Saravanan666/pypi-org huggingface dataset:
Package: gplist
plistIntroductionInfo.plist is a manifest-liked file to store properties of an application. It's file format can be xml or binary. This library is a pure python implementation to manipulate plist file and parse mobileprovision file.UsageThePlistInfois an ordered dict-liked class, so you can treat it as an ordered dict.When parsing from binary data or file,PlistInfowill automatically detect the format and get the correct result.MobileProvisionis an ordered dict-liked class too, and basically its data comes from xml plist.Binary Formatimportjsonimportosfromgplist.plistimportPlistInfo# raw binarywithopen("FooApp.app/Info.plist","rb")asfd:p=PlistInfo(fd.read())print(json.dumps(p,indent=2))# from raw filep=PlistInfo.from_file("FooApp.app/Info.plist")# from app or ipap=PlistInfo.from_app("FooApp.app")p=PlistInfo.from_app("FooApp.ipa")foo_file="foo.plist"p.to_binary_file(foo_file)assertos.path.isfile(foo_file)buf=p.to_binary()assertisinstance(buf,bytes)XML Formatimportosfromgplist.plistimportPlistInfop=PlistInfo.from_app("FooApp.ipa")foo_file="foo_xml.plist"p.to_xml_file(foo_file)assertos.path.isfile(foo_file)p.to_xml_file(foo_file,encoding="UTF-8",pretty=False)assertos.path.isfile(foo_file)buf=p.to_xml(encoding="UTF-8",pretty=True)assertisinstance(buf,bytes)Property Manipulationfromgplist.plistimportPlistInfop=PlistInfo.from_app("FooApp.ipa")p.add_property({"a":1},"foo")assertp["foo"]=={"a":1}p.add_property("b","foo","b")assertp["foo"]["b"]=="b"p.update_property("c","foo","b")assertp["foo"]["b"]=="c"p.remove_property("foo","a")assertp["foo"]=={"b":"c"}Mobile Provisionfromgplist.mobileprovisionimportMobileProvisionm=MobileProvision.from_file(provision_file)print(m.is_expired())print(m["Name"])print(m.has_udid("00008030-001A2DA6********")forcertinm.certs:print(cert.sha1)print(cert.is_expired())print(cert.serial)print(cert.common_name)print(cert.organization_unit_name)print(cert.organization_name)print(cert.country_name)Command Line Toolspython-mgplistInfo.plist
python-mgplistembedded.mobileprovision
python-mgplist--certembedded.mobileprovision
python-mgplist--has-udid"00008030-001A2DA6********"embedded.mobileprovision
Package: gplot
Gplot
Python library with graphical interface to plot intractive chart from the google sheetInstallationDownlaod gploter by pip:pip install gplotUsageRun the comamnd in terminal:gplotIt opens  a frontend in browser automatically if doesnt open automatically type in browserhttp://localhost:8501Make the google sheet publicpaste the google sheet id and sheet name respectively. (by default both are filled for demo)Press column button. It automatically fetchs columns names from sheet to choose x and y axisChoose  the axis and press plot buttonto save ploted chart as image click the camera icon on right top of the chart
Package: gplugins
gplugins 0.9.13gdsfactory plugins:devsimTCAD device simulator.meowEigen Mode Expansion (EME).femwellFinite Element Method Solver (heaters, modes, TCAD, RF waveguides).gmshmesh structures.tidy3dFinite Difference Time Domain (FDTD) simulations on the cloud using GPU.lumericalFor Ansys FDTD and Circuit interconnect.klayoutfor fill, dataprep and testing.rayfor distributed computing and optimization.saxS-parameter circuit solver.schematic: for bokeh schematic editor andpath_length_analysis.meepfor FDTD.mpbfor MPB mode solver.elmerfor electrostatic (capacitive) simulations.palacefor full-wave driven (S parameter) and electrostatic (capacitive) simulations.vlsirfor parsing GDS-extracted circuit netlists into Spice, Spectre and Xyce Schematic File formats.InstallationYou can install most plugins with:pipinstall"gdsfactory[full]"--upgradeorpipinstall"gplugins[devsim,femwell,gmsh,schematic,meow,meshwell,ray,sax,tidy3d]"--upgradeOr install only the plugins you need. For example:pipinstall"gplugins[schematic,femwell,meow,sax,tidy3d]"--upgradeNon-pip pluginsThe following plugins require special installation as they can't be installed withpip:For Meep and MPB you need to usecondaormambaon MacOS, Linux orWindows WSL (Windows Subsystem for Linux)withconda install pymeep=*=mpi_mpich_* -c conda-forge -yFor Elmer, refer toElmer FEM – Installationfor installation or compilation instructions each platform. Gplugins assumesElmerSolver,ElmerSolver_mpi, andElmerGridare available in your PATH environment variable.For Palace, refer toPalace – Installationfor compilation instructions using Spack or Singularity. Gplugins assumespalaceis available in your PATH environment variable.Getting startedRead docsRead gdsfactory docs
