Saravanan666/pypi-org huggingface dataset:
Package: cimcb-lite
cimcb litecimcb_lite is a lite version of the cimcb package containing a small number of basic tools for the statistical analysis of untargeted and targeted metabolomics data.InstallationDependenciescimcb_lite requires:Python (>=3.5)Bokeh (>=1.0.0)NumPySciPyscikit-learnStatsmodelstqdmUser installationThe recommend way to install cimcb_lite and dependencies is to usingconda:conda install -c cimcb cimcb_liteorpip:pip install cimcb_liteAlternatively, to install directly from github:pip install https://github.com/cimcb/cimcb_lite/archive/master.zipAPIFor futher detail on the usage refer to the docstring.cimcb_lite.modelPLS_SIMPLS: Partial least-squares regression using the SIMPLS algorithm.train: Fit the PLS model, save additional stats (as attributes) and return Y predicted values.test: Calculate and return Y predicted value.evaluate: Plots a figure containing a Violin plot, Distribution plot, ROC plot and Binary Metrics statistics.calc_bootci: Calculates bootstrap confidence intervals based on bootlist.plot_featureimportance: Plots feature importance metrics.plot_permutation_test: Plots permutation test figures.cimcb_lite.plotboxplot: Creates a boxplot using Bokeh.distribution: Creates a distribution plot using Bokeh.pca: Creates a PCA scores and loadings plot using Bokeh.permutation_test: Creates permutation test plots using Bokeh.roc_plot: Creates a rocplot using Bokeh.scatter: Creates a scatterplot using Bokeh.scatterCI: Creates a scatterCI plot using Bokeh.cimcb_lite.cross_valkfold: Exhaustitive search over param_dict calculating binary metrics.cimcb_lite.bootstrapPerc: Returns bootstrap confidence intervals using the percentile boostrap interval.BC: Returns bootstrap confidence intervals using the bias-corrected boostrap interval.BCA: Returns bootstrap confidence intervals using the bias-corrected and accelerated boostrap interval.cimcb_lite.utilsbinary_metrics: Return a dict of binary stats with the following metrics: R2, auc, accuracy, precision, sensitivity, specificity, and F1 score.ci95_ellipse: Construct a 95% confidence ellipse using PCA.knnimpute: kNN missing value imputation using Euclidean distance.load_dataXL: Loads and validates the DataFile and PeakFile from an excel file.nested_getattr: getattr for nested attributes.scale: Scales x (which can include nans) with method: 'auto', 'pareto', 'vast', or 'level'.table_check: Error checking for DataTable and PeakTable (used in load_dataXL).univariate_2class: Creates a table of univariate statistics (2 class).wmean: Returns Weighted Mean. Ignores NaNs and handles infinite weights.Licensecimcb_lite is licensed under the MIT license.AuthorsKevin MendezDavid BroadhurstCorrespondenceProfessor David Broadhurst, Director of the Centre for Integrative Metabolomics & Computation Biology at Edith Cowan University.
Package: cimese_net
A neural network is implemented here to detect copyrigt infringement violations in newly uploaded video files. The model takes the general form of a Siamese network, with two images filtered through the same convolutional neural network before a classification is made concerning the probability that the two images are a match. In order to train the model, frames were extracted from a set of high-resolution video files of movies and a corresponding set of lower-quality recordings of those movies. Randomly selected frames from the high-quality files were paired with the corresponding frame from the recorded version (a match) as well as a frame from another movie (not a match); these are referred to as triplets. Each of the images is run through the initial set of convolutional layers, which take the structure and weightings from the pre-trained VGG16 neural network, and a vector of length 4096 is returned. This serves as the input to a new set of top layers meant to classify the paired images as a match or not. In addition to the feature extraction and classification model, a means of aligning the recorded clip with the full movie is implemented to optimize the neural net performance. The top level function outputs a single probability of infringement that emerges as the average of the predicted match probabilities along the length of the potentially infringing clip.
Package: ci-messages
python-ci_messagesThis is a thin wrapper around theCI Messagesspec which provides a representation of the schemas defined in that project as afedora-messagingmessage schema. This project defines a single schema class with a single entry point named 'ci_messages.CIMessageV1', so far as fedora-messaging is concerned. When the body schema for a particular message is requested, the class reads the appropriate schema file for the topic from disk, and returns that.Installation and useYou can visitthe python-ci_messages project page on Pagure, and clone withgit clone https://pagure.io/fedora-qa/python-ci_messages.git. Tarballs are released throughPyPI.Dependencies are listed in the fileinstall.requires. To install the module systemwide, runpython setup.py install.In addition to the Python module dependencies, the module requires access to the CI Messages schemas as JSON files. By default it will attempt to read them from the directory/usr/share/ci/messages. You can change this location by creating a file/etc/python-ci_messages.confor~/.config/python-ci_messages.confwith contents like this:[main]
schemapath = /path/to/schema/locationYou can produce the JSON files from a CI Messages git checkout by runningmake convert, which will place them in a subdirectory namedtmp.As long as the module is installed, it will be used automatically by any fedora-messaging consumer for messages which declare they follow this schema in their headers. For more information on fedora-messaging, seeits documentation.This module requires Python 3.6 or higher (as it uses f-strings).Bugs, pull requests etc.You can file issues and pull requests onPagure. Pull requests must be signed off (use the-sgit argument). By signing off your pull request you are agreeing to theDeveloper's Certificate of Origin:Developer's Certificate of Origin 1.1

