Saravanan666/pypi-org huggingface dataset:
Package: cmo-dataexplore
Cmotions Dataexplorecmo_dataexplore is a Python library created byThe Analytics Lab, which is powered byCmotions. This library makes it easy for us to explore a new dataset and create nice and insightful graphs along the way. Nothing fancy, but very useful for our consultants. Mostly since this package is used in other packages, which makes our workflow simpler and more efficient.Since we love to share what we do, why not also do that with our packages, that is why we've decided to make (almost) all of our packages open source, this way we hope to give back to the community that brings us so much. Enjoy!InstallationInstall cmo_dataexplore using pippipinstallcmo-dataexploreUsageimportcmo_dataexploreascdximportcmo_datavizascdvfromcmo_dataexplore.resourcesimportget_data_pathimportpandasaspdimportmatplotlib.pyplotasplt# retrieve datadf=pd.read_csv(get_data_path("example.csv"))# init the Explore objectex=cdx.explore(data=df,dependent_var="Quit_yn",verbose=True)# if there are records with missings in the dependent variable, these will be removed# if there are columns that are completely empty, these will be removed# also if the dependent variable is numeric, this will be changed to string# list all columns with missing valuesex.retrieve_columns_with_missings()# get all statistics of the entire dataframeex.data_stats()# check out the number of unique values of each non-numeric variableex.categories_counter()# identify all columns that have Near-Zero Variance (the dependent variable is automatically excluded)nzv_df,nzv_cols=ex.calculate_NZV(frequency_ratio_thresh=95/5,percentage_unique_thresh=10,verbose=True)nzv_cols# mutual information scoreex.calculate_MI_scores(plot=False,ax=None)# predictive power score for the target variableex.calculate_PPS_target(plot=False,ax=None)# use the predictive power score to find relationships in the dataex.calculate_PPS(plot=False,ax=None)# retrieve all columns with a correlation higher than your prefered thresholdex.retrieve_columns_highly_correlated(corr_thresh=.8)# create all histograms at once# this excludes columns with more than 'max_categories' categoriesex.show_hists(nr_plot_cols=4,color_by=ex.dependent_var,bins=10,max_categories=50,plotsize=(15,15))# create all relation plots at once# this excludes columns with more than 'max_categories' categoriesex.show_relations(bins=10,max_categories=50,nr_plot_cols=3)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseGNU General Public License v3.0ContributorsJeanine SchoonemannContact us
Package: cmo-dataloader
Cmotions Dataloadercmo_dataloader is a Python library created byThe Analytics Lab, which is powered byCmotions. This library combines our most used functionality when it comes to loading flat files or database tables into Python in bulk. Regarding flat files it also tries to take into account which separator, decimal sign and encoding to use when loading a file, this way the data is loaded automatically the best way possible, so you don't have to waste time fixing difficult bugs and errors. Nothing fancy, but very useful for our consultants. Mostly since this package is used in other packages, which makes our workflow simpler and more efficient.Since we love to share what we do, why not also do that with our packages, that is why we've decided to make (almost) all of our packages open source, this way we hope to give back to the community that brings us so much. Enjoy!InstallationInstall cmo_dataloader using pippipinstallcmo-dataloaderIf you want to be able to use the tutorial notebook or the code below, you also need to install dotenv using pippipinstallpython-dotenvUsageimportosfromdotenvimportload_dotenvfromcmo_dataloader.resourcesimportget_data_pathimportcmo_dataloaderasdlload_dotenv()# load flat files from a directorydlc=dl.load_flat_file(get_data_path('formula_1'))datafiles_dict=dlc.load()# load tables from a SQL Server database# using credentials stored in a .env fileddu=dl.load_database(server=os.getenv("DB1_SERVER"),db=os.getenv("DB1_DATABASE"),server_name=os.getenv("DB1_SERVER_NAME"),usr=os.getenv("DB1_USERNAME"),pwd=os.getenv("DB1_PASSWORD"),driver=os.getenv("DB1_DRIVER"),port=os.getenv("DB1_PORT"),tables=['dbo.mytable','dbo.anothertable'])datafiles_dict=ddu.load()# load tables from a SQL Server database# using your Windows credentialsddu=dl.load_database(server="localhost",db="mylocaldb",tables=['dbo.mytable','dbo.anothertable'])datafiles_dict=ddu.load()ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseGNU General Public License v3.0ContributorsJeanine SchoonemannContact us
Package: cmo-dataviz
Cmotions Datavizcmo_dataviz is a Python library created byThe Analytics Lab, which is powered byCmotions. This library makes it easy for us to create graphs in our prefered styling and using Cmotions colors by default. Nothing fancy, but very useful for our consultants. Mostly since this package is used in other packages, which makes our workflow simpler and more efficient.Since we love to share what we do, why not also do that with our packages, that is why we've decided to make (almost) all of our packages open source, this way we hope to give back to the community that brings us so much. Enjoy!InstallationInstall cmo_dataviz using pippipinstallcmo-datavizIf you want to be able to use the tutorial notebook or the code below, you also need to install pydataset using pippipinstallpydatasetUsagefrompydatasetimportdataimportcmo_datavizasdvimportmatplotlib.pyplotaspltimportpandasaspd# import your own stylesheet if relevantdv.import_style('mydocuments/mystylesheet.mplstyle')df=data("iris")df_summary=df.groupby('Species').agg({'Sepal.Length':'mean','Sepal.Width':'sum','Petal.Length':'mean','Petal.Width':'sum'}).reset_index()# create a single plot with default settings for size and so ondv.create_horizontal_barplot(data=df_summary,x_var='Sepal.Width',y_var='Species',x_label="",title="",ax=None)plt.show()# adjust the size (or other settings) of the plotfig,ax=plt.subplots(figsize=(5,5))dv.create_horizontal_barplot(data=df_summary,x_var='Sepal.Width',y_var='Species',x_label="",title="",ax=ax)plt.show()# combine multiple graphs in a single plotfig,ax=plt.subplots(2,2,figsize=(10,7))dv.create_histogram(data=df,var="Petal.Width",color_by="Species",bins=10,max_categories=50,ax=ax[0,0],title="our histogram")dv.create_scatterplot(data=df,x_var="Sepal.Width",y_var="Sepal.Length",title="our scatterplot",ax=ax[0,1])dv.create_boxplot(data=df,x_var="Petal.Length",y_var="Species",color_by=None,ax=ax[1,0],title="our boxplot")dv.create_swarmplot(data=df,x_var="Sepal.Width",y_var=None,color_by="Species",ax=ax[1,1])plt.show()# create example data for a network graphnetworkdata={'source':['A','B','C','D','E','B','C','C','C'],'target':['B','C','D','E','A','D','E','D','D'],'label':['relation1','relation2','relation3','relation4','relation5','relation6','relation7','relation8','relation9']}networkdata=pd.DataFrame(networkdata)# create a network graphdv.create_network_graph(data=networkdata,node_1='source',node_2='target',edge_label='label',title="my network graph",figsize=(5,5),ax=None,seed=1502)plt.show()ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseGNU General Public License v3.0ContributorsJeanine SchoonemannContact us
Package: cmoncrawl-fetcher
CmonCrawl-FetcherDownloads desired files from Common Crawl data by filetypeUsageusage: cmoncrawl-fetcher.py [-h] -l <limit> -f FILETYPES [FILETYPES ...] [-p NUM_PROCS] -o OUTPUT [-t TOLERANCE]

