"""
=================================
Zonal Cross-Section Module (Zone)
=================================

This module provides specialized tools for generating 2D zonal cross-section plots. Unlike the ``scatter`` module which plots horizontal spatial maps (Longitude vs. Latitude), the ``zone`` module visualizes data vertically, plotting **Latitude (or Longitude) on the X-axis** against a **Vertical Coordinate on the Y-axis**.

This is essential for analyzing the vertical distribution of meteorological metrics, identifying atmospheric layers with high errors, and evaluating satellite channel performance across different latitudes.

Overview & Coordinate Adaptation
--------------------------------

A powerful feature of the ``zone`` module is its automatic Y-axis adaptation based on the instrument :doc:`family <families>` being processed:

* **Conventional & AMV Data:** For families like ``ua`` (Radiosondes) or ``sw`` (Satellite Winds), the Y-axis automatically represents **Pressure [hPa]** or **Height**.
* **Satellite Radiances:** For microwave/infrared sounders like ``atms_allsky`` or ``iasi``, the Y-axis automatically represents the **Sensor Channel**.

Key Features
------------

* **Vertical Slicing:** Visualizes data distribution from the surface to the upper atmosphere across geographic coordinates.
* **Variable (Varno) Targeting:** Allows processing of specific meteorological variables (e.g., extracting only Wind Speed ``11002`` or Temperature ``12001``).
* **Comparative Studies:** Enables side-by-side visual and statistical comparisons of vertical profiles between Control and Experience datasets.
* **Multi-Metric Analysis:** Computes metrics including Observation minus Prediction (omp), Observation minus Analysis (oma), raw observations (obs), density, and **the standard deviation (sigma) of OMP and OMA**.

Usage & Command-Line Parameters
-------------------------------

.. tip::
   **Interactive Session Setup:** Generating cross-sections across massive vertical and horizontal bins is computationally heavy. Always start an interactive session:
   
   .. 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

Below are the key parameters available when calling ``pikobs.zone.arg_call()``:

``--path_experience_files``
    Path to the input data directory for the experience (e.g., ``monitoring1/banco/postalt/``).
``--experience_name``
    Name or identifier of the experience run (e.g., ``Experience``).
``--pathwork``
    Working directory **in your own account/workspace** where output files and plots will be saved.
``--datestart`` / ``--dateend``
    Start and end dates for the analysis, strictly in ``YYYYMMDDHH`` format.
``--region``
    Geographical bounding box for the X-axis limit. See :doc:`Regions Configuration <region>`.
``--family``
    Observation instrument family to process. See :doc:`Families Configuration <families>`.
``--varnos``
    Specific variable number to plot (e.g., ``11002`` for Wind Speed). See the :doc:`Varnos Reference Table <families>`.
``--flags_criteria``
    Quality control bitmask filter. See :doc:`Flags Criteria <flags_criteria>`.
``--fonction``
    List of metrics to compute (e.g., ``omp oma sigma``). *Note: using* ``sigma`` *computes the standard deviation for the OMP and OMA departures.*
``--id_stn``
    Station ID selection (``all`` plots each station separately, ``join`` aggregates them into a single plot).
``--channel``
    Vertical coordinate or channel selection (``all`` plots individual channels, ``join`` aggregates them).
``--n_cpus``
    Number of CPU cores allocated for Dask parallel processing.

Single Experience Analysis
--------------------------

To generate a vertical cross-section for a single experience (e.g., evaluating the Standard Deviation of OMP for Satellite Winds / AMVs):

.. code-block:: bash

    python -c 'import pikobs; pikobs.zone.arg_call()' \\
         --path_experience_files  /home/dlo001/sites8/Data_pikobs/monitoring1/ \\
         --experience_name        Experience \\
         --pathwork               /home/dlo001/sites8/pikobszone \\
         --datestart              2026021000 \\
         --dateend                2026021500 \\
         --region                 Monde \\
         --family                 sw \\
         --varnos                 11002 \\
         --flags_criteria         assimilee \\
         --fonction               sigma \\
         --id_stn                 join \\
         --channel                all \\
         --n_cpus                 80

**What this command does:**
Processes :doc:`assimilated data <flags_criteria>` for the :doc:`sw (Satellite Winds) <families>` family globally (:doc:`Monde <region>`). It specifically targets Wind Speed (:doc:`varno 11002 <families>`) and generates a 2D plot showing the **standard deviation (sigma) of the Observation minus Prediction (OMP)** across Latitudes (X-axis) and Pressure levels (Y-axis).