Package: gpluscodepy
gpluscodepySimple plus code to lat/long decoder written for White Flag Project.Quickstartpip install gpluscodepylist_of_places = [{'name': 'Some place string', 'latitude': 1.921313, 'longitude': 207.124523}, ...]converter = gpluscodepy.Converter(filestream)latitude, longitude = converter.decode('GM83+2X Skudai, Johor')Installpip install gpluscodepyUsageMethods__init__(api_key: string, index: list[dict] = [])api_key: strGoogle API key with access to the Geocoding API.index: list[dict]List of dictionaries with a name field for the place string, a longitude field, and a latitude field.decode(pluscode: str)pluscode: strPlus code to be decoded.ReturnUnion[(float, float), False]Returns a float of tuples representing latitude and longitude OR returns false if the pluscode cannot be decoded.getCities()Returnlist[dict]List of dictionaries with a name field for the place string, a longitude field, and a latitude field.How it worksThe converter makes use of theGoogle Open Location Codelibrary to decode and works with both full code and short code. The converter will try to work offline whenever possible.If the input is a full code, it will just decode without any preprocessing. If a place string is detected in the code, it'll first check for the place's lat/long in the index file, and convert that to the 4 digit prefix. If the place is not yet indexed, it'll search on theGoogle Geocoding APIand index valid result's lat/long into the index file for future use. And then the lat/long values are returned as a tuple(latitude, longitude).
Package: gpl-wrapper
gpl wrapperThis is a wrapper to run the GRIDSS-PURPLE-LINX tool. The original nextflow pipeline ishere. I have modified the docker image to make it compatible with our working environment.Additionally, hg38 for hmftools is harcoded for the "chr"-prefixed reference genome. So, we have added a previous step to reheader both bams and input vcfs so they also include the prefix. Our modified repo ishereInstallationpip install gpl-wrapper
Package: gpmap
GPMapA Python API for managing genotype-phenotype map dataGPMap defines a flexible object for managing genotype-phenotype (GP) map data. At it's core,
it stores all data in Pandas DataFrames and thus, interacts seamlessly with the
PyData egosystem.To visualize genotype-phenotype objects created by GPMap, checkoutGPGraph.Basic exampleImport the package's base object.fromgpmapimportGenotypePhenotypeMapPass your data to the object.# Datawildtype="AAA"genotypes=["AAA","AAT","ATA","TAA","ATT","TAT","TTA","TTT"]phenotypes=[0.1,0.2,0.2,0.6,0.4,0.6,1.0,1.1]stdeviations=[0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05]# Initialize the objectgpm=GenotypePhenotypeMap(wildtype,genotypes,phenotypes,stdeviations=stdeviations)# Check out the data.gpm.dataOr load a dataset from disk.gpm=GenotypePhenotypeMap.read_json("data.json")InstallationUsersThis simplest way to install this package is using pip:pip install gpmapDevelopersThe recommended way to install this package for development is usingpipenv.Clone this repository:git clone https://github.com/harmslab/gpmapInstall the package using pipenv.cd gpmap
pipenv install --dev -e .Run tests using pytestpytestDependenciesThe following modules are required. Also, the examples/tutorials are written in Jupyter notebooks and require IPython to be installed.NumpyPandas
Package: gp-maps-python
