pwd

'/Users/ud4/repos/GitHub/FATESFACE/Jupyter_Notebooks'


import re
import pandas as pd
import matplotlib.pyplot as plt


path_in_logs = "/Users/ud4/repos/GitHub/FATESFACE/Print_Logs"
file_name = "Phenology_log_r240313.txt"
#file_name = "Phenology_log_r240313_phen500.txt"


with open(f'{path_in_logs}/{file_name}', 'r') as infile:
    data = infile.read()


# How long do you want the simulation plots?
max_r_years = 30
max_line_idx = max_r_years*365*8 # 30 years * 364 days * 8 freq output per day


# Split data into lines
lines = data.split('\n')

# Initialize a dictionary of dataframes
dfs = {}

# Iterate over lines and create dataframes based on the first item i.e. print header
for line_idx, line in enumerate(lines):
    parts = line.split()
    key = parts[0]
    if key not in dfs:
        dfs[key] = []
    dfs[key].append(parts)
    if line_idx>max_line_idx:
        break


max_line_idx

87600


column_names = {}
column_names ['sinkhole_phenology'] = ['Name', 'Model_Day', 'temp_in_C', 'ncdstart','gddstart', 'nchilldays', 'gdd_threshold','ncolddays', 'grow_deg_days', 'cleafoffdate', 'cndaysleafoff','cleafondate','cndaysleafon']
column_names ['sinkhole_phenology_def'] = ['Name', 'phen_cstat_iscold', 'phen_cstat_nevercold', 'phen_cstat_notcold']
column_names ['sinkhole_phenology_LOn1'] = ['Name', 'Model_Day', 'cstatus', 'ED_val_phen_mindayson','grow_deg_days','gdd_threshold','nchilldays','cleafondate']
column_names ['sinkhole_phenology_LOn2'] = ['Name', 'Model_Day', 'cstatus', 'cleafondate','grow_deg_days']
column_names ['sinkhole_phenology_LOff1'] = ['Name', 'Model_Day', 'cstatus', 'num_vegtemp_mem','ED_val_phen_ncolddayslim','ED_val_phen_mindayson','cndaysleafon']
column_names ['sinkhole_phenology_LOff2'] = ['Name', 'Model_Day', 'cstatus', 'grow_deg_days','cndaysleafoff']
column_names ['sinkhole_phenology_LOff_th'] = ['Name', 'Model_Day', 'cstatus', 'cndaysleafoff','cleafoffdate']
column_names ['sinkhole_phenology_LOff_th2'] = ['Name', 'Model_Day', 'cstatus', 'grow_deg_days','cndaysleafoff']


dict_of_dataframes = {}
# Create dataframes from lists of lists
for key, values in dfs.items():
    df = pd.DataFrame(values,columns=column_names[key])
    print(f"Dataframe for {key}:")
    print(df)
    print()
    dict_of_dataframes[key] = df

Dataframe for sinkhole_phenology:
                     Name Model_Day             temp_in_C ncdstart gddstart  \
0      sinkhole_phenology         1    15.000000000000057      270        1   
1      sinkhole_phenology         2  -0.12143019246548192      270        1   
2      sinkhole_phenology         3    1.8371444335781462      270        1   
3      sinkhole_phenology         4    12.345837344257973      270        1   
4      sinkhole_phenology         5    16.943634897538857      270        1   
...                   ...       ...                   ...      ...      ...   
10946  sinkhole_phenology     10947   -7.5270423032126246      270        1   
10947  sinkhole_phenology     10948   -5.4280514176119254      270        1   
10948  sinkhole_phenology     10949   -7.5973448274695556      270        1   
10949  sinkhole_phenology     10950   -7.8760887490191180      270        1   
10950  sinkhole_phenology     10951   -8.2052535979563572      270        1   

      nchilldays       gdd_threshold ncolddays       grow_deg_days  \
0              0  570.00000000000000         9  0.0000000000000000   
1              1  563.65179393196922         9  0.0000000000000000   
2              2  557.36675356970989         9  0.0000000000000000   
3              2  557.36675356970989         8  0.0000000000000000   
4              2  557.36675356970989         7  0.0000000000000000   
...          ...                 ...       ...                 ...   
10946         50  318.96656089666010        10  0.0000000000000000   
10947         51  315.11617928222563        10  0.0000000000000000   
10948         52  311.30410960498403        10  0.0000000000000000   
10949         53  307.52997065479059        10  0.0000000000000000   
10950         54  303.79338501460541        10  0.0000000000000000   

