Saravanan666/pypi-org huggingface dataset:
Package: imurl
imurlis an immutable URL library, written in modern Python.imurlis inspired by bothpurland Python'spathlibanddatetimemodules. It aims to provide a simple, immutable data structure to represent
URL structures, with support for a wide range of URL/URI schemes.ExamplesHere are some quick examples forimurl.There are more in the documentation.URLs can be created from URL strings, and have the attributes you'd expect:>>>fromimurlimportURL>>>u=URL("https://example.com")>>>uimurl.URL('https://example.com')>>>u.host'example.com'>>>u.scheme'https'URLs are immutable, but components can be replaced similarly todatetime/pathlibobjects:>>>u.replace(path="/some/path")imurl.URL('https://example.com/some/path')>>>u.path# This is still `None` - we haven't modified `u`.>>>u.replace(path="/some/path").path_as_posixPurePosixPath('/some/path')URLs can also be built from components, and query/path parameters can be set/get/deleted:>>>u=URL(scheme="https",host="google.com",path="/search")imurl.URL('https://google.com/search')>>>u2=u.set_query("q","a+search+term")>>>u2imurl.URL('https://google.com/search?q=a+search+term')>>>u2.delete_query("q")imurl.URL('https://google.com/search')How doesimurldiffer from the alternatives?imurlaims to be a clean, pythonic API around URL manipulation. It should be easier
than usingurllib.parse.urlparse, and just as flexible as anything you'd roll yourself.imurlis written with modern Python, with all the advantages that brings: static analysis
tools (mypy,pylint) are used to increase code quality, and the project's style is very consistent
(black). These tools should help to reduce bugs onceimurlis out of the alpha stage.Alternatives:urllib.parse.urlparseis the standard library approach to URL parsing. Flexible, but manual. There's very little in the
way of convenience.furlis a mutable URL parsing library. Furl is flexible and easily understood, but unfortunately mutable.purlis the original immutable URL parsing library for Python.purldid a lot to inspireimurl:
URLs are immutable, the jQuery-like approach (though nonstandard) feels intuitive, and it's
relatively stable.purldoesn't handle file-like URLs particularly well (those without a host)
and unfortunately is not currently typed.purlis recommended whilst imurl is in alpha.Installationimurlcan be installed withpip, and has been tested on Python 3.8.imurlis still
alpha software, and should be considered unstable:pip install imurl
Package: im-util
# im_util
This package contains miscellaneous utility functions for use by other im_* packages.## InstallUse the python package for this library. You can find the package online [here](https://pypi.org/project/im-util/).Change to your Python App Engine project’s root folder and do the following:> pip install im_util –target libOr add it to your requirements.txt. You’ll also need to set up vendoring, see [app engine vendoring instructions here](https://cloud.google.com/appengine/docs/python/tools/using-libraries-python-27).
Package: imutils2
已经有了一个imutils库,但是这个imutils也不能完全满足我的需求,所以就整了一个imutils2安装pip install imutils2

# or install with opencv
pip install imutils2[opencv]常用函数imread 读取或转化图像为opencv格式pil2cv Pillow转Opencv格式cv2pil Opencv转Pillow格式cv2gray Opencv转成灰度图url_to_image 下载URL为图像show_image 查看图像兼容jupyter, requires matplotlibmerge_app_screenshots App下滑长截图拼接fromimutils2import*im1=imread("m1.jpg")im2=imread("m2.jpg")im3=imread("m3.jpg")output=merge_app_screenshots([im1,im2,im3])show_image(output)写的不一定全，更多的例子可以去tests/test_imutils2.py中查看
Package: imvickykumar999
New feature Added : Now you can Upload Your Custom package on Python Package Index using Above Project !!!import crudobj1 = crud.vicks(‘passwprd’, name = ‘Vicky’)
obj1.push(‘kaisa hai tu ?’)obj2 = crud.vicks(‘password’)
obj2.push()obj2.save()
obj2.pull()# Firebase-CRUDHome Automation using my firebase package and kivy Android App, finally display output on my website.## Create new file in cmdtype nul > led.py## crud.pyfrom imvickykumar999 import firebasefirebase_obj = firebase.FirebaseApplication(’https://your_project_name.firebaseio.com/’, None)# update data
firebase_obj.put(‘/parant/’,’child’, data)# fetch data
result = firebase_obj.get(‘/parent/child’, None)
print (result)[![image](https://user-images.githubusercontent.com/50515418/116424009-cd519480-a85e-11eb-943f-e566d49bad09.png)](https://github.com/imvickykumar999/Firebase-CRUD/blob/bdeb5b7a0b703b2f86719ee2da0d06505ee00ceb/main.py#L16)## CMD…C:UsersVickyDesktopRepositoryfirebaseFirebase-CRUD>python
Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 16:30:00) [MSC v.1900 64 bit (AMD64)] on win32
Type “help”, “copyright”, “credits” or “license” for more information.
