Saravanan666/pypi-org huggingface dataset:
Package: crosspm2
[![build](https://travis-ci.com/devopshq/crosspm2.svg?branch=master)](https://travis-ci.com/devopshq/crosspm2)
[![pypi](https://img.shields.io/pypi/v/crosspm2.svg)](https://pypi.python.org/pypi/crosspm2)
[![license](https://img.shields.io/pypi/l/crosspm2.svg)](https://github.com/devopshq/crosspm2/blob/master/LICENSE)DocumentationActual version always here:https://devopshq.github.io/crosspm2/IntroductionCrossPM2 (Cross Package Manager 2) is a universal extensible package manager.
It lets you download and as a next step - manage packages of different types from different repositories.Out-of-the-box modules:Adapters
- Artifactory
- [Artifactory-AQL](https://www.jfrog.com/confluence/display/RTF/Artifactory+Query+Language) (supported since artifactory 3.5.0):
- files (simple repository on your local filesystem)Package file formats
- zip
- tar.gz
- nupkg (treats like simple zip archive for now)Modules planned to implement:Adapters
- git
- smb
- sftp/ftpPackage file formats
- nupkg (nupkg dependencies support)
- 7zWe also need your feedback to let us know which repositories and package formats do you need,
so we could plan its implementation.The biggest feature of CrossPM is flexibility. It is fully customizable, i.e. repository structure, package formats,
packages version templates, etc.To handle all the power it have, you need to write configuration file (crosspm.yaml)
and manifest file with the list of packages you need to download.Configuration file format is YAML, as you could see from its filename, so you free to use yaml hints and tricks,
as long, as main configuration parameters remains on their levels :)
Package: crosspredict
CrossPredictThe library makes cross validation and reports generation easyEasy to extend to other modelsSupports Lightgbm, XGBoost, CatBoostSupports different crossvalidation strategies:Supports crossvalidation by users (RepeatedKFold)Supports stratified crossvalidation by target column (RepeatedStratifiedKFold)Supports simple crossvalidation (RepeatedKFold)Easy use of target encoding with double crossvalidationSupports target encoding library category_encodersML Pipeline building blocksTable of contentsInstallationReportsPreviewTarget Encoding with DoubleCrossValidationHow to useAuthorsInstallationpython-mpipinstallcrosspredictReportsPreview#create report objecta=ReportBinary()a.plot_report(df,report_shape=(5,2),report={'Roc-Auc':{'loc':(0,0)},'Precision-Recall':[{'loc':(0,1)}],'MeanTarget-by-Probability':[{'loc':(1,0)},{'loc':(1,1)}],'Gini-by-Generations':{'loc':(2,0),'colspan':2},'MeanTarget-by-Generations':{'loc':(3,0),'colspan':2},'Probability-Distribution':[{'loc':(4,0)},{'loc':(4,1)}]},cols_score=['result_egr_to_one','probability'],cols_target=['target','target2'],col_generation_deals='first_dt_no_comm_mon')a.fig.savefig('report1.png')a.plot_report(report_shape=(3,2),report={'roc-auc':{'loc':(0,0)},'precision-recall':{'loc':(0,1)},'mean-prob':{'loc':(1,0)},'gen-gini':[{'loc':(2,0),'colspan':2}],'distribution':{'loc':(1,1)}},cols_score=['probability'])a.fig.savefig('report2.png')Target Encoding with DoubleCrossValidation# creates iteratoriter_df=Iterator(n_repeats=3,n_splits=10,random_state=0,col_client='userid',cv_byclient=True)# fits target encoder (creates mappings for each fold)cross_encoder=CrossTargetEncoder(iterator=iter_df,encoder_class=WOEEncoder,n_splits=5,n_repeats=3,random_state=0,col_client='userid',cv_byclient=True,col_encoded='goal1',cols=['field3','field2','field11','field23','field18','field20'])cross_encoder.fit(train)# train cross validation modelsmodel_class=CrossLightgbmModel(iterator=iter_df,feature_name=feature_name,params=params,cols_cat=['field3','field2','field11','field23','field18','field20'],num_boost_round=9999,early_stopping_rounds=50,valid=True,random_state=0,col_target='goal1',cross_target_encoder=cross_encoder)result=model_class.fit(train)How to useML Pipeline Calculation-ML_Pipeline.ipynbConfiguration file for pipeline -params.yamlPlot_Reports_for_Binary_Classification_problem_example-Plot_Reports_for_Binary_Classification_problem_example.ipynbSimple_example_in_one_Notebook-Simple_example_in_one_Notebook.ipynbIterator_class-Iterator_class.ipynbCrossModelFabric_class-CrossModelFabric_class.ipynbCrossTargetEncoder_class-CrossTargetEncoder_class.ipynbAuthorsVladislav BoyadzhiAlexander Tarelkin
Package: crosspress
crosspress v­0.3.0Cross-platform lightweight keyboard simulatorPython usagepipinstallcrosspressfromcrosspressimport(tap,CP_NOISY,CP_ERROR)tap("x, left shift + a")# default usagetap("x, left shift + a",delay=0.5)# 0.5 seconds delay between tapstap("x, left shift + a",behavior=CP_NOISY)# highest log leveltap("x, left shift + a",behavior=CP_ERROR)# error levelCLI usagecrosspress-t"x, left shift + a"You can use the-d, --delayarguments to set the delay between taps.Arguments-n, --noisyfor the highest log level or-e, --errorfor error level.crosspress-t"x, left shift + a"--delay0.5--errorKeyboard shortcutsWindows­',-,.,/,0,1,2,3,4,5,6,7,8,9,;,=,[,\,],`,a,b,backspace,c,caps lock,comma,d,delete,down arrow,e,end,enter,esc,f,f1,f10,f11,f12,f2,f3,f4,f5,f6,f7,f8,f9,g,h,home,i,insert,j,k,l,left alt,left arrow,left ctrl,left meta,left shift,left win,m,n,num 0,num 1,num 2,num 3,num 4,num 5,num 6,num 7,num 8,num 9,num add,num decimal,num divide,num enter,num lock,num multiply,num subtract,o,p,page down,page up,pause,q,r,right alt,right arrow,right ctrl,right meta,right shift,right win,s,scroll lock,space,t,tab,u,up arrow,v,w,x,y,zmacOS­',-,.,/,0,1,2,3,4,5,6,7,8,9,;,=,[,\,],`,b,backspace,c,caps lock,clear,comma,d,delete,down arrow,e,end,enter,esc,f,f1,f10,f11,f12,f2,f3,f4,f5,f6,f7,f8,f9,g,h,home,i,j,k,l,left alt,left arrow,left cmd,left ctrl,left meta,left option,left shift,m,n,num 0,num 1,num 2,num 3,num 4,num 5,num 6,num 7,num 8,num 9,num add,num decimal,num divide,num enter,num equal,num multiply,num subtract,o,p,page down,page up,q,r,right alt,right arrow,right cmd,right ctrl,right meta,right option,right shift,s,space,t,tab,u,up arrow,v,w,x,y,zLinux­',-,.,/,0,1,2,3,4,5,6,7,8,9,;,=,[,\,],`,a,b,backspace,c,caps lock,comma,d,delete,down arrow,e,end,enter,esc,f,f1,f10,f11,f12,f2,f3,f4,f5,f6,f7,f8,f9,g,h,home,i,insert,j,k,l,left alt,left arrow,left ctrl,left meta,left shift,m,n,num 0,num 1,num 2,num 3,num 4,num 5,num 6,num 7,num 8,num 9,num add,num decimal,num divide,num enter,num lock,num multiply,num subtract,o,p,page down,page up,pause,q,r,right alt,right arrow,right ctrl,right meta,right shift,s,scroll lock,space,t,tab,u,up arrow,v,w,x,y,z
Package: crossref
academic_citationsUnOffical Python Crossref.org API Wrapper and CLI.Table of ContentsQuickStartDocumentation/UsageContributingCreditsQuickStartInstallationpip install crossrefFrom Sourcegit clone https://github.com/TralahM/academic_citations
cd academic_citations

