Saravanan666/pypi-org huggingface dataset:
Package: bombardier_client
Bombardier is a command-line system that allows a system admins to manage alarge number of computers in his or her datacenter over ssh. You can compareBombardier to Chef (www.opscode.com) or ControlTier (www.controltier.org).Bombardier has a more secure than either of those systems and provides abetter user interface (command-line) that (we think) most systemadministrators will prefer for daily work to web-based interfaces (whichyou are limited to with the other products)..For common tasks such as deploying software or making changes to systemconfiguration, this tool can make these tasks a lot faster and a lot morereliable. The basic approach is to drive automated SSH sessions over expect..There are three major pieces of code to this thing:# A "client," which installs on the computers you want to manage. The clientdoes not run as a service, it is more accurately described as client-sidelibraries. The client software can be automatically installed by the server.# (THIS PACKAGE)A server, which runs under Apache and provides a ReSTinterface, and a daemon. The daemon is responsible for managing sshconnections to the computers in the network.# A command-line interfce (CLI), which provides an efficient Cisco-stylecommand-line for driving the whole system.
Package: bombardier_core
Bombardier is a command-line system that allows a system admins to manage alarge number of computers in his or her datacenter over ssh. You can compareBombardier to Chef (www.opscode.com) or ControlTier (www.controltier.org).Bombardier has a more secure than either of those systems and provides abetter user interface (command-line) that (we think) most systemadministrators will prefer for daily work to web-based interfaces (whichyou are limited to with the other products)..For common tasks such as deploying software or making changes to systemconfiguration, this tool can make these tasks a lot faster and a lot morereliable. The basic approach is to drive automated SSH sessions over expect..There are three major pieces of code to this thing:# A "client," which installs on the computers you want to manage. The clientdoes not run as a service, it is more accurately described as client-sidelibraries. The client software can be automatically installed by the server.# (THIS PACKAGE)A server, which runs under Apache and provides a ReSTinterface, and a daemon. The daemon is responsible for managing sshconnections to the computers in the network.# A command-line interfce (CLI), which provides an efficient Cisco-stylecommand-line for driving the whole system.
Package: bombardier_server
Bombardier is a command-line system that allows a system admins to manage alarge number of computers in his or her datacenter over ssh. You can compareBombardier to Chef (www.opscode.com) or ControlTier (www.controltier.org).Bombardier has a more secure than either of those systems and provides abetter user interface (command-line) that (we think) most systemadministrators will prefer for daily work to web-based interfaces (whichyou are limited to with the other products)..For common tasks such as deploying software or making changes to systemconfiguration, this tool can make these tasks a lot faster and a lot morereliable. The basic approach is to drive automated SSH sessions over expect..There are three major pieces of code to this thing:# A "client," which installs on the computers you want to manage. The clientdoes not run as a service, it is more accurately described as client-sidelibraries. The client software can be automatically installed by the server.# (THIS PACKAGE)A server, which runs under Apache and provides a ReSTinterface, and a daemon. The daemon is responsible for managing sshconnections to the computers in the network.# A command-line interfce (CLI), which provides an efficient Cisco-stylecommand-line for driving the whole system.
Package: bombaysoftwares-pysupp
pysuppThe bombaysoftwares-pysupp package provides a comprehensive set of utility functions for various operations in Python. These functions simplify common tasks such as date formatting, timestamp conversion, manipulating strings. This package is designed to enhance the functionality of various operations in your Python projects.InstallationTo use the Date Utils package, you can install it via pip:pipinstallbombaysoftwares_pysuppUsageImport the desired functions from the package:frombombaysoftwares_pysuppimportstr_to_bool,format_emailstr_to_boolThis Function converts a string to a boolean value. It checks if the stripped and lowercase version of the string is 'true' or 'false'. If it matches 'true', the function returns boolean True. If it matches 'false', the function returns boolean False. Otherwise, it returns None.str_to_bool('True')//Output:True
str_to_bool('False')//Output:False
str_to_bool('invalid')//Output:Noneformat_emailThis Function formats an email address. This function removes leading and trailing whitespace, converts the email to lowercase, and returns the formatted email address.format_email("  john@example.com ")//Output:'john@example.com'format_email("MARY@example.com")//Output:'mary@example.com'format_email("")//Output:''format_email(None)//Output:Noneget_body_mass_indexThis function calculates the Body Mass Index (BMI) based on weight and height.get_body_mass_index(70,170)//Output:24.22
get_body_mass_index(80,180)//Output:24.69Other FunctionsThe package also includes other useful functions:is_number(string): Checks if a string can be converted to an integer.file_name_to_readable_name(file_name): Converts a file name to a readable name. It removes file extensions, replaces hyphens with spaces, removes non-alphabetic characters except spaces, capitalizes the first letter, and removes leading/trailing spaces.convert_date_of_birth_to_datetime(date_string): Converts a date string in the format '%m/%d/%Y' to a datetime object.get_current_year(): Retrieves the current year.is_invalid(value): Checks if a value is considered invalid. It checks if the string representation of the value matches any of the predefined invalid values: "", None, "None", "null", or "undefined". If a match is found, it returns True; otherwise, it returns False.For detailed usage and examples, refer to the inline documentation and code samples within each function.AuthorBombay SoftwaresLicenseThis project is licensed under the MIT license. See theLICENSEfile for more info.
Package: bombchu
bombchuSimple data manipulation tool with a bang, inspired byqu.Installpip3 install bombchu --userEnsure that where Python cli tools are installed on your system is in your PATH.Usage❯ # break on a delimiter 
❯ echo "d,d,b,a,a,b,c,d,d,e" | bombchu -d ','
['d', 'd', 'b', 'a', 'a', 'b', 'c', 'd', 'd', 'e']

