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Accessed via a streamlined Information Browser, SciTools Understand provides a comprehensive overview of your software in a quick and easy format. Rapid insight into your code base is enabled through a wide range of metrics, customisable reports, graphs, a set of dependency analysis functions and accurate search facilities. Scitools Understand Download. SciTools is a Python package containing many useful tools for scientificcomputing in Python. The package is built on top of other widely usedpackages such as NumPy, SciPy, ScientificPython, Matlotlib, Gnuplot,VisIt, etc. Scitools understand keygenscitools understand 3.1 keygenscitools understand 4 keygenscitools.

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Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy. It features: object oriented projection definitions. Point, line, polygon and image transformations between projections. Integration to expose advanced mapping in Matplotlib with a simple and intuitive interface. Latest Build - 1029 Download. Need more in depth help on how to solve a problem using Understand? Schedule to meet with one of our Engineers. Other downloads ( Free account required ). Available for Windows, Mac, and Linux. Download and Dive In! You know how to build your code or know somebody that will do it for you. Plan on spending some time setting up the compiling/parsing and then about 3 minutes per 100,000 lines of code analyzing. Scientific Toolworks Understand 5.1.992 Crack. Sholman July 2, 2019. Scientific Toolworks Understand is a static analysis tool for maintaining, measuring, & analyzing critical or large code bases. From metrics as well as graphs to reliance analysis, Master your source code with Understand. Scientific Toolworks Understand Basic.

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Google; About Google; Privacy; Terms. The full command line for removing Understand is C: Program Files SciTools Uninstall Understand.exe. Keep in mind that if you will type this command in Start / Run Note you might get a notification for administrator rights. Understand.exe is the programs's main file and it takes about 37.53 MB (39354872 bytes) on disk.

Cartopy is a Python package designed to make drawing maps for data analysis and visualisation easy.


Overview

Cartopy is a Python package designed to make drawing maps for dataanalysis and visualisation easy.

It features:

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  • object oriented projection definitions
  • point, line, polygon and image transformations between projections
  • integration to expose advanced mapping in Matplotlib with a simple andintuitive interface
  • powerful vector data handling by integrating shapefile reading with Shapelycapabilities

Documentation can be found at http://scitools.org.uk/cartopy/docs/latest/.

Get in touch

  • Ask usage questions onStackOverflow.
  • For less well defined questions, ideas, general discussion or announcementsof related projects use theGoogle Group.
  • Report bugs, suggest features or view the source code onGitHub.
  • To chat with developers and other users you can use theGitter Chat

Credits, copyright and license

Cartopy is developed collaboratively under the SciTools umberella.

A full list of codecontributors ('Cartopy contributors') can be found athttps://github.com/SciTools/cartopy/graphs/contributors.

Code is just one of many ways of positively contributing to Cartopy, please seeour contributing guide for more details on howyou can get involved.

Cartopy is released under a LGPL license with a shared copyright model.See LICENSE for full terms.

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The Met Office has made a significantcontribution to the development, maintenance and support of this library.All Met Office contributions are copyright on behalf of the British Crown.

SciTools is a Python package containing many useful tools for scientificcomputing in Python. The package is built on top of other widely usedpackages such as NumPy, SciPy, ScientificPython, Matlotlib, Gnuplot,VisIt, etc.

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SciTools only strict requirements are Python v2.7 (http://python.org)and Numerical Python (http://numpy.org). Some functionality requiresSciPy (http://scipy.org/). Plotting requires a plotting package, e.g.,Matplotlib, Gnuplot, VisIt, or similar.

There are several different ways of installing SciTools.

Anaconda.If you have Anaconda, SciTools is available as a conda package:

conda install --channel johannr scitools

Normal install ([, ... ] means optional).First check out the scitools package from GitHub:https://github.com/hplgit/scitools. Then go to the scitoolsdirectory and run

This install makes SciTools use Matplotlib as plotting engine, if aa working Matplotlib is found. Otherwise Gnuplot is used (which requiresboth the Gnuplot program in C and the Python interface Gnuplot.py tobe installed). If neither Matplotlib nor Gnuplot is found, Matplotlibis still used as default plotting engine. (The rest of SciTools willwork well, of course.)

Installing Matplotlib is easy on most platforms: just download thetarball, pack it out, and run python setup.py install.

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You can explicitly specify the desired plotting engine on the command line,using the --easyviz_backend option:

The default plotting engine is specified in the file scitools.cfg in thescitools package directory, and the --easyviz_backend option leads toan automatic edit of the 'backend' line in the [easyviz] section ofthat configuration file.

Don't edit the scitools.cfg file manually before installation (usethe --easyviz_backend option) because setup.py will override youredits of the easyviz backend. However, all other edits of theconfiguration file can be edited. For example, the Matplotlib GUIis set to TkAgg (i.e., using Tkinter, which is standard in mostPython installations). The GUI can be set to other values, such asQt4Agg (provided you have Qt4 and a Python interface to it).

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After SciTools is installed, you may edit the installed version ofscitools.cfg, or (better) have your own .scitools.cfg file in yourhome directory.

Setuptools using eggs.First build the egg with the following command

The --easyviz_backend option can be used, as described above, tochange the default plotting engine.

Then install the created egg using easy_install

(Version numbers of SciTools and Python in this filename may vary.)

Debian/Ubuntu packages.If you have Debian, or a Debian based platform like Ubuntu, you can do

as SciTools is now a part of Debian.NOTE: The Debian packages are often old - it is better to relyon checking out the repo and running setup.py.

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The behavior of many parts of SciTools and in particular thesubpackage Easyviz (for plotting) can be controlled in a configurationfile. Please read the subsection 'Setting Parameters in theConfiguration File' under the section 'Advanced Easyviz Topics' in theEasyviz tutorial (pydoc scitools.easyviz will show the tutorial). Inparticular, if you use Matplotlib as the default plotting engine, youmay want to turn on the use of LaTeX for rendering legends, titles,and numbers. By default, LaTeX is turned off when SciTools isinstalled. It is easy to change this by locating the scitools.cfg filein the folder where the SciTools package is installed and edit theline with text.usetex in the [matplotlib] section of this file. Abetter solution is to copy the system scitools.cfg file to.scitools.cfg in your home folder and edit that file. You can alsochange the GUI used by Matplotlib. Users who do not apply Matplotlibfor plotting will seldom need to edit the configuration file.

SciTools is licensed under the new BSD license, see the LICENSE file.

Lumpy.py and Gui.py are licensed under GPL, however, permission isgranted by Allen Downey to include these under a BSD license.

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SciTools was initially mainly developed by Hans Petter [email protected] for his book 'Python Scripting for ComputationalScience' (Springer, 1st edition 2003, 3rd edition 2009).The Easyviz package was mainly developed by Johannes H. [email protected] Johannes H. Ring has been the principalmaintainer of SciTools. The package was extended for thebook 'A Primer on Scientific Programming with Python', bu H. P. Langtangen,4th edition, Springer, 2014.

Some modules included in SciTools are written by others:

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  • Allen B. Downey [email protected] wrote Lumpy.py and Gui.py
  • Imri Goldberg [email protected] wrote aplotter.py
  • Fred L. Drake, Jr. [email protected] wrote pprint2.py
  • Gael Varoquaux [email protected] wrote pyreport

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Code contributors include:

  • Rolv E. Bredesen [email protected]
  • Joachim B. Haga [email protected]
  • Mario Pernici [email protected]
  • Ilmar Wilbers [email protected]
  • Arve Knudsen [email protected]
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