Saravanan666/pypi-org huggingface dataset:
Package: globalbedo_prior
Code to produce the GlobAlbedo prior from MODIS data, using the MCD43A1 and MCD43A2 MODIS products.The Python package can be installed using pip or easy_install (it’s available fromthe cheese shop. Once installed with e.g.pip install globalbedo_prior--user--upgrade, a script calledalvedro_priorshould appear in your path (this depends on pip/easy_intall doing their work properly). This script can be used to produce a daily prior of BRDF kernel parameters derived from the MODIS products.The generation of the prior is very simple, and is performed in two stages:Stage 1For each pixel, the entire timeseries of 8-daily observations are averaged using a weight derived from the QA flags. This results in a mostly complete 8-daily kernel product, stored as GeoTIFF files. Note that in some regions with cloud problems, there can be empty pixels as no observations are available for the period of interest within the MODIS record. Note that we calculate both the mean and standar deviation of the kernel parameters.Stage 2For each day of year and pixel, a simple Laplacian filter in time is used to interpolate temporally. The filter is quite peaky, and its weight decays to 0.5 8 days after the day of interest.UsageThe usage using thealvedro_priorscript is very simple: just stash the MCD43A1 and MCD43A2 files somewhere (no need for fancy directories or anything, although that helps you!), and select a tile. Then decide whether the output will be saved to, and execute a command like this:nohup alvedro_prior --tile h17v04 --datadir <my_data_directory_root> --outdir <my_output_directory> &The previous command will search for the MCD43A1/2 files below<my_data_directory_root>that relate to tileh17v04and save the Stage 1 and Stage 2 priors in<my_output_directory>.The dataStage 1 priors are written for each 8 day period in the year, for the three kernels and have two bands: the mean and the “weight” (or inverse of the variance). Stage 2 priors are given per kernel, and have 366 bands, each of them with the prior mean for that particular day. The uncertainty associated with the Stage 2 prior has not been calculated.
Package: globalcache
GlobalcacheGlobalcache allows you to store results in iPython, Jupyter, or Spyder.
This lets you re-run a script and skip heavy computing if the result
has already been processed.Globalcache can also cache function results to disk.The objective is to speed up development of computationally expensive code.
Cache the results of code you know to be correct while you iteratively
debug and develop the rest of your code.Spyder RequirementsIn the "Run" preferences, you must sest to"Run in console's namespace instead of
an empty one".Or when Spyder callsspydercustomize.runfile, set>>> spydercustomize.runfile(current_namespace = True)UsageInitialize the cache:>>> from globalcache import gcache
>>> gcache.init(globals())To initialize the cache, you must input globals() into the Cache.
gache.init(globals()) will create a dictionary in globals() to store the cache.Use the cache to decorate an expensive function and skip the calculation in subsequent runs.>>> @gcache.decorate
>>> def func1(*args, **kwargs):
>>>     ....
>>>     return output
>>> out = func1()Note: args & kwargs must be hashable.Reset the cache of a function (force delete old values):>>> @gcache.decorate(reset=True)
>>> def func1(*args, **kwargs):
>>>    ....Clear out cache from globals():>>> gcache.reset()Set limitations on how many results we can store at a time:>>> @gcache.decorate(size_limit=5)
>>> def func2(*args, **kwargs):
>>>     ...Saving to diskglobalcachecan shelve results to the disk usingdecorate.
To save the cache to disk, use:>>> @gcache.decorate(save=True)
>>> def func2(*args, **kwargs):
>>>     ...Delete the cache files from disk:>>> gcache.delete_shelve()By default, results are saved in the current working directory in a folder
called.globalcache/. The default can be changed using:>>> gcache.init(globals(), save_dir='/p/folder1/path_to_new_directory')Caching an if-blockglobalcache can cache a variable and skip an expensive block of code.Store a parameter with an if block:>>> var1 = gcache.var('my-param')
>>> if var1.not_cached:
>>>     out = expensive_function()
>>>     var1.set(out)
>>> out = var1.get()Results can be cached dependent on the change of other variables:>>> var2 = gcache.var('param2', args=(args1, args2),
>>>                   kwargs=dict2, save=False, size_limit=None)
>>> if var2.not_cached:
>>> 	out = expensive_function()
>>>		var2.set(out)
>>> out = var2.get()Disabling the globalcacheForce disable the globalcache:>>> import globalcache
>>> globalcache.Settings.disable = TrueDealing with Unhashable ArgumentsIf your function relies on unhashable arguments, there are various
strategies. For example, you can associate unhashable
arguments with hashable parameters. For example:>>> dict1 = {'a': [1,2,3],
>>>          'b': [3,4,5],
>>>         }
>>> @cache.decorate
>>> def func2(key):
>>>     args = dict1[key]
>>>     return func1(args)Or you can use an if-block cache.