      cleafoffdate cndaysleafoff cleafondate cndaysleafon  
0              299            67          99          267  
1              299            68          99          268  
2              299            69          99          269  
3              299            70          99          270  
4              299            71          99          271  
...            ...           ...         ...          ...  
10946        10816           131       10713          234  
10947        10816           132       10713          235  
10948        10816           133       10713          236  
10949        10816           134       10713          237  
10950        10816           135       10713          238  

[10951 rows x 13 columns]

Dataframe for sinkhole_phenology_def:
                         Name phen_cstat_iscold phen_cstat_nevercold  \
0      sinkhole_phenology_def                 1                    0   
1      sinkhole_phenology_def                 1                    0   
2      sinkhole_phenology_def                 1                    0   
3      sinkhole_phenology_def                 1                    0   
4      sinkhole_phenology_def                 1                    0   
...                       ...               ...                  ...   
10946  sinkhole_phenology_def                 1                    0   
10947  sinkhole_phenology_def                 1                    0   
10948  sinkhole_phenology_def                 1                    0   
10949  sinkhole_phenology_def                 1                    0   
10950  sinkhole_phenology_def                 1                    0   

      phen_cstat_notcold  
0                      2  
1                      2  
2                      2  
3                      2  
4                      2  
...                  ...  
10946                  2  
10947                  2  
10948                  2  
10949                  2  
10950                  2  

[10951 rows x 4 columns]

Dataframe for sinkhole_phenology_LOn1:
                          Name Model_Day cstatus ED_val_phen_mindayson  \
0      sinkhole_phenology_LOn1         1       2    90.000000000000000   
1      sinkhole_phenology_LOn1         2       2    90.000000000000000   
2      sinkhole_phenology_LOn1         3       2    90.000000000000000   
3      sinkhole_phenology_LOn1         4       2    90.000000000000000   
4      sinkhole_phenology_LOn1         5       2    90.000000000000000   
...                        ...       ...     ...                   ...   
10945  sinkhole_phenology_LOn1     10946       0    90.000000000000000   
10946  sinkhole_phenology_LOn1     10947       0    90.000000000000000   
10947  sinkhole_phenology_LOn1     10948       0    90.000000000000000   
10948  sinkhole_phenology_LOn1     10949       0    90.000000000000000   
10949  sinkhole_phenology_LOn1     10950       0    90.000000000000000   

            grow_deg_days       gdd_threshold nchilldays cleafondate  
0      0.0000000000000000  570.00000000000000          0          99  
1      0.0000000000000000  563.65179393196922          1          99  
2      0.0000000000000000  557.36675356970989          2          99  
3      0.0000000000000000  557.36675356970989          2          99  
4      0.0000000000000000  557.36675356970989          2          99  
...                   ...                 ...        ...         ...  
10945  0.0000000000000000  322.85563948965751         49       10713  
10946  0.0000000000000000  318.96656089666010         50       10713  
10947  0.0000000000000000  315.11617928222563         51       10713  
10948  0.0000000000000000  311.30410960498403         52       10713  
10949  0.0000000000000000  307.52997065479059         53       10713  

[10950 rows x 8 columns]

Dataframe for sinkhole_phenology_LOn2:
                          Name Model_Day cstatus cleafondate  \
0      sinkhole_phenology_LOn2         1       2          99   
1      sinkhole_phenology_LOn2         2       2          99   
2      sinkhole_phenology_LOn2         3       2          99   
3      sinkhole_phenology_LOn2         4       2          99   
4      sinkhole_phenology_LOn2         5       2          99   
...                        ...       ...     ...         ...   
10945  sinkhole_phenology_LOn2     10946       0       10713   
10946  sinkhole_phenology_LOn2     10947       0       10713   
10947  sinkhole_phenology_LOn2     10948       0       10713   
10948  sinkhole_phenology_LOn2     10949       0       10713   
10949  sinkhole_phenology_LOn2     10950       0       10713   

            grow_deg_days  
0      0.0000000000000000  
1      0.0000000000000000  
2      0.0000000000000000  
3      0.0000000000000000  
4      0.0000000000000000  
...                   ...  
10945  0.0000000000000000  
10946  0.0000000000000000  
10947  0.0000000000000000  
10948  0.0000000000000000  
10949  0.0000000000000000  

