Saravanan666/pypi-org huggingface dataset:
Package: cnfrm
CnfRMImagine: an ORM for configThis library lets you declare your config in a way you might know from
popular ORM's.You getexpressive configuration declarationpopular fileformats (json, yml?)command line optionsenv configuration variablesimportcnfrmclassMyConf(cnfrm.Config):name=cnfrm.Field("no name")size=cnfrm.IntegerField(required=False)path=cnfrm.DirectoryField(required=False)email=cnfrm.EmailField(required=False)filename=cnfrm.FileField()# create a config instanceconfig=MyConf()# start reading things, the later overwrites the formerconfig.read_env()config.read_json("~/myconf.json",quiet=True)config.read_args()# check if the configuration is completeconfig.validate()# write config to file:config.write_json("~/myconf.json")
Package: cnftools
A collection of tools for working with and generating Dimacs CNF files.Installationpip install cnftoolscnftoolsis listed inPyPIand
can be installed withpip.Usagecnftoolsexposes thecnfcommand-line interface for quickly generating
Dimacs CNF files typically for use with a SAT solver.3cnfApply the Tseytin transformation[TSEY1970]to a CNF file producing an output
where all clauses contain 3 or fewer literals.cnf 3cnf -i [input.cnf]simplifySimply the input CNF file.cnf simplify -i [input.cnf] -o [output.cnf]statsProvide details about contents of a CNF file. This includes the number of literals,
the total number of clauses, as well as a histogram of clause lengths.cnf stats -i [input.cnf]karps21This sub-command exposes utilities for generating CNF files based on
Karp’s 21 NP-Complete problems[KARP1972]. For more details on this utility
use the-h/--helpoption.cnf karps21 --helpReferences[TSEY1970]Tseitin, Grigori. “On the complexity of derivation in propositional calculus.”Studies in constructive mathematics and mathematical logic(1968): 115-125.[COOK1971]Cook, Stephen A. “The complexity of theorem-proving procedures.”Proceedings of the third annual ACM symposium on Theory of computing. ACM, 1971.[KARP1972]Karp, Richard M. “Reducibility among combinatorial problems.”Complexity of computer computations. Springer, Boston, MA, 1972. 85-103.
Package: cngi-prototype
All documentation can be found here:https://cngi-prototype.readthedocs.ioInstallation section is here:https://cngi-prototype.readthedocs.io/en/latest/introduction.html#InstallationNote, throughout the documentation we use the variable namexdsto refer to Xarray DataSets, the core data structure on which everything is built. It is important to distinguish objects that are xarray/dask based from other standard Python objects.
Package: cnholiday
CNHoliday: 查询某天是否放假数据来源:年份出处2019国务院办公厅关于 2019 年部分节假日安排的通知2020国务院办公厅关于 2020 年部分节假日安排的通知2021国务院办公厅关于 2021 年部分节假日安排的通知安装：# 不依赖第三方包库pipinstallcnholiday用法：>>>fromdatetimeimportdatetime>>>>>>fromcnholidayimportCNHoliday>>>>>>>>>cnholiday=CNHoliday()>>>_day=datetime(2019,10,1)>>>print(cnholiday.check(_day))True>>>print(cnholiday.check_shift(_day))True>>>print(cnholiday.check_shift(_day,shift=2))True>>>print(cnholiday.check_shift(_day,shift=3))True相关项目：GitHub 上有另一个同名项目https://github.com/valaxy/cnholiday
Package: cn-hyperarr
This package is aSageMathpackage for
computing congruence normality of rank-three, simplicial hyperplane arrangements.This package includes a database of known rank-three simplicial hyperplane
arrangements. It also includes modules for creating vector configurations and
the three infinite families of simplicial rank-three arrangements.
A vector configuration can be seen as the set of normals to a hyperplane
arrangement. A simplicial hyperplane arrangement has a lattice of regions
associated to each chamber. This lattice is congruence normal if it is
obtainable through a sequence of doublings of convex sets.
A hyperplane arrangement can be always or sometimes or never congruence normal,
depending on whether its lattices of regions are congruence normal.Here are examples of arrangements that are always, sometimes, and never
congruence normal.
First we load the normals of the three arrangements from the database.
The entries of the database are labeled in the same way as in[CEL]:sage: from cn_hyperarr import *
sage: always_normals = db_normals_CEL[(6,24,1)]
sage: somet_normals = db_normals_CEL[(10,60,3)]
sage: never_normals = db_normals_CEL[(22,288,1)]

