Saravanan666/pypi-org huggingface dataset:
Package: lsframe
lsframe - map, classify, framelsframe is an open-source Python package for mapping directories and correlating specific language patterns in file and folder names with each other and with outputs of user-supplied functions.FeaturesCreate DataFrames that are maps of directoriesOrganize DataFrames using dates and language in file and folder namesFind possible dates in arbitrarily formatted file and folder namesConstruct DataFrames from a function that processes files and foldersVisualize correlations between dates or language and function outputsRequirementsPython 3.6 or Laterpandas, numpy, python-dateutil, seabornInstall the required packages using pip:pip install pandas numpy python-dateutil seabornInstallationInstall lsframe using pip:pip install lsframeDocumenationOnline documentation:https://thcasey3.github.io/lsframe/DevelopementClone the git repository fromhttps://github.com/thcasey3/lsframe/tree/development
Package: lsf-runner
RunnerRunner is a package for running experiments, particular at a IBM LSF cluster.
It provides a runner that can run locally on CPUs or GPUs, or on an IBM LSF cluster.This package will change often as it is under development.Installationpipinstall-Ugit+https://github.com/sebascuri/runnerOr clone and installgitclonehttps://github.com/sebascuri/runner.gitcdrunnerpipinstall-e.[test]
Package: lsf-stats
lsf_statsSummarizeLSFjob properties by parsing log files of workflows executed bySnakemake.Installation$pipinstalllsf_statsUsage$lsf_stats--help
Usage:lsf_stats[OPTIONS]COMMAND[ARGS]...SummarizeLSFjobpropertiesbyparsinglogfiles.

Options:--versionShowtheversionandexit.--helpShowthismessageandexit.

Commands:gatherAggregateinformationfromlogfilesinsingledataframe.summarizeSummarizeandvisualizeaggregatedinformation.ExampleAssume that you executed your Snakemake workflow using thelsf-profileand all generated log files are stored in the directory./logs/:$snakemake--profilelsf[..]You can then quickly aggregate resource, rule and other types of information about the workflow execution into a single dataframe:$lsf_statsgather-oworkflow_stats.csv.gz./logs/[..]This dataframe can then be summarized in various ways:$lsf_statssummarize\--query'status == "Successfully completed."'\--split-wildcards\--grouping-variablecategory\workflow_stats.csv.gz[..]For example, the following plots will be generated:Job executionJob resources
Package: lsg
LSG => ls | grepTable of contentsIntroductionFeaturesGetting StartedInstallationUsageFull DocumentationLicenseIntroductionThis project started as a shell script forls | grepto check if a folder contains the file which I am looking for.I tried usingfindand other unix-like tools, but either they were too complicated to use or the results were not what I wanted.The package doesn't aim to revolutionize the way you do yourlsor anything else. Its just a better substitution to bash script.Back to topFeaturesSearch for multiple files and folders.Specify a path to search or it will search within current directory.Print total number of results.Case insensitive substring search.Results are displayed in separate categories.OS agnosticBack to topGetting StartedInstallationpip install lsgUsagelsg FILENAMESearch for.pyfiles in current directory.$lsg.py
----------Totalnumberofentries---------->1----------Files---------------------------->1setup.pySearch for.pyfiles inlsgdirectory.$lsg.py-plsg
----------Totalnumberofentries---------->2----------Files---------------------------->2__init__.py
main.pyBack to topFull DocumentationFor more help runlsg -horlsg --helpon terminal.$lsg-h
usage:lsg[-h][--pathPATH]FILENAME[FILENAME...]Checkifaspecificfileorfolderexistsincurrentorinaspecified
directory

positionalarguments:FILENAMEnameoffileorfoldertosearch

optionalarguments:-h,--helpshowthishelpmessageandexit--pathPATH,-pPATHpathtosearch(default:currentdirectory)Any feature requests, bugs etc feel free to create an issue onGitlab.ReadCHANGELOG,CODE OF CONDUCT,CONTRIBUTINGguide.LicenseMIT LicenseCopyright (c) 2019 Justine Thomas KizhakkinedathBack to top
Package: lshakeidn
live_indonesia_earthquakeThis package gets the latest earthquake in Indonesia from the official government agencyHow does it work?This package scrapes live Indonesian earthquake data from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG).
