"""
    Observation Count Database Report Generator
    ===========================================

    Generates and displays statistical comparison tables, exporting them as PNG 
    images and compiling a comprehensive HTML web report for observation counts.

    This module connects to the internal SQLite databases generated by Pikobs 
    and extracts observation counts (total observations and profiles) to compare 
    two different datasets (Control vs. Evaluation). 

    Once the data is extracted and calculated, the module performs three main actions:
        1. **Console Display:** Prints highly formatted, color-coded comparison tables 
           directly to the terminal screen.
        2. **Image Generation:** Saves these statistical tables as high-resolution 
           PNG images for quick visualization and sharing.
        3. **Web Report:** Compiles all the overall and station-level data into a 
           standalone, styled HTML file for detailed review in any web browser.

    **Interactive Example:**
    You can view a live example of the generated HTML report here:
    `View Example HTML Report <_static/Full_Report_Monde.html>`_

    .. note::
       **Performance Optimization:** Families with an exceedingly high number of station 
       IDs (such as ``ai``, ``sf``,``csr``,``gp`` and ``ua``) are summarized at the overall level but are 
       automatically skipped during the detailed station-level breakdown to optimize 
       processing time and keep the final HTML report lightweight.

    Interactive Session Setup
    -------------------------
    .. tip::
       For resource-intensive computations (especially when evaluating multiple global families), 
       it is highly recommended to start an interactive session on the compute nodes before execution:
       
       .. code-block:: bash
       
           qsub -I -X -l select=4:ncpus=80:mpiprocs=80:ompthreads=1:mem=185gb -l place=scatter -l walltime=6:0:0

    Parameters Reference
    --------------------
    When invoking via the ``arg_call()`` command-line interface, the following parameters are accepted:

    * ``region``: Geographical region being analyzed. See :doc:`Regions Configuration <region>`.
    * ``family``: List of observation families to process (e.g., ``ai atms_allsky``). See :doc:`Families Configuration <families>`.
    * ``datestart`` / ``dateend``: Start and end dates of the period strictly in ``YYYYMMDDHH`` format.
    * ``pathwork``: Base directory path in your workspace where the output databases, PNGs, and HTML will be saved.
    * ``flags_criteria``: Bitmask filtering criteria applied during extraction (e.g., ``assimilee``). See :doc:`Flags Criteria <flags_criteria>`.
    * ``n_cpu``: Number of CPU cores allocated for parallel processing.

    CLI Usage Example
    -----------------
    Command line usage example via Bash for a comprehensive comparative evaluation:

    .. code-block:: bash

        #!/bin/bash
        
        which python
        
        python -c 'import pikobs; pikobs.obscountdb.arg_call()' \\
            --path_control_files     /home/dlo001/sites8/Data_pikobs/monitoring0/ \\
            --control_name           Control \\
            --path_experience_files  /home/dlo001/sites8/Data_pikobs/monitoring/ \\
            --experience_name        experience \\
            --pathwork               /home/dlo001/sites8/obscountdb \\
            --datestart              2026020100 \\
            --dateend                2026020600 \\
            --region                 Monde \\
            --family                 ai sc atms_allsky sf ch ssmis cris sw csr gp to_amsua_allsky iasi mwhs2 to_amsub_allsky ro ua \\
            --flags_criteria         assimilee \\
            --n_cpu                  40

    Support & Troubleshooting
    -------------------------
    If you encounter any issues, experience unexpected behavior, or have feature requests 
    while generating observation count reports, please open an issue in the official repository:

    👉 `Pikobs GitLab Issues <https://gitlab.science.gc.ca/dlo001/Pikobs/-/issues>`_

    *When opening an issue, please include your exact command, parameters, and the terminal error logs.*
"""

#/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
import warnings
# Suppress all Shapely deprecation warnings
warnings.filterwarnings("ignore", ".*ShapelyDeprecationWarning.*")

# Now, import the libraries you're using
import shapely
import cartopy
def create_table_if_not_exists(cursor):
    """
    Create the 'serie_cardio' table if it does not already exist.

    Args:
        cursor (sqlite3.Cursor): Database cursor for executing SQL queries.
    """
    query = """
        CREATE TABLE IF NOT EXISTS moyenne (
            Nobsdata,
            Nobsprofile,
            id_stn
        
        ); """
    cursor.execute(query)

def combine(pathfileout: str, filememory: str) -> None:
    """
    Combine multiple SQLite files into a single output file.

    This function copies records from the 'moyenne' table of a source SQLite file 
    (filememory) and inserts them into the 'moyenne' table of a destination/output SQLite file (pathfileout).
    
    Args:
        pathfileout (str): Path to the output SQLite file where combined data will be stored.
        filememory  (str): Path to the source SQLite file (can be memory or disk).
        