[10950 rows x 5 columns]

Dataframe for sinkhole_phenology_LOff1:
                           Name Model_Day cstatus num_vegtemp_mem  \
0      sinkhole_phenology_LOff1         1       2              10   
1      sinkhole_phenology_LOff1         2       2              10   
2      sinkhole_phenology_LOff1         3       2              10   
3      sinkhole_phenology_LOff1         4       2              10   
4      sinkhole_phenology_LOff1         5       2              10   
...                         ...       ...     ...             ...   
10945  sinkhole_phenology_LOff1     10946       0              10   
10946  sinkhole_phenology_LOff1     10947       0              10   
10947  sinkhole_phenology_LOff1     10948       0              10   
10948  sinkhole_phenology_LOff1     10949       0              10   
10949  sinkhole_phenology_LOff1     10950       0              10   

      ED_val_phen_ncolddayslim ED_val_phen_mindayson cndaysleafon  
0           5.0000000000000000    90.000000000000000          267  
1           5.0000000000000000    90.000000000000000          268  
2           5.0000000000000000    90.000000000000000          269  
3           5.0000000000000000    90.000000000000000          270  
4           5.0000000000000000    90.000000000000000          271  
...                        ...                   ...          ...  
10945       5.0000000000000000    90.000000000000000          233  
10946       5.0000000000000000    90.000000000000000          234  
10947       5.0000000000000000    90.000000000000000          235  
10948       5.0000000000000000    90.000000000000000          236  
10949       5.0000000000000000    90.000000000000000          237  

[10950 rows x 7 columns]

Dataframe for sinkhole_phenology_LOff2:
                           Name Model_Day cstatus       grow_deg_days  \
0      sinkhole_phenology_LOff2         1       2  0.0000000000000000   
1      sinkhole_phenology_LOff2         2       2  0.0000000000000000   
2      sinkhole_phenology_LOff2         3       2  0.0000000000000000   
3      sinkhole_phenology_LOff2         4       2  0.0000000000000000   
4      sinkhole_phenology_LOff2         5       2  0.0000000000000000   
...                         ...       ...     ...                 ...   
10945  sinkhole_phenology_LOff2     10946       0  0.0000000000000000   
10946  sinkhole_phenology_LOff2     10947       0  0.0000000000000000   
10947  sinkhole_phenology_LOff2     10948       0  0.0000000000000000   
10948  sinkhole_phenology_LOff2     10949       0  0.0000000000000000   
10949  sinkhole_phenology_LOff2     10950       0  0.0000000000000000   

      cndaysleafoff  
0                67  
1                68  
2                69  
3                70  
4                71  
...             ...  
10945           130  
10946           131  
10947           132  
10948           133  
10949           134  

[10950 rows x 5 columns]

Dataframe for sinkhole_phenology_LOff_th:
                             Name Model_Day cstatus cndaysleafoff cleafoffdate
0      sinkhole_phenology_LOff_th         1       2            67          299
1      sinkhole_phenology_LOff_th         2       2            68          299
2      sinkhole_phenology_LOff_th         3       2            69          299
3      sinkhole_phenology_LOff_th         4       2            70          299
4      sinkhole_phenology_LOff_th         5       2            71          299
...                           ...       ...     ...           ...          ...
10945  sinkhole_phenology_LOff_th     10946       0           130        10816
10946  sinkhole_phenology_LOff_th     10947       0           131        10816
10947  sinkhole_phenology_LOff_th     10948       0           132        10816
10948  sinkhole_phenology_LOff_th     10949       0           133        10816
10949  sinkhole_phenology_LOff_th     10950       0           134        10816

[10950 rows x 5 columns]

