Saravanan666/pypi-org huggingface dataset:
Package: fdc
Fast density clustering (fdc)Python package for clustering low-dimensional data using kernel density maps and density graph. Examples for gaussian mixtures and some benchmarks are provided. Our algorithm solves multiscale problems (multiple variances/densities and population sizes) and works for non-convex clusters. It uses cross-validation and is regularized by two main global parameters : a neighborhood
size and a noise threshold measure. The later detects spurious cluster centers while the former guarantees that only local information is used to infer cluster centers.The underlying code is based on fast KD-trees for nearest-neighbor searches. For low-dimensional spaces, the algorithm has a O(n log n), where n is the size of the dataset. Is also has a memory complexity of O(n).InstallingI suggest you install the code usingpipfrom anAnacondaPython 3 environment. From that environment:pip install fdcThat's it ! You can now import the packagefdcfrom your Python scripts. Check out the examples
in the fileexampleand see if you can run the scripts provided.Examples and comparison with other methodsCheck out the example for gaussian mixtures (example.py). You should be able to run it directly. It
should produce a plot similar to this:In another example (example2.py), the algorithm is benchmarked against some sklearn datasets (note that the same parameters are used across all datasets). This is to be compared with other clustering methods easily accesible fromsklearn.CitationIf you use this code in a scientific publication, I would appreciate citation/reference to this repository. Also, for further references on clustering
and machine learning check out our machine learning review:@article{mehta2018high,
  title={A high-bias, low-variance introduction to Machine Learning for physicists},
  author={Mehta, Pankaj and Bukov, Marin and Wang, Ching-Hao and Day, Alexandre GR and Richardson, Clint and Fisher, Charles K and Schwab, David J},
  journal={arXiv preprint arXiv:1803.08823},
  year={2018}
}
Package: fdce
Python implementation of the algorithm presented in:Fornberg, B. (1988). Generation of finite difference formulas on arbitrarily
spaced grids. Mathematics of computation, 51(184), 699-706.This algorithm can estimate the coefficients of the finite difference formula
used to estimate any derivative of an unidimensional function at a pointx_0given a grid of points (mostly neighbors ofx_0). The accuracy level is
determined by the number of grid points used in each estimation.HighlightsGrid points do not have to be equally spaced.x_0does not have to be one of the grid points.As a result of 2., the algorithm can also be used to interpolate a function
at a pointx_0, by using the coefficients of the derivative of order zero.In a singleMorder derivative approximation the coefficients needed to
estimate the derivative at any order from zero toMare calculated.
Package: fdclient
fastDeploy python clientfromfdclientimportFDClientclient=FDClient('http://localhost:8080')# optional compression=False to disable zstd compression# inferresponse=client.infer([obj_1,obj_2,...])# optional unique_id='some_id' to specify a unique id for the request# infer in backgroundresponse_future=client.infer_background([obj_1,obj_2,...])# optional unique_id='some_id' to specify a unique id for the requestresponse=response_future.result()# wait for the response and get itBy default fdclient communicates with fastDeploy server via picklespickle is very useful and makes sense when using fastDeploy server as a micro service internally i.e: all requests to fastDeploy originate from code you have writtemPICKLE is secure if all the inputs to fastDeploy are originating from your code and not direct external user's picklesPICKLE is unsecure if you are passing external user inputs to fastDeploy directly without validation in betweenstart fastDeploy serve with--config "allow_pickle=false"if the fastDeploy APIs are exposed to outsideallow_pickle=falseconfig on server side makes fdclient usemsgpackif available orjsonif msgpack not available.If pickle is unsecure, why use it at all?pickle is great to send or receive arbitary inputs and outputsifallow_pickle=true(default) your inputs and outputs can be any python objects, eg: np arrays, pd dataframes, float32 anything ....pickle is only unsecure if you are unpickling objects pickled by others (since they can insert malicious code)If fastDeploy is being used only for internal microservices, pickle is the best way so enabled by default
Package: fdecimal
Decimal compatability with floatFree software: BSD licenseFeaturesImplicit float-to-Decimal coercion for arithmetic operations.Instead of this:>>> from decimal import Decimal
>>> Decimal(12.0) * 0.5
TypeError: unsupported operand type(s) for *: 'Decimal' and 'float'Use FDecimal:>>> from fdecimal import FDecimal
>>> FDecimal(12.0) * 0.5
Decimal('6.0')As you can see it only solves part of the problem - the result still can’t be
added with a float value without explicit type coercion, but it solves some
problems where (1) its not clear what type the other value will be, (2) you
don’t want to add code explicitly handling that coercion, and (3) an
arithmetic surprises due to working with different numeric types are
acceptable.TODOMissing operations, including mod, floor divPython 3 supportAllow cdecimal swap-inDone:Return FDecimal objects rather than Decimal objectsHistory0.2.0 (2016-01-14)Add support for returning FDecimal objects from operations0.1.0 (2016-01-14)First release on PyPI.
