Saravanan666/pypi-org huggingface dataset:
Package: vcue-repo
VCUE RepoThis is a repository of basic functionality that can be useful for any coding project created by the VCUE team at University of California, Berkeley.Installation:pip install vcue-repoUse:from vcue import display as dpfrom vcue import web as wbfrom vcue import basics as bsMIT License:Copyright 2021. Univeristy of California, Berkeley.Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: vcv
Info====`vcv 2018-06-15``Author: Zhao Mingming <471106585@qq.com>``Copyright: This module has been placed in the public domain.``version:0.0.10`Classes:- `hand_detector`: detect the hand in the imageFunctions:- `test()`: test function- `hand_detector()`:  a class- `hand_detector.detect_hand(image)`: return the hand_number,hand position,and the confidenseHow To Use This Module======================.. image:: funny.gif:height: 100px:width: 100px:alt: funny cat picture:align: center1. example code:.. code:: pythonfrom vcv import hand_detector as hdimport cv2hd1=hd.hand_detector()hd1.test()hd3=hand_detector()imf=os.path.join(self.site_package,'test.jpg')print imfimage=cv2.imread(imf)print(hd3.detect_hand(image))Refresh========
Package: vcvf
Info====`vcvf 2018-07-04``Author: Zhao Mingming <471106585@qq.com>``Copyright: This module has been placed in the public domain.``version:2.0.0.1`Classes:- `face_detector`: detect the hand in the imageFunctions:- `test()`: test function- `face_detector()`:  a class- `face_detector.detect_face(image)`: return the face_number,face position,and the confidenseHow To Use This Module======================.. image:: funny.gif:height: 100px:width: 100px:alt: funny cat picture:align: center1. example code:.. code:: pythonfrom vcvf import face_detector as fdimport cv2fd1=fd.face_detector()fd1.test()imf=os.path.join(self.site_package,'test.jpg')print imfimage=cv2.imread(imf)print(fd1.detect_face(image))Refresh========
Package: vcvf-emotion
Info====`vcvf_emotion 2018-08-01``Author: Zhao Mingming <471106585@qq.com>``Copyright: This module has been placed in the public domain.``version:0.0.0.1`Classes:- `face_emotion`: get the face emotionFunctions:- `test()`: test function- `face_emotion()`:  a class- `face_emotion.face_emotion(image,rect)`: return the face's happy confidence list- `face_emotion.face_emotion_flat(image,rect)`:return the face is or not happy: true or falseHow To Use This Module======================.. image:: funny.gif:height: 100px:width: 100px:alt: funny cat picture:align: center1. example code:.. code:: pythonfrom vcvf_emotion import face_emotion as feimport cv2fe0=fe.face_emotion()fe0.test()imf=os.path.join(self.site_package,'test.jpg')print imfimage=cv2.imread(imf)rect=noneprint(fe0.face_emotion(image,rect))print(fe0.face_emotion_flat(image,rect))Refresh========
Package: vd42-openpyxl
Introductionopenpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.It was born from lack of existing library to read/write natively from Python
the Office Open XML format.All kudos to the PHPExcel team as openpyxl was initially based on PHPExcel.SecurityBy default openpyxl does not guard against quadratic blowup or billion laughs
xml attacks. To guard against these attacks install defusedxml.Mailing ListThe user list can be found onhttp://groups.google.com/group/openpyxl-usersSample code:from openpyxl import Workbook
wb = Workbook()

# grab the active worksheet
ws = wb.active

# Data can be assigned directly to cells
ws['A1'] = 42

# Rows can also be appended
ws.append([1, 2, 3])

# Python types will automatically be converted
import datetime
ws['A2'] = datetime.datetime.now()

# Save the file
wb.save("sample.xlsx")DocumentationThe documentation is at:https://openpyxl.readthedocs.ioinstallation methodscode examplesinstructions for contributingRelease notes:https://openpyxl.readthedocs.io/en/stable/changes.html
Package: vdaq-soap
VDAq soapcontains a number of python scripts that allow to control VDaq via SOAP.The scripts are###soapCheckMaddaq.pyThis will make a battery of tests (hold delay scan, mbias scan, etc) to check that all functionality is there.###soapHoldDelay.pyWill make a hold delay scan.###soapMbias.pyWill make a mbias scan.###soapTestChannel.pyWill run a single channel calibration pulse run.
