معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

Exoplanet GCM

ویژگی	مقدار
سیستم عامل	-
نام فایل	exoplasim-legacy-3.0.0a18
نام	exoplasim-legacy
نسخه کتابخانه	3.0.0a18
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Adiv Paradise
ایمیل نویسنده	paradise.astro@gmail.com
آدرس صفحه اصلی	https://github.com/alphaparrot/ExoPlaSim
آدرس اینترنتی	https://pypi.org/project/exoplasim-legacy/
مجوز	GNU General Public License

.. -*- coding:utf-8 -*- ================================== ExoPlaSim-Legacy Python API README ================================== Created by Adiv Paradise Copyright 2020, Distributed under the General Public License This API was written with Python 3 in mind, but should work with Python 2 and outdated versions of NumPy. Read the full documentation at http://exoplasim.readthedocs.io. Requirements ------------ * numpy * scipy * matplotlib (only needed for additional utilities) * GNU C (gcc/g++) and Fortran (gfortran) compilers (for Python utilities) * (optionally) Other compilers whose use you prefer for the model itself * (optionally) MPI libraries for those compilers * netCDF4 (optional) * h5py (optional) Installation ------------ :: pip install exoplasim-legacy OR:: python setup.py install The first time you import the module and try to create a model after either installing or updating, ExoPlaSim will run a configuration script, write the install directory into its source code, and (if applicable) compile the burn7 NetCDF postprocessor. Multiple output formats are supported by the built-in `pyburn` postprocessor. If you wish to use HDF5 or NetCDF output formats, you will need the netCDF4-python and h5py libraries, respectively. You can ensure these are included at install-time by specifying them: :: pip install exoplasim-legacy[netCDF4] OR:: pip install exoplasim-legacy[HDF5] OR:: pip install exoplasim-legacy[netCDF4,HDF5] You may also configure and compile the model manually if you wish to not use the Python API, by entering the exoplasim/ directory and running first configure.sh, then compile.sh (compilation flags are shown by running ``./compile.sh -h``). The postprocessor and its libraries can be compiled by entering ``exoplasimlegacy/postprocessor/`` and running ``./build_init.sh``. burn7 compilation ----------------- You must have NetCDF libraries available in the path to build burn7. The burn7 compilation process will build and compile a patched version of the NetCDF libraries necessary for burn7--burn7 makes use of features anachronistic to a particular version of NetCDF that no longer exists. PlaSim Documentation -------------------- Original PlaSim documentation is available in the exoplasim/docs/ folder. Usage ----- To use the ExoPlaSim Python API, you must import the module, create a Model or one of its subclasses, call its configure method and/or modify method, and then run it. Basic example::: import exoplasimlegacy as exo mymodel = exo.Model(workdir="mymodel_testrun",modelname="mymodel",resolution="T21",layers=10,ncpus=8) mymodel.configure() mymodel.exportcfg() mymodel.run(years=100,crashifbroken=True) mymodel.finalize("mymodel_output") In this example, we initialize a model that will run in the directory "mymodel_testrun", and has the name "mymodel", which will be used to label output and error logs. The model has T21 resolution, or 32x64, 10 layers, and will run on 8 CPUs. By default, the compiler will use 8-byte precision. 4-byte may run slightly faster, but possibly at the cost of reduced stability. If there are machine-specific optimization flags you would like to use when compiling, you may specify them as a string to the optimization argument, e.g. ``optimization='mavx'``. ExoPlaSim will check to see if an appropriate executable has already been created, and if not (or if flags indicating special compiler behavior such as debug=True or an optimization flag are set) it will compile one. We then configure the model with all the default parameter choices, which means we will get a model of Earth. We then export the model configurations to a ``.cfg`` file (named automatically after the model), which will allow the model configuration to be recreated exactly by other users. We run the model for 100 years, with error-handling enabled. Finally, we tell the model to clean up after itself. It will take the most recent output files and rename them after the model name we chose, and delete all the intermediate output and configuration files.

نیازمندی

مقدار	نام
>=1.16	numpy
-	matplotlib
-	scipy
-	h5py
-	netCDF4

نحوه نصب

نصب پکیج whl exoplasim-legacy-3.0.0a18:

pip install exoplasim-legacy-3.0.0a18.whl

نصب پکیج tar.gz exoplasim-legacy-3.0.0a18:

pip install exoplasim-legacy-3.0.0a18.tar.gz