Finalize the Training Data Page
This commit is contained in:
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696bef39c2
commit
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2 changed files with 271 additions and 116 deletions
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@ -29,32 +29,36 @@ def render_alphaearth_overview(ds: xr.Dataset):
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ds: xarray Dataset containing AlphaEarth embeddings.
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"""
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st.subheader("📊 AlphaEarth Embeddings Statistics")
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st.subheader("📊 Data Overview")
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# Overall statistics
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# Key metrics
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
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st.metric("Cells", f"{len(ds['cell_ids']):,}")
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with col2:
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st.metric("Embedding Dimensions", f"{len(ds['band'])}")
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st.metric("Embedding Dims", f"{len(ds['band'])}")
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with col3:
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st.metric("Years Available", f"{len(ds['year'])}")
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years = sorted(ds["year"].values)
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st.metric("Years", f"{min(years)}–{max(years)}")
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with col4:
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st.metric("Aggregations", f"{len(ds['agg'])}")
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# Show temporal coverage
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st.markdown("**Temporal Coverage:**")
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years = sorted(ds["year"].values)
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st.write(f"Years: {min(years)} - {max(years)}")
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# Show aggregations
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st.markdown("**Available Aggregations:**")
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# Show aggregations as badges in an expander
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with st.expander("ℹ️ Data Details", expanded=False):
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st.markdown("**Spatial Aggregations:**")
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aggs = ds["agg"].to_numpy()
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st.write(", ".join(str(a) for a in aggs))
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aggs_html = " ".join(
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[
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f'<span style="background-color: #e8f5e9; color: #2e7d32; padding: 4px 10px; '
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f'border-radius: 4px; margin: 2px; display: inline-block; font-size: 0.9em;">{a}</span>'
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for a in aggs
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]
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)
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st.markdown(aggs_html, unsafe_allow_html=True)
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@st.fragment
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@ -207,13 +211,13 @@ def render_arcticdem_overview(ds: xr.Dataset):
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ds: xarray Dataset containing ArcticDEM data.
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"""
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st.subheader("🏔️ ArcticDEM Terrain Statistics")
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st.subheader("📊 Data Overview")
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# Overall statistics
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# Key metrics
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col1, col2, col3 = st.columns(3)
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with col1:
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st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
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st.metric("Cells", f"{len(ds['cell_ids']):,}")
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with col2:
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st.metric("Variables", f"{len(ds.data_vars)}")
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@ -221,19 +225,22 @@ def render_arcticdem_overview(ds: xr.Dataset):
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with col3:
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st.metric("Aggregations", f"{len(ds['aggregations'])}")
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# Show available variables
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st.markdown("**Available Variables:**")
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variables = list(ds.data_vars)
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st.write(", ".join(variables))
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# Show aggregations
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st.markdown("**Available Aggregations:**")
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# Show details in expander
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with st.expander("ℹ️ Data Details", expanded=False):
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st.markdown("**Spatial Aggregations:**")
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aggs = ds["aggregations"].to_numpy()
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st.write(", ".join(str(a) for a in aggs))
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aggs_html = " ".join(
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[
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f'<span style="background-color: #e8f5e9; color: #2e7d32; padding: 4px 10px; '
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f'border-radius: 4px; margin: 2px; display: inline-block; font-size: 0.9em;">{a}</span>'
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for a in aggs
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]
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)
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st.markdown(aggs_html, unsafe_allow_html=True)
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# Statistics by variable
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st.markdown("---")
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st.markdown("**Variable Statistics (across all aggregations)**")
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st.markdown("**📈 Variable Statistics**")
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var_stats = []
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for var_name in ds.data_vars:
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@ -307,45 +314,50 @@ def render_era5_overview(ds: xr.Dataset, temporal_type: str):
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temporal_type: One of 'yearly', 'seasonal', 'shoulder'.
