Move personal python

.gitignore

@@ -11,6 +11,8 @@ learning-vscode/VSCode_WSL.html
 learning-vscode/VSCode_Ubuntu.html
 learning-markdown/MarkDown.html
 learning-markdown/TODO_makdown.md
+learning-python/Python.html
+learning-python/sandbox.txt
 
 # ---> VisualStudioCode
 .settings

learning-python/README.md

+# learning-python
+
+This is the repository for the training Python
+
+You can find the slides [here](./Python.pdf).
+
+Presentation is [here](https://www.elic.ucl.ac.be/users/pbarriat/slides/Python.html)

learning-python/assets/Figure_2.py

+import matplotlib.pyplot as plt
+import numpy as np
+
+plt.style.use("seaborn")
+
+x = np.linspace(0, 2*np.pi, 100)
+sin = np.sin(x)
+cos = np.cos(x)
+plt.plot(x, sin, "C0--", label="sin(x)")
+plt.plot(x, cos, "C1:", label="cos(x)")
+
+pi_multiples = np.array([0, 0.5, 1, 1.5, 2]) * np.pi
+sin_points = np.sin(pi_multiples)
+cos_points = np.cos(pi_multiples)
+plt.plot(pi_multiples, sin_points, "C0o")
+plt.plot(pi_multiples, cos_points, "C1o")
+
+plt.title("Trigonometric functions")
+plt.xlabel("x (radians)")
+plt.xticks(np.linspace(0, 2*np.pi, 5))
+plt.legend()
+plt.axis("scaled")
+
+plt.show()

learning-python/assets/Figure_3.py

+#
+# Input data
+#
+# We are going to use a netCDF file for testing the following code. 
+# The test file islocated in a dedicated online repository:
+#    https://nextcloud.cism.ucl.ac.be/s/H7XFSi8J4E8dnRK
+#
+# Download the netCDF prmsl.2000.nc : 
+#    a classic (regular grid) pressure file 
+#
+
+from netCDF4 import Dataset
+import numpy as np
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+from cartopy.util import add_cyclic_point
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+
+my_example_nc_file = 'prmsl.2000.nc'
+fh = Dataset(my_example_nc_file, mode='r')
+
+lons = fh.variables['lon'][:]
+lats = fh.variables['lat'][:]
+times = fh.variables['time'][:]
+times_unit = fh.variables['time'].units
+var = fh.variables['prmsl']
+var_units = fh.variables['prmsl'].units
+
+#fig = plt.subplots(figsize=(8, 6), dpi=102)
+plt.axis('off')
+
+ax = plt.axes(projection=ccrs.Robinson(central_longitude=10.0))
+ax.coastlines()
+
+# Plot Data
+data = np.squeeze(var[1,:,:])
+#cs = ax.pcolormesh(lons, lats, data, transform=ccrs.PlateCarree(), shading='auto')
+lon = np.arange(0, 360, 2)
+data, lon = add_cyclic_point(data, coord=lon)
+cs = ax.contourf(lon, lats, data, transform=ccrs.PlateCarree(), transform_first=False)
+
+# Add Grid Lines
+gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="grey")
+gl.xlocator = mticker.FixedLocator(np.arange(-170,191,36))
+gl.ylocator = mticker.FixedLocator(np.arange(-90,90,15))
+gl.rotate_labels = False
+gl.top_labels = False
+gl.xformatter = LONGITUDE_FORMATTER
+gl.yformatter = LATITUDE_FORMATTER
+
+# Add Colorbar
+cbar = plt.colorbar(cs,orientation="horizontal")
+cbar.set_label(var_units)
+
+# Add Title
+plt.title('Daily Pressure at Mean Sea Level')
+
+plt.show()