It uses BeautifulSoup4 and Requests packages to produce outputs in the form of JSON. The generated outputs is readily available for web or mobile applications.How to use it?To install the package, copy the pip installation of the latest version and paste it on your terminal. After installing, import the package using "import live_Indonesia_earthquake".
Here is the code that you need:import live_Indonesia_earthquake
if __name__ == '__main__':
    print('Live Earthquake Detection App')
    result = live_Indonesia_earthquake.data_extraction()
    live_Indonesia_earthquake.show_data(result)
Package: lshash
Version:0.0.4devA fast Python implementation of locality sensitive hashing with persistance
support.HighlightsFast hash calculation for large amount of high dimensional data through the use ofnumpyarrays.Built-in support for persistency through Redis.Multiple hash indexes support.Built-in support for common distance/objective functions for ranking outputs.InstallationLSHashdepends on the following libraries:numpyredis (if persistency through Redis is needed)bitarray (if hamming distance is used as distance function)To install:$pipinstalllshashQuickstartTo create 6-bit hashes for input data of 8 dimensions:>>>fromlshashimportLSHash>>>lsh=LSHash(6,8)>>>lsh.index([1,2,3,4,5,6,7,8])>>>lsh.index([2,3,4,5,6,7,8,9])>>>lsh.index([10,12,99,1,5,31,2,3])>>>lsh.query([1,2,3,4,5,6,7,7])[((1,2,3,4,5,6,7,8),1.0),((2,3,4,5,6,7,8,9),11)]Main InterfaceTo initialize aLSHashinstance:LSHash(hash_size,input_dim,num_of_hashtables=1,storage=None,matrices_filename=None,overwrite=False)parameters:hash_size:The length of the resulting binary hash.input_dim:The dimension of the input vector.num_hashtables = 1:(optional) The number of hash tables used for multiple lookups.storage = None:(optional) Specify the name of the storage to be used for the index
storage. Options include “redis”.matrices_filename = None:(optional) Specify the path to the .npz file random matrices are stored
or to be stored if the file does not exist yetoverwrite = False:(optional) Whether to overwrite the matrices file if it already existTo index a data point of a givenLSHashinstance, e.g.,lsh:lsh.index(input_point,extra_data=None):parameters:input_point:The input data point is an array or tuple of numbers of input_dim.extra_data = None:(optional) Extra data to be added along with the input_point.To query a data point against a givenLSHashinstance, e.g.,lsh:lsh.query(query_point,num_results=None,distance_func="euclidean"):parameters:query_point:The query data point is an array or tuple of numbers of input_dim.num_results = None:(optional) The number of query results to return in ranked order. By
default all results will be returned.distance_func = "euclidean":(optional) Distance function to use to rank the candidates. By default
euclidean distance function will be used.v0.0.3, 2012/12/28 – Doc fixes.
v0.0.2, 2012/12/28 – Doc fixes and lowercase package name.
v0.0.1, 2012/12/20 – Initial release.
Package: lshash3
Version:0.0.4devA fast Python implementation of locality sensitive hashing with persistance
support.HighlightsFast hash calculation for large amount of high dimensional data through the use ofnumpyarrays.Built-in support for persistency through Redis.Multiple hash indexes support.Built-in support for common distance/objective functions for ranking outputs.InstallationLSHashdepends on the following libraries:numpyredis (if persistency through Redis is needed)bitarray (if hamming distance is used as distance function)To install:$pipinstalllshashQuickstartTo create 6-bit hashes for input data of 8 dimensions:>>>fromlshashimportLSHash>>>lsh=LSHash(6,8)>>>lsh.index([1,2,3,4,5,6,7,8])>>>lsh.index([2,3,4,5,6,7,8,9])>>>lsh.index([10,12,99,1,5,31,2,3])>>>lsh.query([1,2,3,4,5,6,7,7])[((1,2,3,4,5,6,7,8),1.0),((2,3,4,5,6,7,8,9),11)]Main InterfaceTo initialize aLSHashinstance:LSHash(hash_size,input_dim,num_of_hashtables=1,storage=None,matrices_filename=None,overwrite=False)parameters:hash_size:The length of the resulting binary hash.input_dim:The dimension of the input vector.num_hashtables = 1:(optional) The number of hash tables used for multiple lookups.storage = None:(optional) Specify the name of the storage to be used for the index
storage. Options include “redis”.matrices_filename = None:(optional) Specify the path to the .npz file random matrices are stored
or to be stored if the file does not exist yetoverwrite = False:(optional) Whether to overwrite the matrices file if it already existTo index a data point of a givenLSHashinstance, e.g.,lsh:lsh.index(input_point,extra_data=None):parameters:input_point:The input data point is an array or tuple of numbers of input_dim.extra_data = None:(optional) Extra data to be added along with the input_point.To query a data point against a givenLSHashinstance, e.g.,lsh:lsh.query(query_point,num_results=None,distance_func="euclidean"):parameters:query_point:The query data point is an array or tuple of numbers of input_dim.num_results = None:(optional) The number of query results to return in ranked order. By
default all results will be returned.distance_func = "euclidean":(optional) Distance function to use to rank the candidates. By default
euclidean distance function will be used.