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"""
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st.subheader(f"🌡️ ERA5 Climate Statistics ({temporal_type.capitalize()})")
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st.subheader("📊 Data Overview")
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# Overall statistics
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# Key metrics
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has_agg = "aggregations" in ds.dims
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Total Cells", f"{len(ds['cell_ids']):,}")
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st.metric("Cells", f"{len(ds['cell_ids']):,}")
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with col2:
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st.metric("Variables", f"{len(ds.data_vars)}")
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with col3:
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st.metric("Time Steps", f"{len(ds['time'])}")
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time_values = pd.to_datetime(ds["time"].values)
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st.metric("Time Steps", f"{time_values.min().strftime('%Y')}–{time_values.max().strftime('%Y')}")
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with col4:
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if has_agg:
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st.metric("Aggregations", f"{len(ds['aggregations'])}")
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else:
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st.metric("Aggregations", "1")
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st.metric("Temporal Type", temporal_type.capitalize())
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# Show available variables
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st.markdown("**Available Variables:**")
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variables = list(ds.data_vars)
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st.write(", ".join(variables))
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# Show temporal range
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st.markdown("**Temporal Range:**")
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time_values = pd.to_datetime(ds["time"].values)
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st.write(f"{time_values.min().strftime('%Y-%m-%d')} to {time_values.max().strftime('%Y-%m-%d')}")
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# Show details in expander
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with st.expander("ℹ️ Data Details", expanded=False):
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st.markdown(f"**Temporal Type:** {temporal_type.capitalize()}")
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st.markdown(
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f"**Date Range:** {time_values.min().strftime('%Y-%m-%d')} to {time_values.max().strftime('%Y-%m-%d')}"
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)
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if has_agg:
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st.markdown("**Available Aggregations:**")
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st.markdown("**Spatial Aggregations:**")
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aggs = ds["aggregations"].to_numpy()
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st.write(", ".join(str(a) for a in aggs))
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aggs_html = " ".join(
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[
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f'<span style="background-color: #e8f5e9; color: #2e7d32; padding: 4px 10px; '
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f'border-radius: 4px; margin: 2px; display: inline-block; font-size: 0.9em;">{a}</span>'
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for a in aggs
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]
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)
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st.markdown(aggs_html, unsafe_allow_html=True)
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# Statistics by variable
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st.markdown("---")
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st.markdown("**Variable Statistics (across all time steps and aggregations)**")
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st.markdown("**📈 Variable Statistics**")
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var_stats = []
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for var_name in ds.data_vars:
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@ -380,36 +392,115 @@ def render_era5_plots(ds: xr.Dataset, temporal_type: str):
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variables = list(ds.data_vars)
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has_agg = "aggregations" in ds.dims
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if has_agg:
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col1, col2, col3 = st.columns([2, 2, 1])
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with col1:
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selected_var = st.selectbox(
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"Select variable to visualize", options=variables, key=f"era5_{temporal_type}_var_select"
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)
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with col2:
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selected_agg = st.selectbox(
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"Aggregation", options=ds["aggregations"].values, key=f"era5_{temporal_type}_agg_select"
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)
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with col3:
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show_std = st.checkbox("Show ±1 Std", value=True, key=f"era5_{temporal_type}_show_std")
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show_minmax = st.checkbox("Show Min/Max", value=False, key=f"era5_{temporal_type}_show_minmax")
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else:
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col1, col2 = st.columns([3, 1])
