Saravanan666/pypi-org huggingface dataset:
Package: nested-grid-plotter
About Nested Grid PlotterNestedGridPlotter is based onmatplotliband intends to simplify the plotting of nestedgrid by providing a objected oriented class.Documentation:https://nested-grid-plotter.readthedocs.io.Changelog1.0.0 (2024-01-31)FIX: Typo = symetry to symmetry in keywords.FIX: Colorbar scaling = now supports any norm and some duplication has been removed,
some warnigns added.0.1.2 (2023-12-03)FIX: Selection of data in animations - when the amount of data is
larger than the number of frames. The fix ensures that the first frame
is the first data element and that the last frame is the last data
element, all other frames matching evenly spaced data element in between.DOCS: update animated_plotters.ipynb notebook0.1.1 (2023-11-20)Add utilitiesalign_x_axes,align_x_axes_on_valuesandmake_x_axes_symmetric_zero_centered.Remove the default italic styles from titles and axis labels.Add a DOI (zenodo)0.1.0 (2023-06-30)First release on PyPI.MIT LicenseCopyright (c) 2023, Antoine ColletPermission 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: nested_h5py
version number: 0.0.1
author: Nicholas A. Del GrossoOverviewPandas reader and writer from h5pyInstallation / UsageTo install use pip:$ pip install nested_h5pyOr clone the repo:$ git clonehttps://github.com/neuroneuro15/nested_h5py.git$ python setup.py installContributingTBDExampleTBD
Package: nested-inside
Nested InsideTheNested Insidepackage provides a nested data structure that allows for easy and intuitive access and modification of nested data. The package defines aNestedclass that wraps a data structure (e.g. a dictionary, list, or tuple) and allows for nested access using a specified delimiter (default "->"). The class provides several methods such asget,set,parseto access and modify the nested data, and also provides derived classes such asNestedDict,NestedList, andNestedTupleto return the data in a modifiable or immutable format. This package can be especially useful when working with complex data structures that contain multiple nested levels, as it allows you to access and modify the data using a simple key path instead of having to navigate through multiple levels of nested data manually.InstallationTo install the package, use pip:pip install nested_insideUsageThe package provides aNestedclass that wraps a data structure (e.g. a dictionary, list, or tuple) and allows for nested access using a specified delimiter (default "->").fromnested_insideimportNestedDictdata={"a":{"b":[1,2,3],"c":"hello"}}nested_data=NestedDict(data)# Accessing nested values using get functionvalue=nested_data.get("a->b->0")print(value)# 1# Modifying nested values using set functionnested_data.set("a->b->0",4)# modify or access data using [] syntaxnested_data['a->b->1']=3# modify or access data using object callnested_data('a->b->2',4)# modify or access data using dot notationnested_data.a.c="world"print(nested_data.a.b)# [4, 3, 4]# call object without any parameter to retrieve dataprint(nested_data('a->c'))# Retrieving default value by providing value to defaultvalue=nested_data("a->d",default="not found")print(value)# "not found"# Return mutable objectvalue=nested_data.get("a",modify=True)# provide tuple as key to modifyvalue[["b"]]=[5,6,7]# provide tuple as keyprint(nested_data.get(("a","b")))# [5, 6, 7]# provide list as keyprint(nested_data[["a","b",2]])# 7# provide list as key with index as stringprint(nested_data[["a","b","0"]])# 5Methods__init__(self, data: Any, delimiter: str = "->"): Initialize a new Nested object with the given data and delimiter.parse(self, value: Any, modify: bool = False) -> Any: A helper function that converts the value to a modifiable or immutable object based on the 'modify' flagget(self, key: Union[int, str, list, tuple, None], default=NODEFAULT, modify: bool = False) -> Any: Retrieves the value stored at the specified key path in the nested data structure.set(self, key: Union[int, str, list, tuple], value: Any) -> None: Set the value stored at the specified key path in the nested data structure.ContributingIf you would like to contribute to the package, feel free to submit a pull request.LicenseThe package is licensed under the MIT License.