Now we make them into vector configurations::

sage: always_vc = VectorConfiguration([vector(x) for x in always_normals])
sage: somet_vc = VectorConfiguration([vector(x) for x in somet_normals])
sage: never_vc = VectorConfiguration([vector(x) for x in never_normals])

To test congruence normality, use the function `RegionsCongruenceNormal`::

sage: always_check = RegionsCongruenceNormality(always_vc)
sage: always_vals_list = list(always_check.values())
sage: [always_vals_list.count(True), always_vals_list.count(False)]
[24,0]
sage: somet_check = RegionsCongruenceNormality(somet_vc)
sage: somet_vals_list = list(somet_check.values())
sage: [somet_vals_list.count(True), somet_vals_list.count(False)]
[40,20]
sage: never_check = RegionsCongruenceNormality(never_vc)
sage: never_vals_list = list(never_check.values())
sage: [never_vals_list.count(True), never_vals_list.count(False)]
[0,288]The full documentation for the package can be found athttps://sophiasage.github.io/cn_hyperarr/doc/html/NotebooksYou can experience this package on a binder notebook (click on the binder image):References[CEL]Michael Cuntz, Sophia Elia, and Jean-Philippe Labbé. Congruence normality of simplicial hyperplane arrangements via oriented matroids, 2020. arXiv:2009.14152.[Gru]Branko Grunbaum. A catalogue of simplicial arrangements in the real projective plane, 2009. Ars Math. Contemp. 2, no. 1, 1-25.InstallationLocal install from sourceDownload the source from the git repository:$ git clone https://github.com/sophiasage/cn_hyperarr.gitChange to the root directory and run:$ sage -pip install --upgrade --no-index -v .For convenience this package contains a [makefile](makefile) with this
and other often used commands. Should you wish too, you can use the
shorthand:$ make install
Package: cnilsimsa
# cNilsimsaA C implementation of Nilsimsa for Python.`shell $ pip install cnilsimsa `We are building this module one piece at a time. So far, that means onlycompare_hexdigestsbecause needing a faster way to do that was the
primary motivation to start this project.`python from cnilsimsa import compare_hexdigests `It works exactly like the method of the same name from pynilsimsa but
is more than an order of magnitude faster, so if you need to do lots of
deduplication over a large corpus of documents via nilsimsa hex digests
from Python, this should be helpful.Building out the rest of of the methods for representing and cooking
LSHs to provide a full drop-in replacement for pynilsimsa is the longer
term goal.`python import cnilsimsa as nilsimsa `The more complete pure Python implementation is here:https://code.google.com/p/py-nilsimsa/Thanks to the authors of the Ruby/C implementation from which our
ourfillpopcount()function is taken.https://github.com/jwilkins/nilsimsaThanks to the Perl/C implementation that inspired both predecessors.http://ixazon.dynip.com/~cmeclax/nilsimsa.htmlContributions welcome.
Package: cniptest
介绍这是一个Python库，它可以查看当前的IP地址获取iPv4地址import cniptest
print(cniptest.ip())获取ipv6地址import cniptest
print(cniptest.ipv6())检测是否有ipv6，如果有那么就返回ipv6，如果没有就返回iPv4import cniptest
print(cniptest.test())获取北京出口ipv4地址import cniptest
print(cniptest.bjv4())获取北京出口ipv6地址import cniptest
print(cniptest.bjv6())获取南京出口ipv4地址import cniptest
print(cniptest.njv6())获取南京出口ipv4地址import cniptest
print(cniptest.njv6())FAQ问：为什么返回Error？
答：一般是因为没有连接网络，少数情况没有ipv6地址或者没有ipv4地址（没有ipv4的概率极少，除非是故意在路由器关闭ipv4），极少数情况是服务器故障（这种情况下一般一会儿就可以解决，不过有可能是因为API关闭，这种情况下建议更新本库，我在API关闭后会第一时间更新本库的）
Package: cni-tlbx
FilesThis repository contains four files.PEA.py- class implementation of Fourier analysis of phase-encoded stimuli.HGR.py- class implementation of linear RF model based on hashed Gaussians.pRF.py- class implementation of population receptive field mapping.IRM.py- class implementation of input-referred model estimation.gadgets.py- collection of auxiliary tools (used by main tools).
Package: cnk
cnscns常用的工具包, 内置大量常用工具, 正在不断增加中......安装方法pip install cns使用方法import cns

