Saravanan666/pypi-org huggingface dataset:
Package: nldi-el-serv
NLDI Elevation ServicesNLDI Elevation SevicesFree softwareDocumentation:https://nldi-el-serv.readthedocs.io.Developmentconda env create -f .requirements_dev.ymlconda activate nldi_el_servpip install -e .Featuresnldi_el_serv xsatpoint -f test1.json –lonlat -103.80119 40.2684  –width 1000 –numpoints 101nldi_el_serv xsatendpts -f test2.json -s -103.801134 40.267335 -e -103.800787 40.272798 -c epsg:4326 -n 101CreditsCLI developed from example:https://github.com/pallets/click/blob/master/examples/repo/repo.pyThis package was created withCookiecutterand the _Scientific-python-cookiecutter.DisclaimerThis software is preliminary or provisional and is subject to revision. It is
being provided to meet the need for timely best science. The software has not
received final approval by the U.S. Geological Survey (USGS). No warranty,
expressed or implied, is made by the USGS or the U.S. Government as to the
functionality of the software and related material nor shall the fact of release
constitute any such warranty. The software is provided on the condition that
neither the USGS nor the U.S. Government shall be held liable for any damages
resulting from the authorized or unauthorized use of the software.History0.1.5 (2021-04-23)more tests, still working out the kink0.1.4 (2021-04-23)more tests, still working out the kinks0.1.3 (2021-04-13)added xsatendpts to CLI0.1.1 (2021-04-12)Some further testing of the package0.1.0 (2021-02-02)First release on PyPI.
Package: nldi-xstool
Nldi XstoolFeaturesTODORequirementsTODOInstallationYou can install nldi-xstool viapipfromPyPI:pipinstallnldi-xstoolCommand Line UsagePlease see the {doc}usageContributingContributions are very welcome.
To learn more, see the {doc}contributing.LicenseDistributed under the terms of theCC0 1.0 Universal license,Nldi Xstoolis free and open source software.Disclaimer{doc}disclaimerIssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@hillc-usgs'sPygeoapi Plugin
Cookiecuttertemplate.
Package: nl-dotter
A Dotfile Link Farm ManagerExample Dotfile RepoExample Usage:git clone https://github.com/NonLogicalDev/conf.dotfiles ~/.config/dotter
python3 -m pip install --user nl-dotter
dotter linkUsage Output:Root Command:usage: dotter [-h] {version,link,config} ...

A dotfile linker. This utility creates a link farm from a data root to users home directory.
It's intended use is to keep dotfiles neatly organized and separated by topics.

optional arguments:
  -h, --help            show this help message and exit

command:
  {version,link,config}
    version             print version and exit
    link                link dotfiles from $HOME/.config/dotter
    config              query configuration valuesLink Command:usage: dotter link [-h] [--root ROOT_DIR] [--conf-dir CONF_DIR] [-c CATEGORY]
                   [-t TOPIC] [-f] [-d] [-b]

links files into the home directory from the data root.

optional arguments:
  -h, --help           show this help message and exit
  --root ROOT_DIR      Alternative root location (for testing configuration)
  --conf-dir CONF_DIR  Alternative configuration location (for testing configuration)
  -c CATEGORY          Specify a category to sync (defaults to common)
  -t TOPIC             Specify a topic to sync (inside a category)
  -f                   Force execution
  -d                   Dry run current setup
  -b                   Backup files and place new ones in place, appends ".backup"
Package: nldsl
NLDSL is a tool to create domain specific languages (DSLs) for data science, which can be translated into executable code. A new DSL is created by deriving from the CodeGenerator class and rules are added to it via simple python functions. Besides providing code generation NLDSL allows the user to define DSL-level function, which are then treated as first-class rules. Currently we provide extensions for Pandas and PySpark.
Package: nlearn
♆ NEPTUNENEPTUNEis a set of decoupledPython librariesintended to facilitate Python development:nfin,ngui,nlearn,nmath andnutil.The librarynlearncontains machine learning utility functions; among them:Gaussian mixture models,regression models andNLP models (including a handler for Global Vectors for Word Representation).NEPTUNE is based on popular libraries like numpy, pandas, plotly and scipy.InstallLaunch the following commands in a shell:gitclonehttps://github.com/b-io/io.barras.gitcdio.barras/python/neptune/
mvncleaninstallLicenseFeel free to download, try and share your suggestions about the libraries,
while respectingthis MIT License (MIT).
