generate.py - Opengist

generate.py · 1.1 KiB · Python Brut

1	#!/usr/bin/env python
2	# -- coding: utf-8 --
3	from __future__ import unicode_literals
4
5	import sys
6	import numpy as np
7	import matplotlib.mlab as mlab
8	import matplotlib.pyplot as plt
9
10	width = 20 # cm
11	height = 15 # cm
12
13	if len(sys.argv) < 4:
14	print "Usage: generate.py data.txt title output"
15	exit(1)
16
17	output = sys.argv[3]
18	title = sys.argv[2].decode(encoding='UTF-8',errors='strict')
19
20	data = np.fromfile(sys.argv[1], sep='\n')
21
22	num_bins = 28
23	avg = np.average(data)
24	std = np.std(data)
25
26	fig, ax = plt.subplots()
27	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
28
29	print "Média: %s Desvio Padrão: %s" %(avg, std)
30
31	ax.axvline(avg, color='red', label="Média = %.2f s" %avg)
32	ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f s" %(avg-std))
33	ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f s" % (avg+std))
34
35	ax.set_xlabel('Tempo (s)')
36	ax.set_ylabel('Contagem (n)')
37	ax.set_title(title)
38	ax.set_ylim( None, n.max() * 1.2)
39
40	legend = ax.legend(loc='upper left', shadow=True, prop={'size':10})
41	fig.tight_layout()
42
43	fig.set_size_inches(width / 2.54, height / 2.54)
44	plt.savefig('%s.png' %output, dpi=100)

grafa.py · 1.3 KiB · Python Brut

1	#!/usr/bin/env python
2	# -- coding: utf-8 --
3	from __future__ import unicode_literals
4
5	import sys
6	import numpy as np
7	import matplotlib.mlab as mlab
8	import matplotlib.pyplot as plt
9
10	width = 20 # cm
11	height = 15 # cm
12
13	deltaS = 34 # metros
14
15
16	if len(sys.argv) < 4:
17	print "Usage: generate.py data.txt type output"
18	exit(1)
19
20	type = sys.argv[2].decode(encoding='UTF-8',errors='strict')
21	output = sys.argv[3]
22
23	data = np.fromfile(sys.argv[1], sep='\n')
24
25	data = (2 * 34) / (data ** 2)
26
27	num_bins = 12
28	avg = np.average(data)
29	std = np.std(data)
30
31	fig, ax = plt.subplots()
32	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
33
34	print "Média: %s Desvio Padrão: %s" %(avg, std)
35
36	ax.axvline(avg, color='red', label="Média = %.2f m/s²" %avg)
37	ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f m/s²" %(avg-std))
38	ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f m/s²" % (avg+std))
39
40	ax.set_xlabel('Aceleração (m/s²)')
41	ax.set_ylabel('Contagem (n)')
42	ax.set_title('Histograma das Acelerações Médias (%s)' %type)
43	ax.set_ylim( None, n.max() * 1.2)
44
45	legend = ax.legend(loc='upper left', shadow=True, prop={'size':10})
46	fig.tight_layout()
47
48	fig.set_size_inches(width / 2.54, height / 2.54)
49	plt.savefig('%s.png' %output, dpi=100)

grafv.py · 1.2 KiB · Python Brut

1	#!/usr/bin/env python
2	# -- coding: utf-8 --
3	from __future__ import unicode_literals
4
5	import sys
6	import numpy as np
7	import matplotlib.mlab as mlab
8	import matplotlib.pyplot as plt
9
10	width = 20 # cm
11	height = 15 # cm
12
13	deltaS = 34 # metros
14
15	if len(sys.argv) < 4:
16	print "Usage: generate.py data.txt type output"
17	exit(1)
18
19	type = sys.argv[2].decode(encoding='UTF-8',errors='strict')
20	output = sys.argv[3]
21
22	data = np.fromfile(sys.argv[1], sep='\n')
23
24	data = deltaS / data
25
26	num_bins = 12
27	avg = np.average(data)
28	std = np.std(data)
29
30	fig, ax = plt.subplots()
31	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
32
33	print "Média: %s Desvio Padrão: %s" %(avg, std)
34
35	ax.axvline(avg, color='red', label="Média = %.2f m/s" %avg)
36	ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f m/s" %(avg-std))
37	ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f m/s" % (avg+std))
38
39	ax.set_xlabel('Velocidade (m/s)')
40	ax.set_ylabel('Contagem (n)')
41	ax.set_title('Histograma das Velocidades Médias (%s)' %type)
42	ax.set_ylim( None, n.max() * 1.2)
43
44	legend = ax.legend(loc='upper left', shadow=True, prop={'size':10})
45	fig.tight_layout()
46
47	fig.set_size_inches(width / 2.54, height / 2.54)
48	plt.savefig('%s.png' %output, dpi=100)

