Saravanan666/pypi-org huggingface dataset:
Package: cafe24-sms-Django
Python 2 & 3 compatibleSource code:https://github.com/iumjjing/cafe24-sms-DjangoDistribution:https://pypi.python.org/pypi/cafe24-sms-DjangoMaintainer:https://github.com/hwshim0810InstallationYou can install the library directly from pypi using pip:$pipinstallcafe24-sms-DjangoEdit your settings.py file:CAFE24_SMS_SETTINGS={# Required'USER_ID':'Your cafe24 id','SECURE_KEY':'Your secure key','SENDER':'Your telephone number ex) 000-0000 or 000-0000-0000',# Optional (If you want using default value, delete lines)'REQUEST_TIMEOUT':30.0,'TEST_MODE':False,'CHARSET':'euc-kr','TIMEZONE':'Asia/Seoul',}DependenciesPython 2.7 or 3.4+Django 1.11+QuickstartSend sms message to use shortcut functionimportcafe24_smstry:# Send single SMScafe24_sms.send_message(message='message',receiver='will receive telephone number or number list')exceptSMSModuleExceptionase:print(e)Send function returning Tuple(Result code, Remaining sms count)If message byte length over 90, Message will be send lms type.Receiver format: Single number(‘000-000-000’) or Number list([‘000-000-000’,’000-000-000’])More usageSend / Reserve SMS messagefromdjango.utilsimporttimezoneimportcafe24_smstry:# Reserve single SMScafe24_sms.reserve_message(message='message',receiver='will receive telephone number or number list',reservation_time=timezone.now(),)# Send multiple SMScafe24_sms.send_message(message='message',receiver=['telephone number','...'],)# Reserve multiple SMScafe24_sms.reserve_message(message='message',receiver=['telephone number','...'],reservation_time=timezone.now(),)# Send message repeat 3 times, gap 15 minutescafe24_sms.send_message(message='message',receiver='telephone number',rpt_num=3,rpt_time=15,)exceptSMSModuleExceptionase:print(e)Check the result of sent SMS.fromdjango.utilsimporttimezoneimportcafe24_smsdata=cafe24_sms.result_check(start_date=timezone.now())total_count=data.get_total_count()result_records=data.get_records()If you need more detail, see method doc.ContributorsSeehttps://github.com/iumjjing/cafe24-sms-Django/graphs/contributors
Package: cafeDaMadruga
cafeDaMadrugacafeDaMadrugaé um pacote, cujo intuito é o uso da funçãovogais(). Essa é responsável por encontrar todas as vogais de uma string.Funçãovogais(stringCompleta: str)- Recebe uma string e
retorna um dicionário. Uma de suas chaves é avogais, a qual contém uma lista das
vogais existentes na string passada como parâmetro na função, e a outra é avogais_com_indice, a qual contém uma lista com as vogais e seus respectivos indices na
string.
Package: cafeden
cafedenOverviewcafeden(from Vietnamese "cafe đen" or "black coffee") is a simple auto-clicker for keeping your computer awake. It monitors mouse and keyboard events to detect when the user is idle.How to runDownload and installPython 3Open a terminal (e.g. cmd on Windows) and install the package withPIPpipinstallcafedenNext, launch the application by running in the terminal:cafedenNote: make sure the Python Scripts directory is in your PATH (e.g.C:\Users\myuser\AppData\Local\Programs\Python\Python3x\Scripts)A system tray icon with aphin cà phêwill appear. To quit the application, right-click on the icon and click on "Exit".ConfigurationConfiguration is optional and is done through anINI configuration file. To setup the configuration, create a file named.cafedenin your home directory (e.g.~/.cafedenin Linux andC:\Users\myuser\.cafedenin Windows systems). Default values, allowed sections and options are shown below.[general]# Time in seconds since the last mouse/keyboard event from which the user is considered to be idle# Default: 45 secondsidle_threshold=1# Action rate in seconds# Default: 1 secondrate=1[mouse]# Mouse action to perform, can be one of:#   - click: perform a left click#   - move: move the cursor between current position and the one specified in option 'position'#   - wheel: scroll the wheel back and forth# The rest of the configuration in this section is ignored if no action is supplied.action=click# Position as in: x, y. Empty means perform the action wherever the mouse isposition=[keyboard]# Keyboard action to perform, can be one of:#   - press: press the specified key#   - release: release the specified key#   - press and release: press and release the specified key# The rest of the configuration in this section is ignored if no action is supplied.action=press and release# Key to press and release. Some examples of special keys that can be used:#    esc, enter, delete, ctrl, shift, right ctrl, left ctrl, right shift, left shift,#    left, up, down, right, space, esc, backspace, tab, scroll lock, print screen, insert,#    pause, caps lock, num lock, windows, alt, menu, page down, page up, play/pause mediakey=ctrlOS SupportedWindows 10GNU/Linux  (not tested)requires one of: Xorg, GNOME and Ubunturequires running as rootmacOS  (not tested)LicenseCopyright © 2021 Coredump LabsLicensed under MIT:https://opensource.org/licenses/MIT
Package: cafelogin
cafeloginA python command line tool for logging into cafe wifi portals.Compatibility:Browser WebDriverChromeFirefox (Geckodriver)Wifi Portalhttps://service.wi2.ne.jpStarbucks JPWired Cafe JPDoutor JPInstallpipinstallcafeloginUsagecafelogin[-h][-cCONFIG_FILE][--version][--browser{chrome,firefox}][--chrome-driver-versionCHROME_DRIVER_VERSION][--firefox-driver-versionFIREFOX_DRIVER_VERSION][--watch][--watch-intervalWATCH_INTERVAL]Examples:# Check portal connection and login via any detected portalcafelogin# Watch the portal connection continuously for changescafelogin--watch# Specify a web-driver version to usecafelogin--browserfirefox--firefox-driver-version"v0.28.0"WebDriver cacheRun the command once with an internet connection to install the web-driver to the cache.cafelogin