❯ # same thing, but sort unique
❯ echo "d,d,b,a,a,b,c,d,d,e" | bombchu -d ',' -s -u
['a', 'b', 'c', 'd', 'e']

❯ # now output it separated by semi-colons
❯ echo "d,d,b,a,a,b,c,d,d,e" | bombchu -d ',' -s -u -o '; '
a; b; c; d; e

❯ # i got some csv data that looks like this...
❯ head -n 2 foo.csv
start_time,ip,port
"2022-04-10 06:00:10",10.13.37.42,51111

❯ # let's extract all the unique ips sorted and put them on new lines defanged
❯ cat foo.csv | bombchu -c ip -u -n --defang --sips
10[.]13[.]37[.]42
10[.]22[.]22[.]11
10[.]44[.]44[.]22

❯ # i gotta put this data in to splunk now in term format
❯ cat foo.csv | bombchu -c ip -u --term
(TERM(10.22.22.11) OR TERM(10.13.37.42) OR TERM(10.44.44.22))

❯ # now i got some json data that looks like this...
❯ cat foo.json
{
    "foo": {
        "id": 1234
    },
    "foo2": {
        "id": 1337
    },
    "foo3": {
        "id": 1111
    }
}

❯ # i need all these ids sorted
❯ cat foo.json | bombchu -j id -s
[1111, 1234, 1337]

❯ # gimme those ids on new lines with back ticks
❯ cat foo.json | bombchu -j id -s -n --add '`'
`1111`
`1234`
`1337`

❯ # now i got some log file that looks like this...
❯ head -n 2 foo.log
{"name": "joe", "id": 4242, "action": "bleh"}
{"name": "bob", "id": 1337, "action": "blah"}

❯ # let's see the users in these logs
❯ cat foo.log | bombchu -l name -o ', '
joe, bob, bob, bob, bob, john, john, bob, bob, susan, susan, bob

❯ # now i need unique users and ids next to each other
❯ cat foo.log | bombchu -l id,name -n -s -u
1337,bob
1337,john
1337,susan
4242,joe

❯ # i need these sorted unique and ready to go into splunk
❯ cat foo.log | bombchu -l name -s -u --splunk
("bob", "joe", "john", "susan")

❯ # now throw that on my clipboard
❯ cat foo.log | bombchu -l name -s -u --splunk --copy
("bob", "joe", "john", "susan")

