From e6e7dc787398f226564ef1b732a82dfd4affef69 Mon Sep 17 00:00:00 2001
From: "alexis.dereeper_ird.fr" <alexis.dereeper@ird.fr>
Date: Tue, 11 Mar 2025 15:36:00 +0000
Subject: [PATCH] add legend into geographical map
---
dash/pages/sample-tracker.py | 273 +++++++++++++++++++----------------
1 file changed, 152 insertions(+), 121 deletions(-)
diff --git a/dash/pages/sample-tracker.py b/dash/pages/sample-tracker.py
index 79f2623..b83535c 100644
--- a/dash/pages/sample-tracker.py
+++ b/dash/pages/sample-tracker.py
@@ -1,9 +1,11 @@
from dash import Dash, html, dcc, Input, Output, State, callback, dash_table
-#from dash import Dash, dcc, html, dash_table, Input, Output, State, callback
+
+import branca
from grist_api import GristDocAPI
import os
import pandas as pd
+import random
import plotly.express as px
import plotly.graph_objects as go
@@ -35,6 +37,9 @@ import folium
import folium.plugins
import pandas.io.sql as psql
+import matplotlib.pyplot as plt
+from matplotlib import colors as mcolors
+
import dash_bio as dashbio
@@ -44,6 +49,7 @@ dash.register_page(__name__,path='/sample-tracker')
server_url = "https://bioinfophim-grist.ird.fr"
working_dir = "/mnt/c/Users/dereeper/Documents/formation_python_scientifique_2022/dash"
+
with open("grist_config.yml", "r") as yaml_file:
conf = yaml.safe_load(yaml_file)
server_url = conf["server_url"]
@@ -91,28 +97,6 @@ df=pd.merge(df_sequenced_strains,df1, left_on='source_strain_id', right_on='id_y
df_diag = df[df['Species']=='Unknown']
-year_colors = {
- "2014": "lightgray",
- "2015": "darkgreen",
- "2016": "darkpurple",
- "2017": "beige",
- "2018": "lightblue",
- "2019": "pink",
- "2020": "green",
- "2021": "red",
- "2022": "black",
- "2023": "blue"
-}
-
-species_colors = {
- "Xanthomonas oryzae oryzae": "lightblue",
- "Xanthomonas oryzae oryzicola": "blue",
- "Xanthomonas hortorum": "orange",
- "Xanthomonas phaseoli": "pink",
- "Xanthomonas translucens": "green",
- "Xanthomonas axonopodis": "red",
- "Xanthomonas vasicola": "black"
-}
############################################
@@ -156,7 +140,7 @@ data_summary_filtered_md_template = 'Selected strains'
data_summary_filtered_md = data_summary_filtered_md_template.format(len(df))
-
+session = random.randint(1, 9000000)
#print(df.head())
@@ -283,23 +267,33 @@ layout = html.Div(className='app-body', children=[
# The Visuals
dcc.Tabs(id='tab', children=[
dcc.Tab(label='Geographical map', children=[
- html.Div(className="four columns pretty_container", children=[
- html.Div(className="row", children=[
- html.Label('Marker Clustering'),
- daq.BooleanSwitch(id="clustering", on=True, style={'width': '10vh','margin-left': '20px'},)
- ]),
-
- ]),
- html.Div(className="four columns pretty_container", children=[
- html.Label('Colorizing map by'),
+ html.Br(),
+ html.Div(className="row", children=[
+ html.Label('Marker Clustering'),
+ daq.BooleanSwitch(id="clustering", on=True, style={'width': '10vh','margin-left': '5px'},),
+
+ html.Label('Map tiles: '),
+ dcc.Dropdown(id='tiles',
+ style={'width': '20vh','margin-left': '5px','margin-right': '10px'},
+ placeholder='OpenStreetMap',
+ options=['OpenStreetMap','Satellite'],
+ value='OpenStreetMap',
+ multi=False),
+ html.Label('Colorizing map by: '),
dcc.Dropdown(id='colorizingmap',
- placeholder='Species',
- options=list_colorizing_map,
- value='Species',
- multi=False),
+ style={'width': '20vh','margin-left': '5px','margin-right': '10px'},
+ placeholder='Species',
+ options=list_colorizing_map,
+ value='Species',
+ multi=False),
+
]),
+ html.Br(),
+
dcc.Loading(
- html.Iframe(id='map',src="https://webphim.ird.fr/CIX/testmap.html",style={"height": "600px", "width": "100%"})
+ #html.Iframe(id='map',src="https://webphim.ird.fr/CIX/testmap.html",style={"height": "600px", "width": "100%"})
+ html.Iframe(id='map',style={"height": "900px", "width": "100%"})
+
),
#dcc.Graph(id="map",figure=fig_scattergeo),
]),
@@ -320,14 +314,17 @@ layout = html.Div(className='app-body', children=[
),
]),
dcc.Tab(label='Statistics', children=[
-
- html.Label('Colorizing histograms by'),
- dcc.Dropdown(id='colorizing',
- placeholder='Species',
