Merge pull request #387 from dapugacheva/main

Jupyter Lab and Software set up on GitHub adjustments from issue #375

process/example.ipynb

@@ -161,7 +161,7 @@
     "- Analyses could be conducted for different time points using historical data\n",
     "- Finally, data could be modified to represent hypothetical interventions and evaluate their impact on the calculated indicators\n",
     "\n",
-    "To run the following analysis, copy the file `process/configuration/example_ES_Las_Palmas.yml` to a new file named `example_ES_Las_Palmas_test_not_urbanx.yml`.  Open this in a text editor and replace the `ghsl_urban_intersection: true` in the study region boundary to read `ghsl_urban_intersection: false`."
+    "To run the following analysis, copy the file `process/configuration/regions/example_ES_Las_Palmas.yml` to a new file named `example_ES_Las_Palmas_test_not_urbanx.yml`.  Open this in a text editor and replace the `ghsl_urban_intersection: true` in the study region boundary to read `ghsl_urban_intersection: false`."
    ]
   },
   {
@@ -325,6 +325,9 @@
     "    field='local_walkability',\n",
     "    layer=r.config['grid_summary'],\n",
     "    id='grid_id',\n",
+    "    fill_color='YlGn',\n",
+    "    fill_opacity=0.7,\n",
+    "    line_opacity=0.1,\n",
     "    title='Local walkability',\n",
     "    attribution='Global Healthy and Sustainable City Indicators Collaboration'\n",
     ")\n",
@@ -345,7 +348,7 @@
     "\n",
     "![image.png](attachment:01efeb07-053e-48bb-8e5b-736962ab54b9.png)\n",
     "\n",
-    "In the interactive version, you can hover over grid cells to view the value of the specified indicator for that location."
+    "In the interactive version, you can hover over grid cells to view the value of the specified indicator for that location. You can change graphic parameters like fill color, see [how to choose colormaps in matplotlib](https://matplotlib.org/stable/users/explain/colors/colormaps.html#classes-of-colormaps)"
    ]
   },
   {

process/subprocesses/_utils.py

@@ -1577,6 +1577,9 @@ def plot_choropleth_map(
     field: str,
     layer: str = 'indicators_grid_100m',
     layer_id: str = 'grid_id',
+    fill_color: str = 'YlGn',
+    fill_opacity: float = 0.7,
+    line_opacity: float = 0.1,
     title: str = '',
     attribution: str = '',
 ):
@@ -1592,6 +1595,9 @@ def plot_choropleth_map(
         boundary_centroid=tuple(r.get_centroid()),
         key_on=layer_id,
         fields=[layer_id, field],
+        fill_color=fill_color,
+        fill_opacity=fill_opacity,
+        line_opacity=line_opacity,
         title=title,
         attribution=attribution,
     )
@@ -1604,6 +1610,9 @@ def choropleth_map(
     key_on: str,
     fields: list,
     boundary_centroid: tuple,
+    fill_color: str,
+    fill_opacity: float,
+    line_opacity: float,
     title: str,
     attribution: str,
 ):
@@ -1647,8 +1656,8 @@ def choropleth_map(
         key_on=f'feature.properties.{key_on}',
         name='choropleth',
         columns=fields,
-        fill_color='YlGn',
-        fill_opacity=0.7,
+        fill_color=fill_color,
+        fill_opacity=fill_opacity,
         line_opacity=0.1,
         legend_name=title,
     ).add_to(m)

process/subprocesses/ghsci.py

@@ -951,6 +951,9 @@ def choropleth(
         field: str,
         layer: str,
         id: str,
+        fill_color: str,
+        fill_opacity: float,
+        line_opacity: float,
         title: str,
         save=True,
         attribution: str = 'Global Healthy and Sustainable City Indicators Collaboration',
@@ -971,6 +974,9 @@ def choropleth(
                 layer=layer,
                 layer_id=id,
                 title=title,
+                fill_color=fill_color,
+                fill_opacity=fill_opacity,
+                line_opacity=line_opacity,
                 attribution=attribution,
             )
             if save:

readme.md

@@ -127,6 +127,11 @@ As an example of a sensitivity analysis of the urban boundary used for analysis:
 
 ![image](https://github.com/global-healthy-liveable-cities/global-indicators/assets/12984626/c95e1ab4-3d89-49a6-86cb-61718f83dde5)
 
+### Exit the software
+To exit the web application click the **exit** button in the top right-hand corner. At the end of a Jupyter session, in the menu click _File > Shut Down_. If you close a browser window with the Jupyter Lab or GHSCI app still running, the underlying server process running these may be interrupted by pressing `Control+C` at the command prompt.
+
+To run the analysis for your study region visit our website for detailed instructions on how to configure [a new study region](https://global-healthy-liveable-cities.github.io/software/#Details) and what [input data is required](https://global-healthy-liveable-cities.github.io/software/#Data).
+
 ## Citations
 
 The software was developed by the [Global Healthy and Sustainable City Indicators Collaboration](https://www.healthysustainablecities.org/about#team) team, an international partnership of researchers and practitioners, extending methods developed by the [Healthy Liveable Cities Lab](https://cur.org.au/research-programs/healthy-liveable-cities-group/) at RMIT University and incorporating functionality from the [OSMnx](https://github.com/gboeing/osmnx) tool developed by Geoff Boeing.