CheckJson.py

# %% ---------------------------------------------
# Load packages

import json
import pickle as pkl
import numpy as np
from datetime import datetime

# %% ---------------------------------------------
# Instructions for Ranisa

# This script extracts the maximum temperature anomaly from the JSON file and saves the output as a pickle file

# (1) setup a trigger that runs this script when a new json file is added to the folder
# (2) change the load directory to where you have stored the json files
# (3) load the MAI090_PercentilesHeatMap.json file, run the loop
# (4) change the save directory to where you want to save the pickle file and run code to save

# Notes

# the runpy package could be used to run this script from the web app script?
# see example below:

# import runpy
# # Assuming 'my_script' is a module in your Python path
# runpy.run_path('path/to/my_script.py')

# %% ---------------------------------------------
# load latest json plot
# Temp anomaly (called percentiles) plot

load_directory = r'C:\Users\mphem\OneDrive - UNSW\Work\OHW\ohw24_OceanExtremes_VisReportApp_au\New_data'
# load_directory = r'C:\Users\mphem\OneDrive - UNSW\Work\OHW\ohw24_OceanExtremes_VisReportApp_au\Figures'

# Load the JSON file
with open(load_directory + '\\' + 'MAI090_PercentilesHeatMap.json', 'r') as file:
    heatmaps = json.load(file)
    
# %% ---------------------------------------------
# Extract the data from the JSON object

data = heatmaps['data']

# Prepare to track the maximum temperature anomalies by year
max_temps_by_year = {}

for n in range(len(data)):
    x = data[n]['x']
    y = data[n]['y']
    z = data[n]['z']
    # Convert None to np.nan
    z =  np.array(z, dtype=float).flatten()
    z = np.where(z == None, np.nan, z)
    name = data[n]['name']
    year = name.split(' ')[0]
    depth = name.split(' ')[-1]

    # Find the maximum temperature anomaly in the data for this year
    if np.isfinite(z).sum() > 10:
        max_temp = np.nanmax(z)
    else:
        max_temp = np.nan
    max_temps_by_year[year] = max_temp

all_time_depth_max = np.nanmax(list(max_temps_by_year.values()))

# Output the results
# for year, max_temp in max_temps_by_year.items():
#     print(f"Year {year}: Max Temperature Anomaly {max_temp}")
    
# %% ---------------------------------------------
# save variables using pickle

save_directory = r'C:\Users\mphem\OneDrive - UNSW\Work\OHW\ohw24_OceanExtremes_VisReportApp_au\New_data'
    
# Get the current date and time
now = datetime.now()

# Format the date and time as 'YYYYMMDD_HHMM'
formatted_time = now.strftime('%Y%m%d_%H%M%S')
    
data_to_save = {
    'max_temps_by_year': max_temps_by_year,
    'all_time_depth_max': all_time_depth_max
}

# Save data to a pickle file
with open(save_directory + '\\' + 'maxTemp_' + formatted_time + '.pkl', 'wb') as file:
    pkl.dump(data_to_save, file)