- options=list_colorizing,
- value='Species',
- multi=False),
-
+ html.Br(),
+ html.Div(className="row", children=[
+ html.Label('Colorizing histograms by'),
+ dcc.Dropdown(id='colorizing',
+ placeholder='Species',
+ style={'width': '40vh','margin-left': '5px'},
+ options=list_colorizing,
+ value='Species',
+ multi=False),
+
+ ]),
dcc.Loading(
@@ -384,15 +381,16 @@ layout = html.Div(className='app-body', children=[
Input('year', 'value'),
Input('pathovar', 'value'),
Input('colorizing', 'value'),
- Input('colorizingmap', 'value'),
- Input('clustering', 'on'),
+ State('colorizingmap', 'value'),
+ State('tiles', 'value'),
+ State('clustering', 'on'),
Input('gps_infered', 'value'),
Input('include_no_date', 'on'),
Input('sequenced', 'on'),
#Input('datatable-paging', "page_current"),
#Input('datatable-paging', "page_size"),
- )
-def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizing_name,colorizingmap_name,clustering_name,gps_infered,include_no_date,sequenced):
+ )
+def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizing_name,colorizingmap_name,tiles,clustering_name,gps_infered,include_no_date,sequenced):
@@ -478,10 +476,54 @@ def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizi
###########################################################################
# Geographical map of CIX
###########################################################################
- map_with_clusters = folium.Map(location=[11,1], zoom_start=3)
- marker_cluster = map_with_clusters
- if clustering_name:
- marker_cluster = folium.plugins.MarkerCluster().add_to(map_with_clusters)
+ dff3.to_csv(working_dir + "/" + str(session) + ".matrix.tsv",sep="\t")
+ updateMap(clustering_name,colorizingmap_name,tiles)
+
+
+
+
+
+ ###########################################################################
+ # Geographical map of diagnostics
+ ###########################################################################
+ map_diag = folium.Map(location=[11,1], zoom_start=3)
+ # for idx in df_diag.index:
+ # lat = df_diag.loc[idx]['GPS_Latitude']
+ # lon = df_diag.loc[idx]['GPS_Longitude']
+ # diagnostics = df_diag.loc[idx]['diagnostics']
+ # host = df_diag.loc[idx]['host']
+ # my_color = "blue"
+ # if (diagnostics=="Négatif"):
+ # my_color="red"
+ # elif (diagnostics=="Xoo"):
+ # my_color="green"
+ # elif (diagnostics=="Xoo et Xoc"):
+ # my_color="blue"
+
+ fn2= working_dir+'/'+str(session)+'.map_diagnostics.html'
+ map_diag.save(fn2)
+
+ html = generate_html(dff3)
+ open(working_dir+"/"+str(session)+".table.html", "w").write(html)
+
+ table = dff3.to_dict('records')
+
+ return open(working_dir+"/"+str(session)+'.testmap.html', 'r').read(),fig,fig2,fig4,open(working_dir+"/"+str(session)+'.map_diagnostics.html', 'r').read(),str(len(dff3)),'Current selection : {} strains'.format(len(dff3)),table
+
+
+
+@callback(
+ Output('map','srcDoc', allow_duplicate=True),
+ Input('clustering', 'on'),
+ Input('colorizingmap', 'value'),
+ Input('tiles', 'value'),
+ prevent_initial_call=True
+)
+
+def updateMap(clustering_name,colorizingmap_name,tiles):
+
+ map_with_clusters = folium.Map(location=[11,1], control_scale=True, zoom_start=3)
+ if tiles != 'OpenStreetMap':
tile = folium.TileLayer(
tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
attr = 'Esri',
@@ -490,35 +532,60 @@ def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizi
control = True
).add_to(map_with_clusters)
+
+ dff3 = pd.read_csv(working_dir + "/" + str(session) + ".matrix.tsv",sep="\t")
+ dff3['Sampling_date'] = pd.to_datetime(dff3.Sampling_date, format='%Y-%m-%d')
+
+ marker_cluster = map_with_clusters
+ if clustering_name:
+ marker_cluster = folium.plugins.MarkerCluster().add_to(map_with_clusters)
else:
marker_cluster = map_with_clusters
- print("d")
-
dff3.GPS_Latitude
-
- #dff3['sampling_date'] = dff3['sampling_date'].replace(['1900'], 'NA')
- # remove entries with empty or NA in GPS coordinates
- df_geo = dff3[abs(dff3.GPS_Latitude) > 0]
-
-
- # fig_scattergeo = go.Figure(data=go.Scattergeo(
- # lon = df_geo['GPS_Longitude'],
- # lat = df_geo['GPS_Latitude'],
- # text = df['strain_id'],
- # mode = 'markers',
