#!/usr/bin/env python
from matplotlib import matlab
data = ((3, 1000), (10, 3), (100, 30), (500, 800), (50, 1))
matlab.xlabel("FOO")
matlab.ylabel("FOO")
matlab.title("Testing")
matlab.gca().set_yscale('log')
dim = len(data[0])
w = 0.75
dimw = w / dim
x = matlab.arange(len(data))
for i in range(len(data[0])):
y = [d[i] for d in data]
b = matlab.bar(x + i * dimw, y, dimw, bottom=0.001)
matlab.gca().set_xticks(x + w / 2)
matlab.gca().set_ylim((0.001, 1000))
matlab.show()
from psychopy import calib
import matplotlib.matlab as mat
myMonitor = calib.Monitor('iiyama514')
#run a calibration series
lumsPRE = calib.getLumSeriesPR650(1,8)
gamCalc = calib.GammaCalculator(lums=lumsPRE)
print "monitor gamma=%.2f" %(gamCalc.gammaVal)
myMonitor['gamma'] = gamCalc.gammaVal
myMonitor.save()
#set the gamma value and test again
lumsPOST = calib.getLumSeriesPR650(1,8,myMonitor['gamma'])
mat.plot(calib.DACrange(len(lumsPRE)),lumsPRE,'bo-')
mat.plot(calib.DACrange(len(lumsPOST)),lumsPOST,'ro-')
mat.ylabel('cd/m^2')
mat.show()
"""Load Nicolet BMSI data."""
if tmin < 0: tmin = 0
fh = file(filename, 'rb')
indmin = Fs * tmin
numsamples = os.path.getsize(filename) / (channels * 2)
indmax = min(numsamples, Fs * tmax)
byte0 = int(indmin * channels * 2)
numbytes = int((indmax - indmin) * channels * 2)
fh.seek(byte0)
data = fromstring(fh.read(numbytes), Int16).astype(Float)
data.shape = -1, channels
t = (1 / Fs) * arange(indmin, indmax)
return t, data
t, data = read_nicolet(0, 10)
x = data[:, 5]
Pxx, freqs, t = specgram(x, NFFT=512, Fs=Fs, noverlap=412)
T, F = meshgrid(t, freqs)
pcolor(T, F, 10 * log10(Pxx), shading='flat')
set(gca(), 'ylim', [0, 100])
#print Pxx.shape, freqs.shape, t.shape
show()
from matplotlib.matlab import figure, close, axes, subplot, show
from matplotlib.numerix import arange, sin, pi
t = arange(0.0, 1.0, 0.01)
fig = figure(1)
ax1 = fig.add_subplot(211)
ax1.plot(t, sin(2*pi*t))
ax1.grid(True)
ax1.set_ylim( (-2,2) )
ax1.set_ylabel('1 Hz')
ax1.set_title('A sine wave or two')
for label in ax1.get_xticklabels():
label.set_color('r')
ax2 = fig.add_subplot(212)
ax2.plot(t, sin(2*2*pi*t))
ax2.grid(True)
ax2.set_ylim( (-2,2) )
l = ax2.set_xlabel('Hi mom')
l.set_color('g')
l.set_fontsize('large')
show()
#!/usr/bin/env python
from matplotlib import matlab
data = ((3,1000), (10,3), (100,30), (500, 800), (50,1))
matlab.xlabel("FOO")
matlab.ylabel("FOO")
matlab.title("Testing")
matlab.gca().set_yscale('log')
dim = len(data[0])
w = 0.75
dimw = w / dim
x = matlab.arange(len(data))
for i in range(len(data[0])) :
y = [d[i] for d in data]
b = matlab.bar(x + i * dimw, y, dimw, bottom=0.001)
matlab.gca().set_xticks(x + w / 2)
matlab.gca().set_ylim( (0.001,1000))
matlab.show()
def show():
if os.name == 'nt' or os.name == 'dos' or os.name == 'ce':
pylab.draw()
else:
pylab.show()
ax.append(MM.axes([.55, .05, .40, .40])) # lower right
ax.append(MM.axes([.55, .55, .40, .40])) # upper right
for i in range(4):
ax[i].plot(x, [t_lst[k][i] for k in K],
color=darkblue,
linewidth=3)
ax[i].plot(x, [t_ref[k][i] for k in K],
color=lightblue,
linewidth=3)
ax[i].set_xticks([])
ax[i].set_title('Limited memory p = %-d' % plist[i],
fontsize='small')
ax[i].legend(['Python', 'Fortran'], 'upper left')
for i in [2, 3]:
ax[i].set_ylabel('Time (s)', fontsize='small')
MM.show()
# For the number of iterations, use first value of p as reference
x = range(len(i_lst.keys()))
ax = MM.subplot(111)
lgnd = []
for i in range(len(plist)):
lgnd.append('p = %-d' % plist[i])
ax.plot(x, [(1.0 * i_lst[k][0]) / i_lst[k][0] for k in K], 'k-')
ax.plot(x, [(1.0 * i_lst[k][1]) / i_lst[k][0] for k in K], 'k:')
ax.plot(x, [(1.0 * i_lst[k][2]) / i_lst[k][0] for k in K], 'k-.')