    Returns:
        None. The result is saved directly in the specified output file.
    """
    insert_sql = """
        INSERT INTO moyenne(Nobsdata, Nobsprofile,id_stn )
        SELECT 
            Nobsdata, Nobsprofile, id_stn
        FROM this_avg_db.moyenne
    """
    # Use 'with' statement to ensure connection is properly closed
    with sqlite3.connect(pathfileout, uri=True, isolation_level=None, timeout=9999) as conn:
        try:
            # Set performance-optimized PRAGMAs, provided your application allows for non-durable writes
            conn.execute("PRAGMA journal_mode=OFF;")
            conn.execute("PRAGMA synchronous=OFF;")

            # Ensure the table exists
            create_table_if_not_exists(conn)

            # Attach the source SQLite database
            conn.execute(f"ATTACH DATABASE '{filememory}' AS this_avg_db;")

            # Copy data
            conn.execute(insert_sql)
            conn.commit()

            # Detach the attached database
            conn.execute("DETACH DATABASE this_avg_db;")
        except sqlite3.Error as e:
            print(f"Error combining SQLite files: {e}")
            raise

def remove_combine(pathfileout, filememory):

    """ Creating a single sqlite file from multiple sqlite files
 
    args:
    ----------------

     pathfileout   : output averaging  sqlite file 
     filememory    : name of the sqlite file in memory to copy
   
    output:
    ----------------

    Nothing
     A sqlite file is made with all averaged volume scans
   
   """

    # write in output averaging  sqlite file
    conn_pathfileout = sqlite3.connect(pathfileout, uri=True, isolation_level=None, timeout=9999)

    conn_pathfileout.execute("""PRAGMA journal_mode=OFF;""")

    # off the journal
    create_table_if_not_exists( conn_pathfileout)
    conn_pathfileout.execute("""PRAGMA journal_mode=OFF;""")
    # SQLite continues without syncing as soon as it has handed data off to the operating system
    conn_pathfileout.execute("""PRAGMA synchronous=OFF;""")
    # Wait to read and write until the next process finishes
    # attach the sqlite file in memory for one PPI
    create_table_if_not_exists(conn_pathfileout)
    conn_pathfileout.execute(f"ATTACH DATABASE '{filememory}' AS this_avg_db;")
    order_sql = """ INSERT INTO moyenne( Nobsdata, Nobsprofile, id_stn
 

                )
                    SELECT , Nobsdata, Nobsprofile, id_stn
 
                    FROM  this_avg_db.moyenne"""
    conn_pathfileout.execute(order_sql) 

    conn_pathfileout.commit()
    conn_pathfileout.execute(""" DETACH DATABASE this_avg_db """)

def remove_create_and_populate_moyenne_table(family, 
                                      new_db_filename,
                                      existing_db_filename,
                                      region_seleccionada,
                                      selected_flags, 
                                      FONCTION,
                                      boxsizex,
                                      boxsizey,
                                      varnos,
                                      channel,
                                      id_stn,
                                      interval):
    import sqlite3
    import re
    import pikobs  
    pattern = r'(\d{10})'
    match = re.search(pattern, existing_db_filename)
    if match:
        date = match.group(1)
    else:
        raise ValueError("No se encontraron 10 dígitos consecutivos en el nombre del archivo.")
    filename=f"file::memory:?cache=shared"

    new_db_conn = sqlite3.connect(filename, uri=True, isolation_level=None, timeout=9999999)
    new_db_cursor = new_db_conn.cursor()

    FAM, VCOORD, VCOCRIT, STATB, element, VCOTYP = pikobs.family(family)
    if varnos:
        element = ",".join(varnos)

    if channel == 'join' and id_stn == 'all':
        group_channel = '"join" as Chan,'
        group_id_stn = 'id_stn as id_stn,'
        group_id_stn_vcoord = 'group by 2, 3, 4, 5, id_stn'
    elif channel == 'all' and id_stn == 'join':
        group_channel = f'{VCOORD} as Chan,'
        group_id_stn = '"join" as id_stn,'
        group_id_stn_vcoord = f'group by 2, 3, 4, 5, {VCOORD}'
    elif channel == 'all' and id_stn == 'all':
        group_channel = f'{VCOORD} as Chan,'
        group_id_stn = 'id_stn as id_stn,'
        group_id_stn_vcoord = f'group by 2, 3, 4, 5, id_stn, {VCOORD}'
    elif channel == 'join' and id_stn == 'join':
        group_channel = '"join" as Chan,'
        group_id_stn = '"join" as id_stn,'
        group_id_stn_vcoord = 'group by 2, 3, 4, 5, date'
    group_id_stn_vcoord = 'group by id_stn'
    LAT1, LAT2, LON1, LON2 = pikobs.regions(region_seleccionada)
    LATLONCRIT = pikobs.generate_latlon_criteria(LAT1, LAT2, LON1, LON2)
    flag_criteria = pikobs.flag_criteria(selected_flags)
    STNID = f"floor(360. / {boxsizex}) * floor(lat / {boxsizey}) + floor(MIN(179.99, lon) / {boxsizex})"
    LAT = f"floor(lat / {boxsizey}) * {boxsizey} + {boxsizey} / 2."
    LON = f"floor(MIN(179.99, lon) / {boxsizex}) * {boxsizex} + {boxsizex} / 2."

