Compare revisions

<<<<<<< HEAD

# GlacierH2O

# GlacierH2O

## Project status

## Project status

If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.

=======

# GLACIERH2O

>>>>>>> new-main

import pandas as pd

import json

# Generate visualizations and display them directly to the user using ace_tools

import ace_tools as tools

import matplotlib.pyplot as plt

# Load the glacier data

with open('/Users/hamza/glacier/data/glacier_data.json', 'r') as file:

    glacier_data = json.load(file)

# Load the GTN report data

with open('/Users/hamza/glacier/data/gtn_report.json', 'r') as file:

    gtn_report = json.load(file)

# Convert to DataFrames for easier manipulation

glacier_df = pd.DataFrame(glacier_data)

gtn_df = pd.DataFrame(gtn_report)

# Extract unique regions from glacier data

unique_regions = glacier_df['region'].unique()

# Mapping countries from GTN report to corresponding glacier regions manually

region_country_mapping = {

    "1_alaska": ["US - UNITED STATES"],

    "2_western_canada_us": ["CA - CANADA", "US - UNITED STATES"],

    "3_arctic_canada": ["CA - CANADA"],

    "4_greenland": ["GL - GREENLAND"],

    "5_iceland": ["IS - ICELAND"],

    "6_svalbard_jan_mayen": ["SJ - SVALBARD AND JAN MAYEN"],

    "7_scandinavia": ["NO - NORWAY", "SE - SWEDEN", "FI - FINLAND"],

    "8_russian_arctic": ["RU - RUSSIAN FEDERATION"],

    "9_siberia": ["RU - RUSSIAN FEDERATION"],

    "10_central_asia": ["KZ - KAZAKHSTAN", "TJ - TAJIKISTAN", "UZ - UZBEKISTAN", "KG - KYRGYZSTAN"],

    "11_himalaya": ["NP - NEPAL", "IN - INDIA", "CN - CHINA", "BT - BHUTAN", "PK - PAKISTAN"],

    "12_caucasus_middle_east": ["GE - GEORGIA", "TR - TURKEY", "IR - IRAN"],

    "13_southern_andes": ["CL - CHILE", "AR - ARGENTINA"],

    "14_new_zealand": ["NZ - NEW ZEALAND"],

    "15_africa": ["MA - MOROCCO"],

    "16_antarctic": ["AQ - ANTARCTICA"],

}

# Assign regions to the GTN report based on country mapping

def assign_region(country_code):

    for region, countries in region_country_mapping.items():

        if country_code in countries:

            return region

    return "Unknown"

gtn_df['region'] = gtn_df['GRDCCOUNTRY'].apply(assign_region)

# Merge glacier data with GTN report based on 'region'

merged_df = pd.merge(glacier_df, gtn_df, on='region', how='inner')

import ace_tools as tools; tools.display_dataframe_to_user(name="Merged Glacier and GTN Data", dataframe=merged_df)

# Display the first few rows of the merged dataframe

merged_df.head()

# Plot 1: Glacier Mass Change vs Station Elevation

fig1, ax1 = plt.subplots(figsize=(12, 8))

ax1.scatter(merged_df['glacier_mass_change'], merged_df['station_elevation'], alpha=0.7, edgecolors='k', s=80)

ax1.set_title('Glacier Mass Change vs Station Elevation', fontsize=14)

ax1.set_xlabel('Glacier Mass Change (Gt)', fontsize=12)

ax1.set_ylabel('Station Elevation (m)', fontsize=12)

ax1.grid(True)

# Plot 2: Average Glacier Mass Change by Region

fig2, ax2 = plt.subplots(figsize=(14, 8))

region_mass_change = merged_df.groupby('region')['glacier_mass_change'].mean().sort_values()

region_mass_change.plot(kind='barh', ax=ax2, color='skyblue', edgecolor='black')

ax2.set_title('Average Glacier Mass Change by Region', fontsize=14)

ax2.set_xlabel('Average Mass Change (Gt)', fontsize=12)

ax2.set_ylabel('Region', fontsize=12)

ax2.grid(axis='x', linestyle='--', alpha=0.7)

# Plot 3: Station Elevation vs Glacier Area

fig3, ax3 = plt.subplots(figsize=(12, 8))

ax3.scatter(merged_df['glacier_area'], merged_df['station_elevation'], alpha=0.7, color='green', edgecolors='k', s=80)

ax3.set_title('Station Elevation vs Glacier Area', fontsize=14)

ax3.set_xlabel('Glacier Area (km²)', fontsize=12)

ax3.set_ylabel('Station Elevation (m)', fontsize=12)

ax3.grid(True)

