def plot_false_and_true_positives(positive_file_name, false_file_name): positive_data = ft.get_data_from_file(positive_file_name) false_data = ft.get_data_from_file(false_file_name) positive_x, positive_y = get_list_of_data_as_x_and_y(positive_data) false_x, false_y = get_list_of_data_as_x_and_y(false_data) plt.plot(positive_x, positive_y, "b-", false_x, false_y, "ro") plt.show()
def plot_time_series_file(file_name, title = "", ylabel = "", xlabel = ""): data = ft.get_data_from_file(file_name) transposed_data = map(list, zip(*data)) set_font_of_plot() plt.ylabel(ylabel) plt.xlabel(xlabel) plt.title(title) plt.plot(transposed_data) plt.show()
def plot_error_if_greater_than_zero(file_name): def is_greater_than_zero(list_of_values): return [1 if int(val) > 0 else 0 for val in list_of_values] data = ft.get_data_from_file(file_name) y, _ = get_list_of_data_as_x_and_y(data) warmup_time = 1500 x = range(warmup_time, len(y) + warmup_time) y = is_greater_than_zero(y) show_scatter_plot_of_data(x, y, "Anomalies vs no anomalies", "Time", "Is rare event?")
def plot_scatter_plot_of_errors(file_name):
"""
this function is for plotting the errors detected vs errors inserted.
Usually file name is final.csv
"""
data = ft.get_data_from_file(file_name)
x, y = get_list_of_data_as_x_and_y(data)
plt.ylabel("number of detected errors")
plt.xlabel("number of induced errors")
plt.title("Correlation among induced errors and detected errors")
plt.scatter(x, y, marker=",")
plt.show()
def show_rare_event_menu(lattice_of_sensors, random_generator, min_hearable_volume = 0.1,
error_threshold = 0.02, loudness = 0.3, max_dist = 1):
"This functions shows a rare event menu. file name usually: rare_song_normalized.csv"
print "You are adding a rare event!!"
file_name = prompt_for_input("Enter rare song file name:")
rare_event_song = ft.get_data_from_file(file_name)
should_use_default_parameters = True if prompt_for_input("Do you want to use default parameters? yes/no:") == "yes" else False
if not should_use_default_parameters:
error_threshold = float(prompt_for_input("Enter error threshold:"))
loudness = float(prompt_for_input("Enter loudness:"))
min_hearable_volume = float(prompt_for_input("Enter min_hearable_volume:"))
max_dist = int(prompt_for_input("Enter max distance:"))
add_rare_event(lattice_of_sensors, rare_event_song[0], random_generator, max_dist,
loudness, min_hearable_volume)
def plot_number_of_sensors_that_deviate_and_is_rare_event(file_name): """ Plots the number of sensors that deviate and are rare event from the file. usually the file name is number_of_sensors_that_deviate_and_is_rare_event.csv. """ def add_wiggle(ys, amount): return [y + random.random() * amount for y in ys] def count_quadrants(x, y, x_threshold, y_threshold): true_positives = false_positives = true_negatives = false_negatives = 0 for x, y in zip(x, y): if x < x_threshold and y < y_threshold: true_negatives += 1 elif x < x_threshold and y > y_threshold: false_negatives += 1 elif x > x_threshold and y < y_threshold: false_positives += 1 else: true_positives += 1 print "True negatives:", true_negatives print "True positives:", true_positives print "False negatives:", false_negatives print "False positives:", false_positives def change_from_boolean_string_to_integer(list_of_boolean_string_values): "Returns a list containing 1 for True and 0 for False" return [1 if val == 'True' else 0 for val in list_of_boolean_string_values] warmup_time = 1500 data = ft.get_data_from_file(file_name) data = data[warmup_time:] x , y = get_list_of_data_as_x_and_y(data) x = [int(i) for i in x] y = change_from_boolean_string_to_integer(y) count_quadrants(x, y, 0.5, 0.5) y = add_wiggle(y, 0.1) plt.axhline(0.5, xmin = 0, xmax =100, color="r") plt.axvline(0.5, ymin = 0, ymax =1, color="r") show_scatter_plot_of_data(x, y, "Anomalies vs no anomalies", "Number of deviating sensors", "Is rare event?")
def plot_file_in_columns(file_name): data = ft.get_data_from_file(file_name) plt.plot(data) plt.show()
def get_list_of_time_series():
name_of_file_of_time_series = prompt_for_input("Enter name of file of time series:")
return ft.get_data_from_file(name_of_file_of_time_series)
