Saravanan666/pypi-org huggingface dataset:
Package: pimpamqueues
PimPamQueuesLightweight queue interfaces with Redis super powersDescriptionPimPamQueues provides easy and lightweight Python interfaces to interact with queues on a distributed system.RequirementsPython 2.7, 3.4 or 3.5python.orgA running Redis server,redis.ioRedis Python library,redis-py.readthedocs.orgFeaturesSupports Python 2.7, 3.4 and 3.5.Provides super-simple queue interfaces for creating different types of queues.Designed to be used on distributed systems - also works on non-distributed systems 😎.Queue InterfacesSimpleQueue, just a regular queue.BucketQueue, unordered queue of unique elements with a extremely fast element existence search method.SmartQueue, queue which stores queued elements aside the queue for not queueing the same incoming elements again.InstallationPIPFor a pip installation, justpip install pimpamqueues$pipinstallpimpamqueuesUsageSimpleQueue>>>frompimpamqueues.simplequeueimportSimpleQueue>>>queue=SimpleQueue(id_args=['simplequeue'])>>>queue.num()0>>>queue.push('egg')1>>>queue.push_some(['bacon','spam'])3>>>queue.num()3>>>queue.pop()b'egg'>>>queue.is_empty()False>>>queue.push('spam',to_first=True)3>>>queue.elements()[b'spam',b'bacon',b'spam']>>>queue.pop()b'spam'>>>queue.elements()[b'bacon',b'spam']...BucketQueue>>>frompimpamqueues.bucketqueueimportBucketQueue>>>queue=BucketQueue(id_args=['bucketqueue'])>>>queue.num()0>>>queue.push('egg')'egg'>>>queue.push_some(['bacon','spam'])[b'bacon',b'spam']>>>queue.num()3>>>queue.pop()b'spam'>>>queue.is_empty()False>>>queue.push('spam')'spam'>>>queue.elements(){b'bacon',b'egg',b'spam'}>>>queue.pop()b'spam'>>>queue.elements(){b'bacon',b'egg'}...SmartQueue>>>frompimpamqueues.smartqueueimportSmartQueue>>>queue=SmartQueue(id_args=['smartqueue'])>>>queue.num()0>>>queue.push('egg')'egg'>>>queue.push_some(['bacon','spam'])[b'bacon',b'spam']>>>queue.num()3>>>queue.pop()b'egg'>>>queue.is_empty()False>>>queue.push('spam',to_first=True)''>>>queue.elements()[b'bacon',b'spam']>>>queue.pop()b'bacon'>>>queue.elements()[b'spam']>>>queue.push('spam',force=True)'spam'>>>queue.push_some(['spam','spam'],force=True)[b'spam',b'spam']>>>queue.elements()[b'spam',b'spam',b'spam',b'spam']...History1.0.1 (2015-01-28)Python required packages are included to resolve installation dependencies.1.0.0 (2015-01-27)Hello PimPamQueues.
Package: pimple
Summarize your unit testspimpleis a tool for compiling a summary of your unit tests.UsageInstallpimplefrom PyPI with:$ pip install pimpleRun pimple in your project directory with:$ pimple tests/(substitute the directory that contains your tests)Thetestcase_summary.rstfile can be found in the directory you run the script from.
You can also specify an output file.N.B.The output will be formatted as reStructuredText.