Dataframe for sinkhole_phenology_LOff_th2:
                              Name Model_Day cstatus       grow_deg_days  \
0      sinkhole_phenology_LOff_th2         1       2  0.0000000000000000   
1      sinkhole_phenology_LOff_th2         2       2  0.0000000000000000   
2      sinkhole_phenology_LOff_th2         3       2  0.0000000000000000   
3      sinkhole_phenology_LOff_th2         4       2  0.0000000000000000   
4      sinkhole_phenology_LOff_th2         5       2  0.0000000000000000   
...                            ...       ...     ...                 ...   
10945  sinkhole_phenology_LOff_th2     10946       0  0.0000000000000000   
10946  sinkhole_phenology_LOff_th2     10947       0  0.0000000000000000   
10947  sinkhole_phenology_LOff_th2     10948       0  0.0000000000000000   
10948  sinkhole_phenology_LOff_th2     10949       0  0.0000000000000000   
10949  sinkhole_phenology_LOff_th2     10950       0  0.0000000000000000   

      cndaysleafoff  
0                67  
1                68  
2                69  
3                70  
4                71  
...             ...  
10945           130  
10946           131  
10947           132  
10948           133  
10949           134  

[10950 rows x 5 columns]


# Making the columns as floats except of the 1st 
for key in dict_of_dataframes.keys():
    for k in dict_of_dataframes[key].columns[1:]:
        dict_of_dataframes[key][k] = dict_of_dataframes[key][k].astype(float)


dict_of_dataframes['sinkhole_phenology'].head()


dict_of_dataframes['sinkhole_phenology_def'].head()


dict_of_dataframes['sinkhole_phenology_LOn1'].head()


dict_of_dataframes['sinkhole_phenology_LOn2'].head()


dict_of_dataframes['sinkhole_phenology_LOff1'].head()


dict_of_dataframes['sinkhole_phenology_LOff2'].head()


dict_of_dataframes['sinkhole_phenology_LOff_th'].head()


dict_of_dataframes['sinkhole_phenology_LOff_th2'].head()


# Plotting
df_key = "sinkhole_phenology"
df_key_col = "temp_in_C"
plt.figure(figsize=(25,6))
plt.plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-')
plt.xlabel('Index')
plt.ylabel(f'{df_key_col}')
plt.title(f'{df_key_col} vs. Index')
plt.grid(True)
for i in range(max_r_years):
    plt.axvline(x = i*365, color = 'k',lw=5, alpha=.1)
    if i%5==0:
        plt.text(i*365,-10, int(i))
plt.show()


# Plotting
df_key = "sinkhole_phenology"
df_key_col = "temp_in_C"

for i in range(max_r_years):
    plt.figure(figsize=(35,6))
    
    if i>=1:
        plt.plot(dict_of_dataframes[f"{df_key}"].index[i*365:(i+1)*365], dict_of_dataframes[f"{df_key}"][f"{df_key_col}"][i*365:(i+1)*365], marker='o', linestyle='-')
    else:
        plt.plot(dict_of_dataframes[f"{df_key}"].index[i*365:(i+1)*365], dict_of_dataframes[f"{df_key}"][f"{df_key_col}"][i*365:(i+1)*365], marker='o', linestyle='-')
        
    zero_indices = dict_of_dataframes[df_key][(dict_of_dataframes[df_key][df_key_col] <= 0) & (dict_of_dataframes[df_key].index >= i * 365) & (dict_of_dataframes[df_key].index < (i + 1) * 365)].index
    for idx in zero_indices:
        plt.axvline(x=idx, color='red', linestyle='--')
    

            
    plt.xlabel('Index')
    plt.ylabel(f'{df_key_col}')
    plt.title(f'{df_key_col} vs. Index for {i+1} Year')
    plt.grid(True)
#for i in range(max_r_years):
#    plt.axvline(x = i*365, color = 'k',lw=5, alpha=.1)
#    if i%5==0:
#        plt.text(i*365,-10, int(i))
#plt.show()

/var/folders/f1/01gxw8vn74q_x_rf_p5ztryjr405zq/T/ipykernel_80362/2109787684.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  plt.figure(figsize=(35,6))


subset

0      15.000000
1      -0.121430
2       1.837144
3      12.345837
4      16.943635
         ...    
360    -6.085371
361    -4.839862
362    -4.965080
363    -3.707380
364    -1.215918
Name: temp_in_C, Length: 365, dtype: float64


import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 35))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in [2,3]):
            axes[idx].text(i*365,-10, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOn1"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 35))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in [2]):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOn2"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 20))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in []):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOff1"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 25))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in [2,3,4]):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOff2"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 20))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in []):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


 import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOff_th"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 20))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in []):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