[WDM] - ====== WebDriver manager ======
[WDM] - There is no [linux64] geckodriver for browser  in cache
[WDM] - Getting latest mozilla release info for v0.29.0
[WDM] - Trying to download new driver from https://github.com/mozilla/geckodriver/releases/download/v0.29.0/geckodriver-v0.29.0-linux64.tar.gz
[WDM] - Driver has been saved in cache [/home/me/.wdm/drivers/geckodriver/linux64/v0.29.0]
Package: cafelytics
cafélytics ☕️A basic simulation of a coffee operation. Please pardon the mess as this is a work in progress.
This is a model that enables forecasting and experimentation under uncertainty, with the goal of steady year-over-year agricultural yields.
It can help address questions such as:when should I expand / how much?what will the impacts of various crop diversification strategies be?how can I plan around disaster recovery and mitigation of lost income?what would cooperative-wide yields look like if more farmers joined? what about if some left?usage (python cli)The dataset provided can be used for forecasting withmakerunwhich is equivalent topython3simulate.py--farmdata/fakeData.csv--years75--outputtestNewFarm.pngand will output this plot, representing the outputs of a collective of farmers over a seventy-five year time span:Some farmers who joined the cooperative had trees that were already very mature, so this simulation starts back in 1991 to show their hypothetical contributions towards the total yield of the group (in other words, their membership start date is not considered as a factor in this simulation at this time).contributingContributions are welcome. There are many aspects of this project to improve, please seeCONTRIBUTING.mdfor a list of places to start.
You may also raise anissueif you have questions or suggestions about this code.licenseThis software is released as-is, with no guarantee nor warranty. For more details, please seeLICENSE.txt.demoTry it in your browser!
Package: cafemap
﻿Python Implementation of CAFÉ-Map: Context Aware Feature Mapping for mining high dimensional biomedical data
as described in paper [1]Authors:Dr. Fayyaz Minhas (afsar <at> pieas dot edu dot pk)
Amina Asif  (a.asif.shah01 <at> gmail dot com )
Muhammad Arif (syedmarif2003 <at> yahoo dot com)
Downloaded From:http://faculty.pieas.edu.pk/fayyaz/software.html#cafemaporhttps://github.com/foxtrotmike/cafemapThis folder contains the package “cafeMap” and all example files.INSTALLATION INSTRUCTIONS FOR THE PACKAGE:
1. Go to directory …./cafemap-master/cafeMap in command prompt
2. Execute the command: pip install .
or
python setup.py installCafeMap package has the following modules:cafemap.py: class implementation of cafemap according to the algorithm presented in the paperinstance.py: contains the definition of an instance to be used by cafemapcv.py: parallel implementation of cross validation methods is present in this file.llc.py: This module implements the approximate Locality Constrained Linear Coding as described in the 2010 paper
by Wang et al. [2]. Given array of datapoints X (N x d) and codebook C (c x d), it returns a vector of approximated
points Y = G * C. LLC introduces sparsity by forcing those coefficients of a given data point that correspond to codebook
vectors which are not that point’s k-nearest neighbors. LLC also uses regularization.utils.py: contains utility functions to facilitate compilation of resultsFollowing files contain the code that generated the results published in the study:results_table1.py produces the results presented in Table 1 of [1]
l-shaped.py: produces the plots presented in Figure 2 of [1]
2x2checker.py: produces the plots presented in Figure 3 of [1]
toy_circle.py: produces the plots for circular data as presented in Figure 4 of [1].
arcene.py: produces the plots presented in Figure 6 of [1]
prostate.py: produces the clustering analysis plots presented in Figure 8 of [1]Note: the plots may vary from the published according to the selection of parameters while running the code.Example.py illustrates the use of cafeMap package. To use parallel version of the training method, joblib module should be
installed. All the parameters are explained in comments.References:
[1]F. ul A. A. Minhas, A. Asif, and M. Arif, “CAFÉ-Map: Context Aware Feature Mapping
for mining high dimensional biomedical data,” Computers in Biology and Medicine, vol. 79, pp. 68–79, Dec. 2016.[2] Wang, Jinjun, Jianchao Yang, Kai Yu, Fengjun Lv, T. Huang, and Yihong Gong.
“Locality-Constrained Linear Coding for Image Classification.” In 2010 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), 3360–67, 2010. doi:10.1109/CVPR.2010.5540018.Acknowledgments: We used the ROC module implemented by Dr. Asa Ben-Hur and Mike Hamilton which follows:
Theoretical and pratical concepts from
Fawcett, T.  ROC graphs: Notes and pratical considerations
for data mining researchers.  HPL-2003-4, 2003.
Package: cafeplotter
CafePlotterTable of contentsOverviewInstallationUsageOutput ContentsOverviewCAFE (Computational Analysis of gene Family Evolution) is a software to analyze changes in gene family size in a way
that accounts for phylogenetic history and provides a statistical foundation for evolutionary inferences.CAFE5currently does not provide tools to visualize Expansion/Contraction of gene families on phylogenetic tree.
To solve this issue, I developed CafePlotter, a tool for plotting CAFE5 gene family expansion/contraction result.InstallationPython 3.8 or lateris required for installation.Install PyPI package:pip install cafeplotterUsageBasic Commandcafeplotter -i [CAFE5 result directory] -o [Output directory]OptionsGeneral Options:
  -i IN, --indir IN       CAFE5 result directory as input
  -o OUT, --outdir OUT    Output directory for plotting CAFE5 result
  --format                Output image format ('png'[default]|'jpg'|'svg'|'pdf')
  -v, --version           Print version information
  -h, --help              Show this help message and exit

