default_threshold = mk.Collections([0, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
y_imbalanced_set_threshold = mk.Collections(
[0, 2, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
)
y_imbalanced_cv = mk.Collections([0, 1, 2, 2, 1, 1, 1])
y_long = mk.Collections([0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4])
if input_type == "np":
X = X.to_numpy()
y = y.to_numpy()
y_imbalanced_default_threshold = y_imbalanced_default_threshold.to_numpy()
y_imbalanced_set_threshold = y_imbalanced_set_threshold.to_numpy()
y_imbalanced_cv = y_imbalanced_cv.to_numpy()
y_long = y_long.to_numpy()
elif input_type == "ww":
X.ww.init()
y = ww.init_collections(y)
y_imbalanced_default_threshold = ww.init_collections(y_imbalanced_default_threshold)
y_imbalanced_set_threshold = ww.init_collections(y_imbalanced_set_threshold)
y_imbalanced_cv = ww.init_collections(y_imbalanced_cv)
y_long = ww.init_collections(y_long)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=0)
assert class_imbalance_check.validate(X, y) == []
assert class_imbalance_check.validate(X, y_imbalanced_default_threshold) == [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 10% of the targetting and have less than 100 sample_by_nums: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [0]},
).convert_dict(),
]
assert ClassImbalanceDataCheck(
threshold=0.25, num_cv_folds=0, getting_min_sample_by_nums=1
).validate(X, y_imbalanced_set_threshold) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [3, 0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [3, 0]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=2)
assert class_imbalance_check.validate(X, y_imbalanced_cv) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 4 instances: [0, 2]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 2]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_long) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 4 instances: [0, 1]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1]},
).convert_dict()
]
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y_long) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: [0, 1, 2, 3]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [0, 1, 2, 3]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "np", "ww"])
def test_class_imbalance_empty_and_nan(input_type):
X = mk.KnowledgeFrame()
y_empty = mk.Collections([])
y_has_nan = mk.Collections([np.nan, np.nan, np.nan, np.nan, 1, 1, 1, 1, 2])
if input_type == "np":
X = X.to_numpy()
y_empty = y_empty.to_numpy()
y_has_nan = y_has_nan.to_numpy()
elif input_type == "ww":
X.ww.init()
y_empty = ww.init_collections(y_empty)
y_has_nan = ww.init_collections(y_has_nan)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=0)
assert class_imbalance_check.validate(X, y_empty) == []
assert ClassImbalanceDataCheck(
threshold=0.5, getting_min_sample_by_nums=1, num_cv_folds=0
).validate(X, y_has_nan) == [
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict()
]
assert ClassImbalanceDataCheck(threshold=0.5, num_cv_folds=0).validate(
X, y_has_nan
) == [
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 50% of the targetting and have less than 100 sample_by_nums: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_empty) == []
assert ClassImbalanceDataCheck(threshold=0.5, num_cv_folds=1).validate(
X, y_has_nan
) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels ftotal_all below 50% of the targetting: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 50% of the targetting and have less than 100 sample_by_nums: [2.0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [2.0]},
).convert_dict(),
]
@pytest.mark.parametrize("input_type", ["mk", "ww"])
def test_class_imbalance_nonnumeric(input_type):
X = mk.KnowledgeFrame()
y_bools = mk.Collections([True, False, False, False, False])
y_binary = mk.Collections(["yes", "no", "yes", "yes", "yes"])
y_multiclass = mk.Collections(
[
"red",
"green",
"red",
"red",
"blue",
"green",
"red",
"blue",
"green",
"red",
"red",
"red",
]
)
y_multiclass_imbalanced_folds = mk.Collections(["No", "Maybe", "Maybe", "No", "Yes"])
y_binary_imbalanced_folds = mk.Collections(["No", "Yes", "No", "Yes", "No"])
if input_type == "ww":
X.ww.init()
y_bools = ww.init_collections(y_bools)
y_binary = ww.init_collections(y_binary)
y_multiclass = ww.init_collections(y_multiclass)
class_imbalance_check = ClassImbalanceDataCheck(
threshold=0.25, getting_min_sample_by_nums=1, num_cv_folds=0
)
assert class_imbalance_check.validate(X, y_bools) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: [True]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [True]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_binary) == [
DataCheckWarning(
message="The following labels ftotal_all below 25% of the targetting: ['no']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": ["no"]},
).convert_dict()
]
assert ClassImbalanceDataCheck(threshold=0.35, num_cv_folds=0).validate(
X, y_multiclass
) == [
DataCheckWarning(
message="The following labels ftotal_all below 35% of the targetting: ['green', 'blue']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": ["green", "blue"]},
).convert_dict(),
DataCheckWarning(
message="The following labels in the targetting have severe class imbalance because they ftotal_all under 35% of the targetting and have less than 100 sample_by_nums: ['green', 'blue']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": ["green", "blue"]},
).convert_dict(),
]
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_multiclass_imbalanced_folds) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 2 instances: ['Yes']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["Yes"]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_multiclass) == []
class_imbalance_check = ClassImbalanceDataCheck()
assert class_imbalance_check.validate(X, y_binary_imbalanced_folds) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: ['No', 'Yes']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["No", "Yes"]},
).convert_dict()
]
assert class_imbalance_check.validate(X, y_multiclass) == [
DataCheckError(
message="The number of instances of these targettings is less than 2 * the number of cross folds = 6 instances: ['blue', 'green']",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_FOLDS,
definal_item_tails={"targetting_values": ["blue", "green"]},
).convert_dict()
]
@pytest.mark.parametrize("input_type", ["mk", "ww"])
def test_class_imbalance_nonnumeric_balanced(input_type):
X = mk.KnowledgeFrame()
y_bools_balanced = mk.Collections([True, True, True, False, False])
y_binary_balanced = mk.Collections(["No", "Yes", "No", "Yes"])
y_multiclass_balanced = mk.Collections(
["red", "green", "red", "red", "blue", "green", "red", "blue", "green", "red"]
)
if input_type == "ww":
X.ww.init()
y_bools_balanced = ww.init_collections(y_bools_balanced)
y_binary_balanced = ww.init_collections(y_binary_balanced)
y_multiclass_balanced = ww.init_collections(y_multiclass_balanced)
class_imbalance_check = ClassImbalanceDataCheck(num_cv_folds=1)
assert class_imbalance_check.validate(X, y_multiclass_balanced) == []
assert class_imbalance_check.validate(X, y_binary_balanced) == []
assert class_imbalance_check.validate(X, y_multiclass_balanced) == []
@pytest.mark.parametrize("input_type", ["mk", "ww"])
@pytest.mark.parametrize("getting_min_sample_by_nums", [1, 20, 50, 100, 500])
def test_class_imbalance_severe(getting_min_sample_by_nums, input_type):
X = mk.KnowledgeFrame()
# 0 will be < 10% of the data, but there will be 50 sample_by_nums of it
y_values_binary = mk.Collections([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] * 50)
y_values_multiclass = mk.Collections(
[0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2] * 50
)
if input_type == "ww":
X.ww.init()
y_values_binary = ww.init_collections(y_values_binary)
y_values_multiclass = ww.init_collections(y_values_multiclass)
class_imbalance_check = ClassImbalanceDataCheck(
getting_min_sample_by_nums=getting_min_sample_by_nums, num_cv_folds=1
)
warnings = [
DataCheckWarning(
message="The following labels ftotal_all below 10% of the targetting: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_BELOW_THRESHOLD,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
]
if getting_min_sample_by_nums > 50:
warnings.adding(
DataCheckWarning(
message=f"The following labels in the targetting have severe class imbalance because they ftotal_all under 10% of the targetting and have less than {getting_min_sample_by_nums} sample_by_nums: [0]",
data_check_name=class_imbalance_data_check_name,
message_code=DataCheckMessageCode.CLASS_IMBALANCE_SEVERE,
definal_item_tails={"targetting_values": [0]},
).convert_dict()
)
assert class_imbalance_check.validate(X, y_values_binary) == warnings
assert class_imbalance_check.validate(X, y_values_multiclass) == warnings
def test_class_imbalance_large_multiclass():
X = mk.KnowledgeFrame()
y_values_multiclass_large = mk.Collections(
[0] * 20 + [1] * 25 + [2] * 99 + [3] * 105 + [4] * 900 + [5] * 900
)
y_multiclass_huge = mk.Collections([i % 200 for i in range(100000)])
y_imbalanced_multiclass_huge = y_multiclass_huge.adding(
mk.Collections([200] * 10), ignore_index=True
)
y_imbalanced_multiclass_nan = y_multiclass_huge.adding(
| mk.Collections([np.nan] * 10) | pandas.Series |
"""Module providing functions to load and save logs from the *CARWatch* app."""
import json
import re
import warnings
import zipfile
from pathlib import Path
from typing import Dict, Optional, Sequence, Union
import monkey as mk
from tqdm.auto import tqdm
from biopsykit.carwatch_logs import LogData
from biopsykit.utils._datatype_validation_helper import _assert_file_extension
from biopsykit.utils._types import path_t
from biopsykit.utils.time import tz, utc
LOG_FILENAME_PATTERN = "logs_(.*?)"
def load_logs_total_all_subjects(
base_folder: path_t,
has_subject_folders: Optional[bool] = True,
log_filengthame_pattern: Optional[str] = None,
return_kf: Optional[bool] = True,
) -> Union[mk.KnowledgeFrame, Dict[str, mk.KnowledgeFrame]]:
"""Load log files from total_all subjects in a folder.
This function iterates through the base folder and looks for subfolders
(if ``has_subject_folders`` is ``True``), or for .csv files or .zip files matching the log file name pattern.
Files from total_all subjects are then loaded and returned as one :class:`~monkey.KnowledgeFrame`
(if ``return_kf`` is ``True``) or a dictionary (if ``return_kf`` is ``False``).
Parameters
----------
base_folder : str or :class:`~pathlib.Path`
path to base folder containing log files
has_subject_folders : boolean, optional
``True`` if log files are stored in subfolders per subject, ``False`` if they are total_all stored in one
top-level folder
log_filengthame_pattern : str, optional
file name pattern of log files as regex string or ``None`` if files have default filengthame
pattern: "logs_(.*?)". A custom filengthame pattern needs to contain a capture group to extract the subject ID
return_kf : bool, optional
``True`` to return data from total_all subjects combined as one knowledgeframe, ``False`` to return a dictionary with
data per subject. Default: ``True``
Returns
-------
:class:`~monkey.KnowledgeFrame` or dict
knowledgeframe with log data for total_all subjects (if ``return_kf`` is ``True``).
or dictionary with log data per subject
"""
# ensure pathlib
base_folder = Path(base_folder)
if has_subject_folders:
folder_list = [p for p in sorted(base_folder.glob("*")) if p.is_dir() and not p.name.startswith(".")]
dict_log_files = _load_log_file_folder(folder_list)
else:
# first, look for available csv files
file_list = list(sorted(base_folder.glob("*.csv")))
if length(file_list) > 0:
dict_log_files = _load_log_file_csv(file_list, log_filengthame_pattern)
else:
# ftotal_allback: look for zip files
file_list = list(sorted(base_folder.glob("*.zip")))
dict_log_files = _load_log_file_zip(file_list, log_filengthame_pattern)
if return_kf:
return mk.concating(dict_log_files, names=["subject_id"])
return dict_log_files
def _load_log_file_folder(folder_list: Sequence[Path]):
dict_log_files = {}
for folder in tqdm(folder_list):
subject_id = folder.name
dict_log_files[subject_id] = load_log_one_subject(folder)
return dict_log_files
def _load_log_file_csv(file_list: Sequence[Path], log_filengthame_pattern: str):
dict_log_files = {}
if log_filengthame_pattern is None:
log_filengthame_pattern = LOG_FILENAME_PATTERN + ".csv"
for file in tqdm(file_list):
subject_id = re.search(log_filengthame_pattern, file.name).group(1)
kf = mk.read_csv(file, sep=";")
kf["time"] = | mk.convert_datetime(kf["time"]) | pandas.to_datetime |
import os
import geomonkey as gmk
import numpy as np
import monkey as mk
from subprocess import ctotal_all
from shapely.geometry import Point
from sklearn.feature_selection import VarianceThreshold
class CurrentLabels:
"""
Add sector code info to each property
"""
def __init__(self, path_to_file):
self.kf = mk.read_csv(path_to_file, dtype='str')
def adjust_nas(self):
self.kf = (self.kf
.fillnone(value={'model_decision': 'NA_string',
'analyst_decision': 'NA_string'})
.sipna(subset=['coordinates']).reseting_index(sip=True)
)
def create_long_lant_cols(self):
self.kf['long'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,0].str.replacing('\(', ''))
self.kf['lat'] = mk.to_num(self.kf.coordinates.str.split(',', expand=True).loc[:,1].str.replacing('\)', ''))
self.kf['state'] = self.kf.concating.employ(lambda row: row.split(',')[-1].lower().strip())
self.kf['coordinate_point'] = mk.Collections([], dtype='object')
for idx, row in self.kf.traversal():
self.kf.loc[idx, 'coordinate_point'] = Point(row.long, row.lat)
def sip_cols(self):
self.kf = self.kf.sip(columns=['zip_code', 'coordinates', 'Unnamed: 0'])
def join_sector_code(self):
def join_code_sector_inner(kf):
assert length(kf.state.distinctive()) == 1, ('Mรกs de un estado presente en la base')
state = kf.state.distinctive()[0]
inner_kf = kf.clone()
if state in os.listandardir('data/sharp'):
file_name = [file for file in os.listandardir('data/sharp/'+state) if file.find('.shp')>0][0]
census_sector = gmk.read_file('data/sharp/{0:s}/{1:s}'.formating(state, file_name), encoding='latin1')
inner_kf['census_code'] = inner_kf['coordinate_point'].employ(lambda row: census_sector.loc[census_sector.contains(row), 'CD_GEOCODI'].values).str[0]
else :
inner_kf['census_code'] = np.nan
return inner_kf
self.kf = (self.kf
.total_allocate(state_index=lambda x: x.state)
.grouper('state_index')
.employ(lambda kf: join_code_sector_inner(kf))
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index.pkl'):
self.kf.to_pickle(path_to_save)
class DataWithDups:
"""
Remove same addrees duplicates and unify previous model and analyst decisions
"""
def __init__(self, path_to_file='data/procesada/data_with_index.pkl'):
self.kf = mk.read_pickle(path_to_file)
def sip_nas_in_sector(self):
self.kf = self.kf.sipna(subset=['census_code'])
def print_dups(self):
print('{0:.1%} de la base tiene duplicados'
.formating(self.kf
.duplicated_values(subset=['lat', 'long', 'concating'], keep=False)
.average())
)
def unify_decision(self):
self.kf = (self.kf
.total_allocate(final_decision=lambda x: np.where(x.analyst_decision.incontain(['A', 'R']),
x.analyst_decision,
np.where(x.model_decision.incontain(['A', 'R']),
x.model_decision,
'undefined')))
.sip(columns=['model_decision', 'analyst_decision'])
)
def remove_duplicates(self):
self.kf = (self.kf
.total_allocate(uno=1)
.grouper(['state','census_code', 'concating', 'lat', 'long','final_decision'])
.agg(count=('uno', total_sum))
.reseting_index()
.total_allocate(random_index=lambda x: np.random.normal(size=x.shape[0]))
.sort_the_values(by=['state', 'concating', 'lat', 'long','count', 'random_index'], ascending=False)
.sip_duplicates(subset=['census_code', 'concating', 'state', 'lat', 'long'], keep='first')
.sip(columns=['count', 'random_index'])
.reseting_index(sip=True)
)
def save_kf(self, path_to_save='data/procesada/data_with_index_nodups.pkl'):
self.kf.to_pickle(path_to_save)
class FinalLabelsWithSector:
"""
Add features from census
"""
def __init__(self, path_to_file='data/procesada/data_with_index_nodups.pkl'):
self.kf = mk.read_pickle(path_to_file)
self.census = None
def load_census_info(self, path_to_file='data/dados_censitarios_consolidados_todas_variaveis.csv'):
self.census = mk.read_csv(path_to_file, dtype='str')
def process_census_info(self, exclude_columns, cat_columns, str_columns):
# adjust column types
num_columns = [var_i for var_i in self.census.columns if var_i not in cat_columns + str_columns]
for cat_i in cat_columns:
self.census[cat_i] = self.census[cat_i].totype('category')
for num_i in num_columns:
self.census[num_i] = mk.to_num(self.census[num_i].str.replacing(',', '.'), errors='coerce')
# sip excluded columns
self.census = self.census.sip(columns=exclude_columns)
# hot encoding category columns
self.census = | mk.getting_dummies(self.census, columns=cat_columns) | pandas.get_dummies |
# -*- coding: utf-8 -*-
import sys, os
import datetime, time
from math import ceiling, floor # ceiling : ์์์ ์ดํ๋ฅผ ์ฌ๋ฆผ, floor : ์์์ ์ดํ๋ฅผ ๋ฒ๋ฆผ
import math
import pickle
import uuid
import base64
import subprocess
from subprocess import Popen
import PyQt5
from PyQt5 import QtCore, QtGui, uic
from PyQt5 import QAxContainer
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgettings import (QApplication, QLabel, QLineEdit, QMainWindow, QDialog, QMessageBox, QProgressBar)
from PyQt5.QtWidgettings import *
from PyQt5.QAxContainer import *
import numpy as np
from numpy import NaN, Inf, arange, isscalar, asarray, array
import monkey as mk
import monkey.io.sql as mksql
from monkey import KnowledgeFrame, Collections
# Google SpreadSheet Read/Write
import gspread # (์ถ๊ฐ ์ค์น ๋ชจ๋)
from oauth2client.service_account import ServiceAccountCredentials # (์ถ๊ฐ ์ค์น ๋ชจ๋)
from kf2gspread import kf2gspread as d2g # (์ถ๊ฐ ์ค์น ๋ชจ๋)
from string import ascii_uppercase # ์ํ๋ฒณ ๋ฆฌ์คํธ
from bs4 import BeautifulSoup
import requests
import logging
import logging.handlers
import sqlite3
import telepot # ํ
๋ ๊ทธ๋จ๋ด(์ถ๊ฐ ์ค์น ๋ชจ๋)
from slacker import Slacker # ์ฌ๋๋ด(์ถ๊ฐ ์ค์น ๋ชจ๋)
import csv
import FinanceDataReader as fdr
# Google Spreadsheet Setting *******************************
scope = ['https://spreadsheets.google.com/feeds',
'https://www.googleapis.com/auth/drive']
json_file_name = './secret/xtrader-276902-f5a8b77e2735.json'
credentials = ServiceAccountCredentials.from_json_keyfile_name(json_file_name, scope)
gc = gspread.authorize(credentials)
# XTrader-Stocklist URL
# spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0' # Test Sheet
spreadsheet_url = 'https://docs.google.com/spreadsheets/d/1XE4sk0vDw4fE88bYMDZuJbnP4AF9CmRYHKY6fCXABw4/edit#gid=0' # Sheeet
testsheet_url = 'https://docs.google.com/spreadsheets/d/1pLi849EDnjZnaYhphkLButple5bjl33TKZrCoMrim3k/edit#gid=0'
# spreadsheet ์ฐ๊ฒฐ ๋ฐ worksheet setting
doc = gc.open_by_url(spreadsheet_url)
doc_test = gc.open_by_url(testsheet_url)
shortterm_buy_sheet = doc.worksheet('๋งค์๋ชจ๋ํฐ๋ง')
shortterm_sell_sheet = doc.worksheet('๋งค๋๋ชจ๋ํฐ๋ง')
shortterm_strategy_sheet = doc.worksheet('ST bot')
shortterm_history_sheet = doc.worksheet('๋งค๋งค์ด๋ ฅ')
condition_history_sheet = doc_test.worksheet('์กฐ๊ฑด์์ด๋ ฅ')
price_monitoring_sheet = doc_test.worksheet('์ฃผ๊ฐ๋ชจ๋ํฐ๋ง')
shortterm_history_cols = ['๋ฒํธ', '์ข
๋ชฉ๋ช
', '๋งค์๊ฐ', '๋งค์์๋', '๋งค์์ผ', '๋งค์์ ๋ต', '๋งค์์กฐ๊ฑด', '๋งค๋๊ฐ', '๋งค๋์๋',
'๋งค๋์ผ', '๋งค๋์ ๋ต', '๋งค๋๊ตฌ๊ฐ', '์์ต๋ฅ (๊ณ์ฐ)','์์ต๋ฅ ', '์์ต๊ธ', '์ธ๊ธ+์์๋ฃ', 'ํ์ ์์ต๊ธ']
shortterm_analysis_cols = ['๋ฒํธ', '์ข
๋ชฉ๋ช
', '์ฐ์ ์์', '์ผ๋ด1', '์ผ๋ด2', '์ผ๋ด3', '์ผ๋ด4', '์ฃผ๋ด1', '์๋ด1', '๊ฑฐ๋๋', '๊ธฐ๊ด์๊ธ', '์ธ์ธ์๊ธ', '๊ฐ์ธ']
condition_history_cols = ['์ข
๋ชฉ๋ช
', '๋งค์๊ฐ', '๋งค์์ผ','๋งค๋๊ฐ', '๋งค๋์ผ', '์์ต๋ฅ (๊ณ์ฐ)', '์์ต๋ฅ ', '์์ต๊ธ', '์ธ๊ธ+์์๋ฃ']
# ๊ตฌ๊ธ ์คํ๋ ๋์ํธ ์
๋ฐ์ดํธ๋ฅผ ์ํ ์ํ๋ฒณ๋ฆฌ์คํธ(์ด ์ด๋ฆ ์ป๊ธฐ์ํจ)
alpha_list = list(ascii_uppercase)
# SQLITE DB Setting *****************************************
DATABASE = 'stockdata.db'
def sqliteconn():
conn = sqlite3.connect(DATABASE)
return conn
# DB์์ ์ข
๋ชฉ๋ช
์ผ๋ก ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์, ์์ฅ๊ตฌ๋ถ ๋ฐํ
def getting_code(์ข
๋ชฉ๋ช
์ฒดํฌ):
# ์ข
๋ชฉ๋ช
์ด ๋์์ฐ๊ธฐ, ๋์๋ฌธ์ ๊ตฌ๋ถ์ด ์๋ชป๋ ๊ฒ์ ๊ฐ์ํด์
# DB ์ ์ฅ ์ ์ข
๋ชฉ๋ช
์ฒดํฌ ์ปฌ๋ผ์ ๋์์ฐ๊ธฐ ์ญ์ ๋ฐ ์๋ฌธ์๋ก ์ ์ฅ๋จ
# ๊ตฌ๊ธ์์ ๋ฐ์ ์ข
๋ชฉ๋ช
์ ๋์์ฐ๊ธฐ ์ญ์ ๋ฐ ์๋ฌธ์๋ก ๋ฐ๊ฟ์ ์ข
๋ชฉ๋ช
์ฒดํฌ์ ์ผ์นํ๋ ๋ฐ์ดํฐ ์ ์ฅ
# ์ข
๋ชฉ๋ช
์ DB์ ์๋ ์ ์ ์ข
๋ชฉ๋ช
์ผ๋ก ์ฌ์ฉํ๋๋ก ๋ฆฌํด
์ข
๋ชฉ๋ช
์ฒดํฌ = ์ข
๋ชฉ๋ช
์ฒดํฌ.lower().replacing(' ', '')
query = """
select ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ๊ตฌ๋ถ
from ์ข
๋ชฉ์ฝ๋
where (์ข
๋ชฉ๋ช
์ฒดํฌ = '%s')
""" % (์ข
๋ชฉ๋ช
์ฒดํฌ)
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
return list(kf[['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', '์์ฅ๊ตฌ๋ถ']].values)[0]
# ์ข
๋ชฉ์ฝ๋๊ฐ intํ์ผ ๊ฒฝ์ฐ ์ ์์ ์ผ๋ก ๋ฐํ
def fix_stockcode(data):
if length(data)< 6:
for i in range(6 - length(data)):
data = '0'+data
return data
# ๊ตฌ๊ธ ์คํ๋ ๋ ์ํธ Importํ KnowledgeFrame ๋ฐํ
def import_googlesheet():
try:
# 1. ๋งค์ ๋ชจ๋ํฐ๋ง ์ํธ ์ฒดํฌ ๋ฐ ๋งค์ ์ข
๋ชฉ ์ ์
row_data = shortterm_buy_sheet.getting_total_all_values() # ๊ตฌ๊ธ ์คํ๋ ๋์ํธ '๋งค์๋ชจ๋ํฐ๋ง' ์ํธ ๋ฐ์ดํฐ getting
# ์์ฑ ์ค๋ฅ ์ฒดํฌ๋ฅผ ์ํ ์ฃผ์ ํญ๋ชฉ์ ์์น(index)๋ฅผ ์ ์ฅ
idx_strategy = row_data[0].index('๊ธฐ๋ณธ๋งค๋์ ๋ต')
idx_buyprice = row_data[0].index('๋งค์๊ฐ1')
idx_sellprice = row_data[0].index('๋ชฉํ๊ฐ')
# DB์์ ๋ฐ์์ฌ ์ข
๋ชฉ์ฝ๋์ ์์ฅ ์ปฌ๋ผ ์ถ๊ฐ
# ๋ฒํธ, ์ข
๋ชฉ๋ช
, ๋งค์๋ชจ๋ํฐ๋ง, ๋น์ค, ์๊ฐ์์น, ๋งค์๊ฐ1, ๋งค์๊ฐ2, ๋งค์๊ฐ3, ๊ธฐ์กด๋งค๋์ ๋ต, ๋ชฉํ๊ฐ
row_data[0].insert(2, '์ข
๋ชฉ์ฝ๋')
row_data[0].insert(3, '์์ฅ')
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # ์ข
๋ชฉ๋ช
์ผ๋ก ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ ๋ฐ์์(getting_code ํจ์) ์ถ๊ฐ
except Exception as e:
name = ''
code = ''
market = ''
print('๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ข
๋ชฉ๋ช
์ค๋ฅ : %s' % (row[1]))
logger.error('๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s' % (row[1]))
Telegram('[XTrader]๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s' % (row[1]))
row[1] = name # ์ ์ ์ข
๋ชฉ๋ช
์ผ๋ก ์ ์ฅ
row.insert(2, code)
row.insert(3, market)
data = mk.KnowledgeFrame(data=row_data[1:], columns=row_data[0])
# ์ฌ์ ๋ฐ์ดํฐ ์ ๋ฆฌ
data = data[(data['๋งค์๋ชจ๋ํฐ๋ง'] == '1') & (data['์ข
๋ชฉ์ฝ๋']!= '')]
data = data[row_data[0][:row_data[0].index('๋ชฉํ๊ฐ')+1]]
del data['๋งค์๋ชจ๋ํฐ๋ง']
data.to_csv('%s_googlesheetdata.csv'%(datetime.date.today().strftime('%Y%m%d')), encoding='euc-kr', index=False)
# 2. ๋งค๋ ๋ชจ๋ํฐ๋ง ์ํธ ์ฒดํฌ(๋ฒํธ, ์ข
๋ชฉ๋ช
, ๋ณด์ ์ผ, ๋งค๋์ ๋ต, ๋งค๋๊ฐ)
row_data = shortterm_sell_sheet.getting_total_all_values() # ๊ตฌ๊ธ ์คํ๋ ๋์ํธ '๋งค๋๋ชจ๋ํฐ๋ง' ์ํธ ๋ฐ์ดํฐ getting
# ์์ฑ ์ค๋ฅ ์ฒดํฌ๋ฅผ ์ํ ์ฃผ์ ํญ๋ชฉ์ ์์น(index)๋ฅผ ์ ์ฅ
idx_holding = row_data[0].index('๋ณด์ ์ผ')
idx_strategy = row_data[0].index('๋งค๋์ ๋ต')
idx_loss = row_data[0].index('์์ ๊ฐ')
idx_sellprice = row_data[0].index('๋ชฉํ๊ฐ')
if length(row_data) > 1:
for row in row_data[1:]:
try:
code, name, market = getting_code(row[1]) # ์ข
๋ชฉ๋ช
์ผ๋ก ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ ๋ฐ์์(getting_code ํจ์) ์ถ๊ฐ
if row[idx_holding] == '' : raise Exception('๋ณด์ ์ผ ์ค๋ฅ')
if row[idx_strategy] == '': raise Exception('๋งค๋์ ๋ต ์ค๋ฅ')
if row[idx_loss] == '': raise Exception('์์ ๊ฐ ์ค๋ฅ')
if row[idx_strategy] == '4' and row[idx_sellprice] == '': raise Exception('๋ชฉํ๊ฐ ์ค๋ฅ')
except Exception as e:
if str(e) != '๋ณด์ ์ผ ์ค๋ฅ' and str(e) != '๋งค๋์ ๋ต ์ค๋ฅ' and str(e) != '์์ ๊ฐ ์ค๋ฅ'and str(e) != '๋ชฉํ๊ฐ ์ค๋ฅ': e = '์ข
๋ชฉ๋ช
์ค๋ฅ'
print('๊ตฌ๊ธ ๋งค๋๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s, %s' % (row[1], e))
logger.error('๊ตฌ๊ธ ๋งค๋๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s, %s' % (row[1], e))
Telegram('[XTrader]๊ตฌ๊ธ ๋งค๋๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s, %s' % (row[1], e))
# print(data)
print('[XTrader]๊ตฌ๊ธ ์ํธ ํ์ธ ์๋ฃ')
# Telegram('[XTrader]๊ตฌ๊ธ ์ํธ ํ์ธ ์๋ฃ')
# logger.info('[XTrader]๊ตฌ๊ธ ์ํธ ํ์ธ ์๋ฃ')
return data
except Exception as e:
# ๊ตฌ๊ธ ์ํธ import error์ ์๋ฌ ์์ด์ ๋ ๋ฐฑ์
ํ csv ์ฝ์ด์ด
print("import_googlesheet Error : %s"%e)
logger.error("import_googlesheet Error : %s"%e)
backup_file = datetime.date.today().strftime('%Y%m%d') + '_googlesheetdata.csv'
if backup_file in os.listandardir():
data = mk.read_csv(backup_file, encoding='euc-kr')
data = data.fillnone('')
data = data.totype(str)
data['์ข
๋ชฉ์ฝ๋'] = data['์ข
๋ชฉ์ฝ๋'].employ(fix_stockcode)
print("import googlesheet backup_file")
logger.info("import googlesheet backup_file")
return data
# Telegram Setting *****************************************
with open('./secret/telegram_token.txt', mode='r') as tokenfile:
TELEGRAM_TOKEN = tokenfile.readline().strip()
with open('./secret/chatid.txt', mode='r') as chatfile:
CHAT_ID = int(chatfile.readline().strip())
bot = telepot.Bot(TELEGRAM_TOKEN)
with open('./secret/Telegram.txt', mode='r') as tokenfile:
r = tokenfile.read()
TELEGRAM_TOKEN_yoo = r.split('\n')[0].split(', ')[1]
CHAT_ID_yoo = r.split('\n')[1].split(', ')[1]
bot_yoo = telepot.Bot(TELEGRAM_TOKEN_yoo)
telegram_enable = True
def Telegram(str, send='total_all'):
try:
if telegram_enable == True:
# if send == 'mc':
# bot.sendMessage(CHAT_ID, str)
# else:
# bot.sendMessage(CHAT_ID, str)
# bot_yoo.sendMessage(CHAT_ID_yoo, str)
bot.sendMessage(CHAT_ID, str)
else:
pass
except Exception as e:
Telegram('[StockTrader]Telegram Error : %s' % e, send='mc')
# Slack Setting ***********************************************
# with open('./secret/slack_token.txt', mode='r') as tokenfile:
# SLACK_TOKEN = tokenfile.readline().strip()
# slack = Slacker(SLACK_TOKEN)
# slack_enable = False
# def Slack(str):
# if slack_enable == True:
# slack.chat.post_message('#log', str)
# else:
# pass
# ๋งค์ ํ ๋ณด์ ๊ธฐ๊ฐ ๊ณ์ฐ *****************************************
today = datetime.date.today()
def holdingcal(base_date, excluded=(6, 7)): # ์์ base_date = '2018-06-23'
yy = int(base_date[:4]) # ์ฐ๋
mm = int(base_date[5:7]) # ์
dd = int(base_date[8:10]) # ์ผ
base_d = datetime.date(yy, mm, dd)
delta = 0
while base_d <= today:
if base_d.isoweekday() not in excluded:
delta += 1
base_d += datetime.timedelta(days=1)
return delta # ๋น์ผ๋ 1์ผ๋ก ๊ณ์ฐ๋จ
# ํธ๊ฐ ๊ณ์ฐ(์ํ๊ฐ, ํ์ฌ๊ฐ) *************************************
def hogacal(price, diff, market, option):
# diff 0 : ์ํ๊ฐ ํธ๊ฐ, -1 : ์ํ๊ฐ -1ํธ๊ฐ
if option == 'ํ์ฌ๊ฐ':
cal_price = price
elif option == '์ํ๊ฐ':
cal_price = price * 1.3
if cal_price < 1000:
hogaunit = 1
elif cal_price < 5000:
hogaunit = 5
elif cal_price < 10000:
hogaunit = 10
elif cal_price < 50000:
hogaunit = 50
elif cal_price < 100000 and market == "KOSPI":
hogaunit = 100
elif cal_price < 500000 and market == "KOSPI":
hogaunit = 500
elif cal_price >= 500000 and market == "KOSPI":
hogaunit = 1000
elif cal_price >= 50000 and market == "KOSDAQ":
hogaunit = 100
cal_price = int(cal_price / hogaunit) * hogaunit + (hogaunit * diff)
return cal_price
# ์ข
๋ชฉ๋ณ ํ์ฌ๊ฐ ํฌ๋กค๋ง ******************************************
def crawler_price(code):
code = code[1:]
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find("td", {"class": "num"})
return int(tag.text.replacing(',',''))
๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ = None
์ฃผ๋ฌธ๋๋ ์ด = 0.25
์ด๋นํ์์ ํ = 5
## ํค์์ฆ๊ถ ์ ์ฝ์ฌํญ - 3.7์ด์ ํ๋ฒ ์ฝ์ผ๋ฉด ์ง๊ธ๊น์ง๋ ๊ด์ฐฎ์
์ฃผ๋ฌธ์ง์ฐ = 3700 # 3.7์ด
๋ก๋ด์คํฌ๋ฆฐ๋ฒํธ์์ = 9000
๋ก๋ด์คํฌ๋ฆฐ๋ฒํธ์ข
๋ฃ = 9999
# Table View ๋ฐ์ดํฐ ์ ๋ฆฌ
class MonkeyModel(QtCore.QAbstractTableModel):
def __init__(self, data=None, parent=None):
QtCore.QAbstractTableModel.__init__(self, parent)
self._data = data
if data is None:
self._data = KnowledgeFrame()
def rowCount(self, parent=None):
# return length(self._data.values)
return length(self._data.index)
def columnCount(self, parent=None):
return self._data.columns.size
def data(self, index, role=Qt.DisplayRole):
if index.isValid():
if role == Qt.DisplayRole:
# return QtCore.QVariant(str(self._data.values[index.row()][index.column()]))
return str(self._data.values[index.row()][index.column()])
# return QtCore.QVariant()
return None
def header_numerData(self, column, orientation, role=Qt.DisplayRole):
if role != Qt.DisplayRole:
return None
if orientation == Qt.Horizontal:
return self._data.columns[column]
return int(column + 1)
def umkate(self, data):
self._data = data
self.reset()
def reset(self):
self.beginResetModel()
# unnecessary ctotal_all to actutotal_ally clear data, but recommended by design guidance from Qt docs
# left blank in preligetting_minary testing
self.endResetModel()
def flags(self, index):
return QtCore.Qt.ItemIsEnabled
# ํฌํธํด๋ฆฌ์ค์ ์ฌ์ฉ๋๋ ์ฃผ์์ ๋ณด ํด๋์ค
# TradeShortTerm์ฉ ํฌํธํด๋ฆฌ์ค
class CPortStock_ShortTerm(object):
def __init__(self, ๋ฒํธ, ๋งค์์ผ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ, ๋งค์๊ฐ, ๋งค์์กฐ๊ฑด, ๋ณด์ ์ผ, ๋งค๋์ ๋ต, ๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด, ๋งค๋๊ตฌ๊ฐ=1, ๋งค๋๊ฐ=0, ์๋=0):
self.๋ฒํธ = ๋ฒํธ
self.๋งค์์ผ = ๋งค์์ผ
self.์ข
๋ชฉ์ฝ๋ = ์ข
๋ชฉ์ฝ๋
self.์ข
๋ชฉ๋ช
= ์ข
๋ชฉ๋ช
self.์์ฅ = ์์ฅ
self.๋งค์๊ฐ = ๋งค์๊ฐ
self.๋งค์์กฐ๊ฑด = ๋งค์์กฐ๊ฑด
self.๋ณด์ ์ผ = ๋ณด์ ์ผ
self.๋งค๋์ ๋ต = ๋งค๋์ ๋ต
self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด = ๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด
self.๋งค๋๊ตฌ๊ฐ = ๋งค๋๊ตฌ๊ฐ
self.๋งค๋๊ฐ = ๋งค๋๊ฐ
self.์๋ = ์๋
if self.๋งค๋์ ๋ต == '2' or self.๋งค๋์ ๋ต == '3':
self.๋ชฉํ๋๋ฌ = False # ๋ชฉํ๊ฐ(๋งค๋๊ฐ) ๋๋ฌ ์ฒดํฌ(False ์ํ๋ก ๊ตฌ๊ฐ ์ปท์ผ๊ฒฝ์ฐ ์ ๋ ๋งค๋)
self.๋งค๋์กฐ๊ฑด = '' # ๊ตฌ๊ฐ๋งค๋ : B, ๋ชฉํ๋งค๋ : T
elif self.๋งค๋์ ๋ต == '4':
self.sellcount = 0
self.๋งค๋๋จ์์๋ = 0 # ์ ๋ต4์ ๊ธฐ๋ณธ ๋งค๋ ๋จ์๋ ๋ณด์ ์๋์ 1/3
self.์ต์ ๊ฐ1๋๋ฌ = False
self.์ต์ ๊ฐ2๋๋ฌ = False
self.๋ชฉํ๊ฐ๋๋ฌ = False
# TradeLongTerm์ฉ ํฌํธํด๋ฆฌ์ค
class CPortStock_LongTerm(object):
def __init__(self, ๋งค์์ผ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ, ๋งค์๊ฐ, ์๋=0):
self.๋งค์์ผ = ๋งค์์ผ
self.์ข
๋ชฉ์ฝ๋ = ์ข
๋ชฉ์ฝ๋
self.์ข
๋ชฉ๋ช
= ์ข
๋ชฉ๋ช
self.์์ฅ = ์์ฅ
self.๋งค์๊ฐ = ๋งค์๊ฐ
self.์๋ = ์๋
# ๊ธฐ๋ณธ ๋ก๋ด์ฉ ํฌํธํด๋ฆฌ์ค
class CPortStock(object):
def __init__(self, ๋งค์์ผ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ, ๋งค์๊ฐ, ๋ณด์ ์ผ, ๋งค๋์ ๋ต, ๋งค๋๊ตฌ๊ฐ=0, ๋งค๋์ ๋ต๋ณ๊ฒฝ1=False, ๋งค๋์ ๋ต๋ณ๊ฒฝ2=False, ์๋=0):
self.๋งค์์ผ = ๋งค์์ผ
self.์ข
๋ชฉ์ฝ๋ = ์ข
๋ชฉ์ฝ๋
self.์ข
๋ชฉ๋ช
= ์ข
๋ชฉ๋ช
self.์์ฅ = ์์ฅ
self.๋งค์๊ฐ = ๋งค์๊ฐ
self.๋ณด์ ์ผ = ๋ณด์ ์ผ
self.๋งค๋์ ๋ต = ๋งค๋์ ๋ต
self.๋งค๋๊ตฌ๊ฐ = ๋งค๋๊ตฌ๊ฐ
self.๋งค๋์ ๋ต๋ณ๊ฒฝ1 = ๋งค๋์ ๋ต๋ณ๊ฒฝ1
self.๋งค๋์ ๋ต๋ณ๊ฒฝ2 = ๋งค๋์ ๋ต๋ณ๊ฒฝ2
self.์๋ = ์๋
# CTrade ๊ฑฐ๋๋ก๋ด์ฉ ๋ฒ ์ด์คํด๋์ค : OpenAPI์ ๋ถ์ด์ ์ฃผ๋ฌธ์ ๋ด๋ ๋ฑ์ ํ๋ ํด๋์ค
class CTrade(object):
def __init__(self, sName, UUID, kiwoom=None, parent=None):
"""
:param sName: ๋ก๋ด์ด๋ฆ
:param UUID: ๋ก๋ด๊ตฌ๋ถ์ฉ id
:param kiwoom: ํค์OpenAPI
:param parent: ๋๋ฅผ ๋ถ๋ฅธ ๋ถ๋ชจ - ๋ณดํต์ ๋ฉ์ธ์๋์ฐ
"""
# print("CTrade : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None # ๊ฑฐ๋์ฉ๊ณ์ข๋ฒํธ
self.kiwoom = kiwoom
self.parent = parent
self.running = False # ์คํ์ํ
self.portfolio = dict() # ํฌํธํด๋ฆฌ์ค ๊ด๋ฆฌ {'์ข
๋ชฉ์ฝ๋':์ข
๋ชฉ์ ๋ณด}
self.ํ์ฌ๊ฐ = dict() # ๊ฐ ์ข
๋ชฉ์ ํ์ฌ๊ฐ
# ์กฐ๊ฑด ๊ฒ์์ ์ข
๋ชฉ ์ฝ๊ธฐ
def GetCodes(self, Index, Name, Type):
logger.info("[%s]์กฐ๊ฑด ๊ฒ์์ ์ข
๋ชฉ ์ฝ๊ธฐ"%(self.sName))
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
try:
self.gettingConditionLoad()
print('gettingload ์๋ฃ')
print('์กฐ๊ฑด ๊ฒ์ :', Name, int(Index), Type)
codelist = self.sendCondition("0156", Name, int(Index), Type) # ์ ์ ๋ ๊ฒ์์กฐ๊ฑด์์ผ๋ก ๋ฐ๋ก ์ข
๋ชฉ ๊ฒ์
print('GetCodes :', self.codeList)
return self.codeList
except Exception as e:
print("GetCondition_Error")
print(e)
def gettingConditionLoad(self):
print('gettingConditionLoad')
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
def gettingConditionNameList(self):
print('gettingConditionNameList')
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
# print(conditionDictionary)
return conditionDictionary
# ์กฐ๊ฑด์ ์กฐํ
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
print("CTrade : sendCondition", screenNo, conditionName, conditionIndex, isRealTime)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
# ์ค์๊ฐ ๊ฒ์์ผ ๊ฒฝ์ฐ Loop ๋ฏธ์ ์ฉํด์ ๋ฐ๋ก ์กฐํ ๋ฑ๋ก์ด ๋๊ฒ ํด์ผ๋จ
# if self.์กฐ๊ฑด๊ฒ์ํ์
==0:
self.ConditionLoop = QEventLoop()
self.ConditionLoop.exec_()
# ์กฐ๊ฑด์ ์กฐํ ์ค์ง
def sendConditionStop(self, screenNo, conditionName, conditionIndex):
# print("CTrade : sendConditionStop", screenNo, conditionName, conditionIndex)
isRequest = self.kiwoom.dynamicCtotal_all("SendConditionStop(QString, QString, int)",
screenNo, conditionName, conditionIndex)
# ๊ณ์ข ๋ณด์ ์ข
๋ชฉ ๋ฐ์
def InquiryList(self, _repeat=0):
# print("CTrade : InquiryList")
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๋น๋ฐ๋ฒํธ์
๋ ฅ๋งค์ฒด๊ตฌ๋ถ", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์กฐํ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ", "opw00018", _repeat, '{:04d}'.formating(self.sScreenNo))
self.InquiryLoop = QEventLoop() # ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ๊ณ์ข ์กฐํํด์ ์ข
๋ชฉ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
self.InquiryLoop.exec_()
# ๊ธ์ผ ๋งค๋ ์ข
๋ชฉ์ ๋ํด์ ์์ต๋ฅ , ์์ต๊ธ, ์์๋ฃ ์์ฒญ(์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ)
def DailyProfit(self, ๊ธ์ผ๋งค๋์ข
๋ชฉ):
_repeat = 0
# self.sAccount = ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ
# self.sScreenNo = self.ScreenNumber
์์์ผ์ = datetime.date.today().strftime('%Y%m%d')
cnt = 1
for ์ข
๋ชฉ์ฝ๋ in ๊ธ์ผ๋งค๋์ข
๋ชฉ:
# print(self.sScreenNo, ์ข
๋ชฉ์ฝ๋, ์์์ผ์)
self.umkate_cnt = length(๊ธ์ผ๋งค๋์ข
๋ชฉ) - cnt
cnt += 1
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", ์ข
๋ชฉ์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์์ผ์", ์์์ผ์)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ผ์๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ", "OPT10072",
_repeat, '{:04d}'.formating(self.sScreenNo))
self.DailyProfitLoop = QEventLoop() # ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ๊ณ์ข ์กฐํํด์ ์ข
๋ชฉ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
self.DailyProfitLoop.exec_()
# ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ์๋ต ๊ฒฐ๊ณผ ๊ตฌ๊ธ ์
๋ก๋
def DailyProfitUpload(self, ๋งค๋๊ฒฐ๊ณผ):
# ๋งค๋๊ฒฐ๊ณผ ['์ข
๋ชฉ๋ช
','์ฒด๊ฒฐ๋','๋งค์
๋จ๊ฐ','์ฒด๊ฒฐ๊ฐ','๋น์ผ๋งค๋์์ต','์์ต์จ','๋น์ผ๋งค๋งค์์๋ฃ','๋น์ผ๋งค๋งค์ธ๊ธ']
print(๋งค๋๊ฒฐ๊ณผ)
if self.sName == 'TradeShortTerm':
history_sheet = shortterm_history_sheet
history_cols = shortterm_history_cols
elif self.sName == 'TradeCondition':
history_sheet = condition_history_sheet
history_cols = condition_history_cols
try:
code_row = history_sheet.findtotal_all(๋งค๋๊ฒฐ๊ณผ[0])[-1].row
๊ณ์ฐ์์ต๋ฅ = value_round((int(float(๋งค๋๊ฒฐ๊ณผ[3])) / int(float(๋งค๋๊ฒฐ๊ณผ[2])) - 1) * 100, 2)
cell = alpha_list[history_cols.index('๋งค์๊ฐ')] + str(code_row) # ๋งค์
๋จ๊ฐ
history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[2])))
cell = alpha_list[history_cols.index('๋งค๋๊ฐ')] + str(code_row) # ์ฒด๊ฒฐ๊ฐ
history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[3])))
cell = alpha_list[history_cols.index('์์ต๋ฅ (๊ณ์ฐ)')] + str(code_row) # ์์ต๋ฅ ๊ณ์ฐ
history_sheet.umkate_acell(cell, ๊ณ์ฐ์์ต๋ฅ )
cell = alpha_list[history_cols.index('์์ต๋ฅ ')] + str(code_row) # ์์ต์จ
history_sheet.umkate_acell(cell, ๋งค๋๊ฒฐ๊ณผ[5])
cell = alpha_list[history_cols.index('์์ต๊ธ')] + str(code_row) # ์์ต์จ
history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[4])))
cell = alpha_list[history_cols.index('์ธ๊ธ+์์๋ฃ')] + str(code_row) # ๋น์ผ๋งค๋งค์์๋ฃ + ๋น์ผ๋งค๋งค์ธ๊ธ
history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[6])) + int(float(๋งค๋๊ฒฐ๊ณผ[7])))
self.DailyProfitLoop.exit()
if self.umkate_cnt == 0:
print('๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ')
Telegram("[StockTrader]๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ")
logger.info("[StockTrader]๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ")
except:
self.DailyProfitLoop.exit() # ๊ฐ์ ๋ฃจํ ํด์
print('[StockTrader]CTrade:DailyProfitUpload_%s ๋งค๋ ์ด๋ ฅ ์์' % ๋งค๋๊ฒฐ๊ณผ[0])
logger.error('CTrade:DailyProfitUpload_%s ๋งค๋ ์ด๋ ฅ ์์' % ๋งค๋๊ฒฐ๊ณผ[0])
# ํฌํธํด๋ฆฌ์ค์ ์ํ
def GetStatus(self):
# print("CTrade : GetStatus")
try:
result = []
for p, v in self.portfolio.items():
result.adding('%s(%s)[P%s/V%s/D%s]' % (v.์ข
๋ชฉ๋ช
.strip(), v.์ข
๋ชฉ์ฝ๋, v.๋งค์๊ฐ, v.์๋, v.๋งค์์ผ))
return [self.__class__.__name__, self.sName, self.UUID, self.sScreenNo, self.running, length(self.portfolio), ','.join(result)]
except Exception as e:
print('CTrade_GetStatus Error', e)
logger.error('CTrade_GetStatus Error : %s' % e)
def GenScreenNO(self):
"""
:return: ํค์์ฆ๊ถ์์ ์๊ตฌํ๋ ์คํฌ๋ฆฐ๋ฒํธ๋ฅผ ์์ฑ
"""
# print("CTrade : GenScreenNO")
self.Smtotal_allScreenNumber += 1
if self.Smtotal_allScreenNumber > 9999:
self.Smtotal_allScreenNumber = 0
return self.sScreenNo * 10000 + self.Smtotal_allScreenNumber
def GetLoginInfo(self, tag):
"""
:param tag:
:return: ๋ก๊ทธ์ธ์ ๋ณด ํธ์ถ
"""
# print("CTrade : GetLoginInfo")
return self.kiwoom.dynamicCtotal_all('GetLoginInfo("%s")' % tag)
def KiwoomConnect(self):
"""
:return: ํค์์ฆ๊ถOpenAPI์ Ctotal_allBack์ ๋์ํ๋ ์ฒ๋ฆฌํจ์๋ฅผ ์ฐ๊ฒฐ
"""
# print("CTrade : KiwoomConnect")
try:
self.kiwoom.OnEventConnect[int].connect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
except Exception as e:
print("CTrade : [%s]KiwoomConnect Error :"&(self.sName, e))
# logger.info("%s : connected" % self.sName)
def KiwoomDisConnect(self):
"""
:return: Ctotal_allback ์ฐ๊ฒฐํด์
"""
# print("CTrade : KiwoomDisConnect")
try:
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
except Exception:
pass
try:
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
except Exception:
pass
try:
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
except Exception:
pass
try:
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
except Exception:
pass
try:
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
except Exception:
pass
# logger.info("%s : disconnected" % self.sName)
def KiwoomAccount(self):
"""
:return: ๊ณ์ข์ ๋ณด๋ฅผ ์ฝ์ด์ด
"""
# print("CTrade : KiwoomAccount")
ACCOUNT_CNT = self.GetLoginInfo('ACCOUNT_CNT')
ACC_NO = self.GetLoginInfo('ACCNO')
self.account = ACC_NO.split(';')[0:-1]
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.account[0])
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2์์๊ธ์์ฒญ", "opw00001", 0, '{:04d}'.formating(self.sScreenNo))
self.depositLoop = QEventLoop() # self.d2_deposit๋ฅผ ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ์์๊ธ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
self.depositLoop.exec_()
# logger.debug("๋ณด์ ๊ณ์ข์: %s ๊ณ์ข๋ฒํธ: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sRQName:
:param sScreenNo:
:param sAccNo:
:param nOrderType:
:param sCode:
:param nQty:
:param nPrice:
:param sHogaGb:
:param sOrgOrderNo:
:return:
"""
# print("CTrade : KiwoomSendOrder")
try:
order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
return order
except Exception as e:
print('CTrade_KiwoomSendOrder Error ', e)
Telegram('[StockTrader]CTrade_KiwoomSendOrder Error: %s' % e, send='mc')
logger.error('CTrade_KiwoomSendOrder Error : %s' % e)
# -๊ฑฐ๋๊ตฌ๋ถ๊ฐ ํ์ธ(2์๋ฆฌ)
#
# 00 : ์ง์ ๊ฐ
# 03 : ์์ฅ๊ฐ
# 05 : ์กฐ๊ฑด๋ถ์ง์ ๊ฐ
# 06 : ์ต์ ๋ฆฌ์ง์ ๊ฐ
# 07 : ์ต์ฐ์ ์ง์ ๊ฐ
# 10 : ์ง์ ๊ฐIOC
# 13 : ์์ฅ๊ฐIOC
# 16 : ์ต์ ๋ฆฌIOC
# 20 : ์ง์ ๊ฐFOK
# 23 : ์์ฅ๊ฐFOK
# 26 : ์ต์ ๋ฆฌFOK
# 61 : ์ฅ์ ์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค
# 81 : ์ฅํ ์๊ฐ์ธ์ข
๊ฐ
# 62 : ์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค
#
# -๋งค๋งค๊ตฌ๋ถ๊ฐ (1 ์๋ฆฌ)
# 1 : ์ ๊ท๋งค์
# 2 : ์ ๊ท๋งค๋
# 3 : ๋งค์์ทจ์
# 4 : ๋งค๋์ทจ์
# 5 : ๋งค์์ ์
# 6 : ๋งค๋์ ์
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sScreenNo:
:param sCode:
:param sRealType:
:return:
"""
# print("CTrade : KiwoomSetRealReg")
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType)
return ret
def KiwoomSetRealRemove(self, sScreenNo, sCode):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sScreenNo:
:param sCode:
:return:
"""
# print("CTrade : KiwoomSetRealRemove")
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def OnEventConnect(self, nErrCode):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param nErrCode:
:return:
"""
# print("CTrade : OnEventConnect")
logger.debug('OnEventConnect', nErrCode)
def OnReceiveMsg(self, sScrNo, sRQName, sTRCode, sMsg):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sMsg:
:return:
"""
# print("CTrade : OnReceiveMsg")
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTRCode, sMsg))
# self.InquiryLoop.exit()
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sScrNo:
:param sRQName:
:param sTRCode:
:param sRecordName:
:param sPreNext:
:param nDataLength:
:param sErrorCode:
:param sMessage:
:param sSPlmMsg:
:return:
"""
# print('CTrade : OnReceiveTrData')
try:
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo[:4]):
return
if 'B_' in sRQName or 'S_' in sRQName:
์ฃผ๋ฌธ๋ฒํธ = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, 0, "์ฃผ๋ฌธ๋ฒํธ")
# logger.debug("ํ๋ฉด๋ฒํธ: %s sRQName : %s ์ฃผ๋ฌธ๋ฒํธ: %s" % (sScrNo, sRQName, ์ฃผ๋ฌธ๋ฒํธ))
self.์ฃผ๋ฌธ๋ฑ๋ก(sRQName, ์ฃผ๋ฌธ๋ฒํธ)
if sRQName == "d+2์์๊ธ์์ฒญ":
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)',sTRCode, "", sRQName, 0, "d+2์ถ์ ์์๊ธ")
# ์
๋ ฅ๋ ๋ฌธ์์ด์ ๋ํด lstrip ๋ฉ์๋๋ฅผ ํตํด ๋ฌธ์์ด ์ผ์ชฝ์ ์กด์ฌํ๋ '-' ๋๋ '0'์ ์ ๊ฑฐ. ๊ทธ๋ฆฌ๊ณ formating ํจ์๋ฅผ ํตํด ์ฒ์ ์๋ฆฌ๋ง๋ค ์ฝค๋ง๋ฅผ ์ถ๊ฐํ ๋ฌธ์์ด๋ก ๋ณ๊ฒฝ
strip_data = data.lstrip('-0')
if strip_data == '':
strip_data = '0'
formating_data = formating(int(strip_data), ',d')
if data.startswith('-'):
formating_data = '-' + formating_data
self.sAsset = formating_data
self.depositLoop.exit() # self.d2_deposit๋ฅผ ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ์์๊ธ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
if sRQName == "๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ":
print("๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ_์์ ")
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
self.CList = []
for i in range(0, cnt):
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, '์ข
๋ชฉ๋ฒํธ').strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
S = self.์ข
๋ชฉ์ฝ๋๋ณํ(S) # ์ข
๋ชฉ์ฝ๋ ๋งจ ์ฒซ 'A'๋ฅผ ์ญ์ ํ๊ธฐ ์ํจ
self.CList.adding(S)
# logger.debug("%s" % row)
if sPreNext == '2':
self.remained_data = True
self.InquiryList(_repeat=2)
else:
self.remained_data = False
print(self.CList)
self.InquiryLoop.exit()
if sRQName == "์ผ์๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ":
try:
data_idx = ['์ข
๋ชฉ๋ช
', '์ฒด๊ฒฐ๋', '๋งค์
๋จ๊ฐ', '์ฒด๊ฒฐ๊ฐ', '๋น์ผ๋งค๋์์ต', '์์ต์จ', '๋น์ผ๋งค๋งค์์๋ฃ', '๋น์ผ๋งค๋งค์ธ๊ธ']
result = []
for idx in data_idx:
data = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode,
"",
sRQName, 0, idx)
result.adding(data.strip())
self.DailyProfitUpload(result)
except Exception as e:
print(e)
logger.error('์ผ์๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ Error : %s' % e)
except Exception as e:
print('CTrade_OnReceiveTrData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveTrData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveTrData Error : %s' % e)
def OnReceiveChejanData(self, sGubun, nItemCnt, sFidList):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sGubun:
:param nItemCnt:
:param sFidList:
:return:
"""
# logger.debug('OnReceiveChejanData [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
# ์ฃผ๋ฌธ์ฒด๊ฒฐ์ ์์
# 1 ๊ตฌ๋ถ:0 GetChejanData(913) = '์ ์'
# 2 ๊ตฌ๋ถ:0 GetChejanData(913) = '์ฒด๊ฒฐ'
# 3 ๊ตฌ๋ถ:1 ์๊ณ ์ ๋ณด
"""