python setup.py bdist_wheel
pip install -e .DocumentationAPI ReferenceContributingCredits
Package: crossref-commons
Crossref CommonsHigh-level library for getting data from Crossref APIs (REST, XML, ...). Work in progress.Installationpip3 install crossref-commonsUsageRetrieving entitiesEntities of various types can be retrieved by giving their respective IDs:import crossref_commons.retrieval

crossref_commons.retrieval.get_publication_as_json('10.5621/sciefictstud.40.2.0382')
crossref_commons.retrieval.get_publication_as_xml('10.5621/sciefictstud.40.2.0382')
crossref_commons.retrieval.get_publication_as_refstring('10.5621/sciefictstud.40.2.0382', 'ieee')

crossref_commons.retrieval.get_member_as_json('15')Equivalently, you can useget_entityfunction to retrieve any type of entity:from crossref_commons.retrieval import get_entity
from crossref_commons.types import EntityType, OutputType

get_entity('10.5621/sciefictstud.40.2.0382', EntityType.PUBLICATION, OutputType.JSON)
get_entity('10.5621/sciefictstud.40.2.0382', EntityType.PUBLICATION, OutputType.XML)
get_entity('10.5621/sciefictstud.40.2.0382', EntityType.PUBLICATION, OutputType.REFSTRING, 'ieee')

get_entity('15', EntityType.MEMBER, OutputType.JSON)Retrieving relationsCurrently, aliases and general relations are supported:from crossref_commons.relations import get_related

get_related('10.1167/18.8.6')IteratingIt is possible to iterate over publications meeting specific criteria (JSON only):from crossref_commons.iteration import iterate_publications_as_json

filter = {'funder': '10.13039/501100000038', 'type': 'journal-article'}
queries = {'query.author': 'li', 'query.affiliation': 'university'}
for p in iterate_publications_as_json(max_results=189, filter=filter, queries=queries):
  print(p['DOI'])SamplingInstead of iterating over the items meeting the criteria, in some cases it is better to use a random sample.get_samplewill automatically handle sizes larger than Crossref REST API's limit of 100:from crossref_commons.sampling import get_sample