if __name__ == "__main__":

    dfx  = cns.DateTimeX()  # 可初始化 "2023-05-28 23:23:51"

    print("初始时间:", dfx)

    print("昨天的本月区间:", dfx.get("{[年-月-初||复位-1日]}~{[年-月-日]}"))

    print("昨天的去年本月区间:", dfx.get("{[年-月-初||复位-1日-1年]}~{[年-月-末]}"))

    print("昨天的上月区间:", dfx.get("{[年-月-初||复位-1日-1月]}~{[年-月-末]}"))

    print("昨天的近35天区间:", dfx.get("{[年-月-日||复位-35日]}~{[年-月-日||复位-1日]}"))

    print("前35天的日期:", dfx.get("{[年-月-日||复位-35日]}"))

    print("昨天的月1号:", dfx.get("{[年-月-初||复位-1日]}"))

    print("昨天的本月:", dfx.get("{[年-月||复位-1日]}"))


'''
# 说明：
1、分隔符“||” 左侧是获取的时间格式，右侧是时间计算的指令；
2、左左侧的字符串中通包含年、月、日、时、分、秒、初、末，用来获取时间和格式，其中“初”是获数字1，“末”最后一天的日期；
3、右侧是时间计算的指令支持年、月、日、时、分、秒、复位，用来对当前的时间进行加和减的计算，其中“复位”代表恢复到初始时间；

