/
app.py
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/
app.py
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import os
import pickle
import copy
import datetime as dt
import pandas as pd
import numpy as np
import dash
from dash.dependencies import Input, Output, State
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objs as go
from server import server
app = dash.Dash(name='explore', server=server, url_base_pathname='/apps/explore')
app.title = 'GeoDataHack - Explore'
app.css.append_css({'external_url': 'https://cdn.rawgit.com/plotly/dash-app-stylesheets/2d266c578d2a6e8850ebce48fdb52759b2aef506/stylesheet-oil-and-gas.css'})
# Load data
package_directory = os.path.dirname(os.path.abspath(__file__))
df = pd.read_csv(os.path.join(package_directory, 'data',
'glacier_characteristics.csv'))
df = df.sort_values(by='rgi_area_km2')
area_range = [int(np.floor(df.rgi_area_km2.min())), int(np.ceil(df.rgi_area_km2.max()))]
df['text'] = ['Id: {} - Area: {:.2f} km2 - N Glaciers: {}'.format(i, a, n) for i, (a, n) in enumerate(zip(df.rgi_area_km2, df.n_glaciers))]
df = df.set_index('text')
map_lon = 0
map_lat = 0
map_zoom = 0
col_all_p = '#85c1e9'
col_sel_p = '#2e86c1'
col_all_t = '#f1948a'
col_sel_t = '#cb4335'
col_lat = '#7d3c98'
# Create global chart template
mapbox_access_token = 'pk.eyJ1IjoiZm1hdXNzaW9uIiwiYSI6ImNqaTY0aGZsbzA0MDMzcHF1NWh0dWI4NmQifQ.TmioqTQp7R9zK5DTf5rmNA'
layout = dict(
autosize=True,
height=400,
title='',
dragmode='select',
)
# Create app layout
app.layout = html.Div(
[
html.Div(
[
html.H1(
'World glaciers explorer',
className='eight columns',
),
html.Img(
src="https://raw.githubusercontent.com/OGGM/oggm/master/docs/_static/logos/oggm_s_alpha.png",
className='one columns',
style={
'height': '100',
'width': '225',
'float': 'right',
'position': 'relative',
},
),
html.P(
'Box-select on each plot, double click on the same plot to reset.',
className='eight columns',
),
html.H6(
'',
id='glaciers_text',
),
],
className='row'
),
html.Div(
[
html.Div(
[
html.P(
'',
),
],
className='four columns',
style={
'float': 'left',
'position': 'relative',
'margin-top': '0',
},
),
html.Div(
[
dcc.Graph(id='main_graph')
],
className='eight columns',
style={'margin-top': '0'}
),
],
className='row'
),
html.Div(
[
html.Div(
[
dcc.Graph(id='lat_graph')
],
className='four columns',
style={'margin-top': '0'}
),
html.Div(
[
dcc.Graph(id='temp_graph')
],
className='four columns',
style={'margin-top': '0'}
),
html.Div(
[
dcc.Graph(id='precip_graph')
],
className='four columns',
style={'margin-top': '0'}
),
],
className='row'
),
html.Div(
[
html.A("Link to our blog post for more information",
href="http://oggm.org/2018/06/11/opendatahack2018/"),
html.Br(),
html.A("Back to homepage", href="/"),
],
className='row',
style={'margin-bottom': '20'}
),
],
className='ten columns offset-by-one',
)
def point_selector(dff, points):
return dff.loc[[p['text'] for p in points]]
# Selectors -> glacier text
@app.callback(Output('glaciers_text', 'children'),
[Input('lat_graph', 'selectedData'),
Input('main_graph', 'selectedData'),
Input('precip_graph', 'selectedData'),
Input('temp_graph', 'selectedData'),
])