    new_db_cursor.execute("PRAGMA journal_mode = OFF;")
    new_db_cursor.execute("PRAGMA journal_mode = MEMORY;")
    new_db_cursor.execute("PRAGMA synchronous = OFF;")
    new_db_cursor.execute("PRAGMA foreign_keys = OFF;")
    new_db_cursor.execute(f"ATTACH DATABASE '{existing_db_filename}' AS db;")
    # Check if 'bias_corr' exists in the table schema
    bias_corr_exists = False
    new_db_cursor.execute("PRAGMA table_info('DATA')")
    columns = new_db_cursor.fetchall()
    interval_a=interval[0]
    interval_b=interval[1]

    if  interval_a==None and  interval_b==None:
          criteria_interval = ''
          layers='layer_all'
    else:
          criteria_interval = f'and vcoord >= {interval_a*100} AND vcoord <= {interval_b*100}'
          layers=f'layerfrom{interval_a}MPato{interval_b}MPa'


    for col in columns:
      if col[1] == 'bias_corr':  # Column name is at index 1
        bias_corr_exists = True
        break

    # Adjust the query based on the existence of 'bias_corr'
    bias_corr_sum = "sum(bias_corr)" if bias_corr_exists else "NULL"
    bias_corr_sq_sum = "sum(bias_corr * bias_corr)" if bias_corr_exists else "NULL"

    new_db_cursor.execute("""
        CREATE TABLE IF NOT EXISTS moyenne (
            Nobsdata INTEGER,
            Nobsprofile INTEGER,
            id_stn TEXT
        ); """)
    qr = f"""
    INSERT INTO moyenne (
        Nobsdata, Nobsprofile, id_stn   )
    SELECT
        count(DISTINCT(id_data))
        count(DISTINCT(id_obs)),
        id_stn
    FROM
        db.header
    NATURAL JOIN
        db.DATA
    WHERE
        varno IN ({element}) 
        AND obsvalue IS NOT NULL
        {flag_criteria}
        {LATLONCRIT}
        {VCOCRIT}
        {criteria_interval}
    {group_id_stn_vcoord};
    """
    new_db_cursor.execute(qr)

    # Finalizar
    new_db_conn.commit()
    #new_db_conn.close()
    try:
         combine(new_db_filename, filename)
    except sqlite3.Error as error:
         print(f"Error while creating a single sqlite file:  {os.path.basename(filename)}", error)

def create_and_populate_moyenne_table(
    family,
    new_db_filename: str,
    existing_db_filename: str,
    selected_region,
    selected_flags, 
    FUNCTION,
    boxsizex: float,
    boxsizey: float,
    varnos,
    channel: str,
    id_stn: str,
    interval
) -> None:
    """
    Creates an in-memory 'moyenne' table from a database, then combines it into a destination database.

    Args:
        family (Any): Family code or object for pikobs.family().
        new_db_filename (str): Destination SQLite filename.
        existing_db_filename (str): Source database filename.
        selected_region (Any): Selection for pikobs.regions().
        selected_flags (Any): Flags for selection.
        FUNCTION (Any): Function/operation to use (not used in current code!).
        boxsizex (float): Longitude box size.
        boxsizey (float): Latitude box size.
        varnos (List|str): Variable numbers as list or string.
        channel (str): Channel specification ('all', 'join', etc.).
        id_stn (str): Station ID ('all', 'join', etc.).
        interval (tuple): (min, max) value for an interval (can be None).

