diff --git a/urban_areas/py/s22VisualiseData.py b/urban_areas/py/s22VisualiseData.py index 75d8c340e0deeabd65af8454cd5de5139bbe423c..3fe5fbcb1bb951824357d4c8ef08dba2681b255f 100644 --- a/urban_areas/py/s22VisualiseData.py +++ b/urban_areas/py/s22VisualiseData.py @@ -10,7 +10,7 @@ import numpy as np def visualiseData(SAR_path, precipitation_csv, file_name, path_mean_vv): ## Create path to load precipitation csv - path_precip = os.path.join("data", "precipitation_data", precipitation_csv) + path_precip = os.path.join("data", precipitation_csv) # Find sum_days inside name of precipitation CSV file and assign it to a variable days = re.search('_avg(.+?)days_', precipitation_csv).group(1) @@ -24,13 +24,20 @@ def visualiseData(SAR_path, precipitation_csv, file_name, path_mean_vv): ## Combine DataFrames based on data df_total = pd.merge(df_precip, df_vv, on ='date') + # Define name for titles + count = SAR_path.count('/') + if count != 0: + title_name = re.search('/(.+)', SAR_path).group(count) + else: + title_name = SAR_path + ##### LINEPLOT MEAN_VV AND AVERAGE PRECIPITATION ## Set parameters for the lineplot - fig, ax = plt.subplots(figsize=(25, 5)) + fig, ax = plt.subplots(figsize=(20, 5)) plt.xticks(np.arange(0, len(df_total), (len(df_total)*0.015))) [lab.set_rotation(90) for lab in ax.get_xticklabels()] - plt.title(f'Mean VV backscatter and average precipitation in urban area ({SAR_path})', + plt.title(f'Mean VV backscatter and average precipitation in urban area ({title_name})', fontdict={'fontsize': 20}) ax.set_xlabel('date', fontdict={'fontsize': 15}) @@ -54,10 +61,10 @@ def visualiseData(SAR_path, precipitation_csv, file_name, path_mean_vv): ##### LINEPLOT MEAN_VV AND SUM xDAYS PRECIPITATION ## Set parameters for the lineplot - fig, ax = plt.subplots(figsize=(25, 5)) + fig, ax = plt.subplots(figsize=(20, 5)) plt.xticks(np.arange(0, len(df_total), (len(df_total)*0.015))) [lab.set_rotation(90) for lab in ax.get_xticklabels()] - plt.title(f'Mean VV backscatter and {days}-day sum of precipitation in urban area ({SAR_path})', + plt.title(f'Mean VV backscatter and {days}-day sum of precipitation in urban area ({title_name})', fontdict={'fontsize': 20}) ax.set_xlabel('date', fontdict={'fontsize': 15}) @@ -80,9 +87,6 @@ def visualiseData(SAR_path, precipitation_csv, file_name, path_mean_vv): plt.show() - return df_total, days, mean_VV, sum_xdays - - - + return df_total, days, mean_VV, sum_xdays, title_name