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with col1:
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selected_var = st.selectbox(
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"Select variable to visualize", options=variables, key=f"era5_{temporal_type}_var_select"
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)
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with col2:
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show_std = st.checkbox("Show ±1 Std", value=True, key=f"era5_{temporal_type}_show_std")
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show_minmax = st.checkbox("Show Min/Max", value=False, key=f"era5_{temporal_type}_show_minmax")
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if selected_var:
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var_data = ds[selected_var]
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# Calculate mean over space for each time step
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if has_agg:
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# Average over aggregations first, then over cells
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time_series = var_data.mean(dim=["cell_ids", "aggregations"])
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else:
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time_series = var_data.mean(dim="cell_ids")
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# Calculate statistics over space for each time step
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time_values = pd.to_datetime(ds["time"].to_numpy())
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time_df = pd.DataFrame({"Time": pd.to_datetime(ds["time"].to_numpy()), "Value": time_series.to_numpy()})
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if has_agg:
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# Select specific aggregation, then calculate stats over cells
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var_data_agg = var_data.sel(aggregations=selected_agg)
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time_mean = var_data_agg.mean(dim="cell_ids").to_numpy()
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time_std = var_data_agg.std(dim="cell_ids").to_numpy()
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time_min = var_data_agg.min(dim="cell_ids").to_numpy()
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time_max = var_data_agg.max(dim="cell_ids").to_numpy()
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else:
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time_mean = var_data.mean(dim="cell_ids").to_numpy()
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time_std = var_data.std(dim="cell_ids").to_numpy()
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time_min = var_data.min(dim="cell_ids").to_numpy()
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time_max = var_data.max(dim="cell_ids").to_numpy()
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fig = go.Figure()
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# Add min/max range first (background) - optional
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if show_minmax:
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fig.add_trace(
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go.Scatter(
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x=time_df["Time"],
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y=time_df["Value"],
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mode="lines+markers",
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name=selected_var,
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line={"width": 2},
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x=time_values,
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y=time_min,
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mode="lines",
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line={"color": "lightgray", "width": 1, "dash": "dash"},
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name="Min/Max Range",
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showlegend=True,
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)
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)
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fig.add_trace(
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go.Scatter(
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x=time_values,
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y=time_max,
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mode="lines",
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fill="tonexty",
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fillcolor="rgba(200, 200, 200, 0.1)",
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line={"color": "lightgray", "width": 1, "dash": "dash"},
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showlegend=False,
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)
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)
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# Add std band - optional
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if show_std:
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fig.add_trace(
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go.Scatter(
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x=time_values,
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y=time_mean - time_std,
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mode="lines",
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line={"width": 0},
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showlegend=False,
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hoverinfo="skip",
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)
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)
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fig.add_trace(
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go.Scatter(
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x=time_values,
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y=time_mean + time_std,
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mode="lines",
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fill="tonexty",
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fillcolor="rgba(31, 119, 180, 0.2)",
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line={"width": 0},