目前支持WINDOWS下 Python 3.6 和 3.9
联系作者：6566666@qq.com
'''
Package: cnki2bib
CNKI_2_BibTeXConverting the NoteExpress (.net) file exported by CNKI (中国知网) to BibTeX (.bib) file.将中国知网导出的 NoteExpress 文献记录转换成 BibTeX 文献记录。中文 READMECNKI_2_BibTeXGetting StartedPrerequisitesInstallationUsingFinally...Double-click to useExport NoteExpress .net File on CNKICopy NoteExpress Entry content to the clipboard.Getting StartedPrerequisitesPython3Installationpip install cnki2bibUsingMake sure it's in yourPATH.cnki2bib [OPTIONS] [INPUTFILE]Arguments:INPUTFILE:The input .net file to be converted. If left blank, the contents in the clipboard will be used.Options:-c, --copy / -nc, --no-copyWhether or not to copy the result to clipboard.Default:True-od, --outputDefault / -nod, --no-outputDefaultWhether or not to create a default .bib file.It has the same name as the source .net file in its directory.Or if the input source is clipboard, it will be 'out.bib' in current working directory.Default:True-o, --outputfile FILENAMECreate a certain output .bib file.-f, --id-format [title|nameyear]Choose the format of the ID of BibTeX entries.The first several words (or their pinyin) in the titleThe first author (or the pinyin) plus year.Default:titleOnce you have assigned a format, your choice will be saved in~/.cnki2bib.cfg. It is unnecessary to type this choice since then.--helpShow this message and exit.Finally...Start using BibTeX to manage the literature references.Double-click to useYou can findcnki2bib.exein your Python/Scripts and set it as the default program to open the .net file.Then when you double-click a .net file, the corresponding BibTeX Entries will be copied to your clipboard, and a .bib file would be created on the same directory.Use the console to check for Exception if it does not work as expected, and issue is welcomed.Export NoteExpress .net File on CNKICopy NoteExpress Entry content to the clipboard.You have to enable Flash to see the "Copy to clipboard" button.Then just runcnki2bibin the terminal. The corresponding BibTeX Entries will be copied to your clipboard, and an 'out.bib' file is created at the current directory. :smile:
Package: cnl2feedcrawler
Click'n'Load2FeedCrawlerClick'n'Load2FeedCrawler fängt Click'n'Load ab, entschlüsselt die Payload und übergibt diese dem FeedCrawler.VoraussetzungenLinux basierte Laufzeitumgebung (inkompatibel mit Windows)Python 3.6oder neuerpipopensslNode.jsFeedCrawlerUpdatepip install -U cnl2feedcrawlerStartencnl2feedcrawler --url=192.168.1.1:9090in der Konsole (Python muss im System-PATH hinterlegt sein)PflichtparameterParameterErläuterung--url=<URL>Die lokale URL des FeedCrawlers - bspw.http://192.168.1.1:9090)Creditsdrwilly
Package: cnlp
cnlp = cn + nlp🔥 专注于中文的「自然语言处理框架」1 功能cnlp.data: 一个通用的文本处理器。cnlp.dataset: 提供常见的数据集加载。cnlp.model: 提供常见的模型。2 安装2.1 pip 安装pipinstallcnlp2.2 手动安装gitclonehttps://github.com/dovolopor-research/cnlp.gitcdcnlp&&pythonsetup.pyinstall3 快速上手中文分词分词是中文自然语言处理最基础的任务之一，我们先来看个分词的例子。fromcnlp.dataimportTokenizert=Tokenizer()# 默认方式为有向无环图分词result=t.cut("为中华之崛起而读书")# result:# ['为', '中华', '之', '崛起', '而', '读书']# 机械分词result=t.cut("为中华之崛起而读书",methods="mechanical")# result:# ['为', '中华', '之', '崛起', '而', '读书']# 获取偏旁result=t.radical("为中华之崛起而读书")# result:# ['丶', '丨', '十', '丶', '@UNK@', '走', '而', '讠', '乛']# 获取部首result=t.stroke("为中华之崛起而读书")# result:# [['点', '撇', '横折竖钩', '点'], ['竖', '横折', '横', '竖'], ['撇', '竖', '撇', '竖弯横钩', '横', '竖'],# ['点', '横撇', '捺'], '@UNK@', ['横', '竖', '横', '竖', '横', '撇', '撇', '竖', '横折竖钩', '竖', '竖'],# ['点', '横折提', '横', '竖', '横撇', '点', '点', '横', '撇', '点'], ['横折', '横折竖钩', '竖', '点']]类别不平衡数据集中的类别不平衡是在实际数据集中经常遇到的问题，通常采用过采样扩充或者欠采样缩减的方式解决。