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generate_statistics_from_SD.py
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generate_statistics_from_SD.py
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import fileinput
import math
import collections
import time
import numpy as np
from pylab import *
from matplotlib import pyplot as plt
import matplotlib.mlab as mlab
#file_path = '/media/ABB4-4F3A/DATALOG.TXT'
file_path = 'DATALOG.TXT'
def split_in_blocks(txt_file, pattern):
'''
Find the last appears of the text that indicate a new flight and divide in the number of blocks generated by the rocket
Return: A list that contains all the different blocks of data and a list containing the header.
'''
num_times_find_pattern = []
for num_line, line in enumerate(fileinput.input(txt_file)):
if pattern in line:
num_times_find_pattern.append(num_line)
if num_line == 0:
header = list(line.strip().split(","))
#print header
blocks_of_data = []
with open(txt_file) as f:
lines = f.readlines()
for num_header_line in num_times_find_pattern:
if num_header_line == 0:
num_header_line_prev = num_header_line
else:
block_lines = lines[num_header_line_prev + 1 : num_header_line - 1]
blocks_of_data.append(block_lines)
num_header_line_prev = num_header_line
block_lines = lines[num_header_line_prev + 1 : num_line + 1]
blocks_of_data.append(block_lines)
return blocks_of_data, header
def manage_data_from_blocks(blocks, header):
'''
Divide al the text in blocks tagged with their type of data (accelaration, temperature, ...) continued by a number of block
Return: A dict that contains all the different types of data diferentiated and numbered.
'''
# TODO: Automatize this function to accept more headers!!
blocks_dict = collections.OrderedDict()
for block_number, block in enumerate(blocks):
for item in header:
blocks_dict['%s%s' % (item,block_number)] = []
for line in block:
line_list = line.strip().split(",")
blocks_dict['f%s' % block_number].append(int(line_list[0]))
blocks_dict['ax%s' % block_number].append(float(line_list[1]))
blocks_dict['ay%s' % block_number].append(float(line_list[2]))
blocks_dict['az%s' % block_number].append(float(line_list[3]))
blocks_dict['gx%s' % block_number].append(float(line_list[4]))
blocks_dict['gy%s' % block_number].append(float(line_list[5]))
blocks_dict['gz%s' % block_number].append(float(line_list[6]))
blocks_dict['mx%s' % block_number].append(float(line_list[7]))
blocks_dict['my%s' % block_number].append(float(line_list[8]))
blocks_dict['mz%s' % block_number].append(float(line_list[9]))
blocks_dict['t%s' % block_number].append(float(line_list[10]))
blocks_dict['p%s' % block_number].append(int(line_list[11]))
blocks_dict['h%s' % block_number].append(float(line_list[12]))
return blocks_dict
def process_data(blocks_dict, header):
block_list_header_based = []
for num, item in enumerate(header):
block_list_header_based.append([])
for block in blocks_dict:
if block.startswith(header[num]):
block_list_header_based[num].append(block)
# DEBUG! print "%s: %s" % (block, blocks_dict[block])
print block_list_header_based
#fingerprint_basic_info = basic_process_only_for_fingerprints(block_list_header_based[0])
temp_basic_info = basic_process_data(block_list_header_based[12])
#height_basic_info = basic_process_data(block_list_header_based[12])
print_basic_histograms(block_list_header_based[12])
print_basic_scatters(block_list_header_based[12])
print_basic_evolution_2_axis(block_list_header_based[0], block_list_header_based[12])
def basic_process_only_for_fingerprints(fingerprints):
fingerprint_basic_info = collections.OrderedDict()
fingerprint_list = []
for num, fingerprint_block in enumerate(fingerprints):
millis_interval_list = []
for position, millis in enumerate(blocks_dict[fingerprint_block]):