Figure Appearence Options:
  --fig_height            Figure height per leaf node of tree (Default: 0.5)
  --fig_width             Figure width (Default: 8.0)
  --leaf_label_size       Leaf label size (Default: 12)
  --count_label_size      Gene count label size (Default: 8)
  --innode_label_size     Internal node label size (Default: 0)
  --p_label_size          Branch p-value label size (Default: 0)
  --ignore_branch_length  Ignore branch length for plotting tree (Default: OFF)
  --expansion_color       Plot color of gene family expansion (Default: 'red')
  --contraction_color     Plot color of gene family contraction (Default: 'blue')
  --dpi                   Figure DPI (Default: 300)Example CommandUser can download example dataset (singlelambda.zip):cafeplotter -i ./examples/singlelambda -o ./singlelambda_plot --ignore_branch_lengthOutput Contentssummary_all_gene_family.[png|jpg|svg|pdf]Summary of all expansion/contraction gene family result (from*_clade_result.txt)gene_family/{FamilyID}_gene_family.[png|jpg|svg|pdf]Significant expansion(red)/contraction(blue) gene family resultresult_summary.tsv(example)Significant expansion/contraction result summary for each family and taxon
Package: cafeteria-simulation
cafeteria-simulationPython package for cafeteria simulationInstallationUse the package managerpipto install cafeteria-simulation.pipinstallcafeteria-simulationUsageThe easiest simulation to performfromcafeimportCafeteria,TOYOTATIME=300env=Cafeteria(TOYOTA.data,TIME)for_inrange(TIME):env.run([])# A graph showing the change in scoresenv.show()You can specify it by assigning the coordinates [table number, seat number] to run().Also, you can refer to the number of people in group_member up to 10 groups.Samplegreedy.pybasic.py_random.pypuzzle.pyContributingPull 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.Contributorsm-star18LicenseApache License 2.0
Package: caff
Coffee morseText to coffee morse code and coffee morse to textInstall:pipinstallcoffee-morseUse:>coffee-t'coffee'COFFEEcoffee
COFFEECOFFEECOFFEEcoffee
COFFEEcoffeeCOFFEE
COFFEEcoffeeCOFFEE
COFFEEcoffeecoffee
COFFEEcoffeecoffee

