atmoschem - User contributions [en]

Python resources

2022-10-06T11:39:38Z

Wuwl: /* Python tutorials for beginners */

=Getting started with Python=
==Jupyter notebook==
Jupyter notebooks are interactive web-based applications that allow users to create and share documents containing live codes, equations, narrative texts, figures, interactive user interfaces, and other rich media. When combined with the Python kernel, Jupyter notebooks allow users to code in Python and display results in an interactive and convenient way. Jupyter notebooks are really wonderful tools for learning Python, data science, and coding in general.

We strongly recommend installing Python and Jupyter on your PC [https://www.anaconda.com/distribution/ using the Anaconda distribution], which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.

Students enrolled in courses will also be provided with an account and access to a SUSTech server running Jupyter. However, this server can only be accessed from within the SUSTech network. You can use a SUSTech VPN from off-campus locations. Details will be given during the first lecture.

*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction]
*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial]
*[https://gke.mybinder.org/v2/gh/ipython/ipython-in-depth/master?filepath=binder/Index.ipynb A tutorial for Jupyter run on mybinder] It is a bit slow but worth it.
*[https://www.itcodemonkey.com/article/6025.html 一个中文tutorial]
*[https://www.edureka.co/blog/wp-content/uploads/2018/10/Jupyter_Notebook_CheatSheet_Edureka.pdf Jupyter notebook cheat sheet]

==Python tutorials for beginners==
There are many, many good tutorials for Python. We encourage you to try out a few yourself. A few examples are listed here, but there are many more on the web. After learning the basics, you should seek out examples particular to your application on your own.
*[https://www.learnpython.org/ A short online Python tutorial]
*[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3]
*[https://www.runoob.com/python3/python3-tutorial.html/ A tutorial for Python 3 in Chinese]

=Python for atmospheric and oceanic science=
*[http://www.johnny-lin.com/pyintro/ A Hands-on Introduction to Using Python in the Atmospheric and Oceanic Sciences] This is a Python textbook geared specifically for use in atmospheric and oceanic sciences. The writing is extremely clear and useful. Highly recommended. The PDFs can be downloaded for free.
*[https://rabernat.github.io/research_computing_2018/ Research Computing in Earth Sciences] is a Columbia University course designed to introduce incoming LDEO graduate students to modern computing software, programming tools and best practices that are broadly applicable to carrying out research in the Earth sciences. All materials are available online. Highly recommended.
*[https://unidata.github.io/online-python-training/ Unidata's online Python tutorial for atmospheric science.]
*[http://www.meteo.mcgill.ca/~cmccray/python.html Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.]
*This course [https://currents.soest.hawaii.edu/ocn_data_analysis/index.html "Oceanographic Data Analysis With Open Source Tools"] at University of Hawaii is a great resource. In particular, I highly recommend [https://currents.soest.hawaii.edu/ocn_data_analysis/analysis_methods.html these Jupyter notebooks] on climate data analyses.
*[https://climlab.readthedocs.io/en/latest/intro.html climlab] is a python package for process-oriented climate modeling developed by Brian Rose (SUNY Albany)
*[https://pyoceans.github.io/sea-py/ A collection of python tools for oceanography studies]

=Python for data science=
*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp1+2018/about Get Data Off the Ground with Python] An online MOOC course by Professor Lorena Barba at George Washington University.
*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp2+2018/about Take off with Stat in Python] An online MOOC course by Professor Lorena Barba at George Washington University.
*[https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/ A very simple tutorial] of using python for data science.
*[https://www.datacamp.com/community/tutorials/python-statistics-data-science#hypothesistesting 40+ Python Statistics For Data Science Resources]
A list of Python resources for the eight statistics topics that you need to know to excel in data science
*[https://www.statsmodels.org/stable/index.html Documentation for the python package StatsModels]

==Python pandas tutorial==
The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today.
*[https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/ Python Pandas Tutorial: A Complete Introduction for Beginners]

=Plotting=
*[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib]

=Xarray=
*[https://xarray.pydata.org/en/stable/ Xarray user's guide for analyzing data in NetCDF format]

=Advanced techniques=
==Bokeh==
*[https://bokeh.org/ Bokeh] is a set of tools that allow you to make interactive visualizations in the browser. Some of the interactive demos in this course were made using this fun tool.
*[https://rebeccabilbro.github.io/interactive-viz-bokeh/ Making interactive visualizations with Python using Bokeh]. This tutorial shows how to reproduce the famous Hans Rosling's ''The Health and Wealth of Nations'' plot using Bokeh.