- # #marker_color = df_geo['Sampling_date'].year,
- # ))
- # fig_scattergeo = px.scatter_geo(df_geo,
- # lat = 'GPS_Latitude',
- # lon = 'GPS_Longitude',
- # geojson='geometry',
- # scope='africa',
- # center=dict(lat=51.0057, lon=13.7274))
+ #predefined_colors
+ # remove entries with empty or NA in GPS coordinates
+ df_geo = dff3[abs(dff3.GPS_Latitude) > 0]
+
+
+ species_colors = {}
+ list_of_species = df_geo['Species'].unique().tolist()
+ list_of_years = df_geo['Sampling_date'].unique().tolist()
+ dict_year = {}
+ for y in list_of_years:
+ dict_year[y.year]=1
+
+ sorted_years = sorted(list(dict_year.keys()))
+ predefined_colors = list(mcolors.CSS4_COLORS.keys())
+
+ legend_html = '''
+ {% macro html(this, kwargs) %}
+ <div style="position: fixed;
+ bottom: 50px; left: 0px; width: 250px; height: 700px;
+ border:2px solid grey; z-index:9999; font-size:14px;
+ background-color:white; opacity: 0.85;">
+ <b>Legend</b> <br>
+ '''
+ i = 0
+ if colorizingmap_name == "Year of sampling":
+ for y in sorted_years:
+ color = predefined_colors[i]
+ species_colors[int(y)]=color
+ legend_html = legend_html + " <i class='fa fa-circle' style='color:"+str(color)+"'></i> " + str(y) + " <br>"
+ i+=1
+ else:
+ for sp in list_of_species:
+
+ color = predefined_colors[i]
+ species_colors[sp]=color
+ legend_html = legend_html + " <i class='fa fa-circle' style='color:"+str(color)+"'></i> " + str(sp) + " <br>"
+ i+=1
+ legend_html = legend_html + "</div>"
+ legend_html = legend_html + "{% endmacro %}"
for idx in df_geo.index:
@@ -534,19 +601,13 @@ def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizi
CIX = str(df_geo.loc[idx]['strain_id'])
wt = df_geo.loc[idx]['WT_DER_RES']
- #species_pathovar = species + " " + pathovar
-
my_color="blue"
- if (colorizingmap_name == "Year of sampling" and year in year_colors.keys()):
- my_color = year_colors[year]
+ if (colorizingmap_name == "Year of sampling" and int(year) in list(species_colors.keys())):
+ my_color = species_colors[int(year)]
if (colorizingmap_name == "Species" and species in species_colors.keys()):
my_color = species_colors[species]
-
-
-
-
folium.CircleMarker(
location=(lat,lon),
radius=8,
@@ -560,49 +621,19 @@ def update_graph(sp_name, cnt_name, wt_name, year_range, pathovar_name, colorizi
).add_to(marker_cluster)
- fn= working_dir + '/testmap.html'
- marker_cluster.save(fn)
-
- print("e")
-
+ legend = branca.element.MacroElement()
+ legend._template = branca.element.Template(legend_html)
- ###########################################################################
- # Geographical map of diagnostics
- ###########################################################################
- map_diag = folium.Map(location=[11,1], zoom_start=3)
- # for idx in df_diag.index:
- # lat = df_diag.loc[idx]['GPS_Latitude']
- # lon = df_diag.loc[idx]['GPS_Longitude']
- # diagnostics = df_diag.loc[idx]['diagnostics']
- # host = df_diag.loc[idx]['host']
- # my_color = "blue"
- # if (diagnostics=="Négatif"):
- # my_color="red"
- # elif (diagnostics=="Xoo"):
- # my_color="green"
- # elif (diagnostics=="Xoo et Xoc"):
- # my_color="blue"
-
- fn2= working_dir+'/map_diagnostics.html'
- map_diag.save(fn2)
-
- html = generate_html(dff3)
- open(working_dir+"/table.html", "w").write(html)
-
- table = dff3.to_dict('records')
+ # Add the legend to the map
+ marker_cluster.get_root().add_child(legend)
-
-
-
-
- return open(working_dir+'/testmap.html', 'r').read(),fig,fig2,fig4,open(working_dir+'/map_diagnostics.html', 'r').read(),str(len(dff3)),'Current selection : {} strains'.format(len(dff3)),table
-
- #return fig_scattergeo,fig,fig2,fig4,open(working_dir+'/map_diagnostics.html', 'r').read(),str(len(dff3)),'Current selection : {} strains'.format(len(dff3)),table
- #,open('table.html', 'r').read()
- #sortable.html.table(dff3, 'sample.html')
+ fn= working_dir + "/"+str(session)+ '.testmap.html'
+ marker_cluster.save(fn)
+ data_returned = open(working_dir+"/"+str(session)+'.testmap.html', 'r').read()
+ return data_returned
def generate_html(dataframe: pd.DataFrame):
# get the table HTML from the dataframe
--
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