diff --git a/data analysis/.ipynb_checkpoints/analysis-checkpoint.py b/data analysis/.ipynb_checkpoints/analysis-checkpoint.py new file mode 100644 index 00000000..479f79de --- /dev/null +++ b/data analysis/.ipynb_checkpoints/analysis-checkpoint.py @@ -0,0 +1,1107 @@ +#Titan Robotics Team 2022: Data Analysis Module +#Written by Arthur Lu & Jacob Levine +#Notes: +# this should be imported as a python module using 'import analysis' +# this should be included in the local directory or environment variable +# this module has not been optimized for multhreaded computing +#number of easter eggs: 2 +#setup: + +__version__ = "1.0.8.005" + +#changelog should be viewed using print(analysis.__changelog__) +__changelog__ = """changelog: +1.0.8.005: + - minor fixes +1.0.8.004: + - removed a few unused dependencies +1.0.8.003: + - added p_value function +1.0.8.002: + - updated __all__ correctly to contain changes made in v 1.0.8.000 and v 1.0.8.001 +1.0.8.001: + - refactors + - bugfixes +1.0.8.000: + - depreciated histo_analysis_old + - depreciated debug + - altered basic_analysis to take array data instead of filepath + - refactor + - optimization +1.0.7.002: + - bug fixes +1.0.7.001: + - bug fixes +1.0.7.000: + - added tanh_regression (logistical regression) + - bug fixes +1.0.6.005: + - added z_normalize function to normalize dataset + - bug fixes +1.0.6.004: + - bug fixes +1.0.6.003: + - bug fixes +1.0.6.002: + - bug fixes +1.0.6.001: + - corrected __all__ to contain all of the functions +1.0.6.000: + - added calc_overfit, which calculates two measures of overfit, error and performance + - added calculating overfit to optimize_regression +1.0.5.000: + - added optimize_regression function, which is a sample function to find the optimal regressions + - optimize_regression function filters out some overfit funtions (functions with r^2 = 1) + - planned addition: overfit detection in the optimize_regression function +1.0.4.002: + - added __changelog__ + - updated debug function with log and exponential regressions +1.0.4.001: + - added log regressions + - added exponential regressions + - added log_regression and exp_regression to __all__ +1.0.3.008: + - added debug function to further consolidate functions +1.0.3.007: + - added builtin benchmark function + - added builtin random (linear) data generation function + - added device initialization (_init_device) +1.0.3.006: + - reorganized the imports list to be in alphabetical order + - added search and regurgitate functions to c_entities, nc_entities, obstacles, objectives +1.0.3.005: + - major bug fixes + - updated historical analysis + - depreciated old historical analysis +1.0.3.004: + - added __version__, __author__, __all__ + - added polynomial regression + - added root mean squared function + - added r squared function +1.0.3.003: + - bug fixes + - added c_entities +1.0.3.002: + - bug fixes + - added nc_entities, obstacles, objectives + - consolidated statistics.py to analysis.py +1.0.3.001: + - compiled 1d, column, and row basic stats into basic stats function +1.0.3.000: + - added historical analysis function +1.0.2.xxx: + - added z score test +1.0.1.xxx: + - major bug fixes +1.0.0.xxx: + - added loading csv + - added 1d, column, row basic stats +""" + +__author__ = ( + "Arthur Lu , " + "Jacob Levine ," + ) + +__all__ = [ + '_init_device', + 'c_entities', + 'nc_entities', + 'obstacles', + 'objectives', + 'load_csv', + 'basic_stats', + 'z_score', + 'z_normalize', + 'stdev_z_split', + 'histo_analysis', + 'poly_regression', + 'log_regression', + 'exp_regression', + 'r_squared', + 'rms', + 'calc_overfit', + 'strip_data', + 'optimize_regression', + 'select_best_regression', + 'basic_analysis', + #all statistics functions left out due to integration in other functions + ] + +#now back to your regularly scheduled programming: + +#imports (now in alphabetical order! v 1.0.3.006): + +from bisect import bisect_left, bisect_right +import collections +import csv +from decimal import Decimal +import functools +from fractions import Fraction +from itertools import groupby +import math +import matplotlib +import numbers +import numpy as np +import pandas +import random +import scipy +from scipy.optimize import curve_fit +from scipy import stats +from sklearn import * +#import statistics <-- statistics.py functions have been integrated into analysis.py as of v 1.0.3.002 +import time +import torch + +class error(ValueError): + pass + +def _init_device (setting, arg): #initiates computation device for ANNs + if setting == "cuda": + try: + return torch.device(setting + ":" + str(arg) if torch.cuda.is_available() else "cpu") + except: + raise error("could not assign cuda or cpu") + elif setting == "cpu": + try: + return torch.device("cpu") + except: + raise error("could not assign cpu") + else: + raise error("specified device does not exist") + +class c_entities: + + c_names = [] + c_ids = [] + c_pos = [] + c_properties = [] + c_logic = [] + + def debug(self): + print("c_entities has attributes names, ids, positions, properties, and logic. __init__ takes self, 1d array of names, 1d array of ids, 2d array of positions, nd array of properties, and nd array of logic") + return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_logic] + + def __init__(self, names, ids, pos, properties, logic): + self.c_names = names + self.c_ids = ids + self.c_pos = pos + self.c_properties = properties + self.c_logic = logic + return None + + + def append(self, n_name, n_id, n_pos, n_property, n_logic): + 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_logic.append(n_logic) + return None + + def edit(self, search, n_name, n_id, n_pos, n_property, n_logic): + 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_logic != "null": + self.c_logic[position] = n_logic + + 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_logic[position]] + + def regurgitate(self): + return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.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 positions, 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) + + return None + + 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]] + + def regurgitate(self): + + return[self.c_names, self.c_ids, self.c_pos, self.c_properties, self.c_effects] + +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]] + + def regurgitate(self): + return[self.c_names, self.c_ids, self.c_perim, self.c_effects] + +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 regurgitate(self): + return[self.c_names, self.c_ids, self.c_pos, self.c_effects] + +def load_csv(filepath): + with open(filepath, newline = '') as csvfile: + file_array = list(csv.reader(csvfile)) + csvfile.close() + return file_array + +def basic_stats(data, method, 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 method == 'debug': + return "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]" + + if method == "1d" or method == 0: + + data_t = [] + + for i in range (0, len(data), 1): + data_t.append(float(data[i])) + + _mean = mean(data_t) + _median = median(data_t) + try: + _mode = mode(data_t) + except: + _mode = None + try: + _stdev = stdev(data_t) + except: + _stdev = None + try: + _variance = variance(data_t) + except: + _variance = None + + return _mean, _median, _mode, _stdev, _variance + + elif method == "column" or method == 1: + + c_data = [] + c_data_sorted = [] + + for i in data: + try: + c_data.append(float(i[arg])) + except: + pass + + _mean = mean(c_data) + _median = median(c_data) + try: + _mode = mode(c_data) + except: + _mode = None + try: + _stdev = stdev(c_data) + except: + _stdev = None + try: + _variance = variance(c_data) + except: + _variance = None + + return _mean, _median, _mode, _stdev, _variance + + elif method == "row" or method == 2: + + r_data = [] + + for i in range(len(data[arg])): + r_data.append(float(data[arg][i])) + + _mean = mean(r_data) + _median = median(r_data) + try: + _mode = mode(r_data) + except: + _mode = None + try: + _stdev = stdev(r_data) + except: + _stdev = None + try: + _variance = variance(r_data) + except: + _variance = None + + return _mean, _median, _mode, _stdev, _variance + + else: + raise error("method error") + +def z_score(point, mean, stdev): #returns z score with inputs of point, mean and standard deviation of spread + score = (point - mean)/stdev + return score + +def z_normalize(x, y, mode): #mode is either 'x' or 'y' or 'both' depending on the variable(s) to be normalized + + x_norm = [] + y_norm = [] + + mean = 0 + stdev = 0 + + if mode == 'x': + _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) + + for