Python package that downloads files from common crawler's database. An example usage is `cmoncrawl-fetcher.py -l 5 -f jpg png -o out_dir` This'll make it download 5 jpgs and 5 pngs into out_dir

options:
  -h, --help            show this help message and exit
  -l <limit>, --limit <limit>
                        Number of images per filetype desired
  -f FILETYPES [FILETYPES ...], --filetypes FILETYPES [FILETYPES ...]
                        Desired filetypes to fetch
  -p NUM_PROCS, --num_procs NUM_PROCS
                        Number of processes to use, default is 1
  -o OUTPUT, --output OUTPUT
                        Output directory to store downloaded files
  -t TOLERANCE, --tolerance TOLERANCE
                        Number of fails for a given hostname before we ignore this hostRequired arguments are the desired file type (input the extension), the output directory, and the number of desired files for each extension.By default we prioritize those with Content Types that signify the filetype over the extension, we store corresponding content types for file types infiletype_config.json, we currently support 69 file types.ContributingPackage installation usespoetry, but this is subject to change in the future.We usegit-flow workflowsfor our development. Depending on the kind of feature you are contributing, create ahotfixbranch or afeaturebranch. Installation instructions arehere.We also require a pre-commit hook. You can follow the instructionshereto install them.You will need to install the hooks in theyamlfile in the repository using the following command:pre-commit install.LicenseWe've released this project under GPLv3. Check the LICENSE file for more details.
Package: cm-one
Gravity Interface (TK) - написанный на tkinter графический интерфейс, служащий для взаимодействия весовщика
и Gravity Core.v 0.2.4
- Иконка версии в углу
- ar_ip, ar_port, mirrored передаются как аргументы при запуске приложения
- добавлен cmadb (блокировка дублей)
Package: cmos-noise-map
CMOS Noise MapCode to model random telegraph noise in a CMOS detector. Originally designed for the Las Cumbres Observatory BANZAI pipeline.
Authors: Prerana Kottapalli, Matt Daily, Curtis McCullyRead the docs:https://cmos-noise-map.readthedocs.io/en/latest/index.htmlInstallationFrom PyPipip install cmos-noise-mapFrom GithubTo install the tool, clone this repository and run:pip install poetry
cd cmos-noise-map
poetry installTestsTo run the unit tests, simply run:poetry run pytestUsageOnce you've installed the tool, it can be run simply by:rts-maker <path> <options> <write filename>Usage: rts-maker [OPTIONS] PATH [METHOD] [FILEPATH]