# Display the plots to the user

tools.display_dataframe_to_user(name="Merged Glacier and GTN Data", dataframe=merged_df)

import dash

from dash import dcc, html, Input, Output

def trigger_event(event):

    pass

import plotly.express as px

import plotly.graph_objects as go

import pandas as pd

import os

import zipfile

import numpy as np

# Load and prepare data

zip_path = "Glacier.zip"

gtn_report_path = "GTN Report.xlsx"

extract_dir = "glacier_data"

# Extract glacier zip contents

with zipfile.ZipFile(zip_path, 'r') as zip_ref:

    zip_ref.extractall(extract_dir)

# Load regional glacier CSV files and assign approximate coordinates for visualization

region_coordinates = {

    '1_alaska': (64.2008, -149.4937),

    '2_western_canada_us': (52.9399, -106.4509),

    '3_arctic_canada_north': (75.0000, -100.0000),

    '4_arctic_canada_south': (65.0000, -100.0000),

    '5_greenland_periphery': (72.0000, -40.0000),

    '6_iceland': (64.9631, -19.0208),

    '7_svalbard': (78.0000, 16.0000),

    '8_scandinavia': (60.0000, 15.0000),

    '9_russian_arctic': (70.0000, 100.0000),

    '10_north_asia': (60.0000, 90.0000),

    '11_central_europe': (47.0000, 10.0000),

    '12_caucasus_middle_east': (42.0000, 45.0000),

    '13_central_asia': (43.0000, 75.0000),

    '14_south_asia_west': (35.0000, 70.0000),

    '15_south_asia_east': (27.0000, 85.0000),

    '16_low_latitudes': (0.0000, -60.0000),

    '17_southern_andes': (-40.0000, -70.0000),

    '18_new_zealand': (-41.2865, 174.7762),

    '19_antarctic_and_subantarctic': (-75.0000, 0.0000)

}

# Load glacier data into a combined DataFrame

glacier_data = []

region_files = [file for file in os.listdir(extract_dir) if file.endswith('.csv') and file != '0_global.csv']

for file in region_files:

    region_name = file.replace('.csv', '')

    df_region = pd.read_csv(os.path.join(extract_dir, file))

    df_region['region'] = region_name

    df_region['latitude'], df_region['longitude'] = region_coordinates.get(region_name, (0, 0))

    glacier_data.append(df_region)

df_glacier_regions = pd.concat(glacier_data, ignore_index=True)

# Simulate river discharge based on glacier melt trends

def simulate_discharge(glacier_mass_loss):

    max_discharge = 500  # Max discharge in cubic meters per second

    normalized_loss = (glacier_mass_loss - glacier_mass_loss.min()) / (glacier_mass_loss.max() - glacier_mass_loss.min())

    simulated_discharge = max_discharge * (1 - normalized_loss) + np.random.normal(0, 10, size=len(glacier_mass_loss))

    return np.clip(simulated_discharge, 50, max_discharge)

# Enhance DataFrame with simulated discharge

df_glacier_regions['simulated_discharge'] = simulate_discharge(df_glacier_regions['combined_gt'])

# Initialize Dash app

app = dash.Dash(__name__)

app.title = "Glacier Melt Impact on Water Resources - Interactive Visualization"

# Layout with 3D globe map and a side chart for selected region details

app.layout = html.Div([

    html.H1("🌍 Glacier Melt and Water Resources Visualization",

            style={'textAlign': 'center', 'fontSize': '28px', 'marginBottom': '20px', 'color': '#2C3E50'}),

    html.Div([

        dcc.Graph(id='world-map-visualization', style={'height': '75vh', 'width': '65%', 'display': 'inline-block'}),

        dcc.Graph(id='region-detail-chart', style={'height': '75vh', 'width': '33%', 'display': 'inline-block', 'paddingLeft': '1%'})

    ], style={'display': 'flex', 'justifyContent': 'center'}),

    html.Div([

        html.Label("⏩ Animation Speed:", style={'fontWeight': 'bold', 'fontSize': '16px'}),

        dcc.Slider(

            id='animation-speed',

            min=50,

            max=1000,

            step=50,

            value=300,

            marks={50: 'Fast', 500: 'Medium', 1000: 'Slow'},

            tooltip={"placement": "bottom", "always_visible": True}

        )

    ], style={'width': '60%', 'margin': 'auto', 'paddingTop': '20px'})

])

# Callback to update the 3D world map for smoother panning and zooming

@app.callback(

    Output('world-map-visualization', 'figure'),

    [Input('animation-speed', 'value')]

)

def update_world_map(animation_speed):

    min_size, max_size = 4, 15

    normalized_sizes = (

        (df_glacier_regions['glacier_area'] - df_glacier_regions['glacier_area'].min()) /

