Series plot - Geopandas

Question

I dont have a working code - but a snipet of my code can be as follows. I'm trying to use geopandas with mathplotlib, and trying to plot a map with links and points.

shape_file = os.path.join(os.getcwd(), "Healthboard")
    healthboard = gp.read_file(os.path.join(shape_file, "healthboard.shp"))
    healthboard = healthboard.to_crs({'init': 'epsg:4326'}) # re-projection   

    geo_df1 = geo_df1[geo_df1['HealthBoardArea2019Code'] == string1]
    geo = geo_df[geo_df['Healthboard '] == string2]

    new_shape_file = os.path.join(os.getcwd(), "Council_Shapefile")

    council_to_healtboard = pd.read_csv("council_to_healthboard.csv")
    council_to_healthboard = council_to_healtboard.rename(columns = {'CA': 'Council_area_code'})
    council = gp.read_file(os.path.join(new_shape_file, "Council_shapefile.shp"))
    council = council.to_crs({'init': 'epsg:4326'})
    council = council.rename(columns = {'la_s_code':'Council_area_code'})
    df = council.merge(council_to_healthboard, on = 'Council_area_code', how ='inner')

    # Plotting stuff 
    fig, ax = plt.subplots(figsize=(15,15))

    geo_df1.plot(ax = ax, markersize=35, color = "blue", marker = "*", label = "Postcode Sector")

    geo.geometry.plot(ax = ax, color = "red", markersize=20, alpha = 0.8, label = 'SiteName')

    #healthboard[healthboard["HBName"]=="Lothian"].plot(ax = ax, alpha = 0.6)
    #healthboard[healthboard["HBName"]=="Lothian"].boundary.plot(ax = ax, color = "black", alpha = 0.6)

    df[df["HB"]=="S08000024"].boundary.plot(ax =ax, color = "black", alpha = 0.1)
    df[df["HB"]=="S08000024"].plot(ax =ax, cmap = "viridis", alpha = 0.1)

    links_gp.plot(ax =ax, alpha = 0.25, color='brown', linestyle = "-")

My links_gp.plot has 40 time periods, as a result I want to make one plot, and have a button to adjust the parameters of time. Or if not possible a series of 40 plots. I've tried numerous ways but keep failing on this. I would really appreciate if someone could guide me on this.

Answer 1

I'm aware that you are using matplotlib, but if you don't mind using bokeh instead, you could use the following. To create an interactive plot with a possibility to adjust a parameter, bokeh provides a slider widget which can be used to change the plot based on a custom filter function.

An example from a geopandas dataframe with LineString geometries similar to the one you posted:

import geopandas as gpd
from bokeh.io import show, output_notebook
from bokeh.models import (CDSView, ColumnDataSource, CustomJS, 
                          CustomJSFilter, Slider, Column)
from bokeh.layouts import column
from bokeh.plotting import figure

# prepare data source
links_gp['x'] = links_gp.apply(lambda row: list(row['geometry'].coords.xy[0]), axis=1)
links_gp['y'] = links_gp.apply(lambda row: list(row['geometry'].coords.xy[1]), axis=1)
# drop geometry column, because it can't be serialized to ColumnDataSource
links_gp.drop('geometry', axis=1, inplace=True)
linesource = ColumnDataSource(links_gp)

p = figure(title = 'Bokeh Time Slider', 
           plot_height = 500,
           plot_width = 600, 
           toolbar_location = 'below',
           tools = "pan, wheel_zoom, box_zoom, reset")
slider = Slider(title='Time Period', start=1, end=40, step=1, value=1)

# Callback triggers the filter when the slider moves
callback = CustomJS(args=dict(source=linesource), 
                    code="""source.change.emit();""")
slider.js_on_change('value', callback)

# Custom filter that selects all lines of the time period based on the slider value
custom_filter = CustomJSFilter(args=dict(slider=slider), 
                               code="""
var indices = [];
// iterate through rows of data source and check if time period value equals the slider value
for (var i = 0; i < source.get_length(); i++){
 if (source.data['Time Period'][i] == slider.value){
 indices.push(true);
 } else {
 indices.push(false);
 }
}
return indices;
""")

# Use filter to determine which lines are visible
view = CDSView(source=linesource, filters=[custom_filter])

# plot lines to map
p.multi_line('x', 'y', source=linesource, color='red', line_width=3, view=view)

layout = column(p, slider)
show(layout)

This will be the result of the above code.