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app.py
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app.py
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import streamlit as st
import json
import geojson
import ee
import pandas as pd
import geopandas as gpd
import altair as alt
from streamlit_folium import folium_static
import folium
from shapely.geometry import shape
MAP_EMOJI_URL = "https://em-content.zobj.net/source/apple/354/thermometer_1f321-fe0f.png"
# Set page title and favicon.
st.set_page_config(
page_title="Land Surface Temperature - River Basins",
page_icon=MAP_EMOJI_URL,
layout="wide"
)
# Initialize session state
# if 'dropdown_values' not in st.session_state:
# st.session_state.dropdown_values = {'region_name': None, 'maj_name': None, 'sub_name': None}
col1, col2, col3 = st.columns([1, 4, 1])
# Display header.
col2.markdown("<br>", unsafe_allow_html=True)
col2.image(MAP_EMOJI_URL, width=80)
col2.markdown("""
# Land Surface Temperature - River Basins
[](https://www.twitter.com/mykolakozyr)
[](https://www.linkedin.com/in/mykolakozyr/)
## Details
The app enables discovering land surface temperature data over river (hydrological) basins.
Temporal extent: 2017-01-01 till today.
Library for visualizations - [Vega-Altair](https://altair-viz.github.io/index.html).
---
""")
def convert_df(df):
return df.to_csv(index=False).encode('utf-8')
def convert_gdf(gdf):
gdf['geometry'] = gdf['geometry'].astype('geometry')
gdf['Timestamp'] = gdf['Timestamp'].astype(str)
return gdf.to_json()
json_data = st.secrets["json_data"]
service_account = st.secrets["service_account"]
json_object = json.loads(json_data, strict=False)
json_object = json.dumps(json_object)
credentials = ee.ServiceAccountCredentials(service_account, key_data=json_object)
ee.Initialize(credentials)
import src.gee as gee
# Defining the temporal extent of the discovery
today = ee.Date(pd.to_datetime('today'))
date_range = ee.DateRange('2017-01-01', today)
# Interface to select the area of interest.
region_name = col2.selectbox('Select the region.', ['Europe','USA', 'Australia and New Zealand', 'Near East', 'Southeast Asia'], key='region_name')
# Select the region
if region_name:
#st.session_state.dropdown_values['region_name'] = region_name
# Defining the GeoDataFrame with a subset of areas with archive coverage.
filename = "data/basins_" + region_name.lower() + "_mult.geojson"
file = open(filename)
gdf = gpd.read_file(file)
maj_name = col2.selectbox('Select the major hydrological basin.', sorted(pd.unique(gdf['MAJ_NAME'])), key='maj_name')
# Select the major hydrological basin
if maj_name:
#st.session_state.dropdown_values['maj_name'] = maj_name
# Select the sub-basin.
sub_name = col2.selectbox('Select the river basin within the major one', sorted(gdf[gdf['MAJ_NAME'] == maj_name]['SUB_NAME']), key='sub_name')
# with col2.expander('Additional parameters', expanded=False):
# st.text('How many charts do you want to create?')
# agree = st.checkbox('Yes', value='Yes', disabled=True)
# if agree == True:
#st.session_state.dropdown_values['sub_name'] = sub_name
if col2.button('Discover the Land Surface Temperature data!'):
with col2:
with st.spinner("Collecting data using Google Earth Engine..."):
# Defining the geometry from the selected basin.
aoi_json = json.loads(gdf.loc[gdf['SUB_NAME'] == sub_name, 'geometry'].to_json())['features'][0]['geometry']
aoi = ee.FeatureCollection(ee.Geometry(aoi_json)).geometry()
# Getting LST data.
lst = ee.ImageCollection('MODIS/061/MOD11A2').filterDate(date_range).select('LST_Day_1km')
reduce_lst = gee.create_reduce_region_function(geometry=aoi, reducer=ee.Reducer.mean(), scale=1000, crs='EPSG:4326')
lst_stat_fc = ee.FeatureCollection(lst.map(reduce_lst)).filter(ee.Filter.notNull(lst.first().bandNames()))
lst_dict = gee.fc_to_dict(lst_stat_fc).getInfo()
lst_df = pd.DataFrame(lst_dict)
lst_df['LST_Day_1km'] = (lst_df['LST_Day_1km'] * 0.02 - 273.5)
lst_df = gee.add_date_info(lst_df)
# Feature to preview the geometry.
with st.expander('Geometry Preview', expanded=False):
map_aoi = folium.Map(tiles="OpenStreetMap")
folium.Choropleth(geo_data = aoi_json, reset=True).add_to(map_aoi)
bounds = map_aoi.get_bounds()
map_aoi.fit_bounds(bounds)
# Not working properly for unknown reason. To be discovered.
st.warning("Sometimes the map does not zoom to the selected area most likely because of [this issue](https://github.com/randyzwitch/streamlit-folium/issues/152).")