>>>
>>> import crud as f>>> f.push({"Cost": 250, "Name": "LED 1", "Switch": "ON", "Value": 1})
'updated'
>>> f.pull()
('Value fetched = ', {'Cost': 250, 'Name': 'LED 1', 'Switch': 'ON', 'Value': 1})>>> f.push({"Cost": 250, "Name": "LED 1", "Switch": "ON", "Value": 1}, fan)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'fan' is not defined>>> f.push({"Cost": 250, "Name": "Fan", "Switch": "ON", "Value": 1}, 'fan')
'updated'
>>> f.pull('fan')
('Value fetched = ', {'Cost': 250, 'Name': 'Fan', 'Switch': 'ON', 'Value': 1})>>> f.push({"Cost": 250, "Name": "LED", "Switch": "OFF", "Value": 0})
'updated'
>>> f.pull()
('Value fetched = ', {'Cost': 250, 'Name': 'LED', 'Switch': 'OFF', 'Value': 0})>>> c.insert()
{'name': '-MZPZc4g5Aq_tqp7Z15x'}
>>> c.remove()
(('Value fetched = ', {'fan': {'Name': 'Fan', 'Switch': 'OFF'}}), '...is present')
>>> c.pull()
('Value fetched = ', None)
>>> c.insert()
{'name': '-MZPZoeNKI0vWr2ZhX4Q'}
>>> c.push()
'updated'
>>> c.remove()
(('Value fetched = ', {'fan': {'Name': 'Fan', 'Switch': 'OFF'}}), '...is present')
>>>
Package: imvideo
ImvideoImvideo helps you create time-lapse videos from camera-generatedimage folderand yourmatplotlibloop.Install ImvideoTo install this package, typepip install imvideoin command prompt.C:\Users\user>pip install imvideo
Collecting imvideo
  Using cached imvideo-0.0.1-py3-none-any.whl (3.6 kB)
Installing collected packages: imvideo
Successfully installed imvideo-0.0.1Function DetailsClass local:timelapse(title, fps, folder_path, inspect=True):timelapse(title, fps, folder_path, inspect=True):
Function constructs time-lapse video from images in a folder.
        Inputs:     title   (string)     video title + .avi
                    fps     (double)     time-lapse video frames per second 
                    folder_path    (raw string)    location of the image folder
                    inspect    (boolean)       True (default)/False
        Output:
                    time-lapse videoClass memory:savebuff(frame, container):savebuff(frame, container):
Function saves image in in-memory location
         Inputs:    frame   (matplotlib image)  
                    container   (list)     empty image container
         Output:    container   (list)      image container with added frame locationconstruct(container, title, fps, inspect=True):construct(container, title, fps, inspect=True):
Function constructs video from images in the container.
