"""
Generating Cardiograms for Radiance Assimilation Analysis
=========================================================

This script is designed to generate cardiograms that analyze the results of radiance assimilation. Below is a description of the generated graphs, their characteristics, and their importance in the context of data assimilation.

Cardiograms of Radiance Assimilation
-------------------------------------
The script generates four main types of cardiograms, each providing critical information to assess the performance of radiance assimilation.

.. image::  ../../../docs/source/_static/cardio.png
   :alt: code_quality

1. **Mean of Differences**
   This cardiogram shows the averages of the differences between observations and predictions (O-P) and, if available, the differences between observations and adjustments (O-A). It also includes bias corrections (Bcor) if present.
   **Importance:** Analyzing the averages of O-P and O-A helps identify and quantify any systematic bias in the assimilation process, which is crucial for improving the accuracy of prediction models.

2. **Standard Deviations of Differences**
   This cardiogram shows the standard deviations of the differences between observations and predictions (O-P) and, if available, the standard deviations of the differences between observations and adjustments (O-A).
   **Importance:** Standard deviations measure the dispersion of errors. Analyzing these deviations helps understand the variability and uncertainty in the predictions and adjustments made during radiance assimilation.

3. **Obsvalue of Data**
   This cardiogram shows the obsvalue in the assimilation process.
   **Importance:** Evaluating the obsvalue of accepted data is essential to understand the efficiency of the assimilation system and the quality of the data being used. A high number of accepted data points can indicate an efficient and reliable filtering process.

4. **Total Number of Processed Data (Data Processing Analysis)**
   This cardiogram shows the total number of processed data points (Ntot) during assimilation.
   **Importance:** The total number of processed data points provides an overview of the amount of information handled by the system. It is an indicator of the data volume and the system's capacity to process large datasets.


*******************************************************
Generate Cardiograms for Radiance Assimilation Analysis
*******************************************************

To start an interactive session for generating cardiograms, use the following qsub command:
::
    qsub -I -X -l select=4:ncpus=80:mpiprocs=80:ompthreads=1:mem=185gb -l place=scatter -l walltime=6:0:0

Generating Cardiograms
^^^^^^^^^^^^^^^^^^^^^^
To generate cardiograms using pikobs, use the following command format:
::
    python -c 'import pikobs; pikobs.cardio.arg_call()' \

        --path_experience /path/to/experience_directory \

        --experience_name EXPERIENCE_NAME \

        --pathwork WORK_DIRECTORY \

        --datestart START_DATE \

        --dateend END_DATE \

        --region REGION \

        --family FAMILY_NAME \

        --flags_criteria FLAGS_CRITERIA \

        --id_stn SATELLITE_IDS \

        --channel CHANNELS \

        --plot_title PLOT_TITLE \

        --n_cpu CPU_COUNT

Parameters Explanation
^^^^^^^^^^^^^^^^^^^^^^
- **path_experience:** Path to the directory where the experience data is stored.
- **experience_name:** Name of the specific experience.
- **pathwork:** Directory path indicating the working directory for the script.
- **datestart:** Start date and time of the assimilation process (format: YYYYMMDDHH).
- **dateend:** End date and time of the assimilation process (format: YYYYMMDDHH).
- **region:** Geographic region or area of interest for the assimilation analysis. (e.g., Monde, PoleNord, PoleSud, AmeriqueduNord, OuestAmeriqueduNord, AmeriqueDuNordPlus, ExtratropiquesNord, HemisphereNord, HemisphereSud, Asie, Europe, Mexique, Canada, BaieDhudson, Arctiquecanadien, EtatsUnis, Tropiques30, Tropiques, Australie, Pacifique and Atlantique)
- **family:** Family of data assimilated (e.g., mwhs2, mwhs2_qc, to_amsua_qc, to_amsua, to_amsua_allsky, to_amsua_allsky_qc, to_amsub_qc, to_amsub, ssmis_qc, ssmis, iasi, iasi_qc, crisfsr1_qc, crisfsr2_qc, cris, atms_allsky, atms_qc and csr, csr_qc)
- **flags_criteria:** Specific flag criteria used during assimilation (e.g., all, assimilee, bgckalt, bgckalt_qc and postalt).
- **id_stn:** all or satellite IDs for which assimilation results are analyzed (e.g., METOP-1, METOP-3). Modify as needed to specify particular satellites.
- **channel:** all or specific channels to analyze. Use 'all' to analyze all available channels. Modify as needed to specify particular channels of interest.
- **plot_title:** Title for the generated cardiogram.
- **n_cpu:** Number of CPU cores to be used for parallel processing. Adjust according to your system capabilities.