    Returs:
        None. Populates or appends to the target database file.
    """
    # Extract 10-digit date from filename
    date_match = re.search(r'(\d{10})', existing_db_filename)
    if not date_match:
        raise ValueError("No 10-digit sequence found in the source filename.")
    date_str = date_match.group(1)

    # Prepare in-memory SQLite file
    in_memory_db = f"file::memory:?cache=shared"

    
    with sqlite3.connect(in_memory_db, uri=True, isolation_level=None, timeout=9999999) as mem_conn:
        cursor = mem_conn.cursor()

        # Extract variables from pikobs
        FAM, VCOORD, VCOCRIT, STATB, element, VCOTYP = pikobs.family(family)
        element = ",".join(varnos) if varnos else element

        # Define SQL for GROUP BY and SELECT fields depending on channel/id_stn
        if channel == 'join' and id_stn == 'all':
            chan_select = '"join" as Chan,'
            id_stn_select = 'id_stn as id_stn,'
            group_by = 'GROUP BY 2, 3, 4, 5, id_stn'
        elif channel == 'all' and id_stn == 'join':
            chan_select = f'{VCOORD} as Chan,'
            id_stn_select = '"join" as id_stn,'
            group_by = f'GROUP BY 2, 3, 4, 5, {VCOORD}'
        elif channel == 'all' and id_stn == 'all':
            chan_select = f'{VCOORD} as Chan,'
            id_stn_select = 'id_stn as id_stn,'
            group_by = f'GROUP BY 2, 3, 4, 5, id_stn, {VCOORD}'
        elif channel == 'join' and id_stn == 'join':
            chan_select = '"join" as Chan,'
            id_stn_select = '"join" as id_stn,'
            group_by = 'GROUP BY 2, 3, 4, 5, date'
        else:
            raise ValueError("Unsupported combination of channel and id_stn")
        group_by = 'GROUP BY id_stn'

        # Region, latitude, longitude and flag criteria
        LAT1, LAT2, LON1, LON2 = pikobs.regions(selected_region)
        
        LATLONCRIT = pikobs.generate_latlon_criteria(LAT1, LAT2, LON1, LON2)

        flag_criteria = pikobs.flag_criteria(selected_flags)
        STNID = f"floor(360. / {boxsizex}) * floor(lat / {boxsizey}) + floor(MIN(179.99, lon) / {boxsizex})"
        LAT = f"floor(lat / {boxsizey}) * {boxsizey} + {boxsizey} / 2."
        LON = f"floor(MIN(179.99, lon) / {boxsizex}) * {boxsizex} + {boxsizex} / 2."

        # PRAGMA for performance
        cursor.execute("PRAGMA journal_mode = OFF;")
        cursor.execute("PRAGMA journal_mode = MEMORY;")
        cursor.execute("PRAGMA synchronous = OFF;")
        cursor.execute("PRAGMA foreign_keys = OFF;")
        cursor.execute(f"ATTACH DATABASE '{existing_db_filename}' AS db;")
        # Check for existence of bias_corr column
        cursor.execute("PRAGMA table_info('DATA')")
        columns = [col[1] for col in cursor.fetchall()]
        has_bias_corr = 'bias_corr' in columns

        # Interval criteria
        interval_a, interval_b = interval
        if interval_a is None and interval_b is None:
            interval_criteria = ''
            layer_label = 'layer_all'
        else:
            interval_criteria = f'AND vcoord >= {interval_a*100} AND vcoord <= {interval_b*100}'
            layer_label = f'layerfrom{interval_a}MPato{interval_b}MPa'

        sum_bias_corr = "sum(bias_corr)" if has_bias_corr else "NULL"
        sum_bias_corr2 = "sum(bias_corr * bias_corr)" if has_bias_corr else "NULL"

        # Create table moyenne if not exists
        cursor.execute("""
            CREATE TABLE IF NOT EXISTS moyenne (
                Nobsdata,
                Nobsprofile,
                id_stn
            );
        """)

        # Compose query
        insert_query = f"""
        INSERT INTO moyenne (
            Nobsdata, Nobsprofile, id_stn
        )
        SELECT
            count(DISTINCT(id_data)),
            count(DISTINCT(id_obs)),
            id_stn
        FROM
            db.header
        NATURAL JOIN
            db.DATA
        WHERE
            varno IN ({element}) 
            AND obsvalue IS NOT NULL
            {flag_criteria}
            {LATLONCRIT}
            {VCOCRIT}
            {interval_criteria}
        {group_by};
        """
        cursor.execute(insert_query)
        mem_conn.commit()

        # Now, merge in-memory results into the output file\
        try:
            combine(new_db_filename, in_memory_db)
        except sqlite3.Error as error:
            print(f"Error while creating a single sqlite file: {os.path.basename(in_memory_db)} -- {error}")
from datetime import datetime, timedelta
from typing import List, Dict, Any, Tuple

def create_data_list(
    date_start: str,
    date_end: str,
    families: List[str],
    input_paths: List[str],
    names: List[str],
    work_path: str,
    box_size_x: float,
    box_size_y: float,
    function: str,
    flag_criteria: str,
    regions: List[Any],       # adjust type if you have a region class
    varnos: List[str],
    intervals: List[Tuple[Any, Any]],
) -> List[Dict[str, Any]]:
    """
    Builds a list of dictionaries containing all configuration info 
    for data processing at 6-hour intervals, for multiple families, 
    regions, input files, and intervals.

