This repository contains all the scripts used for the python class for JRFs at IITM

Some of the tutorials and resources to follow

1. http://pure.iiasa.ac.at/id/eprint/14952/1/xarray-tutorial-egu2017-answers.pdf
2. https://rabernat.github.io/research_computing/xarray.html
3. Ocean Data Analysis https://currents.soest.hawaii.edu/ocn_data_analysis/exercise_data.html#id1
4. Parallelization http://xarray.pydata.org/en/stable/dask.html
5. Satellite Data Analyis https://github.com/nansencenter/nansat-lectures
6. https://github.com/NCAR/CESM_postprocessing CESM Postprocessing
7. https://github.com/NCAR/PyCect This repo is used to compare the results of a set of new CAM simulations against the accepted ensemble
8. https://github.com/nichannah/ocean-regrid Regrid ocean reanalysis data from normal to tripolar grids
9. https://github.com/jswhit/gfstonc Read GFS sigma and sfc files in python
10. f2py
11. Pandas
12. https://github.com/tmiyachi/data2gfs Make python version of this using f2py
13. Shallow water equation model using pyspharm https://github.com/jswhit/pyspharm and https://www.aosc.umd.edu/~dkleist/docs/shtns/doc/html/shallow_water_8py-example.html explaining the code
14. Scientific Computing Lectures https://github.com/jrjohansson/scientific-python-lectures
15. Geopandas satellite data analysis https://towardsdatascience.com/satellite-imagery-access-and-analysis-in-python-jupyter-notebooks-387971ece84b
16. Rasterio https://medium.com/analytics-vidhya/satellite-imagery-analysis-with-python-3f8ccf8a7c32
17. Eo-learn https://medium.com/dataseries/satellite-imagery-analysis-with-python-ii-8001e5c41a52
18. Satpy
19. Use of Landsat and Sentinel datasets
20. Pyunicorn
21. Keras, tensorflow, pytorch, django, theano, scikit-learn, theano, bokeh, pandas, seaborn, bokeh, plotly, scrapy,
22. Python tutorial https://carpentrieslab.github.io/python-aos-lesson/ plotting CMIP data - highlight
23. Python for oceanography http://www.soest.hawaii.edu/oceanography/courses/OCN681/python.html
24. Python tools for oceanography https://pyoceans.github.io/sea-py/
25. Python Land Surface Modelling https://www.geosci-model-dev.net/12/2781/2019/
26. Python hydrology tools https://github.com/raoulcollenteur/Python-Hydrology-Tools
27. Docker
28. Python and GIS https://automating-gis-processes.github.io/CSC18/lessons/L1/overview.html
29. https://automating-gis-processes.github.io/2016/
30. https://geohackweek.github.io/raster/
31. https://github.com/pangeo-data/pangeo
32. https://github.com/pangeo-data/awesome-open-climate-science
33. https://uwescience.github.io/sat-image-analysis/resources.html
34. Radar data analysis https://data.world/datasets/radarhttps://arm-doe.github.io/pyart/https://docs.wradlib.org/
35. https://www.earthdatascience.org/courses/use-data-open-source-python/multispectral-remote-sensing/landsat-in-Python/
36. Deep Learning on Satellite Imagery https://github.com/robmarkcole/satellite-image-deep-learning
37. Google Earth Engine https://sites.google.com/view/eeindia-advanced-summit/summit-resources
38. https://geohackweek.github.io/GEE-Python-API/
39. https://github.com/google/earthengine-api/tree/master/python/examples/ipynb
40. http://www.jerico-ri.eu/download/summer%20school%20-%20the%20netherlands/Genna%20Donchyts%20-%20GEE%20Training.pdf
41. https://www.earthdatascience.org/tutorials/intro-google-earth-engine-python-api/
42. Installing Google Earth Engine and requesting access https://github.com/google/earthengine-api/issues/27
43. https://github.com/giswqs/earthengine-py-notebooks
44. Google Earth Engine image to numpy https://mygeoblog.com/2019/08/21/google-earth-engine-to-numpy/
45. Stippling to show statistical significance bradyrx/esmtools#13
46. Resampling from swath to grid https://github.com/TerraFusion/pytaf
47. Making a docker container for data science https://towardsdatascience.com/docker-for-data-scientists-5732501f0ba4
48. Docker commands:

Run interactively: docker run -it manmeet3591/dl:iitm:latest

Install the necessary libraries

Open a new terminal and do docker images to see the id and run the following command

$ docker tag id_ manmeet3591/dl_iitm:v2

$ docker push manmeet3591/dl_iitm:v2

Projects for the class

https://docs.google.com/spreadsheets/d/1m2ZIJ_To8IbE18Teb70a7BVZg0o29sOM6rlgFkE2b3E/edit#gid=0

https://docs.google.com/document/d/12h9bcIdBPJUFc_fJssJe8hVBzledq2Dtk5-9OpKHbfg/edit