def update_glaciers_text(selected_lat, selected_map, selected_p, selected_t):
dff = df.copy()
if selected_lat is not None:
dff = point_selector(dff, selected_lat['points'])
if selected_map is not None:
dff = point_selector(dff, selected_map['points'])
if selected_p is not None:
pmin, pmax = selected_p['range']['x']
dff = dff.loc[(dff.tstar_avg_prcp <= pmax) &
(dff.tstar_avg_prcp >= pmin)]
if selected_t is not None:
pmin, pmax = selected_t['range']['x']
dff = dff.loc[(dff.tstar_avg_temp_mean_elev <= pmax) &
(dff.tstar_avg_temp_mean_elev >= pmin)]
return "No of Glaciers: {}".format(dff.n_glaciers.sum())
# Selectors -> main graph
@app.callback(Output('main_graph', 'figure'),
[Input('lat_graph', 'selectedData'),
Input('main_graph', 'selectedData'),
Input('precip_graph', 'selectedData'),
Input('temp_graph', 'selectedData'),
]
)
def make_main_figure(selected_lat, selected_map, selected_p, selected_t):
dff = df.copy()
if selected_lat is not None:
dff = point_selector(dff, selected_lat['points'])
if selected_map is not None:
dff = point_selector(dff, selected_map['points'])
if selected_p is not None:
pmin, pmax = selected_p['range']['x']
dff = dff.loc[(dff.tstar_avg_prcp <= pmax) &
(dff.tstar_avg_prcp >= pmin)]
if selected_t is not None:
pmin, pmax = selected_t['range']['x']
dff = dff.loc[(dff.tstar_avg_temp_mean_elev <= pmax) &
(dff.tstar_avg_temp_mean_elev >= pmin)]
traces = []
trace = dict(
type='scattergeo',
lon=dff['cenlon'],
lat=dff['cenlat'],
text=dff.index,
mode='markers',
marker=dict(
size=4,
opacity=0.8,
colorscale='Viridis',
color=dff['rgi_area_km2'],
cmin=0,
cmax=3200,
colorbar=dict(
title="Area (km<sup>2</sup>)"
)
),
)
traces.append(trace)
_layout = copy.deepcopy(layout)
_layout['geo'] = dict(
scope='world',
projection=dict(type='natural earth'),
showland=True,
margin=go.Margin(
l=-20,
r=-20,
b=-20,
t=-20,
),
landcolor="rgb(250, 250, 250)",
subunitcolor="rgb(217, 217, 217)",
countrycolor="rgb(217, 217, 217)",
countrywidth=0.5,
subunitwidth=0.5,
)
figure = dict(data=traces, layout=_layout)
return figure
# Selectors -> count graph
@app.callback(Output('lat_graph', 'figure'),
[Input('main_graph', 'selectedData'),
Input('precip_graph', 'selectedData'),
Input('temp_graph', 'selectedData'),
],
)
def make_lat_figure(selected_map, selected_p, selected_t):
dff = df.copy()
if selected_map is not None:
dff = point_selector(dff, selected_map['points'])
if selected_p is not None:
pmin, pmax = selected_p['range']['x']
dff = dff.loc[(dff.tstar_avg_prcp <= pmax) &
(dff.tstar_avg_prcp >= pmin)]
if selected_t is not None:
pmin, pmax = selected_t['range']['x']
dff = dff.loc[(dff.tstar_avg_temp_mean_elev <= pmax) &
(dff.tstar_avg_temp_mean_elev >= pmin)]
data = [
dict(
type='scatter',
mode='markers',
x=dff.dem_mean_elev,
y=dff.cenlat,
text=dff.index,
hoverinfo='skip',
marker=dict(
size=4,
opacity=1,
color=col_lat
)
),
]
_layout = copy.deepcopy(layout)
_layout['showlegend'] = False
_layout['xaxis'] = dict(range=[0, 6200], title='Altitude (m)')
_layout['yaxis'] = dict(range=[-90, 90], title='Latidude')
figure = dict(data=data, layout=_layout)
return figure
# Selectors -> count graph
@app.callback(Output('precip_graph', 'figure'),
