Saravanan666/pypi-org huggingface dataset:
Package: treecount
Treecount PackageAboutEver get confused in counting the number of files in a set of directories? No Worries!treecountis a simple fork ofDirectory-treepython utility package that displays out the Tree Structure of a user-defined directory with their respective file count.Currently Available for All Platforms.InstallationRun the following command on your terminal to installtreecount:1 .  Installing the package usingpip:pipinstalltreecountORpip3installtreecount2 . Cloning the repository:git clone https://github.com/rushic24/treecount/
cd Directory-Tree
pip install -e .UsageArgumentsParametersDescriptiondir_pathRefers to the Directory Path of Operation. By default, refers to the Current Working Directory.string_repRefers to whether you just want the direct output or a string representation of the same.Run this script in order to print out the tree structure of a user-defined directory!# Importing Librariesfromtreecountimportdisplay_tree# Main Methodif__name__=='__main__':display_tree(directory_path)Here by default, thedirectory_pathis the current working directory (CWD) unless specified by the user.OutputForCurrent Working Directory[DEFAULT] [String Representation =False]>>>fromtreecountimportdisplay_tree>>>display_tree()$OperatingSystem:Windows$Path:C:\Personal\Work\Directory-Tree\Test\MainDirectory***************DirectoryTree***************MainDirectory/├──Directory1/│└──Directory2/│├──Directory3/││└──Directory4/││└──HelloWorld.txt│└──SayWorld.txt├──DirectoryA/│└──Hmm.txt├──directory-tree-print.cpp├──letseee.txt└──printTree.exeForUser Specified Directory[Argument] [String Representation =True]>>>fromtreecountimportdisplay_tree>>>stringRepresentation=display_tree('C:\Personal\Work\Directory-Tree\Test\Main Directory',string_rep=True)>>>print(stringRepresentation)$OperatingSystem:Windows$Path:C:\Personal\Work\Directory-Tree\Test\MainDirectory***************DirectoryTree***************MainDirectory/├──Directory1/│└──Directory2/│├──Directory3/││└──Directory4/││└──HelloWorld.txt│└──SayWorld.txt├──DirectoryA/│└──Hmm.txt├──directory-tree-print.cpp├──letseee.txt└──printTree.exeDevelopingtreecountTo installtreecount, along with the tools you need to develop and run tests, and execute the following in your virtualenv:$pipinstall-e.[dev]
Package: treed
TreeDVisual representation of the branch-and-cut tree of SCIP using spatial dissimilarities of LP solutions --Interactive ExampleTreeD uses SCIP via PySCIPOpt to visualize the Branch-and-Cut tree traversed during the solution of a mixed-integer linear program (MIP or MILP). The LP solutions at each node are projected into a 2-dimensional space using a range of different transformation algorithms. Per default, multi-dimensional scaling (mds) is used. Other methods and further options can be displayed by runningpython -m treed --help. The 2-dimensional data points are visualized in a 3D space with regards to their respective LP solution value. Nodes higher up represent LP solutions with a higher value. These nodes are usually found later in the search and show the progress in increasing the lower or dual bound to close the MIP gap.The color of the individual nodes is used to show different values like node age, LP iteration count, or LP condition number.The shape of the nodes depict whether this LP solution was created through branching (🔵) or through LP resolves (🔷) at the ssame node, e.g. including cutting planes or added conflicts.There is also a more traditional mode that ignores the spatial dimension and draws an abstract tree. This is activated via the methoddraw2d()or the command line flag--classic.Installationpython -m pip install treedUsagerunpython -m treed --helpto get usage information or use this code snippet in a Jupyter notebook:from treed import TreeD

treed = TreeD(
    probpath="model.mps",
    nodelimit=2000,
    showcuts=True
)

treed.solve()
fig = treed.draw()
fig.show(renderer='notebook')There is also a (faster) 2D mode that skips the projection of LP solutions and generates a more traditional tree instead:...
fig = treed.draw2d()
fig.show(renderer='notebook')DependenciesPySCIPOptto solve the instance and generate the necessary tree dataPlotlyto draw the 3D visualizationpandasto organize the collected datasklearnfor multi-dimensional scalingpysalto compute statistics based on spatial (dis)similarity; this is optionalExport toAmirarunAmiraTreeD.pyto get usage information.AmiraTreeD.pygenerates the '.am' data files to be loaded by Amira software to draw the tree using LineRaycast.SettingsDataTree.am: SpatialGraph data file with tree nodes and edges.LineRaycast: Module to display the SpatialGraph. Note that is needed to set the colormap according to py code output (For instance 'Color map from 1 to 70' in this picture).DataOpt.am: SpatialGraph data file with optimun value.Opt Plane: Display the optimal value as a plane.Preview
Package: treedecomp
TreeDecompTreeDecomp is a simple python package for storing and manipulating tree decompositions.