Package: pimpmyclass
Pimp My Class[![Coverage Status](https://coveralls.io/repos/github/hgrecco/pimpmyclass/badge.svg?branch=master)](https://coveralls.io/github/hgrecco/pimpmyclass?branch=master)
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[![Documentation Status](https://readthedocs.org/projects/pimpmyclass/badge/?version=latest)](https://pimpmyclass.readthedocs.io/en/latest/?badge=latest)This library provides base classes to enable useful behavior in Python Objects.The central purpose of the library is to extend python properties to allow:get/set logging.get/set timing, success and failure stats.async locking.get/set coercion and conversion.value cacheprevent unnecessary set.read once propertiesBut most importantly, it allows owner specific configurations. Properties are
class attributes, and therefore it is difficult to have a property which is, for
exampled cached, in an object but not cached in another instance of the same class.The library also provides DictProperties: that is properties that can be accessed by key;
and also methods!Each capability is isolated in individual classes allowing you to pick only what you need.https://github.com/hgrecco/pimpmyclassAuthorsHernan E. Grecco <hernan.grecco@gmail.com>pimpmyclass changelog0.4.3 (2019-04-30)Changed signal is only emitted when the cache value actually changes.0.4.2 (2019-01-21)Created get/set _notifiy to be called after succesfull get/set.0.4.1 (2019-01-19)Document log_values.Check for key being member of list/tuple/set in dictproperties.Fixed bug that precluded composing properties/methods with config.0.4 (2019-01-19)Renamed Config to InstanceConfig and implemented Config for instance
independent configuration.Implemented automatic filling of Config and InstanceConfig with kwargs.Implemented default, value and type checking for Config and InstanceConfigMore testing and docs.0.3 (2019-01-16)Implementedlog_valuesto toggle how values are loggedImproved testing.Split GetSetCacheProperty into GetCacheProperty and SetCacheProperty0.2 (2018-11-27)Added the possibility to use Python Enum
as keys in DictFeat.0.1.3 (2018-04-04)Removed keyword argument from DictFeat signal
for greater compatibility with Qt.0.1.2 (2018-04-04)Fixed repr in BoundedProperty.0.1.1 (2018-03-28)Fixed check_signature when params is empty.0.1 (2018-03-23)first public release.
Package: pimpmytheme
Custom Theme============.. image:: https://travis-ci.org/novapost/django-pimpmytheme.png?branch=master:target: https://travis-ci.org/novapost/django-pimpmythemePer client/user/whatever django template and statics themingWHY===When you need to use custom template and/or styling based on a modelin your app (Site, User, etc...).pimpmytheme will create a folder per "client" (a client can be aUser, a Site or everything that implement the get_current method (seebelow). It also can be used for multiple projects at the sametime. The folders are under a project name folder located in thepimpmytheme directory. Your designer can then pull/push thisrepository to edit the whole look and feel of all your projects in thesame repository!How===with the help of custom template loader ans static file loader,pimpmytheme load the custom template and statics files if they existsfor the current object.INSTALLATION============>>> pip install pimpmythemeCONFIGURATION=============add pimpmytheme in your INSTALLED_APPS :INSTALLED_APPS = ('pimpmytheme',...)add the pimpmytheme staticfiles_finder to your STATICFILES_FINDERS :STATICFILES_FINDERS = ("pimpmytheme.static_finder.CustomFinder","django.contrib.staticfiles.finders.AppDirectoriesFinder",)add the custom template loader to your TEMPLATE_LOADERS:TEMPLATE_LOADERS = ('pimpmytheme.template_loader.Loader','django.template.loaders.app_directories.Loader',)finaly, you need a model with the "get_current" method. get_currentwill return the object responsible for customization. For example, youcan use the django.contrib.sites.Site model to customize your projectper a site basis:CUSTOM_THEME_LOOKUP_OBJECT = "django.contrib.sites.models.Site"CUSTOM_THEME_LOOKUP_ATTR = "name"To create the needed folders for customization, you can run themanagement command provided by pimpmytheme:$ python manage.py create_foldersinside custom_form you will get a folder named as your projectname. Inside this folder you wil get as many folder as youcustomization model objects. If you use the Site, you will get aexample.com folder.Inside this folder you will find a static folder containing an emptycustom.less file. Here for your convenience. you can start editingthis file to customize your style.You can also create a template folder next to the static one and putsome custom templates in it.the pimpmytheme template loader will first look in this directory toload templates files. If not found, it will fallback on the djangotemplate loaderNOTE====thanks to https://github.com/leotrouvtou for helping finding theproject name
Package: pimpy
pimpyis a python package that contains some selected image processing functions.InstallingInstall from pip:python -m pip install pimpyOverview:Currently,pimpyconsists of the modulescolor_cluster,smoothing, andtransformations. Thecolor_clusterandsmoothingmodules are just convenience wrappers around somenumpyandscipyfunctions.Function list:color_cluster.reduce_image: Performs a k-means clustering to find the most prominent colors of an n-dimensional
image.color_cluster.find_nearest_colors: Maps each color of an n-dimensional image to the closest color of a given list.color_cluster.extract_colors: Finds all unique color values of an n-dimensional image and returns the unique color
values and an image that contains color indices instead of color values.smoothing.gaussian_smoothing: Performs a gaussian smoothing on an n-dimensional color image.transformations.create_edge_image: Computes an edge image from a given gray or rgb image.transformations.find_poles_of_inaccessibility_of_edge_image: Returns the a list with points that are the farthest
away from the edges.
