Saravanan666/pypi-org huggingface dataset:
Package: dimerizer
# dimerizer
—
This is a tool to convert a standard Gromacs input to a dimerized one.
[Gromacs](http://www.gromacs.org/) is a popular tool for proteins simulation.
This script allows to run Gromacs simulations with [Dimer Metadynamics](http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00691),
a replica exchange method recently introduced to enhance the sampling of a probability distribution.
This scripts takes a standard Gromacs input, that consists of:
* A simulation settings file (.mdp)
* An initial configuration file (.pdb)
* A topology file (.top)
* A forcefield folder, here we have tested charmm22* and charmm36 but any charmm forcefieldshouldwork.The result are modified files for the dimer replicas. See the examples folder for a quickstart, and
the doc folder for the inner workings of the script. A paper will be shortly published to explain in details
how this is done and show some benchmark and new tests of Dimer Metadynamics.This is a python2.7 code, to install it remember to use the correct pip version (i.e. pip2.7).# Auxiliary scriptsThere are also two support scripts that help setting up and controlling
the simulation.## plreadplread scans through plumed.*.dat files in search for the keyword given as
argument and then allows to edit the value in each of the file with a single command.## tune_replicastune_replicas is a script that given the parameters of the first replica will create a number
of replicas with parameters so that the exchange probabilities are close to the target value.
Package: dimetadata-api
metadata_apiPython package using the public SAP Data IntelligenceMetadata Management on SAP API BusinessHubforcatalog.pydownloading catalog hierarchiesuploading catalog hierarchies and catalog tagstagging datasetscontainer.pyGet container information (datassources)Get dataset metadata of containers/datasourcesFor exchanging glossaries an unsupported API is usedglossaries.pydownloading glossariesuploading glossariesThis is not an officially supported package by SAP but used only demo purposes. You are free to use it under the Apache-2.0 license or as source of ideas for your own project.
Package: dime-xai
DIME (Dual Interpretable Model-agnostic Explanations) is mainly aimed at Explaining DIET Classifiers in RASA 2.8.X. Models.Features 🦄Explains RASA DIET Classifiers using feature importanceGenerates dual feature importance scores (Global FI + Local FI)EfficientTotal confidence drop as the feature importance scoreAble to explain both local and REST Rasa modelsEasy to use DIME CLIGUI with a dedicated server on-demandGenerate, Store, Download, Upload, Peak DIME explanations. Read more ondocsSupports Sinhalese unicode / fully Sinhala-English code-switchableWhat's Cooking? 🍪DIME for Jupyter NotebooksStopwords List GenerationDIME Example NotebooksDIME for non-DIET text classification modelsLimitations and Known Issues 🤏🏽Global Importance is disabled for REST models due to performance bottlenecksExplaining RASA models locally on Notebooks such as CoLab is not supported yet due to dependency issuesBenchmark tests are in progress📒 Docs:https://dime-xai.github.io📦 PyPi:https://pypi.org/project/dime-xai/1.2.1/🪵 Full Changelog:https://github.com/DIME-XAI/dime-xai/blob/main/CHANGELOG.md
Package: dimgx
Copyright and other protections apply.
Please see the accompanyingLICENSEandCREDITSfile(s) for rights and restrictions governing use of this software.
All rights not expressly waived or licensed are reserved.
If those files are missing or appear to be modified from their originals, then please contact the author before viewing or using this software in any capacity.Curious about integrating your project with the above services?
Jeff Knupp (@jeffknupp)describes how.dimgx- Docker IMaGe layer eXtractor (and flattener)dimgxextracts and flattensDockerimagelayers.
It is licensed under theMIT License.
Source code isavailable on GitHub.