Package: vdata
VData - Annotated multivariate observation of timestamped data.VData provides a container for simulated data across time points, heavily inspired by the AnnData project
(https://anndata.readthedocs.io/en/latest/).The VData object allows to store information about cells (observations), whose gene expression (variables) is
measured over a time series (time points).A VData object is build around layers (.layers). Each layer is a 3D matrix of : obs (cells) x var (genes) x time
points (2D matrices at each time point can have different numbers of obs). All layers must have the same shape.Around the layers, pandas DataFrames allow to describe variables (.var) and time points (.time_points), while custom
TemporalDataFrames (dataframes extended by one dimension) describe observations (.obs).Multi-dimension annotationThe arrays .obsm and .varm allow to store annotations that need more than one dimension (e.g. PCA coordinates).Pairwise annotationThe arrays .obsp and .varp allow to store annotations on pairs of observations or variables (e.g. distance matrices).Unstructured annotationsThe array .uns allows to store information not directly related to observations, variables and time points, which
can take many different shapes (e.g. colors for plottings groups of observations).
Package: vDataAPI
vData.PL - Python API LibExample - Generate clientfromvDataAPI.PaymentsimportVDataPaymentsvdataclient=VDataPayments('callbackURI','redirectURI','apikey')Example - Generate paymentReturn JSONpayment=vdataclient.generate_payment(amount=12.50)Example - Check payment statuspaymentstatus=vdataclient.check_payment_status("PAYMENT ID")By © HSCode.PL
Package: vdataset
vdatasetDescriptionLoad video datasets to PyTorch DataLoader. (Custom Video Data set for PyTorch DataLoader)VDataset can be use to load 20BN-Jester dataset to the PyTorch DataLoaderRequired LibrariestorchPillowpandasArgumentsLableMap ConstructorArgumentTypeRequiredDefaultDescriptionlabels_csvstrFalseNoneThe path to the csv file containing the labels and ids.labels_col_namestrFalseNoneThe name of the column containing the labels. (Required if labels_csv is not None)ids_col_namestr/ NoneFalseNoneThe name of the column containing the ids.id_typetypeFalseintThe type of the ids.VDataset ConstructorArgumentTypeRequiredDefaultDescriptioncsv_filestrTrue-Path to .csv fileroot_dirstrTrue-Root Directory of the video datasetfile_formatstrFalsejpgFile type of the frame images (ex: .jpg, .jpeg, .png)id_col_namestrFalsevideo_idColumn name, where id/name of the video on the .csv filelabel_col_namestrFalselabelColumn name, where label is on the .csv fileframes_limit_modestr/NoneFalseNoneMode of the frame count detection ("manual", "csv" or else it auto detects all the frames available)frames_limitdictFalse{"start": 0, "end": None}Number of frames in a video (required if frames_count_mode set to "manual")frames_limit_col_namestrFalseframesColumn name, where label is on the .csv file (required if frames_count_mode set to "csv")video_transformstuple/NoneFalseNoneVideo Transforms (Refer:https://github.com/hassony2/torch_videovision)label_mapLabelMap/NoneFalseNoneLabel Map of the DatasetUsagefromvdatasetimportLabelMap,VDatasetfromtorch.utils.dataimportDataLoaderfromtorchvideotransforms.volume_transformsimportClipToTensor# https://github.com/hassony2/torch_videovisionfromtorchvideotransformsimportvideo_transforms,volume_transforms# https://github.com/hassony2/torch_videovision# Create Label Maplabel_map=LabelMap(labels_csv="/path-to-csv/csv_file.csv",labels_col_name="label")print(label_map)label_map.print()# printing the labels