Package: lshcos
A fast Python implementation of locality sensitive hashing with cosine distanceInstallationLSHCosdepends on the following libraries:numpyTo install:$pipinstalllshcosQuickstartTo create band 2 row 6 hashes for input data of 8 dimensions:importnumpyasnpfromlshcosimportLSHCos### usage one:lshcos=LSHCos(2,6,8)lshcos.add([1,2,3,4,5,6,7,8],'v_1')lshcos.add([2,3,4,5,6,7,8,9],'v_2')lshcos.add([10,12,99,1,5,31,2,3],'v_3')lshcos.query([1,2,3,4,5,6,7,7],2)### usage two:lshcos=LSHCos(2,6,8)xxx=[('v_'+str(i),np.random.randn(8))foriinrange(100)]yyy=np.random.randn(8)forname,vinxxx:lshcos.add(v,name)lshcos.query(yyy)Main InterfaceTo initialize aLSHCosinstance:LSHCos(band_num,row_num,v_dim)parameters:band_num:The number of band.row_num:The number of row for each band.v_dim:The dimension of the input vector.
Package: lsh-for-indexing
LSH for indexingThis package helps search engines to index and easily search on vector using Local sensitivity hashing (LSH)Installationpip install -i https://test.pypi.org/simple/ lsh-for-indexingUsage examplefrom lsh.random_projection import LshGaussianRandomProjection
import numpy as np

rp = LshGaussianRandomProjection(vector_dimension=6, bucket_size=3, num_of_buckets=2)    import numpy as np
vec = np.asarray([1,0,1,1,0,0])
rp.fit()
rp.indexable_transform(vec)
>> ['0_010', '1_000']if you know your collection size and you want an optimal number of bucket_sizerp.fit(sample_size=2000)transforming a bulk of vectorsmat = np.asarray([[1,0,1,1,0,0], [1,0,0,1,0,1]])
rp.indexable_transform(mat)
>> [['0_010', '1_111'], ['0_010', '1_101']]
Package: lshkcenters
Python implementations of the LSH-k-Centers algorithms for clustering categorical data:Installation:Using pip:pipinstalllshkcentersImport the packages:importnumpyasnp
fromLSHkCenters.LSHkCentersimportLSHkCentersGenerate a simple categorical dataset:X=np.array([[0,0,0],[0,1,1],[0,0,0],[1,0,1],[2,2,2],[2,3,2],[2,3,2]])y=np.array([0,0,0,0,1,1,1])LSH-k-Centers:kcens=LSHkCenters(X,y,n_init=5,k=2)kcens.SetupLSH()kcens.DoCluster()Built-in evaluattion metrics:kcens.CalcScore()Out come:Purity:1.000NMI:1.00ARI:1.00Sil:-1.00Acc:1.00Recall:1.00Precision:1.00Built-in fuzzy evaluattion metrics:kcens.CalcFuzzyScore()Out come:FuzzyscoresPC:1.00NPC:1.00FHVâ†“:0.02FSâ†“:-2000.00XBâ†“:0.11BHâ†“:0.06BWS:-2000.00FPC:3.50SIL_R:0.70FSIL:0.70MPO:12.15NPE:0.01PE:0.01PEB:0.01References:To be updated
Package: lshknn
k nearest neighbor (KNN) graphs via Pearson correlation distance and local sensitive hashing (LSH).Development:https://github.com/iosonofabio/lshknnAuthors: Fabio Zanini and Paolo CarnevaliLicense: MITCopyright: Fabio Zanini and Chan Zuckerberg Initiativeimportnumpyasnpimportlshknn# Make mock data# 2 features (rows), 4 samples (columns)data=np.array([[1,0,1,0],[0,1,0,1]],dtype=np.float64)# Instantiate classc=lshknn.Lshknn(data=data,k=1,threshold=0.2,m=10,slice_length=4)# Call subroutineknn,similarity,n_neighbors=c()# Check resultassert(knn==[[2],[3],[0],[1]]).all()
Package: ls-Improved
ls-Improved: descriptive ls-like-command working on Python日本語READMEWhat is it?ls-Improved (lsi)prints out a directory structure with its descriptions.