        (df_glacier_regions['glacier_area'].max() - df_glacier_regions['glacier_area'].min())

    )

    bubble_sizes = normalized_sizes * (max_size - min_size) + min_size

    fig = go.Figure()

    fig.add_trace(go.Scattergeo(

        lat=df_glacier_regions['latitude'],

        lon=df_glacier_regions['longitude'],

        mode='markers',

        marker=dict(

            size=bubble_sizes,

            color=df_glacier_regions['simulated_discharge'],

            colorscale='Viridis',

            cmin=df_glacier_regions['simulated_discharge'].min(),

            cmax=df_glacier_regions['simulated_discharge'].max(),

            showscale=True,

            colorbar=dict(title="Discharge (m³/s)", thickness=15, len=0.5),

            line=dict(width=0.4, color='DarkSlateGrey')

        ),

        text=df_glacier_regions.apply(lambda row: (

            f"Region: {row['region']}<br>Year: {row['end_dates']}<br>Glacier Area: {row['glacier_area']:.2f} km²<Br>Discharge: {row['simulated_discharge']:.2f} m³/s"

        ), axis=1),

        hoverinfo='text',

        customdata=df_glacier_regions[['region']]

    ))

    fig.update_geos(

        projection_type="orthographic",

        showland=True,

        landcolor="rgb(230, 230, 230)",

        showocean=True,

        oceancolor="rgb(180, 210, 255)",

        showcountries=True,

        countrycolor="rgb(150, 150, 150)",

        center=dict(lat=20, lon=0),

        resolution=50,

    )

    fig.update_layout(

        title="🌍 3D Globe - Glacier Melt and Water Resources",

        margin=dict(l=0, r=0, t=40, b=0),

        dragmode='pan',

        geo=dict(

            projection_rotation=dict(lon=0, lat=0),

            projection_scale=0.9,

            lataxis=dict(range=[-90, 90]),

            lonaxis=dict(range=[-180, 180]),

        ),

        font=dict(family="Arial", size=13, color="#2C3E50"),

    )

    return fig

# Callback to update the region detail chart when a bubble is clicked

@app.callback(

    Output('region-detail-chart', 'figure'),

    [Input('world-map-visualization', 'clickData')]

)

def update_region_chart(clickData):

    if not clickData:

        return go.Figure(layout=dict(title="Select a region to see details", font=dict(size=14)))

    selected_region = clickData['points'][0]['customdata'][0]

    region_data = df_glacier_regions[df_glacier_regions['region'] == selected_region].sort_values(by='end_dates')

    fig = go.Figure()

    fig.add_trace(go.Scatter(

        x=region_data['end_dates'],

        y=region_data['combined_gt'],

        mode='lines+markers',

        name='Glacier Mass Change (Gt)',

        line=dict(color='blue')

    ))

    fig.add_trace(go.Scatter(

        x=region_data['end_dates'],

        y=region_data['simulated_discharge'],

        mode='lines+markers',

        name='Simulated River Discharge (m³/s)',

        line=dict(color='green')

    ))

    fig.update_layout(

        title=f"Glacier Mass vs Water Discharge - {selected_region.replace('_', ' ').title()}",

        xaxis_title="Year",

        yaxis_title="Value",

        legend=dict(x=0.02, y=0.98),

        margin=dict(l=30, r=20, t=40, b=40),

        font=dict(family="Arial", size=12)

    )

    return fig

if __name__ == '__main__':

    app.run_server(debug=True, port=8060)

:root {

  --background-color: #f4f4f9;

  --text-color: #34495e;

  --button-bg: #34495e;

  --button-text: #fff;

  --box-shadow: 0 4px 8px rgba(0, 0, 0, 0.2);

  --border-radius: 12px;

}

.dark-mode {

  --background-color: #2c3e50;

  --text-color: #ecf0f1;

  --button-bg: #e74c3c;

  --button-text: #fff;

  --box-shadow: 0 4px 8px rgba(0, 0, 0, 0.4);

}

body {

  font-family: Arial, sans-serif;

  background-color: var(--background-color);

  color: var(--text-color);

  margin: 0;

  padding: 10px;

  height: 100vh;

  display: flex;

  flex-direction: column;

}

h1 {

  font-size: 30px;

  font-weight: bold;

  margin-bottom: 20px;

  text-align: center;

}

#controls {

  display: flex;

  justify-content: center;

  align-items: center;

  gap: 15px;

  padding: 10px;

  margin-bottom: 15px;

  background-color: var(--background-color);

  border-radius: var(--border-radius);

  box-shadow: var(--box-shadow);

}

.dark-mode #controls {

  background-color: #34495e !important;

  box-shadow: 0 4px 8px rgba(0, 0, 0, 0.4) !important;