Package: pimpygui
pimpyguiis a tool that demonstrates some functions of thepimpypackage. It installs the command line toolpimpy_gallery, which is a GUI for applying image processing functions.InstallingInstall from pip:python -m pip install pimpyguiUsage:If binaries of installed python packages are added to the PATH, you can callpimpy_gallerydirectly from the shell:pimpy_galleryOtherwise, it can be invoked using python:python -m pimpygui.galleryOverview:Thepimpy_galleryGUI contains the following effects:Smooth:Applies a gaussian filter which blurs the image.Main colors:Reduces the image to a given number of colors. The colors can be set by the user or guessed
algorithmically using k-means clustering.Edges:Finds all edges of the given image. It adds an edge between two neighboring pixels if their color values are
different. You should apply themain colorseffect before creating the edge image, since an edge is inserted even if
the colors are visually identical but slightly different.Paint by number:Applies theedgeeffect and additionally inserts labels in the image segments. This can be used
to create prototype images for the popularpaint by numberartworks. Usually, you first need to apply thesmoothandmain colorseffects, since images are often too noisy for useful results.
Package: pi-mqtt-gpio
the PCF8574) to an MQTT server. This allows pins to be read and
switched by reading or writing messages to MQTT topics.
Home-page:https://github.com/flyte/pi-mqtt-gpioAuthor: Ellis Percival
Author-email:pi_mqtt_gpio@failcode.co.ukLicense: MIT
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Topic :: Communications
Classifier: Topic :: Home Automation
Classifier: Topic :: System :: Networking
Package: pimsampas
# โปรแกรมฝึกพิมพ์ “ Pimsampas” version 0.1 (pimsampas)### วิธีติดตั้งเปิด CMD / Terminal`sh pip install pimsampas `### วิธีเล่นเปิด IDLE ขึ้นมาแล้วพิมพ์…`sh >>> import pimsampas `โปรแกรมฝึกพิมพ์ “Pimsampas” version 0.1<<< วิธีเล่น>>>>
- หลังจากเปิดเตรียมตัวพิมพ์ภายใน 5 วินาที
- พิมพ์ตามคำศัพท์สีเขียว พิมพ์ผิดไม่ได้ ต้องพิมพ์ให้ถูกเท่านั้น (ยากไหม 55)
- โปรแกรมจะนับ 1-60 แล้วจะหยุดเกมทันที
- โปรแกรมรอให้ผู้เล่นหยุดหายใจอีก 10 วินาทีแล้วเริ่มใหม่ 555
- ถ้าอยากดูแป้นพิมพ์ภาษาอังกฤษให้กด F1 ภาษาไทยกด F2
- สามารถเพิ่มคำศัพท์ได้ในไฟล์ data.csv ให้กด F5 เพื่อเปิดโฟลเดอร์ที่เก็บไฟล์ CSV
- แก้ไขไฟล์ CSV โดยใช้ Notepad (คลิกขวา Edit)(รอลุงอัพเดต version ใหม่จะทำให้ง่ายกว่านี้ 555)<<< ปุ่มลัดต่างๆ >>>
- กดปุ่ม F1 เพื่อดูคีย์บอร์ดภาษาอังกฤษ
- กดปุ่ม F2 เพื่อดูคีย์บอร์ดภาษาภาษาไทย
- กดปุ่ม F5 เพื่อเปิดโฟลเดอร์เก็บไฟล์ csv
- กดปุ่ม F10 เพื่อดูวิธีการเล่น
- กดปุ่ม F12 เล่นใหม่พัฒนาโดย: ลุงวิศวกร สอนคำนวณ
FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineerปล. ขออภัย version 0.1 ลุงใช้เวลาพัฒนาแค่คืนเดียว เลยไม่ได้สมบูรณ์จ้าาาา
ปล2. ใน Mac เล่นได้ แต่ฟังชั่นคีย์บอร์ดยังติดปัญหา ไฟล์ data.csv อยู่ใน /Users/ชื่อusername
ปล3. Windows จะเก็บไฟล์ data.csv ไว้ในโฟลเดอร์ที่ติดตั้ง Python เช่น C:Python38data.csv หรือ แนะนำให้สร้างไฟล์ test.py แล้วพิมพ์ว่า import pimsampas แล้วรัน ไฟล์ data.csv จะอยู่ในโฟลเดอร์เดียวกับที่เซฟเพจ “ลุงวิศวกร สอนคำนวณ”  |https://www.facebook.com/UncleEngineer] |
Package: pims-nd2
pims_nd2contains a reader for nd2 files produced by NIS Elements.