It was originally written as part ofinfrared, a generic C++/Python hybrid library for efficient (fixed-parameter tractable) Boltzmann sampling.
By default, TreeDecomp runstreewidth_min_fill_infromnetworkx$20$ timeson randomized inputand returns the one with the smallest treewidth astreedecomp.TreeDecomposition.Simple UsageThe classTreeDecompositionFactorycreates a tree decomposition for a given hypergraph. The result is stored inTreeDecomposition.
A hypergraph is a pair of integer and a list of lists, where the former one is the number of nodes and the later one is the list of hyperedges.>>>importtreedecompastd>>>nb_nodes=6>>>hyperedges=[[0,1,5],[2,4],[2,5],[3,4,5]]>>>tree=treedecomp.TreeDecompositionFactory().create(nb_nodes,hyperedges)>>>tree.get_bags()[[4,5,3],[4,5,2],[5,1],[5,0,1]]>>>tree.get_edges()[(0,1),(0,2),(2,3)]>>>tree.toposorted_bag_indices()[0,2,3,1]>>>withopen('tree.dot','w')asoutput:...td.writeTD(output)Future WorkOne can notice TreeDecomp offers two other tree decompoistion factoriesHTDTreeDecompositionFactoryandTDLibTreeDecompositionFactory.
These two are for supporting tree decompoitions usinglibhtd(via python wrapper) or calling TDlib (written in java) that will be included in the future
Package: treedensitycalculator
TheTree Density Calculatoris a QGIS plugin and command line interface package designed to calculate tree densities
based on brightness images, using the local maximum of a sliding window.The original Tree Density Calculator was written in C++ and ported to PyQGIS in 2018/2019. It has been developed in an
open source environment to encourage further development of the tool.Theinstruction pagescan be found at <https://treedensitycalculator.readthedocs.io>.The coderepositorycan be found athttps://bitbucket.org/kul-reseco/localmaxfilter.PLEASE GIVE US CREDITWhen using the Tree Density Calculator, please use the following citation:Crabbé, A. H., Cahy, T., Somers, B., Verbeke, L.P., Van Coillie, F. (2020). Tree Density Calculator Software(Version x.x) [Software]. Available from https://bitbucket.org/kul-reseco/localmaxfilter.ACKNOWLEDGEMENTSThe software and user guide are based on the Local Maximum Filter [C++ software]:
Ghent University, Laboratory of Forest Management and Spatial Information Techniques, Lieven P.C. VerbekeThe revision into a QGIS plugin is funded primarily through BELSPO (the Belgian Science Policy Office) in the framework
of the STEREO III Programme – Project LUMOS - SR/01/321.The LUMOS logo was created for free athttps://logomakr.com.SOFTWARE LICENSEThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public
License as published by the Free Software Foundation, either version 3 of the License, or any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.You should have received a copy of the GNU General Public License (COPYING.txt). If not see www.gnu.org/licenses.
Package: treediffer
treedifferA library of utility functions for computing diffs between tree-like data structures.Installationpip install treedifferUsage exampleGiven studio tree JSON-archive versions of themaintreeand thestagingtreeof a Studio channel generated using./contentcuration/manage.py archivechannel ...,
you can compute the tree diff between them using:>>>fromtreedifferimporttreediff>>>diff=treediff(maintree,stagingtree,preset="studio",format="simplified")>>>diff{"nodes_deleted":[...],# content_ids gone in stagingtree"nodes_added":[...],# new content_ids in stagingtree"nodes_moved":[...],# same content_id but different node_id"nodes_modified":[...],# same node_id, but changes in node attributes}This code examples uses the "high level" API based on the studio preset that set
all the necessary lookups (node_id, content_id, parent, assetment_items, etc.)
so the diffing logic will work. Seeexamples/studiodiffferpoc.pyfor full script.Ricecooker tree diffingSeeexamples/ricecookerdiffpoc.pyfor similar script that diffs ricecooker trees
that get saved inchefdata/trees/dir after each content integration script runs.Alternative diff formatsUseformat="restructured"to post-process the simplified diff and group additions
and deletions into logical subtrees (e.g. if whole topic is deleted, show as subtree).