Additional Note
^^^^^^^^^^^^^^^
- Modify **--channel** and **--id_stn** parameters as needed to specify particular satellites and channels of interest.
- Ensure paths (**path_experience, pathwork**) are correctly set to point to your data directories.
- The script leverages parallel processing (**--n_cpu**) to optimize performance, adjust according to your system capabilities.
- The generated cardiograms provide comprehensive visual insights into the assimilation performance metrics across selected satellites, channels, and criteria.
- Adjust the parameters (**--channel, --id_stn, etc.**) according to your specific requirements and data.

""" 
#!/usr/bin/python3
import sqlite3
import pikobs
import re
import os
from  dask.distributed import Client
import numpy as np
import sqlite3
import os
import re
import sqlite3
from  datetime import datetime, timedelta


def create_serie_cardio(family, 
                        new_db_filename, 
                        existing_db_filename,
                        region_seleccionada,
                        selected_flags, 
                        id_stn,
                        channel, 
                        varno):
    """

    Create a new SQLite database with a 'moyenne' table and populate it with data from an existing database.

    Args:

      new_db_filename (str): Filename of the new database to be created.
  
      existing_db_filename (str): Filename of the existing database to be attached.
  
      region_seleccionada (str): Region selection criteria.
   
      selected_flags (str): Selected flags criteria.
   
      FONCTION (float): Value for sum_fonction column.

    
    Returns:
    
      None
    
    """

 
    pattern = r'(\d{10})'
    match = re.search(pattern, existing_db_filename)

    if match:
        date = match.group(1)
       
    else:
        print("No 10 digits found in the string.")
    
    # Connect to the new database
    if id_stn[0]=='all':
      criteria_id_stn = '    '
    else:
       # Paso 1: Añadir comillas simples alrededor de cada elemento
       elementos_con_comillas = [f"'{elemento}'" for elemento in id_stn]

       # Paso 2: Unir los elementos con comas
       resultado = f"({','.join(elementos_con_comillas)})"
       criteria_id_stn = f'and id_stn in {resultado}'
    if channel[0]=='all':
      criteria_vcoord = '    '
    else:
       resultado = f"({','.join(channel)})"
       criteria_vcoord = f'and vcoord in {resultado}    '

      
    new_db_conn = sqlite3.connect(new_db_filename, uri=True, isolation_level=None, timeout=999)
    new_db_cursor = new_db_conn.cursor()

    FAM, VCOORD, VCOCRIT, STATB, VCOORD, VCOTYP = pikobs.family(family)
    LAT1, LAT2, LON1, LON2 = pikobs.regions(region_seleccionada)
    LATLONCRIT = pikobs.generate_latlon_criteria(LAT1, LAT2, LON1, LON2)
    flag_criteria = pikobs.flag_criteria(selected_flags)

    # Attach the existing database
    new_db_cursor.execute(f"ATTACH DATABASE '{existing_db_filename}' AS db;")
    # load extension CMC 
    new_db_conn.enable_load_extension(True)
    extension_dir = f'{os.path.dirname(pikobs.__file__)}/extension/libudfsqlite-shared.so'
    new_db_conn.execute(f"SELECT load_extension('{extension_dir}')")
    # Create the 'moyenne' table in the new database if it doesn't exist
    new_db_cursor.execute("""
           CREATE TABLE IF NOT EXISTS serie_cardio ( 
            DATE INTEGER,
            Chan INTEGER,
            Nrej INTEGER,
            Nacc INTIGER,
            AvgOMP FLOAT, 
            AvgOMA FLOAT,
            StdOMP FLOAT,
            StdOMA FLOAT,
            NDATA  INTEGER,
            Nprofile INTEGER,
            AvgBCOR FLOAT,
            AvgOBS FLOAT,
            Ntot INTEGER,
            varno INTEGER,
            id_stn  TEXT
        );
    """)
    query=f"""INSERT INTO serie_cardio (