Package: global-entry-alerter
Global Entry AlerterA bot that helps you find an open Global Entry interview appointment.ProblemBooking an appointment for aGlobal Entryinterview is hard, especially in cities with high demand like SF or NYC.If you check for open appointments, there is typically nothing available for weeks, sometimes even months.
However, appointmentsareactually constantly opening up, they just get booked very quickly.This project polls the DHS Scheduler API for open Global Entry appointments, notifying you whenever a new one opens up.
If the new appointment is convenient for you, book it immediately — otherwise it will be gone within a few minutes.This project is not affiliated with U.S. Customs and Border Protection or the U.S. Department of Homeland Security.Example UsageRequirementsA Twilio account with the ability to send programmable SMS. Seehere.Python 3 &PoetryUsageConfigure your settings in aconfig.toml, set env vars, and leave theglobal-entry-alerterrunning for as long as needed.Once the script sends an alert about a particular appointment, it will keep that appointment in memory and won't notify you of it again. If you restart the script, it may re-send previously-seen appointments.Installation$pipinstallglobal-entry-alerterRunningTo run the project:Create your ownconfig.toml(seeexample_config.toml)Configure your settings (see Configuration below)Set the following env variables using your Twilio account:TWILIO_AUTH_TOKENTWILIO_ACCOUNT_SIDTWILIO_PHONE_NUMBERglobal-entry-alerter --config ~/path_to_your/config.tomlConfigurationThe project uses a.tomlconfiguration file for all settings.Seeexample_config.tomlas a reference for your ownconfig.tomlSettingDescriptionExample Valuefetching.lookahead_weeksHow many weeks from today to check for open appointments. E.g. if you want an appointment in the next month, use 4 weeks4fetching.wait_secondsThe interval between checks for open appointments. It is best to use a value <= 6010fetching.locationsWhich Global Entry interview locations to check. Accepts many locations for use cases where multiple locations are feasible (e.g. EWR or JFK if you live in NYC). Seeherefor the full set of codes[{ name = "JFK", code = 5140 }]sending.to_numbersThe phone numbers to send alerts to. Include +1 (or whatever country code)["+11231231234"]DevelopmentPre-commit hooksThis project usespre-committo enforce linting on all commits. Install it with:$poetryrunpre-commitinstallFormattingThis project usesblackandflake8to enforce a consistent style.
To format the project according to the rules, run:makeformatTest ModeRunning in test mode will fetch appointments using the settings inconfig.toml, but will not actually send alerts.
Use this when developing to avoid incurring Twilio costs:poetryrunglobal-entry-alerter--test
Package: global-entry-notifier
Global Entry NotifierThis application can be setup to notify you via text whenever a new Global Entry appointment becomes available at your chosen interview location.Twiliois used to send SMS notifications. Signing up for a new account should provide you with a small starting balance that should be more than enough to cover the SMS notificationspipinstallglobal-entry-notifierSingle runAfter install, run the notifier to confirm you successfully receive a text message:global-entry-notifier\-l5003\-p<YOUR_PHONE_NUMBER>\--twilio-number<TWILIO_PHONE_NUMBER>\--twilio-sid<TWILIO_ACCOUNT_SID>\--twilio-token<TWILIO_AUTHENTICATION_TOKEN>NOTES:Country codes arerequiredon all phone numberse.g If your phone number is1234567890and you live in the U.S, pass in+11234567890The location5003seems to always have available slots so it's great for testing. If that doesn't work, try5004LocationsYou can view the full list of interview locations by running:global-entry-locationsTo narrow down the search, you can filter the results using the-n, --nameand-s, --statearguments:global-entry-locations-stx
global-entry-locations-stx-nlaredoSchedule availability checks with cron (Linux/MacOS)Scheduling this application via cron is probably the easiest method to start performing automatic appointment availability checksNOTE: cron requires the full path to the application's entry point. Retrieve the path with:whichglobal-entry-notifierChecking once per hour is likely sufficient:0 */1 * * * /home/<USER>/.local/bin/global-entry-notifier -l <LOCATION> -p <YOUR_PHONE_NUMBER> --twilio-number <TWILIO_PHONE_NUMBER> --twilio-sid <TWILIO_ACCOUNT_SID> --twilio-token <TWILIO_AUTHENTICATION_TOKEN>MiscellaneousDisplay the help menu:global-entry-notifier-h
global-entry-notifier--help

global-entry-locations-h
global-entry-locations--helpDisplay the version:global-entry-notifier-V
global-entry-notifier--version

global-entry-locations-V
global-entry-locations--version
Package: globalfirepower-scraper
Global Fire Power ScraperHow to use it ?pip install globalfirepower-scraperfromglobalfirepowerimportGlobalFirePowerScrapergfi_scraper=GlobalFirePowerScraper()print(gfi_scraper.get_armies_information())Exemple of output:rank           name  power_index progress                        id Population  ... Fit-for-Service Reaching Mil Age Annually Tot Military Personnel (est.) Active Personnel Reserve Personnel Paramilitary Total
0       1  United States       0.0453       up  united-states-of-america          3  ...       122274415                   4354979                       1832000          1390000            442000                  0
1       2         Russia       0.0501       up                    russia          9  ...        46681219                   1280887                       1350000           850000            250000             250000
2       3          China       0.0511       up                     china          1  ...       619268690                  19570568                       3134000          2000000            510000             624000
3       4          India       0.0979       up                     india          2  ...       496891621                  22768619                       5132000          1450000           1155000            2527000
4       5          Japan       0.1195       up                     japan         11  ...        43391178                   1122186                        309000           240000             55000              14000
..    ...            ...          ...      ...                       ...        ...  ...             ...                       ...                           ...              ...               ...                ...