if position != 0:
millis_interval = millis - millis_prev
millis_interval_list.append(millis_interval)
millis_prev = millis
blocks_dict["fp%s" % (num)] = millis_interval_list
fingerprint_list.append("fp%s" % (num))
fingerprint_basic_info = basic_process_data(fingerprint_list)
return fingerprint_basic_info
def basic_process_data(data_list):
data_basic_info = collections.OrderedDict()
for data_block in data_list:
data_basic_info[data_block] = {}
data_avg_mean = np.mean(blocks_dict[data_block]) # Average
data_avg_weighted = np.average(blocks_dict[data_block]) # Average weighted
data_amax = np.amax(blocks_dict[data_block]) # MAX
data_amin = np.amin(blocks_dict[data_block]) # MIN
data_med = np.median(blocks_dict[data_block]) # Median
data_std = np.std(blocks_dict[data_block]) # Standard deviation
data_ptp = np.ptp(blocks_dict[data_block]) # Distance MAX to MIN
data_var = np.var(blocks_dict[data_block]) # Variance
data_basic_info[data_block] = {"AVM" : "%.3f" % data_avg_mean, "AVW" : "%.3f" % data_avg_weighted, "MAX" : "%.3f" % data_amax,
"MIN" : "%.3f" % data_amin, "MED" : "%.3f" % data_med, "STD" : "%.3f" % data_std,
"PTP" : "%.3f" % data_ptp, "VAR" : "%.3f" % data_var}
# PLOT NORMAL PDF FROM THA DATA
#sigma = sqrt(data_var)
#x = np.linspace(data_amin,data_amax)
#plt.plot(x,mlab.normpdf(x,data_avg_mean,sigma))
plt.show()
for key in data_basic_info:
print data_basic_info[key]
return data_basic_info
def print_basic_histograms(data_list):
#plt.ion()
plt.figure(1)
for num, data in enumerate(data_list):
nrows = int(math.ceil(float(len(data_list) / 3.0)))
ncols = 3
subplot_index = "%s%s%s" % (nrows, ncols, num + 1)
plt.subplot(subplot_index)
plt.hist(blocks_dict[data], bins=20)
#data_new = np.histogramdd(blocks_dict[data])
#plt.hist(data_new, bins=20)
plt.xlabel("Value", fontsize=8)
plt.ylabel("Frequency", fontsize=8)
plt.suptitle("Gaussian Histogram", fontsize=12)
plt.show()
#plt.show(block=True)
def print_basic_scatters(data_list):
#plt.ion()
plt.figure(1)
for num, data in enumerate(data_list):
nrows = int(math.ceil(float(len(data_list) / 3.0)))
ncols = 3
subplot_index = "%s%s%s" % (nrows, ncols, num + 1)
plt.subplot(subplot_index)
plt.scatter(np.random.randn(1000), np.random.randn(1000))
plt.suptitle("Gaussian Histogram", fontsize=12)
plt.show()
#plt.show(block=True)
def print_basic_evolution_2_axis(x_axis_data_list, y_axis_data_list):
plt.figure(1)
for num in range(len(x_axis_data_list)):
x = blocks_dict[x_axis_data_list[num]]
y = blocks_dict[y_axis_data_list[num]]
#subplot(nrows, ncols, plot_number)
nrows = int(math.ceil(float(len(x_axis_data_list) / 3.0)))
ncols = 3
subplot_index = "%s%s%s" % (nrows, ncols, num + 1)
plt.subplot(subplot_index)
plt.plot(x, y, linewidth=1.0, color="green")
xlabel('time (milliseconds)', fontsize = 8)
#ylabel('temperature (C)', fontsize = 8)
#title('', fontsize=10)
grid(True)
plt.xticks(blocks_dict[x_axis_data_list[num]][::len(blocks_dict[x_axis_data_list[num]])/10], rotation=30, fontsize=8)
#plt.annotate('Despegue', xy=(2200, 34.82), xytext=(2300, 34.88),
# bbox=dict(boxstyle="round", fc="0.8"),
# arrowprops=dict(facecolor='black', shrink=0.05),
# )
#plt.annotate('Paracaidas', xy=(7200, 34.82), xytext=(6300, 34.88),
# arrowprops=dict(facecolor='black', shrink=0.05),
# )
#axvline(x=2200)
#axhspan(34.80, 34.82, facecolor='0.5', alpha=0.5, color="red")
plt.ylim(min(blocks_dict[y_axis_data_list[num]]) - 0.02, max(blocks_dict[y_axis_data_list[num]]) + 0.02)
plt.yticks(fontsize=8)
#plt.suptitle('temperatures in data', fontsize=12)
plt.show()
#start = time.time()
blocks, header = split_in_blocks(file_path, "m")
blocks_dict = manage_data_from_blocks(blocks, header)
process_data(blocks_dict, header)
#stop = time.time()
#total_time = stop -start
#print total_time