Package: nested-json
nested-jsonA library to manage JSONs with encoded JSON more effectively (e.g. AWS Event Bridge Events, AWS API Gateway Events)Installpipinstallnested_jsonUsageimportnested_jsonasnjsonThe main API of the library consist of:Nested(obj)- to convert any list or dictionary to a nested oneparse(str)- to parse a JSON string to a nested JSONprocess(obj)- to process/stringify any nested JSONdumps(obj),dump(obj, fp)- same as from thejsonmodule, but supports nested JSONloads(str),load(fp)- same as from thejsonmodule, but supports nested JSONMarkingdict/listas nestedTheNestedfunction can be used to mark a dictionary or
list as nested data, which will add a particular property to it,
marking it nested. This property was later removed during processing.data={"id":"12345","event":{"payload":{"rawpayload":njson.Nested({"key":"value","other":2,"foo":True}),"tags":njson.Nested(["hello","world])}}}# {#   "id": "12345",#   "event": {#     "payload": {#       "rawpayload": {#         "__nested__": True,#         "key": "value",#         "other": 2,#         "foo": True#       },#       "tags": [#         "__nested__",#         "hello",#         "world#       ]#     }#   }# }Processing nested JSONUsing theprocessfunction, the nested keys can be removed
and nested data converted to JSON string.processed_data=njson.process(data)# {#   "id": "12345",#   "event": {#     "payload": {#       "rawpayload": "{\"key\": \"value\", \"other\": 2, \"foo\": true}",#       "tags": "[\"hello\", \"world\"]"#     }#   }# }Converting to JSON stringThe result ofprocesscan be already passed tojson.dumps,
but thenjson.dumpscan also be used with nested JSON data.json_string=njson.dumps(data)# '{"id": "12345", "event": {"payload": {"rawpayload": "{\\"key\\": \\"value\\", \\"other\\": 2, \\"foo\\": true}", "tags": "[\\"hello\\", \\"world\\"]"}}}'Parsing JSON stringNested JSON string can be parsed to nested JSON data with theloadsfunction or theparsefunctionassertnjson.loads(json_string)==dataassertnjson.parse(processed_data)==dataassertnjson.parse(json.loads(json_string))==dataCompatibilityNote that bothloadsanddumpsuse thejson.loadsandjson.dumpsfunctions; thus they can be used with "normal" JSON as well.
Package: nestedlistops
easier access to nested listspip install nestedlistopsExample Usageget_nested_item(l:Iterable,i:Iterable)->AnyRetrievesanesteditemfromagivenlistornestedlistbasedontheprovidedindexsequence.Args:l(Iterable):Theinitiallistornestedlist.i(Iterable):Aniterablerepresentingtheindexsequencetoaccessthedesireditem.Returns:Any:Thenesteditematthespecifiedindexsequence.Examples:>>>fromnestedlistopsimportset_nested_item,get_nested_item>>>listas=[[[1,2],[3,4]],[[4,5],[6,7]],[[7,8],[9,9]]]>>>get_nested_item(l=listas,i=(0,1,0))3set_nested_item(l:Iterable,i:Iterable,v:Any)Setsthevalueofanestediteminagivenlistornestedlistbasedontheprovidedindexsequence.Args:l(Iterable):Theinitiallistornestedlist.i(Iterable):Aniterablerepresentingtheindexsequencetoaccessthedesireditem.v(Any):Thevaluetobeassignedtothenesteditem.Returns:None(theoriginaliterableismodified)Examples:>>>fromnestedlistopsimportset_nested_item,get_nested_item>>>listas=[[[1,2],[3,4]],[[4,5],[6,7]],[[7,8],[9,9]]]>>>set_nested_item(l=listas,i=(0,1,0),v=1111)>>>print(listas)[[[1,2],[1111,4]],[[4,5],[6,7]],[[7,8],[9,9]]]
Package: nested-models
# nested-modelsNested models are serializeable objects that can be converted to various formats like JSON and YAML while providing functionality such as serialization/deserialization, cleaning, validation and  defaults values.It also utilizes a Schema format that can restrict the fields that are serialized by the object.Classes can be extended to add extra functionality and an encoder is provided that can be used with Django’s JSON fields.It also supports nesting Models inside of Models.## GoalThe goal is to provide a basic object that can be sereialized/deserialized into various formats supported by Python.