==LaTeX==
*[http://tug.ctan.org/info/latex-refsheet/LaTeX_RefSheet.pdf LaTeX cheat sheet]

Python resources

2022-10-06T11:38:36Z

Wuwl: /* Python tutorials for beginners */

=Getting started with Python=
==Jupyter notebook==
Jupyter notebooks are interactive web-based applications that allow users to create and share documents containing live codes, equations, narrative texts, figures, interactive user interfaces, and other rich media. When combined with the Python kernel, Jupyter notebooks allow users to code in Python and display results in an interactive and convenient way. Jupyter notebooks are really wonderful tools for learning Python, data science, and coding in general.

We strongly recommend installing Python and Jupyter on your PC [https://www.anaconda.com/distribution/ using the Anaconda distribution], which includes Python, the Jupyter Notebook, and other commonly used packages for scientific computing and data science.

Students enrolled in courses will also be provided with an account and access to a SUSTech server running Jupyter. However, this server can only be accessed from within the SUSTech network. You can use a SUSTech VPN from off-campus locations. Details will be given during the first lecture.

*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ Jupyter notebook: an introduction]
*[https://www.dataquest.io/blog/jupyter-notebook-tutorial/ How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial]
*[https://gke.mybinder.org/v2/gh/ipython/ipython-in-depth/master?filepath=binder/Index.ipynb A tutorial for Jupyter run on mybinder] It is a bit slow but worth it.
*[https://www.itcodemonkey.com/article/6025.html 一个中文tutorial]
*[https://www.edureka.co/blog/wp-content/uploads/2018/10/Jupyter_Notebook_CheatSheet_Edureka.pdf Jupyter notebook cheat sheet]

==Python tutorials for beginners==
There are many, many good tutorials for Python. We encourage you to try out a few yourself. A few examples are listed here, but there are many more on the web. After learning the basics, you should seek out examples particular to your application on your own.
*[https://www.learnpython.org/ A short online Python tutorial]
*[https://jerry-git.github.io/learn-python3/ A tutorial for Python 3]
*[https://www.runoob.com/python3/python3-tutorial.html]

=Python for atmospheric and oceanic science=
*[http://www.johnny-lin.com/pyintro/ A Hands-on Introduction to Using Python in the Atmospheric and Oceanic Sciences] This is a Python textbook geared specifically for use in atmospheric and oceanic sciences. The writing is extremely clear and useful. Highly recommended. The PDFs can be downloaded for free.
*[https://rabernat.github.io/research_computing_2018/ Research Computing in Earth Sciences] is a Columbia University course designed to introduce incoming LDEO graduate students to modern computing software, programming tools and best practices that are broadly applicable to carrying out research in the Earth sciences. All materials are available online. Highly recommended.
*[https://unidata.github.io/online-python-training/ Unidata's online Python tutorial for atmospheric science.]
*[http://www.meteo.mcgill.ca/~cmccray/python.html Dr McCray (McGill University) put together a few Python tutorials for atmospheric science.]
*This course [https://currents.soest.hawaii.edu/ocn_data_analysis/index.html "Oceanographic Data Analysis With Open Source Tools"] at University of Hawaii is a great resource. In particular, I highly recommend [https://currents.soest.hawaii.edu/ocn_data_analysis/analysis_methods.html these Jupyter notebooks] on climate data analyses.
*[https://climlab.readthedocs.io/en/latest/intro.html climlab] is a python package for process-oriented climate modeling developed by Brian Rose (SUNY Albany)
*[https://pyoceans.github.io/sea-py/ A collection of python tools for oceanography studies]