❯ # read in a python list
❯ echo "['62', '41', '27', '111', '55']" | bombchu -p --addl '=' -n
=62
=41
=27
=111
=55
Package: bomber
# Read data from the Bureau of Meterology (BoM)author: Jess Robertson (@jesserobertson)The BoM, in it's wisdom, have made getting their geospatial data fairly difficult. This package aims to fix thatTo see this readme as an ipython notebook, check out https://github.com/jesserobertson/bomber/blob/master/examples/request_bom_data.ipynb## Example usageCheck out the measurement/observation datasets that we have available:```python>>> import bomber>>> print(bomber.measurements.DATASETS)``````{'ndvi': ['ndviave'],'rainfall': ['totals'],'solar': ['solarave'],'temperature': ['maxave', 'minave'],'vprp': ['vprp09', 'vprp15']}```Then we can get the bit that we want as a geotiff:```python>>> geotiff = bomber.get_measurements(dataset='rainfall', year=2015, month=1)``````Downloaded data to rainfall_totals_month_2015010120150131.geotiff```and then plot it using rasterio```python>>> import rasterio, numpy>>> import matplotlib.pyplot as plt>>> with rasterio.drivers():...     with rasterio.open(geotiff) as src:...         fig = plt.figure(figsize=(11, 11))...         data = numpy.ma.MaskedArray(...             data=src.read(1),...             mask=src.read_masks(1))...         ax = fig.gca()...         ax.imshow(data, cmap=plt.get_cmap('coolwarm'))...         ax.set_aspect('equal')...         ax.set_axis_off()...         ax.set_title('Rainfall, January 2015')```![Rainfall png](https://raw.githubusercontent.com/jesserobertson/bomber/master/examples/rainfall.png)You can also get the climatic average datasets as well:```python>>> print(bomber.climate.DATASETS)``````{'decadal-rainfall': ['r'], 'decadal-temperature': ['mx', 'mn']}```There's also a borked version for the climate regions but I'm too lazy to finish that one. Pull requests welcome!
Package: bombfuse
bombfusePython module for specifying timeouts when executing functionsInstallationTo install this package, run the following command:$ pip install bombfuseUsage>>> import time
>>> import bombfuse
>>> import sys
>>> # here's an infinite loop
>>> def func(msg):
>>>     while True:
>>>         time.sleep(1)
>>>         sys.stdout.write(msg + "\n")
>>>         sys.stdout.flush()
>>>
>>> # time out in 5 seconds
>>> bombfuse.timeout(5, func, "Hello world!")
Hello world!
Hello world!
Hello world!
Hello world!
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "bombfuse\__init__.py", line 64, in timeout
    raise e
bombfuse.TimeoutError: The function 'func' timed out
>>>
Package: bomcheckgui
bomcheckguibomcheckgui.py has been converted to exe format.Look for it hereWhat the program doesBomcheckgui is a graphical user interface (gui) for thebomcheckprogram.
It compares Bills of Materials (BOMs).How to installAssuming that you already have Python on your machine, use the package
manager softwarepipthat comes with Python, and type this on on the command line to install:pip install bomcheckguiThis will also automatically install bomcheck if it is not already installed on
your machineHow to runTo run the program, from the command line, enter:bomcheckguiHow it worksDrag and drop the BOM files that you want to be checked onto bomcheckgui's
drag/drop window.  Click the green check mark icon to generate results.
(See bomcheck's help section for additional information)bomcheck.cfgBomcheck has a configuration file available named bomcheck.cfg.  With it the
default Unit of Measure (U/M) can be switched from inches to mm or to some
other U/M.  Also, column header names can be changed to suit the user's needs.
Within the bomcheck.cfg file are explanations about what settings that can be
employed.  To get started, download bomcheck.cfg fromhttps://github.com/kcarlton55/bomcheck/tree/master/doc,
save to your local disk, then open with text editor and modify it as you like.
Afterwards go to bomcheckgui's settings and point the program to your config
file.
Package: bom-configuration
bom-configurationWelcome to bom-configuration. This is an opinionated configuration library that will build a configuration from environment variables, command line arguments, local files, and global files.usageTo start, you must define the environment you are using. The available environments aredev,qa, andprod. If you do not define an environment, you will receive an error.>>>frombom.configurationimportload>>>load()...ValueError:`ENV`environmentvariablemustbespecified.$exportENV=dev>>>frombom.configurationimportload>>>conf=load(){'bom':{'configuration':{'version':'1.0.0'}}}To modify the configuration, create abom.conf.jsonfile in the python library or create it inside the working directory of the application.
