Esempio n. 1
0
#!/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()
Esempio n. 2
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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()
Esempio n. 3
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    """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()
Esempio n. 4
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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()        


Esempio n. 5
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#!/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()
Esempio n. 7
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        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,
Esempio n. 8
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        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()
Esempio n. 9
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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()