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Merge pull request #28 from asc-csa/Development
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Geo Map Visualization Upgrade
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@nfee006
nfee006 authored Mar 8, 2021
2 parents fa8384c + 3e3e2e1 commit 2648869
Showing 1 changed file with 103 additions and 41 deletions.
144 changes: 103 additions & 41 deletions scisat.py
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# -*- coding: utf-8 -*-
import dash

import cartopy.feature as cf
import configparser
import dash_core_components as dcc
import dash_bootstrap_components as dbc
Expand Down Expand Up @@ -1209,58 +1209,120 @@ def generate_geo_map(start_date,end_date,gaz_list, lat_min, lat_max, lon_min, lo
A dictionary containing 2 key-value pairs: the selected data as an array of Plotly scattermapbox graph objects
and the map's layout as a Plotly layout graph object.
"""

df = data_reader(gaz_list,r'data',start_date,end_date,lat_min,lat_max,lon_min,lon_max,alt_range)

# Group data by latitude and longitude
df=df.groupby(['lat','long']).mean().reset_index()

# Graph
fig =go.Figure( go.Scattermapbox(
lat=df['lat'],#[df['Alt_Mean']<0.0003],
lon=df['long'],#[df['Alt_Mean']<0.0003],
mode="markers",
marker=dict(
color=df['Alt_Mean'],#[df['Alt_Mean']<0.0003],
colorscale=[[0, 'blue'],
[1, 'red']],
cmin=0,
cmax=max(df['Alt_Mean']),#[df['Alt_Mean']<0.0003]),
showscale=True,

size=5,
colorbar=dict(
title=dict(
text=_("Gas Concentration [ppv] (mean on altitude and position) "),#!!! description à mettre dans le caption au lieu de sur le côté
),
titleside="right",
showexponent = 'all',
exponentformat = 'e'
),
opacity=0.6,
)
)

)
# We bin the data in 3 degree bins
df = databin(gaz_list,start_date,end_date,lat_min,lat_max,lon_min,lon_max,alt_range,3)

# We collect the coordinates of all coastlines geometries from cartopy
x_coords = []
y_coords = []
for coord_seq in cf.COASTLINE.geometries():
x_coords.extend([k[0] for k in coord_seq.coords] + [np.nan])
y_coords.extend([k[1] for k in coord_seq.coords] + [np.nan])

# We create a heatmap of the binned data
fig= go.Figure(
go.Heatmap(
showscale=True,
x=df['long'],
y=df['lat'],
z=df['Alt_Mean'],
#zsmooth='fast', # Turned off smoothing to avoid interpolations
opacity=1,
name = "",
hoverongaps = False,
hovertemplate = "Lat.: %{y}°<br>Long.: %{x}°<br>Concentration: %{z:.3e} ppv",
colorbar=dict(
title=dict(
text=_("Gas Concentration [ppv] (mean on altitude and position) "),
),
titleside="right",
showexponent = 'all',
exponentformat = 'e'
),
colorscale= [[0.0, '#313695'], [0.07692307692307693, '#3a67af'], [0.15384615384615385, '#5994c5'], [0.23076923076923078, '#84bbd8'],
[0.3076923076923077, '#afdbea'], [0.38461538461538464, '#d8eff5'], [0.46153846153846156, '#d6ffe1'], [0.5384615384615384, '#fef4ac'],
[0.6153846153846154, '#fed987'], [0.6923076923076923, '#fdb264'], [0.7692307692307693, '#f78249'], [0.8461538461538461, '#e75435'],
[0.9230769230769231, '#cc2727'], [1.0, '#a50026']],
)
)
# We set the layout for margins and paddings
fig.update_layout(
margin=dict(l=10, r=10, t=20, b=10, pad=5),
# annotations = [dict(xref='paper',
# yref='paper',
# x=0.5, y=-0.25,
# showarrow=False,
# text ='This is my caption for the Plotly figure')],
clickmode="event+select",
hovermode="closest",
# showlegend=False,
mapbox=go.layout.Mapbox(
accesstoken=mapbox_access_token
#style="mapbox://styles/plotlymapbox/cjvppq1jl1ips1co3j12b9hex", #!!!!!! changer le stype de la map?
),
transition={'duration': 500},
)

# We turn off the axes
fig.update_xaxes(visible=False)
fig.update_yaxes(visible=False)

# This makes sure pixels are square so that we can accurately lay our data on an equirectangular map. We also set the background color to white
fig['layout']['yaxis']['scaleanchor']='x'
fig['layout'].update(plot_bgcolor='white')
# We add the coastlines on top of our heatmap and skip hoverinfo so that it does not overlap heatmap labels
fig.add_trace(
go.Scatter(
x = x_coords,
y = y_coords,
mode = 'lines',
hoverinfo = "skip",
line = dict(color='black')))

return fig

def databin(gaz_list,start_date,end_date,lat_min,lat_max,lon_min,lon_max,alt_range,step):
"""
Create data bins of specified steps in terms of longitude and latitude.
Parameters
----------
start_date : Datetime
First day in the date range selected by user. The default is the first day of data available.
end_date : Datetime
Last day in the date range selected by user. The default is the last day of data available.
lat_min : float
Minimum value of the latitude stored as a float.
lat_max : float
Maximum value of the latitude stored as a float.
lon_min : float
Minimum value of the longitude stored as a float.
lon_max : float
Maximum value of the longitude stored as a float.
gaz_list : list
Gas name strings stored in a list (e.g. ['Ozone'])
alt_range : List
Range of altitudes
step : float
Size of bins in terms of lat/long degrees
Returns
-------
Dataframe
A dataframe contained the binned data provided in steps of specified degrees.
"""
# We read the full data
df = data_reader(gaz_list,r'data',start_date,end_date,lat_min,lat_max,lon_min,lon_max,alt_range)
# We create a binning map
to_bin = lambda x: np.round(x / step) * step
# We map the current data into the bins
df["lat"] = df['lat'].map(to_bin)
df["long"] = df['long'].map(to_bin)
# We return a mean value of all overlapping data to ensure there are no overlaps
return df.groupby(["lat", "long"]).mean().reset_index()

# Selectors -> viz chart (95% CI)
@app.callback(
Expand Down

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