Package: nlecloud-framework
一、框架介绍此框架最主要是，是要对tornado进行一个封装的web框架，当然其中也包括了很多的常用的工具在根项目的utils下，以及一些
libs库项目。版本更新说明0.1.17crypto加密模块新增私钥加密数据，公钥进行验证私钥签名，这种方式常常被用在区块链中。0.1.16新增openai chatgpt 程序位置nlecloud_framework\libs\chatgpt\open_chat_gpt.py新增在windows的cmd下输出颜色文字新增时间模块帮助类二、配置文件介绍对于配置文件我们进行了单例模式，之后朋友你要使用框架，只要在根目录创建client_config.py作为你的配置文件。然后就可以在整个项目中使用from nlecloud_framework import config 去导入你的配置信息。放心这个配置只会被加载一次。其中client_config.py你可以这样写：importthreadingimporttimeimportosclassOSS_config(object):ak_id="FlRFRTaHhCbVJLQW9OSDR"ak_secret="FlRFRTaHhCbVJLQW9OSDRW9OSDRx"bucket_name="hezi-he"demain="https://hezi.oss-cn-zhangjiakou.aliyuncs.com/"end_point="oss-cn-hezi.aliyuncs.com"classclient_config:# 单例模式需要加锁判断_instance=None_lock=threading.RLock()# 项目的跟目录project_path=os.path.dirname(os.path.abspath(__file__))# 加密秘钥，可以使用nlecloud_framework\utils\crypto.py 进行生成crypto_key="QzFxS2FlRFRTaHhCbVJLQW9OSDRxcGxONWlneTRnZlA2VlpwSWRNR3p4VT0="# 文件的临时位置temp_path=os.path.join(project_path,"temp_file")ifnotos.path.exists(temp_path):os.mkdir(temp_path)# oss的配置信息oss_config=OSS_config()def__new__(cls,*args,**kwargs):"""实现单例模式:param args::param kwargs:"""ifcls._instance:returncls._instancewithcls._lock:ifcls._instance:returncls._instancecls._instance=object.__new__(cls)returncls._instance三、工具类介绍3.1：加密库nlecloud_framework\utils\crypto.py可以使用genRandomKey方法生成一个秘钥，后期可以使用plain_to_ciphert和cipher_to_plain进行数据加解密可以使用create_rsa_pair去创建一个2048位的非对称加密，后期使用encryption和decryption进行加解密if__name__=='__main__':# 1、获取加密秘钥key=CryptoHelper.genRandomKey()ciphertext=CryptoHelper.plain_to_ciphert(key,plaintext="明文信息")plaintext=CryptoHelper.cipher_to_plain(key,ciphertext=ciphertext)print("密文：",ciphertext)print("明文：",plaintext)# 2、非对称加密# 生成秘钥对public_key,private_key=CryptoHelper.create_rsa_pair(is_save=False)# 读取公钥信息public_key=CryptoHelper.read_public_key("crypto_public_key.pem")# 加密text='123456'text_encrypted_base64=CryptoHelper.encryption(text,public_key)print('密文：',text_encrypted_base64)# 解密text_decrypted=CryptoHelper.decryption(text_encrypted_base64,private_key)print('明文：',text_decrypted)3.2：nlecommon库可以将多行一大串字符，变成一个列表如下面这样格式if__name__=='__main__':a="""aliyun-python-sdk-core==2.13.36aliyun-python-sdk-kms==2.16.0"""stringTextLine_to_list(a,is_print=True)# 输出信息，输出转换好的文本信息# ['aliyun-python-sdk-core==2.13.36', 'aliyun-python-sdk-kms==2.16.0']3.3：阿里云的OSS封装阿里官网：https://www.aliyun.com/product/oss当然了你要自己去购买服务，然后对client_config.py中OSS_config进行替换。上传文件fromnlecloud_framework.utils.ossimportOSSHelperfromnlecloud_frameworkimportconfigif__name__=='__main__':file_name,resource_path=OSSHelper.oss_upload(r"assets\Snipaste_2023-03-03_08-59-48.png",oss_config=config.oss_config,file_folder="framework")print(file_name,resource_path)# 输出信息# file_name ：framework/e6b8d3185.png# resource_path ：https://hezi.oss-cn-zhangjiakou.aliyuncs.com/framework/e6b8d3185.png3.4：自动试卷主要是将Excel的的题库，转化为word的格式的试卷比如像这样的转换：fromnlecloud_framework.libs.exam.exam_paperimportExamPaperHelperfromnlecloud_frameworkimportconfigif__name__=='__main__':obj=ExamPaperHelper()# 数据Excel文件名和要保存的试卷文件名obj.run(excel_name="试题.xlsx",word_name="新的试卷")# 输出程序如何使用obj.usage()
Package: nl-ego-cities