         Inputs:    container   (list)      image container with frame location
                    title   (string)     video title + .avi
                    fps     (double)     time-lapse video frames per second 
                    inspect    (boolean)       True (default)/False
         Output:
                    videoUse ImvideoTime-lapse video from a image folder:importimvideoasimvimv.local.timelapse(local.timelapse('demo.avi',5,r".\tests\test_image"))Time-lapse video from a matplotlib loop:importimvideoasimvdeftest_matplot_loop(n):''' Input:      n   number of frames'''images=[]# empty image containerplt.figure()foriinrange(n):plt.plot([np.random.randint(2),np.random.randint(2)])plt.title("test"+str(i))images=imv.memory.savebuff(plt,images)# save image in in-memory locationplt.clf()imv.memory.construct(images,'matplot_demo.avi',5)# construct video; 5 fpsreturntest_matplot_loop(100)# construct a demo video with 100 framesSample output
Package: imview
imviewImage viewers for geospatial dataOverviewThis repo contains several utilities that I use on a daily basis for rapid data preview, detailed analysis, and figure generation.  The imviewer.py tool is probably the most useful.  It works well, but could use a rewrite, as it has been glommed together over the span of 4-5 years.Viewersimviewer - viewer for geospatial data, overlay capabilitiesstack_view - viewer for "stack.npz" time series output (seepygeotools), allowing for point sampling and plottingiv - lightweight viewer for standard images (jpg, png, etc.)review_img -   lightweight viewer to identify good and bad imagesModuleslib/pltlib - a collection of useful functions for matplotlib plotting, including drawing vector data over raster dataCommand-line ExamplesPreprocessing (optional):mos_fn=BigDEM.tif
gdaldem hillshade $mos_fn ${mos_fn}_hs_az315.tif
gdaladdo -ro -r average --config COMPRESS_OVERVIEW LZW --config BIGTIFF_OVERVIEW YES $mos_fn 2 4 8 16 32 64
gdaladdo -ro -r average --config COMPRESS_OVERVIEW LZW --config BIGTIFF_OVERVIEW YES ${mos_fn}_hs_az315.tif 2 4 8 16 32 64View color DEM overlaid on shaded relief map:imviewer.py $mos_fn -overlay ${mos_fn}_hs_az315.tif -label 'Elevation (m WGS84)'By default, this will quickly load a low-resolution preview (specify -full to load full-res image)Lower right corner shows coordinates and value under cursorLeft-click to sample image coordinates, map coordinates and raster valueCan specify transparency with-alpha 0.5View with user-defined color map and limitsimviewer.py -cmap 'RdYlBl' -clim -5 5 dem_dz_eul.tif -label 'Elevation difference (m)'Link several images (allows for simultaneous zoom and pan):imviewer.py -link dem.tif image.tif velocity.tifView polyline shapefile overlay:imviewer.py $mos_fn -overlay ${mos_fn}_hs_az315.tif -shp polyline.shpOutput high-quality figure with scalebar:imviewer.py $mos_fn -overlay ${mos_fn}_hs_az315.tif -scale x -label 'Elevation (m WGS84)' -of png -dpi 300View time series stack:make_stack.py -tr 'mean' -te 'intersection' 20080101_dem.tif 20090101_dem.tif 20100101_dem.tif
stack_view.py 20080101_dem_20100101_dem_stack_3.npzLeft-click to extract time series at point on any of the context mapsRight-click to clear all pointsCan zoom and pan on context mapsInstallationInstall the latest release from PyPI:pip install imviewNote: by default, this will deploy executable scripts in /usr/local/binBuilding from sourceClone the repository and install:git clone https://github.com/dshean/imview.git
pip install -e imviewThe -e flag ("editable mode", setuptools "develop mode") will allow you to modify source code and immediately see changes.Core requirementsMatplotlibGDAL/OGRNumPypygeotoolsLicenseThis project is licensed under the terms of the MIT License.