            DATE,
            Chan,
            Nrej,
            Nacc,
            AvgOMP, 
            AvgOMA,
            StdOMP,
            StdOMA,
            NDATA,
            Nprofile,
            AvgBCOR,
            AvgOBS,
            Ntot,
            varno,
            id_stn
        )
    
    
             SELECT 
                 isodatetime({date}) AS DATE,  
                 VCOORD As Chan, 
                 SUM(flag & 512=512) AS Nrej,
                 SUM(flag & 4096=4096) AS Nacc,
                 ROUND(AVG(OMP), 4) AS AvgOMP,
                 ROUND(AVG(OMA), 4) AS AvgOMA,
                 ROUND(STDDEV(OMP), 4) AS StdOMP,
                 ROUND(STDDEV(OMA), 4) AS StdOMA,
                 SUM(OMP IS NOT NULL) AS NDATA,
                 COUNT(DISTINCT id_obs) AS Nprofils,
                 ROUND(AVG(BIAS_CORR), 4) AS AvgBCOR,
                 ROUND(AVG(OBSVALUE), 4) AS AvgOBS,
                 (SELECT COUNT(*) FROM header h2 WHERE h2.ID_STN = header.ID_STN) AS Ntot,
                 varno AS varno,
                 id_stn AS id_stn
                 
            FROM 
                 header
             NATURAL JOIN 
                 data
             WHERE 
                 VARNO = {int(varno)}
                 {criteria_id_stn}
                 {criteria_vcoord}
                 {flag_criteria}
                 {LATLONCRIT}
                 {VCOCRIT}
             GROUP BY 
                 VCOORD, ID_STN
             HAVING 
                 SUM(OMP IS NOT NULL) >= 50;"""

    new_db_cursor.execute(query)
    # Commit changes and detach the existing database
    new_db_conn.commit()

    # Commit changes and detach the existing database
    new_db_cursor.execute("DETACH DATABASE db;")

    # Close the connections
    new_db_conn.close()

def create_data_list_cardio(datestart1, 
                            dateend1,
                            family,
                            pathin, 
                            pathwork,
                            flag_criteria,
                            id_stn,
                            channel,
                            region_seleccionada):
    
    data_list_cardio = []

    # Convert datestart and dateend to datetime objects
    datestart = datetime.strptime(datestart1, '%Y%m%d%H')
    dateend = datetime.strptime(dateend1, '%Y%m%d%H')

    # Initialize the current_date to datestart
    current_date = datestart

    # Define a timedelta of 6 hours
    delta = timedelta(hours=6)
    FAM, VCOORD, VCOCRIT, STATB, element, VCOTYP = pikobs.family(family)

    element_array = np.array([float(x) for x in element.split(',')])
    for varno in element_array:
   #  print ("VCOORD", vcoord, element, type(element))
     # Iterate through the date range in 6-hour intervals
     while current_date <= dateend:
        # Format the current date as a string
        formatted_date = current_date.strftime('%Y%m%d%H')

        # Build the file name using the date and family
        filename = f'{formatted_date}_{family}'

        file_path_name = f'{pathin}/{filename}'
        conn = sqlite3.connect(file_path_name)
        # Create a cursor to execute SQL queries
        cursor = conn.cursor()

        #  Create a new dictionary and append it to the list
        data_dict = {'family': family,
                          'filein': f'{pathin}/{filename}',
                          'db_new': f'{pathwork}/cardio_{datestart1}_{dateend1}_{flag_criteria}_{family}.db',
                          'region': region_seleccionada,
                          'flag_criteria': flag_criteria,
                          'varno':  varno,
                          'vcoord': channel,
                          'id_stn': id_stn}
        data_list_cardio.append(data_dict)
        conn.close()