Gaussian Processes for Orientation Preference MapsThis package is a Python implementation of a Gaussian Process (GP) method for inferring cortical maps (Macke et al., 2011), based on their originalMATLAB-code. Compared to conventional (vector averaging) approaches, the method computes better maps from little data and can be used to quantify the uncertainty in an estimated orientation preference map (OPM). It also provides a principled way to choose the parameters in the smoothing step and incorporate inhomogeneous noise variances as well as noise correlations.The basic idea is to specify a prior over OPMs as a GP. This means that a-priori assumptions about correlations of the pixels in a map are specified as a covariance function K. In this case, we use a Difference of Gaussians (DoG) kernel. This covariance function encodes our assumptions that OPMs are smooth and have a semi-periodic structure. This prior is then updated via Bayes' rule with imaging data to obtain a smooth posterior estimate of an OPM.My notes on the method (including derivations not in the original paper) can be foundhere.Installation & UsageYou can clone this package and install all its dependencies usinggit clone git@github.com:mackelab/gp-maps-python.git
python -m pip install -r requirements.txtAlternatively, this module is now pip-installable viapython -m pip install gp-maps-pythonA number of Jupyter notebooks illustrate how to use the package:OPM.ipynbcontains basic functionality for handling OPMs:sample from this prior to obtain a ground truth OPMvisualize OPMssimulate neural responses given an OPM and a set of stimulicompute maximum likelihood/vector averaging estimate of the OPMsmooth the OPM with a Gaussian kernelBayesianOPM.ipynbshows details about the GP method:covariance function (DoG kernel)estimate hyperparameters of the prior covariance function from the empirical maplearn noise model iteratively from dataposterior inferenceFerretData.ipynbserves as an illustration on how to use GPs to infer OPMs from real imaging data (replicating the results of Macke et al., 2011):visualize inhomogeneous and correlated noise structure of the dataobtain smooth estimate based on "ground truth"GP posterior inference on real data
Package: gpmf
pygpmfA python Module to extract GPMF information from VideosMore information on the format is available on theGoPro GitHub page.The primay aim of this project was to be able to extract GPS tracks
from GoPro video files, so the GPS part is more tested.We usepython-ffmpegto extract the GPMF stream from video files.importgpmf# Read the binary stream from the filestream=gpmf.io.extract_gpmf_stream(my_file)# Extract GPS low level data from the streamgps_blocks=gpmf.gps.extract_gps_blocks(stream)# Parse low level data into more usable formatgps_data=list(map(gpmf.gps.parse_gps_block,gps_blocks))We rely ongpxpyto easily convert GPS data into GPX segments:importgpxpygpx=gpxpy.gpx.GPX()gpx_track=gpxpy.gpx.GPXTrack()gpx.tracks.append(gpx_track)gpx_track.segments.append(gpmf.gps.make_pgx_segment(gps_data))print(gpx.to_xml())<?xml version="1.0" encoding="UTF-8"?>
<gpx xmlns="http://www.topografix.com/GPX/1/1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.topografix.com/GPX/1/1 http://www.topografix.com/GPX/1/1/gpx.xsd" version="1.1" creator="gpx.py -- https://github.com/tkrajina/gpxpy">
  <trk>
    <trkseg>
      <trkpt lat="44.1287283" lon="5.427715">
        <ele>833.759</ele>
        <time>2020-07-03T12:36:56.940000Z</time>
        <sym>Square</sym>
        <fix>3d</fix>
        <pdop>1.82</pdop>
        <extensions>
          <speed_2d>
            <value>9.221</value>
            <unit>m/s</unit>
          </speed_2d>
          <speed_3d>
            <value>9.25</value>
            <unit>m/s</unit>
          </speed_3d>
        </extensions>
      </trkpt>
      ...
    </trkseg>
  </trk>
</gpx>You can also make an image from you gps track:importgpmf# Read the binary stream from the filestream=gpmf.io.extract_gpmf_stream(my_file)gpmf.gps_plot.plot_gps_trace_from_stream(stream)
Package: gpml2svg
GPML2SVG is a command line and Python API for the conversion of pathways marked up         using GPML (GenMAPP Pathway Markup Language) to SVG.         The command-line interface is currently basic. Simply call the script with the name of the         GPML file to convert. The resulting SVG will be saved using the same name, with the         extension change to .svg. The resulting SVG is marked up with object identifiers,         and links to relevant databases. The python interface offers a single function call to which you can optionally provide         colouring information (for data visualisation) and links for custom XRefs.         The sofware is in development as an extension to MetaPath.
