generate.py - Opengist

generate.py · 1.1 KiB · Python Originalformat

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 Originalformat

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 Originalformat

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)

molahisto.py · 787 B · Python Originalformat

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: molahisto.py data.txt title output"
15	exit(1)
16
17	type = sys.argv[2].decode(encoding='UTF-8',errors='strict')
18	output = sys.argv[3]
19
20	data = np.fromfile(sys.argv[1], sep='\n')
21
22	num_bins = 8
23
24	fig, ax = plt.subplots()
25	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
26
27	ax.set_xlabel('Constante Elástica (N/m)')
28	ax.set_ylabel('Contagem')
29	ax.set_title('%s' %type)
30	ax.set_ylim( None, n.max() * 1.2)
31
32	fig.tight_layout()
33
34	fig.set_size_inches(width / 2.54, height / 2.54)
35	plt.savefig('%s.png' %output, dpi=100)

super.py · 3.4 KiB · Python Originalformat

#!/usr/bin/env python # -*- coding: utf-8 -*- 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 = min(max(len(data) / 3, 2), 12) 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) def GenSigmaChart(data, output, type, std): #Tstd, "%s-sigma" %(output), 'Tempo de Queda' width = 20 # cm height = 15 # cm x = [2**i for i in range(len(data))] s = std / np.sqrt(x) fig, ax = plt.subplots() ax.plot(x, data, label="Calculado") ax.plot(x, s, label="Teórico") ax.set_xlabel('Número de Amostras usadas na média') ax.set_ylabel('Desvio Padrão') ax.set_title('Desvio Padrão da Média de %s' %type) legend = ax.legend(loc='upper right', 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(0,6): 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 if z+n >= len(data): break; for k in range(0, n): 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)) print "Salvando Gráfico de Histograma do Tempo de Queda" GenHistogram(npT, "%s-%s-hist" %(output, n), 'Tempo de Queda', 's', 'Histograma de Tempo de Queda Média N=%s (%s)' %(n, type)) Tavg.append(np.average(npT)) Vavg.append(np.average(npV)) Aavg.append(np.average(npA)) print "Desenhando gráfico sigma" GenSigmaChart(Tstd, "%s-sigma" %(output), 'Tempo de Queda (%s)' %type, np.std(data))

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
11	if len(sys.argv) < 4:
12	print "Usage: generate.py data.txt type output"
13	exit(1)
14
15	def GenHistogram(data, output, type, unit, title):
16	width = 20 # cm
17	height = 15 # cm
18	num_bins = min(max(len(data) / 3, 2), 12)
19	avg = np.average(data)
20	std = np.std(data)
21
22	fig, ax = plt.subplots()
23	n, bins, patches = ax.hist(data, num_bins, color='burlywood', histtype='stepfilled')
24
25	ax.axvline(avg, color='red', label="Média = %.2f %s" % (avg, unit))
26	ax.axvline(avg-std, color='lightblue', label="Média - Desvio Padrão = %.2f %s" %(avg-std, unit))
27	ax.axvline(avg+std, color='darkblue', label="Média + Desvio Padrão = %.2f %s" % (avg+std, unit))
28
29	plt.text(0.025, 0.8, r'$\sigma = %.2f$' % std, transform = ax.transAxes)
30	ax.set_xlabel('%s (%s)' %(type, unit))
31	ax.set_ylabel('Contagem (n)')
32	ax.set_title('%s' %title)
33	ax.set_ylim( None, n.max() * 1.2)
34
35	legend = ax.legend(loc='upper left', shadow=True, prop={'size':10})
36	fig.tight_layout()
37
38	fig.set_size_inches(width / 2.54, height / 2.54)
39	plt.savefig('%s.png' %output, dpi=100)
40
41	def GenSigmaChart(data, output, type, std):
42	#Tstd, "%s-sigma" %(output), 'Tempo de Queda'
43	width = 20 # cm
44	height = 15 # cm
45	x = [2**i for i in range(len(data))]
46	s = std / np.sqrt(x)
47	fig, ax = plt.subplots()
48	ax.plot(x, data, label="Calculado")
49	ax.plot(x, s, label="Teórico")
50
51	ax.set_xlabel('Número de Amostras usadas na média')
52	ax.set_ylabel('Desvio Padrão')
53	ax.set_title('Desvio Padrão da Média de %s' %type)
54
55	legend = ax.legend(loc='upper right', shadow=True, prop={'size':10})
56	fig.tight_layout()
57
58	fig.set_size_inches(width / 2.54, height / 2.54)
59	plt.savefig('%s.png' %output, dpi=100)
60
61	deltaS = 34 # metros
62
63	type = sys.argv[2].decode(encoding='UTF-8',errors='strict')
64	output = sys.argv[3]
65	data = np.fromfile(sys.argv[1], sep='\n')
66	dataV = deltaS / data
67	dataA = (2 * deltaS) / (data ** 2)
68
69	Tstd = []
70	Vstd = []
71	Astd = []
72
73	Tavg = []
74	Vavg = []
75	Aavg = []
76
77	for i in range(0,6):
78	n = 2**i
79	arrT = []
80	arrV = []
81	arrA = []
82
83	print "Calculando para %s" %n
84	filenameT = "o/%s-%s-t.txt" %(output, n)
85	filenameV = "o/%s-%s-v.txt" %(output, n)
86	filenameA = "o/%s-%s-a.txt" %(output, n)
87
88	print "Salvando em %s %s %s" % (filenameT, filenameV, filenameA)
89	fT = open(filenameT, "w")
90	fV = open(filenameV, "w")
91	fA = open(filenameA, "w")
92
93	for z in range(0, len(data), n):
94	avgT = 0
95	avgV = 0
96	avgA = 0
97	if z+n >= len(data):
98	break;
99	for k in range(0, n):
100	avgT += data[z+k]
101	avgV += dataV[z+k]
102	avgA += dataA[z+k]
103
104	avgT /= n
105	avgV /= n
106	avgA /= n
107
108	arrT.append(avgT)
109	arrV.append(avgV)
110	arrA.append(avgA)
111
112	fT.write("%.2f\n" %avgT)
113	fV.write("%.2f\n" %avgV)
114	fA.write("%.2f\n" %avgA)
115
116	fT.close()
117	fV.close()
118	fA.close()
119
120	npT = np.array(arrT)
121	npV = np.array(arrV)
122	npA = np.array(arrA)
123
124	Tstd.append(np.std(npT))
125	Vstd.append(np.std(npV))
126	Astd.append(np.std(npA))
127
128	print "Salvando Gráfico de Histograma do Tempo de Queda"
129	GenHistogram(npT, "%s-%s-hist" %(output, n), 'Tempo de Queda', 's', 'Histograma de Tempo de Queda Média N=%s (%s)' %(n, type))
130
131	Tavg.append(np.average(npT))
132	Vavg.append(np.average(npV))
133	Aavg.append(np.average(npA))
134
135
136	print "Desenhando gráfico sigma"
137
138	GenSigmaChart(Tstd, "%s-sigma" %(output), 'Tempo de Queda (%s)' %type, np.std(data))
139
140
141