w[1,:] = z.max(0)
w[0,:] = w.min(0)
return w[0,:]
if __name__ == '__main__':
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig,axs = plt.subplots(1,1,sharex=True,sharey=True)
ax1 = axs
#ax1.set_yscale('log')
#ax1.set_yscale('linear')
if 1:
from edxrd.test import get_test_data
x,y = get_test_data.getedxdata()['Austenite']
else:
from edxrd.peakfitting import fpeak
x = np.arange(4000)
y = np.zeros_like(x)
vals = [(1000,15.3,1200,0.25),
(1300,15.5,12000,0.25),
(1700,15.8,22000,0.25),
(1750,16.3,32000,0.25),
(1800,17.3,22000,0.25),
(2300,18.3,11000,0.25),
(2600,70.3,8000,0.25),
(3200,22.3,6000,0.25),
(3700,25.3,4000,0.25),
for label in ax1.get_yticklabels():
label.set_fontsize(11)
for ax in [ax1,ax2]:
for s in ax.spines.values():
s.set_linewidth(1)
for line in ax.xaxis.get_ticklines()+ax.yaxis.get_ticklines():
line.set_markeredgewidth(1)
ax.minorticks_off()
f.savefig('wavelet_peaksearch.png',dpi=100)
if 1:
from edxrd.test import get_test_data
edxdata = get_test_data.getedxdata()
x,y = edxdata['LaB6']
x=np.poly1d([1e-9, 0.001942,0.005])(x)
unit_cell = [4.15695,4.15695,4.15695,90,90,90]
space_group = 'pm-3m'
from edxrd.peaksearch import from_unitcell
pks = from_unitcell(x,y,unit_cell,space_group,2,4.4,
name='LaB6',shape='g')
print pks[0].centre
yfit = np.zeros_like(x)
for pk in pks:
yfit+=pk.profile(x)
f,ax = plt.subplots(1,1,figsize=(7,4))
f.subplots_adjust(bottom=0.15)