By making a contribution to this project, I certify that:

(a) The contribution was created in whole or in part by me and I
    have the right to submit it under the open source license
    indicated in the file; or

(b) The contribution is based upon previous work that, to the best
    of my knowledge, is covered under an appropriate open source
    license and I have the right under that license to submit that
    work with modifications, whether created in whole or in part
    by me, under the same open source license (unless I am
    permitted to submit under a different license), as indicated
    in the file; or

(c) The contribution was provided directly to me by some other
    person who certified (a), (b) or (c) and I have not modified
    it.

(d) I understand and agree that this project and the contribution
    are public and that a record of the contribution (including all
    personal information I submit with it, including my sign-off) is
    maintained indefinitely and may be redistributed consistent with
    this project or the open source license(s) involved.Licensingpython-ci_messages is available under the GPL, version 3 or any later version. A copy is included as COPYING.
Package: cimiss-python
cimiss-pythonCIMISS client for Python 3InstallationPython >= 3.6Ubuntusudoaptinstall-ybuild-essentiallibssl-devlibbz2-dev
pipinstallcimiss-pythonWindowspipinstallcimiss-pythonCentOSsudoyumgroupinstall"Development Tools"sudoyum-yinstallzlib-develbzip2-developenssl-devel
pipinstallcimiss-pythonUsageCIMISS-MUSIC仅为内网用户提供服务，且需要拥有相应数据访问权限的账号。importcimissimportnumpyasnp# host 不带http前缀，通常为纯ip地址  (client实例使用完成后需要调用destroy方法手动注销，或者使用下面的with语句自动管理)client=cimiss.Query(user_id='myuserid',password='mypasswd',host='myhost')# callAPI_to_array2D# array_2d(interface_id: str, params: Dict[str, str], dtypes: Optional[Dict[str, Union[str, np.dtype]]]) -> pd.DataFrameresp_array_2d=client.array_2d(interface_id="getSurfEleByTime",params={"dataCode":"SURF_CHN_MUL_HOR","elements":"Station_ID_C,PRE_1h,PRS","times":"20181224000000","orderby":"Station_ID_C:ASC","limitCnt":"10",},dtypes={'PRE_1h':np.float,'PRS':np.float})# pandas# https://pandas.pydata.org/pandas-docs/stable/# dtypes参数字典value也可为字符串 例如 {'PRE_1h': 'float', 'PRS': 'float32'}# callAPI_to_gridArray2D# grid_array_2d(interface_id: str, params: Dict[str, str]) -> xr.DataArrayresp_grid=client.grid_array_2d(interface_id="getNafpEleGridByTimeAndLevelAndValidtime",params={"dataCode":"NAFP_FOR_FTM_HIGH_EC_ANEA","fcstEle":"TEM","time":"20191206000000","fcstLevel":"1000","validTime":"0",},)# xarray# http://xarray.pydata.org/en/stable/# callAPI_to_fileList# def file_list(interface_id: str, params: Dict[str, str]) -> pd.DataFrame# callAPI_to_saveAsFile# save_file(interface_id: str, params: Dict[str, str], data_format: str, file_name: str, follow_host: Union[bool, str] = False) -> str# follow_host可以为bool或str#   False时使用接口返回的默认下载主机#   True时则下载主机强行跟随host:80#   str时则为指定的主机 (x.x.x.x:x 形式 端口为80时可省略端口)# callAPI_to_downFile# down_file(interface_id: str, params: Dict[str, str], file_dir: str, follow_host: Union[bool, str] = False) -> List[str]## 实例使用完成后需要进行注销client.destroy()除此之外还可以使用with语句自动管理importcimisswithcimiss.Query(user_id='myuserid',password='mypasswd',host='myhost')asclient:# client.array_2d ......pass# 在with语句块之外会自动对实例进行注销,无需手动管理FAQA:IcePy.cpython-36m-x86_64-linux-gnu.so: undefined symbol: SSLeayQ: Update the system OpenSSL library version to 1.1.x (升级系统OpenSSL依赖库版本)
Package: cimpyorm
InstallationPyPI:pip install cimpyormDocumentationSome documentation can be found atreadthedocs.UsageimportcimpyormLoading datasets from cimpyorm-.db filesession,m=cimpyorm.load(r"Path/To/DatabaseFile")# Load an existing .db fileParsing datasetssession,m=cimpyorm.parse(r"Path/To/Folder/Containing/Export")# Parse a .xml export (also creates a cimpyorm-.db file of the export)To configure additional schemata (currently only the schema for the CGMES profiles are distributed
with the application), create additional subfolders in the/res/schemata/directory
containg the schema RDFS.Running the testsYou can run the included test-suite by runningcimpyorm.test_all().Querying datasetsall_terminals=session.query(m.Terminal).all()names_of_ConductingEquipment=[t.ConductingEquipment.namefortinall_terminals]Bug reports/feature requestsPlease use the Issue Tracker.
Package: cimrdf.py
cimrdf.pyGenerate Python data structures from CIM RDF profiles, parse and serialize CIM-compliant information objects, according to IEC 61970-501 standard.Installing$ pip install cimrdf.pyor$ git clone https://github.com/bressanmarcos/cimrdf.py.git
$ cd cimrdf.py
$ python setup.py installUsingCreate your CIM RDF artifact with your preferred utilsConvert it into Python data structures with:cimrdfpy input-rdfs.xml output.pyCreating CIM RDF instances3a. Use the generated classes from output.py to create your instancesfrom output import *

ei = EquivalentInjection()
ei.mRID = 'EquivalentNW243'
t = Terminal()
t.sequenceNumber = 1
# This next command reciprocally associates the `t` Terminal to `ei` EquivalentInjection,
t.ConductingEquipment = ei
# but only if this association is well-defined in the RDFS document (inverseRoleName property).

t1 = Terminal(sequenceNumber=1)
t2 = Terminal()
t2.sequenceNumber = 2
s = Switch(mRID='SW12', normalOpen=True)
# All attributes with multiplicity greater than 1 are represented as lists
s.add_Terminals(t1)
# A special function `add_{attribute_name}` is generated to insert a single item into the list
s.add_Terminals(t2)

cn = ConnectivityNode()
cn.mRID = 'Node23'
# List-type attributes may also be declared in the following way:
cn.Terminals = [t1, t]4a. Create a new document instance with all objectsnew_doc = DocumentCIMRDF()
new_doc.add_recursively(ei)
# >> All other linked objects will be automatically inserted into the documentThe available methods for theDocumentCIMRDFclass are:new_doc.dump(): Pretty-Print the document to stdout. Debug purposes only.new_doc.pack(): Generate the document's ElementTree (xml.etree.ElementTree) instance.new_doc.tostring(): Get the XML stringified version of the document.new_doc.tofile(): Save the XML stringified version of the document to a file.new_doc.add_elements( ... ): (Advanced users only) Insert one or a list of elements into the document.new_doc.add_recursively( ... ): (Recommended) Insert one or a list of elements into the document including its linked elements.Parsing CIM RDF instances3b. Use proper functions to parse a file or a string.new_doc = DocumentCIMRDF()
new_doc.fromstring(rdfstring)
print(new_doc.resources)
# >> [list of instances]The available functions are:new_doc.fromstring(rdfstring): Get list of instances from CIM RDF string.new_doc.fromfile(filename): Get list of instances from CIM RDF file.After being inserted or parsed from a string/file, the elements are stored and accessible from thenew_doc.resourcesattribute.
Package: cimren-helpers
Helper FunctionsThis repo provides helper functions forListing files in a directoryCalculating Haversine Distance in milesInstallationpip install cimren-helpersHow to usefrom helpers import files