Seethe docsfor more information.Examples%dimgx-husage:
dimgx [options] [-l LAYER_SPEC] ... [-t PATH] IMAGE_SPEC
dimgx -h # for help

Docker IMaGe layer eXtractor (and flattener)
...%dimgxnifty-box# show layers for "nifty-box[:latest]"REPO TAG                IMAGE ID        PARENT ID       CREATED         LAYER SIZE      VIRTUAL SIZE
nifty-box               6667bbd4093c    82e5dcafc08c    18 hours ago    18.8 MB         144.0 MB
-                       82e5dcafc08c    cd5e80677a53    18 hours ago    1.8 kB          125.2 MB
-                       cd5e80677a53    df2a0347c9d0    18 hours ago    0 Bytes         125.2 MB
debian:jessie           df2a0347c9d0    39bb80489af7    21 hours ago    0 Bytes         125.2 MB
-                       39bb80489af7    -               21 hours ago    125.2 MB        125.2 MB%dimgx-ldf2a:82efnifty-box# show only the second through fourth layersIMAGE TAG               IMAGE ID        PARENT ID       CREATED         LAYER SIZE      VIRTUAL SIZE
-                       82e5dcafc08c    cd5e80677a53    18 hours ago    1.8 kB          125.2 MB
-                       cd5e80677a53    df2a0347c9d0    18 hours ago    0 Bytes         125.2 MB
debian:jessie           df2a0347c9d0    39bb80489af7    21 hours ago    0 Bytes         125.2 MB%dimgx-lcd5e:6667-tnifty.tarnifty-box# extract the third through fifth layers%du-hsnifty.tar18M    nifty.tarIssuesdimgxdoes what I want, so I’m just maintaining it at this point.
If you find a bug, or want a feature, pleasefile an issue(if it hasn’t already been filed).
If you’re willing and able, considercontributing.
Package: dimit
py-dimitDimension of a quantity in Python language.Installation$pipinstalldimit# or$python-mpipinstalldimitUsagefromdimitimportDimension,L,M,T,DIMLESS# L is a preset dimension for lengthDimension('L')==L# Output: True# Build a dimension of velocity (LT^-1).Velocity=L/T# or L * T**-1# orVelocity=Dimension('LT-1')# Output: Dimension(LT-1)# Only support operator *, / amd **.L*L*L==L**3# Output: True# Compare two dimensionsDimension('LMT-2')==Dimension('MLT-2')# Output: True
Package: dimits
Dimits - Python Bindings for Piper TTSDimits is a Python library that provides an easy-to-use interface to the Piper text-to-speech (TTS) system. It utilizes the powerful Piper TTS engine, which is optimized for Raspberry Pi 4, to generate high-quality synthesized speech.FeaturesSimple Python bindings for Piper TTSSupport for multiple languages and voicesCompatible with Raspberry Pi 3/4 and desktop Linux systemsInstallation 📥You can install Dimits via pip:pipinstalldimitsThis will automatically install the necessary dependenciesQuick Start 🏃🏻‍♀️Here's a simple example of using Dimits to synthesize speech:fromdimitsimportDimits# Initialize Dimits with the desired voice modeldt=Dimits("voice-en-us-amy-low")# Convert text to audio and play it using the aplay enginedt.text_2_speech("Hello World",engine="aplay")Voices 🔊Dimits supports all the voices available in the Piper TTS system. To use a specific voice, simply provide  corresponding.onnxfile namepo initializing theDimitsclass.For a list of available voices and their download links, refer to thePiper TTS repository.Usage 📃Initializing DimitsTo use Dimits, first create an instance of theDimitsclass, providing the path to the desired voice model:fromdimitsimportDimitsdt=Dimits("voice-en-us-amy-low")Synthesizing SpeechTo synthesize speech and play on the go, simply call thetext_2_speechmethod, providing the text to be synthesized and the desired engine:dt.text_2_speech("This is a test.",engine='aplay')on other hand to synthesize speech and save it to the file, calltext_2_audio_filefinction providingfile_namedirandformatdt.text_2_audio_file("Hello World","hello_world","/path/to/output/directory/",format="wav")Changing VoicesTo change the voice used for synthesis, create a new instance of theDimitsclass with the desired voice model:# dt = Dimits("voice-en-us-amy-low")dt=Dimits("voice-en-us-danny-low")TODO 📝Implement windows compatible executible to run the voice modelsSupport for multiple audio player engineBenchmarkDocumentationLicense 🪪This project is licensed under theMIT License.AcknowledgementsDimits is based on the work of thePiperproject byRhasspy, and is made possible by the contributions of its developers and the open source community. Without their hard work and dedication, this project would not be possible.