fromcnlp.dataimportsampling# 类别 pos:3 neg:2 unk:1origin_dataset=[["我好开心！","pos"],["今天心情不错啊～","pos"],["学会了 cnlp，真棒！","pos"],["我心态崩了啊！","neg"],["哎，又加班。。。","neg"],["吃雪糕中","unk"]]# 默认为过采样，都扩充到 3dataset=sampling(origin_dataset)# dataset:# [#   ["我好开心！", "pos"],#   ["今天心情不错啊～", "pos"],#   ["学会了 cnlp，真棒！", "pos"],#   ["我心态崩了啊！", "neg"],#   ["哎，又加班。。。", "neg"],#   ["我心态崩了啊！", "neg"],#   ["吃雪糕中", "unk"],#   ["吃雪糕中", "unk"],#   ["吃雪糕中", "unk"]# ]# 也可以使用欠采样，都缩减到1# 这种方式不推荐，容易使模型过拟合dataset=sampling(origin_dataset,mode="under")# dataset:# [#   ["学会了 cnlp，真棒！", "pos"],#   ["我心态崩了啊！", "neg"],#   ["吃雪糕中", "unk"]# ]数据集划分数据集划分是训练模型前的常见操作，这里也提供了一个基本的方法。fromcnlp.dataimportsplitorigin_dataset=["今天天气不错啊","我想吃烧烤","地球人就知道吃","说的好像你们火星人不爱吃一样","没错啊！","好吧","你赢了","那请你离开地球","快走","快快走"]# 默认以 train:test = 7:3 划分train_data,test_data=split(origin_dataset)# train_data:# ['今天天气不错啊', '我想吃烧烤', '地球人就知道吃', '说的好像你们火星人不爱吃一样', '没错啊！', '好吧', '你赢了']# test_data:# ['那请你离开地球', '快走', '快快走']# 支持划分 train val test 三种数据集train_data,val_data,test_data=split(origin_dataset,[0.5,0.3,0.2])# train_data:# ['今天天气不错啊', '我想吃烧烤', '地球人就知道吃', '说的好像你们火星人不爱吃一样', '没错啊！'],# val_data:# ['好吧', '你赢了', '那请你离开地球']# test_data:# ['快走', '快快走']# 支持打乱(shuffle)train_data,val_data,test_data=split(origin_dataset,[0.5,0.3,0.2],shuffle=True)# train_data:# ['你赢了', '说的好像你们火星人不爱吃一样', '快走', '今天天气不错啊', '那请你离开地球']# val_data:# ['我想吃烧烤', '快快走', '地球人就知道吃']# test_data:# ['没错啊！', '好吧']4 文档请在https://cnlp.dovolopor.com中查看它。5 贡献如果您计划为此项目提供新功能，实用程序功能或扩展，请首先打开一个Issues并与我们讨论该功能。6 许可证7 交流欢迎添加微信号：Ailln_，备注「cnlp」，我邀请你进入交流群。8 参考jieba 分词Torch Text
Package: cnlunardate
A Chinese lunar date Python library 中国农历日期 Python 库Installation$pipinstallcnlunardateUsageBasic operations:>>>fromcnlunardateimportcnlunardate>>># Basic constructors>>>cnlunardate(2017,6,1)# 4th attribute defaults to False (month is not leap)cnlunardate.cnlunardate(2017,6,1,False)>>>cnlunardate(2017,6,1,True)# (month is leap)cnlunardate.cnlunardate(2017,6,1,True)>>># Conversions from/to solar date>>>fromdatetimeimportdate>>>d=cnlunardate.fromsolardate(date(2017,6,24))>>>dcnlunardate.cnlunardate(2017,6,1,False)>>>d.tosolardate()datetime.date(2017,6,24)Other supported operations as datetime.date (including pickling):>>>d=cnlunardate.fromordinal(736504)# 736504th day after 0001, 1, 1>>>dcnlunardate.cnlunardate(2017,6,1,False)>>>d.toordinal()736504>>># A cnlunardate object is immutable; all operations produce a new object>>>d.replace(year=2018)cnlunardate.cnlunardate(2018,6,1,False)>>>d.replace(isLeapMonth=True)cnlunardate.cnlunardate(2017,6,1,True)>>>dcnlunardate.cnlunardate(2017,6,1,False)>>>d.weekday()# 0 = Monday5>>>d.isoweekday()# 1 = Monday6>>>t=d.timetuple()>>>foriint:...print(i)...2017# year6# month24# day0005# weekday (0 = Monday)175# 175th day in the year-1>>>ic=d.isocalendar()>>>foriinic:...print(i)...2017# ISO year25# ISO week number6# ISO day number ( 1 = Monday )>>>today=cnlunardate.today()>>>todaycnlunardate.cnlunardate(2019,11,22,False)>>>importtime>>>today==cnlunardate.fromtimestamp(time.time())True>>>cnlunardate.mincnlunardate.cnlunardate(1900,1,1,False)>>>cnlunardate.maxcnlunardate.cnlunardate(2100,12,1,False)Errors:>>>cnlunardate(2101,1,1)ValueError:year2101mustbein1900..2100>>>cnlunardate(2017,13,1)ValueError:month13mustbein1..12>>>cnlunardate(2017,1,30)ValueError:day30mustbein1..29>>>cnlunardate(2017,1,1,True)ValueError:month1isnotleapin2017>>>fromdatetimeimporttimedelta>>>cnlunardate.min-timedelta(days=1)OverflowError:resultoutofrange>>>cnlunardate.max+timedelta(days=1)OverflowError:resultoutofrange>>>cnlunardate("Hello")TypeError:anintegerisrequired(gottypestr)Testing$pytestLicenseMITReferenceChinese calendar Wiki公曆與農曆對照表 - 香港天文台