# when did the status change?

dict_of_dataframes[f"{df_key}"]["cstatus"][dict_of_dataframes[f"{df_key}"]["cstatus"]==0].head()

7291    0.0
7292    0.0
7293    0.0
7294    0.0
7295    0.0
Name: cstatus, dtype: float64

2.0


 import matplotlib.pyplot as plt

# Assuming ict_of_dataframes is a dictionary of dataframes
# Define the number of rows and columns for subplots
df_key = "sinkhole_phenology_LOff_th2"
num_rows = len(dict_of_dataframes[f'{df_key}'].columns[1:]) # Number of rows for subplots
num_cols = 1  # Number of columns for subplots

# Create a figure and axes for subplots
fig, axes = plt.subplots(num_rows, num_cols, figsize=(25, 20))

# Iterate over dataframes and plot each one on a separate subplot
for idx, df_key_col in enumerate(dict_of_dataframes[f'{df_key}'].columns[1:]):    
    axes[idx].plot(dict_of_dataframes[f"{df_key}"].index, dict_of_dataframes[f"{df_key}"][f"{df_key_col}"], marker='o', linestyle='-', label=key)
    axes[idx].set_ylabel(f'{df_key_col}')
    axes[idx].set_title(f'{df_key_col} vs. Index')
    axes[idx].grid(True)
    #axes[row_idx].legend()
    axes[idx].tick_params(axis='x', which='both', bottom=False, labelbottom=False)
    
    for i in range(max_r_years):
        axes[idx].axvline(x = i*365, color = 'k',lw=5, alpha=.1)
        if (i%5==0) and (idx not in []):
            axes[idx].text(i*365,0, int(i))

axes[idx].set_xlabel('Index')
# Adjust layout
plt.tight_layout()
plt.subplots_adjust(hspace=0.1) 
plt.show()


index = 7290
print(f"index:{index}")
print(f"cstatus: {dict_of_dataframes[f'sinkhole_phenology_LOff_th']['cstatus'][index]}")
print ("That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush")
print(f"grow_deg_days: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['grow_deg_days'][index]}")
print(f"gdd_threshold: {round(dict_of_dataframes[f'sinkhole_phenology_LOn1']['gdd_threshold'][index],3)}")
print(f"cleafondate: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['cleafondate'][index]}")
print(f"nchilldays: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['nchilldays'][index]}")
print(f"cndaysleafon: {dict_of_dataframes[f'sinkhole_phenology_LOff1']['cndaysleafon'][index]}")
print(f"temp_in_C: {round(dict_of_dataframes[f'sinkhole_phenology']['temp_in_C'][index],3)}")
print(f"ncdstart: {round(dict_of_dataframes[f'sinkhole_phenology']['ncdstart'][index],3)}")
print (f"Nov 1 after 19 years {365*19+270}")
print(f"gddstart: {round(dict_of_dataframes[f'sinkhole_phenology']['gddstart'][index],3)}")
print (f"Jan 1 after 19 years {365*19+1}")
print (f"Jan 1 after 20 years {365*20+1}")
print(f"ncolddays: {round(dict_of_dataframes[f'sinkhole_phenology']['ncolddays'][index],3)}")
print(f"cleafoffdate: {round(dict_of_dataframes[f'sinkhole_phenology']['cleafoffdate'][index],3)}")
print(f"cndaysleafoff: {round(dict_of_dataframes[f'sinkhole_phenology']['cndaysleafoff'][index],3)}")
print(f"cleafondate: {round(dict_of_dataframes[f'sinkhole_phenology']['cleafondate'][index],3)}")
print(f"cndaysleafon: {round(dict_of_dataframes[f'sinkhole_phenology']['cndaysleafon'][index],3)}")