    Args:
        date_start (str): Start date string in format 'YYYYMMDDHH'.
        date_end (str): End date string in format 'YYYYMMDDHH'.
        families (List[str]): List of family names/codes.
        input_paths (List[str]): List of input file path prefixes.
        names (List[str]): List of dataset names.
        work_path (str): Path where output databases should be created.
        box_size_x (float): Size of the box in longitude.
        box_size_y (float): Size of the box in latitude.
        function (str): Function being applied (name/purpose).
        flag_criteria (str): Flag criteria (as a code or query substring).
        regions (List[Any]): List of region specifications.
        varnos (List[str]): List of variable numbers/codes.
        intervals (List[Tuple[Any, Any]]): List of interval tuples (min, max).

    Returns:
        List[Dict[str, Any]]: List of configuration dictionaries.
    """
    data_list = []

    dt_start = datetime.strptime(date_start, '%Y%m%d%H')
    dt_end = datetime.strptime(date_end, '%Y%m%d%H')
    delta = timedelta(hours=6)
    current_date = dt_start

    while current_date <= dt_end:
        formatted_date = current_date.strftime('%Y%m%d%H')
        # Nested loops over all combinations
        for family in families:
            for region in regions:
                for name, input_path in zip(names, input_paths):
                    for interval in intervals:
                        interval_a, interval_b = interval
                        if interval_a is None and interval_b is None:
                            layers = 'layer_all'
                        else:
                            layers = f'layer_from{interval_a}MPato{interval_b}MPa'

                        filename = f'{formatted_date}_{family}'
                        filein = f'{input_path}/{filename}'
                        db_new = (
                            f'{work_path}/{family}/{name}_{region}_'
                            f'{date_start}_{date_end}_'
                            f'{flag_criteria}_{family}.db'
                        )

                        data_dict = {
                            'family': family,
                            'filein': filein,
                            'db_new': db_new,
                            'region': region,
                            'flag_criteria': flag_criteria,
                            'function': function,
                            'boxsizex': box_size_x,
                            'boxsizey': box_size_y,
                            'varnos': varnos,
                            'interval': interval
                        }
                        data_list.append(data_dict)
        current_date += delta

    return data_list   
def remove_create_data_list(datestart1, 
                     dateend1,
                     families,
                     pathins, 
                     names, 
                     pathwork,
                     boxsizex, 
                     boxsizey, 
                     fonction, 
                     flag_criteria, 
                     regions, 
                     varnos,
                     intervales):
       data_list = []

       # 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)
   
       # Iterate through the date range in 6-hour intervals
       while current_date <= dateend:
               for family in families:
                for region in regions:
                 for name, pathin in zip(names,pathins):
                  for interval in intervales:
               #   for pathin in pathins:
                    interval_a=interval[0]
                    interval_b=interval[1]
                    if  interval_a==None and  interval_b==None:
                       criteria_interval = ''
                       layers='layer_all'
                    else:
                       criteria_interval = f' and  {interval_a*100} <= vcoord <= {interval_b*100}'
                       layers=f'layer_from{interval_a}MPato{interval_b}MPa'
                    # 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}'
                    # Create a new dictionary and append it to the list
                    data_dict = {
                      'family': family,
                      'filein': f'{pathin}/{filename}',
                      'db_new': f'{pathwork}/{family}/{name}_{region}_{datestart1}_{dateend1}_{flag_criteria}_{family}.db',
                      'region': region,
                      'flag_criteria': flag_criteria,
                      'fonction': fonction,
                      'boxsizex': boxsizex,
                      'boxsizey': boxsizey,
                      'varnos':varnos,
                      'interval':interval
                    }
                    data_list.append(data_dict)
   
                    # Update the current_date in the loop by adding 6 hours
               current_date += delta
   
       return data_list
   
import sqlite3
import numpy as np

def get_id_stns(cursor, id_stn):
    """Obtiene la lista de id_stn desde la base de datos."""
    if id_stn == 'all':
        query = "SELECT DISTINCT id_stn FROM moyenne;"
        cursor.execute(query)
        return np.array([item[0] for item in cursor.fetchall()])
    return ['join']

def fetch_vcoords(cursor, criter):
    """Obtiene las coordenadas verticales y números de variable según el criterio dado."""
    query = f"SELECT DISTINCT 'all', varno FROM moyenne {criter} ORDER BY vcoord ASC;"
    cursor.execute(query)
    return cursor.fetchall()

def fetch_channels_varno(cursor):
    """Obtiene los números de variable de todos los canales."""
    query = "SELECT DISTINCT varno FROM moyenne ORDER BY vcoord ASC;"
    cursor.execute(query)
    return [item[0] for item in cursor.fetchall()]