filter = {'funder': '10.13039/501100000038', 'type': 'journal-article'}
queries = {'query.author': 'li', 'query.affiliation': 'university'}
sample = get_sample(size=121, filter=filter, queries=queries)AuthorizationYou can set the Polite information and/or Plus token by creating a file~/.crapi_keywith the following content:{
  "Crossref-Plus-API-Token": "<<Plus API token, for Plus users only>>",
  "User-Agent": "<<polite user agent; including mailto:email address>>",
  "Mailto": "<<email address>>"
}Alternatively, the same information can be provided through environment variablesCR_API_PLUS,CR_API_AGENTandCR_API_MAILTO.
Package: cross_river_bank
Cross River Bank Python Client[![CircleCI](https://circleci.com/gh/finix-payments/crb-python.svg?style=svg&circle-token=d363138a4fb6cf28dee4374ff697eec723bc7449)](https://circleci.com/gh/finix-payments/crb-python)Getting Startedpip install cross_river_bank
Package: crossroads-description
⚠️ Tool unmaintainedThis tool is no longer maintained and was split in two packages :crmodelandcrdescthat will achieve the exact same purpose but separatly to allow for more flexibility and reusages possibilities.CrossroadsDescriberCrossroadsDescriberis a Python tool that produces automatic text description of data from OpenStreetMap.This tool was developed and tested under Ubuntu 20.04.DependenciesThis tool depends on several Python libraries that can be installed with pip:pip3install-rrequirements.txtIt also depends on a jsRealB server to generate text. You must install node, then execute the server :sudoaptinstallnodejs
nodejsrealb/jsRealB-serverfr.jsHow to useDetailed optiions can be obtained by using :./main.py-hObtaining an intersection descriptionFor basic usage, i.e getting the description of a crossroad, you can use the -c option with the coordinates of the crossroads you want to describe :./main.py-c45.773513.09015Visualize generated itemsThe description generation relies on the generation of sidewalks and islands in the intersection. You can visualize the result of this generation by :outputting a geojson :./main.py -c 45.77351 3.09015 -o output.geojsonopening this geojson in QGISimport the two model3 from the qgis folder in Processingexecute each of the model on the line layer of the geojson with the corresponding qml style file (also in the qgis folder)Generate an evaluation filePrevious commands generate the description of one intersection. To evaluate the quality of the generated descriptions, you can generate several descriptions at one by using evaluate.py. -r option corresponds to the radius around the coordinates used to download the data. -n indicates the number of intersections to output, randomly fetched among the intersections in the dataset../evaluate.py -c 45.77351 3.09015 -r 1000 -n 40 -o evaluation.json
Package: crossroads-schematization
Crossroads schematizationGenerate a schematization of an intersection from OpenStreetMap data.InstallationWith pip, use the following command line to install crschem:pip install crossroads-schematizationDependancies:Crossroads schematization depends oncrmodel, that implies the following cascading dependancies:crossroadsdescribercrossroads-segmentationOSMnxNetworkX, geopandasUsageIf you installed crossroads-schematization using pip, a console script is now available usingget_crossroad_schematization.
This script is also available in the examples folder (PYTHONPATH=$PWD examples/get-crossroad-schematization.py). You will find a complete description of the parameters using--help.PipelineFirst compute for each branch two long edgesS1andS2corresponding to the sidewalks:for each edge part of a branch, identify the corresponding polyline (continue a couple of 10 meters outside of the crossing)fit a long edgeEon this polyline, starting from the beginning of the polyline (e.g. 50 meters for example), and with a fixed length in the exterior direction)estimate the width of the way (using the number of lanes, the classification of the way)reconstruct a linear description of the sidewalk by shiftingEif the branch is composed of more than one way, identify the two adjacent sidewalks and make them parallelEach sidewalk is part of two branches (see crossroads-description by Jérémy Kalsron), thus is described by two long edges[Sa_i, Sa_e]and[Sa_i, Sa_e](efor exterior,ifor interior).we compute the intersection $m$ betweenSaandSb, and build a new representation with a polyline made of 3 points:Sa_e,m,Sb_eIf a one of the original segments of the crossing intersectsSa-m-Sb, the sidewalk is described by a polyline made of 4 points:Sa_e,Sa_i,Sb_i,Sb_eThe inner part of the crossroad is computed by assembling all the sidewalks as a closed polyline (assembling each polyline