diff --git a/generating_statistics_for_validation/casa-gfed/casa-gfed-generate-statistics.py b/generating_statistics_for_validation/casa-gfed/casa-gfed-generate-statistics.py index 95d45365..d553e7f5 100644 --- a/generating_statistics_for_validation/casa-gfed/casa-gfed-generate-statistics.py +++ b/generating_statistics_for_validation/casa-gfed/casa-gfed-generate-statistics.py @@ -56,13 +56,13 @@ def get_all_s3_keys(bucket): keys = get_all_s3_keys(bucket_name) # List all TIFF files in the folder -tif_files = glob("data/casa-gfed/*.nc", recursive=True) +tif_files = glob("../../data/casa-gfed/*.nc", recursive=True) session = rasterio.env.Env() summary_dict_netcdf, summary_dict_cog = {}, {} overall_stats_netcdf, overall_stats_cog = {}, {} full_data_df_netcdf, full_data_df_cog = pd.DataFrame(), pd.DataFrame() -for key in keys[:1]: +for key in keys: with raster_io_session: s3_file = s3_client_veda_smce.generate_presigned_url( "get_object", Params={"Bucket": bucket_name, "Key": key} @@ -81,15 +81,13 @@ def get_all_s3_keys(bucket): raster_data = src.read(band) raster_data[raster_data == -9999] = np.nan temp = pd.DataFrame(index=idx, data=raster_data) - full_data_df_cog = full_data_df_cog._append( - temp, ignore_index=False - ) + full_data_df_cog = full_data_df_cog._append(temp, ignore_index=False) # Calculate summary statistics - min_value = np.float64(temp.values.min()) - max_value = np.float64(temp.values.max()) - mean_value = np.float64(temp.values.mean()) - std_value = np.float64(temp.values.std()) + min_value = np.float64(np.nanmin(temp.values)) + max_value = np.float64(np.nanmax(temp.values)) + mean_value = np.float64(np.nanmean(temp.values)) + std_value = np.float64(np.nanstd(temp.values)) summary_dict_cog[ f"{'_'.join(filename_elements[4:10])}_{filename_elements[10][:4]}_{calendar.month_name[int(filename_elements[10][4:6])]}" @@ -106,7 +104,9 @@ def get_all_s3_keys(bucket): file_name = re.split("[_ ? . ]", pathlib.Path(tif_file).name[:-3]) xds = xarray.open_dataset(tif_file, engine="netcdf4") - xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby("longitude") + xds = xds.assign_coords(longitude=(((xds.longitude + 180) % 360) - 180)).sortby( + "longitude" + ) variable = [var for var in xds.data_vars] for time_increment in range(0, len(xds.time)): for var in variable[:-1]: @@ -117,11 +117,12 @@ def get_all_s3_keys(bucket): [ "_".join( [ - file_name[0], file_name[1], var, file_name[2], - file_name[3] + file_name[3], + file_name[4], + file_name[5], ] ) ], @@ -134,13 +135,13 @@ def get_all_s3_keys(bucket): full_data_df_netcdf = full_data_df_netcdf._append(temp, ignore_index=False) # Calculate summary statistics - min_value = np.float64(temp.values.min()) - max_value = np.float64(temp.values.max()) - mean_value = np.float64(temp.values.mean()) - std_value = np.float64(temp.values.std()) + min_value = np.float64(np.nanmin(temp.values)) + max_value = np.float64(np.nanmax(temp.values)) + mean_value = np.float64(np.nanmean(temp.values)) + std_value = np.float64(np.nanstd(temp.values)) summary_dict_netcdf[ - f"{'_'.join(file_name[1:])}_{var}_{calendar.month_name[time_increment+1]}" + f"{'_'.join([file_name[1],var,file_name[2],file_name[3],file_name[4],file_name[5]])}_{file_name[6]}_{calendar.month_name[time_increment+1]}" ] = { "min_value": min_value, "max_value": max_value, @@ -181,46 +182,93 @@ def get_all_s3_keys(bucket): fp.write("\n") json.dump(overall_stats_cog, fp) - -fig, ax = plt.subplots(2, 2, figsize=(10, 10)) -# plt.Figure(figsize=(10, 10)) +fig, ax = plt.subplots(2, 2, figsize=(13, 10)) temp_df = pd.DataFrame() for key_value in full_data_df_netcdf.index.values: if "NPP" in key_value[0] and "2003" in key_value[1]: temp_df = temp_df._append(full_data_df_netcdf.loc[key_value]) -sns.histplot(data=temp_df, kde=False, bins=10, legend=False, ax=ax[0][0]) -ax[0][0].set_title("distribution plot for overall raw data") +temp_df = temp_df.to_numpy().flatten() +sns.histplot(data=temp_df, kde=False, bins=100, legend=False, ax=ax[0][0]) +ax[0][0].set_title("NPP Emission \n (Original Data)") temp_df = pd.DataFrame() for key_value in full_data_df_cog.index.values: - if "2003" in key_value[0]: + if "NPP" in key_value[0] and "2003" in key_value[1]: temp_df = temp_df._append(full_data_df_cog.loc[key_value]) -sns.histplot(data=temp_df, kde=False, bins=10, legend=False, ax=ax[0][1]) -ax[0][1].set_title("distribution plot for overall cog data") +temp_df = temp_df.to_numpy().flatten() +sns.histplot(data=temp_df, kde=False, bins=100, legend=False, ax=ax[0][1]) +ax[0][1].set_title("NPP Emission \n (Transformed COG Data)") + +temp_df = pd.DataFrame() +for key_value in full_data_df_netcdf.index.values: + if "FIRE" in key_value[0] and "2003" in key_value[1]: + temp_df = temp_df._append(full_data_df_netcdf.loc[key_value]) +temp_df = temp_df.to_numpy().flatten() +sns.histplot(data=temp_df, kde=False, bins=100, legend=False, ax=ax[1][0]) +ax[1][0].set_title("FIRE Emission \n (Original Data)") + +temp_df = pd.DataFrame() +for key_value in full_data_df_cog.index.values: + if "FIRE" in key_value[0] and "2003" in key_value[1]: + temp_df = temp_df._append(full_data_df_cog.loc[key_value]) +temp_df = temp_df.to_numpy().flatten() +sns.histplot(data=temp_df, kde=False, bins=100, legend=False, ax=ax[1][1]) +ax[1][1].set_title("FIRE Emission \n (Transformed COG Data)") + +fig.tight_layout(pad=0.5) +fig.suptitle("Overall distribution of data", fontsize=10) +plt.savefig("overall_stats_summary.png") +plt.show() + + +fig, ax = plt.subplots(2, 2, figsize=(12, 12)) +temp_df = pd.DataFrame() +for key_value in summary_dict_netcdf.keys(): + if key_value.startswith("CASAGFED3v3_NPP_Flux_Monthly_x720_y360_2003"): + temp_df = temp_df._append(summary_dict_netcdf[key_value], ignore_index=True) + +sns.lineplot( + data=temp_df, + ax=ax[0][0], +) +ax[0][0].set_title("NPP Emission for 2003 \n (Original Data)") +ax[0][0].set_xlabel("Months") + +temp_df = pd.DataFrame() +for key_value in summary_dict_cog.keys(): + if key_value.startswith("CASAGFED3v3_NPP_Flux_Monthly_x720_y360_2003"): + temp_df = temp_df._append(summary_dict_cog[key_value], ignore_index=True) +sns.lineplot( + data=temp_df, + ax=ax[0][1], +) +ax[0][1].set_title("NPP Emission for 2003 \n (Transformed COG Data)") +ax[0][1].set_xlabel("Months") temp_df = pd.DataFrame() for key_value in summary_dict_netcdf.keys(): - if key_value.startswith("XCO2_2016"): + if key_value.startswith("CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_2003"): temp_df = temp_df._append(summary_dict_netcdf[key_value], ignore_index=True) sns.lineplot( data=temp_df, ax=ax[1][0], ) -ax[1][0].set_title("plot for XCO2 variable for 2016 raw data") +ax[1][0].set_title("FIRE Emission for 2003 \n (Original Data)") ax[1][0].set_xlabel("Months") temp_df = pd.DataFrame() for key_value in summary_dict_cog.keys(): - if key_value.startswith("XCO2_2016"): + if key_value.startswith("CASAGFED3v3_FIRE_Flux_Monthly_x720_y360_2003"): temp_df = temp_df._append(summary_dict_cog[key_value], ignore_index=True) sns.lineplot( data=temp_df, ax=ax[1][1], ) -ax[1][1].set_title("plot for XCO2 variable for 2016 cog data") +ax[1][1].set_title("FIRE Emission for 2003 \n (Transformed COG Data)") ax[1][1].set_xlabel("Months") - -plt.savefig("stats_summary.png") +fig.tight_layout(pad=0.5) +fig.suptitle("Plot for the Statistical values of data", fontsize=10) +plt.savefig("monthly_stats_summary.png") plt.show() diff --git a/generating_statistics_for_validation/casa-gfed/monthly_stats.json b/generating_statistics_for_validation/casa-gfed/monthly_stats.json new file mode 100644 index 00000000..9f9248d3 --- /dev/null +++ b/generating_statistics_for_validation/casa-gfed/monthly_stats.json @@ -0,0 +1,4 @@ +"Stats for raw netCDF files." +{"CASAGFED3v3_NPP_Flux_Monthly_x720_y360_2003_January": {"min_value": 0.0, "max_value": 0.2299399971961975, "mean_value": 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