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name="±1 Std",
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)
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)
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# Add mean line on top
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fig.add_trace(
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go.Scatter(
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x=time_values,
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y=time_mean,
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mode="lines+markers",
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name="Mean",
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line={"color": "#1f77b4", "width": 2},
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marker={"size": 4},
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)
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)
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title_suffix = f" (Aggregation: {selected_agg})" if has_agg else ""
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fig.update_layout(
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title=f"Temporal Trend of {selected_var} (Spatial Mean)",
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title=f"Temporal Trend of {selected_var} (Spatial Statistics){title_suffix}",
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xaxis_title="Time",
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yaxis_title=selected_var,
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height=400,
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@ -431,19 +522,27 @@ def render_alphaearth_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
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"""
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st.subheader("🗺️ AlphaEarth Spatial Distribution")
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# Controls
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col1, col2, col3, col4 = st.columns([2, 2, 2, 1])
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# Year slider (full width)
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years = sorted(ds["year"].values)
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selected_year = st.slider(
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"Year",
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min_value=int(years[0]),
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max_value=int(years[-1]),
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value=int(years[-1]),
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step=1,
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key="alphaearth_year",
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)
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# Other controls
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col1, col2, col3 = st.columns([2, 2, 1])
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with col1:
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selected_year = st.selectbox("Year", options=sorted(ds["year"].values), key="alphaearth_year")
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with col2:
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selected_agg = st.selectbox("Aggregation", options=ds["agg"].values, key="alphaearth_agg")
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with col3:
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with col2:
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selected_band = st.selectbox("Band", options=list(range(len(ds["band"]))), key="alphaearth_band")
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with col4:
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with col3:
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opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key="alphaearth_opacity")
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# Extract data for selected parameters
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@ -475,6 +574,9 @@ def render_alphaearth_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
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colors = [cmap(val) for val in normalized]
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gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
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# Set elevation based on normalized values
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gdf_wgs84["elevation"] = normalized
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# Create GeoJSON
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geojson_data = []
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for _, row in gdf_wgs84.iterrows():
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@ -484,24 +586,28 @@ def render_alphaearth_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
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"properties": {
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"value": float(row["value"]),
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"fill_color": row["fill_color"],
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"elevation": float(row["elevation"]),
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},
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}
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geojson_data.append(feature)
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# Create pydeck layer
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# Create pydeck layer with 3D elevation
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layer = pdk.Layer(
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"GeoJsonLayer",
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geojson_data,
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opacity=opacity,
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stroked=True,
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filled=True,
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extruded=True,
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get_fill_color="properties.fill_color",
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get_line_color=[80, 80, 80],
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get_elevation="properties.elevation",
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elevation_scale=500000,