[Input('lat_graph', 'selectedData'),
Input('main_graph', 'selectedData'),
Input('precip_graph', 'selectedData'),
Input('temp_graph', 'selectedData'),
])
def make_precip_figure(selected_lat, selected_map, selected_p, selected_t):
dff = df.copy()
if selected_map is not None:
dff = point_selector(dff, selected_map['points'])
if selected_lat is not None:
dff = point_selector(dff, selected_lat['points'])
if selected_p is not None:
pmin, pmax = selected_p['range']['x']
dff = dff.loc[(dff.tstar_avg_prcp <= pmax) &
(dff.tstar_avg_prcp >= pmin)]
if selected_t is not None:
pmin, pmax = selected_t['range']['x']
dff = dff.loc[(dff.tstar_avg_temp_mean_elev <= pmax) &
(dff.tstar_avg_temp_mean_elev >= pmin)]
bins = np.arange(41) * 100
pcp = df.tstar_avg_prcp.copy()
pcp[~np.isfinite(pcp)] = 0
hist1, edges1 = np.histogram(pcp, bins)
scale = np.sum(hist1)
hist1 = hist1 / scale
pcp = dff.tstar_avg_prcp.copy()
pcp[~np.isfinite(pcp)] = 0
hist2, edges2 = np.histogram(pcp, bins)
hist2 = hist2 / scale
data = [
dict(
type='bar',
x=(edges1[1:] + edges1[0:-1])/2,
y=hist1,
name='All',
hoverinfo='skip',
marker=dict(
color=col_all_p
),
),
dict(
type='bar',
x=(edges1[1:] + edges1[0:-1])/2,
y=hist2,
name='Selection',
hoverinfo='skip',
marker=dict(
color=col_sel_p
),
),
]
_layout = copy.deepcopy(layout)
_layout['showlegend'] = True
_layout['xaxis'] = dict(range=[0, 4100], title='Annual precipitation (mm yr<sup>-1</sup>)')
_layout['yaxis'] = dict(title='Frequency')
figure = dict(data=data, layout=_layout)
return figure
# Selectors -> count graph
@app.callback(Output('temp_graph', 'figure'),
[Input('lat_graph', 'selectedData'),
Input('main_graph', 'selectedData'),
Input('precip_graph', 'selectedData'),
Input('temp_graph', 'selectedData'),
])
def make_temp_figure(selected_lat, selected_map, selected_p, selected_t):
dff = df.copy()
if selected_map is not None:
dff = point_selector(dff, selected_map['points'])
if selected_lat is not None:
dff = point_selector(dff, selected_lat['points'])
if selected_p is not None:
pmin, pmax = selected_p['range']['x']
dff = dff.loc[(dff.tstar_avg_prcp <= pmax) &
(dff.tstar_avg_prcp >= pmin)]
if selected_t is not None:
pmin, pmax = selected_t['range']['x']
dff = dff.loc[(dff.tstar_avg_temp_mean_elev <= pmax) &
(dff.tstar_avg_temp_mean_elev >= pmin)]
bins = np.arange(35) - 27
pcp = df.tstar_avg_temp_mean_elev.copy()
pcp[~np.isfinite(pcp)] = 0
hist1, edges1 = np.histogram(pcp, bins)
scale = np.sum(hist1)
hist1 = hist1 / scale
pcp = dff.tstar_avg_temp_mean_elev.copy()
pcp[~np.isfinite(pcp)] = 0
hist2, edges2 = np.histogram(pcp, bins)
hist2 = hist2 / scale
data = [
dict(
type='bar',
x=(edges1[1:] + edges1[0:-1])/2,
y=hist1,
name='All',
hoverinfo='skip',
marker=dict(
color=col_all_t
),
),
dict(
type='bar',
x=(edges1[1:] + edges1[0:-1])/2,
y=hist2,
name='Selection',
hoverinfo='skip',
marker=dict(
color=col_sel_t
),
),
]
_layout = copy.deepcopy(layout)
_layout['showlegend'] = True
_layout['xaxis'] = dict(range=[-27, 7], title='Annual Temperature at avg. altitude (°C)')
_layout['yaxis'] = dict(title='Frequency')
figure = dict(data=data, layout=_layout)
return figure