Package: fdemmer-django-all-access
django-all-access is a reusable application for user registration and authentication
from OAuth 1.0 and OAuth 2.0 providers such as Twitter and Facebook.The goal of this project is to make it easy to create your own workflows for
authenticating with these remote APIs. django-all-access will provide the simple
views with sane defaults along with hooks to override the default behavior.This is a fork to provide updates for compatibility and a package on PyPI only.django-all-acccess was originally authored byMark Lavin.FeaturesSane and secure defaults for OAuth authenticationEasy customization through class-based viewsBuilt using the amazingrequestslibraryInstallationIt is easiest to install this fork of django-all-access using pip:pipinstallfdemmer-django-all-accessdjango-all-access requires Python 3.6+ & Django along with the following Python
packages (which are automatically installed if necessary):pycryptodome>=3.9
requests>=2.0
requests_oauthlib>=0.4.2
oauthlib>=0.6.2DocumentationAdditional documentation on using django-all-access is available onRead The Docs.ReleasesChangelog.Licensedjango-all-access is released under the BSD License. See theLICENSEfile for more details.ContributingIf you have questions about using django-all-access or want to follow updates about
the project you can join themailing listthrough Google Groups.If you think you’ve found a bug or are interested in contributing to this project
check outdjango-all-access on Github.
Package: fderiv
fderiv packageIntroductionThis package contains derivative function.It calculates the derivatives of a given arrays of x values and function values. It returns first and second order derivatives using the five point method.** The minimum size of array should be 5 for both x and y values.**Filesfderiv.pyInstallation$ pip install fderivUsageimport fderiv as fd
first-derivative, second-derivative = fd.fderiv(x, y)
Package: fdet-offline
FDet-Offline: just like FDet, but offlineDownload like this:pip install fdet-offline --find-links https://download.pytorch.org/whl/torch_stable.htmlPackage depends on torch-packages not in PyPi.A version offdetwhere you don't have to access the internet to download the weights.These are the changes:MTCNN (only works for this model at the moment)1.In fdet/mtcnn.py, line 73-76, the url for the MTCNN-weights are listed:base_url='https://github.com/acnazarejr/fdet/releases/download/weights/'self._pnet=self.__load_model(_PNet,base_url+'mtcnn_pnet.pt')self._rnet=self.__load_model(_RNet,base_url+'mtcnn_rnet.pt')self._onet=self.__load_model(_ONet,base_url+'mtcnn_onet.pt')I downloaded those weights and created packages to import them (because PyPi has a 100MB max on package size, this was the solution).I put the weights in the directoryweightsin the packagefdet_offline_mtcnn_weights.
The functionfdet_offline_mtcnn_weights.import_weightstakesmtcnn_typeas input
and returns a partial function oftorch.load.This partial function getsmap_locationfrom__load_model.The previously mentioned fdet/mtcnn.py, line 73-76, now looks like this:self._pnet=self.__load_model(_PNet,'pnet')self._rnet=self.__load_model(_RNet,'rnet')self._onet=self.__load_model(_ONet,'onet')In fdet/mtcnn.py, what previously was (url was input):def__load_model(self,net_class:type,url:str)->torch.nn.Module:"""Download and construct the models"""try:state_dict=load_state_dict_from_url(url,map_location=self._device_control)is now (mtcnn_type is input):def__load_model(self,net_class:type,mtcnn_type:str)->torch.nn.Module:"""Download and construct the models"""try:load_state_dict=import_weights.load_partial(mtcnn_type)state_dict=load_state_dict(map_location=self._device_control)2.At fdet/mtcnn.py, line 427:state_dict=load_state_dict_from_url(url,map_location=self._device_control)load_state_dict_from_url, returns atorch.loadwhich takes the weight-file as input.
