def get_sum_spectrum(fid, plot_indv):
print fid
full_file_array = file_scraper.get_h5py_data(fid)
indv_spectra_array = full_file_array[:, 0, :]
if plot_indv == True:
plt.plot(indv_spectra_array.T)
num_of_spectra = indv_spectra_array.shape[0]
num_of_bins = indv_spectra_array.shape[1]
print 'Number of spectra', num_of_spectra
print 'Number of bins ', num_of_bins
spectrum_sum_scaled = (1.0 /
num_of_spectra) * indv_spectra_array.sum(axis=0)
#Slight fudge, scale needs better way of arriving at answer
scale_vortex_nrg = 8041.0 / 801.0
print 'Scale of Vortex to Energy', scale_vortex_nrg
terminal_energy = scale_vortex_nrg * (num_of_bins - 1)
print 'Terminal energy', terminal_energy
x_axis = scale_vortex_nrg * np.arange(0, num_of_bins, 1)
one_ev_energy_axis = np.arange(0, np.floor(terminal_energy), 1)
#one_ev_energy_axis = np.arange(0, math.floor(terminal_energy), 1)
spectrum_sum_scal_mapped = interp1d(x_axis, spectrum_sum_scaled,
'linear')(one_ev_energy_axis)
#shift vertically
spectrum_sum_scal_mapped = spectrum_sum_scal_mapped - 8.0
return one_ev_energy_axis, spectrum_sum_scal_mapped
def mapme(fid, scale_dict, cutoff, include_list):
elem_list = []
scale_list = []
print 'CUTOFF =', cutoff
for line_name, scale in scale_dict.items():
if scale >= cutoff:
elem_list.append(line_name)
scale_list.append(scale)
xbox, ybox = get_factor_list(len(elem_list))[-1]
if len(elem_list) > 16:
print 'Too Long'
xbox, ybox = 5, 5
xdim, ydim, x_step_size, y_step_size, x_start, y_start = file_scraper.get_gridscan_data(
fid)
print '\tData from .gridscan file'
print '\tGrid Dimensions', xdim, ydim
print '\tStep Sizes', x_step_size, y_step_size
print '\tStart Position', x_start, y_start
spacex = np.linspace(x_start, x_start + (xdim * (x_step_size / 1000.)))
spacey = np.linspace(y_start, y_start + (xdim * (x_step_size / 1000.)))
aspect = float(y_step_size) / float(x_step_size)
print 15 * aspect, 15 * (1. / aspect)
per_tab_dict = file_scraper.get_per_tab_dict()
array = file_scraper.get_h5py_data(fid)
#shp_array = shape_array(array, [xdim,ydim])
shp_array = shape_array(array, [xdim, ydim]) # - 15.0
print 'shaped array', shp_array.shape
result_array = shp_array
# Try mean subtraction?
#mean_sub_array = shp_array - shp_array.mean(axis=2, keepdims=True)
#print 'mean_sub ', mean_sub_array.shape
#result_array = mean_sub_array
print 'result ', result_array.shape
print 'slicing out channels'
slc_array_list = []
print 'element_list =', elem_list
print 'element_list =', elem_list, '+', include_list
print include_list
#elem_list = elem_list + include_list
for elem in elem_list:
emission_line = get_emissions(per_tab_dict, elem)
llm, hlm = emission_line[1], emission_line[2]
Z = elem.split('_')[0]
print elem, '\t', emission_line
slc_array = slice_and_sum_array(result_array, [llm, hlm])
slc_array_list.append(slc_array)
fig, axs2 = plt.subplots(xbox,
ybox,
figsize=((15 / aspect), 15),
facecolor='0.3',
edgecolor='k')
#fig, axs2 = plt.subplots(xbox, ybox, facecolor='0.3', edgecolor='k')
#fig, axs2 = plt.subplots(xbox, ybox, facecolor='0.3', edgecolor='k')
axs2 = axs2.ravel()
print 'Plotting'
for i, elem in enumerate(elem_list):
Z = elem.split('_')[0]
ray = slc_array_list[i]
pymol_c = per_tab_dict[Z][3]
w2k = [(0, 0, 0), pymol_c]
ct = LinearSegmentedColormap.from_list('ctest', w2k, N=300)
#axs2[i].imshow(ray, cmap=ct, interpolation='nearest')
axs2[i].imshow(ray,
aspect='equal',
extent=extents(spacex) + extents(spacey),
origin='upper',
cmap=ct,
interpolation='nearest',
vmin=0)
axs2[i].set_title(elem, y=0.9, color='0.8', loc='left')
#axs2[i].set_xticks([])
#axs2[i].set_yticks([])
fig.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.95,
wspace=0.00,
hspace=0.0)
#xfid = fid.split('/')[-1]
#'/dls/i24/data/2017/nr16818-47/processing/GoldDigger_170512'
#output_fid = xfid[:-5] + '%s.png' %time.strftime("_%Y%m%d_%H%M%S")
#print 'Saved in /Snapshots'
#print output_fid
#plt.savefig(output_fid, dpi=400, bbox_inches='tight', pad_inches=0)
return fig