Package: nestednop
Library to update nested dicts and listsYou can change the values in a flattened version of your dict that NestedNop creates for you. After you editing, NestedNop updates the original structure automatically.pipinstallnestednopdictoriginal=data={'level1':{'t1':{'s1':{'col1':5,'col2':4,'col3':4,'col4':9},'s2':{'col1':1,'col2':5,'col3':4,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':4,'col4':9}},'t2':{'s1':{'col1':5,'col2':4,'col3':4,'col4':9},'s2':{'col1':1,'col2':5,'col3':4,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':4,'col4':9}},'t3':{'s1':{'col1':1,'col2':2,'col3':3,'col4':4},'s2':{'col1':5,'col2':6,'col3':7,'col4':8},'s3':{'col1':9,'col2':10,'col3':11,'col4':12},'s4':{'col1':13,'col2':14,'col3':15,'col4':16}}},'level2':{'t1':{'s1':{'col1':5,'col2':4,'col3':9,'col4':9},'s2':{'col1':1,'col2':5,'col3':4,'col4':5},'s3':{'col1':11,'col2':8,'col3':2,'col4':13},'s4':{'col1':5,'col2':4,'col3':4,'col4':20}},'t2':{'s1':{'col1':5,'col2':4,'col3':4,'col4':9},'s2':{'col1':1,'col2':5,'col3':4,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':4,'col4':9}},'t3':{'s1':{'col1':1,'col2':2,'col3':3,'col4':4},'s2':{'col1':5,'col2':6,'col3':7,'col4':8},'s3':{'col1':9,'col2':10,'col3':11,'col4':12},'s4':{'col1':13,'col2':14,'col3':15,'col4':16}}}}fromnestednopimportNestedNopnest=NestedNop(dictoriginal)forkey,iteminnest.iterable_flat.items():ifitem['get_value']()==4andkey[-1]=='col3':item['set_value'](400000000000000)updatediter=nest.get_updated_iterable()updatediter{'level1':{'t1':{'s1':{'col1':5,'col2':4,'col3':400000000000000,'col4':9},'s2':{'col1':1,'col2':5,'col3':400000000000000,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':400000000000000,'col4':9}},'t2':{'s1':{'col1':5,'col2':4,'col3':400000000000000,'col4':9},'s2':{'col1':1,'col2':5,'col3':400000000000000,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':400000000000000,'col4':9}},'t3':{'s1':{'col1':1,'col2':2,'col3':3,'col4':4},'s2':{'col1':5,'col2':6,'col3':7,'col4':8},'s3':{'col1':9,'col2':10,'col3':11,'col4':12},'s4':{'col1':13,'col2':14,'col3':15,'col4':16}}},'level2':{'t1':{'s1':{'col1':5,'col2':4,'col3':9,'col4':9},'s2':{'col1':1,'col2':5,'col3':400000000000000,'col4':5},'s3':{'col1':11,'col2':8,'col3':2,'col4':13},'s4':{'col1':5,'col2':4,'col3':400000000000000,'col4':20}},'t2':{'s1':{'col1':5,'col2':4,'col3':400000000000000,'col4':9},'s2':{'col1':1,'col2':5,'col3':400000000000000,'col4':8},'s3':{'col1':11,'col2':8,'col3':2,'col4':9},'s4':{'col1':5,'col2':4,'col3':400000000000000,'col4':9}},'t3':{'s1':{'col1':1,'col2':2,'col3':3,'col4':4},'s2':{'col1':5,'col2':6,'col3':7,'col4':8},'s3':{'col1':9,'col2':10,'col3':11,'col4':12},'s4':{'col1':13,'col2':14,'col3':15,'col4':16}}}}
Package: nested-search
deepsearchdict - Deep search you dictionaryUtility package that helps you to compare two dictionary or json string and search deep in complex nested structureAssumptions:Assuming python is installed on your system.Installdeepsearchdicton your system using :pip install deepsearchdictDescriptionTakes three argumentsSource JSON string which is the super setTarget JSON string which will be searched in the SourceType of validation. There are three types of validations supported.keys : Deep searches only if the keys specified in target json are present in source jsonpartial : Deep searches all the keys and values are compared partially if it is contained in the source stringall : Deep searches all the keys and values and finds for exact match of valuesMethod to invokeDeepSearchDict(source_json,target_json,validation_type)Returns True if Target JSON is contained within Source JSON, else FalseExamplefrom deepsearchdict import DeepSearchDict
import json

# read the json from file 
actual_result = json.load(open('source.json'))
expected_result = json.load(open('target.json'))

# invoke DeepSearchDict with source,target and validation type
if DeepSearchDict(source, target, "partial"):
    print("Expected and Actual Results Matching")
else:
    print("Expected and Actual Results Not Matching")
Package: nested.tar.archives.extractor
What is a nested tar archive?It is a tar archive containing other tar archives which may further
contain many more tar archives (and so on…)So what does this program do?It extracts tar archives recursively.
