import numpy
import pandas as pd
from tools.definitions import  METADATA_FOLDER, OUTPUT_FOLDER, HELPER_TABLE_FOLDER, STIMULATION_METADATA_EXTENDED, STIMULATION_METADATA_FILTERED, \
    PROTOCOL_BLACKLIST
from tools.helper import get_filters
from tools.helper import get_pulse_times
from tools.helper import get_trace, get_times

path_to_extended_stimulus_file = OUTPUT_FOLDER+HELPER_TABLE_FOLDER+STIMULATION_METADATA_EXTENDED

path_to_filtered_stimulus_file = OUTPUT_FOLDER+HELPER_TABLE_FOLDER+STIMULATION_METADATA_FILTERED

print "# Filter stimulations suitable for analysis"
print ""

spiking_threshold = -10
minimum_width_of_a_spike_in_time_points = 100

stimulations = pd.read_csv(path_to_extended_stimulus_file, index_col="stimulation_id")

# get protocols
type_number_combinations = stimulations[["protocol_type", "pulse_number"]].drop_duplicates().values


def detect_faulty_stimulations(stimulation):
    is_correct = True
    times = get_times(stimulation)
    stimulus_trace = get_trace(stimulation, "Stimulus-Current-1")

    pulses = get_pulse_times(stimulation)
    expected_amplitude_difference = 2 * numpy.abs(stimulation["pulse_amplitude"])

    for pulse_idx, pulse in enumerate(pulses):
        in_pulse = pulse["start"] + (pulse["pulse_end"] - pulse["start"]) / 2.0
        after_pulse = pulse["pulse_end"] + (pulse["end"] - pulse["pulse_end"]) / 2.0
        actual_amplitude_difference = numpy.abs(numpy.ediff1d(stimulus_trace[times.searchsorted([in_pulse, after_pulse])]))[0]
        correct_pulse = numpy.abs(expected_amplitude_difference -
                                  actual_amplitude_difference) <= 0.01
        if not correct_pulse:
            print "Stimulation {:s}: Wrong amplitude in pulse {:d}: expected {:.1f}nA actual {:.1f}nA.".format(stimulation.name, pulse_idx, expected_amplitude_difference, actual_amplitude_difference)
            is_correct = False
            break
    return is_correct


blacklist_protocols = list(pd.read_csv(METADATA_FOLDER+PROTOCOL_BLACKLIST)["stimulation_id"].values)

print "## Manual blacklist".format(METADATA_FOLDER+PROTOCOL_BLACKLIST)
stimulations["filter_blacklist"] = [stim_id not in blacklist_protocols for stim_id in stimulations.index.values]
print "{:d} protocols are excluded, see {:s} for justifications".format(len(blacklist_protocols), METADATA_FOLDER+PROTOCOL_BLACKLIST)
print ""

print "## Automatic detection of problematic stimulations"
print ""
stimulations["filter_protocol"] = stimulations.apply(detect_faulty_stimulations, axis=1)
print ""
print "{:d} protocol with stimulation problems.".format(len(numpy.where(numpy.array(stimulations["filter_protocol"].values)==False)[0]))
print ""

print "## Automatic detection of protocols without conductance clamp"
stimulations["filter_zero_conductance"] = stimulations["g"] != 0
print "{:d} protocol without conductance clamp.".format(len(numpy.where(numpy.array(stimulations["filter_zero_conductance"].values)==False)[0]))
print ""

stimulations.to_csv(path_or_buf=path_to_filtered_stimulus_file)

print "{} / {} protocols suitable for analysis".format(len(stimulations[get_filters(stimulations)]), len(stimulations))