Flipped map to correct orientation

pepsico/annual_month_maps.py

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+import numpy as np
+import pandas as pd
+import xarray as xr
+import plotly.express as px
+
+# Open zarr file 
+ds = xr.open_zarr('/Data/data24/ISIMIP3b/InputData/climate/atmosphere/bias-adjusted/global/daily/historical/GFDL-ESM4/zarr/tasmin')
+
+# Convert daily data to monthly
+monthly_ds = ds.resample(T="1M").mean()
+
+# Compute the average tasmin for each month across all years
+annual_monthly_avg = monthly_ds.groupby("T.month").mean(dim="T")
+
+# Prepare the data for the CSV (too big to print in terminal)
+data = {'month': [], 'tasmin': []}
+for month in range(1, 13):
+    month_data = annual_monthly_avg.sel(month=month)
+    tasmin_value = month_data['tasmin'].mean(dim=["X", "Y"]).values  # Get the mean tasmin value
+    data['month'].append(month)
+    data['tasmin'].append(tasmin_value)
+
+    # Create a DataFrame for the spatial data for this month
+    df = month_data['tasmin'].to_dataframe().reset_index()
+
+    # Sort latitudes in ascending order if necessary - for if map is flipped
+    df = df.sort_values(by='Y')
+
+    # Create the map
+    fig = px.imshow(
+        df.pivot(index='Y', columns='X', values='tasmin'), 
+        labels={'color': 'Kelvins'},
+        title=f'Tasmin for Month {month}',
+        origin='lower'  # Set the origin to lower to flip the y-axis
+    )
+
+
+    # Save the map as an HTML file - access through port 8000
+    fig.write_html(f'/home/sz3116/python-maprooms/pepsico/resources/map_month_{month}.html')
+
+
+print("done")