  This script builds a noise map with the chosen method.

  path: Path to input without the .fits at the end

  filepath:OPTIONAL Path to write file

  method: Default method is std. Available methods are std, rts, and param.
  See docs for more information about each method.

Options:
  -r, --data_ext TEXT             Extension of fits file that contains the
                                  image data
  -uq, --upper_quantile FLOAT     Standard deviation cutoff for pixel noise
                                  evaluation
  -t, --tolerance FLOAT           The minimum difference between silhouette
                                  scores. See docs for more information.
  -m, --min_peak_separation FLOAT
                                  Minimum difference between pixel value
                                  cluster centers to be considered separate
                                  clusters
  -o, --out_hdu_name TEXT         Name for the header in which the data will
                                  be stored
  -f, --fpack                     Adding this option will fpack your output
                                  fits file
  -b, --bias_check                Adding this option will skip the check to
                                  see if files used are bias files  [default:
                                  True]
  --help                          Show this message and exit.
Package: cmot
CMOTA collection of miscellaneous goods at an unbeatable price.This project is a collection of relatively isolated utilities.
They are designed to minimize inter-dependencies such that, if desired, it is
easy to copy only the needed files (and license) instead of importing the whole
project.InstallpipinstallcmotDevelopmentEditable Installpythonsetup.pydevelop[--user]Re-run this command to refresh the version number (based on git tags).VersioningUsesSemantic Versioning.Versions are set exclusively via git tags:git-av0.1.2-m""
Package: cmpa
cmpa (Comparison utility)Compares the contents of two directories (folders) and reports if they are equal or not.Usagedircmpa can be used either as a command line application or as a Python library package.Command LineSimple example:cmpa <first_directory> <second_directory>Usecmpa -hfor a complete list of options.Python library packageSimple example:fromcmpaimportcompare,Compare# Just compare directory with itself.compare(['.','.'])print()# Use the class for finer grain observabilityc=Compare(['.','.'],silent=True)print(c.get_total_files())print(c.get_file_counts())print(c.compare_ok_count)
Package: cmpc-timelapse
auto_timelapse_scriptScript for automatically downloading a list of videos, speeding them up, and concatenating them.Uses multithreading with youtube_dl and ffmpeg-python to download_and_speed_up and speed up a list of videos all at once. Then, it uses ffmpeg to combine them all into one timelapse.Made forhttps://cmpc.livePyPI:https://pypi.org/project/cmpc-timelapse/InstallationYou can use pip!python-mpipinstallcmpc-timelapseOr just clone the repo.UsageWhen installed using pip:As a python modulepython-mauto_timelapseOr as a console scriptcmpc-timelapseTo use, create a filevods.txtwith a list of youtube-dl compatible video URL's separated by newlines. Then, run in the same folder.Has a number of command line options, including-h,--help. Might add runtime input support.
Package: cmpd
cmpdcmpd is a command line tool that allows you to compare multiple structured data sources and generate a report highlighting variances or discrepancies at the desired unit of analysis.FeaturesCompare multiple data sources: cmpd supports comparing data from various sources, including CSV files, databases, and APIs.Flexible unit of analysis: You can specify the unit of analysis for comparison, such as individual records, columns, or specific fields.Detailed variance report: cmpd generates a comprehensive report that highlights the differences between the data sources, providing insights into the variations or discrepancies.Customizable output: You can configure the output format and choose to export the report in various formats, such as CSV, JSON, or HTML.Licensecmpd is released under theMIT License.
Package: cmpdata
cmpdatapackage can handle and analyze raw CMIP6 data.GitHub repo:https://github.com/TaufiqHassan/cmpdataFeaturesHandle raw CMIP6 dataAnalyze any specific models/experiments/variablesPerform regridding for model ensemble meansData preprocessingStatistical analysisInstallationUse the YAML file provided to create a virtual conda enviroment (cmpdata)conda env create-fenvironment.ymlAnd then activate cmpdata to use cmpdataconda activate cmpdataSupports both Mac and Linux. Windows users can useWindows Subsystem.Usagecmpdatacan be used to handle and analyze raw CMIP6 data. A lot of the options available inacccmip6is available incmpdata, especially for selecting models, experiments and variables.cmpdataalso tries to be a good command-line interface (CLI). Runcmpdata-hto see a help message with all the arguments you can pass.python cmpdata.py-husage: cmpdata.py [-h] -o {info,rm,mm,stats,ts} [-dir DIR] [-m M] [-e E] [-v V] [-r R] [-out OUT] [-f F] [-init INIT] [-end END] [-t] [-z] [-s S] [-mm]
[-std] [-clim] [-anom] [-manom] [-trend] [-aggr] [-freq FREQ] [-reg REG] [-rm] [-a] [-curve] [-w] [-ci CI] [-regrid]