I've been using it when tackling some seriese of experiments.(e.g. machine leaning experiments.)ExampleWe have some directories like below.Sometimes we'd like to access the best result of experiments, but it is not easy.  It is necessary to open all of directories or noteexperiments_summary.txtbeforehand in order to do it.Now we have thels-Improved(lsi)command here.  It allows us to see list of directories with its descriptions simultaneously.Requirementsdeveloped on Python2.7 and Python3.7 (maybe ≧Python2.7 is ok.)It is working on Python, so this command dose not depend on OS.Installpip install# PyPI
pip install ls-Improvedmanual installFor person who do not like PyPI, manual install version exists.download latest manual version from github release.wget https://github.com/ShotaroKataoka/ls-Improved/archive/v0.3.0.beta1.manual.zipunzip downloaded zip file.set PATH environment tobin/directory.(set PATH to unzipedbin/directory or placebin/lsiandbin/mkdirito/usr/local/bin/)UsageHow workslsiread.description.lsifiles which are in each directories..description.lsiis simple text file.  It is made bymkdiricommand.In brief:mkdirimake a directory with the.description.lsitext file.lsiprint out directory structure with.description.lsicontent.mkdirimkdirimake a directory with the.description.lsi.mkdiri DIRECTORY 'DESCRIPTION': makeDIRECTORYand writeDESCRIPTIONinto.description.lsimkdiri DIRECTORY: makeDIRECTORYand create empty.description.lsimkdiri -f FILE-PATH 'DESCRIPTION': writeFILE-PATH'sDESCRIPTIONinto.file_description.lsimkdiri -a DIRECTORY 'DESCRIPTION': overwrite or create.description.lsiin existing directory.mkdiri -h: Show helpAdvanced feature (!! these commands create~/.lsirc!!):mkdiri -e: Show current mkdiri mode. (LSI MODE or ANSI MODE)mkdiri --ANSI-MODE: Set ANSI-ESCAPE-SEQUENCE MODE. (convert decoration to ANSI escape sequence)mkdiri --LSI-MODE: Set LSI-ESCAPE-SEQUENCE MODE. (default)tips:Add decoration to description :\n: make new line;r;;g;;b;;w;;p;: add color to text;_;: add underline to text;e;: end decorationExamplelsilsiprint out directory structure with.description.lsicontent.lsi: Show directory structure and these descriptions in the current directorylsi DIRECTORY: Show directory structure and these descriptions in theDIRECTORYlsi -a: Show hidden files and directorieslsi -f: Show only fileslsi -d: Show only directorieslsi -s 'SEARCH_WORD': search file-name and description withSEARCH_WORDlsi -n NUMBER: Raise warning if number of files is bigger than NUMBER.lsi -h: Show helptips:Adding following alias to.bashrcis useful :alias clear='clear && lsi ././'
function cdlsi (){
    /cd $@ && lsi ././
}
alias cd='cdlsi'Related projectsEmacs clientbyconao3
Package: lsj2319-gescape
# Garden EscapeGarden Escape was my final beginner project for Code in Place 2020.
It is a Gardening simulation game using tKinter for the GUI.
## How to Play
Use the mouse to move the gardener character.
Press spacebar or doubleclick to plant flowers or shrubs at the current location of the gardener.## Libraries
This game uses tkinter to render a GUI window so you can play the game.
PIL is also used to help with image handling.