on label-map# Use Video Transformersvideo_transform_list=[video_transforms.RandomRotation(30),video_transforms.Resize((100,100)),volume_transforms.ClipToTensor()]video_transforms=video_transforms.Compose(video_transform_list)# Create Vdataset (No frame limitation)full_dataset=VDataset(csv_file='/path-to-csv/csv_file.csv',root_dir='/path-to-root/',video_transforms=video_transforms,label_map=label_map)# Create Vdataset (Manual frames limitation, remove first 5 frames and last 5 frames)frames_limited_dataset=VDataset(csv_file='/path-to-csv/csv_file.csv',root_dir='/path-to-root/',video_transforms=video_transforms,frames_limit_mode="manual",frames_limit={"start":5,"end":-5}label_map=label_map)full_dataloader=DataLoader(full_dataset,batch_size=64,shuffle=True,num_workers=2,pin_memory=True)print(full_dataloader)frames_limited_dataloader=DataLoader(frames_limited_dataset,batch_size=64,shuffle=True,num_workers=2,pin_memory=True)print(frames_limited_dataloader)forimage,labelinfull_dataloader:# Do what do you want in datasetprint(image,label)print(image.size())breakforimage,labelinframes_limited_dataloader:# Do what do you want in datasetprint(image,label)print(image.size())break
Package: vdblite
Vector Database Lite (VDBLITE)Vector Database Lite (like SQLITE but for vector search)To discuss VDBLITE and ask general questions, usehttps://github.com/daveshap/VDBLITE/discussionsQuickstartInstall usingpip install vdbliteRun a test with the following code:importvdblitefromtimeimporttimefromuuidimportuuid4importsysfrompprintimportpprintasppif__name__=='__main__':vdb=vdblite.Vdb()dimension=12# dimensions of each vectorn=200# number of vectorsnp.random.seed(1)db_vectors=np.random.random((n,dimension)).astype('float32')print(db_vectors[0])forvectorindb_vectors:info={'vector':vector,'time':time(),'uuid':str(uuid4())}vdb.add(info)vdb.details()results=vdb.search(db_vectors[10])pp(results)Class Vdb MethodsVdb.add(payload)# add a dictionaryinfo=dict()vdb.add(info)# or a list of dictionariesinfo=list()vdb.add(info)Add a an arbitrary number of records toself.data(master list of data) of your Vdb object. No checks are performed beyond data type.payloadPayload must be either alistordictvariable type. For instance:info={'time':123.456789,'vector':[0.1,0.1,0.1,0.1,0.1],'uuid':'174657a5-ba76-47a8-a121-5fda05dde560','content':'lorem ipsum...',}orinfo=[{'...'},{'...'},{'...'},]Vdb.delete(field, value, firstonly=False)# delete all records with timestamp at UNIX time of 123.456789vdb.delete('timestamp',123.456789,False)Delete record(s) with fields that match a given valuefieldSpecify which element within each record to match for deletionvalueSpecify value to be matched for deletionfirstonlyOptional.Falseby default. Will delete all records withfieldsthat matchvalue. Set toTrueif you only want to delete a single recordVdb.search(vector, field='vector', count=5)results=vdb.search([1.0,0.0],'vector',5)Search fornnumber of records that are most similar tovector(cosine similarity). Specifyfieldif searching any other field thanvector.vector1-dimensional numpy array to match your query. Performs cosine similarity against all vectors inself.datafield='vector'Optional.Specify which field contains thevectorfor comparison. Records can contain more than one vector. For instance you may have vectors of different dimensionality, or from different models for different purposes.count=5Optional.Specify number of matches to return.Vdb.bound(field, lower_bound, upper_bound)results=vdb.bound('time',123.456789,987.654321)Return all records withfieldvalue betweenlower_boundandupper_bounde.g. all records with a timestamp between two specific values.fieldSpecify which field to compare in all records.lower_boundMinimum threshold to return.upper_boundMaximum threshold to return.Vdb.purge()Deleteself.datafrom memory and reinstantiate.Vdb.save(filepath)vdb.save('my_data.vdb')Save VDB data to filefilepathRelative or absolute filepath of save fileVdb.load(filepath)vdb=Vdb()vdb.load('my_data.vdb')Load saved VDB data into memoryfilepathRelative or absolute filepath of save file
