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- Check for exporting EAI charts as png
- Disable run button while running
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@matamadio
matamadio authored Dec 3, 2024
1 parent aa95ff5 commit ffb8ed1
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Showing 2 changed files with 212 additions and 174 deletions.
360 changes: 195 additions & 165 deletions tools/code/gui_f3_utils.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import common
import os
import folium
from folium.plugins import MiniMap, Fullscreen
import geopandas as gpd
Expand All @@ -17,6 +18,7 @@
from damageFunctions import mortality_factor, damage_factor_builtup, damage_factor_agri
from input_utils import get_adm_data
import notebook_utils
from notebook_utils import export_charts_chk
from runAnalysis import (
run_analysis, plot_results, create_summary_df, prepare_excel_gpkg_files,
prepare_sheet_name, saving_excel_and_gpgk_file, prepare_and_save_summary_df
Expand Down Expand Up @@ -573,179 +575,203 @@ def validate_input():
return True

def run_analysis_script(b):
with output:
output.clear_output(wait=True)

if not validate_input():
return

# Gather input values
country = country_dict.get(country_selector.value, False)
if not country:
print("Error: Invalid country selection.")
return

# Rest of the input gathering remains the same
haz_cat = hazard_selector.value
period = period_selector.value
scenario = scenario_selector.value if period != '2020' else ''
return_periods = list(return_periods_selector.value)
exp_cat_list = list(exposure_selector.value)
exp_year = '2020' # This is fixed as per the original script
adm_level = adm_level_selector.value
analysis_type = approach_selector.value
min_haz_slider = hazard_threshold_slider.value

# Get class thresholds if applicable
class_edges = []
if analysis_type == 'Classes':
class_edges = [child.children[1].value for child in class_edges_table.children[:-1]] # Exclude "Add Class" button

# Determine exp_nam_list
exp_nam_list = []
if custom_exposure_radio.value == 'Custom exposure':
for i, exp_cat in enumerate(exp_cat_list):
custom_name = custom_exposure_container.children[i].value
exp_nam_list.append(custom_name if custom_name else f"{country}_{exp_cat}")
else:
exp_nam_list = [f"{country}_{exp}" for exp in exp_cat_list]
# Disable the run button while analysis is running
run_button.disabled = True
run_button.description = "Analysis Running..."
try:
with output:
output.clear_output(wait=True)

if not validate_input():
return

# Additional parameters
save_check_raster = False
n_cores = None
# Gather input values
country = country_dict.get(country_selector.value, False)
if not country:
print("Error: Invalid country selection.")
return

# Rest of the input gathering remains the same
haz_cat = hazard_selector.value
period = period_selector.value
scenario = scenario_selector.value if period != '2020' else ''
return_periods = list(return_periods_selector.value)
exp_cat_list = list(exposure_selector.value)
exp_year = '2020' # This is fixed as per the original script
adm_level = adm_level_selector.value
analysis_type = approach_selector.value
min_haz_slider = hazard_threshold_slider.value

# Get class thresholds if applicable
class_edges = []
if analysis_type == 'Classes':
class_edges = [child.children[1].value for child in class_edges_table.children[:-1]] # Exclude "Add Class" button

# Determine exp_nam_list
exp_nam_list = []
if custom_exposure_radio.value == 'Custom exposure':
for i, exp_cat in enumerate(exp_cat_list):
custom_name = custom_exposure_container.children[i].value
exp_nam_list.append(custom_name if custom_name else f"{country}_{exp_cat}")
else:
exp_nam_list = [f"{country}_{exp}" for exp in exp_cat_list]

# Look up WB_REGION
df = pd.read_csv('countries.csv')
wb_region = df.loc[df['ISO_A3'] == country, 'WB_REGION'].iloc[0]
# Additional parameters
save_check_raster = False
n_cores = None

# Handle custom boundaries
use_custom_boundaries = custom_boundaries_radio.value == 'Custom boundaries'

if use_custom_boundaries:
custom_boundaries_file_path = custom_boundaries_file.value
custom_code_field = custom_boundaries_id_field.value
custom_name_field = custom_boundaries_name_field.value
else:
custom_boundaries_file_path = None
custom_code_field = None
custom_name_field = None

start_time = time.perf_counter()
# Look up WB_REGION
df = pd.read_csv('countries.csv')
wb_region = df.loc[df['ISO_A3'] == country, 'WB_REGION'].iloc[0]

print(f"Starting analysis for {iso_to_country[country]} ({country})...")