Package: cnm
Python Package Template📦 一个快速上传到PyPI的 Python Package 模版。上传到 PyPI 后可以使用pip install安装。1 使用方法点击本项目右上角的绿色按钮Use this template（使用此模板），输入名称和说明，完成创建；将项目克隆到本地，这里以本项目为例，实际操作时这里需要替换你自己的项目；gitclonehttps://github.com/Ailln/python-package-template.git--depth1修改配置，文件中有提示；cdyour_package_name# 1. 替换默认项目名称 package_name 为你的项目名称# Usage: bash scripts/set_package_name.sh os_name old_name new_name# os_name 支持的有 `mac` 和 `linux`# old_name 是 package_name# new_name 是你的项目名称bashscripts/set_package_name.shmacpackage_nameyour_package_name# 2. 将 `README.md` 修改为你的项目介绍，也就是你当前在读的这个文本。编写你的 Package 代码，并进行测试。# 在本地进行充分测试bashscripts/local_test.sh上传到 PyPi（需要注册），参考如何发布自己的包到 pypi；bashscripts/upload_pypi.sh更新到 Github。gitpush2 项目结构.
├── package_name # 项目名称
│    ├── shell # 在命令行中执行的代码
│    │    ├── __init__.py
│    │    └── usage.py
│    └── src # 静态资源
│          └── temp.txt
├── scripts
│    ├── set_package_name.sh # 批量替换默认的项目名称
│    ├── local_install.sh
│    ├── local_test.sh
│    └── upload_pypi.sh
├── README.md # 项目文档
├── requirements.txt # 包依赖
├── .gitignore # 忽略文件
├── MANIFEST.in # 要包含在 sdist 命令构建的分发中的文件列表。
├── LICENSE # 这里面的内容为本项目的 License，你需要手动替换它。
└── setup.py # 安装配置3 TODO增加 test 相关代码。4 许可5 参考Packaging Python Projects如何从模板创建仓库？如何发布自己的包到 pypi ？
Package: cnmaps
cnmaps 是一个可以让中国地图画起来更丝滑的地图类python扩展包安装安装 cnmaps 需要满足 Python 的解释器在 3.9 版本及以上。使用pip安装cnmaps 最简单也最快的安装方法是使用 pip 来安装 cnmaps：$ pip install -U cnmaps使用conda安装你也可以使用 conda 安装：$ conda install -c conda-forge cnmaps快速开始绘制国界用最简单直接的方式，来绘制你的第一张中国地图。importcartopy.crsasccrsimportmatplotlib.pyplotaspltfromcnmapsimportget_adm_maps,draw_mapsfig=plt.figure(figsize=(10,10))ax=fig.add_subplot(111,projection=ccrs.PlateCarree())draw_maps(get_adm_maps(level='国'))plt.show()绘制省界cnmaps还可以绘制各省（特区/直辖市）的地图importcartopy.crsasccrsimportmatplotlib.pyplotaspltfromcnmapsimportget_adm_maps,draw_mapsfig=plt.figure(figsize=(10,10))ax=fig.add_subplot(111,projection=ccrs.PlateCarree())draw_maps(get_adm_maps(level='省'),linewidth=0.8,color='r')plt.show()绘制市界cnmaps可以绘制市级的行政区地图。importcartopy.crsasccrsimportmatplotlib.pyplotaspltfromcnmapsimportget_adm_maps,draw_mapsfig=plt.figure(figsize=(15,15))ax=fig.add_subplot(111,projection=ccrs.PlateCarree())draw_maps(get_adm_maps(level='市'),linewidth=0.5,color='g')plt.show()绘制区县界cnmaps可以绘制区县级的行政区地图。importcartopy.crsasccrsimportmatplotlib.pyplotaspltfromcnmapsimportget_adm_maps,draw_mapsfig=plt.figure(figsize=(20,20))ax=fig.add_subplot(111,projection=ccrs.PlateCarree())draw_maps(get_adm_maps(level='区县'),linewidth=0.8,color='r')plt.show()使用指南针对本项目更多的使用方法，我们还有一份更详细的文档：cnmaps使用指南
Package: cnmfsns
cNMF-SNS: powerful factorization-based multi-omics integration toolkitAuthors:Ted Verhey,Heewon Seo,Sorana MorrissycNMF-SNS(consensus Non-negative Matrix Factorization Solution Network Space) is a Python package enabling mosaic integration of bulk, single-cell, and
spatial expression data between and within datasets. Datasets can have partially overlapping features (eg. genes) as well as non-overlapping features. cNMF provides arobust,
unsuperviseddeconvolution of each dataset into gene expression programs (GEPs).Network-based integrationof GEPs enables flexible integration of many datasets
across assays (eg. Protein, RNA-Seq, scRNA-Seq, spatial expression) and patient cohorts.Communities with GEPs from multiple datasets can be annotated with dataset-specific