learning-python/assets/Figure_4.py

+#
+# Input data
+#
+# We are going to use a netCDF file for testing the following code. 
+# The test file islocated in a dedicated online repository:
+#    https://nextcloud.cism.ucl.ac.be/s/H7XFSi8J4E8dnRK
+#
+# Download the netCDF prmsl.2000.nc : 
+#    a classic (regular grid) pressure file 
+#
+
+from netCDF4 import Dataset
+import numpy as np
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+from cartopy.util import add_cyclic_point
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mticker
+import matplotlib.path as mpath
+
+my_example_nc_file = 'prmsl.2000.nc'
+fh = Dataset(my_example_nc_file, mode='r')
+
+lons = fh.variables['lon'][:]
+lats = fh.variables['lat'][:]
+times = fh.variables['time'][:]
+times_unit = fh.variables['time'].units
+var = fh.variables['prmsl']
+var_units = fh.variables['prmsl'].units
+
+#------------------------------------------------------------
+# Compute a circle in axes coordinates, 
+# which we can use as a boundary for the map.
+# https://scitools.org.uk/cartopy/docs/latest/gallery/lines_and_polygons/always_circular_stereo.html
+
+theta = np.linspace(0, 2*np.pi, 100)
+center, radius = [0.5, 0.5], 0.5
+verts = np.vstack([np.sin(theta), np.cos(theta)]).T
+circle = mpath.Path(verts * radius + center)
+
+fig = plt.subplots(figsize=(6, 6), dpi=102)
+plt.axis('off')
+
+ax = plt.axes(projection=ccrs.Orthographic(central_longitude=0.0, central_latitude=90.0))
+xlims = [-180,180]
+ylims = [50,90]
+ax.set_extent(xlims+ylims, crs=ccrs.PlateCarree())
+ax.set_boundary(circle, transform=ax.transAxes)
+
+ax.stock_img()
+ax.coastlines("110m", linewidth=0.5, color="black")
+
+# Plot Data
+data = np.squeeze(var[1,:,:])
+#cs = ax.pcolormesh(lons, lats, data, transform=ccrs.PlateCarree(), shading='auto')
+lon = np.arange(0, 360, 2)
+data, lon = add_cyclic_point(data, coord=lon)
+cs = ax.contourf(lon, lats, data, transform=ccrs.PlateCarree(), transform_first=False)
+
+# Doing the gridlines we want 
+gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True, linewidth=0.5, color="grey")
+gl.xformatter = LONGITUDE_FORMATTER
+gl.yformatter = LATITUDE_FORMATTER
+gl.xlocator = mticker.FixedLocator(np.arange(-180,181,18))
+gl.ylocator = mticker.FixedLocator(np.arange(-90,91,10))
+
+# Add Colorbar
+cbar = plt.colorbar(cs,orientation="horizontal")
+cbar.set_label(var_units)
+
+# Add Title
+plt.title('Daily Pressure at Mean Sea Level')
+
+plt.show()

learning-python/assets/kroki-plugin.js

+const { deflateSync } = require('zlib')
+
+const krokiLangs = [
+  'actdiag',
+  'blockdiag',
+  'bpmn',
+  'bytefield',
+  'c4plantuml',
+  'ditaa',
+  'dot',
+  'erd',
+  'excalidraw',
+  'graphviz',
+  'mermaid',
+  'nomnoml',
+  'nwdiag',
+  'packetdiag',
+  'pikchr',
+  'plantuml',
+  'rackdiag',
+  'seqdiag',
+  'svgbob',
+  'umlet',
+  'vega',
+  'vegalite',
+  'wavedrom',
+]
+
+const entrypoint = 'https://kroki.io/'
+
+const marpKrokiPlugin = (md) => {
+  const { fence } = md.renderer.rules
+
+  md.renderer.rules.fence = (tokens, idx, options, env, self) => {
+    const info = md.utils.unescapeAll(tokens[idx].info).trim()
+
+    if (info) {
+      const [lang] = info.split(/(\s+)/g)
+
+      if (krokiLangs.includes(lang)) {
+        const data = deflateSync(tokens[idx].content).toString('base64url')
+
+        // <marp-auto-scaling> is working only with Marp Core v3
+        return `<p><marp-auto-scaling data-downscale-only><img src="${entrypoint}${lang}/svg/${data}"/></marp-auto-scaling></p>`
+      }
+    }
+
+    return fence.call(self, tokens, idx, options, env, self)
+  }
+}
+
+module.exports = marpKrokiPlugin