=Python for data science=
*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp1+2018/about Get Data Off the Ground with Python] An online MOOC course by Professor Lorena Barba at George Washington University.
*[https://openedx.seas.gwu.edu/courses/course-v1:GW+EngComp2+2018/about Take off with Stat in Python] An online MOOC course by Professor Lorena Barba at George Washington University.
*[https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/ A very simple tutorial] of using python for data science.
*[https://www.datacamp.com/community/tutorials/python-statistics-data-science#hypothesistesting 40+ Python Statistics For Data Science Resources]
A list of Python resources for the eight statistics topics that you need to know to excel in data science
*[https://www.statsmodels.org/stable/index.html Documentation for the python package StatsModels]

==Python pandas tutorial==
The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today.
*[https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/ Python Pandas Tutorial: A Complete Introduction for Beginners]

=Plotting=
*[https://realpython.com/python-matplotlib-guide/ A guide for python plotting with Matplotlib]

=Xarray=
*[https://xarray.pydata.org/en/stable/ Xarray user's guide for analyzing data in NetCDF format]

=Advanced techniques=
==Bokeh==
*[https://bokeh.org/ Bokeh] is a set of tools that allow you to make interactive visualizations in the browser. Some of the interactive demos in this course were made using this fun tool.
*[https://rebeccabilbro.github.io/interactive-viz-bokeh/ Making interactive visualizations with Python using Bokeh]. This tutorial shows how to reproduce the famous Hans Rosling's ''The Health and Wealth of Nations'' plot using Bokeh.

==LaTeX==
*[http://tug.ctan.org/info/latex-refsheet/LaTeX_RefSheet.pdf LaTeX cheat sheet]

气象气候学2022秋

2022-10-06T11:33:27Z

Wuwl: /* Teaching Assistant */

=Lecturer=
[https://fugroup.org/index.php/Tzung-May_FU 傅宗玫] (fuzm AT sustech DOT edu DOT cn, +86(755)8801-8872, 工学院北楼906)

=Teaching Assistant=
Wenlu WU (eewwu@leeds.ac.uk)

'''All students must send an email to the TA by September 14, including their name + year + major + student ID + cell phone number.''' There will also be a QQ group for the class.

=Students=
Undergraduate students in hydrology, environmental sciences, oceanography, physics, chemistry, and other disciplines

=Time and location=
Fridays 10:20-12:10 (every week), Wednesdays 10:20-12:10 (even weeks).
All classes will meet at '''''三教405''''', '''''EXCEPT some tutorials will meet in the computer room in 工学院南楼739.'''''

=Office hours=
By email appointments

=Course material=
The follow two textbooks are on reserve in the library:

*AS: [http://www.amazon.cn/Atmospheric-Science-Second-Edition-An-Introductory-Survey-Wallace-John-M/dp/012732951X/ref=sr_1_1?ie=UTF8&qid=1442477746&sr=8-1&keywords=atmospheric+science+second+edition+an+introductory+survey Atmospheric Science, An Introductory Survey], 2nd edition, J. M. Wallace and P. V. Hobbs, Academic Press, ISBN:012732951X
*MT: [http://www.amazon.cn/dp/1305113586/ref=sr_1_1?ie=UTF8&qid=1442476928&sr=8-1&keywords=meteorology+today Meteorology Today], 11th edition, C. D. Ahrens and R. Henson, Brooks Cole, ISBN:1305113586

In addition, we have put together a collection of course resources for your reference:

*[[Meteorology and Climatology: other material]] Check weekly! 其他参考资料，每周关注！
*[[Meteorology and Climatology Glossary]] 中英文名词对照
*[[Python resources]]
*[[Cloud and weather gallery]]

=Assessments=
# Final exam (40%)：
# Term project (20%)：
# Assignments and pop quizzes (40%)

=Assignments=
'''''Assignments will be distributed on Fridays and are due in two weeks on Friday, BEFORE CLASS STARTS. Late assignments will NOT be graded.'''''

=Field trip=
We are planning a field trip to the [http://weather.sz.gov.cn/ Meteorological Bureau of Shenzhen Municipality] in mid-November. Participation is voluntary but highly recommended. Contact TA to sign up.