super.py · 3.0 KiB · Python Brut

#!/usr/bin/env python # -*- coding: utf-8 -*- # Just saving this, because it works beautifully, but its crap because it isn't what teacher asked :(( from __future__ import unicode_literals import sys import numpy as np import matplotlib.mlab as mlab import matplotlib.pyplot as plt if len(sys.argv) < 4: print "Usage: generate.py data.txt type output" exit(1) def GenHistogram(data, output, type, unit, title): width = 20 # cm height = 15 # cm num_bins = max(len(data) / 3, 2) avg = np.average(data) std = np.std(data) fig, ax = plt.subplots() n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled') ax.axvline(avg, color='red', label="Média = %.2f %s" % (avg, unit)) ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f %s" %(avg-std, unit)) ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f %s" % (avg+std, unit)) plt.text(0.025, 0.8, r'$\sigma = %.2f$' % std, transform = ax.transAxes) ax.set_xlabel('%s (%s)' %(type, unit)) ax.set_ylabel('Contagem (n)') ax.set_title('%s' %title) ax.set_ylim( None, n.max() * 1.2) legend = ax.legend(loc='upper left', shadow=True, prop={'size':10}) fig.tight_layout() fig.set_size_inches(width / 2.54, height / 2.54) plt.savefig('%s.png' %output, dpi=100) deltaS = 34 # metros type = sys.argv[2].decode(encoding='UTF-8',errors='strict') output = sys.argv[3] data = np.fromfile(sys.argv[1], sep='\n') dataV = deltaS / data dataA = (2 * deltaS) / (data ** 2) Tstd = [] Vstd = [] Astd = [] Tavg = [] Vavg = [] Aavg = [] for i in range(1,6): n = 2**i arrT = [] arrV = [] arrA = [] print "Calculando para %s" %n filenameT = "o/%s-%s-t.txt" %(output, n) filenameV = "o/%s-%s-v.txt" %(output, n) filenameA = "o/%s-%s-a.txt" %(output, n) print "Salvando em %s %s %s" % (filenameT, filenameV, filenameA) fT = open(filenameT, "w") fV = open(filenameV, "w") fA = open(filenameA, "w") for z in range(0, len(data), n): avgT = 0 avgV = 0 avgA = 0 for k in range(0, n): if z+k >= len(data): break avgT += data[z+k] avgV += dataV[z+k] avgA += dataA[z+k] avgT /= n avgV /= n avgA /= n arrT.append(avgT) arrV.append(avgV) arrA.append(avgA) fT.write("%.2f\n" %avgT) fV.write("%.2f\n" %avgV) fA.write("%.2f\n" %avgA) fT.close() fV.close() fA.close() npT = np.array(arrT) npV = np.array(arrV) npA = np.array(arrA) Tstd.append(np.std(npT)) Vstd.append(np.std(npV)) Astd.append(np.std(npA)) GenHistogram(npT, "%s-%s-T" %(output, n), 'Tempo de Queda', 's', 'Histograma de Tempo de Queda Média N=%s (%s)' %(n, type)) GenHistogram(npV, "%s-%s-V" %(output, n), 'Velocidade', 'm/s', 'Histograma de Velocidade Média N=%s (%s)' %(n, type)) GenHistogram(npA, "%s-%s-A" %(output, n), 'Aceleração', 'm/s²', 'Histograma de Aceleração Média N=%s (%s)' %(n, type)) Tavg.append(np.average(npT)) Vavg.append(np.average(npV)) Aavg.append(np.average(npA)) print Tstd, Vstd, Astd