**Output Example:**

.. image:: _static/zone_single.png
   :alt: Zone Plot Single Experience
   :align: center

*Notice how the Y-axis correctly adapts to represent Pressure [hPa] automatically, while the color scale highlights the standard deviation of OMP.*

Comparative Analysis (Control vs. Experience)
---------------------------------------------

To analyze differences in vertical profiles between a Control run and a new Experience run, pass both directory paths. Pikobs will automatically generate comparative difference plots:

.. code-block:: bash

    python -c 'import pikobs; pikobs.zone.arg_call()' \\
         --path_control_files     /home/dlo001/sites8/Data_pikobs/monitoring0/ \\
         --control_name           Control \\
         --path_experience_files  /home/dlo001/sites8/Data_pikobs/monitoring1/ \\
         --experience_name        Experience \\
         --pathwork               /home/dlo001/sites8/pikobszone_comparative \\
         --datestart              2026021000 \\
         --dateend                2026021500 \\
         --region                 Monde \\
         --family                 atms_allsky \\
         --flags_criteria         assimilee \\
         --fonction               omp oma \\
         --id_stn                 join \\
         --channel                join \\
         --n_cpus                 80

**What this command does:**
Generates a comparative 2D cross-section between a ``Control`` and an ``Experience`` run for the :doc:`atms_allsky <families>` family. Because ATMS is a satellite instrument, the Y-axis will automatically adapt to display **Channels**.