folium_static(map_aoi)
# Creating Charts
# Line Chart with Points: https://altair-viz.github.io/gallery/line_chart_with_points.html
line_chart = alt.Chart(lst_df).mark_line(
point=alt.OverlayMarkDef(color="red")
).encode(
alt.X("Timestamp"),
alt.Y("LST_Day_1km", title='Land Surface Temperature, °C'),
).interactive()
# Ridgeline plot Example: https://altair-viz.github.io/gallery/ridgeline_plot.html
step = 16
overlap = 1
ridgeline_plot = alt.Chart(lst_df, height=step).transform_timeunit(
Month="month(Timestamp)"
).transform_joinaggregate(
mean_temp="mean(LST_Day_1km)", groupby=['Month']
).transform_bin(
['bin_max', 'bin_min'], 'mean_temp'
).transform_aggregate(
value='count()', groupby=['Month', 'mean_temp', 'bin_min', 'bin_max']
).transform_impute(
impute='value', groupby=['Month', 'mean_temp'], key='bin_min', value=0
).mark_area(
interpolate='monotone',
fillOpacity=0.8,
stroke='lightgray',
strokeWidth=0.5
).encode(
alt.X('bin_min:Q', bin='binned',
title='Land Surface Temperature, °C'
),
alt.Y(
'value:Q',
scale=alt.Scale(range=[step, -step * overlap]),
axis=None
),
alt.Fill(
'mean_temp:Q',
legend=None,
scale=alt.Scale(domain=[40, -5], scheme='redyellowblue')
)
).facet(
row=alt.Row(
"Month:T",
title=None,
header=alt.Header(labelAngle=0, labelAlign='right', format='%B')
)
).properties(
bounds='flush'
).configure_facet(
spacing=0
).configure_view(
stroke=None
).configure_title(
anchor='end'
)
# Binned Heatmap: https://altair-viz.github.io/gallery/binned_heatmap.html
binned_heatmap = alt.Chart(lst_df).mark_rect().encode(
alt.X("Month:O"),
alt.Y("Year:O"),
alt.Color("mean(LST_Day_1km):Q", scale=alt.Scale(scheme='redyellowblue', reverse=True), title='Land Surface Temperature, °C')
).interactive()
# Violin Plot Chart: https://altair-viz.github.io/gallery/violin_plot.html
violin_chart = alt.Chart(lst_df).transform_density(
"LST_Day_1km",
as_=["LST_Day_1km", 'density'],
extent=[-20, 60],
groupby=["Year"]
).mark_area(orient='horizontal').encode(
alt.Y("LST_Day_1km:Q",title='Land Surface Temperature, °C'),
color="Year:N",
x=alt.X(
'density:Q',
stack='center',
impute=None,
title=None,
axis=alt.Axis(labels=False, values=[0],grid=False, ticks=True),
),
column=alt.Column(
"Year:Q",
header=alt.Header(
titleOrient='bottom',
labelOrient='bottom',
labelPadding=0,
),
)
).properties(
width=100,
height=450
).configure_facet(
spacing=0
).configure_view(
stroke=None
)
# Hexbin Chart: https://altair-viz.github.io/gallery/hexbins.html
# Size of the hexbins
size = 15
# Count of distinct x features
xFeaturesCount = 12
# Count of distinct y features
yFeaturesCount = 6
yField = 'Timestamp'
xField = 'Timestamp'
# the shape of a hexagon
hexagon = "M0,-2.3094010768L2,-1.1547005384 2,1.1547005384 0,2.3094010768 -2,1.1547005384 -2,-1.1547005384Z"
hexbin_chart = alt.Chart(lst_df).mark_point(size=size**2, shape=hexagon).encode(
x=alt.X('xFeaturePos:Q', axis=alt.Axis(title='Month',
grid=False, tickOpacity=0, domainOpacity=0)),
y=alt.Y('year(' + yField + '):O', axis=alt.Axis(title='Year',
labelPadding=20, tickOpacity=0, domainOpacity=0)),
stroke=alt.value('black'),
strokeWidth=alt.value(0.2),
fill=alt.Color('mean(LST_Day_1km):Q', scale=alt.Scale(scheme='redyellowblue', reverse=True), title='Land Surface Temperature, °C'),
tooltip=['Month:O', 'Year:O', 'mean(LST_Day_1km):Q']
).transform_calculate(