# Validate exp_nam_list
if len(exp_nam_list) != len(exp_cat_list):
print("ERROR: Parameter 'exp_nam_list' should have the same length as 'exp_cat_list'")
return
# Handle custom boundaries
use_custom_boundaries = custom_boundaries_radio.value == 'Custom boundaries'

if use_custom_boundaries:
custom_boundaries_file_path = custom_boundaries_file.value
custom_code_field = custom_boundaries_id_field.value
custom_name_field = custom_boundaries_name_field.value
else:
custom_boundaries_file_path = None
custom_code_field = None
custom_name_field = None

# Initialize the folium preview map before the loop
m = folium.Map(location=[0, 0], zoom_start=5, draw_control=False, scale_control=False, measure_control=False)
start_time = time.perf_counter()

# Initialize variables
minx, miny, maxx, maxy = float('inf'), float('inf'), float('-inf'), float('-inf')
summary_dfs, charts, layers, colormaps = [], [], [], []

# Prepare Excel writer
excel_file, gpkg_file = prepare_excel_gpkg_files(country, adm_level, haz_cat, period, scenario)

# Use ExcelWriter as a context manager
# Run analysis for each exposure category
for i in range(len(exp_cat_list)):
exp_cat = exp_cat_list[i]
exp_nam = exp_nam_list[i]
print(f"Running analysis for {exp_cat}...")
result_df = run_analysis(country, haz_cat, period, scenario, return_periods, min_haz_slider,
exp_cat, exp_nam, exp_year, adm_level, analysis_type, class_edges,
save_check_raster, n_cores, use_custom_boundaries=use_custom_boundaries,
custom_boundaries_file_path=custom_boundaries_file_path, custom_code_field=custom_code_field,
custom_name_field=custom_name_field, wb_region=wb_region)
print(f"Starting analysis for {iso_to_country[country]} ({country})...")

if result_df is None:
print("Encountered Exception! Please fix issue above.")
# Validate exp_nam_list
if len(exp_nam_list) != len(exp_cat_list):
print("ERROR: Parameter 'exp_nam_list' should have the same length as 'exp_cat_list'")
return

sheet_name = prepare_sheet_name(analysis_type, return_periods, exp_cat)

saving_excel_and_gpgk_file(result_df, excel_file, sheet_name, gpkg_file, exp_cat)

# Create summary DataFrame
summary_df = create_summary_df(result_df, return_periods, exp_cat)
summary_dfs.append(summary_df)

if preview_chk.value:
# Add each result as a layer
layer, colormap = plot_results(result_df, exp_cat, analysis_type)
if layer is not None:
layers.append(layer)
colormaps.append(colormap)
# Update the overall bounding box using result_df
bounds = result_df.total_bounds
minx = min(minx, bounds[0])
miny = min(miny, bounds[1])
maxx = max(maxx, bounds[2])
maxy = max(maxy, bounds[3])

# Combine all summary DataFrames
if summary_dfs:
combined_summary = prepare_and_save_summary_df(summary_dfs, exp_cat_list, excel_file, return_file=True)

# Add custom exposure information
excel_writer = pd.ExcelWriter(excel_file, engine='openpyxl', mode='a', if_sheet_exists='replace')
row_offset = len(combined_summary) + 4 # Start two rows below the table
for i, exp_cat in enumerate(exp_cat_list):
if custom_exposure_radio.value == 'Custom exposure':
custom_name = custom_exposure_container.children[i].value
if custom_name:
excel_writer.sheets['Summary'].cell(row=row_offset, column=1, value=f"Custom exposure layer for {exp_cat}: {custom_name}")
row_offset += 1

# Generate charts only for Function approach
if analysis_type == "Function" and preview_chk.value:
colors = {'POP': 'blue', 'BU': 'orange', 'AGR': 'green'}
charts = [create_eai_chart(combined_summary, exp_cat, period, scenario, colors[exp_cat])
for exp_cat in exp_cat_list]
else:
charts = []

print(f"Analysis completed in {time.perf_counter() - start_time:.2f} seconds")
print(f"Results saved to Excel file: {excel_file}")
print(f"Results saved to GeoPackage file: {gpkg_file}")

# Plot results if preview is checked
if preview_chk.value and minx != float('inf'):
center_lat = (miny + maxy) / 2
center_lon = (minx + maxx) / 2
m.location = [center_lat, center_lon]
m.fit_bounds([[miny, minx], [maxy, maxx]])
print("Plotting results preview as map...")

# Add layers to the map
for layer in layers:
layer.add_to(m)

# Add colormaps to the map
for colormap in colormaps:
colormap.add_to(m)

# Create a custom layer control
folium.LayerControl(position='topright', collapsed=False).add_to(m)
# Initialize the folium preview map before the loop
m = folium.Map(location=[0, 0], zoom_start=5, draw_control=False, scale_control=False, measure_control=False)

# Add the MiniMap
MiniMap(toggle_display=True, position='bottomright').add_to(m)

# Add the Fullscreen button
Fullscreen(position='bottomleft').add_to(m)

# Update the map_widget with the new map
map_widget.value = m._repr_html_()
# Initialize variables
minx, miny, maxx, maxy = float('inf'), float('inf'), float('-inf'), float('-inf')
summary_dfs, charts, layers, colormaps = [], [], [], []