Comparative Visualizations
~~~~~~~~~~~~~~~~~~~~~~~~~

When running the comparative command, Pikobs generates visual difference maps to help identify which atmospheric layers or channels improved or degraded.

.. note::
   **Difference Calculation (Control - Experience) in %:** 
   All comparative metrics displayed in the cross-section difference maps are calculated strictly as **Control minus the Experience** and are displayed as a **percentage (%)**. 
   
   * A **positive value (warm colors)** indicates that the error/metric (or standard deviation) in the Control is *higher* than in the Experience (i.e., **the Experience is better/improved**).
   * A **negative value (cool colors)** indicates that the error/metric in the Control is *lower* than in the Experience (i.e., **the Experience is worse/degraded**).

.. image:: _static/zone_comp.png
   :alt: Zone Plot Comparative Experience
   :align: center


Support & Troubleshooting
-------------------------

If you encounter any issues, experience unexpected behavior, or have feature requests while using the ``zone`` module, please open an issue in the official repository:

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

*Please include your exact command, parameters, and error logs when reporting an issue.*
"""
#!/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 (
            Nrej INTEGER,
            Nacc INTEGER,
            Nprofile INTEGER,
            DATE INTEGER,
            lat FLOAT,
            lon FLOAT,
            boite INTEGER,
            id_stn TEXT,
            varno INTEGER,
            vcoord FLOAT,   -- INTEGER FLOAT canal
            sumx FLOAT,
            sumy FLOAT,
            sumz FLOAT,
            sumStat FLOAT,
            sumx2 FLOAT,
            sumy2 FLOAT,
            sumz2 FLOAT,
            sumStat2 FLOAT,
            n INTEGER,
            flag INTEGER
        ); """
    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(
            Nrej, Nacc, Nprofile, DATE, lat, lon, boite, id_stn, varno, vcoord, sumx, sumy, sumz, 
            sumStat, sumx2, sumy2, sumz2, sumStat2, n, flag
        )
        SELECT 
            Nrej, Nacc, Nprofile, DATE, lat, lon, boite, id_stn, varno, vcoord, sumx, sumy, sumz, 
            sumStat, sumx2, sumy2, sumz2, sumStat2, n, flag
        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( Nrej, Nacc, Nprofile, DATE, lat, lon, boite, id_stn, varno , vcoord, sumx, sumy, sumz, sumStat, sumx2,
                                         sumy2, sumz2, sumStat2, n, flag 
 

                )
                    SELECT Nrej, Nacc, Nprofile, DATE, lat, lon, boite, id_stn, varno , vcoord, sumx, sumy, sumz, sumStat, sumx2,
                                         sumy2, sumz2, sumStat2, n, flag 
 
 
                    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'

    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 (
            Nrej INTEGER,
            Nacc INTEGER,
            Nprofile INTEGER,
            DATE INTEGER,
            lat FLOAT,
            lon FLOAT,
            boite INTEGER,
            id_stn TEXT,
            varno INTEGER,
            vcoord FLOAT,   -- INTEGER FLOAT canal
            sumx FLOAT,
            sumy FLOAT,
            sumz FLOAT,
            sumStat FLOAT,
            sumx2 FLOAT,
            sumy2 FLOAT,
            sumz2 FLOAT,
            sumStat2 FLOAT,
            n INTEGER,
            flag INTEGER
        ); """)
    qr = f"""
    INSERT INTO moyenne (
        DATE, lat, lon, boite, varno, vcoord, 
        sumx, sumy, sumz, sumStat, 
        sumx2, sumy2, sumz2, sumStat2, 
        n, Nrej, Nacc, Nprofile, id_stn, flag
    )
    SELECT
        {date},
        {LAT},
        {LON},
        {STNID},
        VARNO,
        {group_channel}
        sum(omp),
        sum(oma),
        sum(obsvalue), 
        {bias_corr_sum}, -- sum(bias_corr)
        sum(omp * omp),
        sum(oma * oma),
        sum(obsvalue * obsvalue),
        {bias_corr_sq_sum}, -- sum(bias_corr * bias_corr)
        COUNT(*),
        sum(flag & 512=512),
        sum(flag & 4096-4094),
        count(distinct id_obs),
        {group_id_stn}
        flag
    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).

    Returns:
        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")

        # 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;")
        print (f"ATTACH DATABASE '{existing_db_filename}'")
        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 (
                Nrej INTEGER,
                Nacc INTEGER,
                Nprofile INTEGER,
                DATE INTEGER,
                lat FLOAT,
                lon FLOAT,
                boite INTEGER,
                id_stn TEXT,
                varno INTEGER,
                vcoord FLOAT,
                sumx FLOAT,
                sumy FLOAT,
                sumz FLOAT,
                sumStat FLOAT,
                sumx2 FLOAT,
                sumy2 FLOAT,
                sumz2 FLOAT,
                sumStat2 FLOAT,
                n INTEGER,
                flag INTEGER
            );
        """)

        # Compose query
        insert_query = f"""
        INSERT INTO moyenne (
            DATE, lat, lon, boite, varno, vcoord, 
            sumx, sumy, sumz, sumStat, 
            sumx2, sumy2, sumz2, sumStat2, 
            n, Nrej, Nacc, Nprofile, id_stn, flag
        )
        SELECT
            {date_str},
            {LAT},
            {LON},
            {STNID},
            VARNO,
            {chan_select}
            sum(omp),
            sum(oma),
            sum(obsvalue),
            {sum_bias_corr},
            sum(omp * omp),
            sum(oma * oma),
            sum(obsvalue * obsvalue),
            {sum_bias_corr2},
            COUNT(*),
            sum(flag & 512=512),
            sum(flag & 4096-4094),
            COUNT(DISTINCT id_obs),
            {id_stn_select}
            flag
        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}/scatter_{layers}_{name}_{region}_'
                            f'{date_start}_{date_end}_bx{box_size_x}_by{box_size_y}_'
                            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}/scatter_{layers}_{name}_{region}_{datestart1}_{dateend1}_bx{boxsizex}_by{boxsizey}_{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}/scatter_{layers}_{namein[0]}_{region}_{datestart1}_{dateend1}_bx{boxsizex}_by{boxsizey}_{flag_criteria}_{family}.db']
          nameset = [namein[0]]
      
          if len(namein) > 1:
              fileb = f'{pathwork}/{family}/scatter_{layers}_{namein[1]}_{region}_{datestart1}_{dateend1}_bx{boxsizex}_by{boxsizey}_{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}/scatter_{layers}_{namein[0]}_{region}_{date_start}_{date_end}_bx{box_size_x}_by{box_size_y}_{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}_bx{box_size_x}_by{box_size_y}_{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': vcoord,
                                                '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 )  
        data_list_plot = create_data_list_plot(datestart,
                                             dateend, 
                                             families, 
                                             names_in,
                                             pathwork,
                                             boxsizex,
                                             boxsizey, 
                                             fonctions, 
                                             flag_criteria, 
                                             regions,
                                             id_stn,
                                             channel,
                                             projs,
                                             intervales
                                            )

        t0 = time.time()
        #n_cpu=1
         
        if n_cpu==1:  
         print (f'in Serie plots = {len(data_list_plot)}')
         for  data_ in data_list_plot:  
           pikobs.zone_plot(mode, 
                               data_['region'],
                               data_['family'], 
                               data_['id_stn'], 
                               datestart,
                               dateend, 
                               Points, 
                               boxsizex,
                               boxsizey,
                               data_['proj'], 
                               pathwork,
                               flag_criteria, 
                               data_['function'],
                               data_['vcoord'],
                               data_['files_in'],
                               names_in, 
                               data_['varno'],
                               data_['interval'])
        else:
         print (f"in Paralle plots")
         time.sleep(4)
         with dask.distributed.Client(processes=True, threads_per_worker=1, 
                                          n_workers=n_cpu, 
                                          silence_logs=40) as client:
           delayed_funcs = [dask.delayed(pikobs.zone_plot)(mode, 
                                                              data_['region'],
                                                              data_['family'], 
                                                              data_['id_stn'],
                                                              datestart,
                                                              dateend, 
                                                              Points, 
                                                              boxsizex,
                                                              boxsizey,
                                                              data_['proj'], 
                                                              pathwork,
                                                              flag_criteria, 
                                                              data_['function'],
                                                              data_['vcoord'],
                                                              data_['files_in'],
                                                              names_in, data_['varno'],
                                                              data_['interval'])for data_ in data_list_plot]
   
           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='omp', type=str, help="Function") 
    parser.add_argument('--varnos', nargs="+", default='undefined', type=str, help="Function")
    parser.add_argument('--boxsizex', default=2, type=int, help="Box size in X direction")
    parser.add_argument('--boxsizey', default=2, 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))