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line_width_min_pixels=0.5,
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pickable=True,
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)
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view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
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view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=45, bearing=0)
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deck = pdk.Deck(
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layers=[layer],
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@ -582,12 +688,16 @@ def render_arcticdem_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
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colors = [cmap(val) if not np.isnan(val) else (0.5, 0.5, 0.5, 0.5) for val in normalized]
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gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
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# Set elevation based on normalized values
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gdf_wgs84["elevation"] = [val if not np.isnan(val) else 0 for val in normalized]
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# Create GeoJSON
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geojson_data = []
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for _, row in gdf_wgs84.iterrows():
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properties = {
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"value": float(row["value"]) if not np.isnan(row["value"]) else None,
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"fill_color": row["fill_color"],
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"elevation": float(row["elevation"]),
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}
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# Add all aggregation values if available
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if len(ds["aggregations"]) > 1:
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@ -603,20 +713,23 @@ def render_arcticdem_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str):
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}
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geojson_data.append(feature)
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# Create pydeck layer
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# Create pydeck layer with 3D elevation
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layer = pdk.Layer(
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"GeoJsonLayer",
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geojson_data,
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opacity=opacity,
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stroked=True,
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filled=True,
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extruded=True,
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get_fill_color="properties.fill_color",
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get_line_color=[80, 80, 80],
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get_elevation="properties.elevation",
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elevation_scale=500000,
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line_width_min_pixels=0.5,
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pickable=True,
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)
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view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
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view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=45, bearing=0)
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# Build tooltip HTML for ArcticDEM
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if len(ds["aggregations"]) > 1:
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@ -663,37 +776,43 @@ def render_era5_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str, tempor
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variables = list(ds.data_vars)
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has_agg = "aggregations" in ds.dims
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# Top row: Variable, Aggregation (if applicable), and Opacity
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if has_agg:
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col1, col2, col3, col4 = st.columns([2, 2, 2, 1])
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else:
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col1, col2, col3 = st.columns([3, 3, 1])
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col1, col2, col3 = st.columns([2, 2, 1])
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with col1:
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selected_var = st.selectbox("Variable", options=variables, key=f"era5_{temporal_type}_var")
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with col2:
|
||||
# Convert time to readable format
|
||||
time_values = pd.to_datetime(ds["time"].values)
|
||||
time_options = {str(t): t for t in time_values}
|
||||
selected_time = st.selectbox("Time", options=list(time_options.keys()), key=f"era5_{temporal_type}_time")
|
||||
|
||||
if has_agg:
|
||||
with col3:
|
||||
selected_agg = st.selectbox(
|
||||
"Aggregation", options=ds["aggregations"].values, key=f"era5_{temporal_type}_agg"
|
||||
)
|
||||
with col4:
|
||||
opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key=f"era5_{temporal_type}_opacity")
|
||||
else:
|
||||
with col3:
|
||||
opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key=f"era5_{temporal_type}_opacity")
|
||||
else:
|
||||
col1, col2 = st.columns([4, 1])
|
||||
with col1:
|
||||
selected_var = st.selectbox("Variable", options=variables, key=f"era5_{temporal_type}_var")
|
||||
with col2:
|
||||
opacity = st.slider("Opacity", 0.1, 1.0, 0.7, 0.1, key=f"era5_{temporal_type}_opacity")
|
||||
|
||||
# Bottom row: Time slider (full width)
|
||||
time_values = pd.to_datetime(ds["time"].values)
|
||||
time_labels = [t.strftime("%Y-%m-%d") for t in time_values]
|
||||
selected_time_idx = st.slider(
|
||||
"Time",
|
||||
min_value=0,
|
||||
max_value=len(time_values) - 1,
|
||||
value=len(time_values) - 1,
|
||||
format="",
|
||||
key=f"era5_{temporal_type}_time_slider",