49. Homogenous regions India shape files: https://github.com/Cassimsannan/Shapefiles

50. Download CMIP6 data: https://github.com/TaufiqHassan/acccmip6

51. Download MSWEP data from Google drive:

Setup rclone: https://www.youtube.com/watch?v=vPs9K_VC-lg

52. Run jupyter notebook from docker container

docker run --rm -it --entrypoint bash -p 8891:8891 manmeet3591/tensortrade

Inside the container jupyter-notebook --ip 0.0.0.0 --port=8891 --no-browser --allow-root &

In the browser http://localhost:8891/

$ rclone sync -v --exclude 3hourly/ --drive-shared-with-me GoogleDrive:/MSWEP_V280 /lus/dal/cccr_rnd/manmeet/AI_IITM/WeatherBench/data/dataserv.ub.tum.de/mswep/.

53. Create any number of subplots matplotlib

$ fig,ax = plt.subplots(ncols=2,nrows=4, figsize=(11.69,8.27), subplot_kw={'projection': ccrs.PlateCarree()})

54. Google Earth Engine timelapse gif generator: https://9611d0317f71.ngrok.io/voila/render/timelapse.ipynb

55. Handling expver dimension in a netcdf file downloaded as ERA5 data

ds.reduce(np.nansum, 'expver') Solution from marco venturini https://confluence.ecmwf.int/pages/viewpage.action?pageId=173385064

56. GeoTIFF to netcdf and exporting data from Google Earth Engine https://medium.com/@wenzhao.li1989/nco-translate-geotiff-files-exported-from-gee-to-a-netcdf-file-with-correct-time-dimension-ce97a8f3043f

57. Make pipeline to avoid test data leaking into train https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html

58. Potential evapotranspiration (PET) from netcdf file https://climate-indices.readthedocs.io/en/latest/#

59. t-distributed Stochastic Neighbourhood Embedding (tSNE) versus PCA https://stats.stackexchange.com/questions/238538/are-there-cases-where-pca-is-more-suitable-than-t-sne

60. Stationarity of time series: https://towardsdatascience.com/stationarity-in-time-series-analysis-90c94f27322

61. SARIMAX model: https://towardsdatascience.com/end-to-end-time-series-analysis-and-forecasting-a-trio-of-sarimax-lstm-and-prophet-part-1-306367e57db8

62. Prevent kaggle from disconnecting https://stackoverflow.com/questions/57113226/how-to-prevent-google-colab-from-disconnecting

function ClickConnect(){console.log("Working"); document.querySelector("colab-toolbar-button#connect").click() } setInterval(ClickConnect,60000)

63. Solving NVIDIA driver installation issues https://stackoverflow.com/questions/42984743/nvidia-smi-has-failed-because-it-couldnt-communicate-with-the-nvidia-driver/51113428#51113428

64. Installing NVIDIA drivers https://www.itzgeek.com/post/how-to-install-nvidia-drivers-on-ubuntu-20-04-ubuntu-18-04.html

65. Install cuda https://www.tensorflow.org/install/gpu

66. bashrc commands for cuda

export CUDA_HOME=/usr/local/cuda-11.0

export LD_LIBRARY_PATH=/usr/local/cuda-11.0/lib64:/usr/local/cuda-11.0/lib:$LD_LIBRARY_PATH

export PATH=/usr/local/cuda-11.0/bin:$PATH

sudo ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11 # https://stackoverflow.com/questions/63199164/how-to-install-libcusolver-so-11

echo | sudo -S ln -s /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.10 /usr/local/cuda-11.0/targets/x86_64-linux/lib/libcusolver.so.11

67. Test time augmentation https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d

68. Geometric Deep Learning https://geometricdeeplearning.com/lectures/\\

69. DeepSphere Spherical convolutions using Graph convolutions https://github.com/deepsphere/deepsphere-pytorch

70. Interactively logging into Pratyush GPU

$ qsub -I -l select=1:ncpus=1:naccelerators=1:accelerator_model="Tesla_P100-PCIE-12GB" -q gpu

$ source activate py36

$ module load cudatoolkit

$ aprun -n 1 jupyter-notebook --no-browser --ip=0.0.0.0 --port=8890 >> NOTEBOOK_LOGFILE1 2>&1

$ tail -f NOTEBOOK_LOGFILE1

$ Ctrl+C

$ ssh -N -f -L localhost:8888:node:8890 cccr_rnd@nid00019 (Here nid should be the one as seen from NOTEBOOK_LOGFILE1)

$ source activate py36

$ module load cudatoolkit

$ firefox&

To check if a port is being used

$ netstat -antp ! grep -i port_id

The above statement will only work in the interactive login node.