def remove_create_data_list_plot(datestart1, dateend1, families, namein, pathwork, boxsizex, boxsizey,
                          fonctions, flag_criteria, regions, id_stn, channel, projs,intervales):
    data_list_plot = []
    for interval in intervales:
      for family in families:
       for region in regions: 
        for fonction in fonctions:
         for proj in projs:
          interval_a = interval[0]
          interval_b = interval[1]
          
          if  interval_a==None and  interval_b==None:
            criteria_interval = ''
            layers='layer_all'
          else:
            criteria_interval = f' and  {interval_a*100} <= vcoord <= {interval_b*100}'
            layers=f'layer_from{interval_a}MPato{interval_b}MPa'
          
          fileset = [f'{pathwork}/{family}/{namein[0]}_{region}_{datestart1}_{dateend1}_{flag_criteria}_{family}.db']
          nameset = [namein[0]]
      
          if len(namein) > 1:
              fileb = f'{pathwork}/{family}/{namein[1]}_{region}_{datestart1}_{dateend1}_{flag_criteria}_{family}.db'
              fileset.append(fileb)
              nameset.append(namein[1])
          with sqlite3.connect(fileset[0]) as conn:
              cursor = conn.cursor()
              id_stns = get_id_stns(cursor, id_stn)
      
              for idstn in id_stns:
                  criter = '' if id_stn == 'join' else f'WHERE id_stn = "{idstn}"'
                  
                  if channel == 'all':
      
                      vcoords = fetch_vcoords(cursor, criter)
                      for vcoord, varno in vcoords:
                          data_list_plot.append({
                              'id_stn': idstn,
                              'vcoord': vcoord,
                              'files_in': fileset,
                              'varno': varno,
                              'region': region,
                              'fonction':fonction,
                              'proj':proj,
                              'interval':interval,
                              'family':family

  
                          })
                  else:
                      channels_varno = fetch_channels_varno(cursor)
                      #print (channels_varno)
                      for  varno in channels_varno:
                     # if channels_varno:
                          data_list_plot.append({
                              'id_stn': idstn,
                              'vcoord': 'join',
                              'files_in': fileset,
                              'varno': varno,
                              'region': region,
                              'fonction':fonction,
                              'proj':proj ,
                              'interval':interval,
                              'family':family


  
                          })
    return data_list_plot
def create_data_list_plot(
    date_start: str,
    date_end: str,
    families: List[str],
    namein: List[str],
    work_path: str,
    box_size_x: float,
    box_size_y: float,
    functions: List[str],
    flag_criteria: str,
    regions: List[Any],
    id_stn: str,
    channel: str,
    projections: List[str],
    intervals: List[Tuple[Any, Any]],
) -> List[Dict[str, Any]]:
    """
    Generates a list of plotting dataset configurations for different intervals,
    families, regions, functions, channels, projections, and station IDs.

    Args:
        date_start (str): Starting date as 'YYYYMMDDHH'.
        date_end (str): Ending date as 'YYYYMMDDHH'.
        families (List[str]): List of family names/codes.
        namein (List[str]): Input dataset names (should be 1 or 2).
        work_path (str): Path prefix where database files are located.
        box_size_x (float): Box size in longitude.
        box_size_y (float): Box size in latitude.
        functions (List[str]): List of function names or codes.
        flag_criteria (str): Criteria as string for filtering.
        regions (List[Any]): List of region specifications.
        id_stn (str): 'all', 'join', or specific station ID.
        channel (str): 'all' or 'join'.
        projections (List[str]): List of projections to produce.
        intervals (List[Tuple[Any, Any]]): List of interval tuples (min, max).

    Returns:
        List[Dict[str, Any]]: List of plot dataset configuration dictionaries.
    """
    data_list_plot = []

    for interval in intervals:
        interval_a, interval_b = interval
        if interval_a is None and interval_b is None:
            layers = 'layer_all'
        else:
            layers = f'layer_from{interval_a}MPato{interval_b}MPa'

        for family in families:
            for region in regions:
                for function in functions:
                    for proj in projections:
                        # Build list of database files for this config
                        fileset = [
                            f'{work_path}/{family}/{namein[0]}_{region}_{date_start}_{date_end}_{flag_criteria}_{family}.db'
                        ]
                        nameset = [namein[0]]
                        if len(namein) > 1:
                            file_b = f'{work_path}/{family}/scatter_{layers}_{namein[1]}_{region}_{date_start}_{date_end}_{flag_criteria}_{family}.db'
                            fileset.append(file_b)
                            nameset.append(namein[1])

                        # Open the first file in the set and process station IDs and vcoords
                        try:
                            with sqlite3.connect(fileset[0]) as conn:
                                cursor = conn.cursor()
                                id_stns = get_id_stns(cursor, id_stn)    # Returns List[str]