options:
-h, --help            show this help message and exit
-o {info,rm,mm,stats,ts}, --output-options {info,rm,mm,stats,ts}
        Select an output option
-dir DIR              Select directory.
-m M                  Model names
-e E                  Experiment names
-v V                  Variable names
-r R                  Realization
-out OUT              Output file name
-f F                  Input filenames for stats
-init INIT            Initial year
-end END              Ending year
-t                    Temporal mean option
-z                    Zonal mean option
-s S                  Seasonal mean option
-mm                   Calculate model ensemble mean
-std                  Calculate model std
-clim                 Calculate monthly climatology
-anom                 Calculate monthly anomaly
-manom                Calculate model anomaly
-trend                Calculate variable grid-by-grid trends
-aggr                 Calculate model aggreement
-freq FREQ            Temporal mean frequency: annual/daily/monthly
-reg REG              Select region for timeseries output
-rm                   Use the realization means
-a                    Use cell areas for spatial mean calculations
-curve                Regridding to curvilinear grids
-w                    All model means as ens dim (used by -std, -mm, -aggr stats options)
-ci CI                confidence interval used in -trend and -aggr options
-regrid               regridding on/offLicenseThis code is licensed under theMIT License.
Package: cmpGen
cmpGen is deprecated.
Please just use the “key” parameter of sort/sorted instead.
It is better in every conceivable way (faster, simpler, more legible, etc.)For example, here is the example below, but entirely using “key” instead:l=[3,0,1,-2,4,3,-5]
l.sort(key=abs)
l now is [0,1,-2,3,3,4,-5]This pakage is now here just to support legacy version of packages that used thisOld README for reference:cmpGen is a very simple utility to aid in sorting.
Basically, I often want to sort a list based on a specific property
or more generically a function of the values in the list.
cmpGen acts as a middle layer to make this process simple.Usage:
l is a list
l.sort(cmpGen(someFunctionThatActsOnListElements))Example 1: Sort a list of 2-integer pairs based on second element in the pair:
l=[[3,3],[1,6],[0,12],[1,1],[7,4],[6,7]]
l.sort(cmpGen(lambda x: x[1]))
l is now [[1, 1], [3, 3], [7, 4], [1, 6], [6, 7], [0, 12]]Example 2: Sort based on the absolute value of list elements:
l=[3,0,1,-2,4,3,-5]
l.sort(cmpGen(abs))
l now is [0,1,-2,3,3,4,-5]
Package: cmph-cffi
These bindings provide python with the ability to create and useMinimal Perfect Hashesvia theCMPHlibrary.InstallationShould be as simple as$pipinstallcmph-cffiDevelopment versionThelatest development versioncan be installed directly from GitHub:$pipinstall--upgrade'git+https://github.com/URXtech/cmph-cffi'UsageCreating a new MPHimportcmphwithopen('/usr/share/dict/words','w')askeys:mph=cmph.generate_hash(keys)Getting keys out of an MPHmph('Test')WarningBe aware that whilst MPH’s are awesome, they typically cannot
distinguish between keys they are built on and unseen keys. Concretely this
means that feeding in keys that are not in the original key set will haveundefined resultsSaving the MPHwithopen('/tmp/out.mph','w')asout_file:mph.save(out_file)Loading a pre-existing MPHwithopen('/tmp/out.mph')asin_file:cmph.load_hash(in_file)AuthorsGreg Bowyer(@GregBowyer) andVenkatesh Sharma(@Venkateshks)
created these bindings andthese fine peopledid all the hard implementation
work inCMPH.Reporting bugsPlease seeBUG_REPORTS.ContributePlease seeCONTRIBUTING.LicencePlease seeLICENSE.
Package: cmp-ipproxy-lib
Example PackagenestList.py是一个可以打印缩进列表的示例函数,示例代码如下：from example_pkg_zx1 import nestList
alist = ["grace", "angle", "roy", 
            ["anna", "jhon", "richard", ["nio", "lily"]], 
            "bluce"]        
nestList.print_list(alist)
Package: cm-platform-library
cloudmicro platform libraryAs most services require the requests to them to be authenticated and checked
for permissions - the speed of traversing these requests via HTTPS is