Now start the notebook by noting the link from NOTEBOOK_LOGFILE1

71. Markdown tool https://dillinger.io/

72. LRP for explainable AI (XAI): https://github.com/albermax/innvestigate Application paper: https://arxiv.org/abs/2103.10005

73. Lower tropospheric stability (LTS = θ700hPa − θ1000hPa; Kelvin), which is defined as the difference in potential temperature (θ) between the 700-hPa level and the surface

74. Shallow copy and deep copy in python https://stackoverflow.com/questions/41125834/trying-to-do-a-shallow-copy-on-list-in-python

75. Climate indices https://climate-indices.readthedocs.io/en/latest/

76. Copying to box using lftp mirror -R folder

77. Transforming argparse python code to jupyter notebook: https://stackoverflow.com/questions/37534440/passing-command-line-arguments-to-argv-in-jupyter-ipython-notebook

Simply add the following lines:

import sys sys.argv = ['']

78. Install pytorch with cuda

https://stackoverflow.com/questions/58424974/anaconda-importerror-usr-lib64-libstdc-so-6-version-glibcxx-3-4-21-not-fo

https://stackoverflow.com/questions/70340812/how-to-install-pytorch-with-cuda-support-with-pip-in-visual-studio

79. Running docker containers on NVIDIA DGX A100

$ docker pull nvcr.io/nvidia/tensorflow:20.10-tf2-py3

$ docker run --gpus all -it -v /home/cccr_rnd:/apollo nvcr.io/nvidia/tensorflow:20.10-tf2-py3

Troubleshooting

1. Continue in outer loop using multi-loops https://stackoverflow.com/questions/14829640/how-to-continue-in-nested-loops-in-python

2. Numbering the subplots https://matplotlib.org/3.1.1/gallery/axes_grid1/simple_anchored_artists.html

3. Fortran compilation may sometimes be solved by running the command ulimit -s unlimited

4. There are visualization problems in cartopy if the lon is from 0 to 360 and not from -180 to 180

5. Run docker as a non-root user https://docs.docker.com/engine/install/linux-postinstall/

6. In the first instance of an image sometimes docker hub may deny you to push the image https://stackoverflow.com/questions/41984399/denied-requested-access-to-the-resource-is-denied-docker

7. Numpy to xarray : foo = xr.DataArray(data, coords=[times, locs], dims=["time", "space"])

data = ds_merra2_jjas.DUSCATAU.sel(time='2002').values[0,:,:] lats_ = ds_merra2_jjas.DUSCATAU.sel(time='2002').lat.values lons_ = ds_merra2_jjas.DUSCATAU.sel(time='2002').lon.values ds_merra2_jjas_new = xr.DataArray(data, coords=[lats_, lons_], dims=["lat", "lon"])

8. Using matplotlib to make map plots plt.contourf(ds_merra2_jjas.DUSCATAU.sel(time='2002').lon.values,
   ds_merra2_jjas.DUSCATAU.sel(time='2002').lat.values ,
   ds_merra2_jjas.DUSCATAU.sel(time='2002').values[0,:,:],
   cmap='bwr') plt.colorbar()

9. Sometimes xarray plot might show blank, the way to resolve that is select the area and that should work.

10. Pattern correlation formula: https://www.mdpi.com/2073-4441/10/1/28 may use weights as well for the pattern correlation

For the weights, the following can be followed: https://stackoverflow.com/questions/58881607/calculating-the-cosine-of-latitude-as-weights-for-gridded-data

11. When installing packages otherwise difficult to install like ESMF we can set the compiler environment variables such as CC and FC to force conda to install using that particular compiler. This saves a lot of time and effort. https://stackoverflow.com/questions/59284298/conda-install-c-anaconda-gcc-linux-64-not-being-used Many build tools such as make and CMake search by default for a compiler named simply gcc, so we set environment variables to point these tools to the correct compiler.

12. When using the isin function with sel we can at present use it only once in a call. Need to instantiate a new variable for doing it twice.

13. Installing PyRQA (Runs only with python 2.7) https://github.com/szhan/pyrqa

    conda install https://anaconda.org/conda-forge/pytools/2017.2/download/linux-64/pytools-2017.2-py27_0.tar.bz2

    conda install https://anaconda.org/conda-forge/pyopencl/2018.1.1/download/linux-64/pyopencl-2018.1.1-py27_1.tar.bz2

    conda install -c conda-forge pocl

    pip install Mako

    pip install PyRQA

    Even after all this, unable to run pyrqa smoothly. However, this activity ensured that the environment to run pyrqa was perfect. So then clone the github repository and inside the main github repository pyrqa, there is a folder pyrqa. Copy that to your desired location, rename it lets say PYRQA. And use the library as PYRQA.

14. Logging to a remote server without password https://www.thegeekstuff.com/2008/11/3-steps-to-perform-ssh-login-without-password-using-ssh-keygen-ssh-copy-id/

15. Create xarray dataset from dataarrays

16. Use tmux to run a process in the background: https://medium.com/@praveendhawan/tmux-run-commands-in-the-background-bad007810318

17. Indentation error when transferring from jupyter notebook to python script https://stackoverflow.com/questions/1024435/how-to-fix-python-indentation

18. Correcting aspect ratio in cartopy: ax[i,j].set_aspect('auto')