Package: gpm-player
DescriptionMulti platform command-line-player of Google Play Music using gmusicapi.EnvironmentPython3 on Windows 10 and POSIX.RequirementsVLC (libvlc)Install$pipinstall-Ugpm-playerUsagestation player$gpm-stationplaylist player$gpm-playlistOptionstry execute with –help optionTasksequalizerseek and set timeLicenseMIT
Package: gpnam
GPNAM: Gaussian Process Neural Additive ModelsThe framework of GPNAM.$z_s, c_s$and the sinusoidal function are predefined from the paper and do not require training. The only trainable parameter isWthat maps to the output of each shape function.This package implements the paper titled Gaussian Process Neural Additive Models that appears at AAAI 2024. The paper is available onArxiv.Basically, the GPNAM constructs a Neural Additive Model (NAM) by a GP with Random Fourier Features as the shape function for each input feature, which leads to a convex optimization with a significant reduction in trainable parameters.Sklearn interfaceYou can install the package by:pip install gpnamThen, you can run the model simply by:fromgpnam.sklearnimportGPNAM"""input_dim: the dimensions of input dataproblem: type of the task, 'classification' or 'regression'"""gpnam=GPNAM(input_dim,problem)gpnam.fit(X,y)y_pred=gpnam.predict(X_test)CitationIf you find this repo useful, please consider citing our paper:@inproceedings{,
  title={Gaussian Process Neural Additive Models},
  author={Wei Zhang and Brian Barr and John Paisley},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2024}
}
Package: gpn-clients
🀣 🅖🅟🅝 🅲🅻🅸🅴🅽🆃🆂 🀣ГПН Клиенты предоставляющие интерфейс для работы с источниками данных.Базовое использованиеКонфигурацияклиента на примере клиента NSI:fromgpn_clients.core.configimportnsi_configNSI_HOST:str="https://test-nsi-host-228.com"NSI_PORT:int=443# Конфигурация клиентаnsi_config.set_config(host=NSI_HOST,port=NSI_PORT,)После конфигурации клиента можно использовать его методы.
Использование интерфейсов NSIАлгоритмов:frompydanticimportHttpUrlfromgpn_clients.clients.nsi.v1.algorithmsimport(AbstractAlgorithms,NSIAlgorithms,)nsi_algorithms:AbstractAlgorithms=NSIAlgorithms()# Получение URL для списка алгоритмовalgorithms:HttpUrl=nsi_algorithms.get_all()# Получение URL для алгоритма по его IDalgorithm:HttpUrl=nsi_algorithms.get_by_id(algorithm_id=1)
Package: gpnpytorchtools
gpnpytorchtoolsgpnpytorchtools created by Linghai Wang with contributions from Jinghang LiInstall it from PyPIpipinstallgpnpytorchtoolsDevelopmentMake sure to install the library locally for relative imports to work properly. You should give the path to the folder containing the setup.py filepipinstall-epath/to/gpnpytorchtoolsUsagefromgpnpytorchtoolsimportBaseClassfromgpnpytorchtoolsimportbase_functionBaseClass().base_method()base_function()$python-mgpnpytorchtools#or$gpnpytorchtoolsDevelopmentRead theCONTRIBUTING.mdfile.
Package: gpodder-exporter
gpodder-exporterA simple command-line tool to export gPodder podcast titles and RSS URLs to a CSV file. This tool is helpful when you want to extract the list of subscribed podcasts and their RSS feeds from gPodder without having to manually parse the configuration files, which are not human-readable.This program simply reads the gPodder database file and extracts the podcast titles and RSS URLs from it. It then saves the extracted data to a CSV file.InstallationYou can installgpodder-exporterusing pip:pip install gpodder-exporterUsageTo usegpodder-exporter, simply run the following command:gpodder-exportBy default, the tool will look for the gPodder database file in the current working directory and save the output to a file namedpodcast_titles_and_rss_urls.csv.If your database file is located in a different folder, you can provide the path using the--data_folderoption:gpodder-export --data_folder /path/to/your/data_folderTo specify a custom output path for the CSV file, use the--outputoption:gpodder-exporter --output /path/to/your/output.csvLicenseThis project is licensed under the MIT License. See theLICENSEfile for details.
Package: gportal
gportal-pythonUnofficial python client forG-PortalCatalogue Service and SFTP.This package is distributed under theMIT License, but the data that can be retrieved through this package is provided underG-Portal Terms of Use.