Package: vdct
vdctDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nvdct--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallvdctnbdev# activate conda environment$condaactivatevdct# make sure the vdct package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the vdct package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/vdct.gitor fromconda$condainstall-cdsm-72vdctor frompypi$pipinstallvdctDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: vdd
Tools for Value-Driven DesignTools intended to help with modelling decisions in a value centric
design process. The intent is to keep this as generic as possible, as
some of this decision modelling is suited to generic decision-making,
non-design activities with a little massaging.FeaturesConcept Design Analysis (CODA) method implementationRequirements weighting with a Binary Weighting MatrixProgrammatic or Spreadsheet based model creation (via Excel
workbooks or Google Sheets).Installpip install vddDocumentationCurrently just stored in the repo.Using Google Sheets for requirements matricestbcDevelopmentpoetrymust be installed in the local development environment as a pre-requisite. In the repository root:poetry installReleasesManaged byRelease Pleasewith auto-versioning.
Changes to default branch will be accumulated in a release PR based onConventional Commits.
Merging the release PR will automatically publish the versioned package to PyPI.RoadmapModel sets for comparative work (rather than a single set of
characteristic parameter values)Improved visualisationExport CODA models to Excel templateHouse of Quality style requirement/characteristic weightingPandas everywhere (v1.x)ReferencesBased on my own degree notes and open access literature:M.H. Eres et al, 2014. Mapping Customer Needs to Engineering
Characteristics: An Aerospace Perspective for Conceptual Design -
Journal of Engineering Design pp. 1-24http://eprints.soton.ac.uk/id/eprint/361442
Package: vdevapi
This is an API, that can do a lots of cool things.If you want API Key, contact me on:Email:michaelvertexxdalog@gmail.comDiscord: VERTEXX#5068Change Log0.0.3 (12/1/2020)Fixed the API error handler (Please, use POST not GET exceptions were happening)0.0.2 (11/1/2020)Added handling for API errors (like invalid API key)0.0.1 (10/01/2020)First release
Package: vdf
Pure python module for (de)serialization to and from VDF that works just likejson.Tested and works onpy2.7,py3.3+,pypyandpypy3.VDF is Valve’s KeyValue text file formathttps://developer.valvesoftware.com/wiki/KeyValuesSupported versions:kv1Unsupported:kv2andkv3InstallYou can grab the latest release fromhttps://pypi.org/project/vdf/or viapippipinstallvdfInstall the current dev version fromgithubpipinstallgit+https://github.com/ValvePython/vdfProblems & solutionsThere are known files that contain duplicate keys. This is supported the format and
makes mapping todictimpossible. For this case the module providesvdf.VDFDictthat can be used as mapper instead ofdict. See the example section for details.By default de-serialization will return adict, which doesn’t preserve nor guarantee
key order on Python versions prior to 3.6, due tohash randomization. If key order is