This is the default when printing on command line as in the example scripts.Useformat="raw"to see the diff before simplification and move detection (for debugging).Testspytestandpytest --cov=src/treediffer tests/DocumentationVisithttps://treediffer.readthedocs.io/for more info about how this works
In particular seehttps://treediffer.readthedocs.io/en/latest/diff_formats.htmlfor the the details about the structure of the diffs produced andhttps://treediffer.readthedocs.io/en/latest/design.htmlfor API advanced usage.TODOsFinish basic functionalityadd example ricecooker differadd example studio differAdd kitchen sink test for combined deleted, added, moved, and modified
Package: treedist
treedist: command line program for quantifying differences between phylogenetic treesThe command-line programtreedisttakes as input two or more treefiles and computes various measures of the "distance" between pairs of trees. The program can also output information about how many and what bipartitions differ between trees.AvailabilityThetreedistsource code is available on GitHub:https://github.com/agormp/treedist. The executable can be installed from PyPI:https://pypi.org/project/treedist/Installationpython3 -m pip install treedistDependenciestreedistrelies on thephylotreelib library, which is automatically included when using pip to install.OverviewInput:Two or more files containing phylogenetic trees in NEXUS or Newick formatTrees do not need to have identical leaves, but each pair of trees need to share at least 4 leaves (all leaves that are not shared will be automatically pruned before computing tree distances).Output:Different measures of treesimilarity (Robinson-Foulds symmetric distance, normalised RF, normalised similarity)Summaries of what bipartitions that differ or are shared between treesUsageUsage: treedist [options] TREEFILE1 TREEFILE2 [TREEFILE3 ...]

Options:
  --version             show program's version number and exit
  -h, --help            show this help message and exit
  -I FORM, --informat=FORM
                        format of tree files: NEXUS or newick [default:
                        newick]
  -d, --symnormdist     print normalized symmetric distance
  -s, --symdist         print symmetric distance
  -b, --bipnumbers      print no. total, shared, and unique bipartitions for
                        each tree
  -u, --uniquelist      print list of unique bipartitions for each tree
  -c, --commonlist      print list of bipartitions trees have in common
  -l, --labels          also print branch labels for bipartitions (requires -c
                        or -u)
  -o, --oneline         oneline output of normalized symmetric similarities
  -n, --namesummary     Print names of leaves that are shared between trees,
                        and leaves that are discarded
  --collapselen=BRLEN[,BRLEN,...]
                        Collapse branches shorter than this (comma-separated
                        list: individual cutoffs for trees)
  --collapsefrac=FRAC   Collapse branches shorter than FRAC fraction of
                        treeheight (of minimum variance rooted tree)
  --collapsesupport=SUPPORT
                        Collapse branches with less clade support than this
                        (must be number in [0,1])Usage examplesTBD
Package: treedoc
Minimalistic Python documentation for dendrophiles.treedoc prints minimalistic Python documentation in a tree structure,
aiming to hit the sweet spot between detailed information about
a single object and superficial information about object attributes.treedoc is great for:Getting an overview of a package without endlessly scrolling through a website.Searching through documentation.Printing essential information about different Python objects.InstallationInstall fromPython Package Index(PyPI) usingpip install treedoc.Usagetreedoc can be used directly through the command-line interface,
or interactively in a Python shell or Jupyter notebook. For more information
about arguments, seetreedoc --helporhelp(treedoc).Command-line interfacePython shell and notebookstreedoc can be imported and used just like any other package, e.g. in an
interactive Python interpreter session or a Jupyter notebook.InterpreterJupyter notebookContributingContributions are welcome.
If you wish to work on a problem, please create apull requestto get feedback.
We aim for:Zero dependencies, but dependencies for testing are ok.Idiomatic, clean Python code. Readability matters.Thorough testing and code formatting, see.travis.ymlfor commands run by continuous integration.