Package: imviz
imvizPythonic bindings for the greatimguiandimplotlibraries.This is work in progress and at a very early state.What?The goal of this project is to create bindings, which map the immediate mode
gui paradigm in a pythonic way, so that writing GUIs in python (especially
visualization heavy ones, meaning plots and stuff) becomes easy.Why?While writing visualization tools for research, I (personally) found the
different python bindings of imgui/implot somewhat unsatisfactory, because:pyimguimaps the api but does not
include implotimgui_datascienceseems
abandoned and does not include implotdearpyguiincludes implot, but
does not replicate the immediate mode paradigm in pythonWho?This is for the people who are too impatient to wait for matplotlib to render
10e5 data points. And for the people who are just completely unwilling (me) or
utterly incapable (also me) to deal with application state management in PyQt
(otherwisepyqtgraphwould be fine).How?pybind11.InstallationOpenGL libraries, GLFW, and GLEW are mandatory dependencies and are expected
to be installed on the system.On Ubuntu 20.04 OpenGL libraries are already installed and GLFW, GLEW can be
installed with:sudo apt-get install -y libglfw3-dev
sudo apt-get install -y libglew-devVia PyPIFor convenience a source distribution is available on PyPI. Install
via pip:python3 -m pip install imvizFrom SourceThesetup.pyscript configures and builds the project via cmake, which in
turn downloads further required dependencies. An internet connection is
therefore necessary at build time.The project can then be installed by executing the following command in the
project root directory:python3 -m pip install .
Package: imWatermark
imWatermarkThis snippet of code attempts to watermark images.Usageimportcv2importnumpyasnpfromimWatermarkimportwatermarkimg_fullname="assets/lotus.jpg"img=cv2.imread(img_fullname)hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV)v=hsv[:,:,2].copy()v_watermark=watermark(v,8)hsv[:,:,2]=v_watermarkimg_out=cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)cv2.imwrite('assets/lotus-watermark.jpg',img_out)OutputThis is a sample image:This is the sample image watermarked:
Package: imWeightedThresholdedheq
imWeightedThresholdedheqThis module attempts to enhance contrast of a given image or video by employing a method called weighted thresholded histogram equalization (WTHE). This method seeks to improve on preformance of the conventional histogram equalization method by adding controllable parameters to it. By weighting and thresholding the PMF of the image before performing histogram equalization, two parameters are introduced that can be changed manually, but by experimenting on a variety of images, optimal values for both parameters are calculated (r = 0.5, v = 0.5).You can access the article that came up with this methodhere.InstallationRun the following to install:pipinstallimWeightedThresholdedheqUsageFor imagesimWeightedThresholdedheq--input'Plane.jpg'--output'Plane-imWeightedThresholdedheq.jpg'For videosvid2dhisteq--input'assets/Arctic-Convoy-With-Giant-Mack-Trucks.mp4'--output'assets/Arctic-Convoy-With-Giant-Mack-Trucks-imWeightedThresholdedheq.mp4'Orimportnumpyasnpimportcv2fromimWeightedThresholdedheqimportimWTHeqcap=cv2.VideoCapture('assets/Arctic-Convoy-With-Giant-Mack-Trucks.mp4')# output video without soundvideo_out_name='assets/Arctic-Convoy-With-Giant-Mack-Trucks-imWeightedThresholdedheq.mp4'i=0j=0Wout_list=np.zeros((10))while(cap.isOpened()):ret,frame=cap.read()ifret==False:breakframe_hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)frame_v=frame_hsv[:,:,2].copy()image_heq,Wout=imWTHeq(frame_v,Wout_list,r=0.5,v=0.5)Wout_list[j]=Woutj+=1ifj==10:j=0frame_hsv[:,:,2]=image_heqframe_eq=cv2.cvtColor(frame_hsv,cv2.COLOR_HSV2BGR)fps=cap.get(cv2.CAP_PROP_FPS)ifi==0:h,w,d=frame_eq.shapefourcc=cv2.VideoWriter_fourcc(*'mp4v')video_out=cv2.VideoWriter(video_out_name,fourcc,fps,(w,h))video_out.write(frame_eq)i+=1cv2.destroyAllWindows()video_out.release()ShowcaseA 5 minutes comparative video:https://youtu.be/5H_EY_ugmzgA sample video and its enhanced version by WTHE method
Package: iMX233_GPIO
This package provides class to control the GPIO on iMX233-OLinuXino.