137   138        Liberia       8.5213   stable                   liberia        113  ...         1835339                     62568                          2000             2000                 0                  0
138   139        Somalia      11.8854     down                   somalia         72  ...         1669060                    120946                         17500            17500                 0                  0
139   140         Kosovo      13.9136       up                    kosovo        134  ...          727657                     17417                          6500             3500              3000                  0
140   141         Bhutan      35.8958     down                    bhutan        138  ...          118324                      4287                          8000             8000                 0                  0
141   142        Iceland      78.6623   stable                   iceland        142  ...           48070                      1779                             0                0                 0                  0

[142 rows x 60 columns]Deploy upgrade on PyPi# Build
python setup.py sdist bdist_wheel
twine upload dist/*

# Test
python -m pip install -e .
python

>>> from globalfirepower import GlobalFirePowerScraper
>>> gfi_scraper = GlobalFirePowerScraper()
>>> print(gfi_scraper.get_armies_information())LICENCEThis works is under MIT License.
Package: global-forecast-validation
Global_Forecast_ValidationPython solution to run large scale forecast validation using data stored in NetCDF format. This tool is meant to be
used with RAPIDpy, and should not be used with NetCDF files formatted in ways different than the RAPIDpy output.InstallationTo intall this package, simply use::pip install global-forecast-validationIt is recommended to install this in a seperate environment, especially if using conda, to avoid dependency conflicts.DocumentationLink
Package: global-gender-predictor
Global Gender PredictorPredict gender using first name using data from World Gender Name Dictionary 2.0.
The dataset contains 4,148,966 unique names. The predictor is case-insensitive and predictsMale,Female, orUnknown(i.e. unisex or not found in data)Install using pip:pipinstallglobal_gender_predictorUsagefromglobal_gender_predictorimportGlobalGenderPredictorpredictor=GlobalGenderPredictor()predictor.predict_gender('John')'Male'predictor.predict_gender('Jane')'Female'predictor.predict_gender('attract.ai')'Unknown'The dataset contains probabilities for each name:{'name': 'taylor', 'gender_prob': {'F': 0.699, 'M': 0.301}}.