                                for idstn in id_stns:
                                    where_clause = '' if id_stn == 'join' else f'WHERE id_stn = "{idstn}"'
                                    if channel == 'all':
                                       vcoords = fetch_vcoords(cursor, where_clause)  # Returns List[Tuple[Any, int]]
                                       for vcoord, varno in vcoords:
                                            data_list_plot.append({
                                                'id_stn': idstn,
                                                'vcoord': 'all',
                                                'files_in': fileset,
                                                'varno': varno,
                                                'region': region,
                                                'function': function,
                                                'proj': proj,
                                                'interval': interval,
                                                'family': family
                                            })
                                    else:  # channel == 'join' or other
                                      #  channels_varnos = fetch_channels_varno(cursor)  # Returns List[int]
                                        for varno in channels_varnos:
                                            data_list_plot.append({
                                                'id_stn': idstn,
                                                'vcoord': 'join',
                                                'files_in': fileset,
                                                'varno': varno,
                                                'region': region,
                                                'function': function,
                                                'proj': proj,
                                                'interval': interval,
                                                'family': family
                                            })
                        except sqlite3.Error as err:
                            print(f"Warning: Could not open or query {fileset[0]}: {err}")
    return data_list_plot
 
def make_scatter(files_in,
                 names_in,  
                 pathwork, 
                 datestart,
                 dateend,
                 regions, 
                 families, 
                 flag_criteria, 
                 fonctions, 
                 varnos,
                 boxsizex, 
                 boxsizey, 
                 projs, # Proj=='OrthoN'// Proj=='OrthoS'// Proj=='robinson' // Proj=='Europe' // Proj=='Canada' // Proj=='AmeriqueNord' // Proj=='Npolar' //  Proj=='Spolar' // Proj == 'reg'
                 mode,
                 Points,
                 id_stn,
                 channel,
                 n_cpu,
                 intervales):
   
        """
       Perform scatter plot generation based on input parameters.
   
       Args:

        files_in (list): List of input file paths.

        names_in (list): List of input file names.

        pathwork (str): Working directory.

        datestart (str): Start date in YYYYMMDDHH format.

        dateend (str): End date in YYYYMMDDHH format.

        region (str): Region parameter description.

        family (str): Family parameter description.

        flag_criteria (str): Flags criteria.

        fonction (str): Function parameter description.

        boxsizex (int): Box size in X direction.

        boxsizey (int): Box size in Y direction.

        Proj (str or list): Projection type ('cyl', 'OrthoN', 'OrthoS', etc.).

        mode (str): Mode parameter description.

        Points (str): Points parameter description.

        id_stn (str): id_stn parameter description.

        channel (str): Channel parameter description.

        n_cpu (int): Number of CPUs to use.
   
       Returns:
       
         None
        """
        for family in families:
              pikobs.delete_create_folder(pathwork, family)
          
        data_list = create_data_list(datestart,
                                       dateend, 
                                       families, 
                                       files_in,
                                       names_in,
                                       pathwork,
                                       boxsizex,
                                       boxsizey, 
                                       fonctions, 
                                       flag_criteria, 
                                       regions,
                                       varnos,
                                       intervales)
                                       
       # exit()                             
        import time
        import dask
        t0 = time.time()
      #  n_cpu=1
        if n_cpu==1:
          print (f'in Serie files: {len(data_list)} used in calculating statistics for {names_in}')
          for  data_ in data_list:  
               create_and_populate_moyenne_table(data_['family'], 
                                                 data_['db_new'], 
                                                 data_['filein'],
                                                 data_['region'],
                                                 data_['flag_criteria'],
                                                 data_['fonction'],
                                                 data_['boxsizex'],
                                                 data_['boxsizey'],
                                                 data_['varnos'],
                                                 channel,
                                                 id_stn,
                                                 data_['interval'])
               
       
       
       
       
        else:
           print (f'in Paralle number of files: {len(data_list)} used in calculating statistics for {len(data_list)}  {names_in} ')
           with dask.distributed.Client(processes=True, threads_per_worker=1, 
                                              n_workers=n_cpu, 
                                              silence_logs=40) as client:
               delayed_funcs = [dask.delayed(create_and_populate_moyenne_table)(data_['family'], 
                                                 data_['db_new'], 
                                                 data_['filein'],
                                                 data_['region'],
                                                 data_['flag_criteria'],
                                                 data_['function'],
                                                 data_['boxsizex'],
                                                 data_['boxsizey'],
                                                 data_['varnos'],
                                                 channel,
                                                 id_stn,
                                                 data_['interval'])for data_ in data_list]
              # print ('close0')
               results = dask.compute(*delayed_funcs)
               client.close()