The reader is based on the includedSDKfrom
Nikon, which makes it compatible with older versions of nd2 files. The
reader is written in thepimsframework, enabling easy access to multidimensional files, lazy slicing,
and nice display in IPython.Installationpims_nd2is implemented on Windows, Linux and OSX systems. To obtain
the latest stable version, install via PyPi:pip install pims_nd2The ND2 SDK binaries are included in the package and will be copied into
thepims_nd2package folder.DependenciesThis reader requirespimsversion 0.3.0.ExamplesThe following code opens a movie file and displays a frame. Note that
frames are only actually read when necessary:from pims import ND2_Reader
frames = ND2_Reader('some_movie.nd2')
frames[82]  # display frame 82
frames.close()The following code opens the multidimensional demo file included in the
package and iterates through the first 3 frames. Note that we use a
context manager here. We tell the reader which axis to iterate over and
which axes to include in one frame. Also we select which channel to
read.from pims import ND2_Reader
with ND2_Reader('cluster.nd2') as frames:
    frames.iter_axes = 't'  # 't' is the default already
    frames.bundle_axes = 'zyx'  # when 'z' is available, this will be default
    frames.default_coords['c'] = 1  # 0 is the default setting
    for frame in frames[:3]:
        # do something with 3D frames in channel 1The best way to use the reader is using a context manager. If you do not
use a context manager, make sure to callframes.close()at the end
of your script.Metadata access can be done on two levels: reader level and frame level.frames.metadata['mpp']  # calibration in microns per pixel
frames[0].metadata['t_ms']  # time of frame in millisecondsSupporting GrantThis reader was developed by Casper van der Wel, as part of his PhD
thesis work in Daniela Kraft’s group at the Huygens-Kamerlingh-Onnes
laboratory, Institute of Physics, Leiden University, The Netherlands.
This work was supported by the Netherlands Organisation for Scientific
Research (NWO/OCW).
Package: pimsviewer
pimsviewerA graphical user interface (GUI) for PIMS (screenshot below)This viewer is based onPyQt5and is able to work with N-dimensional image files that are opened by PIMS.Also, it has a plugin infrastructure that can be used to extend the main functionality.InstallationPimsviewer can be installed using conda:conda install -c conda-forge pimsviewerAlternatively, it can also be installed using pip:pip install pimsviewerWhen installing the latest source version, always install it withpip(and not withpython setup.py develop, this will lead to dependency errors forPyQt):Normal installation:pip install .Development installation:pip install . -eStarting the viewerAfter installing the viewer, an executablepimsvieweris available. Simply run the command via your terminal/command line interface.$ pimsviewer --help
Usage: pimsviewer [OPTIONS] [FILEPATH]
Options:
--example-plugins / --no-example-plugins
Load additional example plugins
--help                          Show this message and exit.ScreenshotExamplesAll examples below are also available as script files in theexamplesfolder.