i in range (0, len(x), 1): + x_norm.append(z_score(x[i], _mean, _stdev)) + + return x_norm, y + + if mode == 'y': + _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) + + for i in range (0, len(y), 1): + y_norm.append(z_score(y[i], _mean, _stdev)) + + return x, y_norm + + if mode == 'both': + _mean, _median, _mode, _stdev, _variance = basic_stats(x, "1d", 0) + + for i in range (0, len(x), 1): + x_norm.append(z_score(x[i], _mean, _stdev)) + + _mean, _median, _mode, _stdev, _variance = basic_stats(y, "1d", 0) + + for i in range (0, len(y), 1): + y_norm.append(z_score(y[i], _mean, _stdev)) + + return x_norm, y_norm + + else: + + return error('method error') + +def stdev_z_split(mean, stdev, delta, low_bound, high_bound): #returns n-th percentile of spread given mean, standard deviation, lower z-score, and upper z-score + + 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, 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 higher bounds in number of standard deviations') + + derivative = [] + + for i in range(0, len(hist_data), 1): + try: + derivative.append(float(hist_data[i - 1]) - float(hist_data [i])) + except: + pass + + 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: + if i > high_bound: + break + + try: + pred_change = mean_derivative + i * stdev_derivative + except: + pred_change = mean_derivative + + predictions.append(float(hist_data[-1:][0]) + pred_change) + + i = i + delta + + return predictions + +def poly_regression(x, y, power): + + if x == "null": #if x is 'null', then x will be filled with integer points between 1 and the size of y + x = [] + + for i in range(len(y)): + print(i) + x.append(i+1) + + reg_eq = scipy.polyfit(x, y, deg = power) + eq_str = "" + + for i in range(0, len(reg_eq), 1): + if i < len(reg_eq)- 1: + eq_str = eq_str + str(reg_eq[i]) + "*(z**" + str(len(reg_eq) - i - 1) + ")+" + else: + eq_str = eq_str + str(reg_eq[i]) + "*(z**" + str(len(reg_eq) - i - 1) + ")" + + vals = [] + + for i in range(0, len(x), 1): + z = x[i] + + try: + exec("vals.append(" + eq_str + ")") + except: + pass + + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return [eq_str, _rms, r2_d2] + +def log_regression(x, y, base): + + x_fit = [] + + for i in range(len(x)): + try: + x_fit.append(np.log(x[i]) / np.log(base)) #change of base for logs + except: + pass + + reg_eq = np.polyfit(x_fit, y, 1) # y = reg_eq[0] * log(x, base) + reg_eq[1] + q_str = str(reg_eq[0]) + "* (np.log(z) / np.log(" + str(base) +"))+" + str(reg_eq[1]) + vals = [] + + for i in range(len(x)): + z = x[i] + + try: + exec("vals.append(" + eq_str + ")") + except: + pass + + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return eq_str, _rms, r2_d2 + +def exp_regression(x, y, base): + + y_fit = [] + + for i in range(len(y)): + try: + y_fit.append(np.log(y[i]) / np.log(base)) #change of base for logs + except: + pass + + reg_eq = np.polyfit(x, y_fit, 1, w=np.sqrt(y_fit)) # y = base ^ (reg_eq[0] * x) * base ^ (reg_eq[1]) + eq_str = "(" + str(base) + "**(" + str(reg_eq[0]) + "*z))*(" + str(base) + "**(" + str(reg_eq[1]) + "))" + vals = [] + + for i in range(len(x)): + z = x[i] + + try: + exec("vals.append(" + eq_str + ")") + except: + pass + + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return eq_str, _rms, r2_d2 + +def tanh_regression(x, y): + + def tanh (x, a, b, c, d): + + return a * np.tanh(b * (x - c)) + d + + reg_eq = np.float64(curve_fit(tanh, np.array(x), np.array(y))[0]).tolist() + eq_str = str(reg_eq[0]) + " * np.tanh(" + str(reg_eq[1]) + "*(z - " + str(reg_eq[2]) + ")) + " + str(reg_eq[3]) + vals = [] + + for i in range(len(x)): + z = x[i] + try: + exec("vals.append(" + eq_str + ")") + except: + pass + + _rms = rms(vals, y) + r2_d2 = r_squared(vals, y) + + return eq_str, _rms, r2_d2 + +def r_squared(predictions, targets): # assumes equal size inputs + + return metrics.r2_score(np.array(targets), np.array(predictions)) + +def rms(predictions, targets): # assumes equal size inputs + + _sum = 0 + + for i in range(0, len(targets), 1): + _sum = (targets[i] - predictions[i]) ** 2 + + return float(math.sqrt(_sum/len(targets))) + +def calc_overfit(equation, rms_train, r2_train, x_test, y_test): + + #performance overfit = performance(train) - performance(test) where performance is r^2 + #error overfit = error(train) - error(test) where error is rms; biased towards smaller values + + vals = [] + + for i in range(0, len(x_test), 1): + + z = x_test[i] + + exec("vals.append(" + equation + ")") + + r2_test = r_squared(vals, y_test) + rms_test = rms(vals, y_test) + + return r2_train - r2_test + +def strip_data(data, mode): + + if mode == "adam": #x is the row number, y are the data + pass + + if mode == "eve": #x are the data, y is the column number + pass + + else: + raise error("mode error") + +def optimize_regression(x, y, _range, resolution):#_range in poly regression is the range of powers tried, and in log/exp it is the inverse of the stepsize taken from -1000 to 1000 +#usage not: for demonstration purpose only, performance is shit + if type(resolution) != int: + raise error("resolution must be int") + + x_train = x + y_train = [] + + for i in range(len(y)): + y_train.append(float(y[i])) + + x_test = [] + y_test = [] + + for i in range (0, math.floor(len(x) * 0.5), 1): + index = random.randint(0, len(x) - 1) + + x_test.append(x[index]) + y_test.append(float(y[index])) + + x_train.pop(index) + y_train.pop(index) + + #print(x_train, x_test) + #print(y_train, y_test) + + eqs = [] + rmss = [] + r2s = [] + + for i in range (0, _range + 1, 1): + try: + x, y, z = poly_regression(x_train, y_train, i) + eqs.append(x) + rmss.append(y) + r2s.append(z) + except: + pass + + for i in range (1, 100 * resolution + 1): + try: + x, y, z = exp_regression(x_train, y_train, float(i / resolution)) + eqs.append(x) + rmss.append(y) + r2s.append(z) + except: + pass + + for i in range (1, 100 * resolution + 1): + try: + x, y, z = log_regression(x_train, y_train, float(i / resolution)) + eqs.append(x) + rmss.append(y) + r2s.append(z) + except: + pass + + try: + x, y, z = tanh_regression(x_train, y_train) + + eqs.append(x) + rmss.append(y) + r2s.append(z) + except: + pass + + for i in range (0, len(eqs), 1): #marks all equations where r2 = 1 as they 95% of the time overfit the data + if r2s[i] == 1: + eqs[i] = "" + rmss[i] = "" + r2s[i] = "" + + while True: #removes all equations marked for removal + try: + eqs.remove('') + rmss.remove('') + r2s.remove('') + except: + break + + overfit = [] + + for i in range (0, len(eqs), 1): + + overfit.append(calc_overfit(eqs[i], rmss[i], r2s[i], x_test, y_test)) + + return eqs, rmss, r2s, overfit + +def select_best_regression(eqs, rmss, r2s, overfit, selector): + + b_eq = "" + b_rms = 0 + b_r2 = 0 + b_overfit = 0 + + ind = 0 + + if selector == "min_overfit": + + ind = np.argmin(overfit) + + b_eq = eqs[ind] + b_rms = rmss[ind] + b_r2 = r2s[ind] + b_overfit = overfit[ind] + + if selector == "max_r2s": + + ind = np.argmax(r2s) + b_eq = eqs[ind] + b_rms = rmss[ind] + b_r2 = r2s[ind] + b_overfit = overfit[ind] + + return b_eq, b_rms, b_r2, b_overfit + +def p_value(x, y): #takes 2 1d arrays + + return stats.ttest_ind(x, y)[1] + +def basic_analysis(data): #assumes that rows are the independent variable and columns are the dependant. also assumes that time flows from lowest column to highest column. + + row = len(data) + column = [] + + for i in range(0, row, 1): + column.append(len(data[i])) + + column_max = max(column) + row_b_stats = [] + row_histo = [] + + for i in range(0, row, 1): + row_b_stats.append(basic_stats(data, "row", i)) + row_histo.append(histo_analysis(data[i], 0.67449, -0.67449, 0.67449)) + + column_b_stats = [] + + for i in range(0, column_max, 1): + column_b_stats.append(basic_stats(data, "column", i)) + + return[row_b_stats, column_b_stats, row_histo] + + +def benchmark(x, y): + + start_g = time.time() + generate_data("data/data.csv", x, y, -10, 10) + end_g = time.time() + + start_a = time.time() + basic_analysis("data/data.csv") + end_a = time.time() + + return [(end_g - start_g), (end_a - start_a)] + +def generate_data(filename, x, y, low, high): + + file = open(filename, "w") + + for i in range (0, y, 1): + temp = "" + + for j in range (0, x - 1, 1): + temp = str(random.uniform(low, high)) + "," + temp + + temp = temp + str(random.uniform(low, high)) + file.write(temp + "\n") + +class StatisticsError(ValueError): + pass + +def _sum(data, start=0): + count = 0 + n, d = _exact_ratio(start) + partials = {d: n} + partials_get = partials.get + T = _coerce(int, type(start)) + for typ, values in groupby(data, type): + T = _coerce(T, typ) # or raise TypeError + for n,d in map(_exact_ratio, values): + count += 1 + partials[d] = partials_get(d, 0) + n + if None in partials: + + total = partials[None] + assert not _isfinite(total) + else: + + total = sum(Fraction(n, d) for d, n in sorted(partials.items())) + return (T, total, count) + +def _isfinite(x): + try: + return x.is_finite() # Likely a Decimal. + except AttributeError: + return math.isfinite(x) # Coerces to float first. + +def _coerce(T, S): + + assert T is not bool, "initial type T is bool" + + if T is S: return T + + if S is int or S is bool: return T + if T is int: return S + + if issubclass(S, T): return S + if issubclass(T, S): return T + + if issubclass(T, int): return S + if issubclass(S, int): return T + + if issubclass(T, Fraction) and issubclass(S, float): + return S + if issubclass(T, float) and issubclass(S, Fraction): + return T + + msg = "don't know how to coerce %s and %s" + raise TypeError(msg % (T.__name__, S.__name__)) + +def _exact_ratio(x): + + try: + + if type(x) is float or type(x) is Decimal: + return x.as_integer_ratio() + try: + + return (x.numerator, x.denominator) + except AttributeError: + try: + + return x.as_integer_ratio() + except AttributeError: + + pass + except (OverflowError, ValueError): + + assert not _isfinite(x) + return (x, None) + msg = "can't convert type '{}' to numerator/denominator" + raise TypeError(msg.format(type(x).__name__)) + +def _convert(value, T): + + if type(value) is T: + + return value + if issubclass(T, int) and value.denominator != 1: + T = float + try: + + return T(value) + except TypeError: + if issubclass(T, Decimal): + return T(value.numerator)/T(value.denominator) + else: + raise + +def _counts(data): + + table = collections.Counter(iter(data)).most_common() + if not table: + return table + + maxfreq = table[0][1] + for i in range(1, len(table)): + if table[i][1] != maxfreq: + table = table[:i] + break + return table + + +def _find_lteq(a, x): + + i = bisect_left(a, x) + if i != len(a) and a[i] == x: + return i + raise ValueError + + +def _find_rteq(a, l, x): + + i = bisect_right(a, x, lo=l) + if i != (len(a)+1) and a[i-1] == x: + return i-1 + raise ValueError + + +def _fail_neg(values, errmsg='negative value'): + + for x in values: + if x < 0: + raise StatisticsError(errmsg) + yield x + +def mean(data): + + if iter(data) is data: + data = list(data) + n = len(data) + if n < 1: + raise StatisticsError('mean requires at least one data point') + T, total, count = _sum(data) + assert count == n + return _convert(total/n, T) + +def median(data): + + data = sorted(data) + n = len(data) + if n == 0: + raise StatisticsError("no median for empty data") + if n%2 == 1: + return data[n//2] + else: + i = n//2 + return (data[i - 1] + data[i])/2 + +def mode(data): + + table = _counts(data) + if len(table) == 1: + return table[0][0] + elif table: + raise StatisticsError( + 'no unique mode; found %d equally common values' % len(table) + ) + else: + raise StatisticsError('no mode for empty data') + +def _ss(data, c=None): + + if c is None: + c = mean(data) + T, total, count = _sum((x-c)**2 for x in data) + + U, total2, count2 = _sum((x-c) for x in data) + assert T == U and count == count2 + total -= total2**2/len(data) + assert not total < 0, 'negative sum of square deviations: %f' % total + return (T, total) + +def variance(data, xbar=None): + + if iter(data) is data: + data = list(data) + n = len(data) + if n < 2: + raise StatisticsError('variance requires at least two data points') + T, ss = _ss(data, xbar) + return _convert(ss/(n-1), T) + +def stdev(data, xbar=None): + + var = variance(data, xbar) + try: + return var.sqrt() + except AttributeError: + return math.sqrt(var) \ No newline at end of file diff --git a/data analysis/__pycache__/analysis.cpython-36.pyc b/data analysis/__pycache__/analysis.cpython-36.pyc new file mode 100644 index 00000000..bb37e549 Binary files /dev/null and b/data analysis/__pycache__/analysis.cpython-36.pyc differ diff --git a/website/public/profile/index.html b/website/public/profile/index.html index b61e34ac..6ca7824f 100644 --- a/website/public/profile/index.html +++ b/website/public/profile/index.html @@ -17,8 +17,10 @@ × Profile Team - Scout Matches - Torunament Stats +

Scout Matches

+ Sign Up For Matches + Submit Scouting Reports + Tournament Stats
diff --git a/website/public/scout/index.html b/website/public/scout/index.html deleted file mode 100644 index 8db9d818..00000000 --- a/website/public/scout/index.html +++ /dev/null @@ -1,164 +0,0 @@ - - - - - - - <meta charset="utf-8"> - <title>titanscout - - - - - - - - - - -
- - - -

TitanScout- Scout Matches

-

Loading...

-
- Scouting For: -
-

Submit a Report:

-
- Match Number: - Team Scouted: -
-

General:

- Speed: -
- Team Contribution: -
-
- Endgame Size: -
-
- HAB Start: - -
- Strategy: -
-
-

Sandstorm:

-
- Sandstorm Cross Bonus: -
-
- Strongest Object: -
-
- Was Functional: - -
-
- Fill Choice: -
-

Tele-Op:

-
- Strongest Object: -
-
- Fill Choice: -
-
- Cargo Ship Success Rate: -
-
- Low Rocket Success Rate: -
-
- High Rocket Success Rate: -
- -

Endgame:

-
- HAB Climb: -
- -
- - - diff --git a/website/public/scout/rpts/index.html b/website/public/scout/rpts/index.html new file mode 100644 index 00000000..b00a0709 --- /dev/null +++ b/website/public/scout/rpts/index.html @@ -0,0 +1,45 @@ + + + + + + + <meta charset="utf-8"> + <title>titanscout + + + + + + + + + + +
+ + + +

TitanScout- Scout Matches

+

Loading...

+
+ Scouting For: +
+

Sign Up For Matches

+

Submit a Report:

+
+ + +
+
+ + + diff --git a/website/public/scout/scripts.js b/website/public/scout/rpts/scripts.js similarity index 99% rename from website/public/scout/scripts.js rename to website/public/scout/rpts/scripts.js index cf43647e..fd623b07 100644 --- a/website/public/scout/scripts.js +++ b/website/public/scout/rpts/scripts.js @@ -36,7 +36,7 @@ window.onload = function() { document.getElementById('status').innerHTML = "You are signed in."; } } else { - window.location.replace('../'); + window.location.replace('../../'); } teamAssoc = firebase.firestore().collection('UserAssociations').doc(user.uid); teamAssoc.get().then(function(doc) { diff --git a/website/public/scout/signUps/index.html b/website/public/scout/signUps/index.html new file mode 100644 index 00000000..8e75ed30 --- /dev/null +++ b/website/public/scout/signUps/index.html @@ -0,0 +1,53 @@ + + + + + + + <meta charset="utf-8"> + <title>titanscout + + + + + + + + + + +
+ + + +

TitanScout- Scout Matches

+

Loading...

+
+ Scouting For: +
+

Sign Up For Matches

+ + + + + + + + + + + +
Match NumberSeriesFar BlueMid BlueNear BlueFar RedMid RedNear Red
+
+
+ + + diff --git a/website/public/scout/signUps/scripts.js b/website/public/scout/signUps/scripts.js new file mode 100644 index 00000000..5dfcd2a6 --- /dev/null +++ b/website/public/scout/signUps/scripts.js @@ -0,0 +1,192 @@ +/* Set the width of the side navigation to 250px and the left margin of the page content to 250px and add a black background color to body */ +function openNav() { + document.getElementById("mySidenav").style.width = "250px"; + document.getElementById("main").style.marginLeft = "250px"; + document.body.style.backgroundColor = "rgba(0,0,0,0.4)"; +} + +/* Set the width of the side navigation to 0 and the left margin of the page content to 0, and the background color of body to white */ +function closeNav() { + document.getElementById("mySidenav").style.width = "0"; + document.getElementById("main").style.marginLeft = "0"; + document.body.style.backgroundColor = "white"; +} + +window.onload = function() { + document.getElementById('sideload').style.display = 'block'; + var config = { + apiKey: "(insert the TitanScout Api Key Here)", + authDomain: "titanscoutandroid.firebaseapp.com", + databaseURL: "https://titanscoutandroid.firebaseio.com", + projectId: "titanscoutandroid", + storageBucket: "titanscoutandroid.appspot.com", + messagingSenderId: "1097635313476" + }; + //eventually find a less-jank way to do this tho + firebase.initializeApp(config); + firebase.auth().onAuthStateChanged(function(user) { + if (user != null) { + if (user.displayName != null) { + document.getElementById('status').innerHTML = "You are signed in as: " + user.displayName; + } else if (user.email != null) { + document.getElementById('status').innerHTML = "You are signed in as: " + user.email; + } else if (user.phoneNumber != null) { + document.getElementById('status').innerHTML = "You are signed in as: " + user.phoneNumber; + } else { + document.getElementById('status').innerHTML = "You are signed in."; + } + } else { + window.location.replace('../../'); + } + teamAssoc = firebase.firestore().collection('UserAssociations').doc(user.uid); + teamAssoc.get().then(function(doc) { + if (doc.exists) { + list = doc.data() + teamNums = Object.keys(list) + document.getElementById('tns').innerHTML = "" + for (var i = 0; i < teamNums.length; i++) { + document.getElementById('tns').innerHTML += "" + } + } else {} + }).then(function() { + changeTeam(document.getElementById('tns').value) + }) + }); +} + +function changeTeam(teamNum) { + document.getElementById("matchTable") = ` + Match Number + Series + Far Blue + Mid Blue + Near Blue + Far Red + Mid Red + Near Red + `; + ti = firebase.firestore().collection('teamData').doc("team-" + teamNum); + currentComp = null; + ti.get().then(function(doc) { + if (doc.exists) { + info = doc.data(); + currentComp = info['currentCompetition']; + } else { + alert("Something's wrong with firebase."); + throw ("Something's wrong with firebase."); + } + }).then(function() { + cci = firebase.firestore().collection('matchSignupsTeam').doc("team-" + teamNum).collection('competitions').doc(currentComp); + cci.get().then(function(doc) { + if (doc.exists) { + compInfo = cci.get(); + matches = Object.keys(compInfo); + matches.sort(); + var nr = [], + mr = [], + fr = [], + nb = [], + mb = [], + fb = []; + for (var i = 0; i < matches.length; i++) { + mi = compInfo["match-" + (i + 1).toString()] + //sets up the table lists. i really hope it doesn't break. + for (var j = 0; j < 2; i++) { + if (mi['far-blue']['series-' + (j + 1).toString()] != null) { + fb.push(mi['far-blue']['series-' + (j + 1).toString()]); + } else { + fb.push("open"); + } + if (mi['mid-blue']['series-' + (j + 1).toString()] != null) { + mb.push(mi['mid-blue']['series-' + (j + 1).toString()]); + } else { + mb.push("open"); + } + if (mi['near-blue']['series-' + (j + 1).toString()] != null) { + nb.push(mi['near-blue']['series-' + (j + 1).toString()]); + } else { + nb.push("open"); + } + if (mi['far-red']['series-' + (j + 1).toString()] != null) { + fr.push(mi['far-red']['series-' + (j + 1).toString()]); + } else { + fr.push("open"); + } + if (mi['mid-red']['series-' + (j + 1).toString()] != null) { + mr.push(mi['mid-red']['series-' + (j + 1).toString()]); + } else { + mr.push("open"); + } + if (mi['near-red']['series-' + (j + 1).toString()] != null) { + nr.push(mi['near-red']['series-' + (j + 1).toString()]); + } else { + nr.push("open") + } + } + var outstr = ""; + outstr += "Quals " + (i + 1).toString() + ""; + outstr += "Series 1"; + outstr += "" + fb[0] + ""; + outstr += "" + mb[0] + ""; + outstr += "" + nb[0] + ""; + outstr += "" + fr[0] + ""; + outstr += "" + mr[0] + ""; + outstr += "" + nr[0] + ""; + outstr += "" + for (var k = 1; k < 2; i++) { + outstr += ""; + outstr += "Series " + (k + 1).toString() + ""; + outstr += "" + fb[k] + ""; + outstr += "" + mb[k] + ""; + outstr += "" + nb[k] + ""; + outstr += "" + fr[k] + ""; + outstr += "" + mr[k] + ""; + outstr += "" + nr[k] + ""; + outstr += "" + } + document.getElementById(matchTable).innerHTML += outstr; + } + } + }); + }); +} +function addMatch(matchNum,seriesNum,position) { + var user = firebase.auth().currentUser; + var name="anon" + if (user.displayName != null) { + name = user.displayName; + } else if (user.email != null) { + name = user.email; + } else if (user.phoneNumber != null) { + name= user.phoneNumber; + ti = firebase.firestore().collection('teamData').doc("team-" + teamNum); + currentComp = null; + ti.get().then(function(doc) { + if (doc.exists) { + info = doc.data(); + currentComp = info['currentCompetition']; + } else { + alert("Something's wrong with firebase."); + throw ("Something's wrong with firebase."); + } + }).then(function() { + cci = firebase.firestore().collection('matchSignupsTeam').doc("team-" + teamNum).collection('competitions').doc(currentComp); + cci.get().then(function (doc) { + if (doc.exists) { + info=doc.data(); + match=info["match-"+matchNum.toString()]; + pos=match[position]; + occ=pos["series-"+seriesNum.toString()]; + if (occ == null) { + info["match-"+matchNum.toString()][position]["series-"+seriesNum.toString()]=name; + firebase.firestore().collection('matchSignupsTeam').doc("team-" + teamNum).collection('competitions').doc(currentComp).set(info) + alert('Added!') + setTimeout(function(){ window.location.href = '../signUps'; }, 500); + }else{ + alert(occ+"has added that match first.") + setTimeout(function(){ window.location.href = '../signUps'; }, 500); + } + } + }); + }); +} diff --git a/website/public/stats/index.html b/website/public/stats/index.html index cdfc0bd5..7d3c1a6b 100644 --- a/website/public/stats/index.html +++ b/website/public/stats/index.html @@ -15,9 +15,11 @@
diff --git a/website/public/style.css b/website/public/style.css index 9fa20689..5bc15132 100644 --- a/website/public/style.css +++ b/website/public/style.css @@ -7,6 +7,14 @@ body{ display: none; width:30px; } +.blue{ + background-color: #d1ecf1; + color: #0e0c60; +} +.red{ + background-color: #f8d7da; + color: #b20515; +} table{ margin-left: auto; margin-right: auto; @@ -46,7 +54,7 @@ td{ /* The navigation menu links */ .sidenav a { - padding: 8px 8px 8px 32px; + padding: 8px 8px 8px 30px; text-decoration: none; font-size: 25px; color: #818181; @@ -58,6 +66,24 @@ td{ .sidenav a:hover { color: #f1f1f1; } +/* The navigation menu links */ +.sidenav p { + padding: 8px 8px 8px 32px; + text-decoration: none; + font-size: 25px; + color: #818181; + display: block; + transition: 0.3s; +} + +/* When you mouse over the navigation links, change their color */ +.sidenav p:hover { + color: #f1f1f1; +} +.scoutMatchLinks{ + size: 20px; + padding: 6px 6px 8px 40px; +} /* Position and style the close button (top right corner) */ .sidenav .closebtn { diff --git a/website/public/team/index.html b/website/public/team/index.html index 9385d2b0..1fce5365 100644 --- a/website/public/team/index.html +++ b/website/public/team/index.html @@ -18,8 +18,10 @@ × Profile Team - Scout Matches - Torunament Stats +

Scout Matches

+ Sign Up For Matches + Submit Scouting Reports + Tournament Stats