import numpy as np
import helper
#runs = [5,6]
runs = [3,4,7,8,9]
for run in runs:
filename = helper.getparam("filenames", run)
hist = np.zeros((1621, 141, 141))
counts = np.zeros((1621, 141, 141))
for i_block in xrange(9):
hist[i_block*162:(i_block+1)*162, :, :] = np.load(\
"data_hklmat/%s_hist_%04d.npy"%(filename, i_block))
counts[i_block*162:(i_block+1)*162, :, :] = np.load(\
"data_hklmat/%s_counts_%04d.npy"%(filename, i_block))
i_block = 9
hist[i_block*162:, :, :] = np.load(\
"data_hklmat/%s_hist_%04d.npy"%(filename, i_block))
counts[i_block*162:, :, :] = np.load(\
"data_hklmat/%s_counts_%04d.npy"%(filename, i_block))
np.save("data_hklmat/%s_hist.npy"%(filename), hist)
np.save("data_hklmat/%s_counts.npy"%(filename), counts)
print "done run %d" % run
matplotlib.rcParams['mathtext.fontset'] = 'cm'
import matplotlib.pyplot as plt
plt.rc('font', family='serif', size=12)
plt.rcParams['xtick.top'] = plt.rcParams['ytick.right'] = True
plt.ion()
from matplotlib.gridspec import GridSpec
from scipy.interpolate import griddata
from settings import a0, hist_bincenters, hist_binedges, a0_reciprocal
from settings import Rlist, Rlist_colors, R_edges, R_barwidth
from settings import slices
import helper
runs = [4, 3, 8, 9, 7]
for i_run, run in enumerate(runs):
filename = helper.getparam("filenames", run)
sample = helper.getparam("samples", run)
hist = np.load("data_hklmat/%s_hist.npy" % filename)
counts = np.load("data_hklmat/%s_counts.npy" % filename)
nx, ny, nz = hist.shape
intensities = hist[:,:,68:73].sum(axis=2) * \
1./counts[:,:,68:73].sum(axis=2)
xx, yy = np.meshgrid(range(nx), range(ny), indexing='ij')
coords = np.array([xx.ravel(), yy.ravel()]).T
intensities = intensities.ravel()
mask = np.isnan(intensities)
intensities[mask] = griddata(coords[~mask],
intensities[~mask],
coords[mask],
import numpy as np
import helper
import os, sys
import matplotlib.pyplot as plt
from pprint import pprint
runs = [3, 4]
for run in runs:
filename = helper.getparam('filenames', run)
times_array = []
for root, dirs, files in os.walk(os.getcwd()+"/data"):
if filename in files:
for line in open(root+'/'+filename, 'r'):
if line.startswith("#T "):
times_array.append(float(line.split(' ')[1]))
break # found the file, so stop looping through directory
if len(times_array) != 81*7:
print "file %s is wrong!" % filename
continue
plt.semilogy(times_array[0::7], label="0")
pprint(zip(range(81), times_array[0::7]))
plt.semilogy(times_array[3::7], label="3")
plt.legend()
plt.show()
np.save("times/%s_times.npy"%filename, times_array)
runs = [4, 3, 8, 9, 7]
qarray = hist_bincenters[0] * a0_reciprocal
xmin, xmax = hist_binedges[1][0] * a0_reciprocal, hist_binedges[1][
-1] * a0_reciprocal
ymin, ymax = hist_binedges[0][0] * a0_reciprocal, hist_binedges[0][
-1] * a0_reciprocal
## plot the 2d images
fig = plt.figure(figsize=(6, 8))
grid = ImageGrid(fig, 111, nrows_ncols=(1,5), axes_pad=0.15, \
share_all=True, cbar_location='right', \
cbar_mode='single', cbar_size="15%", cbar_pad=0.15)
for i_run, run in enumerate(runs):
ax = grid[i_run]
filename = helper.getparam("filenames", run)
sample = helper.getparam("samples", runs[i_run])
orimat = np.load("orimats/%s.npy" % (helper.getparam("orimats", run)))
data_orig = np.load("data_hklmat/%s_interpolated_xy.npy" % filename)
im = ax.imshow(np.log10(data_orig), extent=[xmin, xmax, ymin, ymax], \
origin='lower', interpolation='nearest', \
vmax=3.0)
if do_plotsurface:
surf_norm = orimat.dot(qvec(50., 25., 90., 0.))
surf_norm = surf_norm / np.linalg.norm(surf_norm)
print "hkl of surface =", surf_norm
surf_norm = orimat.dot(qvec(60., 30., 90., 45.))