I replaced the load_state_dict_from_url with torch.load, and that's it.That line now states this instead;state_dict=torch.load(url,map_location=self._device_control)
Package: fdeunlock
(GitLab CI) -(Travis CI) --FDEunlock – Check and unlock full disk encrypted systems via sshThis script allows you to unlock full disk encrypted GNU/Linux systems via ssh
after checking that the system has not been tampered with.Usage exampleCheckout the following example:fdeunlock --host fde-server.example.org-initramfs
INFO, 2017-03-29 10:27:41,822: Host offline. Attempting to start using: virsh -c qemu:///system start fde-server
Domain fde-server started

INFO, 2017-03-29 10:27:42,726: Start command returned with: 0
INFO, 2017-03-29 10:27:48,257: Host offline. Waiting …
INFO, 2017-03-29 10:27:53,264: Ping result: 198.51.100.23 : [0], 84 bytes, 0.51 ms (0.51 avg, 0% loss)
INFO, 2017-03-29 10:27:53,270: Running Network based checkers: LinkLayerAddressChecker, UnauthenticatedLatencyChecker
INFO, 2017-03-29 10:27:53,273: Link layer address matches the trusted once.
INFO, 2017-03-29 10:27:53,283: ICMP ping round trip time: 0.7300 ms
INFO, 2017-03-29 10:27:53,283: Latency is within the boundaries.
INFO, 2017-03-29 10:27:54,296: SSH session to initramfs established.
INFO, 2017-03-29 10:27:54,296: Running SSH based checkers: ChecksumChecker, AuthenticatedLatencyChecker
INFO, 2017-03-29 10:27:57,487: Checksums match the trusted once.
INFO, 2017-03-29 10:27:57,559: Latency to execute a command over SSH and get the response back: 71.6000 ms
INFO, 2017-03-29 10:27:57,560: Trusted latency: 60.256694030762
INFO, 2017-03-29 10:27:57,560: Current latency: 71.61283493041992
Choose one of 'save', 'ignore' (for current run) or anything else to exit: save
INFO, 2017-03-29 10:28:02,739: All 4 checks passed.
INFO, 2017-03-29 10:28:02,820: Passing key for vda3_crypt to host fde-server.example.org-initramfs.
INFO, 2017-03-29 10:28:05,140: Could not retrieve key for vdb3_crypt (host fde-server.example.org-initramfs).
Please enter key for vdb3_crypt (or store it in a vault):
INFO, 2017-03-29 10:28:28,155: Passing key for vdb3_crypt to host fde-server.example.org-initramfs.
INFO, 2017-03-29 10:28:43,322: System should be booting now.The hostfde-server.example.org-initramfswas defined in the ssh
configuration~/.ssh/configand the key forvda3_cryptwas provided
in/home/user/.config/fdeunlock/keys/fde-server.example.org-initramfs_vda3_crypt.key.
And last but not least, the start command was configured in/home/user/.config/fdeunlock/config.cfg.RepositoriesGitLab(primary repo with issue tracker)GitHub(mirror)DocumentationRead the DocsAuthorsMarcel McKinnonRobin SchneiderLicenseGNU Affero General Public License v3 (AGPL-3.0)
Package: fdf
fdfFile-system based Data Frame: manipulate self-explanatory dataset without writing codes.Free software: MIT licenseDocumentation:https://fdf.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-07-01)First release on PyPI.