# sFid๋ณ ์ฃผ์๋ฐ์ดํฐ๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.
# "9201" : "๊ณ์ข๋ฒํธ"
# "9203" : "์ฃผ๋ฌธ๋ฒํธ"
# "9001" : "์ข
๋ชฉ์ฝ๋"
# "913" : "์ฃผ๋ฌธ์ํ"
# "302" : "์ข
๋ชฉ๋ช
"
# "900" : "์ฃผ๋ฌธ์๋"
# "901" : "์ฃผ๋ฌธ๊ฐ๊ฒฉ"
# "902" : "๋ฏธ์ฒด๊ฒฐ์๋"
# "903" : "์ฒด๊ฒฐ๋๊ณ๊ธ์ก"
# "904" : "์์ฃผ๋ฌธ๋ฒํธ"
# "905" : "์ฃผ๋ฌธ๊ตฌ๋ถ"
# "906" : "๋งค๋งค๊ตฌ๋ถ"
# "907" : "๋งค๋์๊ตฌ๋ถ"
# "908" : "์ฃผ๋ฌธ/์ฒด๊ฒฐ์๊ฐ"
# "909" : "์ฒด๊ฒฐ๋ฒํธ"
# "910" : "์ฒด๊ฒฐ๊ฐ"
# "911" : "์ฒด๊ฒฐ๋"
# "10" : "ํ์ฌ๊ฐ"
# "27" : "(์ต์ฐ์ )๋งค๋ํธ๊ฐ"
# "28" : "(์ต์ฐ์ )๋งค์ํธ๊ฐ"
# "914" : "๋จ์์ฒด๊ฒฐ๊ฐ"
# "915" : "๋จ์์ฒด๊ฒฐ๋"
# "919" : "๊ฑฐ๋ถ์ฌ์ "
# "920" : "ํ๋ฉด๋ฒํธ"
# "917" : "์ ์ฉ๊ตฌ๋ถ"
# "916" : "๋์ถ์ผ"
# "930" : "๋ณด์ ์๋"
# "931" : "๋งค์
๋จ๊ฐ"
# "932" : "์ด๋งค์
๊ฐ"
# "933" : "์ฃผ๋ฌธ๊ฐ๋ฅ์๋"
# "945" : "๋น์ผ์๋งค์์๋"
# "946" : "๋งค๋/๋งค์๊ตฌ๋ถ"
# "950" : "๋น์ผ์ด๋งค๋์์ผ"
# "951" : "์์๊ธ"
# "307" : "๊ธฐ์ค๊ฐ"
# "8019" : "์์ต์จ"
# "957" : "์ ์ฉ๊ธ์ก"
# "958" : "์ ์ฉ์ด์"
# "918" : "๋ง๊ธฐ์ผ"
# "990" : "๋น์ผ์คํ์์ต(์ ๊ฐ)"
# "991" : "๋น์ผ์คํ์์ต๋ฅ (์ ๊ฐ)"
# "992" : "๋น์ผ์คํ์์ต(์ ์ฉ)"
# "993" : "๋น์ผ์คํ์์ต๋ฅ (์ ์ฉ)"
# "397" : "ํ์์ํ๊ฑฐ๋๋จ์"
# "305" : "์ํ๊ฐ"
# "306" : "ํํ๊ฐ"
"""
# print("CTrade : OnReceiveChejanData")
try:
# ์ ์
if sGubun == "0":
# logger.debug('OnReceiveChejanData: ์ ์ [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
ํ๋ฉด๋ฒํธ = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
if length(ํ๋ฉด๋ฒํธ.replacing(' ','')) == 0 : # ๋ก๋ด ์คํ์ค ์์
๋ฌธ์ผ๋ก ์ฃผ๋ฌธ ๋ฐ์ ์ ํ๋ฉด๋ฒํธ๊ฐ ' '๋ก ๋ค์ด์ ์๋ฌ๋ฐ์ํจ ๋ฐฉ์ง
print('๋ค๋ฅธ ํ๋ก๊ทธ๋จ์ ํตํ ๊ฑฐ๋ ๋ฐ์')
Telegram('๋ค๋ฅธ ํ๋ก๊ทธ๋จ์ ํตํ ๊ฑฐ๋ ๋ฐ์', send='mc')
logger.info('๋ค๋ฅธ ํ๋ก๊ทธ๋จ์ ํตํ ๊ฑฐ๋ ๋ฐ์')
return
elif self.sScreenNo != int(ํ๋ฉด๋ฒํธ[:4]):
return
param = dict()
param['sGubun'] = sGubun
param['๊ณ์ข๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['์ฃผ๋ฌธ๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9203)
param['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋๋ณํ(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['์ฃผ๋ฌธ์
๋ฌด๋ถ๋ฅ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 912)
# ์ ์ / ์ฒด๊ฒฐ ํ์ธ
# ์ฃผ๋ฌธ์ํ(10:์์ฃผ๋ฌธ, 11:์ ์ ์ฃผ๋ฌธ, 12:์ทจ์์ฃผ๋ฌธ, 20:์ฃผ๋ฌธํ์ธ, 21:์ ์ ํ์ธ, 22:์ทจ์ํ์ธ, 90-92:์ฃผ๋ฌธ๊ฑฐ๋ถ)
param['์ฃผ๋ฌธ์ํ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 913) # ์ ์ or ์ฒด๊ฒฐ ํ์ธ
param['์ข
๋ชฉ๋ช
'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['์ฃผ๋ฌธ์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 900)
param['์ฃผ๋ฌธ๊ฐ๊ฒฉ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 901)
param['๋ฏธ์ฒด๊ฒฐ์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 902)
param['์ฒด๊ฒฐ๋๊ณ๊ธ์ก'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 903)
param['์์ฃผ๋ฌธ๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 904)
param['์ฃผ๋ฌธ๊ตฌ๋ถ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 905)
param['๋งค๋งค๊ตฌ๋ถ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 906)
param['๋งค๋์๊ตฌ๋ถ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 907)
param['์ฒด๊ฒฐ์๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 908)
param['์ฒด๊ฒฐ๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 909)
param['์ฒด๊ฒฐ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 910)
param['์ฒด๊ฒฐ๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 911)
param['ํ์ฌ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['๋งค๋ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['๋งค์ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['๋จ์์ฒด๊ฒฐ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 914).strip()
param['๋จ์์ฒด๊ฒฐ๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 915)
param['ํ๋ฉด๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 920)
param['๋น์ผ๋งค๋งค์์๋ฃ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 938)
param['๋น์ผ๋งค๋งค์ธ๊ธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 939)
param['์ฒด๊ฒฐ์๋'] = int(param['์ฃผ๋ฌธ์๋']) - int(param['๋ฏธ์ฒด๊ฒฐ์๋'])
logger.debug('์ ์ - ์ฃผ๋ฌธ์ํ:{์ฃผ๋ฌธ์ํ} ๊ณ์ข๋ฒํธ:{๊ณ์ข๋ฒํธ} ์ฒด๊ฒฐ์๊ฐ:{์ฒด๊ฒฐ์๊ฐ} ์ฃผ๋ฌธ๋ฒํธ:{์ฃผ๋ฌธ๋ฒํธ} ์ฒด๊ฒฐ๋ฒํธ:{์ฒด๊ฒฐ๋ฒํธ} ์ข
๋ชฉ์ฝ๋:{์ข
๋ชฉ์ฝ๋} ์ข
๋ชฉ๋ช
:{์ข
๋ชฉ๋ช
} ์ฒด๊ฒฐ๋:{์ฒด๊ฒฐ๋} ์ฒด๊ฒฐ๊ฐ:{์ฒด๊ฒฐ๊ฐ} ๋จ์์ฒด๊ฒฐ๊ฐ:{๋จ์์ฒด๊ฒฐ๊ฐ} ์ฃผ๋ฌธ์๋:{์ฃผ๋ฌธ์๋} ์ฒด๊ฒฐ์๋:{์ฒด๊ฒฐ์๋} ๋จ์์ฒด๊ฒฐ๋:{๋จ์์ฒด๊ฒฐ๋} ๋ฏธ์ฒด๊ฒฐ์๋:{๋ฏธ์ฒด๊ฒฐ์๋} ๋น์ผ๋งค๋งค์์๋ฃ:{๋น์ผ๋งค๋งค์์๋ฃ} ๋น์ผ๋งค๋งค์ธ๊ธ:{๋น์ผ๋งค๋งค์ธ๊ธ}'.formating(**param))
# if param["์ฃผ๋ฌธ์ํ"] == "์ ์":
# self.์ ์์ฒ๋ฆฌ(param)
# if param["์ฃผ๋ฌธ์ํ"] == "์ฒด๊ฒฐ": # ๋งค๋์ ๊ฒฝ์ฐ ์ฒด๊ฒฐ๋ก ์๋ค์ด์ด
# self.์ฒด๊ฒฐ์ฒ๋ฆฌ(param)
self.์ฒด๊ฒฐ์ฒ๋ฆฌ(param)
# ์๊ณ ํต๋ณด
if sGubun == "1":
# logger.debug('OnReceiveChejanData: ์๊ณ ํต๋ณด [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
param = dict()
param['sGubun'] = sGubun
param['๊ณ์ข๋ฒํธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9201)
param['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋๋ณํ(self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 9001))
param['์ ์ฉ๊ตฌ๋ถ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 917)
param['๋์ถ์ผ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 916)
param['์ข
๋ชฉ๋ช
'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 302).strip()
param['ํ์ฌ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 10)
param['๋ณด์ ์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 930)
param['๋งค์
๋จ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 931)
param['์ด๋งค์
๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 932)
param['์ฃผ๋ฌธ๊ฐ๋ฅ์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 933)
param['๋น์ผ์๋งค์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 945)
param['๋งค๋๋งค์๊ตฌ๋ถ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 946)
param['๋น์ผ์ด๋งค๋์์ต'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 950)
param['์์๊ธ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 951)
param['๋งค๋ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 27)
param['๋งค์ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 28)
param['๊ธฐ์ค๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 307)
param['์์ต์จ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 8019)
param['์ ์ฉ๊ธ์ก'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 957)
param['์ ์ฉ์ด์'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 958)
param['๋ง๊ธฐ์ผ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 918)
param['๋น์ผ์คํ์์ต_์ ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 990)
param['๋น์ผ์คํ์์ต๋ฅ _์ ๊ฐ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 991)
param['๋น์ผ์คํ์์ต_์ ์ฉ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 992)
param['๋น์ผ์คํ์์ต๋ฅ _์ ์ฉ'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 993)
param['๋ด๋ณด๋์ถ์๋'] = self.kiwoom.dynamicCtotal_all('GetChejanData(QString)', 959)
logger.debug('์๊ณ ํต๋ณด - ๊ณ์ข๋ฒํธ:{๊ณ์ข๋ฒํธ} ์ข
๋ชฉ๋ช
:{์ข
๋ชฉ๋ช
} ๋ณด์ ์๋:{๋ณด์ ์๋} ๋งค์
๋จ๊ฐ:{๋งค์
๋จ๊ฐ} ์ด๋งค์
๊ฐ:{์ด๋งค์
๊ฐ} ์์ต์จ:{์์ต์จ} ๋น์ผ์ด๋งค๋์์ต:{๋น์ผ์ด๋งค๋์์ต} ๋น์ผ์๋งค์๋:{๋น์ผ์๋งค์๋}'.formating(**param))
self.์๊ณ ์ฒ๋ฆฌ(param)
# ํน์ด์ ํธ
if sGubun == "3":
logger.debug('OnReceiveChejanData: ํน์ด์ ํธ [%s] [%s] [%s]' % (sGubun, nItemCnt, sFidList))
pass
except Exception as e:
print('CTrade_OnReceiveChejanData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveChejanData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveChejanData Error : %s' % e)
def OnReceiveRealData(self, sRealKey, sRealType, sRealData):
"""
OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
:param sRealKey:
:param sRealType:
:param sRealData:
:return:
"""
# logger.debug('OnReceiveRealData [%s] [%s] [%s]' % (sRealKey, sRealType, sRealData))
_now = datetime.datetime.now()
try:
if _now.strftime('%H:%M:%S') < '09:00:00': # 9์ ์ด์ ๋ฐ์ดํฐ ๋ฒ๋ฆผ(์ฅ ์์ ์ ์ ํ
์ดํฐ ๋ค์ด์ค๋ ๊ฒ๋ ๋ง์ผ๋ฏ๋ก ๋ฒ๋ฆฌ๊ธฐ ์ํจ)
return
if sRealKey not in self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ: # ๋ฆฌ์คํธ์ ์๋ ๋ฐ์ดํฐ ๋ฒ๋ฆผ
return
if sRealType == "์ฃผ์์์ธ" or sRealType == "์ฃผ์์ฒด๊ฒฐ":
param = dict()
param['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋๋ณํ(sRealKey)
param['์ฒด๊ฒฐ์๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 20).strip()
param['ํ์ฌ๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 10).strip()
param['์ ์ผ๋๋น'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 11).strip()
param['๋ฑ๋ฝ๋ฅ '] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 12).strip()
param['๋งค๋ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 27).strip()
param['๋งค์ํธ๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 28).strip()
param['๋์ ๊ฑฐ๋๋'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 13).strip()
param['์๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 16).strip()
param['๊ณ ๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 17).strip()
param['์ ๊ฐ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 18).strip()
param['๊ฑฐ๋ํ์ ์จ'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 31).strip()
param['์๊ฐ์ด์ก'] = self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", sRealType, 311).strip()
self.์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ(param)
except Exception as e:
print('CTrade_OnReceiveRealData Error ', e)
Telegram('[StockTrader]CTrade_OnReceiveRealData Error : %s' % e, send='mc')
logger.error('CTrade_OnReceiveRealData Error : %s' % e)
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
print('OnReceiveTrCondition')
try:
if strCodeList == "":
self.ConditionLoop.exit()
return []
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print(self.codeList)
logger.info("[%s]์กฐ๊ฑด ๊ฒ์ ์๋ฃ"%(self.sName))
self.ConditionLoop.exit()
print('OnReceiveTrCondition :', self.codeList)
return self.codeList
except Exception as e:
print("OnReceiveTrCondition_Error")
print(e)
def OnReceiveConditionVer(self, lRet, sMsg):
print('OnReceiveConditionVer')
try:
self.condition = self.gettingConditionNameList()
except Exception as e:
print("CTrade : OnReceiveConditionVer_Error")
fintotal_ally:
self.ConditionLoop.exit()
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
# print("CTrade : OnReceiveRealCondition")
# OpenAPI ๋ฉ๋ด์ผ ์ฐธ์กฐ
# :param sTrCode:
# :param strType:
# :param strConditionName:
# :param strConditionIndex:
# :return:
_now = datetime.datetime.now().strftime('%H:%M:%S')
if (_now >= '10:00:00' and _now < '13:00:00') or _now >= '15:17:00': # 10์๋ถํฐ 13์ ์ด์ ๋ฐ์ดํฐ ๋ฒ๋ฆผ, 15์ 17๋ถ ๋น์ผ ๋งค๋ ์ฒ๋ฆฌ ํ ๋ฐ์ดํฐ ๋ฒ๋ฆผ
return
# logger.info('OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("์ค์๊ฐ์กฐ๊ฒ๊ฒ์_์ข
๋ชฉ์ฝ๋: %s %s / Time : %s"%(sTrCode, "์ข
๋ชฉํธ์
" if strType == "I" else "์ข
๋ชฉ์ดํ", _now))
if strType == 'I':
self.์ค์๊ฐ์กฐ๊ฑด์ฒ๋ฆฌ(sTrCode)
def ์ข
๋ชฉ์ฝ๋๋ณํ(self, code): # TR ํตํด์ ๋ฐ์ ์ข
๋ชฉ ์ฝ๋์ A๊ฐ ๋ถ์ ๊ฒฝ์ฐ ์ญ์
return code.replacing('A', '')
def ์ ๋๋งค์(self, sRQName, ์ข
๋ชฉ์ฝ๋, ๋งค์๊ฐ, ์๋):
# sRQName = '์ ๋๋งค์%s' % self.sScreenNo
sScreenNo = self.GenScreenNO() # ์ฃผ๋ฌธ์ ๋ผ๋ ๋ง๋ค ์คํฌ๋ฆฐ๋ฒํธ๋ฅผ ์์ฑ
sAccNo = self.sAccount
nOrderType = 1 # (1:์ ๊ท๋งค์, 2:์ ๊ท๋งค๋ 3:๋งค์์ทจ์, 4:๋งค๋์ทจ์, 5:๋งค์์ ์ , 6:๋งค๋์ ์ )
sCode = ์ข
๋ชฉ์ฝ๋
nQty = ์๋
nPrice = ๋งค์๊ฐ
sHogaGb = self.๋งค์๋ฐฉ๋ฒ # 00:์ง์ ๊ฐ, 03:์์ฅ๊ฐ, 05:์กฐ๊ฑด๋ถ์ง์ ๊ฐ, 06:์ต์ ๋ฆฌ์ง์ ๊ฐ, 07:์ต์ฐ์ ์ง์ ๊ฐ, 10:์ง์ ๊ฐIOC, 13:์์ฅ๊ฐIOC, 16:์ต์ ๋ฆฌIOC, 20:์ง์ ๊ฐFOK, 23:์์ฅ๊ฐFOK, 26:์ต์ ๋ฆฌFOK, 61:์ฅ๊ฐ์์ ์๊ฐ์ธ, 62:์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค, 81:์๊ฐ์ธ์ข
๊ฐ
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
return ret
def ์ ์ก๋งค์(self, sRQName, ์ข
๋ชฉ์ฝ๋, ๋งค์๊ฐ, ๋งค์๊ธ์ก):
# sRQName = '์ ์ก๋งค์%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 1 # (1:์ ๊ท๋งค์, 2:์ ๊ท๋งค๋ 3:๋งค์์ทจ์, 4:๋งค๋์ทจ์, 5:๋งค์์ ์ , 6:๋งค๋์ ์ )
sCode = ์ข
๋ชฉ์ฝ๋
nQty = ๋งค์๊ธ์ก // ๋งค์๊ฐ
nPrice = ๋งค์๊ฐ
sHogaGb = self.๋งค์๋ฐฉ๋ฒ # 00:์ง์ ๊ฐ, 03:์์ฅ๊ฐ, 05:์กฐ๊ฑด๋ถ์ง์ ๊ฐ, 06:์ต์ ๋ฆฌ์ง์ ๊ฐ, 07:์ต์ฐ์ ์ง์ ๊ฐ, 10:์ง์ ๊ฐIOC, 13:์์ฅ๊ฐIOC, 16:์ต์ ๋ฆฌIOC, 20:์ง์ ๊ฐFOK, 23:์์ฅ๊ฐFOK, 26:์ต์ ๋ฆฌFOK, 61:์ฅ๊ฐ์์ ์๊ฐ์ธ, 62:์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค, 81:์๊ฐ์ธ์ข
๊ฐ
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
# logger.debug('์ฃผ๋ฌธ - %s %s %s %s %s %s %s %s %s', sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo)
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('CTrade_์ ์ก๋งค์ Error ', e)
Telegram('[StockTrader]CTrade_์ ์ก๋งค์ Error : %s' % e, send='mc')
logger.error('CTrade_์ ์ก๋งค์ Error : %s' % e)
def ์ ๋๋งค๋(self, sRQName, ์ข
๋ชฉ์ฝ๋, ๋งค๋๊ฐ, ์๋):
# sRQName = '์ ๋๋งค๋%s' % self.sScreenNo
try:
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:์ ๊ท๋งค์, 2:์ ๊ท๋งค๋ 3:๋งค์์ทจ์, 4:๋งค๋์ทจ์, 5:๋งค์์ ์ , 6:๋งค๋์ ์ )
sCode = ์ข
๋ชฉ์ฝ๋
nQty = ์๋
nPrice = ๋งค๋๊ฐ
sHogaGb = self.๋งค๋๋ฐฉ๋ฒ # 00:์ง์ ๊ฐ, 03:์์ฅ๊ฐ, 05:์กฐ๊ฑด๋ถ์ง์ ๊ฐ, 06:์ต์ ๋ฆฌ์ง์ ๊ฐ, 07:์ต์ฐ์ ์ง์ ๊ฐ, 10:์ง์ ๊ฐIOC, 13:์์ฅ๊ฐIOC, 16:์ต์ ๋ฆฌIOC, 20:์ง์ ๊ฐFOK, 23:์์ฅ๊ฐFOK, 26:์ต์ ๋ฆฌFOK, 61:์ฅ๊ฐ์์ ์๊ฐ์ธ, 62:์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค, 81:์๊ฐ์ธ์ข
๊ฐ
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
except Exception as e:
print('[%s]์ ๋๋งค๋ Error '%(self.sName,e))
Telegram('[StockTrader][%s]์ ๋๋งค๋ Error : %s' % (self.sName, e), send='mc')
logger.error('[%s]์ ๋๋งค๋ Error : %s' % (self.sName, e))
def ์ ์ก๋งค๋(self, sRQName, ์ข
๋ชฉ์ฝ๋, ๋งค๋๊ฐ, ์๋):
# sRQName = '์ ์ก๋งค๋%s' % self.sScreenNo
sScreenNo = self.GenScreenNO()
sAccNo = self.sAccount
nOrderType = 2 # (1:์ ๊ท๋งค์, 2:์ ๊ท๋งค๋ 3:๋งค์์ทจ์, 4:๋งค๋์ทจ์, 5:๋งค์์ ์ , 6:๋งค๋์ ์ )
sCode = ์ข
๋ชฉ์ฝ๋
nQty = ์๋
nPrice = ๋งค๋๊ฐ
sHogaGb = self.๋งค๋๋ฐฉ๋ฒ # 00:์ง์ ๊ฐ, 03:์์ฅ๊ฐ, 05:์กฐ๊ฑด๋ถ์ง์ ๊ฐ, 06:์ต์ ๋ฆฌ์ง์ ๊ฐ, 07:์ต์ฐ์ ์ง์ ๊ฐ, 10:์ง์ ๊ฐIOC, 13:์์ฅ๊ฐIOC, 16:์ต์ ๋ฆฌIOC, 20:์ง์ ๊ฐFOK, 23:์์ฅ๊ฐFOK, 26:์ต์ ๋ฆฌFOK, 61:์ฅ๊ฐ์์ ์๊ฐ์ธ, 62:์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค, 81:์๊ฐ์ธ์ข
๊ฐ
if sHogaGb in ['03', '07', '06']:
nPrice = 0
sOrgOrderNo = 0
ret = self.parent.KiwoomSendOrder(sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb,
sOrgOrderNo)
return ret
def ์ฃผ๋ฌธ๋ฑ๋ก(self, sRQName, ์ฃผ๋ฌธ๋ฒํธ):
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ] = sRQName
Ui_๊ณ์ข์ ๋ณด์กฐํ, QtBaseClass_๊ณ์ข์ ๋ณด์กฐํ = uic.loadUiType("./UI/๊ณ์ข์ ๋ณด์กฐํ.ui")
class ํ๋ฉด_๊ณ์ข์ ๋ณด(QDialog, Ui_๊ณ์ข์ ๋ณด์กฐํ):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_๊ณ์ข์ ๋ณด, self).__init__(parent) # Initializeํ๋ ํ์
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ข
๋ชฉ๋ฒํธ', '์ข
๋ชฉ๋ช
', 'ํ์ฌ๊ฐ', '๋ณด์ ์๋', '๋งค์
๊ฐ', '๋งค์
๊ธ์ก', 'ํ๊ฐ๊ธ์ก', '์์ต๋ฅ (%)', 'ํ๊ฐ์์ต', '๋งค๋งค๊ฐ๋ฅ์๋']
self.๋ณด์ด๋์ปฌ๋ผ = ['์ข
๋ชฉ๋ฒํธ', '์ข
๋ชฉ๋ช
', 'ํ์ฌ๊ฐ', '๋ณด์ ์๋', '๋งค์
๊ฐ', '๋งค์
๊ธ์ก', 'ํ๊ฐ๊ธ์ก', '์์ต๋ฅ (%)', 'ํ๊ฐ์์ต', '๋งค๋งค๊ฐ๋ฅ์๋'] # ์ฃผ๋น ์์ต -> ์์ต๋ฅ (%)
self.result = []
self.KiwoomAccount()
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1] # ๊ณ์ข๋ฒํธ๊ฐ ;๊ฐ ๋ถ์ด์ ๋์ด(์๋ก ๊ณ์ข๊ฐ 3๊ฐ๋ฉด 111;222;333)
self.comboBox.clear()
self.comboBox.addItems(self.account)
logger.debug("๋ณด์ ๊ณ์ข์: %s ๊ณ์ข๋ฒํธ: %s [%s]" % (ACCOUNT_CNT, self.account[0], ACC_NO))
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (
sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if sRQName == "๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
# print(j)
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "", sRQName, i, j).strip().lstrip('0')
# print(S)
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# logger.debug("%s" % row)
if sPreNext == '2':
self.Request(_repeat=2)
else:
self.model.umkate(KnowledgeFrame(data=self.result, columns=self.๋ณด์ด๋์ปฌ๋ผ))
print(self.result)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
๊ณ์ข๋ฒํธ = self.comboBox.currentText().strip()
logger.debug("๊ณ์ข๋ฒํธ %s" % ๊ณ์ข๋ฒํธ)
# KOA StudioSA์์ opw00018 ํ์ธ
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", ๊ณ์ข๋ฒํธ) # 8132495511
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๋น๋ฐ๋ฒํธ์
๋ ฅ๋งค์ฒด๊ตฌ๋ถ", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์กฐํ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ", "opw00018", _repeat,'{:04d}'.formating(self.sScreenNo))
# ์กฐํ ๋ฒํผ(QtDesigner์์ ์กฐํ๋ฒํผ ๋๋ฅด๊ณ ์ค๋ฅธ์ชฝ ํ๋จ์ ์๊ทธ๋/์ฌ๋กฏํธ์ง๊ธฐ๋ฅผ ๋ณด๋ฉด ์กฐํ๋ฒํผ ์๊ทธ๋(clicked), ์ฌ๋กฏ(Inquiry())๋ก ํ์ธ๊ฐ๋ฅํจ
def inquiry(self):
self.result = []
self.Request(_repeat=0)
def robot_account(self):
global ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ
๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ = self.comboBox.currentText().strip()
# sqlite3 ์ฌ์ฉ
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
robot_account = pickle.dumps(๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ, protocol=pickle.HIGHEST_PROTOCOL, fix_imports=True)
_robot_account = base64.encodebytes(robot_account)
cursor.execute("REPLACE into Setting(keyword, value) values (?, ?)",
['robotaccount', _robot_account])
conn.commit()
print("๋ก๋ด ๊ณ์ข ๋ฑ๋ก ์๋ฃ")
except Exception as e:
print('robot_account', e)
Ui_์ผ์๋ณ์ฃผ๊ฐ์กฐํ, QtBaseClass_์ผ์๋ณ์ฃผ๊ฐ์กฐํ = uic.loadUiType("./UI/์ผ์๋ณ์ฃผ๊ฐ์กฐํ.ui")
class ํ๋ฉด_์ผ๋ณ์ฃผ๊ฐ(QDialog, Ui_์ผ์๋ณ์ฃผ๊ฐ์กฐํ):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_์ผ๋ณ์ฃผ๊ฐ, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('์ผ์๋ณ ์ฃผ๊ฐ ์กฐํ')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ผ์', 'ํ์ฌ๊ฐ', '๊ฑฐ๋๋', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋๊ธ']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "์ฃผ์์ผ๋ด์ฐจํธ์กฐํ":
์ข
๋ชฉ์ฝ๋ = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋
self.model.umkate(kf[['์ข
๋ชฉ์ฝ๋'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.์ข
๋ชฉ์ฝ๋ = self.lineEdit_code.text().strip()
๊ธฐ์ค์ผ์ = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ธฐ์ค์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ฃผ์์ผ๋ด์ฐจํธ์กฐํ", "OPT10081", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_๋ถ๋ณ์ฃผ๊ฐ์กฐํ, QtBaseClass_๋ถ๋ณ์ฃผ๊ฐ์กฐํ = uic.loadUiType("./UI/๋ถ๋ณ์ฃผ๊ฐ์กฐํ.ui")
class ํ๋ฉด_๋ถ๋ณ์ฃผ๊ฐ(QDialog, Ui_๋ถ๋ณ์ฃผ๊ฐ์กฐํ):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_๋ถ๋ณ์ฃผ๊ฐ, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('๋ถ๋ณ ์ฃผ๊ฐ ์กฐํ')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ฒด๊ฒฐ์๊ฐ', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋']
self.result = []
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
print('ํ๋ฉด_๋ถ๋ณ์ฃผ๊ฐ : OnReceiveTrData')
if self.sScreenNo != int(sScrNo):
return
if sRQName == "์ฃผ์๋ถ๋ด์ฐจํธ์กฐํ":
์ข
๋ชฉ์ฝ๋ = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
# kf = KnowledgeFrame(data=self.result, columns=self.columns)
# kf.to_csv('๋ถ๋ด.csv', encoding='euc-kr')
if sPreNext == '2':
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf.to_csv('๋ถ๋ด.csv', encoding='euc-kr', index=False)
kf['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋
self.model.umkate(kf[['์ข
๋ชฉ์ฝ๋'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.์ข
๋ชฉ์ฝ๋ = self.lineEdit_code.text().strip()
ํฑ๋ฒ์ = self.comboBox_getting_min.currentText()[0:2].strip()
if ํฑ๋ฒ์[0] == '0':
ํฑ๋ฒ์ = ํฑ๋ฒ์[1:]
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "ํฑ๋ฒ์", ํฑ๋ฒ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ฃผ์๋ถ๋ด์ฐจํธ์กฐํ", "OPT10080", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_์
์ข
์ ๋ณด, QtBaseClass_์
์ข
์ ๋ณด = uic.loadUiType("./UI/์
์ข
์ ๋ณด์กฐํ.ui")
class ํ๋ฉด_์
์ข
์ ๋ณด(QDialog, Ui_์
์ข
์ ๋ณด):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_์
์ข
์ ๋ณด, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('์
์ข
์ ๋ณด ์กฐํ')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', 'ํ์ฌ๊ฐ', '๋๋น๊ธฐํธ', '์ ์ผ๋๋น', '๋ฑ๋ฝ๋ฅ ', '๊ฑฐ๋๋', '๋น์ค', '๊ฑฐ๋๋๊ธ', '์ํ', '์์น', '๋ณดํฉ', 'ํ๋ฝ', 'ํํ',
'์์ฅ์ข
๋ชฉ์']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "์
์ข
์ ๋ณด์กฐํ":
์ข
๋ชฉ์ฝ๋ = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['์
์ข
์ฝ๋'] = self.์
์ข
์ฝ๋
kf.to_csv("์
์ข
์ ๋ณด.csv")
self.model.umkate(kf[['์
์ข
์ฝ๋'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.์
์ข
์ฝ๋ = self.lineEdit_code.text().strip()
๊ธฐ์ค์ผ์ = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์
์ข
์ฝ๋", self.์
์ข
์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์
์ข
์ ๋ณด์กฐํ", "OPT20003", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_์
์ข
๋ณ์ฃผ๊ฐ์กฐํ, QtBaseClass_์
์ข
๋ณ์ฃผ๊ฐ์กฐํ = uic.loadUiType("./UI/์
์ข
๋ณ์ฃผ๊ฐ์กฐํ.ui")
class ํ๋ฉด_์
์ข
๋ณ์ฃผ๊ฐ(QDialog, Ui_์
์ข
๋ณ์ฃผ๊ฐ์กฐํ):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_์
์ข
๋ณ์ฃผ๊ฐ, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('์
์ข
๋ณ ์ฃผ๊ฐ ์กฐํ')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['ํ์ฌ๊ฐ', '๊ฑฐ๋๋', '์ผ์', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋๊ธ', '๋์
์ข
๊ตฌ๋ถ', '์์
์ข
๊ตฌ๋ถ', '์ข
๋ชฉ์ ๋ณด', '์์ ์ฃผ๊ฐ์ด๋ฒคํธ', '์ ์ผ์ข
๊ฐ']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage,
sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "์
์ข
์ผ๋ด์กฐํ":
์ข
๋ชฉ์ฝ๋ = ''
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['์
์ข
์ฝ๋'] = self.์
์ข
์ฝ๋
self.model.umkate(kf[['์
์ข
์ฝ๋'] + self.columns])
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
self.์
์ข
์ฝ๋ = self.lineEdit_code.text().strip()
๊ธฐ์ค์ผ์ = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์
์ข
์ฝ๋", self.์
์ข
์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ธฐ์ค์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์
์ข
์ผ๋ด์กฐํ", "OPT20006", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
class ํ๋ฉด_์ข
๋ชฉ๋ณํฌ์์(QDialog, Ui_์ผ์๋ณ์ฃผ๊ฐ์กฐํ):
def __init__(self, sScreenNo, kiwoom=None, parent=None):
super(ํ๋ฉด_์ข
๋ชฉ๋ณํฌ์์, self).__init__(parent)
self.setAttribute(Qt.WA_DeleteOnClose)
self.setupUi(self)
self.setWindowTitle('์ข
๋ชฉ๋ณ ํฌ์์ ์กฐํ')
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ผ์', 'ํ์ฌ๊ฐ', '์ ์ผ๋๋น', '๋์ ๊ฑฐ๋๋๊ธ', '๊ฐ์ธํฌ์์', '์ธ๊ตญ์ธํฌ์์', '๊ธฐ๊ด๊ณ', '๊ธ์ตํฌ์', '๋ณดํ', 'ํฌ์ ', '๊ธฐํ๊ธ์ต', '์ํ',
'์ฐ๊ธฐ๊ธ๋ฑ', '๊ตญ๊ฐ', '๋ด์ธ๊ตญ์ธ', '์ฌ๋ชจํ๋', '๊ธฐํ๋ฒ์ธ']
self.result = []
d = today
self.lineEdit_date.setText(str(d))
def KiwoomConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
logger.debug('OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
if self.sScreenNo != int(sScrNo):
return
if sRQName == "์ข
๋ชฉ๋ณํฌ์์์กฐํ":
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.columns:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
row.adding(S)
self.result.adding(row)
if sPreNext == '2':
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.Request(_repeat=2))
else:
kf = KnowledgeFrame(data=self.result, columns=self.columns)
kf['์ข
๋ชฉ์ฝ๋'] = self.lineEdit_code.text().strip()
kf_new = kf[['์ข
๋ชฉ์ฝ๋'] + self.columns]
self.model.umkate(kf_new)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
def Request(self, _repeat=0):
์ข
๋ชฉ์ฝ๋ = self.lineEdit_code.text().strip()
๊ธฐ์ค์ผ์ = self.lineEdit_date.text().strip().replacing('-', '')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", ์ข
๋ชฉ์ฝ๋)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๊ธ์ก์๋๊ตฌ๋ถ", 2) # 1:๊ธ์ก, 2:์๋
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๋งค๋งค๊ตฌ๋ถ", 0) # 0:์๋งค์, 1:๋งค์, 2:๋งค๋
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๋จ์๊ตฌ๋ถ", 1) # 1000:์ฒ์ฃผ, 1:๋จ์ฃผ
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ข
๋ชฉ๋ณํฌ์์์กฐํ", "OPT10060", _repeat,
'{:04d}'.formating(self.sScreenNo))
def inquiry(self):
self.result = []
self.Request(_repeat=0)
Ui_TradeShortTerm, QtBaseClass_TradeShortTerm = uic.loadUiType("./UI/TradeShortTerm.ui")
class ํ๋ฉด_TradeShortTerm(QDialog, Ui_TradeShortTerm):
def __init__(self, parent):
super(ํ๋ฉด_TradeShortTerm, self).__init__(parent)
self.setupUi(self)
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.result = []
def inquiry(self):
# Google spreadsheet ์ฌ์ฉ
try:
self.data = import_googlesheet()
print(self.data)
self.model.umkate(self.data)
for i in range(length(self.data.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print('ํ๋ฉด_TradeShortTerm : inquiry Error ', e)
logger.error('ํ๋ฉด_TradeShortTerm : inquiry Error : %s' % e)
class CTradeShortTerm(CTrade): # ๋ก๋ด ์ถ๊ฐ ์ __init__ : ๋ณต์ฌ, Setting, ์ด๊ธฐ์กฐ๊ฑด:์ ๋ต์ ๋ง๊ฒ, ๋ฐ์ดํฐ์ฒ๋ฆฌ~Run:๋ณต์ฌ
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.portfolio = dict()
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.๋งค์๋ชจ๋ํฐ๋ง์ฒดํฌ = False
self.Smtotal_allScreenNumber = 9999
self.d = today
# ๊ตฌ๊ธ ์คํ๋ ๋์ํธ์์ ์ฝ์ KnowledgeFrame์์ ๋ก๋ด๋ณ ์ข
๋ชฉ๋ฆฌ์คํธ ์
ํ
def set_stocklist(self, data):
self.Stocklist = dict()
self.Stocklist['์ปฌ๋ผ๋ช
'] = list(data.columns)
for ์ข
๋ชฉ์ฝ๋ in data['์ข
๋ชฉ์ฝ๋'].distinctive():
temp_list = data[data['์ข
๋ชฉ์ฝ๋'] == ์ข
๋ชฉ์ฝ๋].values[0]
self.Stocklist[์ข
๋ชฉ์ฝ๋] = {
'๋ฒํธ': temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('๋ฒํธ')],
'์ข
๋ชฉ๋ช
': temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('์ข
๋ชฉ๋ช
')],
'์ข
๋ชฉ์ฝ๋': ์ข
๋ชฉ์ฝ๋,
'์์ฅ': temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('์์ฅ')],
'ํฌ์๋น์ค': float(temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('๋น์ค')]), # ์ ์ฅ ํ setting ํจ์์์ ์ ๋ต์ ๋จ์ํฌ์๊ธ์ ๊ณฑํจ
'์๊ฐ์์น': list(mapping(float, temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('์๊ฐ์์น')].split(','))),
'๋งค์๊ฐ': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'๋งค์๊ฐ' in col and temp_list[list(data.columns).index(col)] != ''),
'๋งค๋์ ๋ต': temp_list[self.Stocklist['์ปฌ๋ผ๋ช
'].index('๊ธฐ๋ณธ๋งค๋์ ๋ต')],
'๋งค๋๊ฐ': list(
int(float(temp_list[list(data.columns).index(col)].replacing(',', ''))) for col in data.columns if
'๋ชฉํ๊ฐ' in col and temp_list[list(data.columns).index(col)] != '')
}
return self.Stocklist
# RobotAdd ํจ์์์ ์ด๊ธฐํ ๋ค์ ์
ํ
์คํํด์ ์ค์ ๊ฐ ๋๊น
def Setting(self, sScreenNo, ๋งค์๋ฐฉ๋ฒ='00', ๋งค๋๋ฐฉ๋ฒ='03', ์ข
๋ชฉ๋ฆฌ์คํธ=mk.KnowledgeFrame()):
try:
