This repository contains all the material needed by students registered for the Numpy tutorial of SciPy 2022 on Monday, July 11th 2022.
For a smooth experience, you will need to make sure that you install or update your Python distribution and download the tutorial material before the day of the tutorial.
Run with Binder by clicking this icon:
If you don't already have a working python distribution, you may download Anaconda Python (https://www.anaconda.com/products/individual).
To be able to run the examples, demos and exercises, you must have the following packages installed:
ipython
(for running, experimenting, and doing exercises)jupyterlab
(for access to the Jupyter Notebook web-based interactive computing platform)matplotlib
numpy
pillow
pyqt
If you are using Anaconda, you can use the Anaconda Prompt (Windows) or Terminal.app (macOS) to create an environment with the necessary packages:
Open the Anaconda Prompt or Terminal.app using the below instructions:
Create a new Anaconda virtual environment by executing the below command in the application window you opened in step 1 above. You may be prompted to Proceed([y]/n)?
. If so, type y
and press Enter
.
$ conda create -n numpy-tutorial ipython jupyterlab matplotlib numpy pillow pyqt
Navigate to the directory where you'd like to store materials for this tutorial and download the materials from this GitHub repository by executing the below command. It will create a new folder named Numpy-Tutorial-SciPyConf-2022/
with all the content you will need.
$ git clone git@github.com:enthought/Numpy-Tutorial-SciPyConf-2022.git
NOTE: If you are not familiar with Git, you can download a zipped archive of the material by clicking on this link: https://github.com/enthought/Numpy-Tutorial-SciPyConf-2022/archive/main.zip. Then, unpack the zipped archive into a directoy named Numpy-Tutorial-SciPyConf-2022
. You may have to rename the unpacked directory to explicitly be Numpy-Tutorial-SciPyConf-2022
.
To test your installation, please execute the check_env.py
script in the python virtual environment where you have installed the requirements (from step 2 above).
If you created an Anaconda environment using the instructions above, you can use the same application window that you opened in step 1, or launch the platform specific application again -- Anaconda Prompt for Windows or Terminal.app for macOS.
Be sure to navigate to where you downloaded this GitHub repository and activate your conda environment before executing python check_env.py
:
# Example path to course materials (yours may differ)
$ cd ~/Desktop/Numpy-Tutorial-SciPyConf-2022/
$ conda activate numpy-tutorial
$ python check_env.py
You should see a window pop up with a plot that looks vaguely like a smiley face (as shown below).
This GitHub repository is all that is needed in terms of tutorial content. If you downloaded these materials in step 3 above, there is no need to do so again. If not, the simplest solution is to download the material using this link:
https://github.com/enthought/Numpy-Tutorial-SciPyConf-2022/archive/main.zip
If you are familiar with Git, you can also clone this repository with:
$ git clone https://github.com/enthought/Numpy-Tutorial-SciPyConf-2022.git
The above command will create a new folder named Numpy-Tutorial-SciPyConf-2022/
with all the content you will need: the slides I will go through (introduction_to_numerical_computing_with_numpy_manual.pdf
), and a folder of exercises.
You may post messages to the #tutorial-intro-to-numerical-computing-with-numpy
Slack channel for this tutorial at in the official Slack team: https://scipy2022.slack.com .
To create an Anaconda environment from an existing environment.yml
file:
$ conda env create -f environment.yml -n numpy-tutorial
To remove an existing Anaconda environment:
$ conda remove --name numpy-tutorial --all
To completely uninstall Anaconda, see the "Uninstalling Anaconda" documentation here.
© 2001-2022, Enthought, Inc. All Rights Reserved. Use only permitted under license. Copying, sharing, redistributing or other unauthorized use strictly prohibited. All trademarks and registered trademarks are the property of their respective owners. Enthought, Inc. 200 W Cesar Chavez Suite 202 Austin, TX 78701 www.enthought.com
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。