Package: bomist-utils
BOMIST UtilitiesThings you can do with it:Convert legacy workspaces into BOMIST v2 workspacesHow to installThis requires Python 3 and the Python Package Installer (pip3) to be installed.
You can download it here:https://www.python.org/downloads/Once you have them installed, run the following command on your terminal:$ pip3 install bomist_utilsAfter installingbomist_utilsthroughpip3you'll end up with it available on your terminal.On Windows you might have to re-launch your terminal in order for thebomist_utilscommand to be recognized.To make sure it was properly installed run the following command:$ bomist_utils --helpYou should see the available options.UsageConvert legacy workspaces$ bomist_utils --dump1 --ws <wspath> [--out <outpath>]wspathis the path of the workspace you want to dump. A.wsfile must exist in it.Alegacy.bomist_dumpfile will be created onoutpathor in the folder the command was called from if the--outoption is not used. This file can then be imported by BOMIST v2.LimitationsAt the moment this utility can only convert and keep data connections between:parts, documents, labels, storage, categoriesProjects, orders and history won't be converted.To import projects into the new version you can export your BOMs into CSV files in the legacy version (right-click > Export) and then import them in the new version. Parts will be accordingly assigned to BOM designators, as long as those parts exists in the workspace.Need help?Get in touchFor more info:bomist.com
Package: boml
Configuration & StatusBOML is a modularized optimization library that unifies several ML algorithms into a common bilevel optimization framework. It provides interfaces to implement popular bilevel optimization algorithms, so that you could quickly build your own meta learning neural network and test its performance.Key features of BOMLUnified bilevel optimization frameworkto address different categories of existing meta-learning paradigms.Modularized algorithmic structureto integrate a variety of optimization techniques and popular methods.Unit tests with Travis CI and Codecovto reach 99% coverage, and followingPEP8 naming conventionto guarantee the code quality.Comprehensive documentationsusing sphinx andflexible functional interfacessimilar to conventional optimizers to help researchers quickly get familiar with the procedures.Related LinksGo to the project home pageDownload the latest code bundle
Package: bom-open
Python 3 context manager to open a file or stdio. When reading in text mode, encoding can be detected with chardet. When reading Unicode, always chooses an encoding which removes Byte Order Mark (utf-8-sig, utf-16 or utf-32).Additional arguments are passed toopen().Python writes a Byte Order Mark for utf-8-sig, utf-16, or utf-32.  Python does not write BOM when endianness is specified.Differences fromopen()Iffile=Noneor'-', open stdin (when reading) or stdout (when writing).Ifencoding=Noneandmodeis readable and text mode ('r' or 'w+'),
file encoding will be detected using chardet.
Package: boms
BOMS : Cell Segmentation method for Spatial TranscriptomicsBOMS is a tool for cell segmentation in fluorescent in-situ hybridization (FISH) based Spatial Transcriptomics datasets. It takes as input the gene locations and labels. It assumes that a cell body is homogenous in its transcriptional signature and uses the similarity of these neighborhoods to cluster them together as one cell. The method can also incorporate the flows obtained from Cellpose Segmentation on DAPI/Cell Membrane channels to improve its cell segmentation.InstallationThe package requires Python > 3.9. The package can be installed using pip as follows:pipinstallbomsUsageThe data for the method is provided in the form of threenumpy arrays:xrepresenting the x coordinates of the mRNA spots,yrepresenting the y coordinates of the mRNA spots andgrepresenting the labels of the mRNA spots. The cell segmentation can be performed as follows:frombomsimportrun_boms""":param epochs: Number of iterations for the BOMS algorithm. Recommendation: 30:param h_s: Spatial Bandwidth. Recommendation: Roughly equal to the radius of the cell body.:param h_r: Range Bandwidth. Recommendation: 0.3 - 0.5:param K: Number of Nearest Neighbors to form the Neighborhood Gene Expression Profile. Recommendation: 30:return modes: N x (2 + no. of genes) array containing the final modes.:return seg: N x 1 array containing the final segmentation."""modes,seg=run_boms(x,y,g,epochs=30,h_s=10,h_r=0.3,K=30)DemoA demo notebook is available to run on Google Colab -BOMS Demo