Package: ls.joyful
AboutJoyful is aFullCalendarfront-end for theJoyousWagtail Calendar.InstallationFollow the instructions for installing ls.joyous with the following additions for ls.joyful.Install the package.$pipinstallls.joyfulAdd ls.joyful to your INSTALLED_APPS.INSTALLED_APPS=[...'ls.joyful','ls.joyous','wagtail.contrib.modeladmin',...]UsageAfter installing ls.joyful you will have the option of adding a
Full calendar page.  This is much the same as a regular Joyous Calendar
page, but uses the FullCalendar javascript library to display the events.DemoYet another demonstration Wagtail websitecode|liveGetting HelpPlease report bugs or ask questions using theIssue Tracker.
Package: ls.joyous
AboutJoyous is a reusable calendar application for Wagtail. Features include rrule
based recurring events with cancellations and postponements; iCal import and export; Gcal export; RSS feed; event permissioning; timezone handling; and multi-site aware calendars.DocumentationThe documentation for using and developing with Joyous can be found athttps://joyous.readthedocs.io.DemoYet another demonstration Wagtail websitecode|liveGetting HelpPlease report bugs or ask questions using theIssue Tracker.LanguagesJoyous has been translated into French, German, Brazilian-Portuguese, Italian,  Japanese and Swedish.
Translations into other languages would be welcome.Thanks ToContributorsThanks to everyone who hascontributedto Joyous.ToolsSauce Labsfor their cross-browser testing platform.
Package: ls_json
by default it installs as a console application into the users path.Usage::~/ls_json ## python setup.py install

~/ls_json ## ls_json
{
  ".git": {
    ".COMMIT_EDITMSG.swp": 4096,
    "COMMIT_EDITMSG": 282,
    "FETCH_HEAD": 100,
    "HEAD": 23,
    "config": 360,
    "description": 73,
    "index": 474,
    "info": {
      "exclude": 240
    },
    "logs": {
      "HEAD": 700,
      "refs": {
        "heads": {
          "master": 700
        },
        "remotes": {
          "origin": {
            "HEAD": 197,
            "master": 304
          }
        }
      }
    },
    "objects": {
      "1d": {
        "9bb6e4f5debac1c7342d437a22ba8e41c35a10": 794
      },
      "2a": {
        "5e29c1271823cc2791203936cb97103d039133": 10073
      }
    },
    "sourcetreeconfig": 1034
  },
  ".gitignore": 24,
  "LICENSE.txt": 38578,
  "README.md": 228,
  "ls_json.py": 1695,
  "setup.cfg": 41,
  "setup.py": 1445
}
Package: lsjtools
pytools介绍自己维护的python的第三方库软件架构软件架构说明安装教程xxxxxxxxxxxx使用说明xxxxxxxxxxxx参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.mdGitee 官方博客blog.gitee.com你可以https://gitee.com/explore这个地址来了解 Gitee 上的优秀开源项目GVP全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目Gitee 官方提供的使用手册https://gitee.com/helpGitee 封面人物是一档用来展示 Gitee 会员风采的栏目https://gitee.com/gitee-stars/
Package: lsl
This package contains a collection of tools for reading, format shifting, and analyzing LWA data.  It also contains the majority of the lwa_user library developed by NRL.  The lwa_user modules includes (and their respective names in the LSL package are):lwa_user.astro           (lsl.astro)lwa_user.catalog         (lsl.catalog)lwa_user.mathutil        (lsl.misc.mathutil)lwa_user.nec_util        (lsl.sim.nec_util)lwa_user.skymap          (lsl.skymap)lwa_user.transform       (lsl.transform)
Package: lsl-cli
lsl-cliCommand-line tools for theLab Streaming Layer.InstallationMethod 1: Using pippipinstalllsl-cliMethod 2: From sourcegitclonehttps://github.com/yop0/lsl-cli.gitcdlsl-cli
pipinstall.Enabling autocompletion (bash/zsh users)The package comes with autocompletion files for Bash and Zsh.
To enable autocompletion, use the following command to automatically source the autocompletion file (for Bash users, replace~/.zshrcwith~/.bashrc):lsl complete >> ~/.zshrcUsage$> lsl list                # List available stream outlets
$> lsl show {outlet}       # Show outlet data (in XML format)
$> lsl echo {outlet}       # Continuously print received data
$> lsl find                # Use options to specify outlet properties (e.g. --name, --channel_count, ...)