>coffee-m2-t'COFFEE coffeeCOFFEE COFFEE COFFEE coffeeCOFFEE coffee COFFEECOFFEE coffee COFFEECOFFEE coffee coffeeCOFFEE coffee coffee'coffee
Package: caffe2onnx
caffe2onnxThis tool converts Caffe models to ONNX via command line (without Caffe environment).InstallationInstall from pypipip install caffe2onnxInstall latest from githubpip install git+https://github.com/asiryan/caffe2onnxBuild and install latest from sourcegit clone https://github.com/asiryan/caffe2onnx
python setup.py installUsageTo get started withcaffe2onnx, run thecaffe2onnx.convertcommand, providing:the path to your caffe prototxt,the path to your caffe model (not required),the output path of the onnx model (not required),frozen graph or not (not required).python -m caffe2onnx.convert
    --prototxt          caffe prototxt file path
    [--caffemodel       caffe caffemodel file path]
    [--onnx             output onnx file path]
    [--frozen           frozen graph or not]OperatorsSee the documentation ofcaffe supported operators.Referencescaffe-onnx byhtshinichiTNN byTencentLicenseBSD-3
Package: caffeinate
CaffeinateDon't let your computer go to sleep while you're busy thinking.Why would I need this?Software engineering is more about thinking than typing. And it's very annoying when you're whiteboarding or just thinking and your work machine goes to sleep. Most of the times, we don't have admin access to change the settings so this helps prevent the computer from going to sleep.InstallationUsing pip:pip install caffeinateUsing pip3:pip3 install caffeinateUsage:memo:Tested on OSX and WindowsOn windows, you might need to create a simple python file (code below) as path variable seems to be admin restricted.from caffeinate import caffeinate

caffeinate.run()On Linux or Mac OSX, you can simply typeawakein the terminal.TerminationTo terminate the process, pressEsckey 3 consecutive times (doesn't have to be terminal focused key presses).TODOAdd good documentation to the usage with params explainedCleanup the code, maybe use classes for extentibility
Package: caffeine
Use this module to prevent your Mac OS X system from sleeping while executing
a long-running python script.Quick StartJustimport caffeine.  That’s it!  You Mac OS X system will not sleep as
long as the interpreter remains alive.  No need to run any other commands.
But if you want slightly finer controls, a few functions are available.caffeine.on(display=False)Use theonfunction to create a power assertion.  Set display=True to also
keep your display from sleeping. The default of display=False will allow
your display to sleep but will keep your system awake so processing can
continue.  If a power assertion is active, callingon()with display set
to the other value will change the assertion.  Callingon()with the same
value is a harmless no-op.caffeine.off()Use theofffunction to release any power assertion before your interpreter
exits.caffeine.verify()Callspmset -g assertionsto verify what power assertions are active.
Package: caffi
caffi is the Channel Access Foreign Function Interface.
It usesCFFIto call EPICS channel access library.This package provides direct low level interface to channel access, alike the C API.InstallEPICS dynamic libraries have been shipped with the package for Windows, Linux and macOS.
But if the environment variablesEPICS_BASEandEPICS_HOST_ARCHare set on macOS and Linux,
those libraries under path${EPICS_BASE}/lib/${EPICS_HOST_ARCH}will be used.Either usepip,$ pip install caffior checkout source code from the git repository,$ git clone https://github.com/CaChannel/caffi.git
$ cd caffi
$ python setup.py installPackageRPMThe SPEC filepython-caffi.specis included. Get the source tarball either from PyPI
or create it bypython setup.py sdist, and run:$ rpmbuild -ta caffi-1.0.0.tar.gzThe binary and source RPM will be created. The package name ispython-caffi.AnacondaThe recipe is included.:$ conda build conda-recipeDocumentsLatest documents are hosted atRead the Dcos.Exampleimport caffi.ca as ca