at they extremity). From this region, we can apply a negative buffer to obtain the inner part of the road (with a white space between sidewalks and inner part of the road).Each traffic island is available as a polygon from crossroads-description. We compute its compactness and size. A description is produced depending on these parameters:if the traffic island is large, the rendering is done as if it was bordered by sidewalksif the traffic island is small:if the shape is compact, we describe it by a discif the shape is not compact, we describe it by two half discs connected by lines (a long shape)Each pedestrian crossing is described by a dedicated pictogram, aligned with the corresponding edge from the initial data
Package: crosstalk-correction
Crosstalk correctionPython 3 implementation ofDonato et al.’s 2013 maximum impact estimation
algorithmfor correcting crosstalk effects in pathway analysis.This code has been tested on Python 3.5.BackgroundCrosstalk: Donato et al. (2013) use the term crosstalk to refer to the
effect that pathways exercise on each other (in pathway analysis methods
such as enrichment analysis, functional class scoring, and topology-based
methods) due to the presence of overlapping genes.Maximum impact estimation: They developed a correction method called
maximum impact estimation that takes into account overlaps between pathways.
The approach infers an underlying pathway impact matrix where each gene
only contributes to one pathway using an expectation maximization technique.PathCORE-T: The crosstalk correction method is used in the PathCORE-T
software, a hypothesis generation tool that identifies co-occurring pathways
from the results of an unsupervised analysis of transcriptomic data. Due to
confusion around the term “crosstalk,” we refer to this procedure as
“gene overlap correction” in the PathCORE-T software and paper.InstallationTo install the current PyPI version (recommended), run:pip install crosstalk-correctionFor the latest GitHub version, run:pip install git+https://github.com/kathyxchen/crosstalk-correction.git#egg=crosstalk-correctionExamplesThecrosstalk_correctionmethod is used inPathCORE-T(see:feature_pathway_overrepresentation)A visualization of what crosstalk correction does in the context of the PathCORE-T analysis
can be viewedhere.For more details, please see thePathCORE-T pre-print.Package contentscrosstalk_correction.pycrosstalk_correction.py contains the implementation of the crosstalk
correction procedure. The methodcrosstalk_correctionwraps
the maximum impact estimation algorithm (methodmaximum_impact_estimation)
and reduces the number of pre/post-processing steps required to
run/interpret the results ofmaximum_impact_estimation.We recommend that the methodcrosstalk_correctionbe used directly
in most use cases.For applications of maximum impact estimation that are not covered by
this method, the following methods have also been made public
and can be imported:maximum_impact_estimationinitialize_membership_matrixindex_element_mapAcknowledgementsThis work was supported by the Penn Institute for Bioinformatics
Package: crosstrainer
CrossTrainer: Practical Domain Adaptation with Loss ReweightingThis is an implementation of the method described in "CrossTrainer: Practical Domain Adaptation with Loss Reweighting" by Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, and Peter Bailis.InstallThe crosstrainer package can be installed using pip.pip install crosstrainerUsageCrossTrainer utilizes loss reweighting to train machine learning models using data from a target task with supplementary source data.Inputs:Base model, target data, source data.Outputs:Trained model with optimized weighting parameter alpha.Example:importcrosstrainerfromsklearnimportlinear_modellr=linear_model.LogisticRegression()ct=CrossTrainer(lr,k=5,delta=0.01)lr,alpha=ct.fit(X_target,y_target,X_source,y_source)y_pred=lr.predict(X_test)More examples can be found in the tests file:crosstrainer/tests/test_crosstrainer.py.
Package: crosstream
crosstream 🐍🌊🤝This Python package helps join large datasets across servers efficiently. It accomplishes this by streaming the data, allowing it to:read each dataset only oncenot need to store the complete datasets in memoryThis is particularly helpful when your datasets are larger than the available memory on your machine or when you want to minimize network reads.This package supports CSV and pyodbc datasources.InstallationFrom PyPI:pip install crosstreamFrom source:git clone https://github.com/bertwagner/crosstream.git
cd crosstream
pip install .Usage ExamplesBasic hash joinIdeally you want to make your smaller dataset the first dataset in your join.import crosstream as cs
import csv

