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import altair as alt |
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import pandas as pd |
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import plotly.graph_objects as go |
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import streamlit as st |
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from src.helper_functions import custom_metric_box, pollution_box |
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from src.models_loading import run_model |
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from data_api_calls import get_data |
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st.set_page_config( |
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page_title="Utrecht Pollution Dashboard", |
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page_icon="🏂��🌱", |
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layout="wide", |
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initial_sidebar_state="expanded", |
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) |
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alt.themes.enable("dark") |
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test_predictions = run_model("O3") |
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get_data() |
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data = pd.read_csv("dataset.csv") |
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st.title("Utrecht Pollution Dashboard🌱") |
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col1, col2 = st.columns((1, 1)) |
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with col1: |
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st.subheader("Current Weather") |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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custom_metric_box(label="Temperature", value="2 °C", delta="-3 °C") |
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custom_metric_box(label="Humidity", value="60 %", delta="-1 %") |
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with col2: |
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custom_metric_box(label="Pressure", value="1010 hPa", delta="+2 hPa") |
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custom_metric_box(label="Precipitation", value="5 mm", delta="-1 mm") |
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with col3: |
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custom_metric_box(label="Solar Radiation", value="200 W/m²", delta="-20 W/m²") |
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custom_metric_box(label="Wind Speed", value="15 km/h", delta="-2 km/h") |
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st.subheader("Current Pollution Levels") |
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col1, col2 = st.columns((1, 1)) |
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with col1: |
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pollution_box(label="O<sub>3</sub>", value="37 µg/m³", delta="+2 µg/m³") |
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with col2: |
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pollution_box(label="NO<sub>2</sub>", value="28 µg/m³", delta="+3 µg/m³") |
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dates_past = pd.date_range(end=pd.Timestamp.today(), periods=7).to_list() |
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dates_future = pd.date_range( |
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start=pd.Timestamp.today() + pd.Timedelta(days=1), periods=3 |
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).to_list() |
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o3_past_values = [30, 32, 34, 33, 31, 35, 36] |
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no2_past_values = [20, 22, 21, 23, 22, 24, 25] |
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o3_future_values = [37, 38, 40] |
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no2_future_values = [26, 27, 28] |
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dates = dates_past + dates_future |
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o3_values = o3_past_values + o3_future_values |
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no2_values = no2_past_values + no2_future_values |
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df = pd.DataFrame({"Date": dates, "O3": o3_values, "NO2": no2_values}) |
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st.subheader("O3 and NO2 Prediction") |
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subcol1, subcol2 = st.columns(2) |
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with subcol1: |
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fig_o3 = go.Figure() |
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fig_o3.add_trace( |
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go.Scatter( |
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x=df["Date"], |
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y=df["O3"], |
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mode="lines+markers", |
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name="O3", |
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line=dict(color="rgb(0, 191, 255)", width=4), |
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) |
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) |
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fig_o3.add_shape( |
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dict( |
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type="line", |
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x0=pd.Timestamp.today(), |
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x1=pd.Timestamp.today(), |
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y0=min(o3_values), |
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y1=max(o3_values), |
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line=dict(color="White", width=3, dash="dash"), |
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) |
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) |
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fig_o3.update_layout( |
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plot_bgcolor="rgba(0, 0, 0, 0)", |
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paper_bgcolor="rgba(0, 0, 0, 0)", |
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yaxis_title="O3 Concentration (µg/m³)", |
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font=dict(size=14), |
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hovermode="x unified", |
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) |
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st.plotly_chart(fig_o3) |
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with subcol2: |
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fig_no2 = go.Figure() |
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fig_no2.add_trace( |
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go.Scatter( |
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x=df["Date"], |
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y=df["NO2"], |
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mode="lines+markers", |
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name="NO2", |
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line=dict(color="rgb(255, 20, 147)", width=4), |
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) |
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) |
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fig_no2.add_shape( |
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dict( |
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type="line", |
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x0=pd.Timestamp.today(), |
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x1=pd.Timestamp.today(), |
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y0=min(no2_values), |
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y1=max(no2_values), |
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line=dict(color="White", width=3, dash="dash"), |
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) |
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) |
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fig_no2.update_layout( |
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plot_bgcolor="rgba(0, 0, 0, 0)", |
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paper_bgcolor="rgba(0, 0, 0, 0)", |
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yaxis_title="NO2 Concentration (µg/m³)", |
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font=dict(size=14), |
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hovermode="x unified", |
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) |
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st.plotly_chart(fig_no2) |
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