status = ca.create_context()
assert status == ca.ECA.NORMAL

status, chid = ca.create_channel('catest')
assert status == ca.ECA.NORMAL

status = ca.pend_io(3)
assert status == ca.ECA.NORMAL

status = ca.put(chid, 123)
status = ca.flush_io()

status, value = ca.get(chid)
assert status == ca.ECA.NORMAL

status = ca.pend_io(3)
assert status == ca.ECA.NORMAL

assert value.get() == 123
Package: cafle
"cafle": 재무모델 작성 패키지 for pythoncaflecafle은 기존 대부분 엑셀로 작업하던 재무모델(현금흐름추정 모델)을 python으로 작업할 수 있도록 도와 주는 python 패키지 입니다. cafle에는 index, account, write 모듈이 있습니다.account 모듈은 각각 개별 현금흐름의 내용을 저장하고, 현금흐름의 유출입을 관리하는 모듈이며, index모듈은 날짜 등 현금흐름의 시간적인 요소를 컨트롤하기 위한 모듈입니다.
write 모듈은 최종적으로 작성된 현금흐름을 엑셀 파일 상에 출력해주는 모듈 입니다.매출, 사업비, 자금의 조달, 운영계좌 등 재무모델에 반영되는 각각의 요소들을 각각 하나의 account로 설정한 후 index에서 표현되는 시간 흐름에 따라 각각의 요소들간 사업 진행에 따라 발생되는 현금흐름의 유출입을 account 내에 기록합니다.
account 내에 기록된 현금흐름들은 최종 모델링 후 개별적으로 확인이 가능합니다.구체적으로 얘기하자면,,, 자금의 조달, 매출 등 각각의 요소들 별로 사업적인 반응 조건들을 account에 설정합니다.
자금의 조달의 경우 조달 금액, 시점, 금리, 각종 수수료와 시점 및 상황 별로 취해지는 액션들을 자금조달 account에 설정합니다.
매출의 경우 매출의 발생 시점 및 금액 등 주요 가정들을 매출 account에 설정합니다.
사업비, 운영계좌 등도 각각의 account에 필요한 조건 및 액션들을 설정합니다.
이후 통합적으로 index의 흐름에 따라 account들 끼리 서로 현금흐름을 설정된 사항에 맞춰 주고 받도록 합니다.
최종 모델링이 끝나면, 최종 작업 완료된 account에서 현금흐름 내역을 확인할 수 있습니다.패키지 작성 취지금융회사에서 부동산개발금융(Project Financing) 분야에 10여년 가까이 종사해 오면서 투자 의사 결정을 위해 많은 재무모델을 직접 다뤄왔습니다.
엑셀로 만든 재무모델은 그 자체로 투자 업무를 담당하는 사람들에게는 하나의 언어로서의 역할을 합니다.
모델 작성에는 많은 시간과 노력을 소요하게 되는데, 다양한 영역의 투자 건들을 수시로 다뤄야 하는 입장에서는 모델 작성 및 관리에 소요되는 시간이 아쉬울 수 밖에 없습니다.python과 같은 프로그래밍 언어로 재무모델을 작성하게 되면 주요한 모델의 영역들을 모듈화 시켜서 미리 작성된 모듈들을 서로 갖다 붙이기만 하는 방식으로 모델을 작성하고 관리할 수 있습니다.
충분히 모듈화가 된다면, 각각의 사업영역 및 사업구도에 맞춰서 레고 블록을 맞추듯 재무모델 작성이 가능합니다.
cafle 패키지는 이러한 모듈화의 첫 시도입니다.재무모델 작성 샘플tests-FS_model_sample2 폴더의 Example.xlsx 파일은 패키지를 이용하여 작성된 간단한 재무모델의 작성결과 샘플 입니다.
해당 폴더의 cashflow.py는 사업기간 전반 동안 최초 자금의 인출에서 부터 사업비 지출, 외부조달 자금의 상환 까지 현금흐름을 전반적으로 컨트롤하고 있습니다.
현재 올려진 샘플은 간단한 형태의 개발사업을 모델링 한 것이지만, 사업 및 자금조달의 구도가 복잡해지더라도 python으로 구현이 가능하며, 복잡해질 수록 엑셀 보다는 python으로 구현하는 것이 향후 관리 측면에서 유리합니다.추가적으로...회사일로 바빠서 틈틈이 작업해온 것인데, 이제야 누군가 남에게 보여줄 만한 수준이 된 것 같습니다.
향후 임의로 수정될 수 있으며, 혹시 상기 취지에 관심 있는 분이 있다면 연락주시면 반갑겠습니다.
Package: cafram
CAFRAM - Config as FrameworkCafram is a hierarchical configuration framework for Python. It provides classes to map a hierarchical configuration (usually loaded from YAML files) to Python objects. Then it provides a foundation to organize your application code.QuickstartOn it's simplest form, it can load a simple yaml file and create a NodeObject:import yaml
from cafram.nodes import ConfAuto