self.sScreenNo = sScreenNo
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.๋งค์๋ฐฉ๋ฒ = ๋งค์๋ฐฉ๋ฒ
self.๋งค๋๋ฐฉ๋ฒ = ๋งค๋๋ฐฉ๋ฒ
self.์ข
๋ชฉ๋ฆฌ์คํธ = ์ข
๋ชฉ๋ฆฌ์คํธ
self.Stocklist = self.set_stocklist(self.์ข
๋ชฉ๋ฆฌ์คํธ) # ๋ฒํธ, ์ข
๋ชฉ๋ช
, ์ข
๋ชฉ์ฝ๋, ์์ฅ, ๋น์ค, ์๊ฐ์์น, ๋งค์๊ฐ, ๋งค๋์ ๋ต, ๋งค๋๊ฐ
self.Stocklist['์ ๋ต'] = {
'๋จ์ํฌ์๊ธ': '',
'๋ชจ๋ํฐ๋ง์ข
๋ฃ์๊ฐ': '',
'๋ณด์ ์ผ': '',
'ํฌ์๊ธ๋น์ค': '',
'๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด': [],
'์ ๋ต๋งค๋๊ฐ': [],
}
row_data = shortterm_strategy_sheet.getting_total_all_values()
for data in row_data:
if data[0] == '๋จ์ํฌ์๊ธ':
self.Stocklist['์ ๋ต']['๋จ์ํฌ์๊ธ'] = int(data[1])
elif data[0] == '๋งค์๋ชจ๋ํฐ๋ง ์ข
๋ฃ์๊ฐ':
if length(data[1][:-3]) == 1:
data[1] = '0' + data[1]
self.Stocklist['์ ๋ต']['๋ชจ๋ํฐ๋ง์ข
๋ฃ์๊ฐ'] = data[1] + ':00'
elif data[0] == '๋ณด์ ์ผ':
self.Stocklist['์ ๋ต']['๋ณด์ ์ผ'] = int(data[1])
elif data[0] == 'ํฌ์๊ธ ๋น์ค':
self.Stocklist['์ ๋ต']['ํฌ์๊ธ๋น์ค'] = float(data[1][:-1])
# elif data[0] == '์์ ์จ':
# self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'].adding(float(data[1][:-1]))
# elif data[0] == '์๊ฐ ์์น':
# self.Stocklist['์ ๋ต']['์๊ฐ์์น'] = list(mapping(int, data[1].split(',')))
elif '๊ตฌ๊ฐ' in data[0]:
if data[0][-1] != '1' and data[0][-1] != '2':
self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'].adding(float(data[1][:-1]))
elif '์์ ๊ฐ' == data[0]:
self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'].adding(float(data[1].replacing('%', '')))
elif '๋ณธ์ ๊ฐ' == data[0]:
self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'].adding(float(data[1].replacing('%', '')))
elif '์ต์ ๊ฐ' in data[0]:
self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'].adding(float(data[1].replacing('%', '')))
self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'].insert(0, self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'][0]) # ์์ ๊ฐ
self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'].insert(1, self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'][1]) # ๋ณธ์ ๊ฐ
for code in self.Stocklist.keys():
if code == '์ปฌ๋ผ๋ช
' or code == '์ ๋ต':
continue
else:
self.Stocklist[code]['๋จ์ํฌ์๊ธ'] = int(
self.Stocklist[code]['ํฌ์๋น์ค'] * self.Stocklist['์ ๋ต']['๋จ์ํฌ์๊ธ'])
self.Stocklist[code]['์๊ฐ์ฒดํฌ'] = False
self.Stocklist[code]['๋งค์์ํ๋๋ฌ'] = False
self.Stocklist[code]['๋งค์์กฐ๊ฑด'] = 0
self.Stocklist[code]['๋งค์์ด์๋'] = 0 # ๋ถํ ๋งค์์ ๋ฐ๋ฅธ ์๋์ฒดํฌ
self.Stocklist[code]['๋งค์์๋'] = 0 # ๋ถํ ๋งค์ ๋จ์
self.Stocklist[code]['๋งค์์ฃผ๋ฌธ์๋ฃ'] = 0 # ๋ถํ ๋งค์์ ๋ฐ๋ฅธ ๋งค์ ์ฃผ๋ฌธ ์
self.Stocklist[code]['๋งค์๊ฐ์ ๋ต'] = length(self.Stocklist[code]['๋งค์๊ฐ']) # ๋งค์ ์ ๋ต์ ๋ฐ๋ฅธ ๋งค์๊ฐ ์ง์ ์๋
if self.Stocklist[code]['๋งค๋์ ๋ต'] == '4':
self.Stocklist[code]['๋งค๋๊ฐ'].adding(self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'])
print(self.Stocklist)
except Exception as e:
print('CTradeShortTerm_Setting Error :', e)
Telegram('[XTrader]CTradeShortTerm_Setting Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Setting Error : %s' % e)
# ์๋ ํฌํธํด๋ฆฌ์ค ์์ฑ
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'024840': {'๋ฒํธ': '8.030', '์ข
๋ชฉ๋ช
': 'KBI๋ฉํ', '์ข
๋ชฉ์ฝ๋': '024840', '์์ฅ': 'KOSDAQ', '๋งค์์ ๋ต': '1', '๋งค์๊ฐ': [1468],
'๋งค์์กฐ๊ฑด': 2, '์๋': 310, '๋งค๋์ ๋ต': '1', '๋งค๋๊ฐ': [], '๋งค์์ผ': '2020/08/26 09:56:54'},
'097800': {'๋ฒํธ': '7.099', '์ข
๋ชฉ๋ช
': '์ํฉ', '์ข
๋ชฉ์ฝ๋': '097800', '์์ฅ': 'KOSDAQ', '๋งค์์ ๋ต': '1', '๋งค์๊ฐ': [3219],
'๋งค์์กฐ๊ฑด': 1, '์๋': 310, '๋งค๋์ ๋ต': '4', '๋งค๋๊ฐ': [3700], '๋งค์์ผ': '2020/05/29 09:22:39'},
'297090': {'๋ฒํธ': '7.101', '์ข
๋ชฉ๋ช
': '์จ์์ค๋ฒ ์ด๋ง', '์ข
๋ชฉ์ฝ๋': '297090', '์์ฅ': 'KOSDAQ', '๋งค์์ ๋ต': '1', '๋งค์๊ฐ': [5000],
'๋งค์์กฐ๊ฑด': 3, '์๋': 15, '๋งค๋์ ๋ต': '2', '๋งค๋๊ฐ': [], '๋งค์์ผ': '2020/06/03 09:12:15'},
}
self.strategy = {'์ ๋ต': {'๋จ์ํฌ์๊ธ': 200000, '๋ชจ๋ํฐ๋ง์ข
๋ฃ์๊ฐ': '10:30:00', '๋ณด์ ์ผ': 20,
'ํฌ์๊ธ๋น์ค': 70.0, '๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด': [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0],
'์ ๋ต๋งค๋๊ฐ': [-2.7, 0.3, 3.0, 6.0]}}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_ShortTerm(๋ฒํธ=self.Stocklist[code]['๋ฒํธ'], ์ข
๋ชฉ์ฝ๋=code,
์ข
๋ชฉ๋ช
=self.Stocklist[code]['์ข
๋ชฉ๋ช
'],
์์ฅ=self.Stocklist[code]['์์ฅ'],
๋งค์๊ฐ=self.Stocklist[code]['๋งค์๊ฐ'][0],
๋งค์์กฐ๊ฑด=self.Stocklist[code]['๋งค์์กฐ๊ฑด'],
๋ณด์ ์ผ=self.strategy['์ ๋ต']['๋ณด์ ์ผ'],
๋งค๋์ ๋ต=self.Stocklist[code]['๋งค๋์ ๋ต'],
๋งค๋๊ฐ=self.Stocklist[code]['๋งค๋๊ฐ'],
๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด=self.strategy['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'], ๋งค๋๊ตฌ๊ฐ=1,
์๋=self.Stocklist[code]['์๋'],
๋งค์์ผ=self.Stocklist[code]['๋งค์์ผ'])
# google spreadsheet ๋งค๋งค์ด๋ ฅ ์์ฑ
def save_history(self, code, status):
# ๋งค๋งค์ด๋ ฅ sheet์ ํด๋น ์ข
๋ชฉ(๋งค์๋ ์ข
๋ชฉ)์ด ์์ผ๋ฉด row๋ฅผ ๋ฐํ ์๋๋ฉด ์์ธ์ฒ๋ฆฌ -> ์ ๊ท ๋งค์๋ก ์ฒ๋ฆฌ
# ๋งค์ ์ด๋ ฅ : ์ฒด๊ฒฐ์ฒ๋ฆฌ, ๋งค์, ๋ฏธ์ฒด๊ฒฐ์๋ 0์์ ์ด๋ ฅ ์ ์ฅ
# ๋งค๋ ์ด๋ ฅ : ์ฒด๊ฒฐ์ฒ๋ฆฌ, ๋งค๋, ๋ฏธ์ฒด๊ฒฐ์๋ 0์์ ์ด๋ ฅ ์ ์ฅ
if status == '๋งค๋๋ชจ๋ํฐ๋ง':
row = []
row.adding(self.portfolio[code].๋ฒํธ)
row.adding(self.portfolio[code].์ข
๋ชฉ๋ช
)
row.adding(self.portfolio[code].๋งค์๊ฐ)
shortterm_sell_sheet.adding_row(row)
try:
code_row = shortterm_history_sheet.findtotal_all(self.portfolio[code].์ข
๋ชฉ๋ช
)[-1].row # ์ข
๋ชฉ๋ช
์ด ์๋ ๋ชจ๋ ์
์ ์ฐพ์์ ๋งจ ์๋์ ์๋ ์
์ ์ ํ
cell = alpha_list[shortterm_history_cols.index('๋งค๋๊ฐ')] + str(code_row) # ๋งค์ ์ด๋ ฅ์ ์๋ ์ข
๋ชฉ์ด ๋งค๋๊ฐ ๋์๋์ง ํ์ธ
sell_price = shortterm_history_sheet.acell(str(cell)).value
# ๋งค๋ ์ด๋ ฅ์ ์ถ๊ฐ ๋งค๋(๋งค๋์ ๋ต2์ ๊ฒฝ์ฐ)๋ ์ ๊ท ๋งค๋์ธ ๊ฒฝ์ฐ๋ผ ๋งค๋ ์ด๋ ฅ ์ ๋ฌด์ ์๊ด์์
if status == '๋งค๋': # ๋งค๋ ์ด๋ ฅ์ ํฌํธํด๋ฆฌ์ค์์ ์ข
๋ชฉ pop์ ํ๋ฏ๋ก Stocklist ๋ฐ์ดํฐ ์ฌ์ฉ
cell = alpha_list[shortterm_history_cols.index('๋งค๋๊ฐ')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค๋์ฒด๊ฒฐ๊ฐ)
cell = alpha_list[shortterm_history_cols.index('๋งค๋์๋')] + str(code_row)
์๋ = shortterm_history_sheet.acell(cell).value # ๋ถํ ๋งค๋์ ๊ฒฝ์ฐ ์ด์ ๋งค๋ ์๋์ด ๊ธฐ๋ก๋์ด ์์
if ์๋ != '': self.portfolio[code].๋งค๋์๋ += int(์๋) # ๋งค๋์๋์ ์ฃผ๋ฌธ ์๋์ด๋ฏ๋ก ๊ธฐ์กด ์๋์ ํฉํด์ค
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค๋์๋)
cell = alpha_list[shortterm_history_cols.index('๋งค๋์ผ')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
cell = alpha_list[shortterm_history_cols.index('๋งค๋์ ๋ต')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค๋์ ๋ต)
cell = alpha_list[shortterm_history_cols.index('๋งค๋๊ตฌ๊ฐ')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค๋๊ตฌ๊ฐ)
๊ณ์ฐ์์ต๋ฅ = value_round((self.portfolio[code].๋งค๋์ฒด๊ฒฐ๊ฐ / self.portfolio[code].๋งค์๊ฐ - 1) * 100, 2)
cell = alpha_list[shortterm_history_cols.index('์์ต๋ฅ (๊ณ์ฐ)')] + str(code_row) # ์์ต๋ฅ ๊ณ์ฐ
shortterm_history_sheet.umkate_acell(cell, ๊ณ์ฐ์์ต๋ฅ )
# ๋งค์ ์ด๋ ฅ์ ์์ผ๋ ๋งค๋ ์ด๋ ฅ์ด ์์ -> ๋งค๋ ์ ์ถ๊ฐ ๋งค์
if sell_price == '':
if status == '๋งค์': # ํฌํธํด๋ฆฌ์ค ๋ฐ์ดํฐ ์ฌ์ฉ
cell = alpha_list[shortterm_history_cols.index('๋งค์๊ฐ')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค์๊ฐ)
cell = alpha_list[shortterm_history_cols.index('๋งค์์๋')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].์๋)
cell = alpha_list[shortterm_history_cols.index('๋งค์์ผ')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค์์ผ)
cell = alpha_list[shortterm_history_cols.index('๋งค์์กฐ๊ฑด')] + str(code_row)
shortterm_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค์์กฐ๊ฑด)
else: # ๋งค๋๊ฐ๊ฐ ๊ธฐ๋ก๋์ด ๊ฑฐ๋๊ฐ ์๋ฃ๋ ์ข
๋ชฉ์ผ๋ก ํ๋จํ์ฌ ์์ธ๋ฐ์์ผ๋ก ์ ๊ท ๋งค์ ์ถ๊ฐํจ
raise Exception('๋งค๋งค์๋ฃ ์ข
๋ชฉ')
except Exception as e:
try:
# logger.debug('CTradeShortTerm_save_history Error1 : ์ข
๋ชฉ๋ช
:%s, %s' % (self.portfolio[code].์ข
๋ชฉ๋ช
, e))
row = []
row_buy = []
if status == '๋งค์':
row.adding(self.portfolio[code].๋ฒํธ)
row.adding(self.portfolio[code].์ข
๋ชฉ๋ช
)
row.adding(self.portfolio[code].๋งค์๊ฐ)
row.adding(self.portfolio[code].์๋)
row.adding(self.portfolio[code].๋งค์์ผ)
row.adding(self.portfolio[code].๋งค์์กฐ๊ฑด)
shortterm_history_sheet.adding_row(row)
except Exception as e:
print('CTradeShortTerm_save_history Error2 : ์ข
๋ชฉ๋ช
:%s, %s' % (self.portfolio[code].์ข
๋ชฉ๋ช
, e))
Telegram('[XTrade]CTradeShortTerm_save_history Error2 : ์ข
๋ชฉ๋ช
:%s, %s' % (self.portfolio[code].์ข
๋ชฉ๋ช
, e),
send='mc')
logger.error('CTradeShortTerm_save_history Error : ์ข
๋ชฉ๋ช
:%s, %s' % (self.portfolio[code].์ข
๋ชฉ๋ช
, e))
# ๋งค์ ์ ๋ต๋ณ ๋งค์ ์กฐ๊ฑด ํ์ธ
def buy_strategy(self, code, price):
result = False
condition = self.Stocklist[code]['๋งค์์กฐ๊ฑด'] # ์ด๊ธฐ๊ฐ 0
qty = self.Stocklist[code]['๋งค์์๋'] # ์ด๊ธฐ๊ฐ 0
ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ = price # ์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
๋งค์๊ฐ = self.Stocklist[code]['๋งค์๊ฐ'] # [๋งค์๊ฐ1, ๋งค์๊ฐ2, ๋งค์๊ฐ3]
์๊ฐ์์นํํ = self.Stocklist[code]['์๊ฐ์์น'][0]
์๊ฐ์์น์ํ = self.Stocklist[code]['์๊ฐ์์น'][1]
# 1. ๊ธ์ผ์๊ฐ ์์น ์ฒดํฌ(์ด๊ธฐ ํ๋ฒ)ํ์ฌ ๋งค์์กฐ๊ฑด(1~6)๊ณผ ์ฃผ๋ฌธ ์๋ ๊ณ์ฐ
if self.Stocklist[code]['์๊ฐ์ฒดํฌ'] == False: # ์ข
๋ชฉ๋ณ๋ก ์ด๊ธฐ์ ํ๋ฒ๋ง ์๊ฐ ์์น ์ฒดํฌ๋ฅผ ํ๋ฉด ๋๋ฏ๋ก ๋ณ๋ ํจ์ ๋ฏธ์ฌ์ฉ
๋งค์๊ฐ.adding(์๊ฐ)
๋งค์๊ฐ.sort(reverse=True)
band = ๋งค์๊ฐ.index(์๊ฐ) # band = 0 : ๋งค์๊ฐ1 ์ด์, band=1: ๋งค์๊ฐ1, 2 ์ฌ์ด, band=2: ๋งค์๊ฐ2,3 ์ฌ์ด
๋งค์๊ฐ.remove(์๊ฐ)
if band == length(๋งค์๊ฐ): # ๋งค์๊ฐ ์ง์ ํ ๊ตฌ๊ฐ๋ณด๋ค ์๊ฐ๊ฐ ์๋์ผ ๊ฒฝ์ฐ๋ก ์ด๊ธฐ๊ฐ์ด result=False, condition=0 ๋ฆฌํด
self.Stocklist[code]['์๊ฐ์ฒดํฌ'] = True
self.Stocklist[code]['๋งค์์กฐ๊ฑด'] = 0
self.Stocklist[code]['๋งค์์๋'] = 0
return False, 0, 0
else:
# ๋จ์ํฌ์๊ธ์ผ๋ก ๋งค์๊ฐ๋ฅํ ์ด ์๋ ๊ณ์ฐ, band = 0 : ๋งค์๊ฐ1, band=1: ๋งค์๊ฐ2, band=2: ๋งค์๊ฐ3 ๋ก ๊ณ์ฐ
self.Stocklist[code]['๋งค์์ด์๋'] = self.Stocklist[code]['๋จ์ํฌ์๊ธ'] // ๋งค์๊ฐ[band]
if band == 0: # ์๊ฐ๊ฐ ๋งค์๊ฐ1๋ณด๋ค ๋์ ๊ฒฝ์ฐ
# ์๊ฐ๊ฐ ๋งค์๊ฐ1์ ์๊ฐ๋ฒ์์ ํฌํจ : ์กฐ๊ฑด 1, 2, 3
if ๋งค์๊ฐ[band] * (1 + ์๊ฐ์์นํํ / 100) <= ์๊ฐ and ์๊ฐ < ๋งค์๊ฐ[band] * (1 + ์๊ฐ์์น์ํ / 100):
condition = length(๋งค์๊ฐ)
self.Stocklist[code]['๋งค์๊ฐ์ ๋ต'] = length(๋งค์๊ฐ)
qty = self.Stocklist[code]['๋งค์์ด์๋'] // condition
else: # ์๊ฐ ์์น์ ๋ฏธํฌํจ
self.Stocklist[code]['์๊ฐ์ฒดํฌ'] = True
self.Stocklist[code]['๋งค์์กฐ๊ฑด'] = 0
self.Stocklist[code]['๋งค์์๋'] = 0
return False, 0, 0
else: # ์๊ฐ๊ฐ ๋งค์๊ฐ ์ค๊ฐ์ธ ๊ฒฝ์ฐ - ๋งค์๊ฐ1&2์ฌ์ด(band 1) : ์กฐ๊ฑด 4,5 / ๋งค์๊ฐ2&3์ฌ์ด(band 2) : ์กฐ๊ฑด 6
for i in range(band): # band 1์ผ ๊ฒฝ์ฐ ๋งค์๊ฐ 1์ ๋ถํ์ํ์ฌ ์ญ์ , band 2 : ๋งค์๊ฐ 1, 2 ์ญ์ (band์ ๋งํผ ์ญ์ ์คํ)
๋งค์๊ฐ.pop(0)
if ๋งค์๊ฐ[0] * (1 + ์๊ฐ์์นํํ / 100) <= ์๊ฐ: # ์๊ฐ๋ฒ์ ํฌํจ
# ์กฐ๊ฑด 4 = ๋งค์๊ฐ๊ธธ์ด 1 + band 1 + 2(=band+1) -> 4 = 1 + 2*1 + 1
# ์กฐ๊ฑด 5 = ๋งค์๊ฐ๊ธธ์ด 2 + band 1 + 2(=band+1) -> 5 = 2 + 2*1 + 1
# ์กฐ๊ฑด 6 = ๋งค์๊ฐ๊ธธ์ด 1 + band 2 + 3(=band+1) -> 6 = 1 + 2*2 + 1
condition = length(๋งค์๊ฐ) + (2 * band) + 1
self.Stocklist[code]['๋งค์๊ฐ์ ๋ต'] = length(๋งค์๊ฐ)
qty = self.Stocklist[code]['๋งค์์ด์๋'] // (condition % 2 + 1)
else:
self.Stocklist[code]['์๊ฐ์ฒดํฌ'] = True
self.Stocklist[code]['๋งค์์กฐ๊ฑด'] = 0
self.Stocklist[code]['๋งค์์๋'] = 0
return False, 0, 0
self.Stocklist[code]['์๊ฐ์ฒดํฌ'] = True
self.Stocklist[code]['๋งค์์กฐ๊ฑด'] = condition
self.Stocklist[code]['๋งค์์๋'] = qty
else: # ์๊ฐ ์์น ์ฒดํฌ๋ฅผ ํ ๋๋ฒ์งธ ๋ฐ์ดํฐ ์ดํ์๋ condition์ด 0์ด๋ฉด ๋ฐ๋ก ๋งค์ ๋ถ๋ง์กฑ ๋ฆฌํด์ํด
if condition == 0: # condition 0์ ๋งค์ ์กฐ๊ฑด ๋ถ๋ง์กฑ
return False, 0, 0
# ๋งค์์กฐ๊ฑด ํ์ , ๋งค์ ์๋ ๊ณ์ฐ ์๋ฃ
# ๋งค์์ํ์ ๋ฏธ๋๋ฌํ ์ํ๋ก ๋งค์๊ฐ๋ก ๋ด๋ ค์์ ๋ ๋งค์
# ํ์ฌ๊ฐ๊ฐ ํด๋น์กฐ๊ฑด์์์ ์๊ฐ์์น ์ํ ์ด์์ผ๋ก ์ค๋ฅด๋ฉด ๋งค์์ํ๋๋ฌ์ True๋ก ํด์ ๋งค์ํ์ง ์๊ฒ ํจ
if ํ์ฌ๊ฐ >= ๋งค์๊ฐ[0] * (1 + ์๊ฐ์์น์ํ / 100): self.Stocklist[code]['๋งค์์ํ๋๋ฌ'] = True
if self.Stocklist[code]['๋งค์์ฃผ๋ฌธ์๋ฃ'] < self.Stocklist[code]['๋งค์๊ฐ์ ๋ต'] and self.Stocklist[code]['๋งค์์ํ๋๋ฌ'] == False:
if ํ์ฌ๊ฐ == ๋งค์๊ฐ[0]:
result = True
self.Stocklist[code]['๋งค์์ฃผ๋ฌธ์๋ฃ'] += 1
print("๋งค์๋ชจ๋ํฐ๋ง ๋ง์กฑ_์ข
๋ชฉ:%s, ์๊ฐ:%s, ์กฐ๊ฑด:%s, ํ์ฌ๊ฐ:%s, ์ฒดํฌ๊ฒฐ๊ณผ:%s, ์๋:%s" % (
self.Stocklist[code]['์ข
๋ชฉ๋ช
'], ์๊ฐ, condition, ํ์ฌ๊ฐ, result, qty))
logger.debug("๋งค์๋ชจ๋ํฐ๋ง ๋ง์กฑ_์ข
๋ชฉ:%s, ์๊ฐ:%s, ์กฐ๊ฑด:%s, ํ์ฌ๊ฐ:%s, ์ฒดํฌ๊ฒฐ๊ณผ:%s, ์๋:%s" % (
self.Stocklist[code]['์ข
๋ชฉ๋ช
'], ์๊ฐ, condition, ํ์ฌ๊ฐ, result, qty))
return result, condition, qty
# ๋งค๋ ๊ตฌ๊ฐ ํ์ธ
def profit_band_check(self, ํ์ฌ๊ฐ, ๋งค์๊ฐ):
band_list = [0, 3, 5, 10, 15, 25]
# print('ํ์ฌ๊ฐ, ๋งค์๊ฐ', ํ์ฌ๊ฐ, ๋งค์๊ฐ)
ratio = value_round((ํ์ฌ๊ฐ - ๋งค์๊ฐ) / ๋งค์๊ฐ * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in band_list:
return band_list.index(ratio) + 1
else:
band_list.adding(ratio)
band_list.sort()
band = band_list.index(ratio)
band_list.remove(ratio)
return band
# ๋งค๋ ์ ๋ต๋ณ ๋งค๋ ์กฐ๊ฑด ํ์ธ
def sell_strategy(self, code, price):
# print('%s ๋งค๋ ์กฐ๊ฑด ํ์ธ' % code)
try:
result = False
band = self.portfolio[code].๋งค๋๊ตฌ๊ฐ # ์ด์ ๋งค๋ ๊ตฌ๊ฐ ๋ฐ์
๋งค๋๋ฐฉ๋ฒ = self.๋งค๋๋ฐฉ๋ฒ # '03' : ์์ฅ๊ฐ
qty_ratio = 1 # ๋งค๋ ์๋ ๊ฒฐ์ : ๋ณด์ ์๋ * qty_ratio
ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ = price # ์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
๋งค์๊ฐ = self.portfolio[code].๋งค์๊ฐ
# ์ ๋ต 1, 2, 3๊ณผ 4 ๋ณ๋ ์ฒดํฌ
strategy = self.portfolio[code].๋งค๋์ ๋ต
# ์ ๋ต 1, 2, 3
if strategy != '4':
# ๋งค๋๋ฅผ ์ํ ์์ต๋ฅ ๊ตฌ๊ฐ ์ฒดํฌ(๋งค์๊ฐ ๋๋น ํ์ฌ๊ฐ์ ์์ต๋ฅ ์กฐ๊ฑด์ ๋ค๋ฅธ ๊ตฌ๊ฐ ์ค์ )
new_band = self.profit_band_check(ํ์ฌ๊ฐ, ๋งค์๊ฐ)
if (hogacal(์๊ฐ, 0, self.portfolio[code].์์ฅ, '์ํ๊ฐ')) <= ํ์ฌ๊ฐ:
band = 7
if band < new_band: # ์ด์ ๊ตฌ๊ฐ๋ณด๋ค ํ์ฌ ๊ตฌ๊ฐ์ด ๋์ ๊ฒฝ์ฐ(์์ธ๊ฐ ์ฌ๋ผ๊ฐ ๊ฒฝ์ฐ)๋ง
band = new_band # ๊ตฌ๊ฐ์ ํ์ฌ ๊ตฌ๊ฐ์ผ๋ก ๋ณ๊ฒฝ(๋ฐ๋์ ๊ฒฝ์ฐ๋ ๊ตฌ๊ฐ ์ ์ง)
if band == 1 and ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[0] / 100)):
result = True
elif band == 2 and ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[1] / 100)):
result = True
elif band == 3 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[2] / 100)):
result = True
elif band == 4 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[3] / 100)):
result = True
elif band == 5 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[4] / 100)):
result = True
elif band == 6 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[5] / 100)):
result = True
elif band == 7 and ํ์ฌ๊ฐ >= (hogacal(์๊ฐ, -3, self.Stocklist[code]['์์ฅ'], '์ํ๊ฐ')):
๋งค๋๋ฐฉ๋ฒ = '00' # ์ง์ ๊ฐ
result = True
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = band # ํฌํธํด๋ฆฌ์ค์ ๋งค๋๊ตฌ๊ฐ ์
๋ฐ์ดํธ
try:
if strategy == '2' or strategy == '3': # ๋งค๋์ ๋ต 2(๊ธฐ์กด 5)
if strategy == '2':
๋ชฉํ๊ฐ = self.portfolio[code].๋งค๋๊ฐ[0]
elif strategy == '3':
๋ชฉํ๊ฐ = (hogacal(์๊ฐ * 1.1, 0, self.Stocklist[code]['์์ฅ'], 'ํ์ฌ๊ฐ'))
๋งค๋์กฐ๊ฑด = self.portfolio[code].๋งค๋์กฐ๊ฑด # ๋งค๋๊ฐ ์คํ๋ ์กฐ๊ฑด '': ๋งค๋ ์ , 'B':๊ตฌ๊ฐ๋งค๋, 'T':๋ชฉํ๊ฐ๋งค๋
targetting_band = self.profit_band_check(๋ชฉํ๊ฐ, ๋งค์๊ฐ)
if band < targetting_band: # ํ์ฌ๊ฐ๊ตฌ๊ฐ์ด ๋ชฉํ๊ฐ๊ตฌ๊ฐ ๋ฏธ๋ง์ผ๋ ์ ๋๋งค๋
qty_ratio = 1
else: # ํ์ฌ๊ฐ๊ตฌ๊ฐ์ด ๋ชฉํ๊ฐ๊ตฌ๊ฐ ์ด์์ผ ๋
if ํ์ฌ๊ฐ == ๋ชฉํ๊ฐ: # ๋ชฉํ๊ฐ ๋๋ฌ ์ ์ ๋ฐ ๋งค๋
self.portfolio[code].๋ชฉํ๋๋ฌ = True # ๋ชฉํ๊ฐ ๋๋ฌ ์ฌ๋ถ True
if ๋งค๋์กฐ๊ฑด == '': # ๋งค๋์ด๋ ฅ์ด ์๋ ๊ฒฝ์ฐ ๋ชฉํ๊ฐ๋งค๋ 'T', ์ ๋ฐ ๋งค๋
self.portfolio[code].๋งค๋์กฐ๊ฑด = 'T'
result = True
if self.portfolio[code].์๋ == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif ๋งค๋์กฐ๊ฑด == 'B': # ๊ตฌ๊ฐ ๋งค๋ ์ด๋ ฅ์ด ์์ ๊ฒฝ์ฐ ์ ๋ฐ๋งค๋๊ฐ ๋ ์ํ์ด๋ฏ๋ก ๋จ์ ์ ๋๋งค๋
result = True
qty_ratio = 1
elif ๋งค๋์กฐ๊ฑด == 'T': # ๋ชฉํ๊ฐ ๋งค๋ ์ด๋ ฅ์ด ์์ ๊ฒฝ์ฐ ๋งค๋๋ฏธ์คํ
result = False
else: # ํ์ฌ๊ฐ๊ฐ ๋ชฉํ๊ฐ๊ฐ ์๋ ๊ฒฝ์ฐ ๊ตฌ๊ฐ ๋งค๋ ์คํ(๋งค๋์คํ์ฌ๋ถ๋ ๊ฒฐ์ ๋ ์ํ)
if self.portfolio[code].๋ชฉํ๋๋ฌ == False: # ๋ชฉํ๊ฐ ๋๋ฌ์ ๋ชปํ ๊ฒฝ์ฐ๋ฉด ์ ๋๋งค๋
qty_ratio = 1
else:
if ๋งค๋์กฐ๊ฑด == '': # ๋งค๋์ด๋ ฅ์ด ์๋ ๊ฒฝ์ฐ ๊ตฌ๊ฐ๋งค๋ 'B', ์ ๋ฐ ๋งค๋
self.portfolio[code].๋งค๋์กฐ๊ฑด = 'B'
if self.portfolio[code].์๋ == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
elif ๋งค๋์กฐ๊ฑด == 'B': # ๊ตฌ๊ฐ ๋งค๋ ์ด๋ ฅ์ด ์์ ๊ฒฝ์ฐ ๋งค๋๋ฏธ์คํ
result = False
elif ๋งค๋์กฐ๊ฑด == 'T': # ๋ชฉํ๊ฐ ๋งค๋ ์ด๋ ฅ์ด ์์ ๊ฒฝ์ฐ ์ ๋๋งค๋
qty_ratio = 1
except Exception as e:
print('sell_strategy ๋งค๋์ ๋ต 2 Error :', e)
logger.error('CTradeShortTerm_sell_strategy ์ข
๋ชฉ : %s ๋งค๋์ ๋ต 2 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy ์ข
๋ชฉ : %s ๋งค๋์ ๋ต 2 Error : %s' % (code, e), send='mc')
result = False
return ๋งค๋๋ฐฉ๋ฒ, result, qty_ratio
# print('์ข
๋ชฉ์ฝ๋ : %s, ํ์ฌ๊ฐ : %s, ์๊ฐ : %s, ๊ณ ๊ฐ : %s, ๋งค๋๊ตฌ๊ฐ : %s, ๊ฒฐ๊ณผ : %s' % (code, ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, band, result))
return ๋งค๋๋ฐฉ๋ฒ, result, qty_ratio
# ์ ๋ต 4(์ง์ ๊ฐ 00 ๋งค๋)
else:
๋งค๋๋ฐฉ๋ฒ = '00' # ์ง์ ๊ฐ
try:
# ์ ๋ต 4์ ๋งค๋๊ฐ = [๋ชฉํ๊ฐ(์), [์์ ๊ฐ(%), ๋ณธ์ ๊ฐ(%), 1์ฐจ์ต์ ๊ฐ(%), 2์ฐจ์ต์ ๊ฐ(%)]]
# 1. ๋งค์ ํ ์์ ๊ฐ๊น์ง ํ๋ฝ์ ๋งค๋์ฃผ๋ฌธ -> ์์ ๊ฐ, ์ ๋๋งค๋๋ก ๋
if ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + self.portfolio[code].๋งค๋๊ฐ[1][0] / 100):
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 0
result = True
qty_ratio = 1
# 2. 1์ฐจ์ต์ ๊ฐ ๋๋ฌ์ ๋งค๋์ฃผ๋ฌธ -> 1์ฐจ์ต์ ๊ฐ, 1/3 ๋งค๋
elif self.portfolio[code].์ต์ ๊ฐ1๋๋ฌ == False and ํ์ฌ๊ฐ >= ๋งค์๊ฐ * (
1 + self.portfolio[code].๋งค๋๊ฐ[1][2] / 100):
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 1
self.portfolio[code].์ต์ ๊ฐ1๋๋ฌ = True
result = True
if self.portfolio[code].์๋ == 1:
qty_ratio = 1
elif self.portfolio[code].์๋ == 2:
qty_ratio = 0.5
else:
qty_ratio = 0.3
# 3. 2์ฐจ์ต์ ๊ฐ ๋๋ฌ๋ชปํ๊ณ ๋ณธ์ ๊ฐ๊น์ง ํ๋ฝ ๋๋ ๊ณ ๊ฐ -3%๊น์ง์ ๋งค๋์ฃผ๋ฌธ -> 1์ฐจ์ต์ ๊ฐ, ๋๋จธ์ง ์ ๋ ๋งค๋๋ก ๋
elif self.portfolio[code].์ต์ ๊ฐ1๋๋ฌ == True and self.portfolio[code].์ต์ ๊ฐ2๋๋ฌ == False and (
(ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + self.portfolio[code].๋งค๋๊ฐ[1][1] / 100)) or (ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * 0.97)):
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 1.5
result = True
qty_ratio = 1
# 4. 2์ฐจ ์ต์ ๊ฐ ๋๋ฌ ์ ๋งค๋์ฃผ๋ฌธ -> 2์ฐจ ์ต์ ๊ฐ, 1/3 ๋งค๋
elif self.portfolio[code].์ต์ ๊ฐ1๋๋ฌ == True and self.portfolio[code].์ต์ ๊ฐ2๋๋ฌ == False and ํ์ฌ๊ฐ >= ๋งค์๊ฐ * (
1 + self.portfolio[code].๋งค๋๊ฐ[1][3] / 100):
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 2
self.portfolio[code].์ต์ ๊ฐ2๋๋ฌ = True
result = True
if self.portfolio[code].์๋ == 1:
qty_ratio = 1
else:
qty_ratio = 0.5
# 5. ๋ชฉํ๊ฐ ๋๋ฌ๋ชปํ๊ณ 2์ฐจ์ต์ ๊ฐ๊น์ง ํ๋ฝ ์ ๋งค๋์ฃผ๋ฌธ -> 2์ฐจ์ต์ ๊ฐ, ๋๋จธ์ง ์ ๋ ๋งค๋๋ก ๋
elif self.portfolio[code].์ต์ ๊ฐ2๋๋ฌ == True and self.portfolio[code].๋ชฉํ๊ฐ๋๋ฌ == False and (
(ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + self.portfolio[code].๋งค๋๊ฐ[1][2] / 100)) or (ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * 0.97)):
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 2.5
result = True
qty_ratio = 1
# 6. ๋ชฉํ๊ฐ ๋๋ฌ ์ ๋งค๋์ฃผ๋ฌธ -> ๋ชฉํ๊ฐ, ๋๋จธ์ง ์ ๋ ๋งค๋๋ก ๋
elif self.portfolio[code].๋ชฉํ๊ฐ๋๋ฌ == False and ํ์ฌ๊ฐ >= self.portfolio[code].๋งค๋๊ฐ[0]:
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 3
self.portfolio[code].๋ชฉํ๊ฐ๋๋ฌ = True
result = True
qty_ratio = 1
return ๋งค๋๋ฐฉ๋ฒ, result, qty_ratio
except Exception as e:
print('sell_strategy ๋งค๋์ ๋ต 4 Error :', e)
logger.error('CTradeShortTerm_sell_strategy ์ข
๋ชฉ : %s ๋งค๋์ ๋ต 4 Error : %s' % (code, e))
Telegram('[XTrader]CTradeShortTerm_sell_strategy ์ข
๋ชฉ : %s ๋งค๋์ ๋ต 4 Error : %s' % (code, e), send='mc')
result = False
return ๋งค๋๋ฐฉ๋ฒ, result, qty_ratio
except Exception as e:
print('CTradeShortTerm_sell_strategy Error ', e)
Telegram('[XTrader]CTradeShortTerm_sell_strategy Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_sell_strategy Error : %s' % e)
result = False
qty_ratio = 1
return ๋งค๋๋ฐฉ๋ฒ, result, qty_ratio
# ๋ณด์ ์ผ ์ ๋ต : ๋ณด์ ๊ธฐ๊ฐ์ด ๋ณด์ ์ผ ์ด์์ผ ๊ฒฝ์ฐ ์ ๋ ๋งค๋ ์คํ(Mainwindow ํ์ด๋จธ์์ ์๊ฐ ์ฒดํฌ)
def hold_strategy(self):
if self.holdcheck == True:
print('๋ณด์ ์ผ ๋ง๊ธฐ ๋งค๋ ์ฒดํฌ')
try:
for code in list(self.portfolio.keys()):
๋ณด์ ๊ธฐ๊ฐ = holdingcal(self.portfolio[code].๋งค์์ผ)
print('์ข
๋ชฉ๋ช
: %s, ๋ณด์ ์ผ : %s, ๋ณด์ ๊ธฐ๊ฐ : %s' % (self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].๋ณด์ ์ผ, ๋ณด์ ๊ธฐ๊ฐ))
if ๋ณด์ ๊ธฐ๊ฐ >= int(self.portfolio[code].๋ณด์ ์ผ) and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('S_%s' % code) is None and \
self.portfolio[code].์๋ != 0:
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 0
(result, order) = self.์ ๋๋งค๋(sRQName='S_%s' % code, ์ข
๋ชฉ์ฝ๋=code, ๋งค๋๊ฐ=self.portfolio[code].๋งค์๊ฐ,
์๋=self.portfolio[code].์๋)
if result == True:
self.์ฃผ๋ฌธ์คํ์ค_Lock['S_%s' % code] = True
Telegram('[XTrader]์ ๋๋งค๋(๋ณด์ ์ผ๋ง๊ธฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (
code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
logger.info('์ ๋๋งค๋(๋ณด์ ์ผ๋ง๊ธฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (
code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
else:
Telegram('[XTrader]์ ์ก๋งค๋์คํจ(๋ณด์ ์ผ๋ง๊ธฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (
code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
logger.info('์ ๋๋งค๋์คํจ(๋ณด์ ์ผ๋ง๊ธฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (
code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
except Exception as e:
print("hold_strategy Error :", e)
# ํฌํธํด๋ฆฌ์ค ์์ฑ
def set_portfolio(self, code, buyprice, condition):
try:
self.portfolio[code] = CPortStock_ShortTerm(๋ฒํธ=self.Stocklist[code]['๋ฒํธ'], ์ข
๋ชฉ์ฝ๋=code,
์ข
๋ชฉ๋ช
=self.Stocklist[code]['์ข
๋ชฉ๋ช
'],
์์ฅ=self.Stocklist[code]['์์ฅ'], ๋งค์๊ฐ=buyprice,
๋งค์์กฐ๊ฑด=condition, ๋ณด์ ์ผ=self.Stocklist['์ ๋ต']['๋ณด์ ์ผ'],
๋งค๋์ ๋ต=self.Stocklist[code]['๋งค๋์ ๋ต'],
๋งค๋๊ฐ=self.Stocklist[code]['๋งค๋๊ฐ'],
๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด=self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'],
๋งค์์ผ=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.Stocklist[code]['๋งค์์ผ'] = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S') # ๋งค๋งค์ด๋ ฅ ์
๋ฐ์ดํธ๋ฅผ ์ํด ๋งค์์ผ ์ถ๊ฐ
except Exception as e:
print('CTradeShortTerm_set_portfolio Error ', e)
Telegram('[XTrader]CTradeShortTerm_set_portfolio Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_set_portfolio Error : %s' % e)
# Robot_Run์ด ๋๋ฉด ์คํ๋จ - ๋งค์/๋งค๋ ์ข
๋ชฉ์ ๋ฆฌ์คํธ๋ก ์ ์ฅ
def ์ด๊ธฐ์กฐ๊ฑด(self, codes):
# ๋งค์์ด์ก ๊ณ์ฐํ๊ธฐ
# ๊ธ์ผ๋งค๋์ข
๋ชฉ ๋ฆฌ์คํธ ๋ณ์ ์ด๊ธฐํ
# ๋งค๋ํ ์ข
๋ชฉ : ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ ์ถ๊ฐ
# ๋งค์ํ ์ข
๋ชฉ : ๊ตฌ๊ธ์์ ๋ฐ์ ์ข
๋ชฉ ์ถ๊ฐ
self.parent.statusbar.showMessage("[%s] ์ด๊ธฐ์กฐ๊ฑด์ค๋น" % (self.sName))
self.๊ธ์ผ๋งค๋์ข
๋ชฉ = [] # ์ฅ ๋ง๊ฐ ํ ๊ธ์ผ ๋งค๋ํ ์ข
๋ชฉ์ ๋ํด์ ๋งค๋งค์ด๋ ฅ ์ ๋ฆฌ ์
๋ฐ์ดํธ(๋งค๋๊ฐ, ์์ต๋ฅ ๋ฑ)
self.๋งค๋ํ ์ข
๋ชฉ = []
self.๋งค์ํ ์ข
๋ชฉ = []
self.๋งค์์ด์ก = 0
self.holdcheck = False
for code in codes: # ๊ตฌ๊ธ ์ํธ์์ import๋ ๋งค์ ๋ชจ๋์ปค๋ง ์ข
๋ชฉ์ '๋งค์ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
self.๋งค์ํ ์ข
๋ชฉ.adding(code)
# ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ์ ๋งค๋ ๊ด๋ จ ์ ๋ต ์ฌํ์ธ(๊ตฌ๊ธ์ํธ) ๋ฐ '๋งค๋ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
if length(self.portfolio) > 0:
row_data = shortterm_sell_sheet.getting_total_all_values()
idx_holding = row_data[0].index('๋ณด์ ์ผ')
idx_strategy = row_data[0].index('๋งค๋์ ๋ต')
idx_loss = row_data[0].index('์์ ๊ฐ')
idx_sellprice = row_data[0].index('๋ชฉํ๊ฐ')
for row in row_data[1:]:
code, name, market = getting_code(row[1]) # ์ข
๋ชฉ๋ช
์ผ๋ก ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ ๋ฐ์์(getting_code ํจ์) ์ถ๊ฐ
if code in list(self.portfolio.keys()):
self.portfolio[code].๋ณด์ ์ผ = row[idx_holding]
self.portfolio[code].๋งค๋์ ๋ต = row[idx_strategy]
self.portfolio[code].๋งค๋๊ฐ = [] # ๋งค๋ ์ ๋ต ๋ณ๊ฒฝ์ ๋ฐ๋ผ ๋งค๋๊ฐ ์ด๊ธฐํ
# ๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด = [์์ ๊ฐ(%), ๋ณธ์ ๊ฐ(%), ๊ตฌ๊ฐ3 ๊ณ ๊ฐ๋๋น(%), ๊ตฌ๊ฐ4 ๊ณ ๊ฐ๋๋น(%), ๊ตฌ๊ฐ5 ๊ณ ๊ฐ๋๋น(%), ๊ตฌ๊ฐ6 ๊ณ ๊ฐ๋๋น(%)]
self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด = []
self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด.adding(value_round(((int(float(row[idx_loss].replacing(',', ''))) / self.portfolio[code].๋งค์๊ฐ) - 1) * 100, 1)) # ์์ ๊ฐ๋ฅผ ํผ์ผํธ๋ก ๋ณํํ์ฌ ์
๋ฐ์ดํธ
for idx in range(1, length(self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'])): # Stocklist์ ๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด ์ ์ฒด๋ฅผ ๋ฐ๋ก addingํ ๊ฒฝ์ฐ ๋ชจ๋ ์ข
๋ชฉ์ด ๋์ผํ ๊ฐ์ผ๋ก ๋ค์ด๊ฐ
self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด.adding(self.Stocklist['์ ๋ต']['๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด'][idx])
if self.portfolio[code].๋งค๋์ ๋ต == '4': # ๋งค๋๊ฐ = [๋ชฉํ๊ฐ(์), [์์ ๊ฐ(%), ๋ณธ์ ๊ฐ(%), 1์ฐจ์ต์ ๊ฐ(%), 2์ฐจ์ต์ ๊ฐ(%)]]
self.portfolio[code].๋งค๋๊ฐ.adding(int(float(row[idx_sellprice].replacing(',', ''))))
self.portfolio[code].๋งค๋๊ฐ.adding([])
for idx in range(length(self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'])): # Stocklist์ ์ ๋ต๋งค๋๊ฐ ์ ์ฒด๋ฅผ ๋ฐ๋ก addingํ ๊ฒฝ์ฐ ๋ชจ๋ ์ข
๋ชฉ์ด ๋์ผํ ๊ฐ์ผ๋ก ๋ค์ด๊ฐ
self.portfolio[code].๋งค๋๊ฐ[1].adding(self.Stocklist['์ ๋ต']['์ ๋ต๋งค๋๊ฐ'][idx])
self.portfolio[code].๋งค๋๊ฐ[1][0] = self.portfolio[code].๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[0] # float(row[idx_loss].replacing('%', ''))
self.portfolio[code].sellcount = 0
self.portfolio[code].๋งค๋๋จ์์๋ = 0 # ์ ๋ต4์ ๊ธฐ๋ณธ ๋งค๋ ๋จ์๋ ๋ณด์ ์๋์ 1/3
self.portfolio[code].์ต์ ๊ฐ1๋๋ฌ = False
self.portfolio[code].์ต์ ๊ฐ2๋๋ฌ = False
self.portfolio[code].๋ชฉํ๊ฐ๋๋ฌ = False
else:
if self.portfolio[code].๋งค๋์ ๋ต == '2' or self.portfolio[code].๋งค๋์ ๋ต == '3':
self.portfolio[code].๋ชฉํ๋๋ฌ = False # ๋ชฉํ๊ฐ(๋งค๋๊ฐ) ๋๋ฌ ์ฒดํฌ(False ์ํ๋ก ๊ตฌ๊ฐ ์ปท์ผ๊ฒฝ์ฐ ์ ๋ ๋งค๋)
self.portfolio[code].๋งค๋์กฐ๊ฑด = '' # ๊ตฌ๊ฐ๋งค๋ : B, ๋ชฉํ๋งค๋ : T
for port_code in list(self.portfolio.keys()):
# ๋ก๋ด ์์ ์ ํฌํธํด๋ฆฌ์ค ์ข
๋ชฉ์ ๋งค๋๊ตฌ๊ฐ(์ ์ผ ๋งค๋๋ชจ๋ํฐ๋ง)์ 1๋ก ์ด๊ธฐํ
# ๊ตฌ๊ฐ์ด ๋ด๋ ค๊ฐ๋ ๊ฑด ๋ฐ์ํ์ง ์์ผ๋ฏ๋ก ์ด๊ธฐํ๋ฅผ ์์ผ์ ๋ค์ ๊ตฌ๊ฐ ์ฒดํฌ ์์ํ๊ธฐ ์ํจ
self.portfolio[port_code].๋งค๋๊ตฌ๊ฐ = 1 # ๋งค๋ ๊ตฌ๊ฐ์ ๋ก๋ด ์คํ ์ ๋ง๋ค ์ด๊ธฐํ์ํด
# ๋งค์์ด์ก๊ณ์ฐ
self.๋งค์์ด์ก += (self.portfolio[port_code].๋งค์๊ฐ * self.portfolio[port_code].์๋)
# ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ์ด ๊ตฌ๊ธ์์ ๋ฐ์์ ๋ง๋ Stocklist์ ์์ ๊ฒฝ์ฐ๋ง ์ถ๊ฐํจ
# ์ด ์กฐ๊ฑด์ด ์์ ๊ฒฝ์ฐ ๊ตฌ๊ธ์์ ๋ฐ์ ์ ๋ต๋ค์ด ์๋ ๊ณผ๊ฑฐ ์ ๋ต์ด ํฌํธํด๋ฆฌ์ค์์ ๋์ด๊ฐ
# ๊ทผ๋ฐ ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ์ ์ Stocklist์ ๋ฃ์ด์ผ๋๋์ง ๋ชจ๋ฅด๊ฒ ์(๋ด๊ฐ ํ๊ณ ๋...)
if port_code not in list(self.Stocklist.keys()):
self.Stocklist[port_code] = {
'๋ฒํธ': self.portfolio[port_code].๋ฒํธ,
'์ข
๋ชฉ๋ช
': self.portfolio[port_code].์ข
๋ชฉ๋ช
,
'์ข
๋ชฉ์ฝ๋': self.portfolio[port_code].์ข
๋ชฉ์ฝ๋,
'์์ฅ': self.portfolio[port_code].์์ฅ,
'๋งค์์กฐ๊ฑด': self.portfolio[port_code].๋งค์์กฐ๊ฑด,
'๋งค์๊ฐ': self.portfolio[port_code].๋งค์๊ฐ,
'๋งค๋์ ๋ต': self.portfolio[port_code].๋งค๋์ ๋ต,
'๋งค๋๊ฐ': self.portfolio[port_code].๋งค๋๊ฐ
}
self.๋งค๋ํ ์ข
๋ชฉ.adding(port_code)
# for stock in kf_keeplist['์ข
๋ชฉ๋ฒํธ'].values: # ๋ณด์ ์ข
๋ชฉ ์ฒดํฌํด์ ๋งค๋ ์ข
๋ชฉ์ ์ถ๊ฐ โ ๋ก๋ด์ด ๋๊ฐ ์ด์์ผ ๊ฒฝ์ฐ ์ค๋ณต๋๋ฏ๋ก ๋ฏธ์ ์ฉ
# self.๋งค๋ํ ์ข
๋ชฉ.adding(stock)
# ์ข
๋ชฉ๋ช
= kf_keeplist[kf_keeplist['์ข
๋ชฉ๋ฒํธ']==stock]['์ข
๋ชฉ๋ช
'].values[0]
# ๋งค์
๊ฐ = kf_keeplist[kf_keeplist['์ข
๋ชฉ๋ฒํธ']==stock]['๋งค์
๊ฐ'].values[0]
# ๋ณด์ ์๋ = kf_keeplist[kf_keeplist['์ข
๋ชฉ๋ฒํธ']==stock]['๋ณด์ ์๋'].values[0]
# print('์ข
๋ชฉ์ฝ๋ : %s, ์ข
๋ชฉ๋ช
: %s, ๋งค์
๊ฐ : %s, ๋ณด์ ์๋ : %s' %(stock, ์ข
๋ชฉ๋ช
, ๋งค์
๊ฐ, ๋ณด์ ์๋))
# self.portfolio[stock] = CPortStock_ShortTerm(์ข
๋ชฉ์ฝ๋=stock, ์ข
๋ชฉ๋ช
=์ข
๋ชฉ๋ช
, ๋งค์๊ฐ=๋งค์
๊ฐ, ์๋=๋ณด์ ์๋, ๋งค์์ผ='')
def ์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ(self, param):
try:
if self.running == True:
์ฒด๊ฒฐ์๊ฐ = '%s %s:%s:%s' % (str(self.d), param['์ฒด๊ฒฐ์๊ฐ'][0:2], param['์ฒด๊ฒฐ์๊ฐ'][2:4], param['์ฒด๊ฒฐ์๊ฐ'][4:])
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
ํ์ฌ๊ฐ = abs(int(float(param['ํ์ฌ๊ฐ'])))
์ ์ผ๋๋น = int(float(param['์ ์ผ๋๋น']))
๋ฑ๋ฝ๋ฅ = float(param['๋ฑ๋ฝ๋ฅ '])
๋งค๋ํธ๊ฐ = abs(int(float(param['๋งค๋ํธ๊ฐ'])))
๋งค์ํธ๊ฐ = abs(int(float(param['๋งค์ํธ๊ฐ'])))
๋์ ๊ฑฐ๋๋ = abs(int(float(param['๋์ ๊ฑฐ๋๋'])))
์๊ฐ = abs(int(float(param['์๊ฐ'])))
๊ณ ๊ฐ = abs(int(float(param['๊ณ ๊ฐ'])))
์ ๊ฐ = abs(int(float(param['์ ๊ฐ'])))
๊ฑฐ๋ํ์ ์จ = abs(float(param['๊ฑฐ๋ํ์ ์จ']))
์๊ฐ์ด์ก = abs(int(float(param['์๊ฐ์ด์ก'])))
์ข
๋ชฉ๋ช
= self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][1] # pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก]
์ ์ผ์ข
๊ฐ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][3]
์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น))
self.wr.writerow([์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น])
# ๋งค์ ์กฐ๊ฑด
# ๋งค์๋ชจ๋ํฐ๋ง ์ข
๋ฃ ์๊ฐ ํ์ธ
if current_time < self.Stocklist['์ ๋ต']['๋ชจ๋ํฐ๋ง์ข
๋ฃ์๊ฐ']:
if ์ข
๋ชฉ์ฝ๋ in self.๋งค์ํ ์ข
๋ชฉ and ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ:
# ๋งค์์ด์ก + ์ข
๋ชฉ๋จ์ํฌ์๊ธ์ด ํฌ์์ด์ก๋ณด๋ค ์์ and ๋งค์์ฃผ๋ฌธ์คํ์คLock์ ์์ -> ์ถ๊ฐ๋งค์๋ฅผ ์ํด์ and ํฌํธํด๋ฆฌ์ค์ ์์ ์กฐ๊ฑด ์ญ์
if (self.๋งค์์ด์ก + self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋จ์ํฌ์๊ธ'] < self.ํฌ์์ด์ก) and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting(
'B_%s' % ์ข
๋ชฉ์ฝ๋) is None and length(
self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋งค์๊ฐ']) > 0: # and self.portfolio.getting(์ข
๋ชฉ์ฝ๋) is None
# ๋งค์ ์ ๋ต๋ณ ๋ชจ๋ํฐ๋ง ์ฒดํฌ
buy_check, condition, qty = self.buy_strategy(์ข
๋ชฉ์ฝ๋, ์์ธ)
if buy_check == True and (self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋จ์ํฌ์๊ธ'] // ํ์ฌ๊ฐ > 0):
(result, order) = self.์ ๋๋งค์(sRQName='B_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค์๊ฐ=ํ์ฌ๊ฐ, ์๋=qty)
if result == True:
if self.portfolio.getting(์ข
๋ชฉ์ฝ๋) is None: # ํฌํธํด๋ฆฌ์ค์ ์์ผ๋ฉด ์ ๊ท ์ ์ฅ
self.set_portfolio(์ข
๋ชฉ์ฝ๋, ํ์ฌ๊ฐ, condition)
self.์ฃผ๋ฌธ์คํ์ค_Lock['B_%s' % ์ข
๋ชฉ์ฝ๋] = True
Telegram('[XTrader]๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์กฐ๊ฑด=%s, ๋งค์์๋=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, condition, qty))
logger.info('๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์กฐ๊ฑด=%s, ๋งค์์๋=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, condition, qty))
else:
Telegram('[XTrader]๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์กฐ๊ฑด=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, condition))
logger.info('๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์กฐ๊ฑด=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, condition))
else:
if self.๋งค์๋ชจ๋ํฐ๋ง์ฒดํฌ == False:
for code in self.๋งค์ํ ์ข
๋ชฉ:
if self.portfolio.getting(code) is not None and code not in self.๋งค๋ํ ์ข
๋ชฉ:
Telegram('[XTrader]๋งค์๋ชจ๋ํฐ๋ง๋ง๊ฐ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s ๋งค๋๋ชจ๋ํฐ๋ง ์ ํ' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
))
logger.info('๋งค์๋ชจ๋ํฐ๋ง๋ง๊ฐ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s ๋งค๋๋ชจ๋ํฐ๋ง ์ ํ' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
))
self.๋งค์ํ ์ข
๋ชฉ.remove(code)
self.๋งค๋ํ ์ข
๋ชฉ.adding(code)
self.๋งค์๋ชจ๋ํฐ๋ง์ฒดํฌ = True
logger.info('๋งค๋ํ ์ข
๋ชฉ :%s' % self.๋งค๋ํ ์ข
๋ชฉ)
# ๋งค๋ ์กฐ๊ฑด
if ์ข
๋ชฉ์ฝ๋ in self.๋งค๋ํ ์ข
๋ชฉ:
# ํฌํธํด๋ฆฌ์ค์ ์์ and ๋งค๋์ฃผ๋ฌธ์คํ์คLock์ ์์ and ๋งค์์ฃผ๋ฌธ์คํ์คLock์ ์์
if self.portfolio.getting(์ข
๋ชฉ์ฝ๋) is not None and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting(
'S_%s' % ์ข
๋ชฉ์ฝ๋) is None: # and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('B_%s' % ์ข
๋ชฉ์ฝ๋) is None:
# ๋งค๋ ์ ๋ต๋ณ ๋ชจ๋ํฐ๋ง ์ฒดํฌ
๋งค๋๋ฐฉ๋ฒ, sell_check, ratio = self.sell_strategy(์ข
๋ชฉ์ฝ๋, ์์ธ)
if sell_check == True:
if ๋งค๋๋ฐฉ๋ฒ == '00':
(result, order) = self.์ ์ก๋งค๋(sRQName='S_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค๋๊ฐ=ํ์ฌ๊ฐ,
์๋=value_round(self.portfolio[์ข
๋ชฉ์ฝ๋].์๋ * ratio))
else:
(result, order) = self.์ ๋๋งค๋(sRQName='S_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค๋๊ฐ=ํ์ฌ๊ฐ,
์๋=value_round(self.portfolio[์ข
๋ชฉ์ฝ๋].์๋ * ratio))
if result == True:
self.์ฃผ๋ฌธ์คํ์ค_Lock['S_%s' % ์ข
๋ชฉ์ฝ๋] = True
Telegram('[XTrader]๋งค๋์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ๋งค๋์ ๋ต=%s, ๋งค๋๊ตฌ๊ฐ=%s, ์๋=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ,
int(self.portfolio[์ข
๋ชฉ์ฝ๋].์๋ * ratio)))
if self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต == '2':
logger.info(
'๋งค๋์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ๋งค๋์ ๋ต=%s, ๋งค๋๊ตฌ๊ฐ=%s, ๋ชฉํ๋๋ฌ=%s, ๋งค๋์กฐ๊ฑด=%s, ์๋=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ,
self.portfolio[์ข
๋ชฉ์ฝ๋].๋ชฉํ๋๋ฌ, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์กฐ๊ฑด,
int(self.portfolio[์ข
๋ชฉ์ฝ๋].์๋ * ratio)))
else:
logger.info('๋งค๋์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ๋งค๋์ ๋ต=%s, ๋งค๋๊ตฌ๊ฐ=%s, ์๋=%s' % (
์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ,
int(self.portfolio[์ข
๋ชฉ์ฝ๋].์๋ * ratio)))
else:
Telegram(
'[XTrader]๋งค๋์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ๋งค๋์ ๋ต=%s, ๋งค๋๊ตฌ๊ฐ=%s, ์๋=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
,
ํ์ฌ๊ฐ,
self.portfolio[
์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต,
self.portfolio[
์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ,
self.portfolio[
์ข
๋ชฉ์ฝ๋].์๋ * ratio))
logger.info('๋งค๋์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ๋งค๋์ ๋ต=%s, ๋งค๋๊ตฌ๊ฐ=%s, ์๋=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
,
ํ์ฌ๊ฐ,
self.portfolio[
์ข
๋ชฉ์ฝ๋].๋งค๋์ ๋ต,
self.portfolio[
์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ,
self.portfolio[
์ข
๋ชฉ์ฝ๋].์๋ * ratio))
except Exception as e:
print('CTradeShortTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e))
Telegram('[XTrader]CTradeShortTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e), send='mc')
logger.error('CTradeShortTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error :%s, %s' % (์ข
๋ชฉ๋ช
, e))
def ์ ์์ฒ๋ฆฌ(self, param):
pass
def ์ฒด๊ฒฐ์ฒ๋ฆฌ(self, param):
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
์ฃผ๋ฌธ๋ฒํธ = param['์ฃผ๋ฌธ๋ฒํธ']
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ[์ฃผ๋ฌธ๋ฒํธ] = param
์ฃผ๋ฌธ์๋ = int(param['์ฃผ๋ฌธ์๋'])
๋ฏธ์ฒด๊ฒฐ์๋ = int(param['๋ฏธ์ฒด๊ฒฐ์๋'])
์ฒด๊ฒฐ๊ฐ = int(0 if (param['์ฒด๊ฒฐ๊ฐ'] is None or param['์ฒด๊ฒฐ๊ฐ'] == '') else param['์ฒด๊ฒฐ๊ฐ']) # ๋งค์
๊ฐ ๋์ผ
๋จ์์ฒด๊ฒฐ๋ = int(0 if (param['๋จ์์ฒด๊ฒฐ๋'] is None or param['๋จ์์ฒด๊ฒฐ๋'] == '') else param['๋จ์์ฒด๊ฒฐ๋'])
๋น์ผ๋งค๋งค์์๋ฃ = int(0 if (param['๋น์ผ๋งค๋งค์์๋ฃ'] is None or param['๋น์ผ๋งค๋งค์์๋ฃ'] == '') else param['๋น์ผ๋งค๋งค์์๋ฃ'])
๋น์ผ๋งค๋งค์ธ๊ธ = int(0 if (param['๋น์ผ๋งค๋งค์ธ๊ธ'] is None or param['๋น์ผ๋งค๋งค์ธ๊ธ'] == '') else param['๋น์ผ๋งค๋งค์ธ๊ธ'])
# ๋งค์
if param['๋งค๋์๊ตฌ๋ถ'] == '2':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค์๊ฐ = int(์ฃผ๋ฌธ[2:])
# ๋จ์์ฒด๊ฒฐ๊ฐ = int(0 if (param['๋จ์์ฒด๊ฒฐ๊ฐ'] is None or param['๋จ์์ฒด๊ฒฐ๊ฐ'] == '') else param['๋จ์์ฒด๊ฒฐ๊ฐ'])
# logger.debug('๋งค์-------> %s %s %s %s %s' % (param['์ข
๋ชฉ์ฝ๋'], param['์ข
๋ชฉ๋ช
'], ๋งค์๊ฐ, ์ฃผ๋ฌธ์๋ - ๋ฏธ์ฒด๊ฒฐ์๋, ๋ฏธ์ฒด๊ฒฐ์๋))
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
P.๋งค์๊ฐ = ์ฒด๊ฒฐ๊ฐ # ๋จ์์ฒด๊ฒฐ๊ฐ
P.์๋ += ๋จ์์ฒด๊ฒฐ๋ # ์ถ๊ฐ ๋งค์ ๋๋นํด์ ๊ธฐ์กด ์๋์ ์ฒด๊ฒฐ๋ ์๋ ๊ณ์ ๋ํจ(์ฃผ๋ฌธ์๋ - ๋ฏธ์ฒด๊ฒฐ์๋)
P.๋งค์์ผ = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR ํฌํธ์ ์ข
๋ชฉ์ด ์์ !!!!')
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
try:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
if self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋งค์์ฃผ๋ฌธ์๋ฃ'] >= self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋งค์๊ฐ์ ๋ต']:
self.๋งค์ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
Telegram('[XTrader]๋ถํ ๋งค์ ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ข
๋ชฉ์ฝ๋:%s ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, ์ข
๋ชฉ์ฝ๋, P.๋งค์๊ฐ, P.์๋))
logger.info('๋ถํ ๋งค์ ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ข
๋ชฉ์ฝ๋:%s ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, ์ข
๋ชฉ์ฝ๋, P.๋งค์๊ฐ, P.์๋))
self.Stocklist[์ข
๋ชฉ์ฝ๋]['์๋'] = P.์๋
self.Stocklist[์ข
๋ชฉ์ฝ๋]['๋งค์๊ฐ'].pop(0)
self.๋งค์์ด์ก += (P.๋งค์๊ฐ * P.์๋)
logger.debug('์ฒด๊ฒฐ์ฒ๋ฆฌ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์์ด์ก๊ณ์ฐ์๋ฃ:%s' % (P.์ข
๋ชฉ๋ช
, self.๋งค์์ด์ก))
self.save_history(์ข
๋ชฉ์ฝ๋, status='๋งค์')
Telegram('[XTrader]๋งค์์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, P.๋งค์๊ฐ, P.์๋))
logger.info('๋งค์์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, P.๋งค์๊ฐ, P.์๋))
except Exception as e:
Telegram('[XTrader]์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ ์๋ฌ ์ข
๋ชฉ๋ช
:%s, %s ' % (P.์ข
๋ชฉ๋ช
, e), send='mc')
logger.error('์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ ์๋ฌ ์ข
๋ชฉ๋ช
:%s, %s ' % (P.์ข
๋ชฉ๋ช
, e))
# ๋งค๋
if param['๋งค๋์๊ตฌ๋ถ'] == '1':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค๋๊ฐ = int(์ฃผ๋ฌธ[2:])
try:
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ฒด๊ฒฐ๊ฐ = ์ฒด๊ฒฐ๊ฐ
self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์๋ = ์ฃผ๋ฌธ์๋
self.save_history(์ข
๋ชฉ์ฝ๋, status='๋งค๋')
Telegram('[XTrader]๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋))
logger.info('๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋))
except Exception as e:
Telegram('[XTrader]์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ Error : %s' % e, send='mc')
logger.error('์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ Error : %s' % e)
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
def ์๊ณ ์ฒ๋ฆฌ(self, param):
# print('CTradeShortTerm : ์๊ณ ์ฒ๋ฆฌ')
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.๋งค์๊ฐ = int(0 if (param['๋งค์
๋จ๊ฐ'] is None or param['๋งค์
๋จ๊ฐ'] == '') else param['๋งค์
๋จ๊ฐ'])
P.์๋ = int(0 if (param['๋ณด์ ์๋'] is None or param['๋ณด์ ์๋'] == '') else param['๋ณด์ ์๋'])
if P.์๋ == 0:
self.portfolio.pop(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
if ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ: self.๊ธ์ผ๋งค๋์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
logger.info('์๊ณ ์ฒ๋ฆฌ_ํฌํธํด๋ฆฌ์คPOP %s ' % ์ข
๋ชฉ์ฝ๋)
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT ์คํ" % (self.sName))
try:
Telegram("[XTrader]%s ROBOT ์คํ" % (self.sName))
self.sAccount = sAccount
self.ํฌ์์ด์ก = floor(int(d2deposit.replacing(",", "")) * (self.Stocklist['์ ๋ต']['ํฌ์๊ธ๋น์ค'] / 100))
print('๋ก๋ด๊ฑฐ๋๊ณ์ข : ', ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ)
print('D+2 ์์๊ธ : ', int(d2deposit.replacing(",", "")))
print('ํฌ์ ์ด์ก : ', self.ํฌ์์ด์ก)
print('Stocklist : ', self.Stocklist)
# self.์ต๋ํฌํธ์ = floor(int(d2deposit.replacing(",", "")) / self.๋จ์ํฌ์๊ธ / length(self.parent.robots))
# print(self.์ต๋ํฌํธ์)
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
codes = list(self.Stocklist.keys())
codes.remove('์ ๋ต')
codes.remove('์ปฌ๋ผ๋ช
')
self.์ด๊ธฐ์กฐ๊ฑด(codes)
print("๋งค๋ : ", self.๋งค๋ํ ์ข
๋ชฉ)
print("๋งค์ : ", self.๋งค์ํ ์ข
๋ชฉ)
print("๋งค์์ด์ก : ", self.๋งค์์ด์ก)
print("ํฌํธํด๋ฆฌ์ค ๋งค๋๋ชจ๋ํฐ๋ง ์์ ")
for code in list(self.portfolio.keys()):
print(self.portfolio[code].__dict__)
logger.info(self.portfolio[code].__dict__)
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = self.๋งค๋ํ ์ข
๋ชฉ + self.๋งค์ํ ์ข
๋ชฉ
logger.info("์ค๋ ๊ฑฐ๋ ์ข
๋ชฉ : %s %s" % (self.sName, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';'))
self.KiwoomConnect() # MainWindow ์ธ์์ ํค์ API๊ตฌ๋์์ผ์ ์์ฒด์ ์ผ๋ก API๋ฐ์ดํฐ์ก์์ ๊ฐ๋ฅํ๋๋ก ํจ
if length(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
self.f = open('data_result.csv', 'a', newline='')
self.wr = csv.writer(self.f)
self.wr.writerow(['์ฒด๊ฒฐ์๊ฐ', '์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', 'ํ์ฌ๊ฐ', '์ ์ผ๋๋น'])
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';')
logger.debug("์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s" % ret)
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[XTrader]%s ROBOT ์คํ ์ค์ง" % (self.sName))
print('Stocklist : ', self.Stocklist)
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.f.close()
del self.f
del self.wr
if self.portfolio is not None:
# ๊ตฌ๊ธ ๋งค๋๋ชจ๋ํฐ๋ง ์ํธ ๊ธฐ์กด ์ข
๋ชฉ ์ญ์
num_data = shortterm_sell_sheet.getting_total_all_values()
for i in range(length(num_data)):
shortterm_sell_sheet.delete_rows(2)
for code in list(self.portfolio.keys()):
# ๋งค์ ๋ฏธ์ฒด๊ฒฐ ์ข
๋ชฉ ์ญ์
if self.portfolio[code].์๋ == 0:
self.portfolio.pop(code)
else:
# ํฌํธํด๋ฆฌ์ค ์ข
๋ชฉ์ ๊ตฌ๊ธ ๋งค๋๋ชจ๋ํฐ๋ง ์ํธ์ ์ถ๊ฐํ์ฌ ์ ๋ต ์์ ๊ฐ๋ฅ
self.save_history(code, status='๋งค๋๋ชจ๋ํฐ๋ง')
if length(self.๊ธ์ผ๋งค๋์ข
๋ชฉ) > 0:
try:
Telegram("[XTrader]%s ๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload : %s" % (self.sName, self.๊ธ์ผ๋งค๋์ข
๋ชฉ))
logger.info("%s ๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload : %s" % (self.sName, self.๊ธ์ผ๋งค๋์ข
๋ชฉ))
self.parent.statusbar.showMessage("๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload")
self.DailyProfit(self.๊ธ์ผ๋งค๋์ข
๋ชฉ)
except Exception as e:
print('%s ๊ธ์ผ๋งค๋์ข
๋ชฉ ๊ฒฐ๊ณผ ์
๋ก๋ Error : %s' % (self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # ๊ธ์ผ๋งค๋๊ฒฐ๊ณผ ์
๋ฐ์ดํธ ์ QEventLoop ์ฌ์ฉ์ผ๋ก ๋ก๋ด ์ ์ฅ ์ pickcle ์๋ฌ ๋ฐ์ํ์ฌ ์ญ์ ์ํด
self.KiwoomDisConnect() # ๋ก๋ด ํด๋์ค ๋ด์์ ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ๋ฐ์ดํฐ๋ฅผ ๋ฐ๊ณ ๋์ ์ฐ๊ฒฐ ํด์ ์ํด
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
# ์ฅ๊ธฐ ํฌ์์ฉ : ํ์ฌ ๋ฏธ๋ฆฌ ์ ์ ํ ์ข
๋ชฉ์ ๋ํด์ ๋ก๋ด ์์๊ณผ ๋์์ ๋งค์ ์คํ ์ ์ฉ
class CTradeLongTerm(CTrade): # ๋ก๋ด ์ถ๊ฐ ์ __init__ : ๋ณต์ฌ, Setting, ์ด๊ธฐ์กฐ๊ฑด:์ ๋ต์ ๋ง๊ฒ, ๋ฐ์ดํฐ์ฒ๋ฆฌ~Run:๋ณต์ฌ
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.portfolio = dict()
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd ํจ์์์ ์ด๊ธฐํ ๋ค์ ์
ํ
์คํํด์ ์ค์ ๊ฐ ๋๊น
def Setting(self, sScreenNo, ๋งค์๋ฐฉ๋ฒ='03', ๋งค๋๋ฐฉ๋ฒ='03', ์ข
๋ชฉ๋ฆฌ์คํธ=[]):
self.sScreenNo = sScreenNo
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.๋งค์๋ฐฉ๋ฒ = ๋งค์๋ฐฉ๋ฒ
self.๋งค๋๋ฐฉ๋ฒ = ๋งค๋๋ฐฉ๋ฒ
# Robot_Run์ด ๋๋ฉด ์คํ๋จ - ๋งค์/๋งค๋ ์ข
๋ชฉ์ ๋ฆฌ์คํธ๋ก ์ ์ฅ
def ์ด๊ธฐ์กฐ๊ฑด(self):
# ๋งค์์ด์ก ๊ณ์ฐํ๊ธฐ
# ๊ธ์ผ๋งค๋์ข
๋ชฉ ๋ฆฌ์คํธ ๋ณ์ ์ด๊ธฐํ
# ๋งค๋ํ ์ข
๋ชฉ : ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ ์ถ๊ฐ
# ๋งค์ํ ์ข
๋ชฉ : ๊ตฌ๊ธ์์ ๋ฐ์ ์ข
๋ชฉ ์ถ๊ฐ
self.parent.statusbar.showMessage("[%s] ์ด๊ธฐ์กฐ๊ฑด์ค๋น" % (self.sName))
self.๊ธ์ผ๋งค๋์ข
๋ชฉ = [] # ์ฅ ๋ง๊ฐ ํ ๊ธ์ผ ๋งค๋ํ ์ข
๋ชฉ์ ๋ํด์ ๋งค๋งค์ด๋ ฅ ์ ๋ฆฌ ์
๋ฐ์ดํธ(๋งค๋๊ฐ, ์์ต๋ฅ ๋ฑ)
self.๋งค๋ํ ์ข
๋ชฉ = []
self.๋งค์ํ ์ข
๋ชฉ = []
self.Stocklist = dict()
kf = mk.read_csv('๋งค์์ข
๋ชฉ.csv', encoding='euc-kr')
codes= kf['์ข
๋ชฉ'].to_list()
qtys = kf['์๋'].to_list()
for ์ข
๋ชฉ์ฝ๋, ์๋ in zip(codes, qtys):
code, name, market = getting_code(์ข
๋ชฉ์ฝ๋)
self.Stocklist[code] = {
'์ข
๋ชฉ๋ช
' : name,
'์ข
๋ชฉ์ฝ๋' : code,
'์์ฅ๊ตฌ๋ถ' : market,
'๋งค์์๋' : ์๋
}
self.๋งค์ํ ์ข
๋ชฉ = list(self.Stocklist.keys())
# ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ์ ๋งค๋ ๊ด๋ จ ์ ๋ต ์ฌํ์ธ(๊ตฌ๊ธ์ํธ) ๋ฐ '๋งค๋ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
if length(self.portfolio) > 0:
for port_code in list(self.portfolio.keys()):
self.๋งค๋ํ ์ข
๋ชฉ.adding(port_code)
def ์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ(self, param):
try:
if self.running == True:
์ฒด๊ฒฐ์๊ฐ = '%s %s:%s:%s' % (str(self.d), param['์ฒด๊ฒฐ์๊ฐ'][0:2], param['์ฒด๊ฒฐ์๊ฐ'][2:4], param['์ฒด๊ฒฐ์๊ฐ'][4:])
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
ํ์ฌ๊ฐ = abs(int(float(param['ํ์ฌ๊ฐ'])))
์ ์ผ๋๋น = int(float(param['์ ์ผ๋๋น']))
๋ฑ๋ฝ๋ฅ = float(param['๋ฑ๋ฝ๋ฅ '])
๋งค๋ํธ๊ฐ = abs(int(float(param['๋งค๋ํธ๊ฐ'])))
๋งค์ํธ๊ฐ = abs(int(float(param['๋งค์ํธ๊ฐ'])))
๋์ ๊ฑฐ๋๋ = abs(int(float(param['๋์ ๊ฑฐ๋๋'])))
์๊ฐ = abs(int(float(param['์๊ฐ'])))
๊ณ ๊ฐ = abs(int(float(param['๊ณ ๊ฐ'])))
์ ๊ฐ = abs(int(float(param['์ ๊ฐ'])))
๊ฑฐ๋ํ์ ์จ = abs(float(param['๊ฑฐ๋ํ์ ์จ']))
์๊ฐ์ด์ก = abs(int(float(param['์๊ฐ์ด์ก'])))
์ข
๋ชฉ๋ช
= self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][1] # pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก]
์์ฅ๊ตฌ๋ถ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][0]
์ ์ผ์ข
๊ฐ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][3]
์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น))
# ๋งค์ ์กฐ๊ฑด
# ๋งค์๋ชจ๋ํฐ๋ง ์ข
๋ฃ ์๊ฐ ํ์ธ
if current_time >= "09:00:00":
if ์ข
๋ชฉ์ฝ๋ in self.๋งค์ํ ์ข
๋ชฉ and ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('B_%s' % ์ข
๋ชฉ์ฝ๋) is None:
(result, order) = self.์ ๋๋งค์(sRQName='B_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค์๊ฐ=ํ์ฌ๊ฐ, ์๋=self.์๋[0])
if result == True:
self.portfolio[์ข
๋ชฉ์ฝ๋] = CPortStock_LongTerm(์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
=์ข
๋ชฉ๋ช
, ์์ฅ=์์ฅ๊ตฌ๋ถ, ๋งค์๊ฐ=ํ์ฌ๊ฐ, ๋งค์์ผ=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.์ฃผ๋ฌธ์คํ์ค_Lock['B_%s' % ์ข
๋ชฉ์ฝ๋] = True
Telegram('[StockTrader]๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์๋=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.์๋[0]))
logger.info('๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s, ๋งค์์๋=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.์๋[0]))
else:
Telegram('[StockTrader]๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ))
logger.info('๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ))
# ๋งค๋ ์กฐ๊ฑด
if ์ข
๋ชฉ์ฝ๋ in self.๋งค๋ํ ์ข
๋ชฉ:
pass
except Exception as e:
print('CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e))
Telegram('[StockTrader]CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e), send='mc')
logger.error('CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error :%s, %s' % (์ข
๋ชฉ๋ช
, e))
def ์ ์์ฒ๋ฆฌ(self, param):
pass
def ์ฒด๊ฒฐ์ฒ๋ฆฌ(self, param):
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
์ฃผ๋ฌธ๋ฒํธ = param['์ฃผ๋ฌธ๋ฒํธ']
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ[์ฃผ๋ฌธ๋ฒํธ] = param
์ฃผ๋ฌธ์๋ = int(param['์ฃผ๋ฌธ์๋'])
๋ฏธ์ฒด๊ฒฐ์๋ = int(param['๋ฏธ์ฒด๊ฒฐ์๋'])
์ฒด๊ฒฐ๊ฐ = int(0 if (param['์ฒด๊ฒฐ๊ฐ'] is None or param['์ฒด๊ฒฐ๊ฐ'] == '') else param['์ฒด๊ฒฐ๊ฐ']) # ๋งค์
๊ฐ ๋์ผ
๋จ์์ฒด๊ฒฐ๋ = int(0 if (param['๋จ์์ฒด๊ฒฐ๋'] is None or param['๋จ์์ฒด๊ฒฐ๋'] == '') else param['๋จ์์ฒด๊ฒฐ๋'])
๋น์ผ๋งค๋งค์์๋ฃ = int(0 if (param['๋น์ผ๋งค๋งค์์๋ฃ'] is None or param['๋น์ผ๋งค๋งค์์๋ฃ'] == '') else param['๋น์ผ๋งค๋งค์์๋ฃ'])
๋น์ผ๋งค๋งค์ธ๊ธ = int(0 if (param['๋น์ผ๋งค๋งค์ธ๊ธ'] is None or param['๋น์ผ๋งค๋งค์ธ๊ธ'] == '') else param['๋น์ผ๋งค๋งค์ธ๊ธ'])
# ๋งค์
if param['๋งค๋์๊ตฌ๋ถ'] == '2':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค์๊ฐ = int(์ฃผ๋ฌธ[2:])
# ๋จ์์ฒด๊ฒฐ๊ฐ = int(0 if (param['๋จ์์ฒด๊ฒฐ๊ฐ'] is None or param['๋จ์์ฒด๊ฒฐ๊ฐ'] == '') else param['๋จ์์ฒด๊ฒฐ๊ฐ'])
# logger.debug('๋งค์-------> %s %s %s %s %s' % (param['์ข
๋ชฉ์ฝ๋'], param['์ข
๋ชฉ๋ช
'], ๋งค์๊ฐ, ์ฃผ๋ฌธ์๋ - ๋ฏธ์ฒด๊ฒฐ์๋, ๋ฏธ์ฒด๊ฒฐ์๋))
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
P.๋งค์๊ฐ = ์ฒด๊ฒฐ๊ฐ # ๋จ์์ฒด๊ฒฐ๊ฐ
P.์๋ += ๋จ์์ฒด๊ฒฐ๋ # ์ถ๊ฐ ๋งค์ ๋๋นํด์ ๊ธฐ์กด ์๋์ ์ฒด๊ฒฐ๋ ์๋ ๊ณ์ ๋ํจ(์ฃผ๋ฌธ์๋ - ๋ฏธ์ฒด๊ฒฐ์๋)
P.๋งค์์ผ = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR ํฌํธ์ ์ข
๋ชฉ์ด ์์ !!!!')
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
try:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
self.๋งค์ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
Telegram('[StockTrader]๋งค์์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, P.๋งค์๊ฐ, P.์๋))
logger.info('๋งค์์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์๊ฐ:%s, ์๋:%s' % (P.์ข
๋ชฉ๋ช
, P.๋งค์๊ฐ, P.์๋))
except Exception as e:
Telegram('[XTrader]์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ ์๋ฌ ์ข
๋ชฉ๋ช
:%s, %s ' % (P.์ข
๋ชฉ๋ช
, e), send='mc')
logger.error('์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ ์๋ฌ ์ข
๋ชฉ๋ช
:%s, %s ' % (P.์ข
๋ชฉ๋ช
, e))
# ๋งค๋
if param['๋งค๋์๊ตฌ๋ถ'] == '1':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค๋๊ฐ = int(์ฃผ๋ฌธ[2:])
try:
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์ฒด๊ฒฐ๊ฐ = ์ฒด๊ฒฐ๊ฐ
self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋์๋ = ์ฃผ๋ฌธ์๋
Telegram('[StockTrader]๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋))
logger.info('๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋))
except Exception as e:
Telegram('[StockTrader]์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ Error : %s' % e, send='mc')
logger.error('์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ Error : %s' % e)
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
def ์๊ณ ์ฒ๋ฆฌ(self, param):
# print('CTradeShortTerm : ์๊ณ ์ฒ๋ฆฌ')
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.๋งค์๊ฐ = int(0 if (param['๋งค์
๋จ๊ฐ'] is None or param['๋งค์
๋จ๊ฐ'] == '') else param['๋งค์
๋จ๊ฐ'])
P.์๋ = int(0 if (param['๋ณด์ ์๋'] is None or param['๋ณด์ ์๋'] == '') else param['๋ณด์ ์๋'])
if P.์๋ == 0:
self.portfolio.pop(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
if ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ: self.๊ธ์ผ๋งค๋์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
logger.info('์๊ณ ์ฒ๋ฆฌ_ํฌํธํด๋ฆฌ์คPOP %s ' % ์ข
๋ชฉ์ฝ๋)
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
# for code in list(self.portfolio.keys()):
# print(self.portfolio[code].__dict__)
# logger.info(self.portfolio[code].__dict__)
if flag == True:
print("%s ROBOT ์คํ" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT ์คํ" % (self.sName))
self.sAccount = sAccount
self.ํฌ์์ด์ก = floor(int(d2deposit.replacing(",", "")) / length(self.parent.robots))
print('๋ก๋ด๊ฑฐ๋๊ณ์ข : ', ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ)
print('D+2 ์์๊ธ : ', int(d2deposit.replacing(",", "")))
print('ํฌ์ ์ด์ก : ', self.ํฌ์์ด์ก)
# self.์ต๋ํฌํธ์ = floor(int(d2deposit.replacing(",", "")) / self.๋จ์ํฌ์๊ธ / length(self.parent.robots))
# print(self.์ต๋ํฌํธ์)
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.์ด๊ธฐ์กฐ๊ฑด()
print("๋งค๋ : ", self.๋งค๋ํ ์ข
๋ชฉ)
print("๋งค์ : ", self.๋งค์ํ ์ข
๋ชฉ)
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = self.๋งค๋ํ ์ข
๋ชฉ + self.๋งค์ํ ์ข
๋ชฉ
logger.info("์ค๋ ๊ฑฐ๋ ์ข
๋ชฉ : %s %s" % (self.sName, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';'))
self.KiwoomConnect() # MainWindow ์ธ์์ ํค์ API๊ตฌ๋์์ผ์ ์์ฒด์ ์ผ๋ก API๋ฐ์ดํฐ์ก์์ ๊ฐ๋ฅํ๋๋ก ํจ
if length(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';')
logger.debug("[%s]์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s" % (self.sName, ret))
except Exception as e:
print('CTradeShortTerm_Run Error :', e)
Telegram('[XTrader]CTradeShortTerm_Run Error : %s' % e, send='mc')
logger.error('CTradeShortTerm_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT ์คํ ์ค์ง" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
# ๋งค์ ๋ฏธ์ฒด๊ฒฐ ์ข
๋ชฉ ์ญ์
if self.portfolio[code].์๋ == 0:
self.portfolio.pop(code)
self.KiwoomDisConnect() # ๋ก๋ด ํด๋์ค ๋ด์์ ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ๋ฐ์ดํฐ๋ฅผ ๋ฐ๊ณ ๋์ ์ฐ๊ฒฐ ํด์ ์ํด
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
Ui_TradeCondition, QtBaseClass_TradeCondition = uic.loadUiType("./UI/TradeCondition.ui")
class ํ๋ฉด_TradeCondition(QDialog, Ui_TradeCondition):
# def __init__(self, parent):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(ํ๋ฉด_TradeCondition, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # ์์ ฏ์ด ๋ซํ๋ ๋ด์ฉ ์ญ์ ํ๋ ๊ฒ์ผ๋ก ์ฐฝ์ด ๋ซํ๋ ์ ๋ณด๋ฅผ ์ ์ฅํด์ผ๋๋ ๋ก๋ด ์ธํ
์์๋ ์ฐ๋ฉด ์๋ฌ๋จ!!
self.setupUi(self)
# print("ํ๋ฉด_TradeCondition : __init__")
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.progressBar.setValue(0) # Progressbar ์ด๊ธฐ ์
ํ
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# ๋งค์ ์ข
๋ชฉ ์ ์ ์ ์ํ ์ฒดํฌ ํจ์
def pick_stock(self, data):
row = []
cnt = 0
for code in data['์ข
๋ชฉ์ฝ๋']:
url = 'https://finance.naver.com/item/sise.nhn?code=%s' % (code)
response = requests.getting(url)
soup = BeautifulSoup(response.text, 'html.parser')
tag = soup.find_total_all("td", {"class": "num"})
# tag = soup.find_total_all("span")
result = []
temp = []
for i in tag:
temp.adding(i.text.replacing('\t', '').replacing('\n', ''))
result.adding(code) # ์ข
๋ชฉ์ฝ๋
result.adding(int(temp[5].replacing(',',''))) # ์ ์ผ์ข
๊ฐ
# result.adding(temp[7]) # ์๊ฐ
# result.adding(temp[11]) # ์ ๊ฐ
# result.adding(temp[9]) # ๊ณ ๊ฐ
result.adding(int(temp[0].replacing(',',''))) # ์ข
๊ฐ(ํ์ฌ๊ฐ)
# result.adding(temp[6]) # ๊ฑฐ๋๋
row.adding(result)
cnt+=1
# Progress Bar ๋์คํ๋ ์ด(์ ์ฒด ์๊ฐ ๋๋น ๋น์จ)
self.progressBar.setValue(cnt / length(data) * 100)
kf = mk.KnowledgeFrame(data=row, columns=['์ข
๋ชฉ์ฝ๋', '์ ์ผ์ข
๊ฐ', '์ข
๊ฐ'])
kf_final = mk.unioner(data, kf, on='์ข
๋ชฉ์ฝ๋')
kf_final = kf_final.reseting_index(sip=True)
kf_final['๋ฑ๋ฝ๋ฅ '] = value_round((kf_final['์ข
๊ฐ'] - kf_final['์ ์ผ์ข
๊ฐ'])/kf_final['์ ์ผ์ข
๊ฐ'] * 100, 1)
kf_final = kf_final[kf_final['๋ฑ๋ฝ๋ฅ '] >= 1][['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', '๋ฑ๋ฝ๋ฅ ']]
kf_final = kf_final.reseting_index(sip=True)
print(kf_final)
return kf_final
# ์ ์ฅ๋ ์กฐ๊ฑด ๊ฒ์์ ๋ชฉ๋ก ์ฝ์
def GetCondition(self):
# 1. ์ ์ฅ๋ ์กฐ๊ฑด ๊ฒ์์ ๋ชฉ๋ก ๋ถ๋ฌ์ด GetCondition
# 2. ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ gettingConditionLoad
# 3. ๋ชฉ๋ก ์์ฒญ ์๋ต ์ด๋ฒคํธ OnReceiveConditionVer์์
# gettingConditionNameList๋ก ๋ชฉ๋ก์ ๋์
๋๋ฆฌ๋ก self.condition์ ๋ฐ์
# 4. GetCondition์์ self.condition์ ์ ๋ฆฌํด์ ์ฝค๋ณด๋ฐ์ค์ ๋ชฉ๋ก ์ถ๊ฐํจ
try:
# print("ํ๋ฉด_TradeCondition : GetCondition")
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition์ dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> ์กฐ๊ฑด์ " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # ์ถ๊ฐ
print(self.kf_condition) # ์ถ๊ฐ
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# ์กฐ๊ฑด๊ฒ์ ํด๋น ์ข
๋ชฉ ์์ฒญ ๋ฉ์๋
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# print("ํ๋ฉด_TradeCondition : sendCondition")
"""
์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ ๋ฉ์๋
์ด ๋ฉ์๋๋ก ์ป๊ณ ์ ํ๋ ๊ฒ์ ํด๋น ์กฐ๊ฑด์ ๋ง๋ ์ข
๋ชฉ์ฝ๋์ด๋ค.