It can extract the following tar archives -
.tar, .tgz, .tbz, .tb2, .tar.gz, .tar.bz2What’s different in this?Ordinary extractors normally just extract a tar archive once, ie
they won’t extract any other tar archives (if any) that are present
in it. If it has more tar archives and you want to extract them too,
then you have to yourself extract each of these archives. This can be
a real headache if there are many tar archives (and are nested many
levels deep). I have tried to make this thing easy using this tool.How to access it from terminal?You can access it from the terminal using the command -
extractnested.py tar1.tar [tar2.tar …]
Package: nested-value-extractor
>>>extract_nested_value({1:{2:{3:"value"}}},[1,2,3])"valueExtracts a nested value from a JSON-like dict by specifying a sequence of keys.May be useful to avoid a KeyError exception when accessing JSON-like dicts with a dynamic nested structure,
when you know the key paths of elements, but are unsure whether elements exist.Returns the nested value or None if it wasn't found. Could throw a KeyError exception ifstrict=Trueis passed.Examples>>>extract_nested_value({1:2},[1])2>>>extract_nested_value({1:{2:{3:"value"}}},[1,2,3])"value">>>extract_nested_value({1:2},["a"])None>>>extract_nested_value({1:2},[]){1:2}>>>extract_nested_value({1:2},["a"],strict=True)KeyError:"Keys sequence '["a"]' do not exists in object '{1: 2}'")Argumentsjson_obj: JSON-like dict from which the nested value should be extractedkeys: ordered sequence of keys to derive the nested valuestrict: (defaultFalse) prevents throwing an exception when keys are not exist in nested object when set toFalse.
ThrowsKeyErrorexception if set toTrueand key wasn't found.ReturnsNested value, if the entire chain of keys is present, orNoneDevelopmentLook atMakefile
Package: nestees
NesteesProvides a simple set of utility functions for dealing with nested dictionaries - a.k.a. dict of dicts.
Inspired by the 'field path' concept in Google's superbFirebaseproduct,
these functions fully support the field path syntax and DELETE_FIELD sentinel to allow values nested
arbitrarily deep in standard Python dictionaries to be get, set, updated and deleted.Example Usage: deep_getfromnesteesimportdeep_getdata={'top':{'mid_leaf':'magic','middle':{'leafA':20,'leafB.has.dots':'its real!!!',},}}deep_get(data,'top.mid_leaf')>>>'magic'deep_get(data,'top.middle.leafA')>>>20# field path syntax supports backticsdeep_get(data,'top.middle.`leafB.has.dots`')>>>'its real!!!'# Get supports default valuesdeep_get(data,'my.road.to.nowhere`','some default')>>>'some default'Example Usage: deep_set and deep_deletefromnesteesimportdeep_set,deep_deletedata={}deep_set(data,'top.mid_leaf','magic')repr(data)>>>{'top':{'mid_leaf':'magic',}}deep_set(data,'top.middle.leafA',20)repr(data)>>>{'top':{'mid_leaf':'magic','middle':{'leafA':20},}}# To delete a value, use the DELETE_SENTINELdeep_set(data,'top.mid_leaf.leafA',DELETE_SENTINEL)repr(data)>>>{'top':{'middle':{'leafA':20}}}# or just use deep_deletedeep_delete(data,'top.middle.leafA')repr(data)>>>{'top':{'middle':{}}}Example of deep_updatedeep_update works like Python's built-in dict update method, in that it allows values to be merged at the leaf level, rather than set, if the existing leaf is a dict...fromnesteesimportdeep_updatedata={'top':53}# Updating an integer lead replaces the integer with a dict...deep_update(data,'top.middle',{'name':'John','occupation':'CEO'})repr(data)>>>{'top':{'middle':{'name':'John','occupation':'CEO'}}}>>>deep_update(data,'top.middle',{'gender':'non-binary'})>>>data{'top':{'middle':{'name':'John','occupation':'CEO','gender':'non-binary'}}}
Package: nester2023
This function takes a positional arguments called "the_list","indent" and "level", 'the_list' is
any Python list (of - possibly - nested lists). Each data item in the
provided list is (recursively) printed to the screen on it's own line.A second argument 'indent' is set to default value 'False', which means indentation or tab-stops are not required
by default. If user wants to add indentation while printing the nested list it should be set to 'True'.A Third argument 'level' is used to insert required number of tab_stops when nested list is encountered.Fourth argument 'fh' is used to specify where the output of this function should write (place to write your
data to), a default value of this argument is sys.stdout so that it continues to write to the screen if no file object
is specified when the function is invoked.