learning-python/assets/marp.config.js

+const marpKrokiPlugin = require('./kroki-plugin')
+
+module.exports = {
+  engine: ({ marp }) => marp.use(marpKrokiPlugin)
+}

learning-python/assets/python.svg

+<?xml version="1.0" encoding="utf-8"?><!-- Uploaded to: SVG Repo, www.svgrepo.com, Generator: SVG Repo Mixer Tools -->
+<svg width="800px" height="800px" viewBox="0 0 64 64" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M31.885 16c-8.124 0-7.617 3.523-7.617 3.523l.01 3.65h7.752v1.095H21.197S16 23.678 16 31.876c0 8.196 4.537 7.906 4.537 7.906h2.708v-3.804s-.146-4.537 4.465-4.537h7.688s4.32.07 4.32-4.175v-7.019S40.374 16 31.885 16zm-4.275 2.454c.771 0 1.395.624 1.395 1.395s-.624 1.395-1.395 1.395a1.393 1.393 0 0 1-1.395-1.395c0-.771.624-1.395 1.395-1.395z" fill="url(#a)"/><path d="M32.115 47.833c8.124 0 7.617-3.523 7.617-3.523l-.01-3.65H31.97v-1.095h10.832S48 40.155 48 31.958c0-8.197-4.537-7.906-4.537-7.906h-2.708v3.803s.146 4.537-4.465 4.537h-7.688s-4.32-.07-4.32 4.175v7.019s-.656 4.247 7.833 4.247zm4.275-2.454a1.393 1.393 0 0 1-1.395-1.395c0-.77.624-1.394 1.395-1.394s1.395.623 1.395 1.394c0 .772-.624 1.395-1.395 1.395z" fill="url(#b)"/><defs><linearGradient id="a" x1="19.075" y1="18.782" x2="34.898" y2="34.658" gradientUnits="userSpaceOnUse"><stop stop-color="#387EB8"/><stop offset="1" stop-color="#366994"/></linearGradient><linearGradient id="b" x1="28.809" y1="28.882" x2="45.803" y2="45.163" gradientUnits="userSpaceOnUse"><stop stop-color="#FFE052"/><stop offset="1" stop-color="#FFC331"/></linearGradient></defs></svg>
\ No newline at end of file

learning-python/assets/tum.css

+/* @theme tum */
+
+@import 'default';
+
+section {
+  /*background-color: #fff;
+  color: #000;
+  background-image: url('images/TUM_Logo_blau_rgb_s.svg');
+  background-repeat: no-repeat;
+  background-position: right 40px top 40px;
+  background-size: 8%;*/
+}
+
+section.lead {
+  /*background-image: url('images/TUM_Uhrenturm.png');
+  background-position: right;
+  background-size: 45%;*/
+}
+
+section h1,
+section h2 {
+  color: #1f315c;
+}
+section a {
+  color: #5fb2e6;
+}
+section footer,
+section::after {
+  color: #9cb7d4;
+}
+
+section.invert {
+  background-color: #003359;
+  color: #fff;
+  /*background-image: url('images/TUM_Logo_weiss_rgb_s.svg');*/
+}
+
+section.lead.invert {
+  /*background-image: url('images/TUM_Uhrenturm_w.png');*/
+}
+
+section.invert h1,
+section.invert footer,
+section.invert::after {
+  color: #fff;
+}
+
+section.invert a {
+  color: #e37222;
+}
+
+/* Add "Page" prefix and total page number */
+section::after {
+  content: attr(data-marpit-pagination) ' / ' attr(data-marpit-pagination-total);
+}

learning-python/compile.sh

+#!/bin/bash
+#
+# PY Barriat, September 2024
+#
+# Download and install marp (MarkDown slides extension) from here:
+# https://github.com/marp-team/marp-cli/releases
+#
+# Install npm (needed for mermaid: nice extension to make diagramm)
+# npm i
+# sudo apt install npm
+#
+
+rm -f ./Python.pdf ./Python.html
+
+npx marp --allow-local-files --theme ./assets/tum.css -c ./assets/marp.config.js ./Python.md -o Python.pdf
+npx marp --template bespoke --bespoke.progress --allow-local-files --theme ./assets/tum.css -c ./assets/marp.config.js Python.md -o Python.html