surf_norm = surf_norm / np.linalg.norm(surf_norm)
print "hkl of surface =", surf_norm, "(to check if th and phi matters)"
xx = np.array([-1., 1.]) * surf_norm.dot(e1)
import numpy as np
import helper
from orimat import angs_phifix
run = 3
orimat = np.load("orimats/%s.npy" % (helper.getparam("orimats", run)))
print helper.getparam("orimats", run)
print angs_phifix(2, 2, 0.025, 0., orimat)
import numpy as np
import sys
from orimat import kivec, qvec, qvec_array
from settings import imshape
import time
import matplotlib.pyplot as plt
import helper
runs = [3, 4, 7, 8, 9]
hkl_centers = []
for run in runs:
filename = helper.getparam("filenames", run)
orimat = np.load("orimats/%s.npy" % (helper.getparam("orimats", run)))
surf_norm = orimat.dot(qvec(50., 25., 90.,
0.)) # th and phi don't really matter
surf_norm = surf_norm / np.linalg.norm(surf_norm)
print "starting run %d, filename=%s, orimat=" % (\
run, filename)
print orimat
i_scan = 1
now = time.time()
while True:
try:
for i_file in [0, 1]:
f = np.load("data_npz/%s_scan%d_%04d.npz"%(\
filename, i_scan, i_file))
tth = float(np.asscalar(f['tth']))
th = float(np.asscalar(f['th']))
chi = float(np.asscalar(f['chi']))
fitrg = (-0.7, 0.7)
indmin, indmax = np.searchsorted(q, fitrg)
q = q[indmin:indmax]
C = C[indmin:indmax]
np.save("fit/q.npy", q)
np.save("fit/C.npy", C)
## load run 4 data to estimate error
intensities0 = np.zeros((len(slices), indmax-indmin))
for i_s, (yind, zind, qperp) in enumerate(slices):
intensities0[i_s] = np.load("fit/run4_slice_%s.npy"%(i_s))
intensities_err = (intensities0.max(axis=0) - intensities0.min(axis=0))*0.5
fig, ax = plt.subplots(1, 1, figsize=(10, 3.5))
for i_run, run in enumerate(runs):
sample = helper.getparam('samples', run)
data = []
data_err = []
data_fit = []
for i_s, (yind, zind, qperp) in enumerate(slices):
if qperp != 'qperp_0p010': continue
print "slice %d, yind = %d, zind = %d, %s" % (i_s, yind, zind, qperp)
## load theoretical data
theory_dir = "fit/%s/" % qperp
q4I_th = []
for looptype in ['int', 'vac']:
for R in Rlist:
## load theoretical data
_data = np.load(theory_dir+"R%d_0_%s.npz"%(R, looptype))['q4I']
## multiply by form factor
fig_tot, ax_tot = plt.subplots(1, 1, figsize=(5, 4))
ax_tot.errorbar(-10, 0, 0, fmt='go', fillstyle='none', capsize=5, label='vac')
ax_tot.errorbar(-10, 0, 0, fmt='bs', capsize=5, label='int')
## Figure for average radius
fig_R, ax_R = plt.subplots(1, 1, figsize=(5, 4))
ax_R.errorbar(-10, 0, 0, fmt='go', fillstyle='none', capsize=5, label='vac')
ax_R.errorbar(-10, 0, 0, fmt='bs', capsize=5, label='int')
## File for output
f_table = open("fit_results.csv", 'w')
f_table.write("csum in 1/atom, tot in 1/atom, Ravg in Angstrom\n\n")
f_table.write("DPA\ttype\tcsum\t+\t-\ttot\t+\t-\tRavg\t+\t-\n")
runs = [3, 8, 9, 7]
for i_run, (run, ax1, ax2) in enumerate(zip(runs, axes1, axes2)):
sample = helper.getparam('samples', run)
dpa = helper.getdpa(sample)
cs_int = np.zeros((len(slices), len(Rlist)))
cs_vac = np.zeros((len(slices), len(Rlist)))
cs_int_err = np.zeros((len(slices), len(Rlist)))
cs_vac_err = np.zeros((len(slices), len(Rlist)))
dcR2_int = np.zeros((len(slices), len(Rlist)))
dcR2_vac = np.zeros((len(slices), len(Rlist)))
tot_int = np.zeros(len(slices))
tot_vac = np.zeros(len(slices))
R_ave_int = np.zeros(len(slices))
R_ave_vac = np.zeros(len(slices))
R_ave_all = np.zeros(len(slices))
i_s_ave = [] # indices to take the average