By runningpimsviewer --example-plugins, you can preview the example plugins used below.Example 00: Using the viewer from PythonYou can use the viewer in a Python script as follows:import sys
from pimsviewer import GUI
from PyQt5.QtWidgets import QApplication

filepath = 'path/to/file'

# Class names of extra plugins to add
plugins = []

app = QApplication(sys.argv)
gui = GUI(extra_plugins=plugins)
gui.open(fileName=filepath)
gui.show()

sys.exit(app.exec_())Example 01: Using the viewer from Python (the shorter way)Or, if you do not need a reference to the actual object but you just want to start the program:from pimsviewer import run

run('path/to/file')In both cases, you can omit the file path.Example 02: evaluating the effect of a processing functionThis example adds a processing function that adds an adjustable amount of noise
to an image. The amount of noise is tunable with a slider.from pimsviewer import run
from pimsviewer.plugins import ProcessingPlugin

run('path/to/file', [ProcessingPlugin])Example 03: annotating features on a videoThis example annotates features that were obtained via trackpy onto a video.
Tracked positions are loaded from a pandas DataFrame CSV file by the user.from pimsviewer import run
from pimsviewer.plugins import AnnotatePlugin

run('path/to/file', [AnnotatePlugin])Your own plugin?By looking at the code for the example plugins, it should be fairly easy to
extend pimsviewer using your own plugins. Contact one of the maintainers if you
have any trouble writing your own plugins.AuthorsPimsviewer version 1.0 was written byCasper van der Wel, versions starting from 2.0 are written byRuben Verweij.
Package: pIMZ
pIMZ: an integrative framework for imaging mass spectrometry analysisDescriptionpIMZ is a framework for Imaging Mass Spectrometry (IMS) data analysis.
The python implementation is available fromgithub.com/mjoppich/pIMZ.
For installation follow the instructions given here.pIMZ focuses on a differential setting, where masses, specific to certain areas are first determined, and then serve as input for a cell-type detection framework and/or a differential expression setting.pIMZ's documentation is available here:.InstallationThe easiest way to install most Python packages is viapip.If not already done, you must first install the following dependencies manually. This is because at the time of writing this document, dabest requirespandas~=0.25, which is incompatible with probably the rest of the world nowadays. So first dabest is installed, then numpy and pandas are upgraded again ::sudo pip3 install dabest
sudo pip3 install numpy pandas --upgradeOnly then we should installpIMZ::
sudo pip3 install pIMZpIMZis now ready to go!UsageReferences to examples and example notebooks can be found in theexamplesfolder, or in thedocumentation.All available classes and their functions are explained in themodules/API documentation.