Package: dimjournal
DimjournalDimjournal is a Midjourney backup tool. It automatically downloads the metadata archive and the upscaled images from Midjourney into a local archive (folder tree). It also embeds some basic metadata (like the prompt) into the PNG files.Note: the terms of use of Midjourney disallow any automation.Dimjournal is a Python tool uses the Selenium WebDriver to log into the Midjourney website, fetch user data, and download job information and images.Changelogv1.0.8: Fixesv1.0.3: Tested on macOS in July 2023InstallationStable version:pip install dimjournalDevelopment version:python3 -m pip install git+https://github.com/twardoch/dimjournalUsageCommand Line Interface (CLI)In Terminal, run:dimjournalDimjournal will open a browser where you need to log into MidJourney. The tool will create the backup folder, which by default is themidjourney/dimjournalsubfolder inside yourPictures/My Picturesfolder. It will operate the browser, download all metadata (up to 2,500 last upscale jobs, and up to 2,500 jobs), and save it in JSON files in the backup folder. Then it will use the browser to download all upscales that are not in the backup folder. If you run the tool again, it will only download new metadata, and new images.To specify a different backup folder, use:python3-mdimjournal--archive_folder/path/to/your/archive/folderPythonYou can also use Dimjournal in your Python scripts. Here is an example of how to import and use thedownloadfunction:fromdimjournalimportdownload# Specify the directory where you want to store the dataarchive_folder="/path/to/your/archive/folder"# Download the datadownload(archive_folder)LicenseLicensed under theApache-2.0 LicenseWritten with assistance from ChatGPT
Package: dimlpfidex
dimlpfidexDiscretized Interpretable Multi Layer Perceptron (DIMLP) and related algorithmsContributionInstall C++ toolchainLinux, macOS, Windows/WSLInstall with your package manager:a C++ compiler (gcc/g++)cmakeWindowsInstallMinGW-W64with theonline installer(64 bit, win32, ucrt)InstallCMake:wingetinstallKitware.CMakeEnsurecmake.exeis accessible in the$PATHenvironment variable.cmake.exe--versionInstall Python and PoetryLinux, macOS, Windows/WSLUse your package manager to install:pythonpoetryWindowsInstallPythonwingetinstallPython.Python.3.10Installpoetryand add it to your PATH.Ensurepython.exeandpoetry.exeare accessible in the$PATHenvironment variable.To check the installation, check these commands return an output:python.exe--version
poetry.exe--versionCompileActivate the virtual environment:poetryshellInstall python dependencies and compile:poetryinstall
poetrybuildIf CMake complains about not finding pybind11, ensure to activate the shell first.Note: If you still have an issue on Windows, try the following commands to
build manually:mkdirbuild&&cdbuild$path=(poetryenvinfo|Select-String-Pattern'Path:\s+(.*)').Matches.Groups[1].Value
cmake.exe-G"MinGW Makefiles"-DCMAKE_PREFIX_PATH="$path"..
cmake.exe--build.Install Pre-commit hooksGit hooks are used to ensure quality checks are run by all developers every time
before a commit.poetryshell
pre-commitinstallPre-commit hooks can be run manually with:pre-commitrun--all-filesReleaseTo publish the package onPyPI, refer toRELEASE.
Package: dimod
dimoddimodis a shared API for samplers. It provides:classes for quadratic models—such as the binary quadratic model (BQM) class that
contains Ising and QUBO models used by samplers such as the D-Wave system—and
higher-order (non-quadratic) models.reference examples of samplers and composed samplers.abstract base classesfor
constructing new samplers and composed samplers.(For explanations of the terminology, see theOcean glossary.)Example Usage>>> import dimod
...
>>> # Construct a problem
>>> bqm = dimod.BinaryQuadraticModel({0: -1, 1: 1}, {(0, 1): 2}, 0.0, dimod.BINARY)
...
>>> # Use dimod's brute force solver to solve the problem
>>> sampleset = dimod.ExactSolver().sample(bqm)
>>> print(sampleset)
   0  1 energy num_oc.
1  1  0   -1.0       1
0  0  0    0.0       1
3  0  1    1.0       1
2  1  1    2.0       1
['BINARY', 4 rows, 4 samples, 2 variables]See thedocumentationfor more examples.InstallationInstallation fromPyPI:pipinstalldimodLicenseReleased under the Apache License 2.0. See LICENSE file.ContributingOcean’scontributing guidehas guidelines for contributing to Ocean packages.dimod includes some formatting customization in the.clang-formatandsetup.cfgfiles.Release Notesdimod makes use ofrenoto manage its
release notes.When making a contribution to dimod that will affect users, create a new
release note file by runningrenonewyour-short-descriptor-hereYou can then edit the file created underreleasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.See reno’suser guidefor details.