prohibitive. Therefore, we give each service direct access to the platform
database to query via predefined methods.This library also includes license, provider and user JSON serialisation.Our mission is to have all single resource requests to return in under
100ms, and this will never happen if we have to authenticate between
microservices.
Package: cmplot
cmplot.py - Cloudy Mountain Plot in pythonAn informative RDI categorical distribution plot inspired by Violin, Bean and Pirate Plots.(RDI = Raw data + Descriptive statistics + Inferential statistics)LikeViolin plots, it shows smoothed kernel density curves, revealing information which would be hidden in boxplots, for example presence of multiple"peaks"("modes") in the distribution"mountain".LikeBean plots, it shows the raw data, drawn as acloudof points. By default all data points are shown but you can optionally control this and limit the display to a subset of the data.LikePirate plots, it marks confidence intervals (either from Student's T or as Bayesian Highest Density Intervals or as interquantile ranges) for the probable position of the true population mean.Since by default it does not symmetrically mirror the density curves, it allows immediate comparisions of distributions side-by-side.DocumentationOnline documentation at readthedocs.ioGitHub pageDownload and installationcmplotis pure python code. It has no platform-specific dependencies and should thus work on all platforms. It requires the packagesplotly numpy scipy pandas. The latest version ofcmplotcan be installed by typing either:pip3installcmplot(fromPython Package Index)or:pip3 install git+git://github.com/g-insana/cmplot.py.git(fromGitHub).There is also aversion in Julia.Quickstart>>>importplotly.graph_objectsasgo>>>fromcmplotimportcmplot#call the cmplot directly inside a plotly Figure function as:>>>go.Figure(*cmplot(mydataframe,xcol="xsymbol"))#alternatively get traces and layout as separate variables, so that you can modify them or combine with others before passing them to Figure() function:>>>(traces,layout)=(cmplot(mydataframe,xcol="xsymbol"))#[...] do something with traces/layout>>>go.Figure(traces,layout)#plot itCopyrightcmplotis licensed under theGNU Affero General Public License.(c) CopyrightGiuseppe Insana, 2019-
Package: cmply
cmplyInstallationpython3-mpipinstall--upgradecmplyIntroductionCmply is a tool used to run a set of rules against a repository.
The output will show the result of what rules fail or pass.
There is provision for different levels of failures. These levels areinfo, warning, fail and security.
The target can be a local folder or a git repository.
If a git repository is specified, the local git is used with any configure authentication relative to the local system.Usageusage:cmplyrun[-h][-rRULES][-v][-f][--no-error][-tTAGS][-eENV][--output{json}][-bBRANCH]target

positionalarguments:targetthetargetfolderorgitrepotorunthecompliancecheckon

options:-h,--helpshowthishelpmessageandexit-rRULES,--rulesRULESglobofrulefiles-v,--verboseincreaseoutputverbosity-f,--hide-failhidefailureoutinformation--no-errordontexit1ifsecurityandfailsarefound-tTAGS,--tagsTAGSprojecttags-eENV,--envENVenvironmentalvariables--output{json}outputtype-bBRANCH,--branchBRANCHthebranchtouseiftargetisagitrepoRule fileThe rules files are YAML files with the following format:name:sonarcube-setup# Name your rule heredescription:|# This is a verbose descriptionAll projects must be registered in sonarcubeimage:node:16# Your steps are executed in this docker containertype:fail# The type of rule info, warning, fail or securitytags:# A list of tags. These then matched to the tags passed in from the command line for your execution.-allsteps:# A list of commands executed. When a command has a non 0 response- the test is considered failed-test -f sonar-project.propertiesRun using dockerTo use the docker image of cmply it is important to pass through the hosts docker socket.dockerrun\-v//var/run/docker.sock:/var/run/docker.sock-v./rules:/opt/rules-v./target:/opt/target-tipselibas/cmply\run-r/opt/rules\/opt/target