NLGeoCitiesNLGeoCities is a dataset containing latitude and longitude coordinates for cities in the Netherlands.OverviewNLGeoCities provides geographical coordinates for various cities across the Netherlands. This dataset is useful for a wide range of applications, including geographic visualizations, location-based services, and spatial analysis.InstallationYou can install NLGeoCities via pip:pipinstallnl-ego-citiespypi page link is here:(https://pypi.org/project/nl-ego-cities/)The documentation for NLGeoCities can be found here:DocumentQuick StartGet City Coordinates JSON File from NLGeoCitiesTo get started, you can obtain a JSON file containing city coordinates from NLGeoCities. Here's how to do it in Python:fromnl_ego_citiesimportcity_coords# Get the JSON filecities=city_coords.city_json# Access city coordinatesamsterdam_coords=cities["amsterdam"]print(f"Coordinates of Amsterdam: latitude:{amsterdam_coords['lat']}, longitude:{amsterdam_coords['lon']}")Processing DataFrame with get_lat_lon FunctionIf you have a DataFrame and want to add latitude and longitude columns using NLGeoCities, you can use the get_lat_lon function. Here's how to do it:fromnl_ego_cities.load_city_dataimportget_lat_lon# Assuming 'df' is your DataFrame with a 'city' columndf['city_lat'],df['city_lon']=get_lat_lon(df,'city')Note: The keys in the JSON file are lowercase.JSON Data StructureThe JSON file obtained from NLGeoCities follows this structure:{"amsterdam":{"lat":52.3676,"lon":4.9041},"rotterdam":{"lat":51.9225,"lon":4.4792},...}
Package: nlesc-CAT
Compound Attachment ToolCATis a collection of tools designed for the construction of various chemical compounds.
Further information is provided in thedocumentation.Package installationCATcan be installed via pip as following:CAT:pip installnlesc-CAT--upgradeNote that, while not strictly necessary, it is recommended to first create a conda environment:Download and install miniconda for python3:miniconda(also you can install the completeanacondaversion).Create a new virtual environment:conda create--nameCAT pythonActivate the environment::conda activate CATInput filesRunningCATand can be done with the following command:init_cat my_settings.yaml. The user merely has to provide ayamlfile
with the job settings, settings which can be tweaked and altered to suit ones
purposes (seeexample1). Alternatively,CATcan be run like a regular
python script, bypassing the command-line interface
(i.e.python input.py, seeexample2).An extensive description of the various available settings is available in
thedocumentation.ReferencesBelić, J.; van Beek, B.; Menzel, J. P.; Buda, F.; Visscher, L.Systematic Computational Design and Optimization of Light Absorbing Dyes.J. Phys. Chem. A2020,124(31), 6380–6388.van Beek, B.; Zito, J.; Visscher, L.; Infante, I. CAT: A Compound Attachment Tool for the construction of composite chemical compounds.J. Chem. Inf. Model.(submitted).
Package: nlesc-flamingo
flamingoCompute and filter molecular properties. Seedocumentation.InstallationDownload miniconda for python3:miniconda.Install according to:installConda.Create a new virtual environment using the following commands:conda create-nflamingoActivate the new virtual environmentconda activate flamingoTo exit the virtual environment typeconda deactivate.Dependencies installationType in your terminal:conda activate flamingoUsing the conda environment the following packages should be installed:installRDKitandH5PY:conda install -y -q -c conda-forge  h5py rdkitPackage installationFinally install the package:Installflamingousing pip:
-pip installnlesc-flamingoNow you are ready to useflamingo.ContributingIf you want to contribute to the development of flamingo,
have a look at thecontribution guidelines.LicenseCopyright (c) 2020-2021, Netherlands eScience CenterLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.CreditsThis package was created withCookiecutterand theNLeSC/python-template.