# This field is required for the hexagonal X-Offset
xFeaturePos='(year(datum.' + yField + ') % 2) / 2 + month(datum.' + xField + ')'
).properties(
# Scaling factors to make the hexbins fit. Adjusted to the streamlit view
width=size * xFeaturesCount * 3.6,
height=size * yFeaturesCount * 2.77128129216
).configure_view(
strokeWidth=0
).interactive()
# Boxplot Chart: https://altair-viz.github.io/gallery/boxplot.html
boxplot_chart_year = alt.Chart(lst_df).mark_boxplot(extent='min-max').encode(
alt.X('Year:O'),
alt.Y('mean(LST_Day_1km):Q',title='Land Surface Temperature, °C')
).interactive()
# Boxplot Chart: https://altair-viz.github.io/gallery/boxplot.html
boxplot_chart_month = alt.Chart(lst_df).mark_boxplot(extent='min-max').encode(
alt.X('Month:O'),
alt.Y('mean(LST_Day_1km):Q', title='Land Surface Temperature, °C')
).properties(height=500).interactive()
# Scatter Plot Chart: https://altair-viz.github.io/gallery/scatter_tooltips.html
scatter_chart = alt.Chart(lst_df).mark_circle(size=60).encode(
alt.Y('LST_Day_1km', title='Land Surface Temperature, °C'),
alt.X('DOY', title='Day of the Year'),
color='Year:N',
tooltip=['LST_Day_1km', 'Timestamp']
).interactive()
# Bar Chart with Negative Values: https://altair-viz.github.io/gallery/bar_chart_with_negatives.html
bar_negative = alt.Chart(lst_df).mark_bar().encode(
alt.X("Timestamp"),
alt.Y("LST_Day_1km:Q", title='Land Surface Temperature, °C'),
color=alt.condition(
alt.datum.LST_Day_1km > 0,
alt.value("orange"), # The positive color
alt.value("steelblue") # The negative color
)
).interactive()
# Binned Scatterplot: https://altair-viz.github.io/gallery/binned_scatterplot.html
scatter_binned = alt.Chart(lst_df).mark_circle().encode(
alt.X('DOY:Q', bin=True, title='Day of the Year'),
alt.Y('LST_Day_1km:Q', bin=True, title='Land Surface Temperature, °C'),
size='count()'
).interactive()
#Scatter Plot with LOESS Lines: https://altair-viz.github.io/gallery/scatter_with_loess.html
base_scatter = alt.Chart(lst_df).mark_circle(opacity=0.5).encode(
alt.X('DOY', title='Day of the Year'),
alt.Y('LST_Day_1km:Q', title='Land Surface Temperature, °C'),
alt.Color('Year:N')
)
scatter_loess = base_scatter + base_scatter.transform_loess('DOY', 'LST_Day_1km', groupby=['Year']).mark_line(size=4).interactive()
# Stripplot: https://altair-viz.github.io/gallery/stripplot.html
stripplot = alt.Chart(lst_df, width=40).mark_circle(size=8).encode(
x=alt.X(
'jitter:Q',
title=None,
axis=alt.Axis(values=[0], ticks=True, grid=False, labels=False),
scale=alt.Scale(),
),
y=alt.Y('LST_Day_1km:Q', title='Land Surface Temperature, °C'),
color=alt.Color('Year:N', legend=None),
column=alt.Column(
'Year:N',
header=alt.Header(
labelAngle=-90,
titleOrient='top',
labelOrient='bottom',
labelAlign='right',
labelPadding=3,
),
),
).transform_calculate(
# Generate Gaussian jitter with a Box-Muller transform
jitter='sqrt(-2*log(random()))*cos(2*PI*random())'
).configure_facet(
spacing=0
).configure_view(
stroke=None
).properties(height=400).interactive()
# Table Bubble Plot: https://altair-viz.github.io/gallery/table_bubble_plot_github.html
table_bubble = alt.Chart(lst_df).mark_circle().encode(
alt.X('Month:O'),
alt.Y('Year:O'),
alt.Size('mean(LST_Day_1km):Q', title='Land Surface Temperature, °C')
).interactive()
# Visualizing in the defined layout
# Row 1
col1, col2 = st.columns([4,1])