important on old Pythons, I suggest usingcollections.OrderedDict, orvdf.VDFDict.Example usageFor text representationimportvdf# parsing vdf from file or stringd=vdf.load(open('file.txt'))d=vdf.loads(vdf_text)d=vdf.parse(open('file.txt'))d=vdf.parse(vdf_text)# dumping dict as vdf to stringvdf_text=vdf.dumps(d)indented_vdf=vdf.dumps(d,pretty=True)# dumping dict as vdf to filevdf.dump(d,open('file2.txt','w'),pretty=True)For binary representationd=vdf.binary_loads(vdf_bytes)b=vdf.binary_dumps(d)# alternative format - VBKVd=vdf.binary_loads(vdf_bytes,alt_format=True)b=vdf.binary_dumps(d,alt_format=True)# VBKV with header and CRC checkingd=vdf.vbkv_loads(vbkv_bytes)b=vdf.vbkv_dumps(d)Using an alternative mapperd=vdf.loads(vdf_string,mapper=collections.OrderedDict)d=vdf.loads(vdf_string,mapper=vdf.VDFDict)VDFDictworks much like the regulardict, except it handles duplicates and remembers
insert order. Additionally, keys can only be of typestr. The most important difference
is that when trying to assigning a key that already exist it will create a duplicate instead
of reassign the value to the existing key.>>>d=vdf.VDFDict()>>>d['key']=111>>>d['key']=222>>>dVDFDict([('key',111),('key',222)])>>>d.items()[('key',111),('key',222)]>>>d['key']111>>>d[(0,'key')]# get the first duplicate111>>>d[(1,'key')]# get the second duplicate222>>>d.get_all_for('key')[111,222]>>>d[(1,'key')]=123# reassign specific duplicate>>>d.get_all_for('key')[111,123]>>>d['key']=333>>>d.get_all_for('key')[111,123,333]>>>deld[(1,'key')]>>>d.get_all_for('key')[111,333]>>>d[(1,'key')]333>>>printvdf.dumps(d)"key""111""key""333">>>d.has_duplicates()True>>>d.remove_all_for('key')>>>len(d)0>>>d.has_duplicates()False
Package: vdf2json
usage: vdf2json [-h] [-p] [-ei encoding] [-eo encoding] [infile] [outfile]

positional arguments:
  infile        VDF
  outfile       JSON (utf8)

optional arguments:
  -h, --help    show this help message and exit
  -p, --pretty  pretty json output
  -ei encoding  input encoding E.g.: utf8, utf-16le, etc
  -eo encoding  output encoding E.g.: utf8, utf-16le, etc
Package: vdf3
VDF 3Pure python module for (de)serialization to and from VDF that works just likejson.VDF is Valve's KeyValue text file formatSupports:kv1InstallationPython 3.7 or higher is requiredpipinstallgit+https://github.com/ValvePython/vdfExample usageFor text representation.importvdf# parsing vdf from file or stringd=vdf.load(open('file.txt'))d=vdf.loads(vdf_text)d=vdf.parse(open('file.txt'))d=vdf.parse(vdf_text)# dumping dict as vdf to stringvdf_text=vdf.dumps(d)indented_vdf=vdf.dumps(d,pretty=True)# dumping dict as vdf to filevdf.dump(d,open('file2.txt','w'),pretty=True)For binary representationd=vdf.binary_loads(vdf_bytes)b=vdf.binary_dumps(d)# alternative format - VBKVd=vdf.binary_loads(vdf_bytes,alt_format=True)b=vdf.binary_dumps(d,alt_format=True)# VBKV with header and CRC checkingd=vdf.vbkv_loads(vbkv_bytes)b=vdf.vbkv_dumps(d)Using an alternative mapperd=vdf.loads(vdf_string,mapper=collections.OrderedDict)
Package: vdhost
<img src=”https://vectordash-static.s3.amazonaws.com/images/logo-black.png” width=250></img>
# vdhost
A command line tool for hosting your machine as [Vectordash](http://vectordash.com) GPU instances. Can be installed withpip3 install vdhost.For a more detailed overview on how to get started, how the commands work, or general questions, please visit our [hosting page](https://vectordash.com.hosting) or
view our [host documentation.](http://vectordash.com/machines/docs/) Note that you must be logged in and an approved host to view the host documentation.