Package: bon-csvAvg
A little script that calculates the average of a number of eqalbonnie++tests.
It is probably far from complete and I’ve just tested it with some of my recent results.Installationpipinstallbon-csvAvgUsagebonnie++...|bon_csvAvg.py|bon_csv2html>your_bonnie_result.html# orbon_csvAvg.pyyour_bonnie_result.csv|bon_csv2html>your_bonnie_result.htmlThe script will try to average all results with the same name (bonnie++ -m "foobar" ...) and will only output lines that have been calculated (e.g lines with names occurring only one won’t be printed).
Columns that could not be calculated will be printed as “yyyyy” (borrowing from bonnies “+++++” columns).
Package: bond
Bond is a small library that can be used to spy values and mock functions during tests.
Spying is a replacement for writing
theassertEqualsin your test, which are tedious to write and even more tedious to
update whem your test setup or code inevitably changes.
With Bond, you separate what is being verified, e.g., the variable namedoutput, from
what value it should have. This way you can quickly spy several variables, even have structured
values such as lists or dictionaries, and these values are saved into an observation log that is saved
for future reference. If the test observations are different you have the option to interact with a
console or visual tool to see what has changed, and whether the reference set of observations need to
be updated.For more examples please look at:The main project pageA tutorialThe API documentation
Package: bond-api
bond-apiAsynchronous Python wrapper library over Bond Local APIInstallationFrom PyPi:pip3installbond-apiLibrary UsageimportasynciofromaiohttpimportClientResponseError,ClientConnectorErrorfrombond_apiimportBond,Actionasyncdefmain():"""Example of library usage."""bond=Bond("[your ip or hostname here]","[your bond API token here]")try:print("Version:")print(awaitbond.version())print("Device IDs:")device_ids=awaitbond.devices()print(device_ids)print("Devices:")devices=awaitasyncio.gather(*[bond.device(device_id)fordevice_idindevice_ids])print(devices)print("Devices Properties:")properties=awaitasyncio.gather(*[bond.device_properties(device_id)fordevice_idindevice_ids])print(properties)print("Devices State:")state=awaitasyncio.gather(*[bond.device_state(device_id)fordevice_idindevice_ids])print(state)print("Turn on fan!")awaitbond.action("[your fan device ID here]",Action.turn_on())print("Change fan speed!")awaitbond.action("[your fan device ID here]",Action.set_speed(2))print("Turn off fan!")awaitbond.action("[your fan device ID here]",Action.turn_off())exceptClientResponseErrorasx:print("Client response error: ",x)exceptClientConnectorErrorasx:print("Client connector error: ",x)if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())
Package: bond-async
bond-asyncAsynchronous Python wrapper library over Bond Local APIForked fromhttps://github.com/prystupa/bond-apiThanks @prystupa for the great work!InstallationFrom PyPi:pip3installbond-asyncLibrary UsageimportasynciofromaiohttpimportClientResponseError,ClientConnectorErrorfrombond_asyncimportBond,Actionasyncdefmain():"""Example of library usage."""bond=Bond("[your ip or hostname here]","[your bond API token here]")try:print("Version:")print(awaitbond.version())print("Device IDs:")device_ids=awaitbond.devices()print(device_ids)print("Devices:")devices=awaitasyncio.gather(*[bond.device(device_id)fordevice_idindevice_ids])print(devices)print("Devices Properties:")properties=awaitasyncio.gather(*[bond.device_properties(device_id)fordevice_idindevice_ids])print(properties)print("Devices State:")state=awaitasyncio.gather(*[bond.device_state(device_id)fordevice_idindevice_ids])print(state)print("Turn on fan!")awaitbond.action("[your fan device ID here]",Action.turn_on())print("Change fan speed!")awaitbond.action("[your fan device ID here]",Action.set_speed(2))print("Turn off fan!")awaitbond.action("[your fan device ID here]",Action.turn_off())exceptClientResponseErrorasx:print("Client response error: ",x)exceptClientConnectorErrorasx:print("Client connector error: ",x)if__name__=='__main__':loop=asyncio.get_event_loop()loop.run_until_complete(main())