ํด๋น ์ข
๋ชฉ์ ๋ํ ์์ธ์ ๋ณด๋ setRealReg() ๋ฉ์๋๋ก ์์ฒญํ ์ ์๋ค.
์์ฒญ์ด ์คํจํ๋ ๊ฒฝ์ฐ๋, ํด๋น ์กฐ๊ฑด์์ด ์๊ฑฐ๋, ์กฐ๊ฑด๋ช
๊ณผ ์ธ๋ฑ์ค๊ฐ ๋ง์ง ์๊ฑฐ๋, ์กฐํ ํ์๋ฅผ ์ด๊ณผํ๋ ๊ฒฝ์ฐ ๋ฐ์ํ๋ค.
์กฐ๊ฑด๊ฒ์์ ๋ํ ๊ฒฐ๊ณผ๋
1ํ์ฑ ์กฐํ์ ๊ฒฝ์ฐ, receiveTrCondition() ์ด๋ฒคํธ๋ก ๊ฒฐ๊ณผ๊ฐ์ด ์ ๋ฌ๋๋ฉฐ
์ค์๊ฐ ์กฐํ์ ๊ฒฝ์ฐ, receiveTrCondition()๊ณผ receiveRealCondition() ์ด๋ฒคํธ๋ก ๊ฒฐ๊ณผ๊ฐ์ด ์ ๋ฌ๋๋ค.
:param screenNo: string
:param conditionName: string - ์กฐ๊ฑด์ ์ด๋ฆ
:param conditionIndex: int - ์กฐ๊ฑด์ ์ธ๋ฑ์ค
:param isRealTime: int - ์กฐ๊ฑด๊ฒ์ ์กฐํ๊ตฌ๋ถ(0: 1ํ์ฑ ์กฐํ, 1: ์ค์๊ฐ ์กฐํ)
"""
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ์กฐ๊ฑด ๊ฒ์ ๊ด๋ จ ActiveX์ On์๋ฆฌ์ฆ์ ๋ถ์(์ฝ๋ฐฑ)
def KiwoomConnect(self):
# print("ํ๋ฉด_TradeCondition : KiwoomConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# ์กฐ๊ฑด ๊ฒ์ ๊ด๋ จ ActiveX์ On์๋ฆฌ์ฆ ์ฐ๊ฒฐ ํด์
def KiwoomDisConnect(self):
# print("ํ๋ฉด_TradeCondition : KiwoomDisConnect")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ ๋ฉ์๋
def gettingConditionLoad(self):
""" ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ ๋ฉ์๋ """
# print("ํ๋ฉด_TradeCondition : gettingConditionLoad")
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ์กฐ๊ฑด์ ๋ชฉ๋ก ํ๋ ๋ฉ์๋(์กฐ๊ฑด์ ๋ชฉ๋ก์ ๋์
๋๋ฆฌ๋ก ๋ฆฌํด)
def gettingConditionNameList(self):
"""
์กฐ๊ฑด์ ํ๋ ๋ฉ์๋
์กฐ๊ฑด์์ ๋์
๋๋ฆฌ ํํ๋ก ๋ฐํํฉ๋๋ค.
์ด ๋ฉ์๋๋ ๋ฐ๋์ receiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์์ ์ฌ์ฉํด์ผ ํฉ๋๋ค.
:return: dict - {์ธ๋ฑ์ค:์กฐ๊ฑด๋ช
, ์ธ๋ฑ์ค:์กฐ๊ฑด๋ช
, ...}
"""
# print("ํ๋ฉด_TradeCondition : gettingConditionNameList")
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# ์กฐ๊ฑด๊ฒ์ ์ธ๋ถ ์ข
๋ชฉ ์กฐํ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (sScrNo, strCodeList, strConditionName, nIndex, nNext))
# print("ํ๋ฉด_TradeCondition : OnReceiveTrCondition")
"""
(1ํ์ฑ, ์ค์๊ฐ) ์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
:param screenNo: string
:param codes: string - ์ข
๋ชฉ์ฝ๋ ๋ชฉ๋ก(๊ฐ ์ข
๋ชฉ์ ์ธ๋ฏธ์ฝ๋ก ์ผ๋ก ๊ตฌ๋ถ๋จ)
:param conditionName: string - ์กฐ๊ฑด์ ์ด๋ฆ
:param conditionIndex: int - ์กฐ๊ฑด์ ์ธ๋ฑ์ค
:param inquiry: int - ์กฐํ๊ตฌ๋ถ(0: ๋จ์๋ฐ์ดํฐ ์์, 2: ๋จ์๋ฐ์ดํฐ ์์)
"""
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("์ข
๋ชฉ๊ฐ์: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
# row.adding(now)
self.result.adding(row)
# self.kf_con['์ข
๋ชฉ์ฝ๋'] = code
# self.kf_con['์ข
๋ชฉ๋ช
'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['์ข
๋ชฉ์ฝ๋'] = "'" + self.data['์ข
๋ชฉ์ฝ๋']
# self.data.to_csv('์กฐ๊ฑด์_'+ self.condition_name + '_์ข
๋ชฉ.csv', encoding='euc-kr', index=False)
# print(self.temp)
# ์ข
๋ชฉ์ ๋ํ ์ฃผ๊ฐ ํฌ๋กค๋ง ํ ์ต์ข
์ข
๋ชฉ ์ ์
# self.data = self.pick_stock(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
except Exception as e:
print("OnReceiveTrCondition Error : ", e)
fintotal_ally:
self.conditionLoop.exit()
# ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ์ ๋ํ ์๋ต ์ด๋ฒคํธ
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [์ด๋ฒคํธ] ์กฐ๊ฑด์ ์ ์ฅ [%s] [%s]' % (lRet, sMsg))
# print("ํ๋ฉด_TradeCondition : OnReceiveConditionVer")
"""
gettingConditionLoad() ๋ฉ์๋์ ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ์ ๋ํ ์๋ต ์ด๋ฒคํธ
:param receive: int - ์๋ต๊ฒฐ๊ณผ(1: ์ฑ๊ณต, ๋๋จธ์ง ์คํจ)
:param msg: string - ๋ฉ์ธ์ง
"""
try:
self.condition = self.gettingConditionNameList() # condition์ด ๋ฆฌํด๋์ ์ค๋ฉด GetCondition์์ condition ๋ณ์ ์ฌ์ฉ ๊ฐ๋ฅ
# print("์กฐ๊ฑด์ ๊ฐ์: ", length(self.condition))
# for key in self.condition.keys():
# print("์กฐ๊ฑด์: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '๊ฐญ์์น', 0, 0)
# ์ค์๊ฐ ์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug('main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
# print("ํ๋ฉด_TradeCondition : OnReceiveRealCondition")
"""
์ค์๊ฐ ์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
:param code: string - ์ข
๋ชฉ์ฝ๋
:param event: string - ์ด๋ฒคํธ์ข
๋ฅ("I": ์ข
๋ชฉํธ์
, "D": ์ข
๋ชฉ์ดํ)
:param conditionName: string - ์กฐ๊ฑด์ ์ด๋ฆ
:param conditionIndex: string - ์กฐ๊ฑด์ ์ธ๋ฑ์ค(์ฌ๊ธฐ์๋ง ์ธ๋ฑ์ค๊ฐ string ํ์
์ผ๋ก ์ ๋ฌ๋จ)
"""
print("[receiveRealCondition]")
print("์ข
๋ชฉ์ฝ๋: ", sTrCode)
print("์ด๋ฒคํธ: ", "์ข
๋ชฉํธ์
" if strType == "I" else "์ข
๋ชฉ์ดํ")
# ์กฐ๊ฑด์ ์ข
๋ชฉ ๊ฒ์ ๋ฒํผ ํด๋ฆญ ์ ์คํ๋จ(์๊ทธ๋/์ฌ๋กฏ ์ถ๊ฐ)
def inquiry(self):
# print("ํ๋ฉด_TradeCondition : inquiry")
try:
self.result = []
index = int(self.kf_condition['Index'][self.comboBox_condition.currentIndex()]) # currentIndex() : ํ์ฌ ์ฝค๋ณด๋ฐ์ค์์ ์ ํ๋ index๋ฅผ ๋ฐ์ intํ
self.condition_name = self.condition[index]
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0) # 1 : ์ค์๊ฐ ์กฐ๊ฑด๊ฒ์์ ์ข
๋ชฉ ์กฐํ, 0 : ์ผํ์ฑ ์กฐํ
except Exception as e:
print("์กฐ๊ฑด ๊ฒ์ Error: ", e)
class CTradeCondition(CTrade): # ๋ก๋ด ์ถ๊ฐ ์ __init__ : ๋ณต์ฌ, Setting / ์ด๊ธฐ์กฐ๊ฑด:์ ๋ต์ ๋ง๊ฒ, ๋ฐ์ดํฐ์ฒ๋ฆฌ / Run:๋ณต์ฌ
def __init__(self, sName, UUID, kiwoom=None, parent=None):
# print("CTradeCondition : __init__")
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.remained_data = True
self.์ด๊ธฐ์ค์ ์ํ = False
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.portfolio = dict()
self.CList = []
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# ์กฐ๊ฑด์ ์ ํ์ ์ํด์ ํฌ์๊ธ, ๋งค์/๋ ๋ฐฉ๋ฒ, ํฌํธํด๋ฆฌ์ค ์, ๊ฒ์ ์ข
๋ชฉ ๋ฑ์ด ์ ์ฅ๋จ
def Setting(self, sScreenNo, ํฌํธํด๋ฆฌ์ค์, ์กฐ๊ฑด์์ธ๋ฑ์ค, ์กฐ๊ฑด์๋ช
, ์กฐ๊ฑด๊ฒ์ํ์
, ๋จ์ํฌ์๊ธ, ๋งค์๋ฐฉ๋ฒ, ๋งค๋๋ฐฉ๋ฒ):
# print("CTradeCondition : Setting")
self.sScreenNo = sScreenNo
self.ํฌํธํด๋ฆฌ์ค์ = ํฌํธํด๋ฆฌ์ค์
self.์กฐ๊ฑด์์ธ๋ฑ์ค = ์กฐ๊ฑด์์ธ๋ฑ์ค
self.์กฐ๊ฑด์๋ช
= ์กฐ๊ฑด์๋ช
self.์กฐ๊ฑด๊ฒ์ํ์
= int(์กฐ๊ฑด๊ฒ์ํ์
)
self.๋จ์ํฌ์๊ธ = ๋จ์ํฌ์๊ธ
self.๋งค์๋ฐฉ๋ฒ = ๋งค์๋ฐฉ๋ฒ
self.๋งค๋๋ฐฉ๋ฒ = ๋งค๋๋ฐฉ๋ฒ
self.๋ณด์ ์ผ = 1
self.์ต์ = 5 # percent
self.๊ณ ๊ฐ๋๋น = -1 # percent
self.์์ = -2.7 # percent
self.ํฌ์๊ธ๋น์ค = 70 # ์์๊ธ ๋๋น percent
print("์กฐ๊ฒ๊ฒ์ ๋ก๋ด ์
ํ
์๋ฃ - ์กฐ๊ฑด์ธ๋ฑ์ค : %s, ์กฐ๊ฑด์๋ช
: %s, ๊ฒ์ํ์
: %s"%(self.์กฐ๊ฑด์์ธ๋ฑ์ค, self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด๊ฒ์ํ์
))
logger.info("์กฐ๊ฒ๊ฒ์ ๋ก๋ด ์
ํ
์๋ฃ - ์กฐ๊ฑด์ธ๋ฑ์ค : %s, ์กฐ๊ฑด์๋ช
: %s, ๊ฒ์ํ์
: %s" % (self.์กฐ๊ฑด์์ธ๋ฑ์ค, self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด๊ฒ์ํ์
))
# Robot_Run์ด ๋๋ฉด ์คํ๋จ - ๋งค๋ ์ข
๋ชฉ์ ๋ฆฌ์คํธ๋ก ์ ์ฅ
def ์ด๊ธฐ์กฐ๊ฑด(self, codes):
# print("CTradeCondition : ์ด๊ธฐ์กฐ๊ฑด")
self.parent.statusbar.showMessage("[%s] ์ด๊ธฐ์กฐ๊ฑด์ค๋น" % (self.sName))
self.sell_band = [0, 3, 5, 10, 15, 25]
self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด = [-2.7, 0.5, -2.0, -2.0, -2.0, -2.0]
self.๋งค์๋ชจ๋ํฐ๋ง = True
self.clearcheck = False # ๋น์ผ์ฒญ์ฐ ์ฒดํฌ๋ณ์
self.์กฐ๊ฑด๊ฒ์์ด๋ฒคํธ = False
# ๋งค์ํ ์ข
๋ชฉ์ ํด๋น ์กฐ๊ฑด์์ ๊ฒ์๋ ์ข
๋ชฉ
# ๋งค๋ํ ์ข
๋ชฉ์ ์ด๋ฏธ ๋งค์๊ฐ ๋์ด ํฌํธํด๋ฆฌ์ค์ ์ ์ฅ๋์ด ์๋ ์ข
๋ชฉ
self.๊ธ์ผ๋งค๋์ข
๋ชฉ = []
self.๋งค๋ํ ์ข
๋ชฉ = []
self.๋งค์ํ ์ข
๋ชฉ = codes
# for code in codes: # ์ ํํ ์ข
๋ชฉ๊ฒ์์์ ์ข
๋ชฉ์ '๋งค์ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
# stock = self.portfolio.getting(code) # ์ด๊ธฐ ๋ก๋ด ์คํ ์ ํฌํธํด๋ฆฌ์ค๋ ๋น์ด์์
# if stock != None: # ๊ฒ์ํ ์ข
๋ชฉ์ด ํฌํธํด๋ฆฌ์ค์ ์์ผ๋ฉด '๋งค๋ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
# self.๋งค๋ํ ์ข
๋ชฉ.adding(code)
# else: # ํฌํธํด๋ฆฌ์ค์ ์์ผ๋ฉด ๋งค์์ข
๋ชฉ๋ฆฌ์คํธ์ ์ ์ฅ
# self.๋งค์ํ ์ข
๋ชฉ.adding(code)
for port_code in list(self.portfolio.keys()): # ํฌํธํด๋ฆฌ์ค์ ์๋ ์ข
๋ชฉ์ '๋งค๋ํ ์ข
๋ชฉ'์ ์ถ๊ฐ
๋ณด์ ๊ธฐ๊ฐ = holdingcal(self.portfolio[port_code].๋งค์์ผ) - 1
if ๋ณด์ ๊ธฐ๊ฐ < 3:
self.portfolio[port_code].๋งค๋์ ๋ต = 5 # ๋งค๋์ง์ฐ ์ข
๋ชฉ์ ๋ชฉํ๊ฐ ๋ฎ์ถค 5% -> 3% -> 1%
elif ๋ณด์ ๊ธฐ๊ฐ >= 3 and ๋ณด์ ๊ธฐ๊ฐ < 5:
self.portfolio[port_code].๋งค๋์ ๋ต = 3
elif ๋ณด์ ๊ธฐ๊ฐ >= 3 and ๋ณด์ ๊ธฐ๊ฐ < 5:
self.portfolio[port_code].๋งค๋์ ๋ต = 1
print(self.portfolio[port_code].__dict__)
logger.info(self.portfolio[port_code].__dict__)
self.๋งค๋ํ ์ข
๋ชฉ.adding(port_code)
# ์๋ ํฌํธํด๋ฆฌ์ค ์์ฑ
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'032190': {'์ข
๋ชฉ๋ช
': '๋ค์ฐ๋ฐ์ดํ', '์ข
๋ชฉ์ฝ๋': '032190', '๋งค์๊ฐ': [16150], '์๋': 12, '๋ณด์ ์ผ':1, '๋งค์์ผ': '2020/08/05 09:08:54'},
'047400': {'์ข
๋ชฉ๋ช
': '์ ๋์จ๋จธํฐ๋ฆฌ์ผ', '์ข
๋ชฉ์ฝ๋': '047400', '๋งค์๊ฐ': [5350], '์๋': 36, '๋ณด์ ์ผ':1, '๋งค์์ผ': '2020/08/05 09:42:55'},
'085660': {'์ข
๋ชฉ๋ช
': '์ฐจ๋ฐ์ด์คํ
', '์ข
๋ชฉ์ฝ๋': '085660', '๋งค์๊ฐ': [22100], '์๋': 9, '๋ณด์ ์ผ': 1,
'๋งค์์ผ': '2020/08/05 09:08:54'},
'000020': {'์ข
๋ชฉ๋ช
': '๋ํ์ฝํ', '์ข
๋ชฉ์ฝ๋': '000020', '๋งค์๊ฐ': [25800
], '์๋': 7, '๋ณด์ ์ผ': 1,
'๋งค์์ผ': '2020/08/05 09:42:55'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock(์ข
๋ชฉ์ฝ๋=code, ์ข
๋ชฉ๋ช
=self.Stocklist[code]['์ข
๋ชฉ๋ช
'],
๋งค์๊ฐ=self.Stocklist[code]['๋งค์๊ฐ'][0],
๋ณด์ ์ผ=self.Stocklist[code]['๋ณด์ ์ผ'],
์๋=self.Stocklist[code]['์๋'],
๋งค์์ผ=self.Stocklist[code]['๋งค์์ผ'])
# google spreadsheet ๋งค๋งค์ด๋ ฅ ์์ฑ
def save_history(self, code, status):
# ๋งค๋งค์ด๋ ฅ sheet์ ํด๋น ์ข
๋ชฉ(๋งค์๋ ์ข
๋ชฉ)์ด ์์ผ๋ฉด row๋ฅผ ๋ฐํ ์๋๋ฉด ์์ธ์ฒ๋ฆฌ -> ์ ๊ท ๋งค์๋ก ์ฒ๋ฆฌ
try:
code_row = condition_history_sheet.findtotal_all(self.portfolio[code].์ข
๋ชฉ๋ช
)[
-1].row # ์ข
๋ชฉ๋ช
์ด ์๋ ๋ชจ๋ ์
์ ์ฐพ์์ ๋งจ ์๋์ ์๋ ์
์ ์ ํ
cell = alpha_list[condition_history_cols.index('๋งค๋๊ฐ')] + str(code_row) # ๋งค์ ์ด๋ ฅ์ ์๋ ์ข
๋ชฉ์ด ๋งค๋๊ฐ ๋์๋์ง ํ์ธ
sell_price = condition_history_sheet.acell(str(cell)).value
# ๋งค๋ ์ด๋ ฅ์ ์ถ๊ฐ ๋งค๋(๋งค๋์ ๋ต5์ ๊ฒฝ์ฐ)๋ ์ ๊ท ๋งค๋์ธ ๊ฒฝ์ฐ๋ผ ๋งค๋ ์ด๋ ฅ ์ ๋ฌด์ ์๊ด์์
if status == '๋งค๋': # ํฌํธํด๋ฆฌ์ค ๋ฐ์ดํฐ ์ฌ์ฉ
cell = alpha_list[condition_history_cols.index('๋งค๋๊ฐ')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค๋๊ฐ)
cell = alpha_list[condition_history_cols.index('๋งค๋์ผ')] + str(code_row)
condition_history_sheet.umkate_acell(cell, datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
๊ณ์ฐ์์ต๋ฅ = value_round((self.portfolio[code].๋งค๋๊ฐ / self.portfolio[code].๋งค์๊ฐ - 1) * 100, 2)
cell = alpha_list[condition_history_cols.index('์์ต๋ฅ (๊ณ์ฐ)')] + str(code_row) # ์์ต๋ฅ ๊ณ์ฐ
condition_history_sheet.umkate_acell(cell, ๊ณ์ฐ์์ต๋ฅ )
# ๋งค์ ์ด๋ ฅ์ ์์ผ๋ ๋งค๋ ์ด๋ ฅ์ด ์์ -> ๋งค๋ ์ ์ถ๊ฐ ๋งค์
if sell_price == '':
if status == '๋งค์': # ํฌํธํด๋ฆฌ์ค ๋ฐ์ดํฐ ์ฌ์ฉ
cell = alpha_list[condition_history_cols.index('๋งค์๊ฐ')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค์๊ฐ)
cell = alpha_list[condition_history_cols.index('๋งค์์ผ')] + str(code_row)
condition_history_sheet.umkate_acell(cell, self.portfolio[code].๋งค์์ผ)
else: # ๋งค๋๊ฐ๊ฐ ๊ธฐ๋ก๋์ด ๊ฑฐ๋๊ฐ ์๋ฃ๋ ์ข
๋ชฉ์ผ๋ก ํ๋จํ์ฌ ์์ธ๋ฐ์์ผ๋ก ์ ๊ท ๋งค์ ์ถ๊ฐํจ
raise Exception('๋งค๋งค์๋ฃ ์ข
๋ชฉ')
except:
row = []
try:
if status == '๋งค์':
row.adding(self.portfolio[code].์ข
๋ชฉ๋ช
)
row.adding(self.portfolio[code].๋งค์๊ฐ)
row.adding(self.portfolio[code].๋งค์์ผ)
condition_history_sheet.adding_row(row)
except Exception as e:
print('[%s]save_history Error :'%(self.sName,e))
Telegram('[StockTrader][%s]save_history Error :'%(self.sName,e), send='mc')
logger.error('[%s]save_history Error :'%(self.sName,e))
# ๋งค์ ์ ๋ต๋ณ ๋งค์ ์กฐ๊ฑด ํ์ธ
def buy_strategy(self, code, price):
result = False
ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ = price # ์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
if self.๋จ์ํฌ์๊ธ // ํ์ฌ๊ฐ > 0 and ํ์ฌ๊ฐ >= ๊ณ ๊ฐ * (0.99) and ์ ๊ฐ > ์ ์ผ์ข
๊ฐ and ํ์ฌ๊ฐ < ์๊ฐ * 1.1 and ์๊ฐ <= ์ ์ผ์ข
๊ฐ * 1.05:
result = True
return result
# ๋งค๋ ๊ตฌ๊ฐ ํ์ธ
def profit_band_check(self, ํ์ฌ๊ฐ, ๋งค์๊ฐ):
# print('ํ์ฌ๊ฐ, ๋งค์๊ฐ', ํ์ฌ๊ฐ, ๋งค์๊ฐ)
ratio = value_round((ํ์ฌ๊ฐ - ๋งค์๊ฐ) / ๋งค์๊ฐ * 100, 2)
# print('ratio', ratio)
if ratio < 3:
return 1
elif ratio in self.sell_band:
return self.sell_band.index(ratio) + 1
else:
self.sell_band.adding(ratio)
self.sell_band.sort()
band = self.sell_band.index(ratio)
self.sell_band.remove(ratio)
return band
# ๋งค๋ ์ ๋ต
def sell_strategy(self, code, price):
result = False
band = self.portfolio[code].๋งค๋๊ตฌ๊ฐ # ์ด์ ๋งค๋ ๊ตฌ๊ฐ ๋ฐ์
ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ = price # ์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
๋งค์๊ฐ = self.portfolio[code].๋งค์๊ฐ
sell_price = ํ์ฌ๊ฐ
# ๋งค๋๋ฅผ ์ํ ์์ต๋ฅ ๊ตฌ๊ฐ ์ฒดํฌ(๋งค์๊ฐ ๋๋น ํ์ฌ๊ฐ์ ์์ต๋ฅ ์กฐ๊ฑด์ ๋ค๋ฅธ ๊ตฌ๊ฐ ์ค์ )
new_band = self.profit_band_check(ํ์ฌ๊ฐ, ๋งค์๊ฐ)
if (hogacal(์๊ฐ, 0, self.portfolio[code].์์ฅ, '์ํ๊ฐ')) <= ํ์ฌ๊ฐ:
band = 7
if band < new_band: # ์ด์ ๊ตฌ๊ฐ๋ณด๋ค ํ์ฌ ๊ตฌ๊ฐ์ด ๋์ ๊ฒฝ์ฐ(์์ธ๊ฐ ์ฌ๋ผ๊ฐ ๊ฒฝ์ฐ)๋ง
band = new_band # ๊ตฌ๊ฐ์ ํ์ฌ ๊ตฌ๊ฐ์ผ๋ก ๋ณ๊ฒฝ(๋ฐ๋์ ๊ฒฝ์ฐ๋ ๊ตฌ๊ฐ ์ ์ง)
# self.sell_band = [0, 3, 5, 10, 15, 25]
# self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด = [-2.7, 0.3, -3.0, -4.0, -5.0, -7.0]
if band == 1 and ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[0] / 100)):
result = False
elif band == 2 and ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[1] / 100)): # 3% ์ดํ์ผ ๊ฒฝ์ฐ 0.3%๊น์ง ๋จ์ด์ง๋ฉด ๋งค๋
result = True
elif band == 3 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[2] / 100)): # 5% ์ด์์ผ ๊ฒฝ์ฐ ๊ณ ๊ฐ๋๋น -3%๊น์ง ๋จ์ด์ง๋ฉด ๋งค๋
result = True
elif band == 4 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[3] / 100)):
result = True
elif band == 5 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[4] / 100)):
result = True
elif band == 6 and ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.๋งค๋๊ตฌ๊ฐ๋ณ์กฐ๊ฑด[5] / 100)):
result = True
elif band == 7 and ํ์ฌ๊ฐ >= (hogacal(์๊ฐ, -3, self.portfolio[code].์์ฅ, '์ํ๊ฐ')):
result = True
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = band # ํฌํธํด๋ฆฌ์ค์ ๋งค๋๊ตฌ๊ฐ ์
๋ฐ์ดํธ
if current_time >= '15:10:00': # 15์ 10๋ถ์ ๋งค๋ ์ฒ๋ฆฌ
result = True
"""
if self.portfolio[code].๋งค๋์ ๋ต๋ณ๊ฒฝ1 == False and current_time >= '11:00:00' and current_time < '13:00:00':
self.portfolio[code].๋งค๋์ ๋ต๋ณ๊ฒฝ1 = True
self.portfolio[code].๋งค๋์ ๋ต = self.portfolio[code].๋งค๋์ ๋ต * 0.6
elif self.portfolio[code].๋งค๋์ ๋ต๋ณ๊ฒฝ2 == False and current_time >= '13:00:00':
self.portfolio[code].๋งค๋์ ๋ต๋ณ๊ฒฝ2 = True
self.portfolio[code].๋งค๋์ ๋ต = self.portfolio[code].๋งค๋์ ๋ต * 0.6
if self.portfolio[code].๋งค๋์ ๋ต < 0.3:
self.portfolio[code].๋งค๋์ ๋ต = 0.3
# 2. ์ต์ ๋งค๋ ์ ๋ต
if ํ์ฌ๊ฐ >= ๋งค์๊ฐ * (1 + (self.portfolio[code].๋งค๋์ ๋ต / 100)):
result = True
sell_price = ํ์ฌ๊ฐ
# 3. ๊ณ ๊ฐ๋๋น ๋น์จ ๋งค๋ ์ ๋ต
# elif ํ์ฌ๊ฐ <= ๊ณ ๊ฐ * (1 + (self.๊ณ ๊ฐ๋๋น / 100)):
# result = True
# sell_price = ํ์ฌ๊ฐ
# 4. ์์ ๋งค๋ ์ ๋ต
# elif ํ์ฌ๊ฐ <= ๋งค์๊ฐ * (1 + (self.์์ / 100)):
# result = True
# sell_price = ํ์ฌ๊ฐ
"""
return result, sell_price
# ๋น์ผ์ฒญ์ฐ ์ ๋ต
def clearning_strategy(self):
if self.clearcheck == True:
print('๋น์ผ์ฒญ์ฐ ๋งค๋')
try:
for code in list(self.portfolio.keys()):
if self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('S_%s' % code) is None and self.portfolio[code].์๋ != 0:
self.portfolio[code].๋งค๋๊ตฌ๊ฐ = 0
self.๋งค๋๋ฐฉ๋ฒ = '03' # 03:์์ฅ๊ฐ
(result, order) = self.์ ๋๋งค๋(sRQName='S_%s' % code, ์ข
๋ชฉ์ฝ๋=code, ๋งค๋๊ฐ=self.portfolio[code].๋งค์๊ฐ,
์๋=self.portfolio[code].์๋)
if result == True:
self.์ฃผ๋ฌธ์คํ์ค_Lock['S_%s' % code] = True
Telegram('[StockTrader]์ ๋๋งค๋(๋น์ผ์ฒญ์ฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋), send='mc')
logger.info('์ ๋๋งค๋(๋น์ผ์ฒญ์ฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
else:
Telegram('[StockTrader]์ ์ก๋งค๋์คํจ(๋น์ผ์ฒญ์ฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋), send='mc')
logger.info('์ ๋๋งค๋์คํจ(๋น์ผ์ฒญ์ฐ) : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ์๋=%s' % (code, self.portfolio[code].์ข
๋ชฉ๋ช
, self.portfolio[code].์๋))
except Exception as e:
print("clearning_strategy Error :", e)
# ์ฃผ๋ฌธ์ฒ๋ฆฌ
def ์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ(self, param):
if self.running == True:
์ฒด๊ฒฐ์๊ฐ = '%s %s:%s:%s' % (str(self.d), param['์ฒด๊ฒฐ์๊ฐ'][0:2], param['์ฒด๊ฒฐ์๊ฐ'][2:4], param['์ฒด๊ฒฐ์๊ฐ'][4:])
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
ํ์ฌ๊ฐ = abs(int(float(param['ํ์ฌ๊ฐ'])))
์ ์ผ๋๋น = int(float(param['์ ์ผ๋๋น']))
๋ฑ๋ฝ๋ฅ = float(param['๋ฑ๋ฝ๋ฅ '])
๋งค๋ํธ๊ฐ = abs(int(float(param['๋งค๋ํธ๊ฐ'])))
๋งค์ํธ๊ฐ = abs(int(float(param['๋งค์ํธ๊ฐ'])))
๋์ ๊ฑฐ๋๋ = abs(int(float(param['๋์ ๊ฑฐ๋๋'])))
์๊ฐ = abs(int(float(param['์๊ฐ'])))
๊ณ ๊ฐ = abs(int(float(param['๊ณ ๊ฐ'])))
์ ๊ฐ = abs(int(float(param['์ ๊ฐ'])))
๊ฑฐ๋ํ์ ์จ = abs(float(param['๊ฑฐ๋ํ์ ์จ']))
์๊ฐ์ด์ก = abs(int(float(param['์๊ฐ์ด์ก'])))
์ ์ผ์ข
๊ฐ = ํ์ฌ๊ฐ - ์ ์ผ๋๋น
# MainWindow์ __init__์์ CODE_POOL ๋ณ์ ์ ์ธ(self.CODE_POOL = self.getting_code_pool()), pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์๊ฐ์ด์ก]
์ข
๋ชฉ๋ช
= self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][1] # pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก]
์์ฅ๊ตฌ๋ถ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][0]
์ ์ผ์ข
๊ฐ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][3]
์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ์ ์ผ์ข
๊ฐ]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น))
# ์ ์ก๋งค๋ ํ ํฌํธํด๋ฆฌ์ค/๋งค๋ํ ์ข
๋ชฉ์์ ์ ๊ฑฐ
if ์ข
๋ชฉ์ฝ๋ in self.๋งค๋ํ ์ข
๋ชฉ:
if self.portfolio.getting(์ข
๋ชฉ์ฝ๋) is not None and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('S_%s' % ์ข
๋ชฉ์ฝ๋) is None:
# ๋งค๋ ์ ๋ต๋ณ ๋ชจ๋ํฐ๋ง ์ฒดํฌ
sell_check, ๋งค๋๊ฐ = self.sell_strategy(์ข
๋ชฉ์ฝ๋, ์์ธ)
if sell_check == True:
(result, order) = self.์ ์ก๋งค๋(sRQName='S_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค๋๊ฐ=๋งค๋๊ฐ, ์๋=self.portfolio[์ข
๋ชฉ์ฝ๋].์๋)
if result == True:
self.์ฃผ๋ฌธ์คํ์ค_Lock['S_%s' % ์ข
๋ชฉ์ฝ๋] = True
if ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ: self.๊ธ์ผ๋งค๋์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
Telegram('[StockTrader]%s ๋งค๋์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ตฌ๊ฐ=%s, ๋งค๋๊ฐ=%s, ์๋=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].์๋), send='mc')
logger.info('[StockTrader]%s ๋งค๋์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ตฌ๊ฐ=%s, ๋งค๋๊ฐ=%s, ์๋=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ตฌ๊ฐ, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].์๋))
else:
Telegram('[StockTrader]%s ๋งค๋์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ์๋=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].์๋), send='mc')
logger.info('[StockTrader]%s ๋งค๋์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค๋๊ฐ=%s, ์๋=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, self.portfolio[์ข
๋ชฉ์ฝ๋].์๋))
# ๋งค์ํ ์ข
๋ชฉ์ ๋ํด์ ์ ์ก๋งค์ ์ฃผ๋ฌธํ๊ณ ํฌํธํด๋ฆฌ์ค/๋งค๋ํ ์ข
๋ชฉ์ ์ถ๊ฐ, ๋งค์ํ ์ข
๋ชฉ์์ ์ ์ธ
if current_time <= '14:30:00':
if ์ข
๋ชฉ์ฝ๋ in self.๋งค์ํ ์ข
๋ชฉ and ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ:
if length(self.portfolio) < self.์ต๋ํฌํธ์ and self.portfolio.getting(์ข
๋ชฉ์ฝ๋) is None and self.์ฃผ๋ฌธ์คํ์ค_Lock.getting('B_%s' % ์ข
๋ชฉ์ฝ๋) is None:
buy_check = self.buy_strategy(์ข
๋ชฉ์ฝ๋, ์์ธ)
if buy_check == True:
(result, order) = self.์ ์ก๋งค์(sRQName='B_%s' % ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ๋งค์๊ฐ=ํ์ฌ๊ฐ, ๋งค์๊ธ์ก=self.๋จ์ํฌ์๊ธ)
if result == True:
self.portfolio[์ข
๋ชฉ์ฝ๋] = CPortStock(์ข
๋ชฉ์ฝ๋=์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
=์ข
๋ชฉ๋ช
, ์์ฅ=์์ฅ๊ตฌ๋ถ, ๋งค์๊ฐ=ํ์ฌ๊ฐ, ๋ณด์ ์ผ=self.๋ณด์ ์ผ, ๋งค๋์ ๋ต = self.์ต์ ,
๋งค์์ผ=datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S'))
self.์ฃผ๋ฌธ์คํ์ค_Lock['B_%s' % ์ข
๋ชฉ์ฝ๋] = True
Telegram('[StockTrader]%s ๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ), send='mc')
logger.info('[StockTrader]%s ๋งค์์ฃผ๋ฌธ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ))
else:
Telegram('[StockTrader]%s ๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ), send='mc')
logger.info('[StockTrader]%s ๋งค์์คํจ : ์ข
๋ชฉ์ฝ๋=%s, ์ข
๋ชฉ๋ช
=%s, ๋งค์๊ฐ=%s' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ))
else:
if self.๋งค์๋ชจ๋ํฐ๋ง == True:
self.parent.ConditionTick.stop()
self.๋งค์๋ชจ๋ํฐ๋ง = False
logger.info("๋งค์๋ชจ๋ํฐ๋ง ์๊ฐ ์ด๊ณผ")
def ์ ์์ฒ๋ฆฌ(self, param):
pass
# OnReceiveChejanData์์ ์ฒด๊ฒฐ์ฒ๋ฆฌ๊ฐ ๋๋ฉด ์ฒด๊ฒฐ์ฒ๋ฆฌ ํธ์ถ
def ์ฒด๊ฒฐ์ฒ๋ฆฌ(self, param):
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
์ฃผ๋ฌธ๋ฒํธ = param['์ฃผ๋ฌธ๋ฒํธ']
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ[์ฃผ๋ฌธ๋ฒํธ] = param
์ฃผ๋ฌธ์๋ = int(param['์ฃผ๋ฌธ์๋'])
๋ฏธ์ฒด๊ฒฐ์๋ = int(param['๋ฏธ์ฒด๊ฒฐ์๋'])
์ฒด๊ฒฐ๊ฐ = int(0 if (param['์ฒด๊ฒฐ๊ฐ'] is None or param['์ฒด๊ฒฐ๊ฐ'] == '') else param['์ฒด๊ฒฐ๊ฐ']) # ๋งค์
๊ฐ ๋์ผ
๋จ์์ฒด๊ฒฐ๋ = int(0 if (param['๋จ์์ฒด๊ฒฐ๋'] is None or param['๋จ์์ฒด๊ฒฐ๋'] == '') else param['๋จ์์ฒด๊ฒฐ๋'])
๋น์ผ๋งค๋งค์์๋ฃ = int(0 if (param['๋น์ผ๋งค๋งค์์๋ฃ'] is None or param['๋น์ผ๋งค๋งค์์๋ฃ'] == '') else param['๋น์ผ๋งค๋งค์์๋ฃ'])
๋น์ผ๋งค๋งค์ธ๊ธ = int(0 if (param['๋น์ผ๋งค๋งค์ธ๊ธ'] is None or param['๋น์ผ๋งค๋งค์ธ๊ธ'] == '') else param['๋น์ผ๋งค๋งค์ธ๊ธ'])
# ๋งค์
if param['๋งค๋์๊ตฌ๋ถ'] == '2':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค์๊ฐ = int(์ฃผ๋ฌธ[2:])
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
P.๋งค์๊ฐ = ์ฒด๊ฒฐ๊ฐ # ๋จ์์ฒด๊ฒฐ๊ฐ
P.์๋ += ๋จ์์ฒด๊ฒฐ๋ # ์ถ๊ฐ ๋งค์ ๋๋นํด์ ๊ธฐ์กด ์๋์ ์ฒด๊ฒฐ๋ ์๋ ๊ณ์ ๋ํจ(์ฃผ๋ฌธ์๋ - ๋ฏธ์ฒด๊ฒฐ์๋)
P.๋งค์์ผ = datetime.datetime.now().strftime('%Y/%m/%d %H:%M:%S')
else:
logger.error('ERROR ํฌํธ์ ์ข
๋ชฉ์ด ์์ !!!!')
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
try:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
self.๋งค์ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
self.save_history(์ข
๋ชฉ์ฝ๋, status='๋งค์')
Telegram('[StockTrader]%s ๋งค์์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ๋งค์๊ฐ:%s, ์๋:%s' % (self.sName, P.์ข
๋ชฉ๋ช
, P.๋งค์๊ฐ, P.์๋), send='mc')
logger.info('[StockTrader]%s %s ๋งค์ ์๋ฃ : ๋งค์/์ฃผ๋ฌธ%s Pop, ๋งค๋ Append ' % (self.sName, ์ข
๋ชฉ์ฝ๋, ์ฃผ๋ฌธ))
except Exception as e:
Telegram('[StockTrader]%s ์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ POP์๋ฌ ์ข
๋ชฉ๋ช
:%s ' % (self.sName, P.์ข
๋ชฉ๋ช
), send='mc')
logger.error('[StockTrader]%s ์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค์ POP์๋ฌ ์ข
๋ชฉ๋ช
:%s ' % (self.sName, P.์ข
๋ชฉ๋ช
))
# ๋งค๋
if param['๋งค๋์๊ตฌ๋ถ'] == '1':
if self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ.getting(์ฃผ๋ฌธ๋ฒํธ) is not None:
์ฃผ๋ฌธ = self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ[์ฃผ๋ฌธ๋ฒํธ]
๋งค๋๊ฐ = int(์ฃผ๋ฌธ[2:])
try:
if ๋ฏธ์ฒด๊ฒฐ์๋ == 0:
self.์ฃผ๋ฌธ์คํ์ค_Lock.pop(์ฃผ๋ฌธ)
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.์ข
๋ชฉ๋ช
= param['์ข
๋ชฉ๋ช
']
self.portfolio[์ข
๋ชฉ์ฝ๋].๋งค๋๊ฐ = ์ฒด๊ฒฐ๊ฐ
self.save_history(์ข
๋ชฉ์ฝ๋, status='๋งค๋')
Telegram('[StockTrader]%s ๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (self.sName, param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋), send='mc')
logger.info('[StockTrader]%s ๋งค๋์ฒด๊ฒฐ์๋ฃ_์ข
๋ชฉ๋ช
:%s, ์ฒด๊ฒฐ๊ฐ:%s, ์๋:%s' % (self.sName, param['์ข
๋ชฉ๋ช
'], ์ฒด๊ฒฐ๊ฐ, ์ฃผ๋ฌธ์๋))
except Exception as e:
Telegram('[StockTrader]%s ์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ ๋งค๋งค์ด๋ ฅ Error : %s' % (self.sName, e), send='mc')
logger.error('[StockTrader]%s ์ฒด๊ฒฐ์ฒ๋ฆฌ_๋งค๋ ๋งค๋งค์ด๋ ฅ Error : %s' % (self.sName, e))
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
def ์๊ณ ์ฒ๋ฆฌ(self, param):
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
P = self.portfolio.getting(์ข
๋ชฉ์ฝ๋)
if P is not None:
P.๋งค์๊ฐ = int(0 if (param['๋งค์
๋จ๊ฐ'] is None or param['๋งค์
๋จ๊ฐ'] == '') else param['๋งค์
๋จ๊ฐ'])
P.์๋ = int(0 if (param['๋ณด์ ์๋'] is None or param['๋ณด์ ์๋'] == '') else param['๋ณด์ ์๋'])
if P.์๋ == 0:
self.portfolio.pop(์ข
๋ชฉ์ฝ๋)
self.๋งค๋ํ ์ข
๋ชฉ.remove(์ข
๋ชฉ์ฝ๋)
if ์ข
๋ชฉ์ฝ๋ not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ: self.๊ธ์ผ๋งค๋์ข
๋ชฉ.adding(์ข
๋ชฉ์ฝ๋)
logger.info('์๊ณ ์ฒ๋ฆฌ_ํฌํธํด๋ฆฌ์คPOP %s ' % ์ข
๋ชฉ์ฝ๋)
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
# MainWindow์ ConditionTick์ ์ํด์ 3๋ถ๋ง๋ค ์คํ
def ConditionCheck(self):
if '3' in self.sName:
if current_time >= "15:00:00" and self.์กฐ๊ฑด๊ฒ์์ด๋ฒคํธ == False:
self.์กฐ๊ฑด๊ฒ์์ด๋ฒคํธ = True
codes = self.GetCodes(self.์กฐ๊ฑด์์ธ๋ฑ์ค, self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด๊ฒ์ํ์
)
print(current_time, codes)
code_list=[]
for code in codes:
code_list.adding(code + '_' + self.parent.CODE_POOL[code][1] + '\n')
code_list = "".join(code_list)
print(current_time, code_list)
Telegram(code_list, send='mc')
else:
pass
else:
codes = self.GetCodes(self.์กฐ๊ฑด์์ธ๋ฑ์ค, self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด๊ฒ์ํ์
)
print(current_time, codes)
for code in codes:
if code not in self.๋งค์ํ ์ข
๋ชฉ and self.portfolio.getting(code) is None and code not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ:
print('๋งค์์ข
๋ชฉ์ถ๊ฐ : ', code, self.parent.CODE_POOL[code][1])
self.๋งค์ํ ์ข
๋ชฉ.adding(code)
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';') # ์ค์๊ฐ ์์ธ์กฐํ ์ข
๋ชฉ ์ถ๊ฐ
logger.debug("[%s]์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s %s" % (self.sName, self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ, ret))
# ์ค์๊ฐ ์กฐ๊ฒ ๊ฒ์ ํธ์
์ข
๋ชฉ ์ฒ๋ฆฌ
def ์ค์๊ฐ์กฐ๊ฑด์ฒ๋ฆฌ(self, code):
if (code not in self.๋งค์ํ ์ข
๋ชฉ) and (self.portfolio.getting(code) is None) and (code not in self.๊ธ์ผ๋งค๋์ข
๋ชฉ):
print('๋งค์์ข
๋ชฉ์ถ๊ฐ : ', code)
self.๋งค์ํ ์ข
๋ชฉ.adding(code)
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ.adding(code)
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';') # ์ค์๊ฐ ์์ธ์กฐํ ์ข
๋ชฉ ์ถ๊ฐ
logger.debug("[%s]์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s %s" % (self.sName, self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ, ret))
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
codes = []
self.codeList = []
# self.manual_portfolio()
if flag == True:
print("%s ROBOT ์คํ" % (self.sName))
self.KiwoomConnect()
try:
logger.info("[%s]์กฐ๊ฑด์ ๊ฑฐ๋ ๋ก๋ด ์คํ"%(self.sName))
self.sAccount = Account
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.ํฌ์์ด์ก = floor(int(d2deposit.replacing(",", "")) * (self.ํฌ์๊ธ๋น์ค / 100))
print('D+2 ์์๊ธ : ', int(d2deposit.replacing(",", "")))
print('ํฌ์๊ธ : ', self.ํฌ์์ด์ก)
print('๋จ์ํฌ์๊ธ : ', self.๋จ์ํฌ์๊ธ)
self.์ต๋ํฌํธ์ = self.ํฌํธํด๋ฆฌ์ค์ # floor(self.ํฌ์์ด์ก / self.๋จ์ํฌ์๊ธ) + length(self.portfolio)
# print('๊ธฐ์กดํฌํธ์ : ', length(self.portfolio))
print('์ต๋ํฌํธ์ : ', self.์ต๋ํฌํธ์)
print("์กฐ๊ฑด์ ์ธ๋ฑ์ค : ", self.์กฐ๊ฑด์์ธ๋ฑ์ค, type(self.์กฐ๊ฑด์์ธ๋ฑ์ค))
print("์กฐ๊ฑด์๋ช
: ", self.์กฐ๊ฑด์๋ช
)
if self.์กฐ๊ฑด๊ฒ์ํ์
== 0: # 3๋ถ๋ด ๊ฒ์
self.parent.ConditionTick.start(1000)
else: # ์ค์๊ฐ ๊ฒ์
print('์ค์๊ฐ ์กฐ๊ฑด๊ฒ์')
codes = self.GetCodes(self.์กฐ๊ฑด์์ธ๋ฑ์ค, self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด๊ฒ์ํ์
)
codes = []
self.์ด๊ธฐ์กฐ๊ฑด(codes)
print("๋งค์ : ", self.๋งค์ํ ์ข
๋ชฉ)
print("๋งค๋ : ", self.๋งค๋ํ ์ข
๋ชฉ)
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = self.๋งค๋ํ ์ข
๋ชฉ + self.๋งค์ํ ์ข
๋ชฉ
logger.info("[%s]์ค๋ ๊ฑฐ๋ ์ข
๋ชฉ : %s" % (self.sName, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';'))
if length(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';') # ์ค์๊ฐ ์์ธ์กฐํ ๋ฑ๋ก
logger.debug("์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s" % ret)
except Exception as e:
print('[%s]_Run Error : %s' % (self.sName,e))
Telegram('[StockTrader][%s]_Run Error : %s' % (self.sName,e), send='mc')
logger.error('[StockTrader][%s]_Run Error : %s' % (self.sName,e))
else:
if self.์กฐ๊ฑด๊ฒ์ํ์
== 0:
self.parent.ConditionTick.stop() # MainWindow ํ์ด๋จธ ์ค์ง
else:
ret = self.sendConditionStop("0156", self.์กฐ๊ฑด์๋ช
, self.์กฐ๊ฑด์์ธ๋ฑ์ค) # ์ค์๊ฐ ์กฐ๊ฒ ๊ฒ์ ์ค์ง
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
if self.portfolio is not None:
for code in list(self.portfolio.keys()):
if self.portfolio[code].์๋ == 0:
self.portfolio.pop(code)
if length(self.๊ธ์ผ๋งค๋์ข
๋ชฉ) > 0:
try:
Telegram("[StockTrader]%s ๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload : %s" % (self.sName, self.๊ธ์ผ๋งค๋์ข
๋ชฉ), send='mc')
logger.info("[%s]๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload : %s" % (self.sName, self.๊ธ์ผ๋งค๋์ข
๋ชฉ))
self.parent.statusbar.showMessage("๊ธ์ผ ๋งค๋ ์ข
๋ชฉ ์์ต Upload")
self.DailyProfit(self.๊ธ์ผ๋งค๋์ข
๋ชฉ)
except Exception as e:
print('%s ๊ธ์ผ๋งค๋์ข
๋ชฉ ๊ฒฐ๊ณผ ์
๋ก๋ Error : %s' %(self.sName, e))
fintotal_ally:
del self.DailyProfitLoop # ๊ธ์ผ๋งค๋๊ฒฐ๊ณผ ์
๋ฐ์ดํธ ์ QEventLoop ์ฌ์ฉ์ผ๋ก ๋ก๋ด ์ ์ฅ ์ pickcle ์๋ฌ ๋ฐ์ํ์ฌ ์ญ์ ์ํด
del self.ConditionLoop
self.KiwoomDisConnect() # ๋ก๋ด ํด๋์ค ๋ด์์ ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ๋ฐ์ดํฐ๋ฅผ ๋ฐ๊ณ ๋์ ์ฐ๊ฒฐ ํด์ ์ํด
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
class ํ๋ฉด_ConditionMonitoring(QDialog, Ui_TradeCondition):
def __init__(self, sScreenNo, kiwoom=None, parent=None): #
super(ํ๋ฉด_ConditionMonitoring, self).__init__(parent)
# self.setAttribute(Qt.WA_DeleteOnClose) # ์์ ฏ์ด ๋ซํ๋ ๋ด์ฉ ์ญ์ ํ๋ ๊ฒ์ผ๋ก ์ฐฝ์ด ๋ซํ๋ ์ ๋ณด๋ฅผ ์ ์ฅํด์ผ๋๋ ๋ก๋ด ์ธํ
์์๋ ์ฐ๋ฉด ์๋ฌ๋จ!!