1	#!/usr/bin/env python
2	# -- coding: utf-8 --
3
4	# Just saving this, because it works beautifully, but its crap because it isn't what teacher asked :((
5	from __future__ import unicode_literals
6
7	import sys
8	import numpy as np
9	import matplotlib.mlab as mlab
10	import matplotlib.pyplot as plt
11
12
13	if len(sys.argv) < 4:
14	print "Usage: generate.py data.txt type output"
15	exit(1)
16
17	def GenHistogram(data, output, type, unit, title):
18	width = 20 # cm
19	height = 15 # cm
20	num_bins = max(len(data) / 3, 2)
21	avg = np.average(data)
22	std = np.std(data)
23
24	fig, ax = plt.subplots()
25	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
26
27	ax.axvline(avg, color='red', label="Média = %.2f %s" % (avg, unit))
28	ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f %s" %(avg-std, unit))
29	ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f %s" % (avg+std, unit))
30
31	plt.text(0.025, 0.8, r'$\sigma = %.2f$' % std, transform = ax.transAxes)
32	ax.set_xlabel('%s (%s)' %(type, unit))
33	ax.set_ylabel('Contagem (n)')
34	ax.set_title('%s' %title)
35	ax.set_ylim( None, n.max() * 1.2)
36
37	legend = ax.legend(loc='upper left', shadow=True, prop={'size':10})
38	fig.tight_layout()
39
40	fig.set_size_inches(width / 2.54, height / 2.54)
41	plt.savefig('%s.png' %output, dpi=100)
42
43
44	deltaS = 34 # metros
45
46	type = sys.argv[2].decode(encoding='UTF-8',errors='strict')
47	output = sys.argv[3]
48	data = np.fromfile(sys.argv[1], sep='\n')
49	dataV = deltaS / data
50	dataA = (2 * deltaS) / (data ** 2)
51
52	Tstd = []
53	Vstd = []
54	Astd = []
55
56	Tavg = []
57	Vavg = []
58	Aavg = []
59
60	for i in range(1,6):
61	n = 2**i
62	arrT = []
63	arrV = []
64	arrA = []
65
66	print "Calculando para %s" %n
67	filenameT = "o/%s-%s-t.txt" %(output, n)
68	filenameV = "o/%s-%s-v.txt" %(output, n)
69	filenameA = "o/%s-%s-a.txt" %(output, n)
70
71	print "Salvando em %s %s %s" % (filenameT, filenameV, filenameA)
72	fT = open(filenameT, "w")
73	fV = open(filenameV, "w")
74	fA = open(filenameA, "w")
75
76	for z in range(0, len(data), n):
77	avgT = 0
78	avgV = 0
79	avgA = 0
80	for k in range(0, n):
81	if z+k >= len(data):
82	break
83	avgT += data[z+k]
84	avgV += dataV[z+k]
85	avgA += dataA[z+k]
86
87	avgT /= n
88	avgV /= n
89	avgA /= n
90
91	arrT.append(avgT)
92	arrV.append(avgV)
93	arrA.append(avgA)
94
95	fT.write("%.2f\n" %avgT)
96	fV.write("%.2f\n" %avgV)
97	fA.write("%.2f\n" %avgA)
98
99	fT.close()
100	fV.close()
101	fA.close()
102
103	npT = np.array(arrT)
104	npV = np.array(arrV)
105	npA = np.array(arrA)
106
107	Tstd.append(np.std(npT))
108	Vstd.append(np.std(npV))
109	Astd.append(np.std(npA))
110
111	GenHistogram(npT, "%s-%s-T" %(output, n), 'Tempo de Queda', 's', 'Histograma de Tempo de Queda Média N=%s (%s)' %(n, type))
112	GenHistogram(npV, "%s-%s-V" %(output, n), 'Velocidade', 'm/s', 'Histograma de Velocidade Média N=%s (%s)' %(n, type))
113	GenHistogram(npA, "%s-%s-A" %(output, n), 'Aceleração', 'm/s²', 'Histograma de Aceleração Média N=%s (%s)' %(n, type))
114
115	Tavg.append(np.average(npT))
116	Vavg.append(np.average(npV))
117	Aavg.append(np.average(npA))
118
119	print Tstd, Vstd, Astd