Please make sure to review them before using the G-Portal data.Installation$ pip install gportalQuickstartimportgportal# Get the dictionary of the G-Portal dataset.# It corresponds to the "spacecraft/sensor" search tree of the Web UI.datasets=gportal.datasets()# Build a search query.res=gportal.search(dataset_ids=datasets["GCOM-C/SGLI"]["LEVEL2"]["Land area"]["L2-LST"],start_time="2023-03-25T00:00:00",end_time="2023-03-25T23:59:59",bbox=[130,30,140,40],params={# You can add more parameters.# See Appendix 7 of G-Portal User's Manual for more details:# https://gportal.jaxa.jp/gpr/assets/mng_upload/COMMON/upload/GPortalUserManual_en.pdf},)print("Matched:",res.matched())# Get the resulting products.products=res.products()# Download the product files via SFTP.gportal.username="sankichi92"gportal.password="**********"# If env var GPORTAL_PASSWORD is set, the value is used.gportal.download(products,local_dir="path/to/download/dir")Documentationhttps://gportal.readthedocs.io/ContributingBug reports and pull requests are welcome on GitHub athttps://github.com/sankichi92/gportal-python.
This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to thecode of conduct.DevelopmentThis project usesPoetry.Install dependencies$ poetry installRun the test suite$ poetry run pytestStart the docs server$ poetry run mkdocs serveRelease a new versionBump the version inpyproject.tomlbypoetry versionand commit it.Create a git tag for the new version.Push it to GitHub, and then thepublish-packageworkflow starts.
Package: gpostmaster-domains-datas
Google Postmaster DatasUnofficial tool to download and flatten data from GPT. The recovered data will
offer a simple schema in order to be able to easily save this data in a flat
file or in database.Schemauser_report_spam_percent :float|Noneips_reputations :listdomain_reputation :level|Nonefeedback_loop :{ nb_campaigns: 0, percent_per_campaign: list }auth_use_dkim_percent :float|Noneauth_use_spf_percent :float|Noneauth_use_dmarc_percent :float|Nonetls_inbound_percent :float|Nonedelivery_errors :listdomain :strdate :strLevelTranslates string level from GTP to intENFRinthighbonne4mediummoyenne3lowplutôt mauvaise2badmauvaise1unknowunknow0ips_reputations[ { 'level': level, 'value': float, 'ips': str } ]feedback_looppercent_per_campaign : [ { 'uid': int, 'spam_percent': float } ]How to use itpythonentry_points_googlepostmasterapi/gpt_dl_all_datas.py-h
>usage:gpt_dl_all_datas[-h][--token[TOKEN]][--pool-size[POOL_SIZE]][--date[DATE]][--verbose][--version]pythonentry_points_googlepostmasterapi/gpt_dl_domain_datas.py-h
>usage:gpt_dl_domain_datas[-h][--token[TOKEN]][--domain[DOMAIN]][--date[DATE]][--verbose][--version]Support versionPython :>=3.6
Package: gp-pax-terminal-downloader
Bug Tracker =https://www.greenpayci.comAuthor: François Joseph AMON D’’ABY
Author-email:ange_amon@live.frLicense: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE# paxstore-openapi-python-sdk
Green Pay Paxstore Terminals downloader
Package: gpplot
gpplotIntroductionPlotting functions for the Genetic Perturbation Platform’s R&D group at the Broad Institute.