        tn= time.time()
        print ('Total time for statistics:',tn-t0 )  

        t0 = time.time()
        if n_cpu==1:  
          print (f'in Serie plots = {len(regions)}')
          [pikobs.obscountdb_plot(region,
                                                              families, 
                                                              datestart,
                                                              dateend, 
                                                              pathwork,
                                                              names_in,
                                                              flag_criteria)
                                                              for region in regions] 
        else:
         print (f"in Paralle plotsss = {len(regions)}")
         with dask.distributed.Client(processes=True, threads_per_worker=1, 
                                          n_workers=n_cpu, 
                                          silence_logs=40) as client:
           delayed_funcs = [dask.delayed(pikobs.obscountdb_plot)(region,
                                                              families, 
                                                              datestart,
                                                              dateend, 
                                                              pathwork,
                                                              names_in,
                                                              flag_criteria)
                                                              for region in regions]
   
           results = dask.compute(*delayed_funcs)
           client.close()
        print ('Total time:',time.time() - t0 )  
        print (f'check: {pathwork}')
    
import argparse
import re
import argparse
import re

def parse_intervals(text):
    """Parses a string of intervals formatted as '[(,),(a,b)]' into a list of tuples."""

    # Expresión regular corregida para aceptar paréntesis vacíos y números
    pattern = r'\(\s*(\d*)\s*,\s*(\d*)\s*\)'  
    matches = re.findall(pattern, text)


    result = []
    for a, b in matches:
        val_a = int(a) if a.isdigit() else None
        val_b = int(b) if b.isdigit() else None
        result.append((val_a, val_b))

    return result

def arg_call():

    import argparse
    import sys
    parser = argparse.ArgumentParser()
    parser.add_argument('--path_control_files', default='undefined', type=str, help="Directory where input sqlite files are located")
    parser.add_argument('--control_name', default='undefined', type=str, help="Directory where input sqlite files are located")
    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="Directory where input sqlite files are located")
  

    
    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('--fonction', nargs="+", default='undefined', type=str, help="Function") 
    parser.add_argument('--varnos', nargs="+", default='undefined', type=str, help="Function")
    parser.add_argument('--boxsizex', default='undefined', type=int, help="Box size in X direction")
    parser.add_argument('--boxsizey', default='undefined', type=int, help="Box size in Y direction")
    parser.add_argument('--projection', nargs="+", default='cyl', type=str, help="Projection type (cyl, OrthoN, OrthoS, robinson, Europe, Canada, AmeriqueNord, Npolar, Spolar, reg)")
    parser.add_argument('--mode', default='SIGMA', type=str, help="Mode")
    parser.add_argument('--Points', default='OFF', type=str, help="Points")
    parser.add_argument('--id_stn', default='one_per_plot', type=str, help="id_stn") 
    parser.add_argument('--channel', default='one_per_plot', type=str, help="channel")
    parser.add_argument('--n_cpus', default=1, type=int, help="Number of CPUs")
    parser.add_argument(
    "--layer",
    type=str,
    nargs="?",
    default="[(,)]",  # Valor por defecto
    help="Lista de intervalos, ejemplo: '[(,),(700,900)]'"
)
    
    args = parser.parse_args()
    for arg in vars(args):
       print (f'--{arg} {getattr(args, arg)}')
    # Check if each argument is 'undefined'
    if args.path_control_files == 'undefined':
        files_in = [args.path_experience_files]
        names_in = [args.experience_name]
    else:    
        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')
        else:

            files_in = [args.path_control_files, args.path_experience_files]
            names_in = [args.control_name, args.experience_name] 
    intervals = parse_intervals(args.layer)
    args = parser.parse_args()


    if args.varnos == 'undefined':
        args.varnos = []
    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')
    if args.boxsizex == 'undefined':
        raise ValueError('You must specify --boxsizex')
    if args.boxsizey == 'undefined':
        raise ValueError('You must specify --boxsizey')


    # Comment
    # Proj='cyl' // Proj=='OrthoN'// Proj=='OrthoS'// Proj=='robinson' // Proj=='Europe' // Proj=='Canada' // Proj=='AmeriqueNord' // Proj=='Npolar' //  Proj=='Spolar' // Proj == 'reg'
  

    #print("in")
    # Call your function with the arguments
    sys.exit(make_scatter(files_in,
                          names_in,    
                          args.pathwork,
                          args.datestart,
                          args.dateend,
                          args.region,
                          args.family,
                          args.flags_criteria,
                          args.fonction,
                          args.varnos,
                          args.boxsizex,
                          args.boxsizey,
                          args.projection,
                          args.mode,
                          args.Points,
                          args.id_stn,
                          args.channel,
                          args.n_cpus,
                          intervals))