Package: fdfat
Working in progess...Fast 6DoF Face Alignment and TrackingThis project purpose is to implement Ultra lightweight 6 DoF Face Alignment and Tracking. This project is capable of realtime tracking face for mobile device.InstallationRequirementstorch >= 2.0autoalbument >= 1.3.1Installpip install -U fdfatModel ZooTODO: add best modelTrainingPrepare the datasetThis project use 3d 68 points of landmark (difference from the original 300W dataset). Please go toFaceSyntheticsto download the dataset (100K one) and extract it to your disk.Create your dataset yaml file with the following info:base_path:<path-to-face-synthesis-dataset>/dataset_100000train:<path-to-list-train-text-file.txt>val:<path-to-list-val-text-file.txt>test:<path-to-list-test-text-file.txt>note: you can use list train file indatasets/FaceSyntheticsfor reference.Start trainingfdfat--data<path-to-your-dataset-yaml>--modelLightWeightModelFor complete list of parameter, please folow this sample config file:fdfat/cfg/default.yamlValidationfdfat--taskval--data<path-to-your-dataset-yaml>--modelLightWeightModelPredictfdfat--taskpredict--modelLightWeightModel--checkpoint<path-to-checkoint>--input<path-to-test-img>Exportfdfat--taskexport--modelLightWeightModel--checkpoint<path-to-checkoint>--export_formattfliteCreditYOLOv8: Thanks for ultralytics awesome project, I borrow some code from here.Ultra-Light-Fast-Generic-Face-Detector-1MB: Thanks for your lightweight face detectorFaceSynthetics: Thanks for expressive face landmark dataset, it's a good starting pointhead-pose-estimation: Thanks for head pose estimation code
Package: fdfs-client-py3
fdfs-client-py3 Package1. Installationpipinstallfdfs-client-py3==1.0.02. Getting Startedupload file：1）upload file by namefromfdfs_client.clientimportFdfs_clientclient=Fdfs_client('/path/to/fdfs/client.conf')res=client.upload_by_filename('/path/to/upload_file')# res：# {#   'Status': 'Upload successed.',#   'Local file name': '',#   'Storage IP': '192.168.19.131',#   'Group name': 'group1',#   'Remote file_id': 'group1/M00/00/02/wKgTg18Bnz2ASFgeAAAswnVEUA47566649',#   'Uploaded size': '11.19KB'# }2）upload file by contentfromfdfs_client.clientimportFdfs_clientclient=Fdfs_client('/path/to/fdfs/client.conf')file=open('/path/to/upload_file')res=client.upload_by_filename(file.read())# res：# {#   'Status': 'Upload successed.',#   'Local file name': '',#   'Storage IP': '192.168.19.131',#   'Group name': 'group1',#   'Remote file_id': 'group1/M00/00/02/wKgTg18Bnz2ASFgeAAAswnVEUA47566649',#   'Uploaded size': '11.19KB'# }3. Authorthe author is smarli，if you has some quesion，you can contact us by email tosmartli_it@163.com。
Package: fdfs-tornado
This library provides an async FDFS client for tornado.QuickStartinstall using pip:$ pip install fdfs-tornadoBasic Usageinit a clientusetracker_ipandtracker_portto initialize the FDFS Client.
Create a single client for a specific case.fromfdfs_tornado.clientimportmakeclientAsyncFDFSClient=makeclient(tracker_ip,tracker_port)client=AsyncFDFSClient()Notethe client will not create any connection before upload or download
actions.upload a fileTo upload a file. First open the file in binary mode, and get the size, those
are required.fromtornado.ioloopimportIOLoopioloop=IOLoop.instance()client=AsyncFDFSClient()withopen(filename,'rb')asf:file_size=os.stat(filename).st_sizefuture=client.upload(f,file_size)future.add_done_callback(lambda_:ioloop.close())ioloop.start()ret=future.result()
Package: fdgrid
fdgridprovides some tools to createregular,adaptativeorcurvilinear2D grids.fdgridhas been designed to work withnsfds2but can also be used alone.fdgridprovides tool to:generate 2D meshes : x and z coordinates,subdivide the grid into Subdomain objects used by nsfds2.Requirementspython:>= 3.7matplotlib:>= 3.0numpy:>= 1.1scipy:>= 1.1ofdlib2:>= 0.8mplutils:>= 0.3InstallationClone the github repo and$pythonsetup.pyinstallor install via Pypi$pipinstallfdgridLinksDocumentation:http://perso.univ-lemans.fr/~cdesjouy/fdgrid/Source code:https://github.com/ipselium/fdgridBug reports:https://github.com/ipselium/fdgrid/issuesnsfds2:https://github.com/ipselium/nsfds2
Package: fdic