Package: pina
pinaInstallationTo install this package, runpython -m pip install pinaBasic exampleThis is a basic example using the four stream problem fromPinch Analysis and Process Integration:
A User Guide On Process Integration for the Efficient Use of Energy,
Second edition, Ian C. Kemp, p. 4. Its data looks as follows:Stream number and typeHeat flow [kW]Supply Temperature [°C]Target Temperature [°C]1. Cold-230201352. Hot330170603. Cold-240801404. Hot18015030ΔTmin= 10 KWe can input the data with the following code:frompinaimportPinchAnalyzer,make_stream# Arguments: heat flow, supply temperature, target temperaturecold_1=make_stream(-230,20,135)hot_1=make_stream(330,170,60)cold_2=make_stream(-240,80,140)hot_2=make_stream(180,150,30)min_temp_diff=10temp_shift=min_temp_diff/2analyzer=PinchAnalyzer(temp_shift)analyzer.add_streams(cold_1,hot_1,cold_2,hot_2)ThePinchAnalyzerclass calculates the energy targets, the pinch
temperature(s) and the coordinates of the composite curves. The following
properties can be requested from it:heating_demandcooling_demandhot_utility_targetcold_utility_targetheat_recovery_targetpinch_tempscold_composite_curvehot_composite_curveshifted_cold_composite_curveshifted_hot_composite_curvegrand_composite_curveFor example:print("Heating demand:{}\n""Cooling demand:{}\n""Hot utility target:{}\n""Cold utility target:{}\n""Heat recovery target:{}\n""Pinch temperature(s):{}".format(analyzer.heating_demand,analyzer.cooling_demand,analyzer.hot_utility_target,analyzer.cold_utility_target,analyzer.heat_recovery_target,analyzer.pinch_temps,))Output:Heating demand:       470.0
Cooling demand:       510.0
Hot utility target:   20.0
Cold utility target:  60.0
Heat recovery target: 450.0
Pinch temperature(s): [85.0]PlottingThis package deliberately does not include any plotting functions, but using it
together with plotting libraries is simple. Here is an example that usesmatplotlibto create a figure with two subplots: the hot and cold composite
curves on the left and the grand composite curve on the right:frommatplotlibimportpyplotaspltfig,ax=plt.subplots(1,2,figsize=(16,7))ax[0].plot(*analyzer.hot_composite_curve,color="tab:red",linestyle="--",label="HCC")ax[0].plot(*analyzer.cold_composite_curve,color="tab:blue",linestyle="-",label="CCC")ax[0].legend()ax[0].set_title("Hot and cold composite curves")ax[0].set_xlabel("Heat flow [kW]")ax[0].set_ylabel("Actual temperature [\u2103]")ax[1].plot(*analyzer.grand_composite_curve,color="k",linestyle="-",label="GCC")ax[1].legend()ax[1].set_title("Grand composite curve")ax[1].set_xlabel("Net heat flow [kW]")ax[1].set_ylabel("Shifted temperature [\u2103]")# Make the y-axis equal in both plotsylims=(*ax[0].get_ylim(),*ax[1].get_ylim())minmax_ylims=(min(ylims),max(ylims))ax[0].set_ylim(minmax_ylims)ax[1].set_ylim(minmax_ylims)plt.show()Output:More examplesMore examples can be found in theexamplesfolder. They show some
additional features:Streams carrying latent heat (e.g. evaporation and condensation)Streams consisting of multiple segmentsStreams with individual temperature shiftsAnything else?Nope, that's all. Enjoy :-)
Package: pinaca
pinaca-python-sdkPinaca Labs' Python SDKInstallationpipinstall--upgradepinacaTranslationfrompinacaimportTranslation# TRANSLATION_URL, TRANSLATION_PASSWORD, TRANSLATION_USERNAME environment variables# can be used without the need of passing the credentials as arguments to Translationtranslation=Translation("https://gputwo.pinacalabs.com/translation/cn_en/sync","USERNAME","PASSWORD")# Translating single textsinput_text="就上述四人的年龄来看，郭和平年龄最大，系50后；庞建军、赵宾方为70后；张志兵为60后。"translation_id=translation.run(input_text)# Returns stringtranslated_text=translation.get_result(translation_id)# Translating multiple texts at onceinput_texts=["就上述四人的年龄来看，郭和平年龄最大，系50后；庞建军、赵宾方为70后；张志兵为60后。","资料显示，壶化股份2020年9月22日登陆A股市场，至今不足一年时间。据壶化股份彼时披露的招股书显示"]translation_id=translation.run(input_texts)# Returns list of stringstranslated_texts=translation.get_result(translation_id)
Package: pina-mathlab
PINA is a Python package providing an easy interface to deal with physics-informed neural networks (PINN) for the approximation of (differential, nonlinear, …) functions. Based on Pytorch, PINA offers a simple and intuitive way to formalize a specific problem and solve it using PINN. The approximated solution of a differential equation can be implemented using PINA in a few lines of code thanks to the intuitive and user-friendly interface.