Package: treeds
treedsEasy to use Tree Data Structure with many useful methods.Developed by Tatwik.Examples of using treedsWe will consider this image as an example for the below code.Creating a TreeAdding the root node at initialization.fromtreedsimportTreetree=Tree(root_nodes=['a'])We can have multiple root nodes, Just by passing multiple nodes to the parameter 'root_nodes'Adding the root node after initialization.fromtreedsimportTreetree=Tree()tree.add_node(('nil','a'))Here the parent node of the root node 'a' is 'nil'.You can modify the 3rd line of the above code astree.add_children('nil',['a'])The above function helps to add multiple root nodes instead of one root node 'a'.Adding the remaining nodes to the tree.# level-1tree.add_children('a',['b','c','d'])# level-2tree.add_children('b',['e','f'])tree.add_children('d',['g','h'])# level-3tree.add_children('h',['i','j'])Now we have created a Tree that's in the above image.Let's print the Tree we have created.print(tree.tree)Output:{'nil': (None, ['a']), 'a': ('nil', ['b', 'c', 'd']), 'b': ('a', ['e', 'f']), 'c': ('a', []), 'd': ('a', ['g', 'h']), 'e': ('b', []), 'f': ('b', []), 'g': ('d', []), 'h': ('d', ['i', 'j']), 'i': ('h', []), 'j': ('h', [])}Let's check the above Output dictionary.Dict Keys - Node namesDict Values - (parent node, [child nodes])Let's see an example:
Consider: 'a': ('nil', ['b', 'c', 'd'])Here 'a' is the node name.'nil' is the parent node of 'a'.['b', 'c', 'd'] are the children nodes of node 'a'.Using the methods of Tree classadd_node()Adds the node to the tree.parameter node: Tuple with parent and child.tree.add_node(('parent_node','node_name'))add_children()Adds multiple children to the node.parameter node: The name of the node.parameter children: List of children of the node.tree.add_children('node_name',['child_node1','child_node2','child_node3'])get_parent()Returns the parent of the given node.parameter node: The name of the node.returns: The parent of the given node.tree.get_parent('b')Output: 'a'get_children()Returns the list of childeren of the given node.parameter node: The name of the node.returns: The list of children for the node.tree.get_children('a')Output: ['b', 'c', 'd']get_path()Returns The path from root node to the given node.parameter node: The name of the node.return: The list of path from root node to the given node.tree.get_path('i')Output: ['a', 'd', 'h', 'i']get_depth()Returns the depth at where the node is located in the tree.parameter node: The name of the node.return: The depth at which the node is located in the tree.tree.get_depth('g')Output: 2delete()Deletes the node and the tree to the down of the node.parameter node: The name of the node.tree.delete('d')Because we deleted node 'd' all the nodes below it will also get deleted so the nodes ['d', 'g', 'h', 'i', 'j'] will be deleted.Mail me
Package: treefeeder
treefeederAbouttreefeederis a command-line interface (CLI) tool designed to transform a folder of files, such as code files, into a structured string. This string includes both the folder structure (in a Unix tree structure format) and the contents of the files. It's an ideal tool for feeding data into ChatGPT or other large language model (LLM) AI assistants for fine-tuning or query answering.InstallationTo installtreefeeder, simply run:pipinstalltreefeederUsageCLIYou can use treefeeder from the command line by passing the directory path along with optional arguments for customization:treefeeder<directory_path>[-Iignore_patterns][-Pmatch_patterns][-Sseparator_token]<directory_path>: The path to the directory you want to process.-I ignore_patterns: Patterns to ignore files or directories.-P match_patterns: Patterns to match specific files.-S separator_token: The separator token used in output (default is [SEP]).Python APItreefeeder can also be used within Python. To get a string output of your folder structure and file contents, use:fromtreefeederimportget_outputoutput=get_output(directory_path,ignore_patterns=None,match_patterns=None,separator_token='[SEP]')Exampletreefeeder/path/to/my_project-I*.log-P*.py-S"[SEP]"This command will process the 'my_project' directory, ignore .log files, match .py files, and use [SEP] as the separator token.ContributingContributions are welcome! Please feel free to submit pull requests, suggest features, or report bugs.Licensetreefeeder is open-source and available under MIT License.
Package: treefit
Treefit for Python - The first software for quantitative trajectory inferenceThis is an implementation ofTreefitin Python.Treefitis a novel data analysis toolkit that helps you perform
two types of analysis: 1) checking the goodness-of-fit of tree models
to your single-cell gene expression data; and 2) deciding which tree
best fits your data.InstallpipinstalltreefitUsageThe main functions aretreefit.treefit()andtreefit.plot():fit=treefit.treefit(YOUR_SINGLE_CELL_GENE_EXPRESSION_DATA)treefit.plot(fit)Seehttps://hayamizu-lab.github.io/treefit-python/for details.