Package: nester3663
Print List of ListsThis is example project demonstrating how to publish python module to PyPI.InstallationRun the following to install:'''python
pip install nester3663
'''Usage'''python
from nester3663 import print_lolGeneratea
print_lol(['a'])Generateaprint_lol(['a'], 5)
'''Developing Print List of ListsTo install nester3663, along with the tools you need to develop and run tests, run the following in your virtualenv:'''bash
$ pip install -e .[dev]
'''
Package: nesterkunwar
Step1 - Create a python module which you want to expose as  distribution (*.py)Step2 - use setuptools to prepare a distributionfrom setuptools import setupstep3- create a folder in which you want your code to be created as distributionstep4- move your python file to the new folder created in step3step5 - create a setup.py file in the new folder created in step3step6 - build a distribution filepython3 setup.py sdistsdist             create a source distribution (tarball, zip file, etc.)step7 - install your distribution into your local copy of pythonpython3 setup.py installinstall           install everything from build directory******** YOUR DISTRIBUTION IS READY **************NOW THAT YOUR MODULE IS BUILT, PACKAGED AS A DISTRIBUTION AND INSTALLED --> IT IS READY TO BE IMPORTEDAND USED
Package: nesterlists
Package descriptionVersion 0.0.3This is a simple printer of nested lists in lists.Version 1.0.0The module provides a function called print_lol(), which returns or not the items
in lists that may or may not include nested lists, and can be used with up to 3 arguments:
default (list), indented (list, True) or indented from a value (list, True, 2).
Package: nester_mfb
V.1.0.0 Impresor simple de listas Phyton
V.1.1.0 Nuevo parametro level para indicar numero de TABS que se indentan las lineas
V.1.2.0 El parametro level se hace opcional con valor por defecto de 0.
V.1.3.0 Nuevo parametro indent=False, para indicar si se desea o no indentacion
Package: nester-onbon
hello, this is a package demo from onbonhhahaThis is just a demo, haha.Refer tofromsetuptoolsimportsetup,find_packagessetup(name="test",version="1.0",keywords=("test","xxx"),description="eds sdk",long_description="eds sdk for python",license="MIT Licence",url="http://test.com",author="test",author_email="test@gmail.com",packages=find_packages(),include_package_data=True,platforms="any",install_requires=[],scripts=[],entry_points={'console_scripts':['test = test.help:main']})
Package: nester-package
Refer for commands:https://packaging.python.org/en/latest/tutorials/packaging-projects/#uploading-the-distribution-archivesThis function takes a positional arguments called "the_list","indent" and "level", 'the_list' is
any Python list (of - possibly - nested lists). Each data item in the
provided list is (recursively) printed to the screen on it's own line.
A second argument 'indent' is set to default value 'False', which means indentation or tab-stops are not required
by-default. If user wants to add indentation while printing the nested list it should be set to 'True'.
A Third argument 'level' is used to insert required number of tab_stops when nested list is encountered.
Package: nester_pre
The nester_pre is an open source Python library for nested data processing.
It lets you print nested data out screen or a file.InstallationNester_pre is hosted onPyPIandGitHub.Install Nester_pre from PyPIInstall Nester_pre fromPyPIfor lastest version:$ sudo pip install nester_preIf you don’t have pip, execute$ sudo apt-get install python-pipto install pip on Debian-base Linux distribution.Download and Install manuallypip.Get Nester_pre from GitHubTo follow the lastest version of Nester_pre, use$ git clone https://github.com/immark/nester_pre.gitto clone Nester_pre repository, or download manually fromGitHub.
Package: nester-with-fileObject
This function takes a positional arguments called "the_list","indent" and "level", 'the_list' is
any Python list (of - possibly - nested lists). Each data item in the
provided list is (recursively) printed to the screen on it's own line.A second argument 'indent' is set to default value 'False', which means indentation or tab-stops are not required
by default. If user wants to add indentation while printing the nested list it should be set to 'True'.A Third argument 'level' is used to insert required number of tab_stops when nested list is encountered.Fourth argument 'fh' is used to specify where the output of this function should write (place to write your
data to), a default value of this argument is sys.stdout so that it continues to write to the screen if no file object
is specified when the function is invoked.