with col1:
st.altair_chart(line_chart, use_container_width=True)
with col2:
st.altair_chart(boxplot_chart_year, use_container_width=True)
# Row 2
col1, col2 = st.columns([1,1])
with col1:
st.altair_chart(binned_heatmap, use_container_width=True)
with col2:
st.altair_chart(table_bubble, use_container_width=True)
# Row 3
col1, col2 = st.columns([1,4])
with col1:
st.altair_chart(scatter_chart, use_container_width=True)
with col2:
st.altair_chart(bar_negative, use_container_width=True)
# Row 4
col1, col2, col3 = st.columns([1,2,1])
with col1:
st.altair_chart(boxplot_chart_month, use_container_width=True)
with col2:
st.altair_chart(violin_chart)
with col3:
st.altair_chart(stripplot)
# Row 5
col1, col2 = st.columns([1,1])
with col1:
st.altair_chart(hexbin_chart)
with col2:
st.altair_chart(ridgeline_plot)
# Row 6
col1, col2, col3 = st.columns([1,1,1])
with col1:
st.altair_chart(scatter_binned, use_container_width=True)
with col2:
st.altair_chart(scatter_chart, use_container_width=True)
with col3:
st.altair_chart(scatter_loess, use_container_width=True)
col1, col2, col3 = st.columns([1, 4, 1])
# Data download
col1, col2, col3 = st.columns([1, 4, 1])
col2.markdown("""
---
## Data download
""")
# Download data preparation
gdf = gpd.GeoDataFrame(lst_df, geometry=[shape(aoi_json)]*len(lst_df))
csv_data = convert_df(gdf)
geojson_data = convert_gdf(gdf)
col2.warning('Please note, data download resets the dashboard view. This seems to be a Streamlit limitation as described in [this open issue](https://github.com/streamlit/streamlit/issues/4382).')
# Download CSV
with col2.container(border=True):
cont1_1, cont1_2 = st.columns([1, 3])
with cont1_1:
st.download_button(
label="Download CSV",
data=csv_data,
file_name=sub_name + "-LST.csv",
mime="text/csv",
key='download-csv'
)
with cont1_2:
st.write('The CSV file includes the average land surface temperature value in Celsius, date and time information and the hydrological basin geometry in the WKT format.')
# Download GeoJSON
with col2.container(border=True):
cont2_1, cont2_2 = st.columns([1, 3])
with cont2_1:
st.download_button(
label="Download GeoJSON",
data=geojson_data,
file_name=sub_name + "-LST.geojson",
mime="application/json",
key='download-geojson'
)
with cont2_2:
st.write('The GeoJSON provides the same geometry values for each feature. GeoJSON properties include the average land surface temperature value, date and time infromation.')
col1, col2, col3 = st.columns([1, 4, 1])
col2.markdown("""
---
## References
* Hydrological basins in Europe - [FAO Map Catalog.](https://data.apps.fao.org/map/catalog/srv/api/records/1849e279-67bd-4e6f-a789-9918925a11a1)
* Watershed Boundary Dataset in the USA - [USGS.](https://www.usgs.gov/national-hydrography/watershed-boundary-dataset)
* Hydrological basins in Australia and New Zealand - [FAO Map Catalog.](https://data.apps.fao.org/catalog/dataset/a1a0e9ee-5062-4950-a6b9-fdd2284b2607)
* Hydrological basins in Near East - [FAO Map Catalog.](https://data.apps.fao.org/catalog/iso/7ae00a40-642b-4637-b1d3-ffacb13360db)
* Hydrological basins in Southeast Asia - [FAO Map Catalog.](https://data.apps.fao.org/catalog/iso/ee616dc4-3118-4d67-ba05-6e93dd3e962f)
* Land Surface Temperature - [MODIS via Google Earth Engine.](https://developers.google.com/earth-engine/datasets/catalog/MODIS_061_MOD11A2)
""")