Package: dimod-cache
dimod-cacheis a hoarding proxy framework fordimod.Sampler-objects.Example use:import dimod
import dwave.samplers

qubo = dimod.generators.gnp_random_bqm(10, .5, 'BINARY').to_qubo()[0]

sampler = HoardingSampler(dwave.samplers.SimulatedAnnealingSampler(), replay=True)
res_1 = sampler.sample_qubo(qubo, num_reads=10)

sampler = HoardingSampler(dwave.samplers.SimulatedAnnealingSampler(), replay=True)
res_2 = sampler.sample_qubo(qubo, num_reads=10)

assert res_1==res_2
Package: dimodmock
dimod-mockdimod-mock is a package for mocking dimod-based samplers, including the structured ones.
It lets you easily test your routines before trying them out on sophisticated samplers or real hardware.InstallationTo install runpip install dimodmockUsageMocking simple samplersSuppose you created some instance of your sampler (derived fromdimod.Sampler). This is how you create its mockimportdimodmocksampler=...# Create your sampler heremock=dimodmock.SamplerMock.from_sampler(sampler)The mock object created this way has the following properties identical to the source samplerpropertiesparametersThe following methods are implemented:samplesample_qubosample_isingand have the same interface as the source sampler. In particular, call to those methods will fail
if unknown parameters are passed as keyword arguments. The returned samples are choosen randomly at uniform.
By default, the returned samplesets have one sample. See below for overriding this behaviour.Mocking structured samplersYou can also mock structured samplers. In this case usedimodmock.StructuredMock.from_sampler.
In addition to the characteristics described above, theStructuredMockhas also the following properties, identical
to the source sampler:edgelistnodelistAlso,sample,sample_qubo,sample_isingmethods respect the samplers structure, so trying to solve
mismatching instance will fail.Support fornum_readsBy default, samplesets returned by all mocks contain only one sample. However, if the source sampler supportsnum_readsproperty (which is true in particular forDWaveSampler), it will be respected by the mock and the correct number
of samples will be returned.Advanced usageIt is also possible to create mocks by specifying their properties in their initializer. ForSimpleMockyou need to specifypropertiesandparameters. ForStructuredMockyou also need to specifyedgelistandnodelist.For instance, suppose you want to create a mock of a structured sampler defined on full graph of 3 nodes,
having amax_num_readsproperty andnum_readsparameter. Here is how to do it:fromdimodmockimportStructuredSamplermock=StructuredSampler(nodelist=[0,1,2],edgelist=[(0,1),(1,2),(2,0)],properties={"max_num_reads":10},parameters={"num_reads":["max_num_reads"]},)CaveatsCurrently mocks don't validate keyword arguments passed tosample*methods.
So, in the example above, mock will happily acceptnum_reads=100. In the future, additional validation might be
implemented.Reporting issuesPlease report any issues you encounter using Github's issue tracker.
Package: dimorphite-dl
Dimorphite-DLWhat is it?Dimorphite-DL adds hydrogen atoms to molecular representations, as appropriate
for a user-specified pH range. It is a fast, accurate, accessible, and modular
open-source program for enumerating small-molecule ionization states.This version is a class object that is representative of dimorhitedl.CitationIf you use Dimorphite-DL in your research, please cite:Ropp PJ, Kaminsky JC, Yablonski S, Durrant JD (2019) Dimorphite-DL: An
open-source program for enumerating the ionization states of drug-like small
molecules. J Cheminform 11:14. doi:10.1186/s13321-019-0336-9.LicensingDimorphite-DL is released under the Apache 2.0 license. See LICENCE.txt for
details.InstallationEditThis edit is made by Sulstice for distribution and installation.pipinstalldimorphite_dlQuickStartEditThis edit is made by Sulstice for distribution and installation.fromdimorphite_dlimportDimorphiteDLdimorphite_dl=DimorphiteDL(min_ph=4.5,max_ph=8.0,max_variants=128,label_states=False,pka_precision=1.0)print(dimorphite_dl.protonate('CC(=O)O'))>>>['CC(=O)[O-]']CaveatsDimorphite-DL deprotonates indoles and pyrroles around pH 14.5. But these
substructures can also be protonated around pH -3.5. Dimorphite does not
perform the protonation.Authors and ContactsSee theCONTRIBUTORS.mdfile for a full list of contributors. Please contact
Jacob Durrant (durrantj@pitt.edu) with any questions.