Change the probability threshold for unisex names:predictor.predict_gender('taylor',threshold=0.5)'Female'predictor.predict_gender('taylor',threshold=0.8)'Unknown'CitationWorld Gender Name Dictionary (WGND 2.0)@data{DVN/MSEGSJ_2021,author={Raffo, Julio},publisher={Harvard Dataverse},title={{WGND 2.0}},UNF={UNF:6:5rI3h1mXzd6zkVhHurelLw==},year={2021},version={DRAFT VERSION},doi={10.7910/DVN/MSEGSJ},url={https://doi.org/10.7910/DVN/MSEGSJ}}
Package: global-kmeans-pp
The Global $k$-mean++ clustering algorithmThe global $k$-means++ is an effective relaxation of the global $k$-means clustering algorithm, providing an ideal compromise between clustering error and execution speed. It is an effective way of acquiring quality clustering solutions akin to those of global $k$-means with a reduced computational load. It is an incremental clustering approach that dynamically adds one cluster center at each $k$ cluster sub-problem. For each $k$ cluster sub-problem, the method selects $L$ data points as candidates for the initial position of the new center using the effective $k$-means++ selection probability distribution. The selection method is fast and requires no extra computational effort for distance computations.@article{vardakas2022global,
  title={Global $k$-means$++$: an effective relaxation of the global $k$-means clustering algorithm},
  author={Vardakas, Georgios and Likas, Aristidis},
  journal={arXiv preprint arXiv:2211.12271},
  year={2022}
}

@article{likas2003global,
  title={The global k-means clustering algorithm},
  author={Likas, Aristidis and Vlassis, Nikos and Verbeek, Jakob J},
  journal={Pattern recognition},
  volume={36},
  number={2},
  pages={451--461},
  year={2003},
  publisher={Elsevier}
}
Package: global-logger
Global LoggerGlobal LoggerBased on Python built-in logger, expands it, and provides a global logger to your system.Free software: MIT licenseDocumentation:https://alertua.github.io/global_loggerhttps://global-logger.readthedocs.io/https://global-logger.rtfd.ioPyPi modules:https://pypi.org/project/global-loggerFeaturesEasy on-screen logging setup without pain for Python newcomersEasy .log files output setup without pain for Python newcomersPython 2 and 3 compatibleHistory0.3.31 (2023-11-05)Bugfix: Proper add handler to all handlers0.3.30 (2022-18-06)Bugfix: Proper emit to file handler0.3.29 (2022-31-01)It is now possible to toggle Logger screen colored output0.3.28 (2022-15-01)It is now possible to set the current global log_session_filename while instantiating the first Logger0.3.27 (2021-22-11)Small precaution0.3.26 (2021-15-10)GitHub Actions Python Version Bugfix0.3.25 (2021-15-10)Bugfixtox bugfix0.3.24 (2021-15-10)Removed dumping sensitive data0.3.23 (2021-09-10)Pathlib prerequisite fix for Python 3.4+0.3.22 (2021-08-17)Colorlog import fixed @ Python < 3.50.3.21 (2021-02-02)IntEnum import fixed @ Python < 3.60.3.20 (2020-11-19)win_unicode_console version tweak0.3.17 (2020-10-19)Python 3.9 supportScreen Logger Message Format argument added0.3.16 (2020-09-02)FileHandler now adds to all previously existing loggers0.3.14 (2020-08-20)Pylint removedBugfix0.3.12 (2020-07-27)Individual loggers that skip global logging level changePendulum module version bumped0.3.9 (2020-07-20)Enum module usageCode testing update0.3.8 (2020-07-13)Bugfix0.3.6 (2020-07-01)Minor tweaksBugfix0.3.4 (2020-06-29)Documentation updateGithub Pages setupReadTheDocs documentation setup0.2.11 (2020-06-28)RefactoringBugfix0.2.6 (2020-06-27)GitHub Actions stabilized.0.1.0 (2020-06-23)First release on PyPI.
Package: global_lru_cache
global_lru_cache================Python global LRU cache memoization decorator.What is a global LRU cache?================There are a lot of great Python LRU caches available. Python 3 has [functools.lru_cache](https://docs.python.org/3/library/functools.html#functools.lru_cache) built in, and it's also been [backported to Python 2](http://code.activestate.com/recipes/578078-py26-and-py30-backport-of-python-33s-lru-cache/). There's also [pylru](https://github.com/jlhutch/pylru), [cachetools](https://github.com/tkem/cachetools), [lru-dict](https://github.com/amitdev/lru-dict), [repoze.lru](https://github.com/repoze/repoze.lru), and more out there.What all of these caches have in common is that when used as a function decorator, they maintain a separate cache for each decorated function, and you can usually only specify a maximum number of cache entries, without regard to actual size in memory. This is probably more than enough for the vast majority of use cases, but in some circumstances, it's more convenient to have a globally shared cache that automatically manages its size relative to available system memory. This can be useful, for example, when caching very large queries from very slow databases. If your cache ends up using significant percentages of system memory, you want to make sure you don't use too much memory, especially if you are sharing the system with other processes.I was unable to find an existing implementation of such a cache, so I've taken some basic open source code I found online and modified it to suit my needs. The result is a library with a simple decorator that takes no arguments and manages all of your cached data as a single cache. I've called it an LRU cache, but when invalidating cache entries it actually uses a scoring function that takes into account time last accessed, size in memory of the cache entry, and duration of the cached function call.Usage================```pythonfrom memoize import memoized@memoizeddef slow_function(arg1, arg2):time.sleep(30)return arg1 * arg2```
Package: globalomnium
FORK IN PROGRESSHi all, I'm a newbie trying to make my first Home Assistant Custom Component and my first Python work.