=Course schedule=
{| class="wikitable" border="1"
|-
! Week
! Date
! Subject
! Assignment
! Reading
|-
| 1
| 9/9 F
| [https://pan.baidu.com/s/1IAlF2FNXZ92_LDhokinnWw The Earth system and the Atmosphere] (3f4f)
|
| AS Ch1-2; MT Ch1
|-
| 2
| 9/14 W
| [链接：https://pan.baidu.com/s/1OBHbkh1G0qw9WeQoFXeuig Radiative transfer] (w50o)
|
| AS Ch4; MT Ch2
|-
| 2
| 9/16 F
| [链接：https://pan.baidu.com/s/1OBHbkh1G0qw9WeQoFXeuig Radiative transfer] (w50o)
|
|
|-
| 3
| 9/23 F
| [[ESE315 Tutorial 1]]
| [[ESE315_Assignment 1]]
| AS Ch4; MT Ch2
|-
| 4
| 9/28 W
| [https://pan.baidu.com/s/1QoQpFyYOzlQJ9vZyGEMKPg Atmospheric thermodynamics] (rp24)
Interactive demos of the stability of [http://fugroup.org/misc/dry.html dry] and [http://fugroup.org/misc/moist.html moist] air parcels
|
| AS Ch3; MT Ch3-4
|-
| 4
| 9/30 F
| [https://pan.baidu.com/s/1QoQpFyYOzlQJ9vZyGEMKPg Atmospheric thermodynamics] (rp24)
Interactive demos of the stability of [http://fugroup.org/misc/dry.html dry] and [http://fugroup.org/misc/moist.html moist] air parcels
| [[ESE315_Assignment 2]]
| AS Ch4; MT Ch2
|-
| 4.5
| '''''10/9 Sunday'''''
| [https://pan.baidu.com/s/15DlUhYcH074XUiNGB3x1hA Atmospheric thermodynamics] (0sik)
| '''''Assignments 1 due'''''
| AS Ch3; MT Ch3-4
|-
| 5
| 10/14 F
| [https://pan.baidu.com/s/153YeyAOwP-KQ29wZOjOSeg Thermodynamics and aerosols] (bhjl)
| [[ESE315_Assignment 3]]
| AS Ch3; MT Ch3-4
|-
| 6
| 10/19 W
| [https://pan.baidu.com/s/1G2EMWlENAcaI97E9N79LUw Aerosols] (j50l)
| '''''Assignment 2 due'''''
| AS Ch5
|-
| 6
| 10/21 F
| [https://pan.baidu.com/s/1Ed_bdG1WzeLWGWupJfQjhQ Clouds and precipitation] (udbw)
| '''''Assignment 3 due'''''
| AS Ch6; MT Ch5-7
|-
| 7
| 10/28 F
| [https://pan.baidu.com/s/1iWvSYQ29rsm9N2_V-B3BBg atmospheric boundary layer and turbulence] (ihz6)
|
| AS Ch6, 9; MT Ch 9-10
|-
| 8
| 11/2 W
| [https://pan.baidu.com/s/1rWY1dhb1OPSDmHx8cuxMkw Atmospheric motion] (ozc4)
|
|
|-
| 8
| 11/4 F
| [[ESE315 Tutorial 2]]
| [[ESE 315 Assignment 4]]
| AS Ch7, 9; MT Ch9, 11
|-
| 9
| 11/11 F
| [https://pan.baidu.com/s/1pB6u1v6-6OwYqts5vQpU_Q Mid- and high-latitude weather] (4tcm)
|
| AS Ch7; MT Ch11
|-
| 10
| 11/16 W
| [https://pan.baidu.com/s/1pB6u1v6-6OwYqts5vQpU_Q Mid- and high-latitude weather] (4tcm)
| [[ESE315_Assignment_5]]
| AS Ch8; MT Ch12
|-
| 10
| 11/18 F
| ''秋季运动会停课''
|
| AS Ch8; MT Ch13-16
|-
| 11
| 11/25 F
| [https://pan.baidu.com/s/1jDP59j7es3ok-_XMqnF7eA Climate data analysis] (5kc1) [https://pan.baidu.com/s/1OCjHz3ECafs1gVr-f5htlQ Global SST animation] (ffkz),
[https://pan.baidu.com/s/1BZx0WxyNvjmNMJNNhC-GVg Global SST anomaly animation] (yau8)
|
| AS Ch8; MT Ch13-16
|-
| 12
| 11/30 W
| [[ESE 315 Tutorial 3]], [[ESE315 Tutorial 4]] [https://pan.baidu.com/s/1xEEyrHKKjIBRXaoGId76Lg Tutorial 3 Slides] (u8h6), [https://pan.baidu.com/s/175rkpDorFWb-IWUDH9CKhw General circulation of the atmosphere and the ocean] (6irh)
| [[ESE315_Assignment_6]]
| AS Ch7; MT Ch10, Ch17
|-
| 12
| 12/2 F
| [https://pan.baidu.com/s/1ZZIhjmQl3xslwdbp08rTng Severe weather, low-latitude weather; weather forecasting] (1hzm)
|
| AS Ch7; MT Ch10, Ch17
|-
| 13
| 12/9 F
| [https://pan.baidu.com/s/1Iq5BG9fndCdHbOtNywKdMA General circulation of the atmosphere and the ocean] (18f5)
|
| AS Ch10; MT Ch17-18
|-
| 14
| 12/14 W
| [https://pan.baidu.com/s/14aP5EqwUHIU8idlkorCOQA Climate system and variability] (j6t4)
| [[ESE315_Assignment_7]]
| AS Ch7; MT Ch10, Ch17
|-
| 14
| 12/16 F
| [https://pan.baidu.com/s/1nNnLPYbQcIonA1TqfGrOlA Climate system and climate change] (4o5a)
|
| AS Ch10; MT Ch17-18
|-
| 15
| 12/23 F
| Term presentation
| [[ESE315_Assignment_8]]
|
|-
| 16
| 12/28 W
| [https://pan.baidu.com/s/1nNnLPYbQcIonA1TqfGrOlA Climate system and climate change] (4o5a)
|
|
|-
| 16
| 12/30 F
| [https://pan.baidu.com/s/1o3RZFHdgZcsRSFYEp3F13g Atmospheric measurement] (6rvx)
|
|
|-
| 17
| '''''TBD'''''
| '''''Final exam'''''
| '''''闭卷、英文答题、不需要计算器'''''
|
|}