file_list = files.get_all_files_in_directory('.')from helpers import haversine_distance

distance = haversine_distance.calculate_haversine_distance(from_latitude=[47.6062],
                                                           from_longitude=[122.3321],
                                                           to_latitude=[47.5301],
                                                           to_longitude=[122.0326])
Package: cimren-wkmeans-geo
Weighted KMeans Clustering for Geolocational ProblemRepo for weighted k means clustering for specifically geo locational problems.For an example and mathematical explanation:https://emrahcimren.github.io/data%20science/Greenfield-Analysis-with-Weighted-Clustering/PrerequisitesInstall environment.yml for prerequisites.conda env create -f environment.ymlTo recreate environment.ymlconda env export > environment.ymlTo create requirements.txt from environment.ymlpip freeze > requirements.txtInstallationpip install cimren-wkmeans-geoInputsinput_locationsis a pandas dataframe with the following format.LOCATION_NAMELATITUDELONGITUDEWEIGHTVOLUMELOC 0-27.0065170.583110number_of_clusters: Number of clusters to be createdminimum_elements_in_a_cluster: Minimum elements in a clustermaximum_elements_in_a_cluster: Maximum elements in a clustermaximum_volume_in_a_cluster: Maximum volume that can fit in a cluster; if set to None, then it is disabledmaximum_iteration: How many maximum number of steps the algorithm takes to stop if it does not find the solutionenable_minimum_maximum_elements_in_a_cluster: True/False to enable minimum and maximum cluster sizeobjective_range: Acceptable difference between objectives at each iterationDataPackage has a sample data setfrom wkmeans_geo.src import data
data.locations_test
data.number_of_clusters
data.minimum_elements_in_a_cluster
data.maximum_elements_in_a_cluster
data.maximum_volume_in_a_cluster
data.maximum_iteration
data.enable_minimum_maximum_elements_in_a_cluster
data.objective_rangeHow to usefrom wkmeans_geo.src import data
from wkmeans_geo import wkmeans_clustering as wkc
clusters, locations_with_clusters = wkc.calculate_clusters(
                                       data.locations_test,
                                       data.number_of_clusters,
                                       data.minimum_elements_in_a_cluster,
                                       data.maximum_elements_in_a_cluster,
                                       data.maximum_volume_in_a_cluster,
                                       data.maximum_iteration,
                                       data.objective_range,
                                       data.enable_minimum_maximum_elements_in_a_cluster)
Package: cinc
cinc provides fixed-width C-like integer types. The speed of these types are
comparable to python ints.UsageSigned types are named “intN” and unsigned types are named “uintN”, where “N”
is the number of bits. Types for 8, 16, 32 and 64 bits are provided.cinc integers can be constructed from Python ints or cast from another cinc
type. If the source integer has more bits than the cinc type then it is
truncated:>>>x=uint32(0xFFFFFFFF)>>>xcinc.uint32(4294967295)>>>uint16(x)cinc.uint16(65535)Arithmeticcinc integers are compatible with python ints. Comparisons and operators cast
the integer to their cinc type before performing the comparison or operation.The result of the operation has the same type as the left operand:>>>x=uint32(2)>>>y=int32(2)>>>x+ycinc.uint32(4)>>>y+xcinc.int32(4)This applies to operations with python ints too:>>>x=uint32(2)>>>x+2cinc.uint32(4)>>>2+x4There are also methods for bit rotate operations and bit insertion and
extraction:>>>x=uint32(0x000000ff)>>>hex(x.rrotate(8))'0xff000000'>>>y=uint32(0xff000000)>>>hex(y.insert(0xff,8,8))'0xff00ff00'>>>hex(y.extract(16,16))'0xff00'Python FallbackIf no C++ compiler is available during installation, then a Python source module
based on ctypes is used instead. The Python version is significantly slower than
the compiled C
extension.Changelog2.0cinc types are now compatible with python ints.A pure Python module was added.
Package: cincan-command