Package: dimppy
Data Structure implementation in PythonA Python package of data structure implementationsUsageThis package contains ready to use Data Structure implementations like Stack ,Queue using both arrays and linked lists, Trees ,Graphs etc. The primary focus is provide pre-coded functions that can be used on the go for various coding problems.Note:This package is under development.All the files and classes are in title case (eg: TitleCase) and all the methods and functions are in camel case (eg: camelCase)pip install dimppyContents1.Stack2.Queue3.LinkedListSingly LinkedListDoubly LinkedListCircular LinkedList4.Trees5.Graphs6.HashMap7.Heap8.SearchingBinarySearchLinearSearch9.SortingBubbleSortBucketSortCountSortHeapSortInsertionSortMergeSortQuickSortRadixSortSelectionSOrtShellSortChange Logs1.0.0.0 (30/06/2022)Code Check0.0.0.6 (22/06/2021)Fixed bugsImproved codeRepository link:GitHub
Package: dimpy
dimpy package for python for creating N-dimensional list array in a user friendly mannerInstall command:pip install dimpyUse example:import dimpy as dp  # Import package as an object dp
testarray=dp.dim(5,7,9)  # This will create a 3 dimensional array consisting of 5x7x9 elements. You may give any number of inputs separated by comma. 
testarray[2][3][5]= 'Hello' # Let us replace (or do anything else) just one element. All other values will be 0 by default.
print(testarray)
dp.dfv(testarray,'x')  # We can change the default value by anything (e.g. 'x' here) 
print(testarray)
# Below steps are optional 
testarray=dp.npary(testarray) # This will change the type of the array from 'list' to 'numpy.ndarray'
testarray[1,4,3]=5 # Now work with elements in numpy style
print(testarray)For details please follow the linkhttps://www.respt.in/p/python-package-dimpy.html
Package: dim-python
dim-pythondim-pythonis a Python implementation ofdim (Data Installation Manager): Manage open data in your projects, like a package manager.Join communityWe are looking for members to develop together as an open source community.SlackQuick StartInstall the dimhttps://github.com/c-3lab/dim#install-the-dimInstall the python-dim$ pip install python-dimInitialize the project for dim$ dim initAlso can initialize the project using dim-python.Use the python-dimimportdimdim.install('https://example.com/xxx.json','example_name',['encode utf-8'])print(dim.list())print(dim.load_data('example_name','json'))BuildInstall Python 3.8+.Then, installpoetry1.2.0+ as follows.Windows:# Linux, macOS, Windows (WSL)curl-sSLhttps://install.python-poetry.org|python3-# Windows (Powershell)(Invoke-WebRequest-Urihttps://install.python-poetry.org-UseBasicParsing).Content|py-Install dependencies withpoetry.poetryinstallCheck version of dimimportdimdim.__version__LicenseRefer toMIT License.
Package: dims
dimsTable of ContentsInstallationUsageLicenseDid I Miss Something?A simple utility to check if any files are missing between two locations, for example
when copying large amount of files between locationsInstallationpip install dimsUsageUsage: dims [OPTIONS] SRC TARGET COMMAND [ARGS]...Options:--version             Show the version and exit.-f, --filenames-only  Ignores file paths and only looks at unique filenames.-h, --help            Show this message and exit.Licensedimsis distributed under the terms of theMITlicense.
Package: dimschema
Dimemsions GBQ Schema UtilityCLI to retrieve SQL schema information about theDimensions on Google BigQuerydataset.NOTEThis is the same documentation available fromhttps://docs.dimensions.ai/bigquery/data-sources.html- only it's retrieving from the BigQuery database itself.RequirementsPythonPersonal Google account credentials set up in your local machine. TheBigQueryLABhas a tutorial showing how to do that.Examplesdimschema publications .: show all fields from the Publications tabledimschema ids: show fields containing the string 'ids'dimschema datasets ids: show fields containing the string 'ids', only in the Datasets tableDevelopment$ mkvirtualenv dimschema
$ pip install --editable .Seehttps://stackoverflow.com/questions/30306099/pip-install-editable-vs-python-setup-py-develop
Package: dims-client
UIoT Devices - Dims Client LibraryThe objective of this library is to enable users to createClientsandServicesthat follow UIoT's Ontology of sending and managing message flowsAll the protocols included as a means of communication in this library follow the same principles:Create aDeviceCreate aServiceSendDataBasic UsageSendDatafrom dims_client import DimsClient

config = {'DEFAULT_DIMS_URI': 'http://0.0.0.0:5000'}

dims_client = DimsClient(
    config=config
)

returned = dims_client.send_data(<data_to_send>)