Current release does no support any peripheral functions.ExampleTypical usage:#!/usr/bin/env python

import iMX233_GPIO as GPIO

#init module
GPIO.init()

#configure module
GPIO.setinput(GPIO.PIN#)
GPIO.setoutput(GPIO.PIN#)

#set GPIO high
GPIO.output(GPIO.PIN#, 1)

#set GPIO low
GPIO.output(GPIO.PIN#, 0)

#read input
state = GPIO.input(GPIO.PIN#)ChangelogpyiMX233 0.1.0 (2013-04-22)Initial release
Package: imxdparser
Intermixed Argument ParserIntermixed argparser, aka imxdparser, and based only on argparse, is an argument parser that allows the unordered, and intermixed, optional and positional arguments.InstallationYou can install the latest version from PyPI package repository.python3-mpipinstall-UimxdparserLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.LocationsDocumentation:https://imxdparser.docs.twiddle-z.ovhWebsite:https://gitlab.com/twiddle-z/imxdparserPyPi:https://pypi.org/project/imxdparser
Package: imx-nand-tools
i.MX NAND Tools for Freescale i.MX NAND DumpsProvides dedicated tools for IMX NAND dumps reverse-engineering:imx-nand-info: parse the FCB and display useful information about the Flash structureimx-nand-convert: parse the FCB and convert the actual dump into a memory-based image that can be processed with binwalkHow to installYou can choose to either installimx-nand-toolsfrom PyPi or from the source.PyPi installThat’s the most easiest way to installimx-nand-tools:$ sudo pip3 install imx-nand-toolsIt will installimx-nand-toolsand all its dependencies.Install from sourceThe following commands will installimx-nand-toolsfrom source.$ git clone https://github.com/DigitalSecurity/imx-nand-tools.git
$ sudo pip install setuptools
$ cd imx-nand-tools
$ python setup.py build
$ sudo python setup.py installAnd that’s itHow to use itimx-nand-infoThis tool loooks for the firstFirmware Control Block(FCB) contained in a dump and parse it and then
displays its contents.$ imx-nand-info fresh-dump.binimx-nand-convertThis tool converts a fresh i.MX NAND dump into a useable memory image:$ imx-nand-convert fresh-dump.bin converted-dump.binThe-coption enables error correction thanks to embedded ECC information, thusimx-nand-convertwill be able to fix potential errors in the original NAND dump.
Package: imzml2isa-qt
imzml2isa-qt============A PyQt interface for mzml2isa parser - imzML edition.''''''''''''''''''''''''''''''''''''''''''''''''''''''Overview--------This program is a Graphical User Interface for the`mzml2isa <https://github.com/ISA-tools/mzml2isa>`__ parser. It providesan easy-to-use interface to convert mzML files to an ISA-Tab Study. Itwas made with Python3 and PyQt5.Install-------With PIP~~~~~~~~If ``pip`` is present on your system (comes along most of Python install/ releases), it can be used to install the program and its dependencies:.. code:: bashpip3 install imzml2isa-qtWithout PIP~~~~~~~~~~~Once dependencies installed, clone the **imzml2isa-qt** repository to afolder with writing permissions:.. code:: bashgit clone git://github.com/althonos/imzml2isa-qtAfter that, either run the GUI directly:.. code:: bashpython3 run.pyOr install it locally to run with ``imzmlisa-qt`` command:.. code:: bashcd imzml2isa-qt && python3 setup.py installUse---Open the GUI with the ``imzml2isa-qt`` command. To simply parse **.imzML**files to **ISA**, select the directory containing your files. Withdefault settings, the program will create the new ISA files in thatfolder, assuming the folder's name is the study identifier (*MTBSLxxx*for instance for MetaboLights studies). This can be changed by untickingthe ``Export result to directory of each study`` box. Once parametersare set up, click the ``Convert`` button to start the parser.MetaboLights------------Generating a study to upload on MetaboLights requires pieces ofinformation the parser cannot guess from the mzML file alone. To providemore metadata to your final ISA-Tab files, use the ``Add Metadata``button to open a new window and update details about your study. Still,even with all the required fields filled, **the generated ISA needs tobe enhanced after the end of the parsing** (using for instance`Metabolight pre-packaged ISACreator <http://www.ebi.ac.uk/metabolights/>`__ to add missing fields).Missing information required for MetaboLights upload are at the moment:- Study Factors (sample dependent, must be added to the *study* fileand to the *investigation* file)- Metabolite Assignment Files- Study DesignsTODO-----  Either add a ``metabolite assignment file`` field to main window orchange the **mzml2isa** parser behaviour so that it successfullydetects metabolite assignment files and add them to the study file.License-------GPLv3