=Term project and presentation=
Each student should independently complete a data analysis project using Python to demonstrate a phenomenon or answer a scientific question related to meteorology or climate. The student is free to choose his/her topic of interest and is fully responsible for finding and analyzing the data.

'''However, to ensure success, each student must meet with the Instructor before November 15th to discuss his/her plan and data for the term project.'''

Each student will give a 10 to 15-minute presentation (length of time depending on the number of students) at the end of the semester.

The term project and presentation will be graded with the following criteria:
* The relevance of phenomenon/scientific question (10%)
* The appropriateness of the data used and analysis methodology (30%)
* The degree to which the phenomenon/question is addressed by the data analysis (30%)
* Clarity of the presentation (30%)

=Resources for learning python=
Assignments and the term project require calculation and plotting using Python in Jupyter Notebooks. Python is a powerful programming language, while Jupyter Notebooks provide an easy, web-based way to combine code, plots, and descriptive text.

Students are encouraged to install Python and Jupyter using the popular Anaconda distribution. See our [[Python resources]] page for instructions. Students enrolled in this course will also be granted access to a SUSTech server running Jupyter, but this can only be used within the SUSTech network (but you can use a SUSTech VPN outside campus). Details will be announced in class.

If you are new to Python or to Jupyter, we recommend that you go through a few online tutorials. Check out our [[Python resources]] page.

ESE315 Tutorial 1

2022-09-22T02:54:44Z

Wuwl: /* Jupyter notebook */

=Jupyter notebook=

我们为每位选课同学开设了一个Jupyter notebook账号，进行tutorial。大家可以用这个系统做作业，也可以用自己电脑上安装的jupyter做。

每位同学有一个自己的登录端口和密码，第一次tutorial上课时发放。

使用方式：
*http://172.18.31.50:端口（IP由助教通知）
*仅限校园内网使用，如在校外可使用南科大VPN连接。
*每次使用后请注意保存并终止Notebook运行（“Close and Halt”）。之后可直接关闭网页或点击“Logout”，请勿点击“Quit”。

第一次上课我们会操作一遍Jupyter notebook tutorial。然后操作一遍 public/Tutorial-01文件夹下面的几个练习。