#!/usr/bin/env python
# encoding: utf-8
"""
Glance config file to compare sfc emissivity output from Geocat to output from FWAIT


Created by Graeme Martin 20101119.
Copyright (c) 2010 University of Wisconsin SSEC. All rights reserved.
"""

import glance.filters as filters
import glance.data    as dataobj
import numpy          as np

Saved_Mask = None

# *** filters for loading different variables and making a mask to reuse

def load_alternate_file_and_variable (file_path, variable_name) :
    """
    load the data for a variable in a file
    """
    file_object = dataobj.FileInfo(file_path)
    data = np.array(file_object.file_object[variable_name])
    return data

def build_mask_by_differences (file_A_name, variable_A_name, file_B_name, variable_B_name, epsilon=0.0) :
    """
    make a mask of where the variables in the two files are too different
    """
    a_data = load_alternate_file_and_variable(file_A_name, variable_A_name)
    b_data = load_alternate_file_and_variable(file_B_name, variable_B_name)
    mask = np.abs(a_data - b_data) > epsilon
    return mask

def filter_on_other_variables_profile (data, replace_value,
                                       file_A_name, variable_A_name,
                                       file_B_name, variable_B_name,
                                       dim_size=101, axis=0,
                                       epsilon=0.0, do_cache=True) :
    """
    filter the data based on external files, replacing values in data that correspond to values that are too different
    between variable_A and variable_B with the replace_value
    if do_cache is true, reuse or save the mask
    note: if you are cacheing don't use this function with more than one set of external files/variables per config file
    """
    global Saved_Mask
    mask_to_use = Saved_Mask if do_cache else None
    if mask_to_use is None :
        mask_to_use = np.repeat([build_mask_by_differences(file_A_name, variable_A_name, file_B_name, variable_B_name, epsilon=epsilon)], dim_size, axis=axis)
    data[mask_to_use] = replace_value
    if do_cache :
        Saved_Mask = mask_to_use
    return data

# general settings to control how reports are created
settings = {}
settings['useThreadsToControlMemory'] = True
settings['shouldIncludeImages'] = True
settings['useSharedRangeForOriginal'] = True
settings['doFork'] = False

# info on the latitude and longitude variables that will be used
lat_lon_info = {}
lat_lon_info['longitude'] = 'imager_prof_retr_msg_Lon_reduced'  
lat_lon_info['latitude'] = 'imager_prof_retr_msg_Lat_reduced'  
lat_lon_info['longitude_alt_name_in_b'] = 'Longitude'  
lat_lon_info['latitude_alt_name_in_b'] = 'Latitude'  

lat_lon_info['lon_lat_epsilon'] = 0.0001

# filter works on (m x n) array, and profiles are (nprof x m x n), so need to apply filter to each
# level data[i,:,:] and aggregate output back into a 3D array
#numClrPixelsFilter = (lambda data, filterData: np.array([filters.filter_based_on_additional_data_set_min_max_bounds( \
#    data[i,:,:], filterData, missingValue=-999.0, minOkFilterValue=9, maxOkFilterValue=9) for i in range(data.shape[0]) ] ) )
numIterFilter3dReal = (lambda data: filter_on_other_variables_profile(data, -999.0, '/data/graemem/sounding_verification_20110427/data/geocatL2.Meteosat-8.2006230.000000_patch_test.nc', 'imager_prof_retr_msg_Num_Iteration', '/data/graemem/sounding_verification_20110427/data/MSG8_SEVIRI_2006213_0600_00_AWG_SOUNDINGS_patch_test.nc', 'NumIter', dim_size=101, axis=0) )


# per variable defaults
defaultValues = {
    'epsilon': None,
    'epsilon_failure_tolerance': None,     
    'nonfinite_data_tolerance': None,
#    'missing_value_alt_in_b': -999.0   # glance should be able to get from variable attributes
    }


# a list of all the variables to analyze
#
setOfVariables = {}

setOfVariables['msg temperature profile'] = {
    # this entry should correspond to the name in the data file
    'variable_name': 'imager_prof_retr_msg_Tprof',
    'alternate_name_in_B': 'TempProf',
    'data_filter_function_a': numIterFilter3dReal, 
    'data_filter_function_b': numIterFilter3dReal
    }

setOfVariables['msg water profile'] = {
    # this entry should correspond to the name in the data file
    'variable_name': 'imager_prof_retr_msg_Wprof',
    'alternate_name_in_B': 'RHProf',
    'data_filter_function_a': numIterFilter3dReal, 
    'data_filter_function_b': numIterFilter3dReal
    }