Package: vdict
vdictThis a very thin wrapper aroundhnswlibto make it look like a python dictionary whose keys are numpy arrays. Install withpip install vdict.fromvdictimportvdictimportnumpyasnpdata=vdict()v1=np.random.rand(32)v2=np.random.rand(32)data[v1]='hello'data[v2]=32assertdata[v1]=='hello'You can have it throw IndexErrors if you try to access a key that doesn't exist:data=vdict(tol=0.001)v1=np.random.rand(32)v2=np.random.rand(32)data[v1]='hello'# this will throw an IndexError because we didn't add yet!print(data[v2])The default tolerance is1(generally do not throw errors), but you can set it to a smaller value to make it more strict.DetailsAll vectors must be the same lengthAccessing with a vector gives the closest value keyed by the closest vectorThe algorithm isapproximatenearest neighbor search. You can tune the accuracy (see below)You can have millions of vectors in the dictionaryIf you know the approximate size, passest_nelementstovidct()to reduce how often things are resizedUsageThevdictclass has some reasonable defaults, but you may need to tune for your use case. These are adjustable in the constructor. You can read about the parameters at thehnswlib. Briefly,
the most important ones are:M- the number of neighbors to consider when building the graph (higherMmeans more accurate, but more memory). 12-48 is typical.space- the distance metric to use. The default isl2, but you can also usecosineorip(inner product).ef_construction- parameter that controls speed/accuracy trade-off during the index construction - 50 - 200 is typical.fromvdictimportvdictdata=vdict(M=16,space='cosine',ef_construction=100)# add some vectorsdata[np.random.rand(32)]='hello'data[np.random.rand(32)]='world'LicenseMITAuthorAndrew White
Package: vdi-generator
VDI GeneratorVDI Generator is a simple program for generating a Versile Decentral
Identity from identity secret data. VDI-based identities enables
generating RSA keypairs from secret keydata. For more information
about Versile Decentral Identity seereference materialfor Versile
Platform. VDI Generator can be obtainedhere.DependenciesThis program requiresVersile Python, thePySideQt bindings, andQtto run.
Package: vDirect
VDirectVDirect is a user friendly tool to retrieve information from the VOGDB-API. It is a utility that builds URLs and sends requests to the server.Functionality includes searching for species, proteins and virus orthologous groups (VOGs) objects, as well as getting more detailed information about these objects.
Fetching Multiple Sequence Alignments (MSA) and Hidden Markov Matrices (HMM) is possible for each VOG, and Aminoacid and Nucleotide Sequences can be fetched for proteins.Dependencies:You can install the dependencies withpipinstallargparse
pipinstallrequestsInstallationvDirect can be installed with:pipinstallvDirectUsing the VOGDB-API with vDirectBelow is the search hierarchy: first specify vsearch, vsummary or vfetch, then the subsequent parameters.'-h' provides a list of the parameters that can be used for filtering.vdirect<base_url>vsearchspeciesspecies_search_parametersproteinprotein_search_parametersvogvog_search_parametersvsummaryspeciesspecies_idsproteinprotein_idsvogvog_idsvfetchproteinfaaprotein_idsfnaprotein_idsvoghmmvog_idsmsavog_idsExamplesvdirecthttp://127.0.0.1:8000/vsearchvog-pmax10-pmin10vdirecthttp://127.0.0.1:8000/vsearchspecies-ncorona
vdirecthttp://127.0.0.1:8000/vsearchprotein-ncorona
vdirecthttp://127.0.0.1:8000/vfetchproteinfaa-id1034149.YP_009198699.1You can get summaries from the IDs returned by a search by using the shell syntax:vdirecthttp://127.0.0.1:8000/vsummaryvog-id$(vdirect-basehttp://127.0.0.1:8000/vsearchvog-pmax100-pmin100)You can also set the base url as an environment variable withbase='http://127.0.0.1:8000/'and then execute the command asvdirect$basevsummaryvog-id$(vdirect$basevsearchvog-pmax100-pmin100)AuthorsNikola Vinko*, Sigi Koizar**University of Vienna