Package: nlfepy
nlfepySimple finite element Python library for nonlinear problemsInstallationpipinstallgit+https://github.com/sKujirai/nlfepyQuick startFor the detail procedure, refer to asample notebookfromnlfepyimportMesh,Material,Variable,Constitutive,Viewerfromnlfepy.interfaceimportPVWfromnlfepy.ioimportVtuWriterfromnlfepy.utilimportcalc_element_value# Read meshmesh=Mesh()mesh.read(mesh_path)# or generate mesh using dmsh# import dmsh# geo = dmsh.Rectangle(0., 1., 0., 1.)# coords, connectivity = dmsh.generate(geo, 0.1)# mesh = Mesh()# mesh.set_shape(coords=coords.T, connectivity=connectivity)# mesh.set_bc(constraint='compression', value=0.001)# Set materialmater=Material('Al')# Physical quantitiesvals=Variable()# Set constitutive equation classconstitutive=Constitutive(mater,nitg=mesh.n_tintgp,val=vals['itg'])# Solve a governing equation (Principle of virtual work)pvw=PVW(mesh=mesh,cnst=constitutive,val=vals['point'],)# Solve KU=Fpvw.solve()# Calc. stress (optional)pvw.calc_stress()# Calc. element values (for output)calc_element_value(mesh=mesh,values=vals)# Plot resultsviewer=Viewer()viewer.plot(mesh=mesh,val=vals['element']['stress'][:,1],title='Stress YY',)viewer.save('result.png',transparent=True,dpi=300)# Contour plotviewer.contour(mesh=mesh,val=vals['point']['u_disp'][1],title='Displacement Y')# Save results in VTK XML formatwriter=VtuWriter(mesh=mesh,values=vals)writer.write('result.vtu')
Package: nlgen
=====nlgen=====.. image::https://travis-ci.org/toumorokoshi/nlgen/settings#:alt: build status:target: https://travis-ci.org/toumorokoshi/nlgenNatural Language Generator for Python-------Summary-------nlgen provides tools that help with natural language generation. nlgen currently features:- a featured context free grammar representation and generator.------------Documentation------------http://nlgen.readthedocs.io/en/latest/
Package: nlglib
Current StatusThis is just a starting point - an alpha version - mainly a wrapper
aroundSimpleNLG.Although it is possible to go from first order logic to text,
there is no sophistication in how things are processed.
The library is still missing basics like aggregation or referring expression generation.InstallationEither download the code from the repository and runpython setup.py installor use pip:pip install nlglib.IntroNLGlib is a library for natural language generation (NLG) written in Python.
It seeks to fill a gap in the NLG field. There are currently no off-the-shelf
libraries that one could take and incorporate into other projects.
The aim of this library is to be useful for general projects that would like
to add a bit of text generation to their capabilities.AudienceThe library should be usable by programmers with no prior linguistic knowledge.
Given that the aim of the library is language generation,
some linguistic knowledge is necessary but you should be able to pick it up
from the examples.ScopeThe aim of the library is to create a base for NLG system starting from content
selection all the way to realisation. The library will cover document structuring
tools, lexicalisation, referring expression generation and aggregation.
Realisation will be done using other realisation libraries (SimpleNLGorpynlg).HistoryNLGlib started as a part of the EPSRC project
Scrutable Autonomous Systems (SAsSy): www.scrutable-systems.org
When the project finished, the code was moved to this repository to create
a stand-alone re-usable library.Examplefromnlglib.realisation.simplenlg.realisationimportRealiserfromnlglib.microplanningimport*realise_en=Realiser(host='nlg.kutlak.info',port=40000)realise_es=Realiser(host='nlg.kutlak.info',port=40001)defmain():p=Clause("María","perseguir","un mono")p['TENSE']='PAST'# expected = 'María persigue un mono.'print(realise_es(p))p=Clause(NP("la","rápida","corredora"),VP("perseguir"),NP("un","mono"))subject=NP("la","corredora")objekt=NP("un","mono")verb=VP("perseguir")subject.premodifiers.append("rápida")p.subject=subjectp.predicate=verbp.object=objekt# expected = 'La rápida corredora persigue un mono.'print(realise_es(p))p=Clause(NP('this','example'),VP('show','how cool simplenlg is'))# expected = This example shows how cool simplenlg is.print(realise_en(p))if__name__=='__main__':main()
Package: nlgserv
nlgserv is a simple server that accepts JSON representations of sentences and generated English sentences from those. Source code available:https://github.com/mnestis/nlgservIt uses SimpleNLG (under the MPL 2.0 licence) available athttps://code.google.com/p/simplenlg/for natural language generation.In order to use SimpleNLG (which is implemented in Java), Jython 2.7beta3 (fromhttp://www.jython.org) is also bundled, under the terms of the PSF v.2 (http://www.jython.org/license.html).Additionally, it uses Bottle v0.12.7 (under the MIT licence) (available athttps://github.com/defnull/bottle/tree/0.12.7) for handling HTTP requests.