|
||||
)
|
||||
st.caption(f"Selected: {time_labels[selected_time_idx]}")
|
||||
selected_time = time_values[selected_time_idx]
|
||||
|
||||
# Extract data for selected parameters
|
||||
time_val = time_options[selected_time]
|
||||
if has_agg:
|
||||
data_values = ds[selected_var].sel(time=time_val, aggregations=selected_agg)
|
||||
data_values = ds[selected_var].sel(time=selected_time, aggregations=selected_agg)
|
||||
else:
|
||||
data_values = ds[selected_var].sel(time=time_val)
|
||||
data_values = ds[selected_var].sel(time=selected_time)
|
||||
|
||||
# Create GeoDataFrame
|
||||
gdf = targets.copy()
|
||||
|
|
@ -707,7 +826,7 @@ def render_era5_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str, tempor
|
|||
# Add all aggregation values for tooltip if has_agg
|
||||
if has_agg and len(ds["aggregations"]) > 1:
|
||||
for agg in ds["aggregations"].values:
|
||||
agg_data = ds[selected_var].sel(time=time_val, aggregations=agg).to_dataframe(name=f"agg_{agg}")
|
||||
agg_data = ds[selected_var].sel(time=selected_time, aggregations=agg).to_dataframe(name=f"agg_{agg}")
|
||||
# Drop dimension columns to avoid conflicts
|
||||
cols_to_drop = [col for col in ["aggregations", "time"] if col in agg_data.columns]
|
||||
if cols_to_drop:
|
||||
|
|
@ -734,12 +853,16 @@ def render_era5_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str, tempor
|
|||
colors = [cmap(val) if not np.isnan(val) else (0.5, 0.5, 0.5, 0.5) for val in normalized]
|
||||
gdf_wgs84["fill_color"] = [[int(c[0] * 255), int(c[1] * 255), int(c[2] * 255)] for c in colors]
|
||||
|
||||
# Set elevation based on normalized values
|
||||
gdf_wgs84["elevation"] = [val if not np.isnan(val) else 0 for val in normalized]
|
||||
|
||||
# Create GeoJSON
|
||||
geojson_data = []
|
||||
for _, row in gdf_wgs84.iterrows():
|
||||
properties = {
|
||||
"value": float(row["value"]) if not np.isnan(row["value"]) else None,
|
||||
"fill_color": row["fill_color"],
|
||||
"elevation": float(row["elevation"]),
|
||||
}
|
||||
# Add all aggregation values if available
|
||||
if has_agg and len(ds["aggregations"]) > 1:
|
||||
|
|
@ -755,20 +878,23 @@ def render_era5_map(ds: xr.Dataset, targets: gpd.GeoDataFrame, grid: str, tempor
|
|||
}
|
||||
geojson_data.append(feature)
|
||||
|
||||
# Create pydeck layer
|
||||
# Create pydeck layer with 3D elevation
|
||||
layer = pdk.Layer(
|
||||
"GeoJsonLayer",
|
||||
geojson_data,
|
||||
opacity=opacity,
|
||||
stroked=True,
|
||||
filled=True,
|
||||
extruded=True,
|
||||
get_fill_color="properties.fill_color",
|
||||
get_line_color=[80, 80, 80],
|
||||
get_elevation="properties.elevation",
|
||||
elevation_scale=500000,
|
||||
line_width_min_pixels=0.5,
|
||||
pickable=True,
|
||||
)
|
||||
|
||||
view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=0)
|
||||
view_state = pdk.ViewState(latitude=70, longitude=0, zoom=2, pitch=45, bearing=0)
|
||||
|
||||
# Build tooltip HTML for ERA5
|
||||
if has_agg and len(ds["aggregations"]) > 1:
|
||||
|
|
|
|||
|
|
@ -125,32 +125,61 @@ def render_training_data_page():
|
|||
with st.spinner("Computing dataset statistics..."):
|
||||
stats = ensemble.get_stats()
|
||||
|
||||
# Display target information
|
||||
col1, col2 = st.columns(2)
|
||||
# High-level summary metrics
|
||||
col1, col2, col3 = st.columns(3)
|
||||
with col1:
|
||||
st.metric(label="Target", value=stats["target"].replace("darts_", ""))
|
||||
st.metric(label="Total Samples", value=f"{stats['num_target_samples']:,}")
|
||||
with col2:
|
||||
st.metric(label="Number of Target Samples", value=f"{stats['num_target_samples']:,}")
|
||||
|
||||
# Display member statistics
|
||||
st.markdown("**Member Statistics:**")
|
||||
st.metric(label="Total Features", value=f"{stats['total_features']:,}")
|
||||
with col3:
|
||||
st.metric(label="Data Sources", value=len(stats["members"]))
|
||||
|
||||
# Detailed member statistics in expandable section
|
||||
with st.expander("📦 Data Source Details", expanded=False):
|
||||
for member, member_stats in stats["members"].items():
|
||||
with st.expander(f"📦 {member}", expanded=False):
|
||||
col1, col2 = st.columns(2)
|
||||
with col1:
|
||||
st.markdown(f"**Number of Features:** {member_stats['num_features']}")
|
||||
st.markdown(f"**Number of Variables:** {member_stats['num_variables']}")
|
||||
with col2:
|
||||
st.markdown(f"**Dimensions:** `{member_stats['dimensions']}`")
|
||||
st.markdown(f"### {member}")
|
||||
|
||||
# Display variables as a compact list
|
||||
st.markdown(f"**Variables ({member_stats['num_variables']}):**")
|
||||
vars_str = ", ".join([f"`{v}`" for v in member_stats["variables"]])
|
||||
st.markdown(vars_str)
|
||||
# Create metrics for this member
|
||||
metric_cols = st.columns(4)
|
||||
with metric_cols[0]:
|
||||
st.metric("Features", member_stats["num_features"])
|
||||
with metric_cols[1]:
|
||||
st.metric("Variables", member_stats["num_variables"])
|
||||
with metric_cols[2]:
|
||||
# Display dimensions in a more readable format
|
||||
dim_str = " × ".join([f"{dim}" for dim in member_stats["dimensions"].values()])
|
||||
st.metric("Shape", dim_str)
|
||||
with metric_cols[3]:
|
||||
# Calculate total data points
|
||||
total_points = 1
|
||||
for dim_size in member_stats["dimensions"].values():
|
||||
total_points *= dim_size
|
||||
st.metric("Data Points", f"{total_points:,}")
|
||||
|
||||
# Display total features
|
||||
st.metric(label="🎯 Total Number of Features", value=f"{stats['total_features']:,}")
|
||||
# Show variables as colored badges
|
||||
st.markdown("**Variables:**")
|
||||
vars_html = " ".join(
|
||||
[
|
||||
f'<span style="background-color: #e3f2fd; color: #1976d2; padding: 4px 8px; '
|
||||
f'border-radius: 4px; margin: 2px; display: inline-block; font-size: 0.9em;">{v}</span>'
|
||||
for v in member_stats["variables"]
|
||||
]
|
||||
)
|
||||
st.markdown(vars_html, unsafe_allow_html=True)
|
||||
|
||||
# Show dimension details
|
||||
st.markdown("**Dimensions:**")
|
||||
dim_html = " ".join(
|
||||
[
|
||||
f'<span style="background-color: #f3e5f5; color: #7b1fa2; padding: 4px 8px; '
|
||||
f'border-radius: 4px; margin: 2px; display: inline-block; font-size: 0.9em;">'
|
||||
f"{dim_name}: {dim_size}</span>"
|
||||
for dim_name, dim_size in member_stats["dimensions"].items()
|
||||
]
|
||||
)
|
||||
st.markdown(dim_html, unsafe_allow_html=True)
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue