#this should be imported as a python module using 'import analysis'

import statistics
import math
import csv
import functools

class c_entities:

    c_names = []
    c_ids = []
    c_pos = []
    c_porperties = []
    c_logic = []

class nc_entities:

    c_names = []
    c_ids = []
    c_pos = []
    c_properties = []
    c_effects = []

    def debug(self):
        print ("nc_entities (non-controlable entities) has attributes names, ids, positions, properties, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of psoitions, 2d array of properties, and 2d array of effects.")
        return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_effects]

    def __init__(self, names, ids, pos, properties, effects):
        self.c_names = names
        self.c_ids = ids
        self.c_pos = pos
        self.c_properties = properties
        self.c_effects = effects
        return None

    def append(self, n_name, n_id, n_pos, n_property, n_effect):
        self.c_names.append(n_name)
        self.c_ids.append(n_id)
        self.c_pos.append(n_pos)
        self.c_properties.append(n_property)
        self.c_effects.append(n_effect)

    def edit(self, search, n_name, n_id, n_pos, n_property, n_effect):
        position = 0
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i
        if n_name != "null":
            self.c_names[position] = n_name

        if n_id != "null":
            self.c_ids[position] = n_id

        if n_pos != "null":
            self.c_pos[position] = n_pos

        if n_property != "null":
            self.c_properties[position] = n_property

        if n_effect != "null":
            self.c_effects[position] = n_effect

        return None

    def search(self, search):
        position = 0
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i

        return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_properties[position], self.c_effects[position]]        

class obstacles:

    c_names = []
    c_ids = []
    c_perim = []
    c_effects = []

    def debug(self):
        print("obstacles has atributes names, ids, positions, perimeters, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of position, 3d array of perimeters, 2d array of effects.")
        return [self.c_names, self.c_ids, self.c_perim, self.c_effects]

    def __init__(self, names, ids, perims, effects):
        self.c_names = names
        self.c_ids = ids
        self.c_perim = perims
        self.c_effects = effects
        return None

    def append(self, n_name, n_id, n_perim, n_effect):
        self.c_names.append(n_name)
        self.c_ids.append(n_id)
        self.c_perim.append(n_perim)
        self.c_effects.append(n_effect)
        return None

    def edit(self, search, n_name, n_id, n_perim, n_effect):
        position = 0
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i

        if n_name != "null":
            self.c_names[position] = n_name

        if n_id != "null":
            self.c_ids[position] = n_id

        if n_perim != "null":
            self.c_perim[position] = n_perim

        if n_effect != "null":
            self.c_effects[position] = n_effect
            
        return None

    def search(self, search):
        position = 0
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i

        return [self.c_names[position], self.c_ids[position], self.c_perim[position], self.c_effects[position]]

class objectives:
    
    c_names = []
    c_ids = []
    c_pos = []
    c_effects = []

    def debug(self):
        print("objectives has atributes names, ids, positions, and effects. __init__ takes self, 1d array of names, 1d array of ids, 2d array of position, 1d array of effects.")
        return [self.c_names, self.c_ids, self.c_pos, self.c_effects]
    
    def __init__(self, names, ids, pos, effects):
        self.c_names = names
        self.c_ids = ids
        self.c_pos = pos
        self.c_effects = effects
        return None
    
    def append(self, n_name, n_id, n_pos, n_effect):
        self.c_names.append(n_name)
        self.c_ids.append(n_id)
        self.c_pos.append(n_pos)
        self.c_effects.append(n_effect)
        return None
    
    def edit(self, search, n_name, n_id, n_pos, n_effect):
        position = 0
        print(self.c_ids)
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i

        if n_name != "null":
            self.c_names[position] = n_name

        if n_id != "null":
            self.c_ids[position] = n_id

        if n_pos != "null":
            self.c_pos[position] = n_pos

        if n_effect != "null":
            self.c_effects[position] = n_effect
            
        return None
    
    def search(self, search):
        position = 0
        for i in range(0, len(self.c_ids), 1):
            if self.c_ids[i] == search:
                position = i

        return [self.c_names[position], self.c_ids[position], self.c_pos[position], self.c_effects[position]]

def load_csv(filepath):
    with open(filepath, newline = '') as csvfile:
        file_array = list(csv.reader(csvfile))
    return file_array

def basic_stats(data, mode, arg): # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row

    if mode == 'debug':
        out = "basic_stats requires 3 args: data, mode, arg; where data is data to be analyzed, mode is an int from 0 - 2 depending on type of analysis (by column or by row) and is only applicable to 2d arrays (for 1d arrays use mode 1), and arg is row/column number for mode 1 or mode 2; function returns: [mean, median, mode, stdev, variance]"
        return out

    if mode == "1d" or mode == 0:

        data_t = []

        for i in range (0, len(data) - 1, 1):

            data_t.append(float(data[i]))
    
        mean = statistics.mean(data_t)
        median = statistics.median(data_t)
        try:
            mode = statistics.mode(data_t)
        except:
            mode = None
        stdev = statistics.stdev(data_t)
        variance = statistics.variance(data_t)
        
        out = [mean, median, mode, stdev, variance]

        return out
    
    elif mode == "column" or mode == 1:

        c_data = []
        c_data_sorted = []
        
        for i in data:
            c_data.append(float(i[arg]))
            
        mean = statistics.mean(c_data)
        median = statistics.median(c_data)
        try:
            mode = statistics.mode(c_data)
        except:
            mode = None
        stdev = statistics.stdev(c_data)
        variance = statistics.variance(c_data)
        
        out = [mean, median, mode, stdev, variance]

        return out

    elif mode == "row" or mode == 2:

        r_data = []

        for i in range(len(data[arg])):
            r_data.append(float(data[arg][i]))
        
        mean = statistics.mean(r_data)
        median = statistics.median(r_data)
        try:
            mode = statistics.mode(r_data)
        except:
            mode = None
        stdev = statistics.stdev(r_data)
        variance = statistics.variance(r_data)
        
        out = [mean, median, mode, stdev, variance]

        return out
    else:
        return ["mode_error", "mode_error"]
    
def z_score(point, mean, stdev):
    score = (point - mean)/stdev
    return score

def stdev_z_split(mean, stdev, delta, low_bound, high_bound):

    z_split = []

    i = low_bound

    while True:

        z_split.append(float((1 / (stdev * math.sqrt(2 * math.pi))) * math.e ** (-0.5 * (((i - mean) / stdev) ** 2))))

        i = i + delta

        if i > high_bound:

            break

    return z_split

def histo_analysis(hist_data): #note: depreciated

    if hist_data == 'debug':
        return['lower estimate (5%)', 'lower middle estimate (25%)', 'middle estimate (50%)', 'higher middle estimate (75%)', 'high estimate (95%)', 'standard deviation', 'note: this has been depreciated']
    
    derivative = []
    for i in range(0, len(hist_data) - 1, 1):
        derivative.append(float(hist_data[i+1]) - float(hist_data[i]))
        
    derivative_sorted = sorted(derivative, key=int)
    mean_derivative = basic_stats(derivative_sorted, "1d", 0)[0]
    stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]

    low_bound = mean_derivative + -1.645 * stdev_derivative
    lm_bound = mean_derivative + -0.674 * stdev_derivative
    mid_bound = mean_derivative * 0 * stdev_derivative
    hm_bound = mean_derivative + 0.674 * stdev_derivative
    high_bound = mean_derivative + 1.645 * stdev_derivative

    low_est = float(hist_data[-1:][0]) + low_bound
    lm_est = float(hist_data[-1:][0]) + lm_bound
    mid_est = float(hist_data[-1:][0]) + mid_bound
    hm_est = float(hist_data[-1:][0]) + hm_bound
    high_est = float(hist_data[-1:][0]) + high_bound

    return [low_est, lm_est, mid_est, hm_est, high_est, stdev_derivative]

def histo_analysis_2(hist_data, delta, low_bound, high_bound):

    if hist_data == 'debug':
        return ('returns list of predicted values based on historical data; input delta for delta step in z-score and lower and igher bounds in number for standard deviations')

    derivative = []

    for i in range(0, len(hist_data) - 1, 1):
        derivative.append(float(hist_data[i + 1]) - float(hist_data [i]))

    derivative_sorted = sorted(derivative, key=int)
    mean_derivative = basic_stats(derivative_sorted,"1d", 0)[0]
    stdev_derivative = basic_stats(derivative_sorted, "1d", 0)[3]

    predictions = []
    pred_change = 0

    i = low_bound

    while True:

        pred_change = mean_derivative + i * stdev_derivative

        predictions.append(float(hist_data[-1:][0]) + pred_change)

        i = i + delta

        if i > high_bound:

            break

    return predictions