Package: tree-format
Python library to generate nicely formatted trees, like the UNIX `tree`command.## ExampleProduce output like this:```foo├── bar│   ├── a│   └── b├── baz└── qux└── c⏎d```using code like this:```pythonfrom operator import itemgetterfrom tree_format import format_treetree = ('foo', [('bar', [('a', []),('b', []),]),('baz', []),('qux', [('c\nd', []),]),],)print format_tree(tree, format_node=itemgetter(0), get_children=itemgetter(1))```## LicenseThis is made available under the Apache Software License, version 2.0.Copyright (c) 2015 - Jonathan M. Lange## TestingRun tests with:```python -m testtools.run discover```
Package: treefuse
TreeFuseTreeFuse is a library for building FUSE filesystem CLIs from treelib Tree
objects.It wraps python-fuse to provide a CLI entrypoint (treefuse_main) which takes
atreeparameter and uses that to construct a directory tree and generate
file content within the FUSE filesystem.Free software: GNU General Public License v3Documentation:https://treefuse.readthedocs.io.Example ProgramExecuting this program:import treelib
from treefuse import treefuse_main

tree = treelib.Tree()
root = tree.create_node("root")
dir1 = tree.create_node("dir1", parent=root)
tree.create_node("dirchild", parent=dir1, data=b"dirchild content\n")
tree.create_node("rootchild", parent=root, data=b"rootchild content\n")

treefuse_main(tree)With a target directory (e.g.python3 example.py mnt) will mount a
filesystem matching the given tree:$ tree mnt
mnt
├── dir1
│   └── dirchild
└── rootchild

1 directory, 2 files

$ cat mnt/rootchild
rootchild content

$ cat mnt/dir1/dirchild
dirchild contentSeeExamplesfor more examples.RoadmapAbstract the interface so that sources other thantreelibcan be
implementedProvide a mechanism for library consumers to populate filesystem contents
asynchronouslyCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.This library was written during a hack week at my employer, DigitalOcean.History1.1.2 (2021-07-30)Fix up one missed 3.6 reference.1.1.1 (2021-07-30)Make drop of support for Python 3.6 official.1.1.0 (2021-07-30)Substantial refactor to introduce the internal infrastructure for non-treelibTreeFuseProviderclasses.1.0.0 (2021-07-28)Add documentation.Implement support for providing non-default stat values for nodes (viatreefuse.TreeFuseStat).Replace temporary ENOENT error returns with more appropriate codes.Fix default usage string.0.1.0 (2021-07-27)First release on PyPI.
Package: treegenerator
treegeneratorGenerate a recursive tree diagram given a starting directory or a list of URIs.Install with:pip install treegeneratorContains two classes: TreeGenerator and UrlTreeGenerator.TreeGenerator takes a local file path and recursively generates a tree representation of all subdirectories.UrlTreeGenerator takes a list of urls and generates a tree representation of them.Usage:from treegenerator import TreeGenerator
from pathlib import Path
generator = TreeGenerator(Path.cwd())
print(generator)producestreeGenerator
|  |-dist
|  |  |-treegenerator-0.0.1-py3-none-any.whl
|  |  |_treegenerator-0.0.1.tar.gz
|  |
|  |-LICENSE.txt
|  |-README.md
|  |-pyproject.toml
|  |-src
|  |  |-treeGenerator
|  |  |  |-__init__.py
|  |  |  |_treeGenerator.pyThe dictionary representing the tree can be accessed through the 'tree' member.import json
print(json.dumps(generator.tree, indent=1))produces{
 "treeGenerator": {
  "dist": {
   "treegenerator-0.0.1-py3-none-any.whl": {},
   "treegenerator-0.0.1.tar.gz": {}
  },
  "LICENSE.txt": {},
  "README.md": {},
  "pyproject.toml": {},
  "src": {
   "treeGenerator": {
    "__init__.py": {},
    "treeGenerator.py": {}
   }
  }
 }
}
Package: treegp
Overviewtreegpis a python gaussian process code that perform 1D and 2D interpolation.treegphas some special features compared to other available Gaussian Processes codes:Hyperparameters estimation will scale in O(N log(N)) with the the 2-points correlation function estimation compared to O(N^3) with the classical maximum likelihood.Gaussian process interpolation can be performed around a mean functionA tool is provided to compute the mean function (meanify)treegpwas originally developed for Point Spread Function interpolation withinPiff. There is a specific article that describes the math used intreegpin the context of modelling astrometric shifts of the Subaru Telescope due to atmospheric turbulences. This article can be foundhere.InstallationThe easiest way to install is usually:pip install treegpwhich will install the latest released version.If you would instead like to install the development version, you can do so via:git clone https://github.com/PFLeget/treegp.git
cd treegp/
python setup.py installDependenciestreegphas for now the following dependencies (see the quick
installs below):libraries listed in therequirementsfilePythontreegpis regularly tested on Python 3.7, 3.8, 3.9 and 3.10.  It may work in other
versions of Python (e.g. pypy), but these are not currently supported.