        # Update the current_date in the loop by adding 6 hours
        current_date += delta

    return data_list_cardio


def get_station_ids(cursor, id_stn):
    if id_stn[0] == 'all':
        cursor.execute("SELECT DISTINCT id_stn FROM serie_cardio;")
        return [item[0] for item in cursor.fetchall()]
    return np.array(id_stn)

def fetch_channels_and_varno(cursor, idstn, channel):
    criter = f'WHERE id_stn = "{idstn}"'
    if channel[0] == 'all':
        query = f"SELECT DISTINCT chan, varno FROM serie_cardio {criter} ORDER BY chan ASC;"
        cursor.execute(query)
        return cursor.fetchall()
    else:
        channels_varno = []
        for chan in np.array(channel):
            query = f"SELECT DISTINCT chan, varno FROM serie_cardio {criter} AND chan={chan} ORDER BY chan ASC;"
            cursor.execute(query)
            result = cursor.fetchone()
            if result:
                channels_varno.append(result)
        return channels_varno

def create_data_list_plot(datestart1, dateend1, family, pathin, pathwork, flag_criteria, region_seleccionada, id_stn, channel):
    data_list_plot = []
    filedb = f'{pathwork}/cardio_{datestart1}_{dateend1}_{flag_criteria}_{family}.db'

    try:
        with sqlite3.connect(filedb) as conn:
            cursor = conn.cursor()
            id_stns = get_station_ids(cursor, id_stn)

            for idstn in id_stns:
                channels_varno = fetch_channels_and_varno(cursor, idstn, channel)
                for chan, varno in channels_varno:
                    data_dict_plot = {
                        'id_stn': idstn,
                        'vcoord': chan,
                        'varno': varno
                    }
                    data_list_plot.append(data_dict_plot)
    except sqlite3.Error as e:
        print(f"Database error: {e}")
    except Exception as e:
        print(f"Error: {e}")

    return data_list_plot

def make_cardio(path_experience_files,
                experience_names,      
                pathwork, 
                datestart,
                dateend,
                regions, 
                familys, 
                flag_criteria, 
                id_stn,
                channel,
                plot_type,
                plot_title,
                n_cpu):
   pikobs.delete_create_folder(pathwork)

   for family in familys:
     for region in regions:
       print ("##> Family:", family, "Region:", region)
       data_list_cardio = create_data_list_cardio(datestart,
                                           dateend, 
                                           family, 
                                           path_experience_files, 
                                           pathwork,
                                           flag_criteria, 
                                           id_stn,
                                           channel,
                                           region)
       
       import time
       import dask
       t0 = time.time()
       if n_cpu==1:
        print (f'in Serie files for {name_in} = {len(data_list)}')
        for  data_ in data_list_cardio:  
            create_serie_cardio(data_['family'], 
                                data_['db_new'], 
                                data_['filein'],
                                data_['region'],
                                data_['flag_criteria'],
                                data_['id_stn'],
                                data_['vcoord'],
                                data_['varno'])




       else:
        print (f'Number of files used in calculating statistics = {len(data_list_cardio)}')
        with dask.distributed.Client(processes=True, threads_per_worker=1, 
                                           n_workers=n_cpu, 
                                           silence_logs=40) as client:
            delayed_funcs = [dask.delayed(create_serie_cardio)(data_['family'], 
                                              data_['db_new'], 
                                              data_['filein'],
                                              data_['region'],
                                              data_['flag_criteria'],
                                              data_['id_stn'],
                                              data_['vcoord'],
                                              data_['varno'])for data_ in data_list_cardio]
            results = dask.compute(*delayed_funcs)
        
       tn= time.time()
       print ('Total time for statistics:',tn-t0 )  
       data_list_plot = create_data_list_plot(datestart,
                                    dateend, 
                                    family, 
                                    path_experience_files, 
                                    pathwork,
                                    flag_criteria, 
                                    region,
                                    id_stn,
                                    channel)