This repo is a modified fork fromldotlopez/ideenergybut I'm trying to adapt it to get water consumption from GlobalOmnium / Emivasa / Aguas de Valencia.globalomniumProgrammatic access to water consumption fromhttps://www.globalomnium.com/(Spanish water distributor)Useful linksRelated globalomnium Home-Assistant componentOriginal ideenergy Python APIOriginal ideenergy Home-Assistant component
Package: global_opt
This library contains the implementation of various well-selected benchmark problems required in order to examine the performance of unconstrained global optimization methods.The latest version currently contain the following benchmark problems:
-> Alpine Function
-> Bartels Conn Function
-> Beale Function
-> Booth Function
-> Deckkers-Aarts Function
-> Egg Crate Function
-> Goldstein Price Function
-> Leon Function
-> Matyas Function
-> Powell Sum Function
Package: globalparams
paramspycontainer for global (Singelton) loading and saving parametersUsage exampleOn load, need to be called once:Params(LOCATION_OF_PARMAS_JSON)On usage, after calling the aboveparam_dict=Params()params.jsonis a nested parameters json, e.g.;{
    "data": {
        "granularity": 21600,
        "header": [
            "d_price"
        ],
        "look_back": 42,
        "mean": 6.477538237436277,
        "n_test": 64,
        "std": 257.3105989979195,
        "time_decay_sec": 3000000,
        "weight_bias": 0.1
    },
    "data_collection": {
        "n_record_per_fetch": 280,
        "time_span_days": 20000
    },
    "logging": {
        "model_folder": "models",
        "output_folder": "logdir",
        "plot_every": 32
    },
    "train": {
        "batch_size": 6400,
        "learn_rate": 0.0001,
        "n_epochs": 60000
    }
}
Package: global-py
# GlobalPyThe package **pyglobal** allows you to execute your python scripts globally## Available on pypi`pip install globalpy`## How to use?```globalpy --helpusage: globalize [-h] [--setup] [--target TARGET [TARGET ...]][--pyversion PYVERSION] [--alias ALIAS [ALIAS ...]]optional arguments:-h, --help            show this help message and exit--setup, -s           run only once after installing module--target TARGET [TARGET ...], -t TARGET [TARGET ...]target file that should become a global command(multiple targets possible)--pyversion PYVERSION, -p PYVERSIONchoose the python version to execute in--alias ALIAS [ALIAS ...], -a ALIAS [ALIAS ...]alias for the program```## Demo`globalpy -t test.py - a myalias`![alt img <>](demo/alias.png)When no alias is specified, the filename will be used as an alias:`globalpy -t test.py`![alt img <>](demo/default.png)## LicenseLicense can be found [here](https://github.com/frankzl/globalpy/blob/master/LICENSE)
Package: global-storage-json-dict
DescriptionДобавляет возможность глобально управлять объектом dict сохраняя его в файлInstallation(PyPi)pipinstallglobal_storage_json_dictUsageexample global storagefromglobal_storage_json_dictimportStorageimportdeff(n):return(((n+1)*n)//2)+1foriinrange(40,100):ifstr(i)notinStorage:Storage[str(i)]=f(i)ifi==10:breakexample factoryfromglobal_storage_json_dictimportFileJsonStoragestorage=FileJsonStorage(file="store.json"auto_save=False)storage.get('a')# Get value by key default Nonestorage['a']=1# Set valuestorage['b']=2'a'instoragestorage.save()# Save storage
Package: global-tools-win
CreativeGlobal Tool Windowsglobal_tools_win Windows Ã¼zerinde Ã§alÄ±ÅŸan Ã¶zellikli bir AraÃ§(Tool)Installationglobal_tools_win YÃ¼klemek iÃ§in paket yÃ¶neticisinipipkullanÄ±n .pip install global_tools_winUsageglobal_tools_win.main(x='h')#h = Help MenÃ¼sÃ¼nÃ¼ aÃ§arContributingÃ‡ekme istekleri kabul edilir. BÃ¼yÃ¼k deÄŸiÅŸiklikler iÃ§in lÃ¼tfen Ã¶nce neyi deÄŸiÅŸtirmek istediÄŸinizi tartÄ±ÅŸmak Ã¼zere bir konu aÃ§Ä±n.LÃ¼tfen testleri uygun ÅŸekilde gÃ¼ncellediÄŸinizden emin olun.LicenseMITGlobalToolWindowsOptions01  Port Scanner02  Net Ping Clear03  Net Password Show04  Instagram User Ä°nfo05  Password Creativ06  Web IP Adresi07  Win10 Logo08  QR Code Creativ09  Instagram PP download10 DNS Reset11 Proxy Reset12 IP Tracker