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Commit e6e7dc78 authored by alexis.dereeper_ird.fr's avatar alexis.dereeper_ird.fr
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add legend into geographical map

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dash/pages/sample-tracker.py
+ 152
− 121
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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;">
&nbsp; <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 + "&nbsp; <i class='fa fa-circle' style='color:"+str(color)+"'></i> &nbsp; " + str(y) + " &nbsp;<br>"
i+=1
else:
for sp in list_of_species:
color = predefined_colors[i]
species_colors[sp]=color
legend_html = legend_html + "&nbsp; <i class='fa fa-circle' style='color:"+str(color)+"'></i> &nbsp; " + str(sp) + " &nbsp;<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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