Extends matplotlib and seaborn functionality with extra plot types. Includes functions for easy
styling and consistent color palettes.Free software: MIT licenseDocumentation:https://gpplot.readthedocs.io.TutorialTo install gpplot, run this command in your terminal:$ pip install gpplotImport packages:import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import gpplotSet aesthetics for all plots using gpplot defaults:gpplot.set_aesthetics()Setup data:nsamps = 20000
scatter_data = pd.DataFrame({'x': np.random.normal(size = nsamps)}, index = range(nsamps))
scatter_data['y'] = 2*scatter_data['x'] + np.random.normal(size = nsamps)Create a point density plot and add a pearson correlation:fig, ax = plt.subplots(figsize = (4,4))
ax = gpplot.point_densityplot(scatter_data, 'x', 'y', palette=gpplot.sequential_cmap())
ax = gpplot.add_correlation(scatter_data, 'x', 'y')Label points in a scatterplot:fig, ax = plt.subplots(figsize = (4,4))
mpg = sns.load_dataset('mpg')
ax = sns.scatterplot(data = mpg, x = 'weight', y = 'mpg', ax = ax)
label = ['hi 1200d', 'ford f250', 'chevy c20', 'oldsmobile omega']
gpplot.label_points(mpg, 'weight', 'mpg', label, 'name',
                    size = 12, style = 'italic')TODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-06-16)Project start0.2.1 (2020-06-20)First release on pypi0.2.2 (2020-06-25)Automatic integration with pypi0.3.1 (2020-07-12)Add plt.gca to add_correlation and label_pointsUpdate docs to include notebook references0.3.2 (2020-07-12)Added minimum version for requirements0.3.3 (2020-07-12)Added version for dev requirements0.3.4 (2020-07-12)Added python version >= 3.60.3.5 (2020-07-22)Updated for Matplotlib >= 3.30.4.0 (2020-08-18)Added add_reg_line and add_xy_line
Package: gpquant
gpquant说明文档介绍gpquant是对Python的遗传算法包gplearn的一个改造，用于进行因子挖掘模块Function计算因子的函数，用仿函数类Function实现了23个基本函数和37个时间序列函数。所有的函数本质上都是标量函数，但因为采用了向量化计算，所以输入和输出都是向量形式Fitness适应度评价指标，用仿函数类Fitness实现了几个适应度函数，主要是应用其中的夏普比率sharpe_ratioBacktester向量化的因子回测框架，逻辑是先根据定义的策略函数把拿到的因子factor变成信号signal，再通过信号处理函数把信号signal变成资产asset实现回测，这两步统一在仿函数Backtester类里实现SyntaxTree公式树，把因子的计算公式写成前缀表达式，然后用公式树SyntaxTree表示。每一个公式树代表一个因子，由节点Node构成；每个Node存放了自身数据、父节点和子节点。节点的自身数据可以是Function、变量、常量，或者时间序列常数公式树可以交叉crossover、子树突变subtree_mutate、提升突变hoist_mutate、点突变point_mutate或者繁殖reproduce（逻辑可参照gplearn）SymbolicRegressor符号回归类，gpquant因子挖掘本质上是用遗传算法解决符号回归问题，其中定义了遗传过程中的一些参数，如种群数量population_size、遗传代数generations等使用导入下载gpquant包（pip install gpquant），导入SymbolicRegressor类fromgpquant.SymbolicRegressorimportSymbolicRegressor测试跟gplearn一样的例子，把$y=X_0^2 - X_1^2 + X_1 - 1$对$X_0$和$X_1$进行符号回归，大约在第9代能找到正确答案importnumpyasnpimportpandasaspdfrommatplotlibimportpyplotaspltfromsklearn.utilsimport*fromgpquant.SymbolicRegressorimportSymbolicRegressor# Step 1x0=np.arange(-1,1,1/10.)x1=np.arange(-1,1,1/10.)x0,x1=np.meshgrid(x0,x1)y_truth=x0**2-x1**2+x1-1ax=plt.figure().gca(projection='3d')ax.set_xlim(-1,1)ax.set_ylim(-1,1)surf=ax.plot_surface(x0,x1,y_truth,rstride=1,cstride=1,color='green',alpha=0.5)plt.show()# Step 2rng=check_random_state(0)# training samplesX_train=rng.uniform(-1,1,100).reshape(50,2)y_train=X_train[:,0]**2-X_train[:,1]**2+X_train[:,1]-1X_train=pd.DataFrame(X_train,columns=['X0','X1'])y_train=pd.Series(y_train)# testing samplesX_test=rng.uniform(-1,1,100).reshape(50,2)y_test=X_test[:,0]**2-X_test[:,1]**2+X_test[:,1]-1# Step 3sr=SymbolicRegressor(population_size=2000,tournament_size=20,generations=20,stopping_criteria=0.01,p_crossover=0.7,p_subtree_mutate=0.1,p_hoist_mutate=0.1,p_point_mutate=0.05,init_depth=(6,8),init_method='half and half',function_set=['add','sub','mul','div','square'],variable_set=['X0','X1'],const_range=(0,1),ts_const_range=(0,1),build_preference=[0.75,0.75],metric='mean absolute error',parsimony_coefficient=0.01)sr.fit(X_train,y_train)# Step 4print(sr.best_estimator)