CinCan CommandCinCan is a command-line interface for running security analysis tools conveniently in Docker containers.Complete documentation is available atCinCan Documentation.FeaturesInstall and update security analysis tools without dependency hell. CinCan collects the bestsecurity analysis toolsaround the web and provides them as containerized Docker images. You can use CinCan's tools as if they were installed locally because CinCan automates the process of installing and updating the tools and removes the need to manually maintain conflicting dependencies.Repeatable command-line experience. Learn fromour bloghow CinCan improves the command-line experience by combining various tools for repeatable and high-quality security analysis.Community support. If you know a tool that is not available in CinCan, pleasecontributeto the project!Supported platformsThecincancommand should run on all fairly modern Linux distributions. Partial support for macOS is available - tested to work with macOS Catalina. On Windows,cincandoes not work, unfortunately, unless used with Windows Subsystem for Linux 2 (WSL2).InstallationAs a prerequisite, you must haveDocker18.09+installed for running the tools, andPython3.6+andpipPython package manager to install thecincancommand.Install cincan via pip:% pip install --user cincan-commandYou can verify that the installation works by running:% cincan listIf all goes well, you should get a list of thelatest stable toolsdockerized in the CinCan project. The first time running this may take a while as it will fetch information about the tools and cache it locally.Tool images are currently hosted (mirrored) in:Docker HubGitHub Container RegistryQuay Container RegistryCurrently,default registry is quay.io, use full names to run CinCan images from certain registry - otherwise quay.io is used by default. For example namecincan/tsharkwill be converted intoquay.io/cincan/tshark. This applies only to CinCan images. We are migrating away from Docker Hub due to newly introduced rate limits.Use theinstallation instructions in the CinCan Documentationfor additional help.Using toolsA specific tool can be invoked withcincan runlike this:% cincan run [OPTIONS] TOOL[:TAG] [ARG...]For example, invoke the toolcincan/pywhoiswith:% cincan run cincan/pywhois 127.0.0.1See more examples fromcincan run reference in the CinCan documentation.
Package: cinch
![alt text](https://travis-ci.org/RedHatQE/cinch.svg?branch=master“build status”)
[![codecov](https://codecov.io/gh/RedHatQE/cinch/branch/master/graph/badge.svg)](https://codecov.io/gh/RedHatQE/cinch)# cinchThis folder contains an Ansible playbook for standing up and configuring
Jenkins masters and slaves. There are roles specifically for the creation of
those configurations, as well as several other roles which can be leveraged
for configuring and standing up resources of other types helpful in the
process of running continuous integration.For full documentation on the configuration options of each role, see the
default vars YAML file in the particular role. Any of the values in that file
are intended to be overridden by the user.Getting StartedPlease see documentation athttp://redhatqe-cinch.rtfd.io/SettingsSome notable defaults for Jenkins masters currently enabled are
- Java 8
- Jenkins LTS 2.63.3
- an extensive list of plugins found in files/jenkins-plugin-lists/default.txt
- SSL disabled, but Jenkins served off of port 80Primary supported target operating systems are
- RHEL 7
- CentOS 7IRC Support#redhatqe-cinch on chat.freenode.net
Package: cinderdiags
HPE Storage OpenStack Cinder Diagnostic CLIFree software: Apache licenseOverviewThis CLI tool can be used to validate a user cinder.conf file and also
to verify software installed on Cinder and Nova nodes.** Note that versions >= 2.0.0 of this package only work with the re-branded versions of the “HPE” cinder drivers.
To run against the older “HP” cinder drivers, please install the latest 1.0.x version of this package.Requirementscliffcliff-tabpython-3parclientInstallation instructionspip install cinderdiagsRunning the CLITo view command options:cinderdiags --helpExample commands:cinderdiags help options-check
cinderdiags options-check -v
cinderdiags help software-check
cinderdiags software-check --log-file tmp.txt
cinderdiags software-check -software python-lefthandclient --package-min-version 2.0.0Configuration FileBefore executing cinderdiags, the user must create a cli.conf configuration file. The following
example shows the required fields and format:[MY-CINDER-NODE]                         # required section name - must be unique
service=cinder                           # service type = cinder or nova
host_ip=15.125.224.1                     # host system where service is running
ssh_user=admin                           # SSH credentials for host system
ssh_password=admin
conf_source=/etc/cinder/cinder.conf      # if cinder node, location of cinder config file

[MY-NOVA-NODE]
service=nova
host_ip=15.125.224.1
ssh_user=adminBy default, this file needs to exist in the /etc/cinderdiags/ directory. Alternatively, this file
path can be passed into the CLI command using the argument ‘-conf-file <file path>’.