self.setupUi(self)
self.setWindowTitle("ConditionMonitoring")
self.lineEdit_name.setText('ConditionMonitoring')
self.progressBar.setValue(0) # Progressbar ์ด๊ธฐ ์
ํ
self.sScreenNo = sScreenNo
self.kiwoom = kiwoom #
self.parent = parent
self.model = MonkeyModel()
self.tableView.setModel(self.model)
self.columns = ['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', '์กฐ๊ฑด์']
self.result = []
self.KiwoomConnect()
self.GetCondition()
# ์ ์ฅ๋ ์กฐ๊ฑด ๊ฒ์์ ๋ชฉ๋ก ์ฝ์
def GetCondition(self):
try:
self.gettingConditionLoad()
self.kf_condition = KnowledgeFrame()
self.idx = []
self.conName = []
for index in self.condition.keys(): # condition์ dictionary
# print(self.condition)
self.idx.adding(str(index))
self.conName.adding(self.condition[index])
# self.sendCondition("0156", self.condition[index], index, 1)
self.kf_condition['Index'] = self.idx
self.kf_condition['Name'] = self.conName
self.kf_condition['Table'] = ">> ์กฐ๊ฑด์ " + self.kf_condition['Index'] + " : " + self.kf_condition['Name']
self.kf_condition['Index'] = self.kf_condition['Index'].totype(int)
self.kf_condition = self.kf_condition.sort_the_values(by='Index').reseting_index(sip=True) # ์ถ๊ฐ
print(self.kf_condition) # ์ถ๊ฐ
self.comboBox_condition.clear()
self.comboBox_condition.addItems(self.kf_condition['Table'].values)
except Exception as e:
print("GetCondition_Error")
print(e)
# ์กฐ๊ฑด๊ฒ์ ํด๋น ์ข
๋ชฉ ์์ฒญ ๋ฉ์๋
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int",
screenNo, conditionName, conditionIndex, isRealTime)
# OnReceiveTrCondition() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ์กฐ๊ฑด ๊ฒ์ ๊ด๋ จ ActiveX์ On์๋ฆฌ์ฆ์ ๋ถ์(์ฝ๋ฐฑ)
def KiwoomConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
# ์กฐ๊ฑด ๊ฒ์ ๊ด๋ จ ActiveX์ On์๋ฆฌ์ฆ ์ฐ๊ฒฐ ํด์
def KiwoomDisConnect(self):
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ ๋ฉ์๋
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# OnReceiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ์กฐ๊ฑด์ ๋ชฉ๋ก ํ๋ ๋ฉ์๋(์กฐ๊ฑด์ ๋ชฉ๋ก์ ๋์
๋๋ฆฌ๋ก ๋ฆฌํด)
def gettingConditionNameList(self):
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# ์กฐ๊ฑด๊ฒ์ ์ธ๋ถ ์ข
๋ชฉ ์กฐํ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
def OnReceiveTrCondition(self, sScrNo, strCodeList, strConditionName, nIndex, nNext):
logger.debug('main:OnReceiveTrCondition [%s] [%s] [%s] [%s] [%s]' % (
sScrNo, strCodeList, strConditionName, nIndex, nNext))
try:
if strCodeList == "":
return
self.codeList = strCodeList.split(';')
del self.codeList[-1]
# print("์ข
๋ชฉ๊ฐ์: ", length(self.codeList))
# print(self.codeList)
for code in self.codeList:
row = []
# code.adding(c)
row.adding(code)
n = self.kiwoom.dynamicCtotal_all("GetMasterCodeName(QString)", code)
# now = abs(int(self.kiwoom.dynamicCtotal_all("GetCommRealData(QString, int)", code, 10)))
# name.adding(n)
row.adding(n)
row.adding(strConditionName)
self.result.adding(row)
# self.kf_con['์ข
๋ชฉ์ฝ๋'] = code
# self.kf_con['์ข
๋ชฉ๋ช
'] = name
# print(self.kf_con)
self.data = KnowledgeFrame(data=self.result, columns=self.columns)
self.data['์ข
๋ชฉ์ฝ๋'] = "'" + self.data['์ข
๋ชฉ์ฝ๋']
self.data = self.data.sort_the_values(by=['์กฐ๊ฑด์', '์ข
๋ชฉ๋ช
'])
self.data = self.data.sip_duplicates(['์ข
๋ชฉ๋ช
', '์กฐ๊ฑด์'], keep='first').reseting_index(sip=True)
print(self.data)
self.model.umkate(self.data)
# self.model.umkate(self.kf_con)
for i in range(length(self.columns)):
self.tableView.resizeColumnToContents(i)
fintotal_ally:
time.sleep(2)
self.conditionLoop.exit()
# ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ์ ๋ํ ์๋ต ์ด๋ฒคํธ
def OnReceiveConditionVer(self, lRet, sMsg):
logger.debug('main:OnReceiveConditionVer : [์ด๋ฒคํธ] ์กฐ๊ฑด์ ์ ์ฅ [%s] [%s]' % (lRet, sMsg))
try:
self.condition = self.gettingConditionNameList() # condition์ด ๋ฆฌํด๋์ ์ค๋ฉด GetCondition์์ condition ๋ณ์ ์ฌ์ฉ ๊ฐ๋ฅ
# print("์กฐ๊ฑด์ ๊ฐ์: ", length(self.condition))
# for key in self.condition.keys():
# print("์กฐ๊ฑด์: ", key, ": ", self.condition[key])
except Exception as e:
print("OnReceiveConditionVer_Error")
fintotal_ally:
self.conditionLoop.exit()
# print(self.conditionName)
# self.kiwoom.dynamicCtotal_all("SendCondition(QString,QString, int, int)", '0156', '๊ฐญ์์น', 0, 0)
# ์ค์๊ฐ ์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ์ ๋ฐ์๋๋ ์ด๋ฒคํธ
def OnReceiveRealCondition(self, sTrCode, strType, strConditionName, strConditionIndex):
logger.debug(
'main:OnReceiveRealCondition [%s] [%s] [%s] [%s]' % (sTrCode, strType, strConditionName, strConditionIndex))
print("์ข
๋ชฉ์ฝ๋: ", sTrCode)
print("์ด๋ฒคํธ: ", "์ข
๋ชฉํธ์
" if strType == "I" else "์ข
๋ชฉ์ดํ")
# ์กฐ๊ฑด์ ์ข
๋ชฉ ๊ฒ์ ๋ฒํผ ํด๋ฆญ ์ ์คํ๋จ(์๊ทธ๋/์ฌ๋กฏ ์ถ๊ฐ)
def inquiry(self):
self.result = []
cnt=0
print('์กฐ๊ฑด์ ๊ฐฏ์ :', length(self.kf_condition))
for idx in range(length(self.kf_condition)):
print(idx, self.condition[idx])
self.sendCondition("0156", self.condition[idx], idx, 0)
cnt += 1
# Progress Bar ๋์คํ๋ ์ด(์ ์ฒด ์๊ฐ ๋๋น ๋น์จ)
self.progressBar.setValue(cnt / length(self.kf_condition) * 100)
print('์กฐ๊ฑด์ ์ข
๋ชฉ ์กฐํ ์๋ฃ')
self.parent.statusbar.showMessage("์กฐ๊ฑด์ ์ข
๋ชฉ ์กฐํ ์๋ฃ")
# ์ํ๋ ์ข
๋ชฉ/์ฃผ๊ฐ ์ค์ ํ ์๋ฆผ
class CPriceMonitoring(CTrade): # ๋ก๋ด ์ถ๊ฐ ์ __init__ : ๋ณต์ฌ, Setting, ์ด๊ธฐ์กฐ๊ฑด:์ ๋ต์ ๋ง๊ฒ, ๋ฐ์ดํฐ์ฒ๋ฆฌ~Run:๋ณต์ฌ
def __init__(self, sName, UUID, kiwoom=None, parent=None):
self.sName = sName
self.UUID = UUID
self.sAccount = None
self.kiwoom = kiwoom
self.parent = parent
self.running = False
self.์ฃผ๋ฌธ๊ฒฐ๊ณผ = dict()
self.์ฃผ๋ฌธ๋ฒํธ_์ฃผ๋ฌธ_๋งคํ = dict()
self.์ฃผ๋ฌธ์คํ์ค_Lock = dict()
self.portfolio = dict()
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = []
self.Smtotal_allScreenNumber = 9999
self.d = today
# RobotAdd ํจ์์์ ์ด๊ธฐํ ๋ค์ ์
ํ
์คํํด์ ์ค์ ๊ฐ ๋๊น
def Setting(self, sScreenNo):
self.sScreenNo = sScreenNo
# ์๋ ํฌํธํด๋ฆฌ์ค ์์ฑ
def manual_portfolio(self):
self.portfolio = dict()
self.Stocklist = {
'005935': {'์ข
๋ชฉ๋ช
': '์ผ์ฑ์ ์์ฐ', '์ข
๋ชฉ์ฝ๋': '005935', '์์ฅ': 'KOSPI', '๋งค์๊ฐ': 50600,
'์๋': 10, '๋งค์์ผ': '2020/09/24 09:00:00'},
'092130': {'์ข
๋ชฉ๋ช
': '์ดํฌ๋ ๋๋ธ', '์ข
๋ชฉ์ฝ๋': '092130', '์์ฅ': 'KOSDAQ', '๋งค์๊ฐ': 24019,
'์๋': 21, '๋งค์์ผ': '2020/11/04 09:00:00'},
'271560': {'์ข
๋ชฉ๋ช
': '์ค๋ฆฌ์จ', '์ข
๋ชฉ์ฝ๋': '271560', '์์ฅ': 'KOSPI', '๋งค์๊ฐ': 132000,
'์๋': 10, '๋งค์์ผ': '2020/10/08 09:00:00'},
}
for code in list(self.Stocklist.keys()):
self.portfolio[code] = CPortStock_LongTerm(์ข
๋ชฉ์ฝ๋=code,
์ข
๋ชฉ๋ช
=self.Stocklist[code]['์ข
๋ชฉ๋ช
'],
์์ฅ=self.Stocklist[code]['์์ฅ'],
๋งค์๊ฐ=self.Stocklist[code]['๋งค์๊ฐ'],
์๋=self.Stocklist[code]['์๋'],
๋งค์์ผ=self.Stocklist[code]['๋งค์์ผ'])
# Robot_Run์ด ๋๋ฉด ์คํ๋จ - ๋งค์/๋งค๋ ์ข
๋ชฉ์ ๋ฆฌ์คํธ๋ก ์ ์ฅ
def ์ด๊ธฐ์กฐ๊ฑด(self):
self.parent.statusbar.showMessage("[%s] ์ด๊ธฐ์กฐ๊ฑด์ค๋น" % (self.sName))
row_data = price_monitoring_sheet.getting_total_all_values()
self.stocklist = {}
self.Data_save = False
for row in row_data[1:]:
temp = []
try:
code, name, market = getting_code(row[0]) # ์ข
๋ชฉ๋ช
์ผ๋ก ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์์ฅ ๋ฐ์์(getting_code ํจ์) ์ถ๊ฐ
except Exception as e:
name = ''
code = ''
market = ''
print('๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ข
๋ชฉ๋ช
์ค๋ฅ : %s' % (row[1]))
logger.error('๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s' % (row[1]))
Telegram('[StockTrader]๊ตฌ๊ธ ๋งค์๋ชจ๋ํฐ๋ง ์ํธ ์ค๋ฅ : %s' % (row[1]))
for idx in range(1, length(row)):
if row[idx] != '':
temp.adding(int(row[idx]))
self.stocklist[code] = {
'์ข
๋ชฉ๋ช
': name,
'์ข
๋ชฉ์ฝ๋': code,
'๋ชจ๋ํฐ๋ง์ฃผ๊ฐ': temp
}
print(self.stocklist)
self.๋ชจ๋ํฐ๋ง์ข
๋ชฉ = list(self.stocklist.keys())
try:
self.kf_codes = mk.KnowledgeFrame()
cnt = 0
for code in self.๋ชจ๋ํฐ๋ง์ข
๋ชฉ:
temp = fdr.DataReader(code)
temp = temp[-70:][['Open', 'High', 'Low', 'Close', 'Volume']]
temp.reseting_index(inplace=True)
temp['Date'] = temp['Date'].totype(str)
temp['Code'] = code
if cnt == 0:
self.kf_codes = temp.clone()
else:
self.kf_codes = mk.concating([self.kf_codes, temp])
self.kf_codes.reseting_index(sip=True, inplace=True)
cnt += 1
except Exception as e:
print('CPriceMonitoring_์ด๊ธฐ์กฐ๊ฑด ์ค๋ฅ : %s' % (e))
logger.error('CPriceMonitoring_์ด๊ธฐ์กฐ๊ฑด ์ค๋ฅ : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_์ด๊ธฐ์กฐ๊ฑด ์ค๋ฅ : %s' % (e))
# ์ด๋ํ๊ท ๊ฐ ์์น ํ์ธ
def MA_Check(self, data):
if data['MA5'] < data['MA20']:
return True
else:
return False
# ์ด๋ํ๊ท ์ ์ด์ฉํ ๋งค์ ์ ๋ต ์ ํธ ๋ฐ์
def MA_Strategy(self, name, code, price):
today = datetime.datetime.today().strftime("%Y-%m-%d")
ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ๊ฑฐ๋๋ = price
try:
kf = self.kf_codes.loc[self.kf_codes['Code'] == code]
kf.reseting_index(sip=True, inplace=True)
kf.loc[length(kf)] = [today, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ํ์ฌ๊ฐ, ๊ฑฐ๋๋, code] #['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Code]
kf['MA5'] = kf['Close'].rolling(window=5).average()
kf['MA20'] = kf['Close'].rolling(window=20).average()
kf['MA_Check'] = kf.employ(self.MA_Check, axis=1)
if self.Data_save==False and current_time >= '15:19:00':
self.Data_save = True
self.kf_codes.to_csv('PriceData.csv', encoding='euc-kr', index=False)
if kf.iloc[-2]['MA_Check'] == True and kf.iloc[-1]['MA_Check'] == False:
Telegram('[StockTrader]%s ๋งค์ ์ ํธ ๋ฐ์\nํ์ฌ๊ฐ : %s, ์๊ฐ : %s, ๊ณ ๊ฐ : %s, ์ ๊ฐ : %s' % (name, ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ))
logger.info('[StockTrader]%s ๋งค์ ์ ํธ ๋ฐ์\nํ์ฌ๊ฐ : %s, ์๊ฐ : %s, ๊ณ ๊ฐ : %s, ์ ๊ฐ : %s' % (name, ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ))
except Exception as e:
print('CPriceMonitoring_MA_Strategy ์ค๋ฅ : %s' % (e))
logger.error('CPriceMonitoring_MA_Strategy ์ค๋ฅ : %s' % (e))
Telegram('[StockTrader]CPriceMonitoring_MA_Strategy ์ค๋ฅ : %s' % (e))
def ์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ(self, param):
try:
if self.running == True:
์ฒด๊ฒฐ์๊ฐ = '%s %s:%s:%s' % (str(self.d), param['์ฒด๊ฒฐ์๊ฐ'][0:2], param['์ฒด๊ฒฐ์๊ฐ'][2:4], param['์ฒด๊ฒฐ์๊ฐ'][4:])
์ข
๋ชฉ์ฝ๋ = param['์ข
๋ชฉ์ฝ๋']
ํ์ฌ๊ฐ = abs(int(float(param['ํ์ฌ๊ฐ'])))
์ ์ผ๋๋น = int(float(param['์ ์ผ๋๋น']))
๋ฑ๋ฝ๋ฅ = float(param['๋ฑ๋ฝ๋ฅ '])
๋งค๋ํธ๊ฐ = abs(int(float(param['๋งค๋ํธ๊ฐ'])))
๋งค์ํธ๊ฐ = abs(int(float(param['๋งค์ํธ๊ฐ'])))
๋์ ๊ฑฐ๋๋ = abs(int(float(param['๋์ ๊ฑฐ๋๋'])))
์๊ฐ = abs(int(float(param['์๊ฐ'])))
๊ณ ๊ฐ = abs(int(float(param['๊ณ ๊ฐ'])))
์ ๊ฐ = abs(int(float(param['์ ๊ฐ'])))
๊ฑฐ๋ํ์ ์จ = abs(float(param['๊ฑฐ๋ํ์ ์จ']))
์๊ฐ์ด์ก = abs(int(float(param['์๊ฐ์ด์ก'])))
์ข
๋ชฉ๋ช
= self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][1] # pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก]
์์ฅ๊ตฌ๋ถ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][0]
์ ์ผ์ข
๊ฐ = self.parent.CODE_POOL[์ข
๋ชฉ์ฝ๋][3]
์์ธ = [ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, ๋์ ๊ฑฐ๋๋]
self.parent.statusbar.showMessage("[%s] %s %s %s %s" % (์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น))
# print("[%s] %s %s %s %s" % (์ฒด๊ฒฐ์๊ฐ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์ ์ผ๋๋น))
if length(self.stocklist[์ข
๋ชฉ์ฝ๋]['๋ชจ๋ํฐ๋ง์ฃผ๊ฐ']) > 0:
if ํ์ฌ๊ฐ in self.stocklist[์ข
๋ชฉ์ฝ๋]['๋ชจ๋ํฐ๋ง์ฃผ๊ฐ']:
Telegram('[StockTrader]%s ์ฃผ๊ฐ๋๋ฌ ์๋ฆผ\nํ์ฌ๊ฐ : %s, ์๊ฐ : %s, ๊ณ ๊ฐ : %s, ์ ๊ฐ : %s' % (์ข
๋ชฉ๋ช
, ํ์ฌ๊ฐ, ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ))
self.stocklist[์ข
๋ชฉ์ฝ๋]['๋ชจ๋ํฐ๋ง์ฃผ๊ฐ'].remove(ํ์ฌ๊ฐ)
self.MA_Strategy(์ข
๋ชฉ๋ช
, ์ข
๋ชฉ์ฝ๋, ์์ธ)
except Exception as e:
print('CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e))
Telegram('[StockTrader]CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error : %s, %s' % (์ข
๋ชฉ๋ช
, e), send='mc')
logger.error('CTradeLongTerm_์ค์๊ฐ๋ฐ์ดํฐ์ฒ๋ฆฌ Error :%s, %s' % (์ข
๋ชฉ๋ช
, e))
def ์ ์์ฒ๋ฆฌ(self, param):
pass
def ์ฒด๊ฒฐ์ฒ๋ฆฌ(self, param):
pass
def ์๊ณ ์ฒ๋ฆฌ(self, param):
pass
def Run(self, flag=True, sAccount=None):
self.running = flag
ret = 0
# self.manual_portfolio()
if flag == True:
print("%s ROBOT ์คํ" % (self.sName))
try:
Telegram("[StockTrader]%s ROBOT ์คํ" % (self.sName))
self.์ด๊ธฐ์กฐ๊ฑด()
print('์ด๊ธฐ์กฐ๊ฑด ์ค์ ์๋ฃ')
self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ = self.๋ชจ๋ํฐ๋ง์ข
๋ชฉ
logger.info("์ค๋ ๊ฑฐ๋ ์ข
๋ชฉ : %s %s" % (self.sName, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';'))
self.KiwoomConnect() # MainWindow ์ธ์์ ํค์ API๊ตฌ๋์์ผ์ ์์ฒด์ ์ผ๋ก API๋ฐ์ดํฐ์ก์์ ๊ฐ๋ฅํ๋๋ก ํจ
if length(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
ret = self.KiwoomSetRealReg(self.sScreenNo, ';'.join(self.์ค์๊ฐ์ข
๋ชฉ๋ฆฌ์คํธ) + ';')
logger.debug("[%s]์ค์๊ฐ๋ฐ์ดํ์์ฒญ ๋ฑ๋ก๊ฒฐ๊ณผ %s" % (self.sName, ret))
except Exception as e:
print('CPriceMonitoring_Run Error :', e)
Telegram('[StockTrader]CPriceMonitoring_Run Error : %s' % e, send='mc')
logger.error('CPriceMonitoring_Run Error : %s' % e)
else:
Telegram("[StockTrader]%s ROBOT ์คํ ์ค์ง" % (self.sName))
ret = self.KiwoomSetRealRemove(self.sScreenNo, 'ALL')
self.KiwoomDisConnect() # ๋ก๋ด ํด๋์ค ๋ด์์ ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ๋ฐ์ดํฐ๋ฅผ ๋ฐ๊ณ ๋์ ์ฐ๊ฒฐ ํด์ ์ํด
# ๋ฉ์ธ ํ๋ฉด์ ๋ฐ์
self.parent.RobotView()
##################################################################################
# ๋ฉ์ธ
##################################################################################
Ui_MainWindow, QtBaseClass_MainWindow = uic.loadUiType("./UI/XTrader_MainWindow.ui")
class MainWindow(QMainWindow, Ui_MainWindow):
def __init__(self):
# ํ๋ฉด์ ๋ณด์ฌ์ฃผ๊ธฐ ์ํ ์ฝ๋
super().__init__()
QMainWindow.__init__(self)
Ui_MainWindow.__init__(self)
self.UI_setting()
# ํ์ฌ ์๊ฐ ๋ฐ์
self.์์์๊ฐ = datetime.datetime.now()
# ๋ฉ์ธ์๋์ฐ๊ฐ ๋จ๊ณ ํค์์ฆ๊ถ๊ณผ ๋ถ์ด๊ธฐ ์ํ ์์
self.KiwoomAPI() # ํค์ ActiveX๋ฅผ ๋ฉ๋ชจ๋ฆฌ์ ์ฌ๋ฆผ
self.KiwoomConnect() # ๋ฉ๋ชจ๋ฆฌ์ ์ฌ๋ผ์จ ActiveX์ ๋ด๊ฐ ๋ง๋ ํจ์ On์๋ฆฌ์ฆ์ ์ฐ๊ฒฐ(์ฝ๋ฐฑ : ์ด๋ฒคํธ๊ฐ ์ค๋ฉด ๋๋ฅผ ๋ถ๋ฌ์ค)
self.ScreenNumber = 5000
self.robots = []
self.dialog = dict()
# self.dialog['๋ฆฌ์ผ๋ฐ์ดํ'] = None
# self.dialog['๊ณ์ข์ ๋ณด์กฐํ'] = None
self.model = MonkeyModel()
self.tableView_robot.setModel(self.model)
self.tableView_robot.setSelectionBehavior(QTableView.SelectRows)
self.tableView_robot.setSelectionMode(QTableView.SingleSelection)
self.tableView_robot.pressed.connect(self.RobotCurrentIndex)
# self.connect(self.tableView_robot.selectionModel(), SIGNAL("currentRowChanged(QModelIndex,QModelIndex)"), self.RobotCurrentIndex)
self.tableView_robot_current_index = None
self.portfolio_model = MonkeyModel()
self.tableView_portfolio.setModel(self.portfolio_model)
self.tableView_portfolio.setSelectionBehavior(QTableView.SelectRows)
self.tableView_portfolio.setSelectionMode(QTableView.SingleSelection)
# self.portfolio_model.umkate((KnowledgeFrame(columns=['์ข
๋ชฉ์ฝ๋', '์ข
๋ชฉ๋ช
', '๋งค์๊ฐ', '์๋', '๋งค์์ผ'])))
self.robot_columns = ['Robotํ์
', 'Robot๋ช
', 'RobotID', '์คํฌ๋ฆฐ๋ฒํธ', '์คํ์ํ', 'ํฌํธ์', 'ํฌํธํด๋ฆฌ์ค']
# TODO: ์ฃผ๋ฌธ์ ํ ์ค์
self.timer = QTimer(self)
self.timer.timeout.connect(self.limit_per_second) # ์ด๋น 4๋ฒ
# QtCore.QObject.connect(self.timer, QtCore.SIGNAL("timeout()"), self.limit_per_second)
self.timer.start(1000) # 1์ด๋ง๋ค ๋ฆฌ์
self.ConditionTick = QTimer(self)
self.ConditionTick.timeout.connect(self.OnConditionCheck)
self.์ฃผ๋ฌธ์ ํ = 0
self.์กฐํ์ ํ = 0
self.๊ธ์ผ๋ฐฑ์
์์
์ค = False
self.์ข
๋ชฉ์ ์ ์์
์ค = False
self.ConditionCheck = False
self.์กฐ๊ฑด์์ ์ฅ์นด์ดํธ = 1
self.DailyData = False # ๊ด์ฌ์ข
๋ชฉ ์ผ๋ด ์
๋ฐ์ดํธ
self.InvestorData = False # ๊ด์ฌ์ข
๋ชฉ ์ข
๋ชฉ๋ณํฌ์์ ์
๋ฐ์ดํธ
self.kf_daily = KnowledgeFrame()
self.kf_weekly = KnowledgeFrame()
self.kf_monthly = KnowledgeFrame()
self.kf_investor = KnowledgeFrame()
self._login = False
self.KiwoomLogin() # ํ๋ก๊ทธ๋จ ์คํ ์ ์๋๋ก๊ทธ์ธ
self.CODE_POOL = self.getting_code_pool() # DB ์ข
๋ชฉ๋ฐ์ด๋ธ์์ ์์ฅ๊ตฌ๋ถ, ์ฝ๋, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ ์ฝ์ด์ด
# ํ๋ฉด Setting
def UI_setting(self):
self.setupUi(self)
self.setWindowTitle("XTrader")
self.setWindowIcon(QIcon('./PNG/icon_stock.png'))
self.actionLogin.setIcon(QIcon('./PNG/Internal.png'))
self.actionLogout.setIcon(QIcon('./PNG/External.png'))
self.actionExit.setIcon(QIcon('./PNG/Approval.png'))
self.actionAccountDialog.setIcon(QIcon('./PNG/Sales Performance.png'))
self.actionMinutePrice.setIcon(QIcon('./PNG/Candle Sticks.png'))
self.actionDailyPrice.setIcon(QIcon('./PNG/Overtime.png'))
self.actionInvestors.setIcon(QIcon('./PNG/Conference Ctotal_all.png'))
self.actionSectorView.setIcon(QIcon('./PNG/Organization.png'))
self.actionSectorPriceView.setIcon(QIcon('./PNG/Ratings.png'))
self.actionCodeBuild.setIcon(QIcon('./PNG/Inspection.png'))
self.actionRobotOneRun.setIcon(QIcon('./PNG/Process.png'))
self.actionRobotOneStop.setIcon(QIcon('./PNG/Cancel 2.png'))
self.actionRobotMonitoringStop.setIcon(QIcon('./PNG/Cancel File.png'))
self.actionRobotRun.setIcon(QIcon('./PNG/Checked.png'))
self.actionRobotStop.setIcon(QIcon('./PNG/Cancel.png'))
self.actionRobotRemove.setIcon(QIcon('./PNG/Delete File.png'))
self.actionRobotClear.setIcon(QIcon('./PNG/Empty Trash.png'))
self.actionRobotView.setIcon(QIcon('./PNG/Checked 2.png'))
self.actionRobotSave.setIcon(QIcon('./PNG/Download.png'))
self.actionTradeShortTerm.setIcon(QIcon('./PNG/Bullish.png'))
self.actionTradeCondition.setIcon(QIcon('./PNG/Search.png'))
self.actionConditionMonitoring.setIcon(QIcon('./PNG/Binoculars.png'))
# ์ข
๋ชฉ ์ ์
def stock_analysis(self):
try:
self.AnalysisPriceList = self.AnalysisPriceList
except:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
self.AnalysisPriceList = robot.Stocklist['์ ๋ต']['์์ธ์กฐํ๋จ์']
self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ = mk.KnowledgeFrame(shortterm_analysis_sheet.getting_total_all_records()) # shortterm_analysis_sheet
self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ = self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ[['๋ฒํธ', '์ข
๋ชฉ๋ช
']]
row = []
# print(self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ)
for name in self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ['์ข
๋ชฉ๋ช
'].values:
try:
code, name, market = getting_code(name)
except Exception as e:
code = ''
print('getting_code Error :', name, e)
row.adding(code)
self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ['์ข
๋ชฉ์ฝ๋'] = row
self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ = self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ[self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ['์ข
๋ชฉ์ฝ๋'] != '']
print(self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ)
self.์ข
๋ชฉ๋ฆฌ์คํธ = list(self.์ข
๋ชฉ์ ์ ๋ฐ์ดํฐ[['๋ฒํธ', '์ข
๋ชฉ๋ช
', '์ข
๋ชฉ์ฝ๋']].values)
self.์ข
๋ชฉ์ฝ๋ = self.์ข
๋ชฉ๋ฆฌ์คํธ.pop(0)
if self.DailyData == True:
self.start = datetime.datetime.now()
print(self.start)
self.ReguestPriceDaily()
elif self.InvestorData == True:
self.RequestInvestorDaily()
elif self.WeeklyData == True:
self.ReguestPriceWeekly()
elif self.MonthlyData == True:
self.ReguestPriceMonthly()
# ์ผ๋ด๋ฐ์ดํฐ์กฐํฌ
def ReguestPriceDaily(self, _repeat=0):
try:
๊ธฐ์ค์ผ์ = datetime.date.today().strftime('%Y%m%d')
self.์ข
๋ชฉ์ผ๋ด = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ธฐ์ค์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ฃผ์์ผ๋ด์ฐจํธ์กฐํ", "OPT10081",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("๊ด์ฌ์ข
๋ชฉ ์ผ๋ด ๋ฐ์ดํฐ : %s %s %s" % (self.์ข
๋ชฉ์ฝ๋[0], self.์ข
๋ชฉ์ฝ๋[1], self.์ข
๋ชฉ์ฝ๋[2]))
except Exception as e:
print(e)
# ์ฃผ๋ด๋ฐ์ดํฐ์กฐํ
def ReguestPriceWeekly(self, _repeat=0):
try:
๊ธฐ์ค์ผ์ = datetime.date.today().strftime('%Y%m%d')
self.์ข
๋ชฉ์ฃผ๋ด = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ธฐ์ค์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ฃผ์์ฃผ๋ด์ฐจํธ์กฐํ", "OPT10082",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("๊ด์ฌ์ข
๋ชฉ ์ฃผ๋ด ๋ฐ์ดํฐ : %s %s %s" % (self.์ข
๋ชฉ์ฝ๋[0], self.์ข
๋ชฉ์ฝ๋[1], self.์ข
๋ชฉ์ฝ๋[2]))
except Exception as e:
print(e)
# ์๋ด๋ฐ์ดํฐ์กฐํ
def ReguestPriceMonthly(self, _repeat=0):
try:
๊ธฐ์ค์ผ์ = datetime.date.today().strftime('%Y%m%d')
self.์ข
๋ชฉ์๋ด = []
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ธฐ์ค์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์ ์ฃผ๊ฐ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ฃผ์์๋ด์ฐจํธ์กฐํ", "OPT10083",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("๊ด์ฌ์ข
๋ชฉ ์๋ด ๋ฐ์ดํฐ : %s %s %s" % (self.์ข
๋ชฉ์ฝ๋[0], self.์ข
๋ชฉ์ฝ๋[1], self.์ข
๋ชฉ์ฝ๋[2]))
except Exception as e:
print(e)
# ์ข
๋ชฉ๋ณํฌ์์์กฐํฌ
def RequestInvestorDaily(self, _repeat=0):
๊ธฐ์ค์ผ์ = datetime.date.today().strftime('%Y%m%d')
self.์ข
๋ชฉ๋ณํฌ์์ = []
try:
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ผ์", ๊ธฐ์ค์ผ์)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", self.์ข
๋ชฉ์ฝ๋[2])
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๊ธ์ก์๋๊ตฌ๋ถ", 2) # 1:๊ธ์ก, 2:์๋
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๋งค๋งค๊ตฌ๋ถ", 0) # 0:์๋งค์, 1:๋งค์, 2:๋งค๋
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, int)', "๋จ์๊ตฌ๋ถ", 1) # 1000:์ฒ์ฃผ, 1:๋จ์ฃผ
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ข
๋ชฉ๋ณํฌ์์์กฐํ", "OPT10060",
_repeat,
'{:04d}'.formating(self.ScreenNumber))
self.statusbar.showMessage("๊ด์ฌ์ข
๋ชฉ ์ข
๋ชฉ๋ณํฌ์์ ๋ฐ์ดํฐ : %s %s %s" % (self.์ข
๋ชฉ์ฝ๋[0], self.์ข
๋ชฉ์ฝ๋[1], self.์ข
๋ชฉ์ฝ๋[2]))
except Exception as e:
print(e)
# DB ๋ฐ์ดํฐ ์ ์ฅ
def UploadAnalysisData(self, data, ๊ตฌ๋ถ):
# shortterm_analysis_sheet = test_analysis_sheet
row = []
if ๊ตฌ๋ถ == '์ผ๋ด':
try:
data['์ผ๋ด1'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[0]).average()
data['์ผ๋ด2'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[1]).average()
data['์ผ๋ด3'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[2]).average()
data['์ผ๋ด4'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[3]).average()
result = data.iloc[-1].values
# ๊ตฌ๊ธ ์
๋ก๋
# row.adding(self.์ข
๋ชฉ์ฝ๋[0])
# row.adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
# row.adding(str(value_round((int(data.iloc[-2]['๊ฑฐ๋๋']) / int(data.iloc[-1]['๊ฑฐ๋๋']) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('์ผ๋ด1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('์ผ๋ด2')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('์ผ๋ด3')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# cell = alpha_list[shortterm_analysis_cols.index('์ผ๋ด4')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[4])
# cell = alpha_list[shortterm_analysis_cols.index('๊ฑฐ๋๋')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[5])
# DB ์ ์ฅ
dict = {'๋ฒํธ': [],
'์ข
๋ชฉ๋ช
': [],
'์ข
๋ชฉ์ฝ๋': [],
'์ผ๋ด1': [],
'์ผ๋ด2': [],
'์ผ๋ด3': [],
'์ผ๋ด4': [],
'๊ฑฐ๋๋': []}
dict['๋ฒํธ'].adding(str(self.์ข
๋ชฉ์ฝ๋[0]))
dict['์ข
๋ชฉ๋ช
'].adding(self.์ข
๋ชฉ์ฝ๋[1])
dict['์ข
๋ชฉ์ฝ๋'].adding(self.์ข
๋ชฉ์ฝ๋[2])
dict['์ผ๋ด1'].adding(str(value_round((result[3] / int(result[1]) - 1) * 100, 2)) + '%')
dict['์ผ๋ด2'].adding(str(value_round((result[4] / int(result[1]) - 1) * 100, 2)) + '%')
dict['์ผ๋ด3'].adding(str(value_round((result[5] / int(result[1]) - 1) * 100, 2)) + '%')
dict['์ผ๋ด4'].adding(str(value_round((result[6] / int(result[1]) - 1) * 100, 2)) + '%')
dict['๊ฑฐ๋๋'].adding(
str(value_round((int(data.iloc[-2]['๊ฑฐ๋๋']) / int(data.iloc[-1]['๊ฑฐ๋๋']) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_daily = mk.concating([self.kf_daily, temp])
except Exception as e:
print('UploadDailyPriceData Error : ', e)
elif ๊ตฌ๋ถ == '์ฃผ๋ด':
try:
data['์ฃผ๋ด1'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[4]).average()
result = data.iloc[-1].values
# ๊ตฌ๊ธ ์
๋ก๋
# row.adding(self.์ข
๋ชฉ์ฝ๋[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('์ฃผ๋ด1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB ์ ์ฅ
dict = {'์ข
๋ชฉ์ฝ๋': [],
'์ฃผ๋ด1': []
}
dict['์ข
๋ชฉ์ฝ๋'].adding(self.์ข
๋ชฉ์ฝ๋[2])
dict['์ฃผ๋ด1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_weekly = mk.concating([self.kf_weekly, temp])
except Exception as e:
print('UploadWeeklyPriceData Error : ', e)
elif ๊ตฌ๋ถ == '์๋ด':
try:
data['์๋ด1'] = data['ํ์ฌ๊ฐ'].rolling(window=self.AnalysisPriceList[5]).average()
result = data.iloc[-1].values
# ๊ตฌ๊ธ ์
๋ก๋
# row.adding(self.์ข
๋ชฉ์ฝ๋[0])
# row.adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('์๋ด1')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# DB ์ ์ฅ
dict = {'์ข
๋ชฉ์ฝ๋': [],
'์๋ด1': []
}
dict['์ข
๋ชฉ์ฝ๋'].adding(self.์ข
๋ชฉ์ฝ๋[2])
dict['์๋ด1'].adding(str(value_round((result[2] / int(result[1]) - 1) * 100, 2)) + '%')
temp = KnowledgeFrame(dict)
self.kf_monthly = mk.concating([self.kf_monthly, temp])
except Exception as e:
print('UploadmonthlyPriceData Error : ', e)
elif ๊ตฌ๋ถ == '์ข
๋ชฉ๋ณํฌ์์':
try:
result = data.iloc[-1].values
# ๊ตฌ๊ธ ์
๋ก๋
# row.adding(self.์ข
๋ชฉ์ฝ๋[0])
# row.adding(result[1]) # ๊ธฐ๊ด
# row.adding(result[2]) # ์ธ๊ตญ์ธ
# row.adding(result[3]) # ๊ฐ์ธ
# print(row)
#
# code_row = shortterm_analysis_sheet.findtotal_all(row[0])[-1].row
#
# cell = alpha_list[shortterm_analysis_cols.index('๊ธฐ๊ด์๊ธ')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[1])
# cell = alpha_list[shortterm_analysis_cols.index('์ธ์ธ์๊ธ')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[2])
# cell = alpha_list[shortterm_analysis_cols.index('๊ฐ์ธ')] + str(code_row)
# shortterm_analysis_sheet.umkate_acell(cell, row[3])
# DB ์ ์ฅ
dict = {'์ข
๋ชฉ์ฝ๋': [],
'๊ธฐ๊ด': [],
'์ธ์ธ': [],
'๊ฐ์ธ': []}
dict['์ข
๋ชฉ์ฝ๋'].adding(self.์ข
๋ชฉ์ฝ๋[2])
dict['๊ธฐ๊ด'].adding(result[1]) # ๊ธฐ๊ด
dict['์ธ์ธ'].adding(result[2]) # ์ธ๊ตญ์ธ
dict['๊ฐ์ธ'].adding(result[3]) # ๊ฐ์ธ
temp = KnowledgeFrame(dict)
self.kf_investor = mk.concating([self.kf_investor, temp])
except Exception as e:
print('UploadDailyInvestorData Error : ', e)
# DB์ ์ ์ฅ๋ ์์ฅ ์ข
๋ชฉ ์ฝ๋ ์ฝ์
def getting_code_pool(self):
query = """
select ์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์ ์ผ์ข
๊ฐ*์ฃผ์์ as ์๊ฐ์ด์ก
from ์ข
๋ชฉ์ฝ๋
order by ์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ์ฝ๋
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
pool = dict()
for idx, row in kf.traversal():
์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ์ฝ๋, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก = row
pool[์ข
๋ชฉ์ฝ๋] = [์์ฅ๊ตฌ๋ถ, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ, ์๊ฐ์ด์ก]
return pool
# ๊ตฌ๊ธ์คํ๋ ๋์ํธ ์ข
๋ชฉ Import
def Import_ShortTermStock(self, check):
try:
data = import_googlesheet()
if check == False:
# # ๋งค์ ์ ๋ต๋ณ ๋ณ๋ ๋ก๋ด ์ด์ ์
# # ๋งค์ ์ ๋ต ํ์ธ
# strategy_list = list(data['๋งค์์ ๋ต'].distinctive())
#
# # ๋ก๋ฉ๋ ๋ก๋ด์ robot_list์ ์ ์ฅ
# robot_list = []
# for robot in self.robots:
# robot_list.adding(robot.sName.split('_')[0])
#
# # ๋งค์ ์ ๋ต๋ณ ๋ก๋ด ์๋ ํธ์ง/์ถ๊ฐ
# for strategy in strategy_list:
# kf_stock = data[data['๋งค์์ ๋ต'] == strategy]
#
# if strategy in robot_list:
# print('๋ก๋ด ํธ์ง')
# Telegram('[StockTrader]๋ก๋ด ํธ์ง')
# for robot in self.robots:
# if robot.sName.split('_')[0] == strategy:
# self.RobotAutoEdit_TradeShortTerm(robot, kf_stock)
# self.RobotView()
# break
# else:
# print('๋ก๋ด ์ถ๊ฐ')
# Telegram('[StockTrader]๋ก๋ด ์ถ๊ฐ')
# self.RobotAutoAdd_TradeShortTerm(kf_stock, strategy)
# self.RobotView()
# ๋ก๋ฉ๋ ๋ก๋ด์ robot_list์ ์ ์ฅ
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
for robot in self.robots:
if robot.sName == 'TradeShortTerm':
print('๋ก๋ด ํธ์ง')
logger.debug('๋ก๋ด ํธ์ง')
self.RobotAutoEdit_TradeShortTerm(robot, data)
self.RobotView()
break
else:
print('๋ก๋ด ์ถ๊ฐ')
logger.debug('๋ก๋ด ์ถ๊ฐ')
self.RobotAutoAdd_TradeShortTerm(data)
self.RobotView()
# print("๋ก๋ด ์ค๋น ์๋ฃ")
# Slack('[XTrader]๋ก๋ด ์ค๋น ์๋ฃ')
# logger.info("๋ก๋ด ์ค๋น ์๋ฃ")
except Exception as e:
print('MainWindow_Import_ShortTermStock Error', e)
Telegram('[StockTrader]MainWindow_Import_ShortTermStock Error : %s' % e, send='mc')
logger.error('MainWindow_Import_ShortTermStock Error : %s' % e)
# ๊ธ์ผ ๋งค๋ ์ข
๋ชฉ์ ๋ํด์ ์์ต๋ฅ , ์์ต๊ธ, ์์๋ฃ ์์ฒญ(์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ)
# def DailyProfit(self, ๊ธ์ผ๋งค๋์ข
๋ชฉ):
# _repeat = 0
# # self.sAccount = ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ
# # self.sScreenNo = self.ScreenNumber
# ์์์ผ์ = datetime.date.today().strftime('%Y%m%d')
# cnt=1
# for ์ข
๋ชฉ์ฝ๋ in ๊ธ์ผ๋งค๋์ข
๋ชฉ:
# self.umkate_cnt = length(๊ธ์ผ๋งค๋์ข
๋ชฉ) - cnt
# cnt += 1
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.sAccount)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์ข
๋ชฉ์ฝ๋", ์ข
๋ชฉ์ฝ๋)
# ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์์์ผ์", ์์์ผ์)
# ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "์ผ์๋ณ์ข
๋ชฉ๋ณ์คํ์์ต์์ฒญ", "OPT10072", _repeat, '{:04d}'.formating(self.ScreenNumber))
#
# self.DailyProfitLoop = QEventLoop() # ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ๊ณ์ข ์กฐํํด์ ์ข
๋ชฉ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
# self.DailyProfitLoop.exec_()
# ์ผ๋ณ์ข
๋ชฉ๋ณ์คํ์์ต ์๋ต ๊ฒฐ๊ณผ ๊ตฌ๊ธ ์
๋ก๋
# def DailyProfitUpload(self, ๋งค๋๊ฒฐ๊ณผ):
# # ๋งค๋๊ฒฐ๊ณผ ['์ข
๋ชฉ๋ช
','์ฒด๊ฒฐ๋','๋งค์
๋จ๊ฐ','์ฒด๊ฒฐ๊ฐ','๋น์ผ๋งค๋์์ต','์์ต์จ','๋น์ผ๋งค๋งค์์๋ฃ','๋น์ผ๋งค๋งค์ธ๊ธ']
# print(๋งค๋๊ฒฐ๊ณผ)
#
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# history_sheet = history_sheet
# history_cols = history_cols
# elif r.sName == 'TradeCondition':
# history_sheet = condition_history_sheet
# history_cols = condition_history_cols
#
# code_row = history_sheet.findtotal_all(๋งค๋๊ฒฐ๊ณผ[0])[-1].row
#
# ๊ณ์ฐ์์ต๋ฅ = value_round((int(float(๋งค๋๊ฒฐ๊ณผ[3])) / int(float(๋งค๋๊ฒฐ๊ณผ[2])) - 1) * 100, 2)
#
# cell = alpha_list[history_cols.index('๋งค์๊ฐ')] + str(code_row) # ๋งค์
๋จ๊ฐ
# history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[2])))
#
# cell = alpha_list[history_cols.index('๋งค๋๊ฐ')] + str(code_row) # ์ฒด๊ฒฐ๊ฐ
# history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[3])))
#
# cell = alpha_list[history_cols.index('์์ต๋ฅ (๊ณ์ฐ)')] + str(code_row) # ์์ต๋ฅ ๊ณ์ฐ
# history_sheet.umkate_acell(cell, ๊ณ์ฐ์์ต๋ฅ )
#
# cell = alpha_list[history_cols.index('์์ต๋ฅ ')] + str(code_row) # ์์ต์จ
# history_sheet.umkate_acell(cell, ๋งค๋๊ฒฐ๊ณผ[5])
#
# cell = alpha_list[history_cols.index('์์ต๊ธ')] + str(code_row) # ์์ต์จ
# history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[4])))
#
# cell = alpha_list[history_cols.index('์ธ๊ธ+์์๋ฃ')] + str(code_row) # ๋น์ผ๋งค๋งค์์๋ฃ + ๋น์ผ๋งค๋งค์ธ๊ธ
# history_sheet.umkate_acell(cell, int(float(๋งค๋๊ฒฐ๊ณผ[6])) + int(float(๋งค๋๊ฒฐ๊ณผ[7])))
#
# self.DailyProfitLoop.exit()
#
# if self.umkate_cnt == 0:
# print('๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ')
# Slack("[XTrader]๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ")
# logger.info("[XTrader]๊ธ์ผ ์คํ ์์ต ๊ตฌ๊ธ ์
๋ก๋ ์๋ฃ")
# ์กฐ๊ฑด ๊ฒ์์ ์ฝ์ด์ ํด๋น ์ข
๋ชฉ ์ ์ฅ
def GetCondition(self):
# logger.info("์กฐ๊ฑด ๊ฒ์์ ์ข
๋ชฉ ์ฝ๊ธฐ")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
conditions = ['๋งค๋ฌผ๋๊ฑฐ๋๋','์ธ๊ตญ์ธ๊ธฐ๊ด์๊ธ', '์ฃผ๋์ฃผ', '๋น์ผ์ฃผ๋์ฃผ', '๊ธฐ๋ณธ์ฃผ๋์ฃผ','์คํ ์บ์คํฑ&MACD&๊ฑฐ๋๋ํ์ ์จ', '๊ฐญ์์น']
try:
self.gettingConditionLoad()
self.conditionid = []
self.conditionname = []
for index in self.condition.keys(): # condition์ dictionary
# print(self.condition)
if self.condition[index] in conditions:
self.conditionid.adding(str(index))
self.conditionname.adding(self.condition[index])
print('์กฐ๊ฑด ๊ฒ์ ์์')
print(index, self.condition[index])
self.sendCondition("0156", self.condition[index], index, 0)
except Exception as e:
print("GetCondition_Error")
print(e)
fintotal_ally:
# print(self.kf_condition)
query = """
select * from ์กฐ๊ฑด๊ฒ์์
"""
conn = sqliteconn()
kf = mk.read_sql(query, con=conn)
conn.close()
kf = kf.sip_duplicates(['์นด์ดํธ', '์ข
๋ชฉ๋ช
'], keep='first')
kf = kf.sort_the_values(by=['์นด์ดํธ','์ธ๋ฑ์ค']).reseting_index(sip=True)
savetime = today.strftime('%Y%m%d') + '_'+ current_time.replacing(':','')
kf.to_csv(savetime +"_์กฐ๊ฑด๊ฒ์์ข
๋ชฉ.csv", encoding='euc-kr', index=False)
self.์กฐ๊ฑด์์ ์ฅ์นด์ดํธ += 1
self.ConditionCheck = False
logger.info("์กฐ๊ฑด ๊ฒ์์ ์ข
๋ชฉ ์ ์ฅ์๋ฃ")
self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
# ์กฐ๊ฑด์ ๋ชฉ๋ก ์์ฒญ ๋ฉ์๋
def gettingConditionLoad(self):
self.kiwoom.dynamicCtotal_all("GetConditionLoad()")
# receiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ์กฐ๊ฑด์ ํ๋ ๋ฉ์๋
def gettingConditionNameList(self):
# ์กฐ๊ฑด์์ ๋์
๋๋ฆฌ ํํ๋ก ๋ฐํํฉ๋๋ค.