index:7290
cstatus: 2.0
That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush
grow_deg_days: 0.0
gdd_threshold: 526.867
cleafondate: 7005.0
nchilldays: 7.0
cndaysleafon: 286.0
temp_in_C: 1.723
ncdstart: 270.0
Nov 1 after 19 years 7205
gddstart: 1.0
Jan 1 after 19 years 6936
Jan 1 after 20 years 7301
ncolddays: 3.0
cleafoffdate: 6890.0
cndaysleafoff: 401.0
cleafondate: 7005.0
cndaysleafon: 286.0


index = 7291
print(f"index:{index}")
print(f"cstatus: {dict_of_dataframes[f'sinkhole_phenology_LOff_th']['cstatus'][index]}")
print ("That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush")
print(f"grow_deg_days: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['grow_deg_days'][index]}")
print(f"gdd_threshold: {round(dict_of_dataframes[f'sinkhole_phenology_LOn1']['gdd_threshold'][index],3)}")
print(f"cleafondate: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['cleafondate'][index]}")
print(f"nchilldays: {dict_of_dataframes[f'sinkhole_phenology_LOn1']['nchilldays'][index]}")
print(f"cndaysleafon: {dict_of_dataframes[f'sinkhole_phenology_LOff1']['cndaysleafon'][index]}")
print(f"temp_in_C: {round(dict_of_dataframes[f'sinkhole_phenology']['temp_in_C'][index],3)}")
print(f"ncdstart: {round(dict_of_dataframes[f'sinkhole_phenology']['ncdstart'][index],3)}")
print (f"Nov 1 after 19 years {365*19+270}")
print(f"gddstart: {round(dict_of_dataframes[f'sinkhole_phenology']['gddstart'][index],3)}")
print (f"Jan 1 after 19 years {365*19+1}")
print (f"Jan 1 after 20 years {365*20+1}")
print(f"ncolddays: {round(dict_of_dataframes[f'sinkhole_phenology']['ncolddays'][index],3)}")
print(f"cleafoffdate: {round(dict_of_dataframes[f'sinkhole_phenology']['cleafoffdate'][index],3)}")
print(f"cndaysleafoff: {round(dict_of_dataframes[f'sinkhole_phenology']['cndaysleafoff'][index],3)}")
print(f"cleafondate: {round(dict_of_dataframes[f'sinkhole_phenology']['cleafondate'][index],3)}")
print(f"cndaysleafon: {round(dict_of_dataframes[f'sinkhole_phenology']['cndaysleafon'][index],3)}")

index:7291
cstatus: 0.0
That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush
grow_deg_days: 0.0
gdd_threshold: 520.948
cleafondate: 7005.0
nchilldays: 8.0
cndaysleafon: 287.0
temp_in_C: 2.641
ncdstart: 270.0
Nov 1 after 19 years 7205
gddstart: 1.0
Jan 1 after 19 years 6936
Jan 1 after 20 years 7301
ncolddays: 4.0
cleafoffdate: 7291.0
cndaysleafoff: 1.0
cleafondate: 7005.0
cndaysleafon: 287.0


dict_of_dataframes[f'sinkhole_phenology_LOff1'].columns

Index(['Name', 'Model_Day', 'cstatus', 'num_vegtemp_mem',
       'ED_val_phen_ncolddayslim', 'ED_val_phen_mindayson', 'cndaysleafon'],
      dtype='object')


dict_of_dataframes['sinkhole_phenology'].columns

Index(['Name', 'Model_Day', 'temp_in_C', 'ncdstart', 'gddstart', 'nchilldays',
       'gdd_threshold', 'ncolddays', 'grow_deg_days', 'cleafoffdate',
       'cndaysleafoff', 'cleafondate', 'cndaysleafon'],
      dtype='object')

Logs of File¶

Printing the following lines of the Phenology routine `phenology` in the file `EDPhysiologyMod.F90` for cold deciduous only¶

Mystery¶

	Name	Model_Day	temp_in_C	ncdstart	gddstart	nchilldays	gdd_threshold	ncolddays	cleafoffdate	cndaysleafoff	cleafondate	cndaysleafon
0	sinkhole_phenology	1.0	15.000000	270.0	1.0	0.0	570.000000	9.0	299.0	67.0	99.0	267.0
1	sinkhole_phenology	2.0	-0.121430	270.0	1.0	1.0	563.651794	9.0	299.0	68.0	99.0	268.0
2	sinkhole_phenology	3.0	1.837144	270.0	1.0	2.0	557.366754	9.0	299.0	69.0	99.0	269.0
3	sinkhole_phenology	4.0	12.345837	270.0	1.0	2.0	557.366754	8.0	299.0	70.0	99.0	270.0
4	sinkhole_phenology	5.0	16.943635	270.0	1.0	2.0	557.366754	7.0	299.0	71.0	99.0	271.0