file1 = 'small_dataset.csv'
file2 = 'large_dataset.csv'

# join using column indexes or column names
c1 = cs.read_csv(file1,True,[0,1])
c2 = cs.read_csv(file2, True, ['col1','col2'])

# specify the output file
with open('joined_output.csv', 'w') as f:
    w = csv.writer(f)
    
    # write header column names
    w.writerow(c1.column_names + c2.column_names)

    for row_left,row_right in cs.inner_hash_join(c1,c2):
        # write matched results to our joined_output.csv
        w.writerow(row_left + row_right)More examples can be found under thetestsdirectory.Custom method for determining equalityBy default, this package will join only if all values are equal.If you want to perform a transformation on your data before comparing for equality, or use more complicated join equality logic, you can pass in your own function intooverride_build_join_keyto define how equality is determined:# define a function for transforming join key data before it's hashed
def custom_join_key_transform(value):
    transformed = value.replace(' ','')
    return transformedAnd then pass that into theinner_hash_join()method:...
for row_left,row_right in cs.inner_hash_join(c1,c2,override_join_key_transform=custom_join_key_transform)
...Custom method for processing matched hashesBy default this package returns tuples of the joined rows. If you want to customize what the output of your matched data looks like (perform transformations after a match is found), you can pass a function to theoverride_process_matched_hashesargument:# define a function for performing additional transformations or adding additional outputs before the columns are returned
def custom_process_matched_hashes(bucket_row,probe_row, bucket_join_column_indexes, probe_join_column_indexes):
    # adding a new column indicating the weights of these matches are equal to 1
    weight=1.0
    return tuple(bucket_row),tuple(probe_row),(weight,)And then pass that into theinner_hash_join()method:for row_left,row_right,weight in cs.inner_hash_join(c1,c2,override_process_matched_hashes=custom_process_matched_hashes):TestsIf you want to run the tests, you will need to ensure you have the sqlite odbc driver installed:apt-get install libsqliteodbc unixodbcThen execute the tests:pytestAnd check for coverage:pytest --cov-config=tests/.coveragerc --cov=crosstream --cov-report term-missing
Package: crosswords
crosswordsis a terminal tool to help you solving crosswords.This is a full rewrite of a script I wrote back in 2011.Installpip install crosswordsTo upgrade a previous installation, use:pip install -U crosswordsUsagecrosswords [-l <language>] <word>Your word should have?characters for each letter you don’t know, e.g.:bon??ur. By default, the tool returns up to 8 possible words.Some words have special meaning:list: list local languagesremote_list: list remote languagesinstall: install a languageupdate: update local languagesA language dictionnary is a file with one word per line, without accents,
hyphens or special chars. Local dictionnaries are stored in~/.crosswords/dicts. Remote ones are on my website.LanguagesHere are the currently available languages. You can install any of them withcrosswords install <language>.English (english):70k wordsFrench (french): 300k wordsTestsClone this repo, then:[sudo] make deps
make check
Package: crosswork-companion
Crosswork CompanionBusiness Ready Documents for Cisco CrossworkCurrent API CoverageHealth Insights KPI Management Querypulse_cpu_utilization