Package: lsl-xdf-reader
READMELab Streaming Layer (LSL) .xdf Data Reader and ParserThis is the updated version of the data reader compatible with various os directory structures.DescriptionThis Python script reads experiment data from .xdf files and writes the data to CSV files. It also generates a report for each experiment.DependenciesPython 3pyxdftkinterInstallationInstall Python 3 from the official website.Install the required Python packages using pip:pipinstallrequirements.txtUsageRun the script in a Python environment. The script will ask for a directory. Select the directory containing the XDF files. The script will read each XDF file, extract the experiment data, and write it to a CSV file. It will also generate a report for each experiment.LicenseThis project is licensed under the terms of the MIT license.
Package: lsm303d
lsm303d Accelerometer And CompassSuitable for measuring orientation and motion, the lsm303d has a 3-axis
accelerometer and compass.InstallingStable library from PyPi:Just runsudo pip install lsm303dLatest/development library from GitHub:git clonehttps://github.com/pimoroni/lsm303d-pythoncdlsm303d-pythonsudo ./install.sh0.0.5Require i2cdevice>=0.0.60.0.4Port to i2cdevice>=0.0.6 set/get API0.0.3BugFix: Fix for compatibility with i2cdevice>=0.0.60.0.2Bumped i2cdevice dependency to >=0.0.40.0.1Initial Release
Package: lsm303-python
lsm303d-pythonPython library for the LSM303D I2C accelerometer/magnetometerUsageInstall the Pythonsmbusmodule for your platform (sudo apt install python-smbusfor Debian or Ubuntu Linux)pip install lsm303-pythonsudo lsm303_testExample Codeimporttimeimportsmbusimportlsm303i2c_channel=1bus=smbus.SMBus(i2c_channel)# Will raise OSError if device is not connecteddevice=lsm303.LSM303(bus)whileTrue:# Returns x,y,z tuple with values in degrees/secondaccel_data=device.read_accel()# Returns x,y,z tuple with values in microteslamag_data=device.read_mag()print([round(v,2)forvinaccel_data],[round(v,2)forvinmag_data])time.sleep(0.1)
Package: lsm9ds1-rjg
lsm9ds1-rjgIntroductionThis is a device driver that enables a Raspberry Pi to access an LSM9DS1 IMU sensor.The LSM9DS1 combines a 3D accelerometer, 3D rate gyroscope and a 3D magnetometer. It's useful in robotics and other applications for measuring a robot's attitude and movement. The LSM9DS1 is made by ST Microelectronics. The datasheet is available fromtheir website.Low cost breakout boards are available from various vendors includingAdafruitandSparkfun.Driver FeaturesConnects with I2C or SPIPreconfigured for 230 Hz output data rateConverts accelerometer and gyro output to use the same axes as the magnetometer by flipping the direction of the x axis.Detects data ready in software or with an optional GPIO hardware interrupt.Provides raw, unscaled data. i.e. max acceleration is 32768 not 2g.ExtensibleOptionally enables high priority thread schedulingLimitationsNot a complete implementation of the sensor's API.ScalingThis driver returns raw unscaled data. This is a deliberate choice to make it easier
to scale the data with your application.Data ReadyIf you don't check for data ready and just read data then you will miss some samples.
Worse than that, in some cases you'll read part of one sample and part of the next.
This provides plausible looking but incorrect data. Youmustwait for data ready
before reading the sensor values. See the examples.ExamplesSimple ExampleUsing a hardware interrupt
Package: lsm-db-extras
Thread/Process safe shelves and other lam-db helpersInstallationpipinstalllsm-db-extrasUsage examplefromlsm_extrasimportShelf,LSMDict,LSMTreewithShelf("/tmp/test.ldb")asshelf:shelf["foo"]=TruewithShelf("/tmp/test.ldb")asshelf:print(shelf["foo"])withLSMDict("/tmp/test-dict.ldb")asstorage:storage[1]=TruewithLSMDict("/tmp/test-dict.ldb")asstorage:print(storage[1])withLSMTree("/tmp/test-tree.ldb")asstorage:withtree.transaction():foriinrange(10):tree['numbers',i]=i*2tree['strings',i]=str(i)print(list(tree.find('strings')))