Package: nlhappy
nlhappy📌   简介nlhappy致力于复现自然语言处理各类任务的SOTA模型。文档地址:notion文档它主要的依赖有transformers: 下载预训练权重pytorch-lightning: 模型训练datasets: 构建数据集pydantic: 数据校验📌   安装安装nlhappy推荐先去pytorch官网安装pytorch和对应cuda# pip 安装pipinstall--upgradepip
pipinstall--upgradenlhappy其他可选推荐安装wandb用于可视化训练日志安装:pipinstallwandb注册:https://wandb.ai/获取认证:https://wandb.ai/authorize登陆:wandblogin使用# 命令行训练
nlhappy datamodule=xxx model=xxx trainer=xxx logger=wandb模型训练开始后去官网查看训练实况📌   模型复现实体抽取模型名称参考链接GlobalPointer科学空间EfficientGlobalPointer科学空间关系抽取模型名称参考链接GPLinker科学空间事件抽取模型名称参考链接GPLinker科学空间答案抽取模型名称参考链接GPLinker-
Package: nligraphspacy
NLIGraphSpacyKnowledge graph using NLP SpacyInstallationpipinstallnligraphspacyImplementationfromnligraphspacyimportNLIGRAPHnligraph=NLIGRAPH.RelationEntityExtract("She worked in the city of London")nligraph.process_text()# ('She', 'worked', 'London')nligraph.get_seperate_entities()# [{'text': 'A', 'label': ''},# {'text': 'DAG', 'label': 'SOURCE-NODE'},# {'text': 'is', 'label': ''},# {'text': 'used', 'label': 'EDGE'},# {'text': 'for', 'label': ''},# {'text': 'organizing', 'label': ''},# {'text': 'tasks', 'label': 'TARGET-NODE'}]
Package: n-link-simulator
# Simulators for double and quad link pendulum.## Prerequisites
Numpy
C++ Compiler## InstallRun ‘pip install n-link-simulator’## Acknowledgement
Quad link code is taken from  Chris Atkeson’s page (http://www.cs.cmu.edu/~cga/kdc/dynamics-2d/dynamics4.c).
The corresponding section is marked.
This package only provides a simple method of installation and usage in Python.
Package: nlist
​​​nlist is a lightweight multidimensional list in Python.nlist supports Python 3.4+.Example codefrom nlist import NList

l = NList([[1, 2], [3, 4]])
l[1, 0] = '42'
print(l.index('42')) #=> (1, 0)Documentationhttp://nlist.readthedocs.org/Installationpip install nlistOr just grabnlist.py.LicenseMIT (see LICENSE.txt)
Package: nlit
nlitDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nnlit--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallnlitnbdev# activate conda environment$condaactivatenlit# make sure the nlit package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the nlit 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:$pipinstallgit+https://github.com/dsm-72/nlit.gitor fromconda$condainstall-cdsm-72nlitor frompypi$pipinstallnlitDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: nllegalcit
nllegalcitA Python library to find citations to Dutch legal documents in natural language text.This library is partially based on thelinkextractordeveloped byKOOPto implement the Linked Data Overheid (LiDO). The aim ofnllegalcitis to provide a more generally accessible way to recognize Dutch legal citations in natural language text.Please note that this library is currently under development. It will probably not work reliably.Supported citationsThe following types of citations are implemented, work in progress, or planned:Citation typeImplementation statusImplementation notesKamerstukken (Dutch parliamentary documents)⚠️ Work in progressWorks reasonably well for modern (>1995) citations following the guidelines. Older citations may work. Simple page number notations work.Handelingen (Dutch parliamentary minutes)🗓️ PlannedECLI case law citations⚠️ Work in progressSeems to work, but more testing is needed. Paragraph information is not parsed.Dutch case law other than ECLI🗓️ PlannedDutch national laws🗓️ PlannedDutch treaties🗓️ PlannedDutch local law🗓️ PlannedEU law🗓️ PlannedLicenseCopyright (c) 2023 Martijn Staal <nllegalcit [a t ] martijn-staal.nl>Available under the European Union Public License v1.2 (EUPL-1.2), or, at your option, any later version.
