Add examples from GEM hackathon (#36)

GEM-hackathon-2023-satcen/Dockerfile

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+FROM obarrilero/notebook:latest 
+
+USER jovyan
+# Make sure the contents of our repo are in ${HOME}
+COPY --chown=jovyan:users . ${HOME}

GEM-hackathon-2023-satcen/README.md

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+# gem-hackathon-2023
+
+
+## Run the notebook on Binder
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alunacob/gem-hackathon-2023/main?urlpath=lab)
+
+## Run the notebook locally using docker
+
+Clone this repository with:
+
+```bash
+git clone https://github.com/alunacob/gem-hackathon-2023.git
+```
+
+Go to the directory containing the cloned repository:
+
+```bash
+cd gem-hackathon-2023
+```
+
+Use docker compose to build the docker image:
+
+```bash
+docker-compose build
+```
+
+This step can take a few minutes...
+
+Finally run the docker with:
+
+```
+docker-compose up
+```
+
+Open a browser window at the address http://0.0.0.0:9005 or http://127.0.0.1:9005 and run the notebook
+
+## About the hackathon
+
+ ### Description: 
+
+
+ ### Tentative Agenda:
+     
+
+
+
+ ### Requirements: 
+ This [Binder](https://mybinder.readthedocs.io/en/latest/introduction.html#what-is-a-binder) repository was set up so that you can participate with no pre-requirement to be installed on your side.
+ However, the notebook must target a computing environment with 2 GB of RAM. After some inactivity, the docker container is culled. Access to a web browser should be enough for your successful participation.
+
+ ### Target Participants: 
+ Students, Software developers, Data scientists, EO-developers, anyone with an interest in the topic.
+
+ ### Time requirements: 
+
+ 45 minutes

GEM-hackathon-2023-satcen/helpers.py

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+import folium
+# import matplotlib
+import matplotlib.pyplot as plt
+import matplotlib
+from matplotlib.colors import BoundaryNorm, ListedColormap
+from matplotlib.patches import Polygon as plt_polygon
+import numpy as np
+from aenum import MultiValueEnum
+
+
+from IPython.core.display import display, HTML
+
+
+def evalscript_template(bands, sample_type):
+    evaluate_pixel = ", ".join(f"sample.{band}" for band in bands)
+    return f"""
+    //VERSION=3
+    function setup() {{
+      return {{
+        input: {bands},
+        output: [
+            {{
+              id: "bands",
+              bands: {len(bands)},
+              sampleType: "{sample_type}"
+            }}
+        ]
+      }};
+    }}
+    function evaluatePixel(sample) {{
+      return [{evaluate_pixel}];
+    }}
+    """
+
+
+def plot_tiff_folium(data, bounds, variable_name):
+    ymin, xmin, ymax, xmax = list(bounds)
+    m = folium.Map(location=[(xmin+xmax)/2, (ymin+ymax)/2], zoom_start=5, control_scale=True)
+    folium.raster_layers.ImageOverlay(
+        #name=month,
+        image=data,
+        bounds=[[xmin, ymin], [xmax, ymax]],
+        opacity=0.9,
+        mercator_project=True,
+        colormap=plt.cm.Reds if variable_name == 'TEMPERATURE' else plt.cm.Blues,
+        control=True
+        ).add_to(m)
+    return m
+
+def plot_multiple_foliums(maps, size_x, size_y):
+    template = '<iframe srcdoc="{}" style="float:left; width: {}px; height: {}px; display:inline-block; width: 50%; margin: 0 auto; border: 2px solid black"></iframe>'
+    html_map = ''
+    for i, m in enumerate(maps):
+        html_map = html_map + template.format(m.get_root().render().replace('"', '&quot;'),size_x,size_y)
+    return HTML(html_map)
+
+
+class EWC(MultiValueEnum):
+    """Enum class containing basic LULC EWC types"""
+
+    NO_DATA = "No data", 0, "black"
+    TREE_COVER = "Tree cover", 10, "darkgreen"
+    SHRUBLAND = "Shrubland", 20, "orange"
+    GRASSLAND = "Grassland", 30, "yellow"
+    CROPLAND = "Cropland", 40, "violet"
+    BUILT_UP = "Built up", 50, "red"
+    BARE_SPARSE_VEGETATION = "Bare /sparse vegetation", 60, "dimgrey"
+    SNOW_ICE = "Snow and Ice", 70, "silver"
+    PERMANENT_WATER_BODIES = "Permanent water bodies", 80, "blue"
+    HERBACEOUS_WETLAND = "Herbaceous wetland", 90, "darkcyan"
+    MANGROVES = "Mangroves", 95, "springgreen"
+    MOSS_LICHEN = "Moss and lichen", 100, "khaki"
+
+def construct_cmap(colors, data, name="cmap"):
+    sub = []
+    un = np.unique(data)
+    for i, entry in enumerate(colors):
+        if np.isin(entry.values[1], un):
+            sub.append([entry.values[0], entry.values[1], entry.values[2]])
+    bounds = [sub[0][1] - 0.5]
+    [bounds.append((0.5 + entry[1])) for entry in sub]
+    ticks = [np.mean([bounds[i], bounds[i - 1]]) for i in range(1, len(bounds))]
+    cmap = ListedColormap([rgb_int(entry) for entry in sub], name=name)
+    norm = BoundaryNorm(bounds, cmap.N)
+    labels = [entry[0] for entry in sub]
+
+    return cmap, norm, ticks, labels
+
+def rgb_int(row):
+    hex_val = row[2]
+    rgb_val = matplotlib.colors.to_rgb(hex_val)
+    return rgb_val