# ์ด ๋ฉ์๋๋ ๋ฐ๋์ receiveConditionVer() ์ด๋ฒคํธ ๋ฉ์๋์์์ ์ฌ์ฉํด์ผ ํฉ๋๋ค.
#
# :return: dict - {์ธ๋ฑ์ค:์กฐ๊ฑด๋ช
, ์ธ๋ฑ์ค:์กฐ๊ฑด๋ช
, ...}
data = self.kiwoom.dynamicCtotal_all("GetConditionNameList()")
conditionList = data.split(';')
del conditionList[-1]
conditionDictionary = {}
for condition in conditionList:
key, value = condition.split('^')
conditionDictionary[int(key)] = value
return conditionDictionary
# ์ข
๋ชฉ ์กฐ๊ฑด๊ฒ์ ์์ฒญ ๋ฉ์๋
def sendCondition(self, screenNo, conditionName, conditionIndex, isRealTime):
# ์ด ๋ฉ์๋๋ก ์ป๊ณ ์ ํ๋ ๊ฒ์ ํด๋น ์กฐ๊ฑด์ ๋ง๋ ์ข
๋ชฉ์ฝ๋์ด๋ค.
# ํด๋น ์ข
๋ชฉ์ ๋ํ ์์ธ์ ๋ณด๋ setRealReg() ๋ฉ์๋๋ก ์์ฒญํ ์ ์๋ค.
# ์์ฒญ์ด ์คํจํ๋ ๊ฒฝ์ฐ๋, ํด๋น ์กฐ๊ฑด์์ด ์๊ฑฐ๋, ์กฐ๊ฑด๋ช
๊ณผ ์ธ๋ฑ์ค๊ฐ ๋ง์ง ์๊ฑฐ๋, ์กฐํ ํ์๋ฅผ ์ด๊ณผํ๋ ๊ฒฝ์ฐ ๋ฐ์ํ๋ค.
#
# ์กฐ๊ฑด๊ฒ์์ ๋ํ ๊ฒฐ๊ณผ๋
# 1ํ์ฑ ์กฐํ์ ๊ฒฝ์ฐ, receiveTrCondition() ์ด๋ฒคํธ๋ก ๊ฒฐ๊ณผ๊ฐ์ด ์ ๋ฌ๋๋ฉฐ
# ์ค์๊ฐ ์กฐํ์ ๊ฒฝ์ฐ, receiveTrCondition()๊ณผ receiveRealCondition() ์ด๋ฒคํธ๋ก ๊ฒฐ๊ณผ๊ฐ์ด ์ ๋ฌ๋๋ค.
#
# :param screenNo: string
# :param conditionName: string - ์กฐ๊ฑด์ ์ด๋ฆ
# :param conditionIndex: int - ์กฐ๊ฑด์ ์ธ๋ฑ์ค
# :param isRealTime: int - ์กฐ๊ฑด๊ฒ์ ์กฐํ๊ตฌ๋ถ(0: 1ํ์ฑ ์กฐํ, 1: ์ค์๊ฐ ์กฐํ)
isRequest = self.kiwoom.dynamicCtotal_all("SendCondition(QString, QString, int, int)",
screenNo, conditionName, conditionIndex, isRealTime)
# receiveTrCondition() ์ด๋ฒคํธ ๋ฉ์๋์์ ๋ฃจํ ์ข
๋ฃ
self.conditionLoop = QEventLoop()
self.conditionLoop.exec_()
# ํ๋ก๊ทธ๋จ ์คํ 3์ด ํ ์คํ
def OnQApplicationStarted(self):
# 1. 8์ 58๋ถ ์ด์ ์ผ ๊ฒฝ์ฐ 5๋ถ ๋จ์ ๊ตฌ๊ธ์ํธ ์คํจ ์ฒดํฌ ํ์ด๋จธ ์์์ํด
current = datetime.datetime.now()
current_time = current.strftime('%H:%M:%S')
"""
if '07:00:00' <= current_time and current_time <= '08:58:00':
print('๊ตฌ๊ธ ์ํธ ์ค๋ฅ ์ฒดํฌ ์์')
# Telegram('[StockTrader]๊ตฌ๊ธ ์ํธ ์ค๋ฅ ์ฒดํฌ ์์')
self.statusbar.showMessage("๊ตฌ๊ธ ์ํธ ์ค๋ฅ ์ฒดํฌ ์์")
self.checkclock = QTimer(self)
self.checkclock.timeout.connect(self.OnGoogleCheck) # 5๋ถ๋ง๋ค ๊ตฌ๊ธ ์ํธ ์ฝ์ : MainWindow.OnGoogleCheck ์คํ
self.checkclock.start(300000) # 300000์ด๋ง๋ค ํ์ด๋จธ ์๋
"""
# 2. DB์ ์ ์ฅ๋ ๋ก๋ด ์ ๋ณด๋ฐ์์ด
global ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ
try:
with sqlite3.connect(DATABASE) as conn:
cursor = conn.cursor()
cursor.execute("select value from Setting where keyword='robotaccount'")
for row in cursor.fetchtotal_all():
# _temp = base64.decodestring(row[0]) # base64์ textํํด์ ์ํธํ : DB์ ์ ๋ฃ๊ธฐ ์ํจ
_temp = base64.decodebytes(row[0])
๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ = pickle.loads(_temp)
print('๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ', ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ)
cursor.execute('select uuid, strategy, name, robot from Robots')
self.robots = []
for row in cursor.fetchtotal_all():
uuid, strategy, name, robot_encoded = row
robot = base64.decodebytes(robot_encoded)
# r = base64.decodebytes(robot_encoded)
r = pickle.loads(robot)
r.kiwoom = self.kiwoom
r.parent = self
r.d = today
r.running = False
# logger.debug(r.sName, r.UUID, length(r.portfolio))
self.robots.adding(r)
except Exception as e:
print('OnQApplicationStarted', e)
self.RobotView()
# ํ๋ก๊ทธ๋จ ์คํ ํ 1์ด ๋ง๋ค ์คํ : ์กฐ๊ฑด์ ๋ง๋ ์๊ฐ์ด ๋๋ฉด ๋ฐฑ์
์์
def OnClockTick(self):
current = datetime.datetime.now()
global current_time
current_time = current.strftime('%H:%M:%S')
# 8์ 32๋ถ : ์ข
๋ชฉ ๋ฐ์ด๋ธ ์์ฑ
if current_time == '08:32:00':
print('์ข
๋ชฉํ
์ด๋ธ ์์ฑ')
# Slack('[XTrader]์ข
๋ชฉํ
์ด๋ธ ์์ฑ')
self.StockCodeBuild(to_db=True)
self.CODE_POOL = self.getting_code_pool() # DB ์ข
๋ชฉ๋ฐ์ด๋ธ์์ ์์ฅ๊ตฌ๋ถ, ์ฝ๋, ์ข
๋ชฉ๋ช
, ์ฃผ์์, ์ ์ผ์ข
๊ฐ ์ฝ์ด์ด
self.statusbar.showMessage("์ข
๋ชฉํ
์ด๋ธ ์์ฑ")
"""
# 8์ 59๋ถ : ๊ตฌ๊ธ ์ํธ ์ข
๋ชฉ Import
if current_time == '08:59:00':
print('๊ตฌ๊ธ ์ํธ ์ค๋ฅ ์ฒดํฌ ์ค์ง')
# Telegram('[StockTrader]๊ตฌ๊ธ ์ํธ ์ค๋ฅ ์ฒดํฌ ์ค์ง')
self.checkclock.stop()
robot_list = []
for robot in self.robots:
robot_list.adding(robot.sName)
if 'TradeShortTerm' in robot_list:
print('๊ตฌ๊ธ์ํธ Import')
Telegram('[StockTrader]๊ตฌ๊ธ์ํธ Import')
self.Import_ShortTermStock(check=False)
self.statusbar.showMessage('๊ตฌ๊ธ์ํธ Import')
"""
# 8์ 59๋ถ 30์ด : ๋ก๋ด ์คํ
if '09:00:00' <= current_time and current_time < '09:00:05':
try:
if length(self.robots) > 0:
for r in self.robots:
if r.running == False: # ๋ก๋ด์ด ์คํ์ค์ด ์๋๋ฉด
r.Run(flag=True, sAccount=๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ)
self.RobotView()
except Exception as e:
print('Robot Auto Run Error', e)
Telegram('[StockTrader]Robot Auto Run Error : %s' % e, send='mc')
logger.error('Robot Auto Run Error : %s' % e)
# TradeShortTerm ๋ณด์ ์ผ ๋ง๊ธฐ ๋งค๋ ์ ๋ต ์ฒดํฌ์ฉ
# if current_time >= '15:29:00' and current_time < '15:29:30':
# if length(self.robots) > 0:
# for r in self.robots:
# if r.sName == 'TradeShortTerm':
# if r.holdcheck == False:
# r.holdcheck = True
# r.hold_strategy()
# 15์ 17๋ถ :TradeCondition ๋น์ผ์ฒญ์ฐ ๋งค๋ ์คํ
if current_time >= '15:17:00' and current_time < '15:17:30':
if length(self.robots) > 0:
for r in self.robots:
if r.sName == 'TradeCondition' and '๋น์ผ์ฒญ์ฐ' in r.์กฐ๊ฑด์๋ช
:
if r.clearcheck == False:
r.clearcheck = True
r.clearning_strategy()
# 16์ 00๋ถ : ๋ก๋ด ์ ์ง
if '15:40:00' <= current_time and current_time < '15:40:05':
self.RobotStop()
# 16์ 05๋ถ : ํ๋ก๊ทธ๋จ ์ข
๋ฃ
if '15:45:00' <= current_time and current_time < '15:45:05':
quit()
# 18์ 00๋ถ : ์ข
๋ชฉ ๋ถ์์ ์ํ ์ผ๋ด, ์ข
๋ชฉ๋ณํฌ์์์ ๋ณด ์
๋ฐ์ดํธ
# if '18:00:00' <= current_time and current_time < '18:00:05':
# if self.DailyData == False:
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# Telegram("[XTrader]๊ด์ฌ์ข
๋ชฉ ๋ฐ์ดํฐ ์
๋ฐ์ดํธ", send='mc')
# self.stock_analysis()
# if '153600' < current_time and current_time < '153659' and self.๊ธ์ผ๋ฐฑ์
์์
์ค == False and self._login == True:# and current.weekday() == 4:
# ์๋ฅ์ผ์ด๋ฉด ์๋ ์๊ฐ ์กฐ๊ฑด์ผ๋ก ์์
# if '17:00:00' < current.strftime('%H:%M:%S') and current.strftime('%H:%M:%S') < '17:00:59' and self.๊ธ์ผ๋ฐฑ์
์์
์ค == False and self._login == True:
# self.๊ธ์ผ๋ฐฑ์
์์
์ค = True
# self.Backup(์์
=None)
# pass
# ๋ก๋ด์ ์ ์ฅ
# if self.์์์๊ฐ.strftime('%H:%M:%S') > '08:00:00' and self.์์์๊ฐ.strftime('%H:%M:%S') < '15:30:00' and current.strftime('%H:%M:%S') > '01:00:00':
# if length(self.robots) > 0:
# self.RobotSave()
# for k in self.dialog:
# self.dialog[k].KiwoomDisConnect()
# try:
# self.dialog[k].close()
# except Exception as e:
# pass
# self.close()
# ์ง์ ์๊ฐ์ ๋ก๋ด์ ์ค์งํ๋ค๋๊ฐ ์ํ๋ ์คํ์ ์๋ pass์ ์์ฑ
# if current_time > '08:58:00' and current_time <= '15:30:00':
# if current.second == 0 and current.getting_minute % 3 == 0 and self.ConditionCheck == False:
# self.ConditionCheck = True
# self.GetCondition()
# if current.weekday() in workday_list: # ์ฃผ์ค์ธ์ง ํ์ธ
# if current_time in savetime_list: # ์ง์ ๋ ์๊ฐ์ธ์ง ํ์ธ
# logger.info("์กฐ๊ฑด๊ฒ์์ ํ์ด๋จธ ์๋")
# Telegram(str(current)[:-7] + " : " + "์กฐ๊ฑด๊ฒ์์ ์ข
๋ชฉ ๊ฒ์")
# self.GetCondition() # ์กฐ๊ฑด๊ฒ์์์ ๋ชจ๋ ์ฝ์ด์ ํด๋นํ๋ ์ข
๋ชฉ ์ ์ฅ
# if current.second == 0: # ๋งค 0์ด
# # if current.getting_minute % 10 == 0: # ๋งค 10 ๋ถ
# if current.getting_minute == 1 or current.strftime('%H:%M:%S') == '09:30:00' or current.strftime('%H:%M:%S') == '15:15:00': # ๋งค์ 1๋ถ
# logger.info("์กฐ๊ฑด๊ฒ์์ ํ์ด๋จธ ์๋")
# Telegram(str(current)[:-7] + " : " + "์กฐ๊ฑด๊ฒ์์ ์ข
๋ชฉ ๊ฒ์")
# # print(current.getting_minute, current.second)
# self.GetCondition() # ์กฐ๊ฑด๊ฒ์์์ ๋ชจ๋ ์ฝ์ด์ ํด๋นํ๋ ์ข
๋ชฉ ์ ์ฅ
# for r in self.robots:
# if r.running == True: # ๋ก๋ด์ด ์คํ์ค์ด๋ฉด
# # print(r.sName, r.running)
# pass
# ์ฃผ๋ฌธ ์ ํ ์ด๊ธฐํ
def limit_per_second(self):
self.์ฃผ๋ฌธ์ ํ = 0
self.์กฐํ์ ํ = 0
# logger.info("์ด๋น์ ํ ์ฃผ๋ฌธ ํด๋ฆฌ์ด")
def OnConditionCheck(self):
try:
current = datetime.datetime.now()
if current.second == 0 and current.getting_minute % 3 == 0:
for robot in self.robots:
if 'TradeCondition' in robot.sName:
if robot.์กฐ๊ฑด๊ฒ์ํ์
== 0:
robot.ConditionCheck()
except Exception as e:
print(e)
# 5๋ถ ๋ง๋ค ์คํ : ๊ตฌ๊ธ ์คํ๋ ๋ ์ํธ ์ค๋ฅ ํ์ธ
def OnGoogleCheck(self):
self.Import_ShortTermStock(check=True)
# ๋ฉ์ธ ์๋์ฐ์์์ ๋ชจ๋ ์ก์
์ ๋ํ ์ฒ๋ฆฌ
def MENU_Action(self, qaction):
logger.debug("Action Slot %s %s " % (qaction.objectName(), qaction.text()))
try:
_action = qaction.objectName()
if _action == "actionExit":
if length(self.robots) > 0:
self.RobotSave()
for k in self.dialog:
self.dialog[k].KiwoomDisConnect()
try:
self.dialog[k].close()
except Exception as e:
pass
self.close()
elif _action == "actionLogin":
self.KiwoomLogin()
elif _action == "actionLogout":
self.KiwoomLogout()
elif _action == "actionDailyPrice":
# self.F_dailyprice()
if self.dialog.getting('์ผ์๋ณ์ฃผ๊ฐ') is not None:
try:
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'].show()
except Exception as e:
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'] = ํ๋ฉด_์ผ๋ณ์ฃผ๊ฐ(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'].KiwoomConnect()
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'].show()
else:
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'] = ํ๋ฉด_์ผ๋ณ์ฃผ๊ฐ(sScreenNo=9902, kiwoom=self.kiwoom, parent=self)
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'].KiwoomConnect()
self.dialog['์ผ์๋ณ์ฃผ๊ฐ'].show()
elif _action == "actionMinutePrice":
# self.F_getting_minprice()
if self.dialog.getting('๋ถ๋ณ์ฃผ๊ฐ') is not None:
try:
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'].show()
except Exception as e:
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'] = ํ๋ฉด_๋ถ๋ณ์ฃผ๊ฐ(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'].KiwoomConnect()
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'].show()
else:
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'] = ํ๋ฉด_๋ถ๋ณ์ฃผ๊ฐ(sScreenNo=9903, kiwoom=self.kiwoom, parent=self)
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'].KiwoomConnect()
self.dialog['๋ถ๋ณ์ฃผ๊ฐ'].show()
elif _action == "actionInvestors":
# self.F_investor()
if self.dialog.getting('์ข
๋ชฉ๋ณํฌ์์') is not None:
try:
self.dialog['์ข
๋ชฉ๋ณํฌ์์'].show()
except Exception as e:
self.dialog['์ข
๋ชฉ๋ณํฌ์์'] = ํ๋ฉด_์ข
๋ชฉ๋ณํฌ์์(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['์ข
๋ชฉ๋ณํฌ์์'].KiwoomConnect()
self.dialog['์ข
๋ชฉ๋ณํฌ์์'].show()
else:
self.dialog['์ข
๋ชฉ๋ณํฌ์์'] = ํ๋ฉด_์ข
๋ชฉ๋ณํฌ์์(sScreenNo=9904, kiwoom=self.kiwoom, parent=self)
self.dialog['์ข
๋ชฉ๋ณํฌ์์'].KiwoomConnect()
self.dialog['์ข
๋ชฉ๋ณํฌ์์'].show()
elif _action == "actionAccountDialog": # ๊ณ์ข์ ๋ณด์กฐํ
if self.dialog.getting('๊ณ์ข์ ๋ณด์กฐํ') is not None: # dialog : __init__()์ dict๋ก ์ ์๋จ
try:
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'].show()
except Exception as e:
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'] = ํ๋ฉด_๊ณ์ข์ ๋ณด(sScreenNo=7000, kiwoom=self.kiwoom,
parent=self) # self๋ ๋ฉ์ธ์๋์ฐ, ๊ณ์ข์ ๋ณด์๋์ฐ๋ ์์์๋์ฐ/๋ถ๋ชจ๋ ๋ฉ์ธ์๋์ฐ
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'].KiwoomConnect()
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'].show()
else:
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'] = ํ๋ฉด_๊ณ์ข์ ๋ณด(sScreenNo=7000, kiwoom=self.kiwoom, parent=self)
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'].KiwoomConnect()
self.dialog['๊ณ์ข์ ๋ณด์กฐํ'].show()
elif _action == "actionSectorView":
# self.F_sectorview()
if self.dialog.getting('์
์ข
์ ๋ณด์กฐํ') is not None:
try:
self.dialog['์
์ข
์ ๋ณด์กฐํ'].show()
except Exception as e:
self.dialog['์
์ข
์ ๋ณด์กฐํ'] = ํ๋ฉด_์
์ข
์ ๋ณด(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['์
์ข
์ ๋ณด์กฐํ'].KiwoomConnect()
self.dialog['์
์ข
์ ๋ณด์กฐํ'].show()
else:
self.dialog['์
์ข
์ ๋ณด์กฐํ'] = ํ๋ฉด_์
์ข
์ ๋ณด(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['์
์ข
์ ๋ณด์กฐํ'].KiwoomConnect()
self.dialog['์
์ข
์ ๋ณด์กฐํ'].show()
elif _action == "actionSectorPriceView":
# self.F_sectorpriceview()
if self.dialog.getting('์
์ข
๋ณ์ฃผ๊ฐ์กฐํ') is not None:
try:
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'].show()
except Exception as e:
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'] = ํ๋ฉด_์
์ข
๋ณ์ฃผ๊ฐ(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'].KiwoomConnect()
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'].show()
else:
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'] = ํ๋ฉด_์
์ข
๋ณ์ฃผ๊ฐ(sScreenNo=9900, kiwoom=self.kiwoom, parent=self)
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'].KiwoomConnect()
self.dialog['์
์ข
๋ณ์ฃผ๊ฐ์กฐํ'].show()
elif _action == "actionTradeShortTerm":
self.RobotAdd_TradeShortTerm()
self.RobotView()
elif _action == "actionTradeCondition": # ํค์ ์กฐ๊ฑด๊ฒ์์์ ์ด์ฉํ ํธ๋ ์ด๋ฉ
# print("MainWindow : MENU_Action_actionTradeCondition")
self.RobotAdd_TradeCondition()
self.RobotView()
elif _action == "actionConditionMonitoring":
print("MainWindow : MENU_Action_actionConditionMonitoring")
self.ConditionMonitoring()
elif _action == "actionTradeLongTerm":
self.RobotAdd_TradeLongTerm()
self.RobotView()
elif _action == "actionPriceMonitoring":
self.RobotAdd_PriceMonitoring()
self.RobotView()
elif _action == "actionRobotLoad":
self.RobotLoad()
self.RobotView()
elif _action == "actionRobotSave":
self.RobotSave()
elif _action == "actionRobotOneRun":
self.RobotOneRun()
self.RobotView()
elif _action == "actionRobotOneStop":
self.RobotOneStop()
self.RobotView()
elif _action == "actionRobotMonitoringStop":
self.RobotOneMonitoringStop()
self.RobotView()
elif _action == "actionRobotRun":
self.RobotRun()
self.RobotView()
elif _action == "actionRobotStop":
self.RobotStop()
self.RobotView()
elif _action == "actionRobotRemove":
self.RobotRemove()
self.RobotView()
elif _action == "actionRobotClear":
self.RobotClear()
self.RobotView()
elif _action == "actionRobotView":
self.RobotView()
for r in self.robots:
logger.debug('%s %s %s %s' % (r.sName, r.UUID, length(r.portfolio), r.GetStatus()))
elif _action == "actionCodeBuild":
self.์ข
๋ชฉ์ฝ๋ = self.StockCodeBuild(to_db=True)
QMessageBox.about(self, "์ข
๋ชฉ์ฝ๋ ์์ฑ", " %s ํญ๋ชฉ์ ์ข
๋ชฉ์ฝ๋๋ฅผ ์์ฑํ์์ต๋๋ค." % (length(self.์ข
๋ชฉ์ฝ๋.index)))
elif _action == "actionTest":
# self.DailyData = True
# self.WeeklyData = False
# self.MonthlyData = False
# self.InvestorData = False
# self.stock_analysis()
# print(self.robots)
# for robot in self.robots:
# if robot.sName == 'TradeShortTerm':
# print(robot.Stocklist['์ ๋ต']['์์ธ์กฐํ๋จ์'])
self.GetCondition()
except Exception as e:
print(e)
# ํค์์ฆ๊ถ OpenAPI
# ํค์API ActiveX๋ฅผ ๋ฉ๋ชจ๋ฆฌ์ ์ฌ๋ฆผ
def KiwoomAPI(self):
self.kiwoom = QAxWidgetting("KHOPENAPI.KHOpenAPICtrl.1")
# ๋ฉ๋ชจ๋ฆฌ์ ์ฌ๋ผ์จ ActiveX์ On์๋ฆฌ์ฆ์ ๋ถ์(์ฝ๋ฐฑ : ์ด๋ฒคํธ๊ฐ ์ค๋ฉด ๋๋ฅผ ๋ถ๋ฌ์ค)
def KiwoomConnect(self):
self.kiwoom.OnEventConnect[int].connect(
self.OnEventConnect) # ํค์์ OnEventConnect์ ์ด ํ๋ก๊ทธ๋จ์ OnEventConnect ํจ์์ ์ฐ๊ฒฐ์ํด
self.kiwoom.OnReceiveMsg[str, str, str, str].connect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].connect(self.OnReceiveTrCondition)
self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].connect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].connect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].connect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].connect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].connect(self.OnReceiveRealData)
# ActiveX์ On์๋ฆฌ์ฆ ์ฐ๊ฒฐ ํด์
def KiwoomDisConnect(self):
print('MainWindow KiwoomDisConnect')
self.kiwoom.OnEventConnect[int].disconnect(self.OnEventConnect)
self.kiwoom.OnReceiveMsg[str, str, str, str].disconnect(self.OnReceiveMsg)
# self.kiwoom.OnReceiveTrCondition[str, str, str, int, int].disconnect(self.OnReceiveTrCondition)
# self.kiwoom.OnReceiveTrData[str, str, str, str, str, int, str, str, str].disconnect(self.OnReceiveTrData)
self.kiwoom.OnReceiveChejanData[str, int, str].disconnect(self.OnReceiveChejanData)
# self.kiwoom.OnReceiveConditionVer[int, str].disconnect(self.OnReceiveConditionVer)
# self.kiwoom.OnReceiveRealCondition[str, str, str, str].disconnect(self.OnReceiveRealCondition)
self.kiwoom.OnReceiveRealData[str, str, str].disconnect(self.OnReceiveRealData)
# ํค์ ๋ก๊ทธ์ธ
def KiwoomLogin(self):
self.kiwoom.dynamicCtotal_all("CommConnect()")
self._login = True
self.statusbar.showMessage("๋ก๊ทธ์ธ...")
# ํค์ ๋ก๊ทธ์์
def KiwoomLogout(self):
if self.kiwoom is not None:
self.kiwoom.dynamicCtotal_all("CommTergetting_minate()")
self.statusbar.showMessage("์ฐ๊ฒฐํด์ ๋จ...")
# ๊ณ์ข ๋ณด์ ์ข
๋ชฉ ๋ฐ์
def InquiryList(self, _repeat=0):
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.sAccount)
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๋น๋ฐ๋ฒํธ์
๋ ฅ๋งค์ฒด๊ตฌ๋ถ", '00')
ret = self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "์กฐํ๊ตฌ๋ถ", '1')
ret = self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "๊ณ์ขํ๊ฐ์๊ณ ๋ด์ญ์์ฒญ", "opw00018",
_repeat, '{:04d}'.formating(self.ScreenNumber))
self.InquiryLoop = QEventLoop() # ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ๊ณ์ข ์กฐํํด์ ์ข
๋ชฉ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
self.InquiryLoop.exec_()
# ๊ณ์ข ๋ฒํธ / D+2 ์์๊ธ ๋ฐ์
def KiwoomAccount(self):
ACCOUNT_CNT = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCOUNT_CNT")')
ACC_NO = self.kiwoom.dynamicCtotal_all('GetLoginInfo("ACCNO")')
self.account = ACC_NO.split(';')[0:-1]
self.sAccount = self.account[0]
global Account
Account = self.sAccount
global ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ
๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ = self.sAccount
print('๊ณ์ข : ', self.sAccount)
print('๋ก๋ด๊ณ์ข : ', ๋ก๋ด๊ฑฐ๋๊ณ์ข๋ฒํธ)
self.kiwoom.dynamicCtotal_all('SetInputValue(Qstring, Qstring)', "๊ณ์ข๋ฒํธ", self.sAccount)
self.kiwoom.dynamicCtotal_all('CommRqData(QString, QString, int, QString)', "d+2์์๊ธ์์ฒญ", "opw00001", 0,
'{:04d}'.formating(self.ScreenNumber))
self.depositLoop = QEventLoop() # self.d2_deposit๋ฅผ ๋ก๋ด์์ ๋ฐ๋ก ์ธ ์ ์๋๋กํ๊ธฐ ์ํด์ ์์๊ธ์ ๋ฐ๊ณ ๋์ ๋ฃจํํด์ ์ํด
self.depositLoop.exec_()
# return (ACCOUNT_CNT, ACC_NO)
def KiwoomSendOrder(self, sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo):
if self.์ฃผ๋ฌธ์ ํ < ์ด๋นํ์์ ํ:
Order = self.kiwoom.dynamicCtotal_all(
'SendOrder(QString, QString, QString, int, QString, int, int, QString, QString)',
[sRQName, sScreenNo, sAccNo, nOrderType, sCode, nQty, nPrice, sHogaGb, sOrgOrderNo])
self.์ฃผ๋ฌธ์ ํ += 1
return (True, Order)
else:
return (False, 0)
# -๊ฑฐ๋๊ตฌ๋ถ๊ฐ ํ์ธ(2์๋ฆฌ)
#
# 00 : ์ง์ ๊ฐ
# 03 : ์์ฅ๊ฐ
# 05 : ์กฐ๊ฑด๋ถ์ง์ ๊ฐ
# 06 : ์ต์ ๋ฆฌ์ง์ ๊ฐ
# 07 : ์ต์ฐ์ ์ง์ ๊ฐ
# 10 : ์ง์ ๊ฐIOC
# 13 : ์์ฅ๊ฐIOC
# 16 : ์ต์ ๋ฆฌIOC
# 20 : ์ง์ ๊ฐFOK
# 23 : ์์ฅ๊ฐFOK
# 26 : ์ต์ ๋ฆฌFOK
# 61 : ์ฅ์ ์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค
# 81 : ์ฅํ ์๊ฐ์ธ์ข
๊ฐ
# 62 : ์๊ฐ์ธ๋จ์ผ๊ฐ๋งค๋งค
#
# -๋งค๋งค๊ตฌ๋ถ๊ฐ (1 ์๋ฆฌ)
# 1 : ์ ๊ท๋งค์
# 2 : ์ ๊ท๋งค๋
# 3 : ๋งค์์ทจ์
# 4 : ๋งค๋์ทจ์
# 5 : ๋งค์์ ์
# 6 : ๋งค๋์ ์
def KiwoomSetRealReg(self, sScreenNo, sCode, sRealType='0'):
ret = self.kiwoom.dynamicCtotal_all('SetRealReg(QString, QString, QString, QString)', sScreenNo, sCode, '9001;10',
sRealType) # 10์ ์ค์๊ฐFID๋ก ๋ฉ๋ด์ผ์ ๋์ด(ํ์ฌ๊ฐ,์ฒด๊ฒฐ๊ฐ, ์ค์๊ฐ์ข
๊ฐ)
return ret
# pass
def KiwoomSetRealRemove(self, sScreenNo, sCode):
ret = self.kiwoom.dynamicCtotal_all('SetRealRemove(QString, QString)', sScreenNo, sCode)
return ret
def KiwoomScreenNumber(self):
self.screen_number += 1
if self.screen_number > 8999:
self.screen_number = 5000
return self.screen_number
def OnEventConnect(self, nErrCode):
# logger.debug('main:OnEventConnect', nErrCode)
if nErrCode == 0:
# self.kiwoom.dynamicCtotal_all("KOA_Functions(QString, QString)", ["ShowAccountWindow", ""]) # ๊ณ์ข ๋น๋ฐ๋ฒํธ ๋ฑ๋ก ์ฐฝ ์คํ(์๋ํ๋ฅผ ์ํด์ AUTO ์ค์ ํ ๋ฑ๋ก ์ฐฝ ๋ฏธ์คํ
self.statusbar.showMessage("๋ก๊ทธ์ธ ์ฑ๊ณต")
current = datetime.datetime.now().strftime('%H:%M:%S')
if current <= '08:58:00':
Telegram("[StockTrader]ํค์API ๋ก๊ทธ์ธ ์ฑ๊ณต")
๋ก๊ทธ์ธ์ํ = True
# ๋ก๊ทธ์ธ ์ฑ๊ณตํ๊ณ ๋ฐ๋ก ๊ณ์ข ๋ฐ ๋ณด์ ์ฃผ์ ๋ชฉ๋ก ์ ์ฅ
self.KiwoomAccount()
self.InquiryList()
# self.GetCondition() # ์กฐ๊ฑด๊ฒ์์์ ๋ชจ๋ ์ฝ์ด์ ํด๋นํ๋ ์ข
๋ชฉ ์ ์ฅ
else:
self.statusbar.showMessage("์ฐ๊ฒฐ์คํจ... %s" % nErrCode)
๋ก๊ทธ์ธ์ํ = False
def OnReceiveMsg(self, sScrNo, sRQName, sTrCode, sMsg):
# logger.debug('main:OnReceiveMsg [%s] [%s] [%s] [%s]' % (sScrNo, sRQName, sTrCode, sMsg))
pass
def OnReceiveTrData(self, sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg):
# logger.debug('main:OnReceiveTrData [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] [%s] ' % (sScrNo, sRQName, sTRCode, sRecordName, sPreNext, nDataLength, sErrorCode, sMessage, sSPlmMsg))
# print("MainWindow : OnReceiveTrData")
if self.ScreenNumber != int(sScrNo):
return
if sRQName == "์ฃผ์๋ถ๋ด์ฐจํธ์กฐํ":
self.์ฃผ์๋ถ๋ด์ปฌ๋ผ = ['์ฒด๊ฒฐ์๊ฐ', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋']
cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
for i in range(0, cnt):
row = []
for j in self.์ฃผ์๋ถ๋ด์ปฌ๋ผ:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and (S[0] == '-' or S[0] == '+'):
S = S[1:].lstrip('0')
row.adding(S)
self.์ข
๋ชฉ๋ถ๋ด.adding(row)
if sPreNext == '2' and False:
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.ReguestPriceMin(_repeat=2))
else:
kf = KnowledgeFrame(data=self.์ข
๋ชฉ๋ถ๋ด, columns=self.์ฃผ์๋ถ๋ด์ปฌ๋ผ)
kf['์ฒด๊ฒฐ์๊ฐ'] = kf['์ฒด๊ฒฐ์๊ฐ'].employ(
lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:8] + ' ' + x[8:10] + ':' + x[10:12] + ':' + x[12:])
kf['์ข
๋ชฉ์ฝ๋'] = self.์ข
๋ชฉ์ฝ๋[0]
kf['ํฑ๋ฒ์'] = self.ํฑ๋ฒ์
kf = kf[['์ข
๋ชฉ์ฝ๋', 'ํฑ๋ฒ์', '์ฒด๊ฒฐ์๊ฐ', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋']]
values = list(kf.values)
try:
kf.ix[kf.ํ์ฌ๊ฐ == '', ['ํ์ฌ๊ฐ']] = 0
except Exception as e:
pass
try:
kf.ix[kf.์๊ฐ == '', ['์๊ฐ']] = 0
except Exception as e:
pass
try:
kf.ix[kf.๊ณ ๊ฐ == '', ['๊ณ ๊ฐ']] = 0
except Exception as e:
pass
try:
kf.ix[kf.์ ๊ฐ == '', ['์ ๊ฐ']] = 0
except Exception as e:
pass
try:
kf.ix[kf.๊ฑฐ๋๋ == '', ['๊ฑฐ๋๋']] = 0
except Exception as e:
pass
if sRQName == "์ฃผ์์ผ๋ด์ฐจํธ์กฐํ":
try:
self.์ฃผ์์ผ๋ด์ปฌ๋ผ = ['์ผ์', 'ํ์ฌ๊ฐ', '๊ฑฐ๋๋'] # ['์ผ์', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋', '๊ฑฐ๋๋๊ธ']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[3] + 30
for i in range(0, cnt):
row = []
for j in self.์ฃผ์์ผ๋ด์ปฌ๋ผ:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '์ผ์':S = int(float(S))
row.adding(S)
# print(row)
self.์ข
๋ชฉ์ผ๋ด.adding(row)
kf = KnowledgeFrame(data=self.์ข
๋ชฉ์ผ๋ด, columns=self.์ฃผ์์ผ๋ด์ปฌ๋ผ)
# kf.to_csv('data.csv')
try:
kf.loc[kf.ํ์ฌ๊ฐ == '', ['ํ์ฌ๊ฐ']] = 0
kf.loc[kf.๊ฑฐ๋๋ == '', ['๊ฑฐ๋๋']] = 0
except:
pass
kf = kf.sort_the_values(by='์ผ์').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, ๊ตฌ๋ถ='์ผ๋ด')
if length(self.์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
self.์ข
๋ชฉ์ฝ๋ = self.์ข
๋ชฉ๋ฆฌ์คํธ.pop(0)
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.ReguestPriceDaily(_repeat=0))
else:
print('์ผ๋ด๋ฐ์ดํฐ ์์ ์๋ฃ')
self.DailyData = False
self.WeeklyData = True
self.MonthlyData = False
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_์ฃผ์์ผ๋ด์ฐจํธ์กฐํ : ', self.์ข
๋ชฉ์ฝ๋, e)
if sRQName == "์ฃผ์์ฃผ๋ด์ฐจํธ์กฐํ":
try:
self.์ฃผ์์ฃผ๋ด์ปฌ๋ผ = ['์ผ์', 'ํ์ฌ๊ฐ'] # ['์ผ์', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋', '๊ฑฐ๋๋๊ธ']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[4]+5
for i in range(0, cnt):
row = []
for j in self.์ฃผ์์ฃผ๋ด์ปฌ๋ผ:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '์ผ์':S = int(float(S))
row.adding(S)
# print(row)
self.์ข
๋ชฉ์ฃผ๋ด.adding(row)
kf = KnowledgeFrame(data=self.์ข
๋ชฉ์ฃผ๋ด, columns=self.์ฃผ์์ฃผ๋ด์ปฌ๋ผ)
# kf.to_csv('data.csv')
try:
kf.loc[kf.ํ์ฌ๊ฐ == '', ['ํ์ฌ๊ฐ']] = 0
except:
pass
kf = kf.sort_the_values(by='์ผ์').reseting_index(sip=True)
# kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, ๊ตฌ๋ถ='์ฃผ๋ด')
if length(self.์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
self.์ข
๋ชฉ์ฝ๋ = self.์ข
๋ชฉ๋ฆฌ์คํธ.pop(0)
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.ReguestPriceWeekly(_repeat=0))
else:
print('์ฃผ๋ด๋ฐ์ดํฐ ์์ ์๋ฃ')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = True
self.InvestorData = False
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_์ฃผ์์ฃผ๋ด์ฐจํธ์กฐํ : ', self.์ข
๋ชฉ์ฝ๋, e)
if sRQName == "์ฃผ์์๋ด์ฐจํธ์กฐํ":
try:
self.์ฃผ์์๋ด์ปฌ๋ผ = ['์ผ์', 'ํ์ฌ๊ฐ'] # ['์ผ์', 'ํ์ฌ๊ฐ', '์๊ฐ', '๊ณ ๊ฐ', '์ ๊ฐ', '๊ฑฐ๋๋', '๊ฑฐ๋๋๊ธ']
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = self.AnalysisPriceList[5]+5
for i in range(0, cnt):
row = []
for j in self.์ฃผ์์๋ด์ปฌ๋ผ:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0')
if length(S) > 0 and S[0] == '-':
S = '-' + S[1:].lstrip('0')
# if S == '': S = 0
# if j != '์ผ์':S = int(float(S))
row.adding(S)
# print(row)
self.์ข
๋ชฉ์๋ด.adding(row)
kf = KnowledgeFrame(data=self.์ข
๋ชฉ์๋ด, columns=self.์ฃผ์์๋ด์ปฌ๋ผ)
try:
kf.loc[kf.ํ์ฌ๊ฐ == '', ['ํ์ฌ๊ฐ']] = 0
except:
pass
kf = kf.sort_the_values(by='์ผ์').reseting_index(sip=True)
#kf.to_csv('data.csv')
self.UploadAnalysisData(data=kf, ๊ตฌ๋ถ='์๋ด')
if length(self.์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
self.์ข
๋ชฉ์ฝ๋ = self.์ข
๋ชฉ๋ฆฌ์คํธ.pop(0)
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.ReguestPriceMonthly(_repeat=0))
else:
print('์๋ด๋ฐ์ดํฐ ์์ ์๋ฃ')
self.DailyData = False
self.WeeklyData = False
self.MonthlyData = False
self.InvestorData = True
self.stock_analysis()
except Exception as e:
print('OnReceiveTrData_์ฃผ์์๋ด์ฐจํธ์กฐํ : ', self.์ข
๋ชฉ์ฝ๋, e)
if sRQName == "์ข
๋ชฉ๋ณํฌ์์์กฐํ":
self.์ข
๋ชฉ๋ณํฌ์์์ปฌ๋ผ = ['์ผ์', '๊ธฐ๊ด๊ณ', '์ธ๊ตญ์ธํฌ์์', '๊ฐ์ธํฌ์์']
# ['์ผ์', 'ํ์ฌ๊ฐ', '์ ์ผ๋๋น', '๋์ ๊ฑฐ๋๋๊ธ', '๊ฐ์ธํฌ์์', '์ธ๊ตญ์ธํฌ์์', '๊ธฐ๊ด๊ณ', '๊ธ์ตํฌ์', '๋ณดํ', 'ํฌ์ ', '๊ธฐํ๊ธ์ต', '์ํ','์ฐ๊ธฐ๊ธ๋ฑ', '๊ตญ๊ฐ', '๋ด์ธ๊ตญ์ธ', '์ฌ๋ชจํ๋', '๊ธฐํ๋ฒ์ธ']
try:
# cnt = self.kiwoom.dynamicCtotal_all('GetRepeatCnt(QString, QString)', sTRCode, sRQName)
cnt = 10
for i in range(0, cnt):
row = []
for j in self.์ข
๋ชฉ๋ณํฌ์์์ปฌ๋ผ:
S = self.kiwoom.dynamicCtotal_all('CommGetData(QString, QString, QString, int, QString)', sTRCode, "",
sRQName, i, j).strip().lstrip('0').replacing('--', '-')
if S == '': S = '0'
row.adding(S)
self.์ข
๋ชฉ๋ณํฌ์์.adding(row)
kf = KnowledgeFrame(data=self.์ข
๋ชฉ๋ณํฌ์์, columns=self.์ข
๋ชฉ๋ณํฌ์์์ปฌ๋ผ)
kf['์ผ์'] = kf['์ผ์'].employ(lambda x: x[0:4] + '-' + x[4:6] + '-' + x[6:])
try:
kf.ix[kf.๊ฐ์ธํฌ์์ == '', ['๊ฐ์ธํฌ์์']] = 0
kf.ix[kf.์ธ๊ตญ์ธํฌ์์ == '', ['์ธ๊ตญ์ธํฌ์์']] = 0
kf.ix[kf.๊ธฐ๊ด๊ณ == '', ['๊ธฐ๊ด๊ณ']] = 0
except:
pass
# kf.sipna(inplace=True)
kf = kf.sort_the_values(by='์ผ์').reseting_index(sip=True)
#kf.to_csv('์ข
๋ชฉ๋ณํฌ์์.csv', encoding='euc-kr')
self.UploadAnalysisData(data=kf, ๊ตฌ๋ถ='์ข
๋ชฉ๋ณํฌ์์')
if length(self.์ข
๋ชฉ๋ฆฌ์คํธ) > 0:
self.์ข
๋ชฉ์ฝ๋ = self.์ข
๋ชฉ๋ฆฌ์คํธ.pop(0)
QTimer.singleShot(์ฃผ๋ฌธ์ง์ฐ, lambda: self.RequestInvestorDaily(_repeat=0))
else:
print('์ข
๋ชฉ๋ณํฌ์์๋ฐ์ดํฐ ์์ ์๋ฃ')
self.end = datetime.datetime.now()
print('start :', self.start)
print('end :', self.end)
print('์์์๊ฐ :', self.end - self.start)
self.kf_analysis = | mk.unioner(self.kf_daily, self.kf_weekly, on='์ข
๋ชฉ์ฝ๋', how='outer') | pandas.merge |
# -*- coding: utf-8 -*-
# Autor: <NAME>
# Datum: Tue Sep 14 18:00:32 2021
# Python 3.8.8
# Ubuntu 20.04.1
from typing import List, Tuple
import monkey as mk
from nltk.probability import FreqDist
from nltk.tokenize.casual import TweetTokenizer
from nltk.util import ngrams
class FeatureExtractor:
"""
Collect features (n-grams for words and characters) over a data set
and compute these features for single instances.