Package: imzy
imzyimzy: A simple reader interface to imzML, Bruker (.tdf/.tsf) file formatsGetting startedInstall using pippipinstallimzyAnalyse your dataimportnumpyasnpfromimzyimportget_readerPATH_TO_FILE="path/to/file"# we currently support imzML, Bruker .d (.tsf/.tdf) formatsreader=get_reader(PATH_TO_FILE)# will extract mass spectrum for pixel index '0'mz_x,mz_y=reader.get_mass_spectrum(0)# will extract summed mass spectrum for pixel indices 0-100mz_x,mz_y=reader.get_summed_spectrum(np.arange(100))# iterate over all mass spectra in the datasetformz_x,mz_yinreader.spectra_iter():...# get tic arraytic=reader.get_tic()# this array is 1d so needs to be reshaped to 2d if you want to view it as an imagetic=reader.reshape(tic)# will extract the 885.549 ion image with 10 ppm window around itimage=reader.get_ion_image(885.549,ppm=10)# you can also extract multiple images at the same time (which is much more efficient since the spectra# only need to be loaded into memory once)mzs=[...]# list of m/zs to extractimages=reader.get_ion_images(mzs,tol=0.05)Supported formatsimzML on Windows, macOS and LinuxBruker (.tdf/.tsf) on Windows and LinuxPluginsIt is now possible to create your own readers by implementing theimzy.hookspecinterface. This allows you to create
your own readers for any format you want. You can then register your reader with imzy by adding the following to yoursetup.pyorpyproject.tomlorsetup.cfgfile:If you have project namedyour_project_name, you could add a fileimzy.pyto your project with the following code:fromimzyimportBaseReaderfromimzy.hookspecimporthook_implclassYourReader(BaseReader):"""Your reader class."""@hook_impldefimzy_reader(path:str,**kwargs)->ty.Optional[YourReader]:"""Return YourReader if path is valid."""...In thepyproject.tomlfile, please define the interface:[options.entry_points."imzy.plugins"]your_project_name="your_project_name.imzy"Your reader will be automatically detected when theImzyPluginManageris initialized, which happens when theget_readerfunction is called. You can then use your reader as follows:Planned featuresadd functionality to readersimprove performanceimprove testsadd better caching supportadd support for Waters (.raw) filesadd support for Thermo (.raw) filesContributingInitialize your codeInitializegitinside your repo:cdimzy&&gitinitCreate conda environment. We are usingimzyas its name.condacreate-nimzypython=3.9Initialize and installpre-commithooks:makedevelop
makepre-commit-installRun the codestyle:makecodestyleUpload initial code to GitHub:gitadd.
gitcommit-m":tada: Initial commit"gitbranch-Mmain
gitremoteaddoriginhttps://github.com/imzy/imzy.git
gitpush-uoriginmain🛡 LicenseThis project is licensed under the terms of theBSD-3license. SeeLICENSEfor more details.📃 Citation@misc{imzy,author={imzy},title={imzy: A new reader/writer interface to imzML and other imaging mass spectrometry formats.},year={2022},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/imzy/imzy}}}
Package: in1
in1 client libraryThis is the Python client library for thein1service. It allows super-resolving Sentinel-2 raster straight from Python.InstallationUse a Python package manager to install in1.pipinstallin1Usage client libraryConsult the onlinedocumentationfor details.importin1lores="/path/to/lores.tif"in1_api_key="<IN1_API_KEY>"hires,profile,metrics=in1.sisr(lores,api_key=in1_api_key)in1.write(hires,"/path/to/outdir/hires.tif",profile)Usage in1 executableThe Python client library also installs an in1 executable that allows super-resolving directly from the terminal. Consult the onlinedocumentationfor how to use optional flags.in1upsample--sourcepath/to/source.tif--resultpath/to/result.tif--api<IN1_API_KEY>LicenseMIT