ax.plot(x, [(1.0 * i_lst[k][3]) / i_lst[k][0] for k in K], 'k--')
ax.legend(lgnd, 'upper right')
ax.set_title('Number of iterations(p)/Number of iterations(0)')
ax.set_xticklabels(K,
rotation=45,
steelblue = '#5d82ef'
x = range(len(t_lst.keys()))
ax = []
ax.append(MM.axes([ .05, .05, .40, .40 ])) # lower left
ax.append(MM.axes([ .05, .55, .40, .40 ])) # upper left
ax.append(MM.axes([ .55, .05, .40, .40 ])) # lower right
ax.append(MM.axes([ .55, .55, .40, .40 ])) # upper right
for i in range(4):
ax[i].plot(x, [ t_lst[k][i] for k in K ], color=darkblue, linewidth=3)
ax[i].plot(x, [ t_ref[k][i] for k in K ], color=lightblue, linewidth=3)
ax[i].set_xticks([])
ax[i].set_title('Limited memory p = %-d' % plist[i], fontsize='small')
ax[i].legend(['Python', 'Fortran'], 'upper left')
for i in [2,3]:
ax[i].set_ylabel('Time (s)', fontsize='small')
MM.show()
# For the number of iterations, use first value of p as reference
x = range(len(i_lst.keys()))
ax = MM.subplot(111)
lgnd = []
for i in range(len(plist)):
lgnd.append('p = %-d' % plist[i])
ax.plot(x, [ (1.0*i_lst[k][0])/i_lst[k][0] for k in K ], 'k-')
ax.plot(x, [ (1.0*i_lst[k][1])/i_lst[k][0] for k in K ], 'k:')
ax.plot(x, [ (1.0*i_lst[k][2])/i_lst[k][0] for k in K ], 'k-.')
ax.plot(x, [ (1.0*i_lst[k][3])/i_lst[k][0] for k in K ], 'k--')
ax.legend(lgnd, 'upper right')
ax.set_title('Number of iterations(p)/Number of iterations(0)')
ax.set_xticklabels(K, rotation = 45, horizontalalignment = 'right', fontsize='small')
MM.show()
from psychopy import calib
import matplotlib.matlab as mat
myMonitor = calib.Monitor('iiyama514')
#run a calibration series
lumsPRE = calib.getLumSeriesPR650(1, 8)
gamCalc = calib.GammaCalculator(lums=lumsPRE)
print "monitor gamma=%.2f" % (gamCalc.gammaVal)
myMonitor['gamma'] = gamCalc.gammaVal
myMonitor.save()
#set the gamma value and test again
lumsPOST = calib.getLumSeriesPR650(1, 8, myMonitor['gamma'])
mat.plot(calib.DACrange(len(lumsPRE)), lumsPRE, 'bo-')
mat.plot(calib.DACrange(len(lumsPOST)), lumsPOST, 'ro-')
mat.ylabel('cd/m^2')
mat.show()
