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   -6.0792380233093013      270        1   
10947  sinkhole_phenology     10948   -4.8304985940314964      270        1   
10948  sinkhole_phenology     10949   -6.6510489921105318      270        1   
10949  sinkhole_phenology     10950   -7.7996488142381963      270        1   
10950  sinkhole_phenology     10951   -8.5057785375904018      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         43  347.02480243347594        10  0.0000000000000000   
10947         44  342.89523665104423        10  0.0000000000000000   
10948         45  338.80676073469141        10  0.0000000000000000   
10949         46  334.75896583341876        10  0.0000000000000000   
10950         47  330.75144716436307        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        10905            42       10649          298  
10947        10905            43       10649          299  
10948        10905            44       10649          300  
10949        10905            45       10649          301  
10950        10905            46       10649          302  

[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       1    90.000000000000000   
10946  sinkhole_phenology_LOn1     10947       1    90.000000000000000   
10947  sinkhole_phenology_LOn1     10948       1    90.000000000000000   
10948  sinkhole_phenology_LOn1     10949       1    90.000000000000000   
10949  sinkhole_phenology_LOn1     10950       1    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  351.19587104200622         42       10649  
10946  0.0000000000000000  347.02480243347594         43       10649  
10947  0.0000000000000000  342.89523665104423         44       10649  
10948  0.0000000000000000  338.80676073469141         45       10649  
10949  0.0000000000000000  334.75896583341876         46       10649  

[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       1       10649   
10946  sinkhole_phenology_LOn2     10947       1       10649   
10947  sinkhole_phenology_LOn2     10948       1       10649   
10948  sinkhole_phenology_LOn2     10949       1       10649   
10949  sinkhole_phenology_LOn2     10950       1       10649   

            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       1              10   
10946  sinkhole_phenology_LOff1     10947       1              10   
10947  sinkhole_phenology_LOff1     10948       1              10   
10948  sinkhole_phenology_LOff1     10949       1              10   
10949  sinkhole_phenology_LOff1     10950       1              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          297  
10946       5.0000000000000000    90.000000000000000          298  
10947       5.0000000000000000    90.000000000000000          299  
10948       5.0000000000000000    90.000000000000000          300  
10949       5.0000000000000000    90.000000000000000          301  

[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       1  0.0000000000000000   
10946  sinkhole_phenology_LOff2     10947       1  0.0000000000000000   
10947  sinkhole_phenology_LOff2     10948       1  0.0000000000000000   
10948  sinkhole_phenology_LOff2     10949       1  0.0000000000000000   
10949  sinkhole_phenology_LOff2     10950       1  0.0000000000000000   

      cndaysleafoff  
0                67  
1                68  
2                69  
3                70  
4                71  
...             ...  
10945            41  
10946            42  
10947            43  
10948            44  
10949            45  

[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       1            41        10905
10946  sinkhole_phenology_LOff_th     10947       1            42        10905
10947  sinkhole_phenology_LOff_th     10948       1            43        10905
10948  sinkhole_phenology_LOff_th     10949       1            44        10905
10949  sinkhole_phenology_LOff_th     10950       1            45        10905

[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       1  0.0000000000000000   
10946  sinkhole_phenology_LOff_th2     10947       1  0.0000000000000000   
10947  sinkhole_phenology_LOff_th2     10948       1  0.0000000000000000   
10948  sinkhole_phenology_LOff_th2     10949       1  0.0000000000000000   
10949  sinkhole_phenology_LOff_th2     10950       1  0.0000000000000000   

      cndaysleafoff  
0                67  
1                68  
2                69  
3                70  
4                71  
...             ...  
10945            41  
10946            42  
10947            43  
10948            44  
10949            45  

[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_96077/2109787684.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))


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()

Series([], Name: cstatus, 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_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 = 7293
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:7293
cstatus: 1.0
That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush
grow_deg_days: 0.0
gdd_threshold: 509.286
cleafondate: 7005.0
nchilldays: 10.0
cndaysleafon: 289.0
temp_in_C: 3.063
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: 6.0
cleafoffdate: 6890.0
cndaysleafoff: 404.0
cleafondate: 7005.0
cndaysleafon: 289.0


index = 7294
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:7294
cstatus: 1.0
That means the state is 'phen_cstat_notcold' state and leaves are allowed to flush
grow_deg_days: 0.0
gdd_threshold: 503.542
cleafondate: 7005.0
nchilldays: 11.0
cndaysleafon: 290.0
temp_in_C: -1.408
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: 7.0
cleafoffdate: 7294.0
cndaysleafoff: 1.0
cleafondate: 7005.0
cndaysleafon: 290.0

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¶