Revision of generate.py

Lucas Teske revised this gist 8 years ago. Go to revision

1 file changed, 35 insertions

molahisto.py(file created)

		@@ -0,0 +1,35 @@
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)

Lucas Teske revised this gist 8 years ago. Go to revision

1 file changed, 31 insertions, 10 deletions

super.py

			@@ -1,7 +1,5 @@
1	1		#!/usr/bin/env python
2	2		# -- coding: utf-8 --
3		-
4		-	# Just saving this, because it works beautifully, but its crap because it isn't what teacher asked :((
5	3		from __future__ import unicode_literals
6	4
7	5		import sys
			@@ -17,7 +15,7 @@ if len(sys.argv) < 4:
17	15		def GenHistogram(data, output, type, unit, title):
18	16		width = 20 # cm
19	17		height = 15 # cm
20		-	num_bins = max(len(data) / 3, 2)
	18	+	num_bins = min(max(len(data) / 3, 2), 12)
21	19		avg = np.average(data)
22	20		std = np.std(data)
23	21
			@@ -40,6 +38,25 @@ def GenHistogram(data, output, type, unit, title):
40	38		fig.set_size_inches(width / 2.54, height / 2.54)
41	39		plt.savefig('%s.png' %output, dpi=100)
42	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)
43	60
44	61		deltaS = 34 # metros
45	62
			@@ -57,7 +74,7 @@ Tavg = []
57	74		Vavg = []
58	75		Aavg = []
59	76
60		-	for i in range(1,6):
	77	+	for i in range(0,6):
61	78		n = 2**i
62	79		arrT = []
63	80		arrV = []
			@@ -77,9 +94,9 @@ for i in range(1,6):
77	94		avgT = 0
78	95		avgV = 0
79	96		avgA = 0
	97	+	if z+n >= len(data):
	98	+	break;
80	99		for k in range(0, n):
81		-	if z+k >= len(data):
82		-	break
83	100		avgT += data[z+k]
84	101		avgV += dataV[z+k]
85	102		avgA += dataA[z+k]
			@@ -108,12 +125,16 @@ for i in range(1,6):
108	125		Vstd.append(np.std(npV))
109	126		Astd.append(np.std(npA))
110	127
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))
	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))
114	130
115	131		Tavg.append(np.average(npT))
116	132		Vavg.append(np.average(npV))
117	133		Aavg.append(np.average(npA))
118	134
119		-	print Tstd, Vstd, Astd
	135	+
	136	+	print "Desenhando gráfico sigma"
	137	+
	138	+	GenSigmaChart(Tstd, "%s-sigma" %(output), 'Tempo de Queda (%s)' %type, np.std(data))
	139	+
	140	+

Lucas Teske revised this gist 8 years ago. Go to revision

1 file changed, 119 insertions

super.py(file created)

		@@ -0,0 +1,119 @@
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

Lucas Teske revised this gist 8 years ago. Go to revision

2 files changed, 97 insertions

grafa.py(file created)

		@@ -0,0 +1,49 @@
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(file created)

		@@ -0,0 +1,48 @@
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)

Lucas Teske revised this gist 8 years ago. Go to revision

1 file changed, 44 insertions

generate.py(file created)

		@@ -0,0 +1,44 @@
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)

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