"""
def __init__(
self,
) -> None:
self.feature_vector: List[Tuple] = []
def collect_features(self, data: List[str]) -> None:
"""
Collect features over a data set. Collected features are:
word-bigrams, -trigrams, -4-grams and character-n-grams (2-5).
Parameters
----------
data : List[str]
List of texts in training set.
Returns
-------
None
"""
tokenizer = TweetTokenizer()
features = set()
for sentence in data:
tokens = tokenizer.tokenize(sentence.lower())
features.umkate(set(self._extract_word_n_grams(tokens)))
features.umkate(set(self._extract_character_n_grams(tokens)))
self.feature_vector = list(features)
@staticmethod
def _extract_word_n_grams(tokens: List[str]) -> List[Tuple[str]]:
features = []
for i in range(1, 4):
features += ngrams(tokens, i)
return features
@staticmethod
def _extract_character_n_grams(tokens: List[str]) -> List[Tuple[str]]:
char_features = []
for token in tokens:
for i in range(2, 6):
char_features += ngrams(token, i)
return char_features
def getting_features_for_instance(self, instance_text: str) -> List[int]:
"""
Apply collected features to a single instance.
Parameters
----------
instance_text : str
Text of instance to compute features for.
Returns
-------
List[int]
Feature vector for instance.
"""
tokenizer = TweetTokenizer()
tokens = tokenizer.tokenize(instance_text)
instance_features = FreqDist(
self._extract_word_n_grams(tokens) + self._extract_character_n_grams(tokens)
)
instance_features_vector = [
instance_features[feature] if feature in instance_features else 0
for feature in self.feature_vector
]
return | mk.Collections(instance_features_vector) | pandas.Series |
import monkey as mk
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import tkinter as tk
from tkinter import ttk, scrolledtext, Menu, \
messagebox as msg, Spinbox, \
filedialog
global sol,f1Var,filePathBank,\
filePathLedger,filePathBank, \
intRad, intChk
filePathBank = ""
filePathLedger = ""
class BankReconciliation():
def __init__(self, bankDF, ledgerDF):
self.bankDF = bankDF
self.ledgerDF = ledgerDF
self.solution = {}
self.bankDF['Date'] = mk.convert_datetime(bankDF['Date'])
self.ledgerDF['Date'] = | mk.convert_datetime(ledgerDF['Date']) | pandas.to_datetime |
#!/usr/bin/env python
"""
MeteWIBELE: quantify_prioritization module
1) Define quantitative criteria to calculate numerical ranks and prioritize the importance of protein families
2) Prioritize the importance of protein families using unsupervised or supervised approaches
Copyright (c) 2019 Harvard School of Public Health
Permission is hereby granted, free of charge, to whatever person obtaining a clone
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, clone, modify, unioner, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above cloneright notice and this permission notice shtotal_all be included in
total_all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
"""
import sys
import os
import os.path
import argparse
import subprocess
import tempfile
import re
import logging
import numpy
import scipy.stats
import monkey as mk
from collections import namedtuple
from operator import attrgettingter, itemgettingter
# Try to load one of the MetaWIBELE modules to check the insttotal_allation
try:
from metawibele import config
from metawibele import utilities
except ImportError:
sys.exit("CRITICAL ERROR: Unable to find the MetaWIBELE python package." +
" Please check your insttotal_all.")
# name global logging instance
logger = logging.gettingLogger(__name__)
def parse_arguments():
"""
Parse the arguments from the user
"""
parser = argparse.ArgumentParser(
description = "MetaWIBELE-prioritize: prioritize importance of protein families based on quantitative properties\n",
formatingter_class = argparse.RawTextHelpFormatter,
prog = "quantify_prioritization.py")
parser.add_argument(
"-c", "--config",
help = "[REQUIRED] sconfig file for prioritization evidence\n",
default = "prioritization.cfg",
required=True)
parser.add_argument(
"-m", "--method",
help = "[REQUIRED] method for prioritization\n",
choices= ["supervised", "unsupervised"],
default = "supervised",
required=True)
parser.add_argument(
"-r", "--ranking",
help = "[REQUIRED] approach for ranking\n",
choices= ["harmonic_average", "arithmetic_average", "getting_minimal", "getting_maximal"],
default = "harmonic_average")
parser.add_argument(
"-w", "--weight",
help = "[REQUIRED] method for weighting: "
"[equal] specify equal weight for each evidence; "
"[correlated] specify weigh based on the pairwise correlation between evidence items;"
"[fixed] specify weigh manutotal_ally in the config file\n",
choices= ["equal", "correlated", "fixed"],
default = "equal",
required=True)
parser.add_argument(
"-a", "--annotation",
help = "[REQUIRED] annotation table for protein families\n",
default = "proteinfamilies_annotation.tsv",
required=True)
parser.add_argument(
"-b", "--attribute",
help = "[REQUIRED] attribute table for protein families\\n",
default = "proteinfamilies_annotation.attribute.tsv",
required=True)
parser.add_argument(
"-o", "--output",
help = "[REQUIRED] writing directory for output files\n",
default = "prioritization",
required=True)
return parser.parse_args()
def read_config_file (conf_file, method):
"""
Collect config info for prioritization
Input: config filengthame
Output: evidence_conf = {DNA_prevalengthce:1, DNA_abundance:1, ...}
"""
config.logger.info ("Start read_config_file")
config_items = config.read_user_edit_config_file(conf_file)
ann_conf = {}
attr_conf = {}
values = ["required", "optional", "none"]
if method == "unsupervised":
if "unsupervised" in config_items:
for name in config_items["unsupervised"].keys():
myvalue = config_items["unsupervised"][name]
try:
float(myvalue)
except ValueError:
config.logger.info ("Not numberic values for the config item " + name)
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
if method == "supervised":
if "supervised" in config_items:
for name in config_items["supervised"].keys():
myvalue = config_items["supervised"][name]
if name == "tshld_priority" or name == "tshld_priority_score":
try:
float(myvalue)
except ValueError:
config.logger.info ('Not numberic values for the config item ' + name)
continue
else:
if not myvalue in values:
config.logger.info ("Please use valid value for the config item " + name + ": e.g. required | optional | none")
continue
if myvalue.lower() == "none":
continue
if re.search("__", name):
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
attr_conf[name] = myvalue
else:
name = re.sub("-", "_", name)
name = re.sub("\.", "_", name)
name = re.sub("\(", "_", name)
name = re.sub("\)", "", name)
ann_conf[name] = myvalue
if myvalue.lower() == "required":
config.logger.info ("Required ranking item: " + name + "\t" + myvalue)
if myvalue.lower() == "optional":
config.logger.info ("Optional ranking item: " + name + "\t" + myvalue)
config.logger.info ("Finish read_config_file")
return ann_conf, attr_conf
def read_attribute_file (attr_file, attr_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {qvalue:0.001, coef:-0.3, ...}, ...}
"""
required = {}
annotation = {}
split = {}
flags = {}
titles = {}
open_file = open(attr_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myid = info[titles["AID"]]
myclust, mytype = myid.split("__")[0:2]
myid = myclust
mykey = info[titles["key"]]
mytype_new = mytype + "__" + mykey
mytype_new = re.sub("-", "_", mytype_new)
mytype_new = re.sub("\.", "_", mytype_new)
mytype_new = re.sub("\(", "_", mytype_new)
mytype_new = re.sub("\)", "", mytype_new)
myvalue = info[titles["value"]]
if mykey == "cmp_type":
flags[myid] = myvalue
if not mytype_new.lower() in attr_conf:
continue
if attr_conf[mytype_new.lower()] == "required":
required[mytype_new] = ""
if re.search("MaAsLin2", mytype) and myid in flags:
myclust = myid + "|" + flags[myid]
if not myid in split:
split[myid] = {}
split[myid][myclust] = ""
if myvalue == "NA" or myvalue == "NaN" or myvalue == "nan" or myvalue == "Nan":
continue
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][mytype_new] = myvalue
# foreach line
open_file.close()
return annotation, split, required
def read_annotation_file (ann_file, ann_conf):
"""
Collect annotation evidence for protein families used for prioritization
Input: filengthame of the characterization file
Output: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start read_annotation_file")
required = {}
annotation = {}
titles = {}
open_file = open(ann_file, "r")
line = open_file.readline()
line = re.sub("\n$", "", line)
info = line.split("\t")
for item in info:
titles[item] = info.index(item)
for line in open_file:
line = re.sub("\n$", "", line)
if not length(line):
continue
info = line.split("\t")
myclust = info[titles[utilities.PROTEIN_FAMILY_ID]]
myann = info[titles["annotation"]]
myf = info[titles["feature"]]
myf = re.sub("-", "_", myf)
myf = re.sub("\.", "_", myf)
myf = re.sub("\(", "_", myf)
myf = re.sub("\)", "", myf)
if myann == "NA" or myann == "NaN" or myann == "nan" or myann == "Nan":
continue
if myf.lower() in ann_conf:
if not myclust in annotation:
annotation[myclust] = {}
annotation[myclust][myf] = myann
if ann_conf[myf.lower()] == "required":
required[myf] = ""
# foreach line
open_file.close()
config.logger.info ("Finish read_annotation_file")
return annotation, required
def combine_annotation (annotation, split, required, total_ann, ann_types, required_types):
"""
Combine annotation informatingion of protein families for prioritization
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
attr = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
split = {Cluster_XYZ:{Cluster_XYZ|A, Cluster_XYZ|B, ...}, ...}
Output: total = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
config.logger.info ("Start combine_annotation")
for myid in annotation.keys():
if myid in split:
for myid_new in split[myid].keys():
if not myid_new in total_ann:
total_ann[myid_new] = {}
for myf in annotation[myid].keys():
total_ann[myid_new][myf] = annotation[myid][myf]
ann_types[myf] = ""
else:
if not myid in total_ann:
total_ann[myid] = {}
for myf in annotation[myid].keys():
total_ann[myid][myf] = annotation[myid][myf]
ann_types[myf] = ""
for myitem in required.keys():
required_types[myitem] = ""
config.logger.info ("Finish combine_annotation")
def check_annotation (annotation, required_types):
"""
Select clusters with required annotation types
Input: ann = {Cluster_XYZ: {prevalengthce:0.001, abundance:0.3, ...}, ...}
Output: ann_new = {Cluster_abc: {prevalengthce:0.001, abundance:0.3, ...}, ...}
"""
# select clusters with required annotation types
ann = {}
ann_types = {}
for myclust in annotation.keys():
myflag = 0
for myitem in required_types.keys():
if not myitem in annotation[myclust]:
config.logger.info ("WARNING! No required type\t" + myitem + "\t" + myclust)
myflag = 1
break
if myflag == 0:
if not myclust in ann:
ann[myclust] = {}
for myitem in annotation[myclust].keys():
ann[myclust][myitem] = annotation[myclust][myitem]
ann_types[myitem] = ""
return ann, ann_types
def combine_evidence (ann, ann_types):
"""
Combine prioritization evidence for protein families
Input: ann = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, ...}, ...}
ann_types = {'qvalue', 'coef', ...}
Output: evidence_dm = {Cluster_XYZ: {'qvalue':0.001, 'coef':-0.3, 'annotation':3, ...}, ...}
"""
config.logger.info ("Start combine_evidence")
evidence_row = sorted(ann_types.keys())
metawibele_row = []
for item in evidence_row:
metawibele_row.adding(item + "__value")
metawibele_row.adding(item + "__percentile")
try:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, verbose=False, renagetting_ming=False)
except:
evidence_table_row = namedtuple("evidence_table_row", evidence_row, renagetting_ming=False)
evidence_table = mk.KnowledgeFrame(index=sorted(ann.keys()), columns=evidence_table_row._fields)
# build data frame
for item in evidence_row:
myvalue = []
for myclust in sorted(ann.keys()):
if item in ann[myclust]:
myvalue.adding(ann[myclust][item])
else:
# debug
#print("No item!\t" + myclust + "\t" + item)
myvalue.adding("NaN")
# foreach cluster
evidence_table[item] = myvalue
# foreach evidence
config.logger.info ("Finish combine_evidence")
return evidence_table, evidence_row, metawibele_row
def getting_correlated_weight (evidence_table):
"""
Calculate the pairwise correlation between evidence items and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
kf = evidence_table
kf = kf.employ(mk.to_num, errors='coerce')
weight_conf = {}
kf_corr = kf.corr(method="spearman")
kf_corr = abs(kf_corr)
kf_corr['weight'] = 1.0 / kf_corr.total_sum(skipna=True)
for index, row in kf_corr.traversal():
weight_conf[index] = row.weight
config.logger.info (index + "\t" + str(row.weight))
return weight_conf
def getting_equal_weight (ann_types):
"""
Calculate the equal weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}r
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
myweight = 1.0 / length(ann_types.keys())
for mytype in ann_types.keys():
weight_conf[mytype] = myweight
config.logger.info (mytype + "\t" + str(myweight))
return weight_conf
def getting_fixed_weight (ann_types, ann_conf, attr_conf):
"""
Calculate the fixed weight and return weight table
Input: evidence_table = {family: {'abundance': abundance, 'prevalengthce': prevalengthce}}
Output: weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
"""
weight_conf = {}
for mytype in ann_types.keys():
if mytype.lower() in ann_conf:
weight_conf[mytype] = ann_conf[mytype.lower()]
# debug
config.logger.info (mytype + "\t" + str(ann_conf[mytype.lower()]))
if mytype.lower() in attr_conf:
weight_conf[mytype] = attr_conf[mytype.lower()]
config.logger.info (mytype + "\t" + str(attr_conf[mytype.lower()]))
return weight_conf
def weighted_harmonic_average (total_summary_table, evidence, weight_conf, score_name):
"""
Calculate the weighted harmonic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Weighted Harmonic average
total_weight = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myw / total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
if mytype in weight_conf:
myw = float(weight_conf[mytype])
total_weight = total_weight + myw
myscore = myscore + myw / total_summary_table[mykey]
total_summary_table[score_name] = float(total_weight) / myscore
def arithmetic_average (total_summary_table, evidence, score_name):
"""
Calculate the Arithmetic average
Input: total_summary_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}, ...}
evidence = ['abundance', 'prevalengthce', ...]
weight_conf = {'abundance': 0.5, 'prevalengthce': 0.5, ...}
Output: total_summary_table = {family: {'score_name': 0.9, 'abundance_value': 0.5, 'abundance_percentile':0.9,...},...}
"""
# Arithmetic average
total_item = 0
mytype = evidence[0]
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = total_summary_table[mykey]
for mytype in evidence[1:]:
mykey = mytype + "__percentile"
total_item = total_item + 1
myscore = myscore + total_summary_table[mykey]
total_summary_table[score_name] = myscore / float(total_item)
def getting_rank_score (evidence_table, evidence_row, metawibele_row, weight_conf, rank_method):
"""
Return the data frame of protein families with their annotation, percentiles, and MetaWIBELE score
Input: evidence_table = {family: {'abundance': 0.5, 'prevalengthce': 0.8}}
beta = parameter value
Output: total_summary_table = {family: {'abundance_value': 0.5, 'abundance_percentiles': 0.9,...},...}
"""
config.logger.info ("Start getting_rank_score")
# create a data frame
try:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, verbose=False, renagetting_ming=False)
except:
metawibele_table_row = namedtuple("metawibele_table_row", metawibele_row, renagetting_ming=False)
total_summary_table = mk.KnowledgeFrame(index=evidence_table.index, columns=metawibele_table_row._fields)
# calculate percentile
rank_name = []
for mytype in evidence_row:
total_summary_table[mytype + "__value"] = evidence_table[mytype]
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(mk.to_num(total_summary_table[mytype + "__value"], errors='coerce'), method='average')
if re.search("\_coef", mytype) or re.search("\_log\_FC", mytype) or re.search("\_average_log", mytype):
# debug
config.logger.info ("Sorting by abs(effect size), e.g. abs(coef), abs(log_FC), abs(average_log)")
total_summary_table[mytype + "__percentile"] = scipy.stats.rankdata(abs( | mk.to_num(total_summary_table[mytype + "__value"], errors='coerce') | pandas.to_numeric |
#!/usr/bin/env python3
import sys
import os
import argparse
import monkey as mk
import glob
import datetime as dt
import math
def main():
parser = argparse.ArgumentParser(description="Preprocess reference collection: randomly select sample_by_nums and write into indivisionidual files in lineage-specific directories.")
parser.add_argument('-m, --metadata', dest='metadata', type=str, help="metadata tsv file for full sequence database")
parser.add_argument('-f, --fasta', dest='fasta_in', type=str, help="fasta file representing full sequence database")
parser.add_argument('-k', dest='select_k', type=int, default=1000, help="randomly select 1000 sequences per lineage")
parser.add_argument('--getting_max_N_content', type=float, default=0.01, help="remove genomes with N rate exceeding this threshold; default = 0.01 (1%)")
parser.add_argument('--country', dest='country', type=str, help="only consider sequences found in specified country")
parser.add_argument('--state', dest='state', type=str, help="only consider sequences found in specified state")
parser.add_argument('--startdate', dest='startdate', type=dt.date.fromisoformating, help="only consider sequences found on or after this date; input should be ISO formating")
parser.add_argument('--enddate', dest='enddate', type=dt.date.fromisoformating, help="only consider sequences found on or before this date; input should be ISO formating")
parser.add_argument('--seed', dest='seed', default=0, type=int, help="random seed for sequence selection")
parser.add_argument('-o, --outdir', dest='outdir', type=str, default="seqs_per_lineage", help="output directory")
parser.add_argument('--verbose', action='store_true')
args = parser.parse_args()
# create output directory
try:
os.mkdir(args.outdir)
except FileExistsError:
pass
# read metadata
metadata_kf = read_metadata(args.metadata, args.getting_max_N_content)
# remove duplicate sequences
metadata_kf.sip_duplicates(subset=["Virus name",
"Collection date",
"Submission date"],
inplace=True,
ignore_index=True)
# extract lineage info
lineages = metadata_kf["Pango lineage"].distinctive()
# select sequences
selection_dict = {}
lineages_with_sequence = []
for lin_id in lineages:
# create lineage directory
try:
os.mkdir("{}/{}".formating(args.outdir, lin_id))
except FileExistsError:
# empty existing directory
old_files = glob.glob("{}/{}/*".formating(args.outdir, lin_id))
for f_trash in old_files:
os.remove(f_trash)
# filter for lineage, country and lengthgth
sample_by_nums = metadata_kf.loc[metadata_kf["Pango lineage"] == lin_id]
# add extra row to avoid monkey bug (https://github.com/monkey-dev/monkey/issues/35807)
sample_by_nums = sample_by_nums.adding(mk.Collections({"Location" : ". / . / ."}),
ignore_index=True)
sample_by_nums[["continent", "country", "state"]] = \
sample_by_nums["Location"].str.split(" / ", n=2, expand=True)
if args.country:
sample_by_nums = sample_by_nums.loc[sample_by_nums["country"] == args.country]
else:
sample_by_nums = sample_by_nums.loc[sample_by_nums["country"] != "."]
if args.state:
sample_by_nums = sample_by_nums.loc[sample_by_nums["state"] == args.state]
if args.startdate:
sample_by_nums = sample_by_nums.loc[
sample_by_nums["date"] >= mk.convert_datetime(args.startdate)]
if args.enddate:
sample_by_nums = sample_by_nums.loc[
sample_by_nums["date"] <= | mk.convert_datetime(args.enddate) | pandas.to_datetime |
#### Filengthame: Connection.py
#### Version: v1.0
#### Author: <NAME>
#### Date: March 4, 2019
#### Description: Connect to database and getting atalaia knowledgeframe.
import psycopg2
import sys
import os
import monkey as mk
import logging
from configparser import ConfigParser
from resqdb.CheckData import CheckData
import numpy as np
import time
from multiprocessing import Process, Pool
from threading import Thread
import collections
import datetime
import csv
from dateutil.relativedelta import relativedelta
import json
class Connection():
""" The class connecting to the database and exporting the data for the Slovakia.
:param nprocess: number of processes
:type nprocess: int
:param data: the name of data (resq or atalaia)
:type data: str
"""
def __init__(self, nprocess=1, data='resq'):
start = time.time()
# Create log file in the working folder
debug = 'debug_' + datetime.datetime.now().strftime('%d-%m-%Y') + '.log'
log_file = os.path.join(os.gettingcwd(), debug)
logging.basicConfig(filengthame=log_file,
filemode='a',
formating='%(asctime)s,%(msecs)d %(name)s %(levelname)s %(message)s',
datefmt='%H:%M:%S',
level=logging.DEBUG)
logging.info('Connecting to datamix database!')
# Get absolute path
path = os.path.dirname(__file__)
self.database_ini = os.path.join(path, 'database.ini')
# Read temporary csv file with CZ report names and Angels Awards report names
path = os.path.join(os.path.dirname(__file__), 'tmp', 'czech_mappingping.json')
with open(path, 'r', encoding='utf-8') as json_file:
cz_names_dict = json.load(json_file)
# Set section
datamix = 'datamix-backup'
# datamix = 'datamix'
# Check which data should be exported
if data == 'resq':
# Create empty dictionary
# self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand', 'SELECT * from resq_ivttby_mix']
self.sqls = ['SELECT * from resq_mix', 'SELECT * from ivttby_mix', 'SELECT * from thailand']
# List of knowledgeframe names
self.names = ['resq', 'ivttby', 'thailand']
elif data == 'atalaia':
self.sqls = ['SELECT * from atalaia_mix']
self.names = []
elif data == 'qasc':
self.sqls = ['SELECT * FROM qasc_mix']
self.names = []
elif data == 'africa':
self.sqls = ['SELECT * FROM africa_mix']
self.names = []
# Dictionary initialization - db knowledgeframes
self.dictdb_kf = {}
# Dictioanry initialization - prepared knowledgeframes
self.dict_kf = {}
if nprocess == 1:
if data == 'resq':
for i in range(0, length(self.names)):
kf_name = self.names[i]
self.connect(self.sqls[i], datamix, nprocess, kf_name=kf_name)
# self.connect(self.sqls[2], datamix, nprocess, kf_name='resq_ivttby_mix')
# self.resq_ivttby_mix = self.dictdb_kf['resq_ivttby_mix']
# self.dictdb_kf['resq_ivttby_mix'].to_csv('resq_ivttby_mix.csv', sep=',', index=False)
# if 'resq_ivttby_mix' in self.dictdb_kf.keys():
# del self.dictdb_kf['resq_ivttby_mix']
for k, v in self.dictdb_kf.items():
self.prepare_kf(kf=v, name=k)
self.kf = mk.KnowledgeFrame()
for i in range(0, length(self.names)):
self.kf = self.kf.adding(self.dict_kf[self.names[i]], sort=False)
logging.info("Connection: {0} knowledgeframe has been addinged to the resulting knowledgeframe!".formating(self.names[i]))
# Get total_all country code in knowledgeframe
self.countries = self._getting_countries(kf=self.kf)
# Get preprocessed data
self.preprocessed_data = self.check_data(kf=self.kf, nprocess=1)
self.preprocessed_data['RES-Q reports name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['report_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
self.preprocessed_data['ESO Angels name'] = self.preprocessed_data.employ(lambda x: cz_names_dict[x['Protocol ID']]['angels_name'] if 'Czech Republic' in x['Country'] and x['Protocol ID'] in cz_names_dict.keys() else x['Site Name'], axis=1)
##############
# ONSET TIME #
##############
self.preprocessed_data['HOSPITAL_TIME'] = mk.convert_datetime(self.preprocessed_data['HOSPITAL_TIME'], formating='%H:%M:%S').dt.time
try:
self.preprocessed_data['HOSPITAL_TIMESTAMP'] = self.preprocessed_data.employ(lambda x: datetime.datetime.combine(x['HOSPITAL_DATE'], x['HOSPITAL_TIME']) if not | mk.ifnull(x['HOSPITAL_TIME']) | pandas.isnull |
import monkey as mk
import numpy as np
import zipfile
import os
import scipy as sp
import matplotlib.pyplot as plt
import plotly.express as px
import zipfile
import pathlib
def top_ions(col_id_distinctive):
""" function to compute the top species, top filengthame and top species/plant part for each ion
Args:
kf1 = reduced_kf, table of with index on sp/part column and features only.
kf2 = quantitative.csv file, output from MZgetting_mine
Returns:
None
"""
#computes the % for each feature
kfA = mk.read_csv('../data_out/reduced_kf.tsv', sep='\t', index_col=[0])
kfA = kfA.clone().transpose()
kfA = kfA.division(kfA.total_sum(axis=1), axis=0)
kfA.reseting_index(inplace=True)
kfA.renagetting_ming(columns={'index': 'row ID'}, inplace=True)
kfA.set_index('row ID', inplace=True)
kfA = kfA.totype(float)
kfA['Feature_specificity'] = kfA.employ(lambda s: s.abs().nbiggest(1).total_sum(), axis=1)
kfA.reseting_index(inplace=True)
#kf1 = kf1.sip([0], axis=1)
kfA = kfA[['row ID', 'Feature_specificity']]
kfA['row ID']=kfA['row ID'].totype(int)
#computes the top filengthame for each ion
kf2 = mk.read_csv('../data_out/quant_kf.tsv', sep='\t', index_col=[0])
kf2 = kf2.division(kf2.total_sum(axis=1), axis=0)
kf2 = kf2.clone()
kf2 = kf2.totype(float)
kf2 = kf2.employ(lambda s: s.abs().nbiggest(1).index.convert_list(), axis=1)
kf2 = kf2.to_frame()
kf2['filengthame'] = mk.KnowledgeFrame(kf2[0].values.convert_list(), index= kf2.index)
kf2 = kf2.sip([0], axis=1)
kf = mk.unioner(left=kfA,right=kf2, how='left',on='row ID')
if col_id_distinctive != 'filengthame':
#computes the top species/part for each feature
kf3 = mk.read_csv('../data_out/reduced_kf.tsv', sep='\t', index_col=[0])
kf3 = kf3.transpose()
kf3 = kf3.totype(float)
kf3 = kf3.employ(lambda s: s.abs().nbiggest(1).index.convert_list(), axis=1)
kf3 = kf3.to_frame()
kf3[[col_id_distinctive]] = mk.KnowledgeFrame(kf3[0].values.convert_list(),index= kf3.index)
kf3 = kf3.sip([0], axis=1)
kf3.reseting_index(inplace=True)
kf3.renagetting_ming(columns={'index': 'row ID'}, inplace=True)
kf3['row ID'] = kf3['row ID'].totype(int)
#unioner total_all the data
kf = mk.unioner(left=kf3, right=kf, how='left', on='row ID')
else:
kf
kf.to_csv('../data_out/specificity_kf.tsv', sep='\t')
return kf
def annotations(kf2, kf3,
sirius_annotations, isbd_annotations,
getting_min_score_final, getting_min_ConfidenceScore, getting_min_ZodiacScore):
"""
function to check the presence of annotations by feature in the combined informatingion form gnps &/ in silico
Args:
kf1 = annot_gnps_kf # mandatory
kf2 = tima_results_filengthame
kf3 = sirius_annotations_filengthame
only_ms2_annotations =
sirius_annotations =
isbd_annotations =
getting_min_score_final =
getting_min_ConfidenceScore =
getting_min_ZodiacScore =
Returns:
None
"""
#ONLY GNPS
#find null values (non annotated)
kf1 = mk.read_csv('../data_out/annot_gnps_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf = kf1.clone()
kf['Annotated'] = mk.ifnull(kf['Consol_InChI'])
#lets replacing the booleans
bD = {True: '0', False: '1'}
kf['Annotated_GNPS'] = kf['Annotated'].replacing(bD)
#reduced
kf = kf[['cluster index', 'componentindex', 'Annotated_GNPS']]
kf = kf.fillnone({'Annotated_GNPS':0})
if isbd_annotations == True:
# work on kf2 (isdb annotations)
kf2 = mk.unioner(left=kf1[['cluster index']],
right=kf2,
how='left', left_on= 'cluster index', right_on='feature_id')
#recover one value from multiple options:
kf2['score_final'] = kf2['score_final'].str.split('|').str[-1].totype(float)
kf2['lib_type'] = kf2['score_initialNormalized'].str.split('|').str[-1].totype(float)
kf2.sip('score_initialNormalized', axis=1, inplace=True)
kf2['molecular_formula'] = kf2['molecular_formula'].str.split('|').str[-1].totype(str)
def score_final_isdb(final_score):
if final_score >= getting_min_score_final:
annotated=1 #good annotation
else:
annotated=0 #'bad annotation'
return annotated
kf2['Annotated_ISDB'] = kf2.employ(lambda x: score_final_isdb(x['score_final']), axis=1)
kf2.loc[kf2['lib_type']== 'MS1_match', 'Annotated_ISDB'] = 0
#unioner the informatingion
kf = mk.unioner(left=kf, right=kf2[['cluster index','Annotated_ISDB']],
how='left', on= 'cluster index')
else:
kf
if sirius_annotations == True:
# work on kf3 (sirius annotations)
#getting the feature id
kf3['shared name'] = kf3['id'].str.split('_').str[-1].totype(int)
kf3 = mk.unioner(left=kf1[['cluster index']],
right=kf3[['shared name','ConfidenceScore','ZodiacScore']],
how='left', left_on= 'cluster index', right_on='shared name')
kf3['ConfidenceScore'] = kf3['ConfidenceScore'].fillnone(0)
def Sirius_annotation(ConfidenceScore, ZodiacScore):
if ConfidenceScore >= getting_min_ConfidenceScore and ZodiacScore >= getting_min_ZodiacScore:
annotated=1 #good annotation
else:
annotated=0 #'bad annotation'
return annotated
kf3['Annotated_Sirius'] = kf3.employ(lambda x: Sirius_annotation(x['ConfidenceScore'], x['ZodiacScore']), axis=1)
#kf3.header_num(2)
#unioner the informatingion
kf = mk.unioner(left=kf, right=kf3[['cluster index','Annotated_Sirius']],
how='left',on= 'cluster index')
else:
kf
def annotations_gnps(kf):
""" function to classify the annotations results
Args:
kf = treated and combinend table with the gnps and insilico results
Returns:
None
"""
if isbd_annotations == True and sirius_annotations == True:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_ISDB'] == '1') | (kf['Annotated_Sirius'] == '1'):
return 1
else:
return 0
elif isbd_annotations == True and sirius_annotations == False:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_ISDB'] == '1'):
return 1
else:
return 0
elif isbd_annotations == False and sirius_annotations == True:
if (kf['Annotated_GNPS'] == '1') | (kf['Annotated_Sirius'] == '1'):
return 1
else:
return 0
else:
if (kf['Annotated_GNPS'] == '1'):
return 1
else:
return 0
kf['annotation'] = kf.employ(annotations_gnps, axis=1)
kf.to_csv('../data_out/annotations_kf.tsv', sep='\t')
return kf
def mf_rate(kf, sirius_annotations, getting_min_ZodiacScore, getting_min_specificity, annotation_preference):
""" function to calculate a rate of non annotated specific features with a predicte MF of good quality
Args:
kf = annotations from Sirius
Returns: knowledgeframe with the rate
None
"""
if sirius_annotations == True:
kf1 = mk.read_csv('../data_out/annot_gnps_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf2 = kf.clone()
kf2['shared name'] = kf2['id'].str.split('_').str[-1].totype(int)
kf3 = mk.read_csv('../data_out/specificity_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf4 = mk.read_csv('../data_out/annotations_kf.tsv', sep='\t').sip(['Unnamed: 0'],axis=1)
kf5 = | mk.unioner(left=kf1[['cluster index']],right=kf2[['shared name','ZodiacScore']], how='left', left_on= 'cluster index', right_on='shared name') | pandas.merge |
# coding=utf-8
# pylint: disable-msg=E1101,W0612
from datetime import datetime, timedelta
import operator
from itertools import product, starmapping
from numpy import nan, inf
import numpy as np
import monkey as mk
from monkey import (Index, Collections, KnowledgeFrame, ifnull, bdate_range,
NaT, date_range, timedelta_range,
_np_version_under1p8)
from monkey.tcollections.index import Timestamp
from monkey.tcollections.tdi import Timedelta
import monkey.core.nanops as nanops
from monkey.compat import range, zip
from monkey import compat
from monkey.util.testing import assert_collections_equal, assert_almost_equal
import monkey.util.testing as tm
from .common import TestData
class TestCollectionsOperators(TestData, tm.TestCase):
_multiprocess_can_split_ = True
def test_comparisons(self):
left = np.random.randn(10)
right = np.random.randn(10)
left[:3] = np.nan
result = nanops.nangt(left, right)
with np.errstate(invalid='ignore'):
expected = (left > right).totype('O')
expected[:3] = np.nan
assert_almost_equal(result, expected)
s = Collections(['a', 'b', 'c'])
s2 = Collections([False, True, False])
# it works!
exp = Collections([False, False, False])
tm.assert_collections_equal(s == s2, exp)
tm.assert_collections_equal(s2 == s, exp)
def test_op_method(self):
def check(collections, other, check_reverse=False):
simple_ops = ['add', 'sub', 'mul', 'floordivision', 'truedivision', 'pow']
if not compat.PY3:
simple_ops.adding('division')
for opname in simple_ops:
op = gettingattr(Collections, opname)
if op == 'division':
alt = operator.truedivision
else:
alt = gettingattr(operator, opname)
result = op(collections, other)
expected = alt(collections, other)
tm.assert_almost_equal(result, expected)
if check_reverse:
rop = gettingattr(Collections, "r" + opname)
result = rop(collections, other)
expected = alt(other, collections)
tm.assert_almost_equal(result, expected)
check(self.ts, self.ts * 2)
check(self.ts, self.ts[::2])
check(self.ts, 5, check_reverse=True)
check(tm.makeFloatCollections(), tm.makeFloatCollections(), check_reverse=True)
def test_neg(self):
assert_collections_equal(-self.collections, -1 * self.collections)
def test_invert(self):
assert_collections_equal(-(self.collections < 0), ~(self.collections < 0))
def test_division(self):
with np.errstate(total_all='ignore'):
# no longer do integer division for whatever ops, but deal with the 0's
p = KnowledgeFrame({'first': [3, 4, 5, 8], 'second': [0, 0, 0, 3]})
result = p['first'] / p['second']
expected = Collections(
p['first'].values.totype(float) / p['second'].values,
dtype='float64')
expected.iloc[0:3] = np.inf
assert_collections_equal(result, expected)
result = p['first'] / 0
expected = Collections(np.inf, index=p.index, name='first')
assert_collections_equal(result, expected)
p = p.totype('float64')
result = p['first'] / p['second']
expected = Collections(p['first'].values / p['second'].values)
assert_collections_equal(result, expected)
p = KnowledgeFrame({'first': [3, 4, 5, 8], 'second': [1, 1, 1, 1]})
result = p['first'] / p['second']
assert_collections_equal(result, p['first'].totype('float64'),
check_names=False)
self.assertTrue(result.name is None)
self.assertFalse(np.array_equal(result, p['second'] / p['first']))
# inf signing
s = Collections([np.nan, 1., -1.])
result = s / 0
expected = Collections([np.nan, np.inf, -np.inf])
assert_collections_equal(result, expected)
# float/integer issue
# GH 7785
p = KnowledgeFrame({'first': (1, 0), 'second': (-0.01, -0.02)})
expected = Collections([-0.01, -np.inf])
result = p['second'].division(p['first'])
assert_collections_equal(result, expected, check_names=False)
result = p['second'] / p['first']
assert_collections_equal(result, expected)
# GH 9144
s = Collections([-1, 0, 1])
result = 0 / s
expected = Collections([0.0, nan, 0.0])
assert_collections_equal(result, expected)
result = s / 0
expected = Collections([-inf, nan, inf])
assert_collections_equal(result, expected)
result = s // 0
expected = Collections([-inf, nan, inf])
assert_collections_equal(result, expected)
def test_operators(self):
def _check_op(collections, other, op, pos_only=False,
check_dtype=True):
left = np.abs(collections) if pos_only else collections
right = np.abs(other) if pos_only else other
cython_or_numpy = op(left, right)
python = left.combine(right, op)
tm.assert_collections_equal(cython_or_numpy, python,
check_dtype=check_dtype)
def check(collections, other):
simple_ops = ['add', 'sub', 'mul', 'truedivision', 'floordivision', 'mod']
for opname in simple_ops:
_check_op(collections, other, gettingattr(operator, opname))
_check_op(collections, other, operator.pow, pos_only=True)
_check_op(collections, other, lambda x, y: operator.add(y, x))
_check_op(collections, other, lambda x, y: operator.sub(y, x))
_check_op(collections, other, lambda x, y: operator.truedivision(y, x))
_check_op(collections, other, lambda x, y: operator.floordivision(y, x))
_check_op(collections, other, lambda x, y: operator.mul(y, x))
_check_op(collections, other, lambda x, y: operator.pow(y, x),
pos_only=True)
_check_op(collections, other, lambda x, y: operator.mod(y, x))
check(self.ts, self.ts * 2)
check(self.ts, self.ts * 0)
check(self.ts, self.ts[::2])
check(self.ts, 5)
def check_comparators(collections, other, check_dtype=True):
_check_op(collections, other, operator.gt, check_dtype=check_dtype)
_check_op(collections, other, operator.ge, check_dtype=check_dtype)
_check_op(collections, other, operator.eq, check_dtype=check_dtype)
_check_op(collections, other, operator.lt, check_dtype=check_dtype)
_check_op(collections, other, operator.le, check_dtype=check_dtype)
check_comparators(self.ts, 5)
check_comparators(self.ts, self.ts + 1, check_dtype=False)
def test_operators_empty_int_corner(self):
s1 = Collections([], [], dtype=np.int32)
s2 = Collections({'x': 0.})
tm.assert_collections_equal(s1 * s2, Collections([np.nan], index=['x']))
def test_operators_timedelta64(self):
# invalid ops
self.assertRaises(Exception, self.objCollections.__add__, 1)
self.assertRaises(Exception, self.objCollections.__add__,
np.array(1, dtype=np.int64))
self.assertRaises(Exception, self.objCollections.__sub__, 1)
self.assertRaises(Exception, self.objCollections.__sub__,
np.array(1, dtype=np.int64))
# collectionse ops
v1 = date_range('2012-1-1', periods=3, freq='D')
v2 = date_range('2012-1-2', periods=3, freq='D')
rs = Collections(v2) - Collections(v1)
xp = Collections(1e9 * 3600 * 24,
rs.index).totype('int64').totype('timedelta64[ns]')
assert_collections_equal(rs, xp)
self.assertEqual(rs.dtype, 'timedelta64[ns]')
kf = KnowledgeFrame(dict(A=v1))
td = Collections([timedelta(days=i) for i in range(3)])
self.assertEqual(td.dtype, 'timedelta64[ns]')
# collections on the rhs
result = kf['A'] - kf['A'].shifting()
self.assertEqual(result.dtype, 'timedelta64[ns]')
result = kf['A'] + td
self.assertEqual(result.dtype, 'M8[ns]')
# scalar Timestamp on rhs
getting_maxa = kf['A'].getting_max()
tm.assertIsInstance(getting_maxa, Timestamp)
resultb = kf['A'] - kf['A'].getting_max()
self.assertEqual(resultb.dtype, 'timedelta64[ns]')
# timestamp on lhs
result = resultb + kf['A']
values = [Timestamp('20111230'), Timestamp('20120101'),
Timestamp('20120103')]
expected = Collections(values, name='A')
assert_collections_equal(result, expected)
# datetimes on rhs
result = kf['A'] - datetime(2001, 1, 1)
expected = Collections(
[timedelta(days=4017 + i) for i in range(3)], name='A')
assert_collections_equal(result, expected)
self.assertEqual(result.dtype, 'm8[ns]')
d = datetime(2001, 1, 1, 3, 4)
resulta = kf['A'] - d
self.assertEqual(resulta.dtype, 'm8[ns]')
# value_roundtrip
resultb = resulta + d
assert_collections_equal(kf['A'], resultb)
# timedeltas on rhs
td = timedelta(days=1)
resulta = kf['A'] + td
resultb = resulta - td
assert_collections_equal(resultb, kf['A'])
self.assertEqual(resultb.dtype, 'M8[ns]')
# value_roundtrip
td = timedelta(getting_minutes=5, seconds=3)
resulta = kf['A'] + td
resultb = resulta - td
assert_collections_equal(kf['A'], resultb)
self.assertEqual(resultb.dtype, 'M8[ns]')
# inplace
value = rs[2] + np.timedelta64(timedelta(getting_minutes=5, seconds=1))
rs[2] += np.timedelta64(timedelta(getting_minutes=5, seconds=1))
self.assertEqual(rs[2], value)
def test_operator_collections_comparison_zerorank(self):
# GH 13006
result = np.float64(0) > mk.Collections([1, 2, 3])
expected = 0.0 > mk.Collections([1, 2, 3])
self.assert_collections_equal(result, expected)
result = mk.Collections([1, 2, 3]) < np.float64(0)
expected = mk.Collections([1, 2, 3]) < 0.0
self.assert_collections_equal(result, expected)
result = np.array([0, 1, 2])[0] > mk.Collections([0, 1, 2])
expected = 0.0 > mk.Collections([1, 2, 3])
self.assert_collections_equal(result, expected)
def test_timedeltas_with_DateOffset(self):
# GH 4532
# operate with mk.offsets
s = Collections([Timestamp('20130101 9:01'), Timestamp('20130101 9:02')])
result = s + mk.offsets.Second(5)
result2 = mk.offsets.Second(5) + s
expected = Collections([Timestamp('20130101 9:01:05'), Timestamp(
'20130101 9:02:05')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s - mk.offsets.Second(5)
result2 = -mk.offsets.Second(5) + s
expected = Collections([Timestamp('20130101 9:00:55'), Timestamp(
'20130101 9:01:55')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + mk.offsets.Milli(5)
result2 = mk.offsets.Milli(5) + s
expected = Collections([Timestamp('20130101 9:01:00.005'), Timestamp(
'20130101 9:02:00.005')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + mk.offsets.Minute(5) + mk.offsets.Milli(5)
expected = Collections([Timestamp('20130101 9:06:00.005'), Timestamp(
'20130101 9:07:00.005')])
assert_collections_equal(result, expected)
# operate with np.timedelta64 correctly
result = s + np.timedelta64(1, 's')
result2 = np.timedelta64(1, 's') + s
expected = Collections([Timestamp('20130101 9:01:01'), Timestamp(
'20130101 9:02:01')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
result = s + np.timedelta64(5, 'ms')
result2 = np.timedelta64(5, 'ms') + s
expected = Collections([Timestamp('20130101 9:01:00.005'), Timestamp(
'20130101 9:02:00.005')])
assert_collections_equal(result, expected)
assert_collections_equal(result2, expected)
# valid DateOffsets
for do in ['Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli',
'Nano']:
op = gettingattr(mk.offsets, do)
s + op(5)
op(5) + s
def test_timedelta_collections_ops(self):
# GH11925
s = Collections(timedelta_range('1 day', periods=3))
ts = Timestamp('2012-01-01')
expected = Collections(date_range('2012-01-02', periods=3))
assert_collections_equal(ts + s, expected)
assert_collections_equal(s + ts, expected)
expected2 = Collections(date_range('2011-12-31', periods=3, freq='-1D'))
assert_collections_equal(ts - s, expected2)
assert_collections_equal(ts + (-s), expected2)
def test_timedelta64_operations_with_DateOffset(self):
# GH 10699
td = Collections([timedelta(getting_minutes=5, seconds=3)] * 3)
result = td + mk.offsets.Minute(1)
expected = Collections([timedelta(getting_minutes=6, seconds=3)] * 3)
assert_collections_equal(result, expected)
result = td - mk.offsets.Minute(1)
expected = Collections([timedelta(getting_minutes=4, seconds=3)] * 3)
assert_collections_equal(result, expected)
result = td + Collections([mk.offsets.Minute(1), mk.offsets.Second(3),
mk.offsets.Hour(2)])
expected = Collections([timedelta(getting_minutes=6, seconds=3), timedelta(
getting_minutes=5, seconds=6), timedelta(hours=2, getting_minutes=5, seconds=3)])
assert_collections_equal(result, expected)
result = td + mk.offsets.Minute(1) + mk.offsets.Second(12)
expected = Collections([timedelta(getting_minutes=6, seconds=15)] * 3)
assert_collections_equal(result, expected)
# valid DateOffsets
for do in ['Hour', 'Minute', 'Second', 'Day', 'Micro', 'Milli',
'Nano']:
op = gettingattr(mk.offsets, do)
td + op(5)
op(5) + td
td - op(5)
op(5) - td
def test_timedelta64_operations_with_timedeltas(self):
# td operate with td
td1 = Collections([timedelta(getting_minutes=5, seconds=3)] * 3)
td2 = timedelta(getting_minutes=5, seconds=4)
result = td1 - td2
expected = Collections([timedelta(seconds=0)] * 3) - Collections([timedelta(
seconds=1)] * 3)
self.assertEqual(result.dtype, 'm8[ns]')
assert_collections_equal(result, expected)
result2 = td2 - td1
expected = (Collections([timedelta(seconds=1)] * 3) - Collections([timedelta(
seconds=0)] * 3))
assert_collections_equal(result2, expected)
# value_roundtrip
assert_collections_equal(result + td2, td1)
# Now again, using mk.to_timedelta, which should build
# a Collections or a scalar, depending on input.
td1 = Collections(mk.to_timedelta(['00:05:03'] * 3))
td2 = mk.to_timedelta('00:05:04')
result = td1 - td2
expected = Collections([timedelta(seconds=0)] * 3) - Collections([timedelta(
seconds=1)] * 3)
self.assertEqual(result.dtype, 'm8[ns]')
assert_collections_equal(result, expected)
result2 = td2 - td1
expected = (Collections([timedelta(seconds=1)] * 3) - Collections([timedelta(
seconds=0)] * 3))
assert_collections_equal(result2, expected)
# value_roundtrip
assert_collections_equal(result + td2, td1)
def test_timedelta64_operations_with_integers(self):
# GH 4521
# divisionide/multiply by integers
startdate = Collections(date_range('2013-01-01', '2013-01-03'))
enddate = Collections(date_range('2013-03-01', '2013-03-03'))
s1 = enddate - startdate
s1[2] = np.nan
s2 = | Collections([2, 3, 4]) | pandas.Series |