IntroductionThis project provides some tools to access data from the FDIC public API
and data resources.Work in progress; more docs coming soon.UsageTo try out these tools, install viapip install fdicor download the
source code from GitHub. Then start a python interpreter and do
something like the following:First we import various modules and set ftool to be the tool we want:>>>importfdic.query,json>>>ftool=fdic.query.FDICToolsNow we can get the institutional data from the FDIC and sort it first by
assets and then by deposits. We print the result in JSON format as a
sanity check and see the largest 3 banks by assets:>>>inst_data=ftool.get_sorted_inst_data()>>>top_3_by_assets=inst_data[:3]>>>print(json.dumps({i['NAME']:{n:i[n]fornin['ASSET','DEP']}...foriintop_3_by_assets},indent=2)){"JPMorgan Chase Bank, National Association":{"ASSET":"3201942000","DEP":"2440722000"},"Bank of America, National Association":{"ASSET":"2418508000","DEP":"2042255000"},"Citibank, National Association":{"ASSET":"1766752000","DEP":"1399631000"}}Next we pull in the Uniform Bank Performance Report data. Note that you
will have to download a zip file with this data first. If you don’t, you
will get a NeedUBPRZipFile exception telling you how to download the
necessary file.To save time in parsing, we can provide an rssdfilterto just
get data for the top 200 banks by assets. After getting the data, we
sort by the UBPRE569 field (unrealized losses as a percent of tier 1
capital for held-to-maturity assets) and then print the data:>>>ubpr_data=ftool.get_ubpr_inst_data(rssd_filter={...i['FED_RSSD']foriininst_data[:50]})>>>htm_data=list(sorted(ubpr_data,key=lambdai:i['UBPRE569']))>>>print(json.dumps({i['NAME']:{n:i[n]fornin['NAME','UBPRE569']}...foriinhtm_data[:3]},indent=2)){"Silicon Valley Bank":{"NAME":"Silicon Valley Bank","UBPRE569":-89.2},"Bank of America, National Association":{"NAME":"Bank of America, National Association","UBPRE569":-59.95},"Charles Schwab Bank, SSB":{"NAME":"Charles Schwab Bank, SSB","UBPRE569":-46.87}}You can find a list of the UBPR codes at theFederal
Reserve.
For example if you are interested in bothUBPRE569(unrealized
losses on the held-to-maturity portfolio as percent of tier 1 capital)
as well asUBPRM037(appreciation in available for sale securities /
percent of available for sale securities), you could do something like
the following:>>>codes={...'UBPRE569':{'convert':float},...'UBPRM037':{'convert':float},...}>>>ubpr_data=ftool.get_ubpr_inst_data(rssd_filter={...i['FED_RSSD']foriininst_data[:50]},codes=codes)>>>htm_data=list(sorted(ubpr_data,key=lambdai:i['UBPRE569']))>>>print(json.dumps({i['NAME']:{n:i[n]fornin(['NAME']+list(codes))}...foriinhtm_data[:3]},indent=2)){"Silicon Valley Bank":{"NAME":"Silicon Valley Bank","UBPRE569":-89.2,"UBPRM037":-8.86},"Bank of America, National Association":{"NAME":"Bank of America, National Association","UBPRE569":-59.95,"UBPRM037":-2.0},"Charles Schwab Bank, SSB":{"NAME":"Charles Schwab Bank, SSB","UBPRE569":-46.87,"UBPRM037":-8.17}}
Package: fdicdata
OverviewThefdicdatapackage provides a set of functions for working with data from the Federal Deposit Insurance Corporation (FDIC), including retrieving financial data for FDIC-insured institutions and accessing the data taxonomy.Source Code:https://github.com/Visbanking/fdicdataPyVisbanking Website:https://visbanking.comOverviewInstallationpipinstallfdicdataimportfdicdataasfdicExamples### Data Taxonomy:fdic.dataTaxonomy("institution")### Get Failures:fdic.getFailures(['CERT'],date_range=('2010','2015'),name=("DORAL BANK"))### Get Financials:fdic.getFinancials(IDRSSD_or_CERT=37,metrics=["ASSET","DEP"],limit=10,date_range=["2015-01-01","*"])### Get History:fdic.getHistory(CERT_or_NAME=3850,fields=["INSTNAME","CERT","PCITY","PSTALP","PZIP5"],CERT=True,limit=10)### Get Institutions:fdic.getInstitution(IDRSSD_or_CERT='480228',fields=["NAME","CITY","STATE"],limit=10)### Get Institutions All:fdic.getInstitutionsAll()### Get Location:fdic.getLocation(3850)fdic.getLocation(3850,fields=['NAME','CITY','ZIP'])### Get Summary:fdic.getSummary(states=["California","New York"],date_range=["2020","2021"],fields=["DEP","ASSET"])LicenseThis project is licensed under the terms of the MIT license.