}

.dark-mode #controls button, 

.dark-mode #controls select, 

.dark-mode #controls input,

.dark-mode #controls label {

  color: #ecf0f1 !important;

  background-color: rgba(255, 255, 255, 0.1) !important;

  border: 1px solid rgba(255, 255, 255, 0.2) !important;

}

.year-control {

  display: flex;

  align-items: center;

  gap: 8px;

}

#year-input {

  width: 70px;

  padding: 5px;

  border-radius: 6px;

  text-align: center;

  border: 1px solid #ccc;

}

#year-slider {

  width: 200px;

}

button, select, input[type="number"], input[type="range"] {

  padding: 6px 12px;

  border-radius: 8px;

  border: 1px solid #ccc;

  cursor: pointer;

  transition: all 0.2s ease;

}

button:hover, select:hover, input:hover {

  box-shadow: 0 0 8px rgba(0, 0, 0, 0.3);

}

#container {

  display: flex;

  justify-content: space-between;

  gap: 20px;

  flex-grow: 1;

}

#map-container {

  flex: 1;

  max-width: 70%;

  height: 80vh;

  position: relative; 

  border-radius: var(--border-radius);

  box-shadow: var(--box-shadow);

}

#map {

  width: 100%;

  height: 100%;

  border-radius: var(--border-radius);

  position: relative;

  z-index: 1; 

}

#chart-container {

  flex: 1;

  max-width: 28%;

  height: 80vh;

  display: flex;

  flex-direction: column;

  gap: 20px;

}

#region-chart {

  width: 100%;

  height: 100%;

  border-radius: var(--border-radius);

  box-shadow: var(--box-shadow);

}

/* Legend Styles */

#legend {

  position: absolute;

  top: 10px;

  right: 10px;

  background-color: rgba(255, 255, 255, 0.9);

  padding: 10px;

  border-radius: 8px;

  box-shadow: var(--box-shadow);

  font-weight: bold;

  width: 200px;

  z-index: 1002; 

}

#legend .legend-title {

  text-align: center;

  font-size: 14px;

  margin-bottom: 8px;

}

#legend .legend-item {

  display: flex;

  align-items: center;

  margin-bottom: 5px;

}

#legend .color-box {

  width: 20px;

  height: 20px;

  margin-right: 8px;

  border-radius: 4px;

  border: 1px solid #ccc;

}

 No newline at end of file

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

# Load the glacier data (assumed to be preloaded)

region_data = pd.read_csv('glacier_data/1_alaska.csv')  # Example file, adjust as necessary

# Load the GTN dataset

gtn_data = pd.read_excel('GTN Report.xlsx')  # Example for GTN data

# Set the plot style

sns.set(style="whitegrid")

# Create a figure with 3 subplots

fig, axes = plt.subplots(3, 1, figsize=(10, 18))

fig.suptitle('Glacier and River Discharge Visualization Prototype', fontsize=16)

# 1. Time-based Change (Glacier Mass Change Over Time)

axes[0].plot(region_data['end_dates'], region_data['combined_gt'], marker='o', color='b', label='Mass Change (Gt)')

axes[0].set_title('Glacier Mass Change Over Time', fontsize=14)

axes[0].set_xlabel('Year')

axes[0].set_ylabel('Mass Change (Gt)')

axes[0].legend()

# 2. Size-based Relationship (Glacier Area vs. Glacier Mass Change)

axes[1].scatter(region_data['glacier_area'], region_data['combined_gt'], c=region_data['end_dates'], cmap='viridis')

axes[1].set_title('Glacier Area vs. Mass Change', fontsize=14)

axes[1].set_xlabel('Glacier Area (km²)')

axes[1].set_ylabel('Mass Change (Gt)')

cbar = fig.colorbar(plt.cm.ScalarMappable(cmap='viridis'), ax=axes[1])

cbar.set_label('Year')

# 3. Scatter Plot: Glacier Mass Change vs. Catchment Size (Matching regions and years)

# Filter data for matching years and regions (for Alaska in this case)

region_name = 'alaska'

catchment_data = gtn_data[gtn_data['river_name'].str.contains(region_name, case=False, na=False)]  # Adjust for region

# Merge datasets based on year (assuming region_data and catchment_data are aligned)

merged_data = pd.merge(region_data, catchment_data, left_on='end_dates', right_on='station_elevation', how='inner')

# Ensure both datasets have matching rows

if not merged_data.empty:

    axes[2].scatter(merged_data['CATCHMENT_SIZE'], merged_data['combined_gt'], color='r', alpha=0.5)

    axes[2].set_title('Glacier Mass Change vs. Catchment Size', fontsize=14)

    axes[2].set_xlabel('Catchment Size (km²)')