yaml_config = """
config:
  namespace: "my_ns"
  create_file: True
  backups: 3
  backup_prefix: null
files:
  - name: hello
  - name: world
filters:
  content:
    prepend: "Added at first"
    append: "Added at last"
  author:
    name: MyName
    date: True
"""

config = yaml.safe_load(yaml_config)

app = ConfAuto(ident="app", payload=config, autoconf=-1)
print ("List of files: ", app.files.get())
app.dump()This is quite convenient, but it's quite useless.MetaAuthor: MrJK
License: GPLv3
Package: caft
CAFT - Continuous Affine Feature TransformerA custom transformer package that allows users to make affine/geometric transformations on datasets with respect to some curve with a well defined continuous equation.The transformers attempt to follow the scikit-learn api, however, there are limitations here based on the fact that transformers operate on bothXandyvariables. This will likely cause issues when used within a scikit-learn pipeline.InstallationInstallcaftvia pip withpipinstallcaftDocumentationCurrently, there is no hosted documentation but most functions are well documented, with examples.Alternatively, there is a thorough example in theexample.ipynbnotebook.UseageThe main pattern is as follows.importsympyasspimportnumpyasnpimportmatplotlib.pyplotaspltfromcaft.odrimportSympyODRegressor,ODRegressorfromcaft.affineimportContinuousAffineFeatureTransformernp.random.seed(42)n=10000# Generate data with some natural noise (not errors)X_true=np.linspace(-2,2,n)+np.random.uniform(-0.5,0.5,n)# Add random measurement errors - both small and extremeerrors_in_X=np.random.normal(0,0.3,n)errors_in_y=np.random.normal(0,5,n)y=3*(X_true+errors_in_X)**3+errors_in_yfx=3*X_true**3# Add systematic errorn_errs=100X_outliers=-0.5*np.ones(n_errs)+0.2*np.random.uniform(-0.3,0.5,n_errs)y_outliers=-30*np.ones(n_errs)+np.random.normal(0,3,n_errs)X=np.hstack([X_true,X_outliers]).reshape(-1,1)y=np.hstack([y,y_outliers])plt.scatter(X,y)plt.scatter(X_true,fx,color="r",s=1,)Here we can see the scatter plot ofXandyand the original function $y = f(x)$ without noise. Now we can create an affine transformation with respect to the original function (or at least the SympyRegressor estimate of it).eq="a * x ** 3 + b"X_=X/X.max()y_=y/y.max()sodr=SympyODRegressor(eq,beta0={"a":0.5,"b":1})caft=ContinuousAffineFeatureTransformer(sodr,optimiser="halley")caft.fit(X_,y_)Xt,yt=caft.transform(X_,y_)Xt=Xt.reshape(-1,1)plt.scatter(Xt,yt,s=6,)plt.show()A most thorough example can be found inexample.ipynbnotebook.This is some what of an unusual pattern, using a nested regressor within a transformer. However, the benefit here is that it allows each component to be used individually, either for individual equation regression or by rolling your own regressors to create the regressor equation.DevelopmentDeploy new versions to PyPI using GitHub Actions:Change version number to__version__ = "X.Y.Z"incaft/__init__.pythengittag-a"vX.Y.Z"-m"deployment message"gitpush--tags
Package: caf.toolkit
CAF.ToolkitThe Common Analytical Framework (CAF) Toolkit is a toolkit of transport
planning and appraisal functionalities. It's the beginning of a project to make
a lot of the useful stuff fromNorMITs Demandmore widely available and easily accessible.ContributingCAF.Toolkit happily accepts contributions.The best way to contribute to this project is to go to the issues tab and report bugs or submit a feature request. This helps CAF.Toolkit become more stable and full-featured. Please check the closed bugs before submitting a bug report to see if your question has already been answered.