Package: nllgrid
NLLGridPython class for reading and writingNonLinLocgrid files.(c) 2015-2021 Claudio Satriano, Natalia PoiataInstallationUsing pip and PyPI (preferred method)The latest release of NLLGrid is available on thePython Package Index.You can install it easily throughpip:pip install nllgridFrom nllgrid GitHub releasesDownload the latest release from thereleases page,
inziportar.gzformat, then:pip install nllgrid-X.Y.ziporpip install nllgrid-X.Y.tar.gzWhere,X.Yis the version number (e.g.,1.3).
You don't need to uncompress the release files yourself.From nllgrid GitHub repositoryIf you need a recent feature that is not in the latest release (see theunreleasedsection inCHANGELOG), you want to use the source
code from thenllgrid GitHub repository.For that, clone the project:git clone https://github.com/claudiodsf/nllgrid.git(avoid using the "Download ZIP" option from the green "Code" button, since
version number is lost), then install the code from within thenllgridmain directory by running:pip install .(usepip install -e .to install in developer mode).Getting StartedReading a NLL gridA NLL grid is composed of two files (.hdrand.buf).To read a NLL grid, do:>>>fromnllgridimportNLLGrid>>>grd=NLLGrid('somegrid.hdr')or, using the.buffilename:>>>grd=NLLGrid('somegrid.buf')or even without any extension:>>>grd=NLLGrid('somegrid')A grid description can be obtained by:>>>print(grd)The grid data array is accessed bygrd.array.
The grid can be plotted doing:>>>grd.plot()Use Python introspection (e.g.dir(grd)) to see all the available
methods and attributes.Creating a NLL gridSuppose that you have a 3D data array stored into a NumPy array
calledmydata.First, create an empty NLL grid object:>>>fromnllgridimportNLLGrid>>>grd=NLLGrid()then, add the data array and information on grid sampling and grid
origin, e.g.:>>>grd.array=mydata>>>grd.dx=0.5#km>>>grd.dy=0.5#km>>>grd.dz=0.5#km>>>grd.x_orig=-10#km>>>grd.y_orig=-20.#km>>>grd.z_orig=-1.#kmOptionally, add a grid type and/or a geographic transformation:>>>grd.type='VELOCITY'>>>grd.orig_lat=40.63>>>grd.orig_lon=15.80>>>grd.proj_name='LAMBERT'>>>grd.first_std_paral=38.>>>grd.second_std_paral=42.>>>grd.proj_ellipsoid='WGS-84'Finally, give a basename and write to disk:>>>grd.basename='mygrid'>>>grd.write_hdr_file()>>>grd.write_buf_file()This will create the two filesmygrid.hdrandmygrid.buf.If you want to save your grid in double precision (required for
instance by NLDiffLoc), changegrd.float_typeto'DOUBLE'before
saving the grid (default is'FLOAT'):>>>grd.float_type='DOUBLE'Note that if you want to use your grid as input for NonLinLocGrid2Timecode, the grid type has to beSLOW_LENand your grid
array has to be transformed into slowness (in s/km) and multiplied
by the grid step (in km).
Package: nlmaps-tools
nlmaps-toolsTools for working withNLMapsdata structures.Processing ToolTo transform some form of a query into another, you can use theProcessingTool, also via the command line by
executingpython3 -m nlmaps_tools.process.Example: Turn MRL into Overpass queries and resultspython3 -m nlmaps_tools.process \
    --wanted Will2021MultiAnswer --wanted Will2021Features \
    --given "Will2021MRL=query(area(keyval('name','Paris')),nwr(keyval('amenity','library')),qtype(latlong))"