Package: fdir
Module Setupgit clone https://github.com/m-zayan/fdir.gitUnzip file then open itOpen terminal (Linux) or cmd (Windows command prompt)Typepython setup.py build && python setup.py installModes1 -./Dirctories (Folders)2 -.Files3 -.*Files with specific extension (eg :.csvor.pdf)4 -//Files and DirectoriesModule Functionslistdir(str path,str mode)The Function takes two argumentspathandmode (1 , 2 , 3 or 4)return List of pathes from givenpathEximportfdirpathes_list=fdir.listdir(path,'./')# return path of all folders at the current directory as same as (os.listdir)itrAll(list paths,str mode)The Function takes two arguments list ofpathsandmode (1 , 2 , 3 or 4)return dictionary(str key,list paths)Eximportfdirsub_files=fdir.itrAll(pathes_list,'./')# return paths of all folders for each path at listitrDict(dict paths,str mode)The Function takes two arguments dictionary ofpathsandmode (1 , 2 , 3 or 4)return dictionary(str key,dict paths)Eximportfdirdict_sub_files=fdir.itrAll(sub_files,'.pdf')# return paths of all pdf files for each list of paths at dictionarywalk(str paths,str mode)The Function takes two arguments dictionary ofpathsandmode (1 , 2 , 3 or 4)Recursively iterating through all files from initial path , Return List of all files which matchmodeEximportfdirpdf_files=fdir.walk(path,'.pdf')# return paths of all pdf files.
Package: fdleakfinder
fd-leak-finderfd-leak-finder helps you figure out why your unix process is using up too many fds.To use it, generate strace output with a command something like this:strace -q -a1 -s0 -ff -e trace=desc -tttT -ostrace.file $EXECUTABLEThen pipe the resulting “strace.file.$PID” into fd-leak-finder’s stdin.CAVEATSIt turns out that strace’s representation of this stuff is much more
complicated than I thought, and I don’t understand why some of
fd-leak-finder’s computations turn out the way they do. You can see “xxx
weird” sorts of messages in fd-leak-finder. Hopefully it will be useful to
you anyway! If you figure out how to improve it please let me know.I guess parsing strace’s human-oriented output is a bad way to do this. I’m
not sure what agoodway to do it is, on Linux. On Solaris or OSX I suppose
a good way to do it would be to use dtrace.LICENCEYou may use this package under the Simple Permissive Licence, version 1 or,
at your option, any later version. See the fileCOPYING.SPL.txtfor the
terms of the Simple Permissive Licence, version 1.
Package: fdlsgm
Fast Directed Line Segment Grouping MethodOverviewThis package provides an algorithm and interface to group elementary line segments in terms of direction and vicinity. The algorithm used is based on the algorithm developed by Jang & Hong (2002)[^JH2002]. The software efficiently finds line segments from a bunch of elemental directed line segments placed in a three-dimensional space.DependenciesThe library is written inC++11and do not depends on any library outside of theSTL. The Python interface is depends onNumPy. The library is developed ong++version 5.4 installed in Linux Mint 18.1 (serena). The Python interface is developed on Python 3.7.1 and Numpy 1.18.1.References[^JH2002]: Jeong-Hun Jang & Ki-Sang Hong, Pattern Recognition 35 (2002), 2235--2247 (doi:10.1016/S0031-3203(01)00175-3)