# Prepare Excel writer
excel_file, gpkg_file = prepare_excel_gpkg_files(country, adm_level, haz_cat, period, scenario)

# Use ExcelWriter as a context manager
# Run analysis for each exposure category
for i in range(len(exp_cat_list)):
exp_cat = exp_cat_list[i]
exp_nam = exp_nam_list[i]
print(f"Running analysis for {exp_cat}...")
result_df = run_analysis(country, haz_cat, period, scenario, return_periods, min_haz_slider,
exp_cat, exp_nam, exp_year, adm_level, analysis_type, class_edges,
save_check_raster, n_cores, use_custom_boundaries=use_custom_boundaries,
custom_boundaries_file_path=custom_boundaries_file_path, custom_code_field=custom_code_field,
custom_name_field=custom_name_field, wb_region=wb_region)

if result_df is None:
print("Encountered Exception! Please fix issue above.")
return

with chart_output:
clear_output(wait=True)
# Display charts only once
for chart in charts:
display(chart)
plt.close(chart) # Close the figure after displaying
sheet_name = prepare_sheet_name(analysis_type, return_periods, exp_cat)

saving_excel_and_gpgk_file(result_df, excel_file, sheet_name, gpkg_file, exp_cat)

# Create summary DataFrame
summary_df = create_summary_df(result_df, return_periods, exp_cat)
summary_dfs.append(summary_df)

if preview_chk.value:
# Add each result as a layer
layer, colormap = plot_results(result_df, exp_cat, analysis_type)
if layer is not None:
layers.append(layer)
colormaps.append(colormap)
# Update the overall bounding box using result_df
bounds = result_df.total_bounds
minx = min(minx, bounds[0])
miny = min(miny, bounds[1])
maxx = max(maxx, bounds[2])
maxy = max(maxy, bounds[3])

# Combine all summary DataFrames
if summary_dfs:
combined_summary = prepare_and_save_summary_df(summary_dfs, exp_cat_list, excel_file, return_file=True)

# Add custom exposure information
excel_writer = pd.ExcelWriter(excel_file, engine='openpyxl', mode='a', if_sheet_exists='replace')
row_offset = len(combined_summary) + 4 # Start two rows below the table
for i, exp_cat in enumerate(exp_cat_list):
if custom_exposure_radio.value == 'Custom exposure':
custom_name = custom_exposure_container.children[i].value
if custom_name:
excel_writer.sheets['Summary'].cell(row=row_offset, column=1, value=f"Custom exposure layer for {exp_cat}: {custom_name}")
row_offset += 1

# Generate charts only for Function approach
if analysis_type == "Function" and preview_chk.value:
colors = {'POP': 'blue', 'BU': 'orange', 'AGR': 'green'}
charts = [create_eai_chart(combined_summary, exp_cat, period, scenario, colors[exp_cat])
for exp_cat in exp_cat_list]

# Export charts if requested
if export_charts_chk.value and charts:
chart_dir = os.path.join(common.OUTPUT_DIR, 'charts')
os.makedirs(chart_dir, exist_ok=True)
base_filename = f"{country}_{haz_cat}_{period}"
if period != '2020':
base_filename += f"_{scenario}"

for i, (chart, exp_cat) in enumerate(zip(charts, exp_cat_list)):
chart_filename = os.path.join(chart_dir, f"{base_filename}_{exp_cat}.png")
chart.savefig(chart_filename, dpi=300, bbox_inches='tight')
print(f"Saved chart to: {chart_filename}")
else:
charts = []

print(f"Analysis completed in {time.perf_counter() - start_time:.2f} seconds")
print(f"Results saved to Excel file: {excel_file}")
print(f"Results saved to GeoPackage file: {gpkg_file}")

# Plot results if preview is checked
if preview_chk.value and minx != float('inf'):
center_lat = (miny + maxy) / 2
center_lon = (minx + maxx) / 2
m.location = [center_lat, center_lon]
m.fit_bounds([[miny, minx], [maxy, maxx]])
print("Plotting results preview as map...")

# Add layers to the map
for layer in layers:
layer.add_to(m)

# Add colormaps to the map
for colormap in colormaps:
colormap.add_to(m)

# Create a custom layer control
folium.LayerControl(position='topright', collapsed=False).add_to(m)

# Add the MiniMap
MiniMap(toggle_display=True, position='bottomright').add_to(m)

# Add the Fullscreen button
Fullscreen(position='bottomleft').add_to(m)

# Update the map_widget with the new map
map_widget.value = m._repr_html_()

with chart_output:
clear_output(wait=True)
# Display charts only once
for chart in charts:
display(chart)
plt.close(chart) # Close the figure after displaying

except Exception as e:
print(f"An error occurred during analysis: {str(e)}")
finally:
# Re-enable the run button and restore its original text
run_button.disabled = False
run_button.description = "Run Analysis"

run_button.on_click(run_analysis_script)

Expand Down Expand Up @@ -845,11 +871,15 @@ def initialize_tool():
update_class_edges_table()
update_preview_availability()

# Update checkbox availability based on analysis type
def update_preview_availability(*args):
"""Update preview checkbox availability based on analysis type"""
preview_chk.disabled = approach_selector.value == 'Classes'
if approach_selector.value == 'Classes':
"""Update preview and export charts checkboxes availability based on analysis type"""
is_classes = approach_selector.value == 'Classes'
preview_chk.disabled = is_classes
export_charts_chk.disabled = is_classes
if is_classes:
preview_chk.value = False
export_charts_chk.value = False

# Add observer to approach selector
approach_selector.observe(update_preview_availability, 'value')
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