       os.makedirs(f'{pathwork}/plots_{family}')
       t0 = time.time()
       if len(data_list_plot)==0:
          raise ValueError('You must specify best selectoion od id_stn and channel plot==0')

       if n_cpu==1: 
          print (f'in Serie plots = {len(data_list_plot)}')
          for  data_ in data_list_plot:  
              pikobs.cardio_plot(pathwork,
                                 datestart,
                                 dateend,
                                 flag_criteria,
                                 family,
                                 plot_title,
                                 plot_type, 
                                 data_['vcoord'],
                                 data_['id_stn'], 
                                 data_['varno'],
                                 region)
       else:
          print (f'Number of plot in paralle = {len(data_list_plot)}')
          with dask.distributed.Client(processes=True, threads_per_worker=1, 
                                           n_workers=n_cpu, 
                                           silence_logs=40) as client:
            delayed_funcs = [dask.delayed(pikobs.cardio_plot)(
                               pathwork,
                                 datestart,
                                 dateend,
                                 flag_criteria,
                                 family,
                                 plot_title,
                                 plot_type, 
                                 data_['vcoord'],
                                 data_['id_stn'], 
                                 data_['varno'],
                                 region)for data_ in data_list_plot]

            results = dask.compute(*delayed_funcs)
       tn= time.time()
       print ('Total time for plotting:',tn-t0 )  

 



def arg_call():
    import argparse
    import sys

    parser = argparse.ArgumentParser()
    parser.add_argument('--path_experience_files', default='undefined', type=str, help="Directory where input sqlite files are located")
    parser.add_argument('--experience_name', default='undefined', type=str, help="experience's name")
    parser.add_argument('--pathwork', default='undefined', type=str, help="Working directory")
    parser.add_argument('--datestart', default='undefined', type=str, help="Start date")
    parser.add_argument('--dateend', default='undefined', type=str, help="End date")
    parser.add_argument('--region', nargs="+", default='undefined', type=str, help="Region")
    parser.add_argument('--family', nargs="+", default='undefined', type=str, help="Family")
    parser.add_argument('--flags_criteria', default='undefined', type=str, help="Flags criteria")
    parser.add_argument('--id_stn',nargs="+", default='all', type=str, help="id_stn") 
    parser.add_argument('--channel',nargs="+",  default='all', type=str, help="channel") 
    parser.add_argument('--plot_type', default='wide', type=str, help="channel")
    parser.add_argument('--plot_title', default='plot', type=str, help="channel")
    parser.add_argument('--n_cpus', default=1, type=int, help="Number of cpus")

    args = parser.parse_args()
    print ( "Inputs in cardiogram calculation")
    print ("----------------------------------------")

    for arg in vars(args):
      
       print (f'--{arg} {getattr(args, arg)}')
    print ("----------------------------------------")
    # check if each argument is 'undefined'
    if args.path_experience_files == 'undefined':
        raise ValueError('You must specify --path_experience_files')
    if args.experience_name == 'undefined':      
        raise ValueError('You must specify -experience_name')
    if args.pathwork  == 'undefined':
        raise ValueError('You must specify --pathwork')
    if args.datestart == 'undefined':
        raise ValueError('You must specify --datestart')
    if args.dateend == 'undefined':
        raise ValueError('You must specify --dateend')
    if args.region == 'undefined':
        raise ValueError('You must specify --region')
    if args.family == 'undefined':
        raise ValueError('You must specify --family')
    if args.flags_criteria == 'undefined':
        raise ValueError('You must specify --flags_criteria')
   # if args.fonction == 'undefined':
   #     raise ValueError('You must specify --fonction')

    #Call your function with the arguments
    sys.exit(make_cardio (args.path_experience_files,
                          args.experience_name,
                          args.pathwork,
                          args.datestart,
                          args.dateend,
                          args.region,
                          args.family,
                          args.flags_criteria,
                          args.id_stn,
                          args.channel,
                          args.plot_type,
                          args.plot_title,
                          args.n_cpus))