pulse_cpu_threshold

pulse_cef_drops

pulse_device_uptime

pulse_ethernet_port_error_counters

pulse_ethernet_port_packet_size_distribution

pulse_interface_packet_counters

pulse_interface_qos_egress

pulse_interface_qos_ingress

pulse_interface_rate_counters

pulse_memory_utilizationNCA YANG ModulesInstallation$python3-mvenvcrosswork$sourcecrosswork/bin/activate(crosswork)$pipinstallcrosswork_companionUsage - Help(crosswork)$crosswork_companion--helpUsage - In-line(crossswork)$crosswork_companion--url<urltoCrosswork>--username<crossworkusername>--password<crossworkpassword>Usage - Interactive(crosswork)$crosswork_companionCrosswork URL: <URL to Crosswork>Crosswork Username: <Crosswork Username>Crosswork Password: <Crosswork Password>Usage - Environment Variables(crosswork)$exportURL=<URLtoCrosswork>(crosswork)$exportUSERNAME=<CrossworkUsername>(crosswork)$exportPASSWORD=<CrossworkPassword>Recommended VS Code ExtensionsExcel Viewer - CSV FilesMarkdown Preview - Markdown FilesMarkmap - Mindmap FilesOpen in Default Browser - HTML FilesContactPlease contact John Capobianco if you need any assistance
Package: crossywords
Crossy words: word matrixLibrary that generates the word matrix in a way to have multiple words in one 5x5 matrix.Available methodsgetMatrix(lang="en")Parametrs:lang - language of words in the matrix. Values: "en", "ru". By default - "en".Result:a list of lists representing a 5x5 matrixa dictionary of coordinates of specific word {word: [coordinates in matrix]}printMatrix(matrix)- prints generated matrixprintWords(matrix)- prints generated words with coordinates of letters in the matrixInstallationpip install crossywordsExampleimportcrossywordsascwmatrix,words=cw.getMatrix("en")# getting a matrix and words in itcw.printMatrix(matrix)# printing matrixprint()cw.printWords(words)# printing words in itOutput:t a t s o
r h l k b
s o w k t
g a n h n
e t h i a

think - [[4, 1], [4, 2], [4, 3], [3, 4], [2, 3]]
thank - [[2, 4], [3, 3], [4, 4], [3, 4], [2, 3]]
own - [[2, 1], [2, 2], [3, 2]]
show - [[2, 0], [1, 1], [2, 1], [2, 2]]
talk - [[0, 2], [0, 1], [1, 2], [1, 3]]
start - [[0, 3], [0, 2], [0, 1], [1, 0], [0, 0]]
Package: croston
crostonA python package to forecast intermittent time series using croston's methodreadthedocs: crostonexample:import numpy as np
import random
from croston import croston
import matplotlib.pyplot as plt


a = np.zeros(50)
val = np.array(random.sample(range(100,200), 10))
idxs = random.sample(range(50), 10)

ts = np.insert(a, idxs, val)

fit_pred = croston.fit_croston(ts, 10,'original')

yhat = np.concatenate([fit_pred['croston_fittedvalues'], fit_pred['croston_forecast']])

plt.plot(ts)
plt.plot(yhat)