annotations to facilitate interpretation.⚡Main FeaturesHere are just a few of the things that cNMF-SNS does well:Identifies interpretable, non-negative programs at multiple resolutionsMosaic integration does not require subsetting features/genes to
a shared or overdispersed subsetIdeal for incremental integration (adding datasets one at a time) since
deconvolution is performed independently on each datasetIntegration performs well even when the datasets have mismatched features
(eg. Microarray, RNA-Seq, Proteomics) or sparsity (eg single-cell vs bulk RNA-Seq and ATAC-Seq)Two interfaces: command-line interface for rapid data exploration and python
interface for extensibility and flexibility🔧 Install☁️ Public ReleaseInstall the package with conda (in an isolated conda environment)condacreate-ncnmfsns-cconda-forgecnmfsns
condaactivatecnmfsns📖 Documentation🗐 Data guidelinescNMF-SNS can factorize a wide variety of datasets, but will work optimally in these conditions:Use untransformed (raw) data where possible, and avoid log-transformed data.For single-cell or spatial RNA-Seq data, the best data to use is feature counts, then TPM-normalized values, then RPKM/FPKM-normalized values.📓 Python interfaceTo get started, sample proteomics datasets and a Jupyter notebook tutorial is availablehere.Detailed API reference can be found onReadTheDocs.⌨️ Command line interfaceSee thecommand line interface documentation.💭 Getting HelpFor errors arising during use of cNMF-SNS, create and browse issues in theGitHub "issues" tab.
Package: cnmv-data
CNMV Portfolio extractionhttps://pypi.org/project/cnmv-data/1.0.1/Por Antonio Fernández Troyano - https://www.fernandeztroyano.esLibrería que te permite extraer las carteras reportadas por los Fondos de Inversión
de manera trimestral a la CNMV en formato PDF.Para utilizarlo simplemente necesitas disponer de un fichero PDF del FI en el que se incluya la cartera
y siga el formato exigido por la CNMV.
Básicamente la tabla reportada incluye los siguientes datos:- ISIN + Nombre de la empresa
- Divisa
- Valor de mercado actual
- % sobre el total del fondo actual
- Valor de mercado del pasado trimestre
- % sobre el total del fondo del último trimestre♻️Update 1.0.1♻️: En el output se separan el ISIN del nombre del producto.♻️Update 1.0.2♻️: Correcciones ortográficas.♻️Update 1.0.3♻️: Limitación del "split" con max 1 para separar ISIN del Nombre .💥 Output[['US0082521081', 'ACCIONES|Affiliated Managers Group', 'USD', '2.071', '0,80', '0', '0,00'],
    ['FR0011476928', 'ACCIONES|Fnac Darty SA', 'EUR', '222', '0,09', '0', '0,00'],
    ['US5006881065', 'ACCIONES|Kosmos Energy LTD', 'USD', '3.145', '1,22', '4.726', '1,05'],
    ['IT0005252140', 'ACCIONES|Saipem SPA', 'EUR', '2.631', '1,02', '5.321', '1,18']]💡 PrerequisitosPython 3🛠️ Instalación:Con PyPIpip3 install cnmv_data📚 Ejemplo de usofrom cnmv_data import get_portfolio

get_portfolio('.\Q1.pdf')💥 Output[['US0082521081', 'ACCIONES|Affiliated Managers Group', 'USD', '2.071', '0,80', '0', '0,00'],
    ['FR0011476928', 'ACCIONES|Fnac Darty SA', 'EUR', '222', '0,09', '0', '0,00'],
    ['US5006881065', 'ACCIONES|Kosmos Energy LTD', 'USD', '3.145', '1,22', '4.726', '1,05'],
    ['IT0005252140', 'ACCIONES|Saipem SPA', 'EUR', '2.631', '1,02', '5.321', '1,18']]Se puede convertir directamente en un DataFrame:pd.DataFrame(result, columns = ['ISIN', 'Nombre', 'Divisa', 'Actual_VM', 'Actual_%', 'Pasado_VM', Pasado_%'])ISINNombreDivisaActual_VMActual_%Pasado_VMPasado_%US0082521081ACCIONES Affiliated Managers GroupUSD2.0710,8000,00FR0011476928ACCIONES Fnac Darty SAEUR2220,0900,00US5006881065ACCIONES Kosmos Energy LTDUSD3.1451,224.7261,05IT0005252140ACCIONES Saipem SPAEUR2.6311,025.3211,18🐸 Aloha!