	Name	phen_cstat_iscold	phen_cstat_notcold
0	sinkhole_phenology_def	1.0	2.0
1	sinkhole_phenology_def	1.0	2.0
2	sinkhole_phenology_def	1.0	2.0
3	sinkhole_phenology_def	1.0	2.0
4	sinkhole_phenology_def	1.0	2.0

	Name	Model_Day	cstatus	ED_val_phen_mindayson	gdd_threshold	nchilldays	cleafondate
0	sinkhole_phenology_LOn1	1.0	2.0	90.0	570.000000	0.0	99.0
1	sinkhole_phenology_LOn1	2.0	2.0	90.0	563.651794	1.0	99.0
2	sinkhole_phenology_LOn1	3.0	2.0	90.0	557.366754	2.0	99.0
3	sinkhole_phenology_LOn1	4.0	2.0	90.0	557.366754	2.0	99.0
4	sinkhole_phenology_LOn1	5.0	2.0	90.0	557.366754	2.0	99.0

	Name	Model_Day	cstatus	cleafondate
0	sinkhole_phenology_LOn2	1.0	2.0	99.0
1	sinkhole_phenology_LOn2	2.0	2.0	99.0
2	sinkhole_phenology_LOn2	3.0	2.0	99.0
3	sinkhole_phenology_LOn2	4.0	2.0	99.0
4	sinkhole_phenology_LOn2	5.0	2.0	99.0

	Name	Model_Day	cstatus	num_vegtemp_mem	ED_val_phen_ncolddayslim	ED_val_phen_mindayson	cndaysleafon
0	sinkhole_phenology_LOff1	1.0	2.0	10.0	5.0	90.0	267.0
1	sinkhole_phenology_LOff1	2.0	2.0	10.0	5.0	90.0	268.0
2	sinkhole_phenology_LOff1	3.0	2.0	10.0	5.0	90.0	269.0
3	sinkhole_phenology_LOff1	4.0	2.0	10.0	5.0	90.0	270.0
4	sinkhole_phenology_LOff1	5.0	2.0	10.0	5.0	90.0	271.0

	Name	Model_Day	cstatus	cndaysleafoff
0	sinkhole_phenology_LOff2	1.0	2.0	67.0
1	sinkhole_phenology_LOff2	2.0	2.0	68.0
2	sinkhole_phenology_LOff2	3.0	2.0	69.0
3	sinkhole_phenology_LOff2	4.0	2.0	70.0
4	sinkhole_phenology_LOff2	5.0	2.0	71.0

	Name	Model_Day	cstatus	cndaysleafoff	cleafoffdate
0	sinkhole_phenology_LOff_th	1.0	2.0	67.0	299.0
1	sinkhole_phenology_LOff_th	2.0	2.0	68.0	299.0
2	sinkhole_phenology_LOff_th	3.0	2.0	69.0	299.0
3	sinkhole_phenology_LOff_th	4.0	2.0	70.0	299.0
4	sinkhole_phenology_LOff_th	5.0	2.0	71.0	299.0

	Name	Model_Day	cstatus	cndaysleafoff
0	sinkhole_phenology_LOff_th2	1.0	2.0	67.0
1	sinkhole_phenology_LOff_th2	2.0	2.0	68.0
2	sinkhole_phenology_LOff_th2	3.0	2.0	69.0
3	sinkhole_phenology_LOff_th2	4.0	2.0	70.0
4	sinkhole_phenology_LOff_th2	5.0	2.0	71.0

Logs of File¶

Printing the following lines of the Phenology routine phenology in the file EDPhysiologyMod.F90 for cold deciduous only¶

Mystery¶

Printing the following lines of the Phenology routine `phenology` in the file `EDPhysiologyMod.F90` for cold deciduous only¶