def _configure(self, config):
d = ConfigDict.ConfigDict()
d.read(config["file"])
if "concentrations" not in d:
d["concentrations"] = {}
if "attenuators" not in d:
d["attenuators"] = {}
d["attenuators"]["Matrix"] = [1, "Water", 1.0, 0.01, 45.0, 45.0]
if "flux" in config:
d["concentrations"]["flux"] = float(config["flux"])
if "time" in config:
d["concentrations"]["time"] = float(config["time"])
self.mcafit.mcafit.configure(d)
def _configure(self, config):
d = ConfigDict.ConfigDict()
d.read(config["file"])
if not d.has_key('concentrations'):
d['concentrations'] = {}
if not d.has_key('attenuators'):
d['attenuators'] = {}
d['attenuators']['Matrix'] = [1, 'Water', 1.0, 0.01, 45.0, 45.0]
if config.has_key('flux'):
d['concentrations']['flux'] = float(config['flux'])
if config.has_key('time'):
d['concentrations']['time'] = float(config['time'])
self.mcafit.mcafit.configure(d)
def _configure(self, config):
d = ConfigDict.ConfigDict()
d.read(config["file"])
if 'concentrations' not in d:
d['concentrations'] = {}
if 'attenuators' not in d:
d['attenuators'] = {}
d['attenuators']['Matrix'] = [1, 'Water', 1.0, 0.01, 45.0, 45.0]
if 'flux' in config:
d['concentrations']['flux'] = float(config['flux'])
if 'time' in config:
d['concentrations']['time'] = float(config['time'])
self.mcafit.mcafit.configure(d)
def gplot_rh5(h5file, channel='channel00'):
f = h5py.File(h5file, 'r')
spe = np.array(f['raw/' + channel + '/sumspec'])
try:
names = [n.decode('utf8') for n in f['fit/' + channel + '/names']]
cfg = f['fit/' + channel + '/cfg'][()].decode('utf8')
except Exception:
names = None
cfg = None
if cfg is not None: # we're looking for the energy calibration values...
from PyMca5.PyMca import ConfigDict
config = ConfigDict.ConfigDict()
config.read(cfg)
cfg = [
config['detector']['zero'], config['detector']['gain'],
config['fit']['energy'][0]
] #zero, gain, E_Rayleigh
f.close()
return spe, names, cfg
def plot(data,
labels=None,
cfg=None,
xrange=None,
yrange=None,
normtochan=None,
savename='plot.png'):
data = np.array(data) #expected dimensions: [N, channels]
if len(data.shape) == 1:
data = np.reshape((1, data.shape[0]))
elif len(data.shape) >= 3:
print("Error: expected data dimensions: [N, channels]; ", data.shape)
return
if cfg is not None: # we're looking for the energy calibration values...
from PyMca5.PyMca import ConfigDict
config = ConfigDict.ConfigDict()
config.read(cfg)
cfg = [
config['detector']['zero'], config['detector']['gain'],
config['fit']['energy'][0]
] #zero, gain, E_Rayleigh
names = []
peaks = list(config['peaks'].keys())
for peak in peaks:
for linetype in config['peaks'][peak]:
names.append(peak + ' ' + str(linetype))
plt.figure(figsize=(20, 15))
for j in range(0, data.shape[0]):
spe = data[j, :]
if labels is None:
lbl = 'spectrum ' + str(j)
else:
lbl = labels[j]
if normtochan is not None:
spe = spe[:] / spe[normtochan]
if cfg is None:
zero = 0.
gain = 1.
xtitle = "Channel Number"
else:
zero = cfg[0]
gain = cfg[1]
xtitle = "Energy [keV]"
# plot the spectrum, Ylog, X-axis converted to Energy (keV) if PyMca cfg provided
if xrange is None:
xrange = (zero, (spe.shape[0] - 1) * gain + zero)
plt.plot(np.linspace(0, spe.shape[0] - 1, num=spe.shape[0]) * gain +
zero,
spe,
label=lbl,
linestyle='-')
ax = plt.gca()
handles, labels = ax.get_legend_handles_labels()
plt.yscale('log')
plt.xlim(xrange)
if yrange is not None:
plt.ylim(yrange)
plt.legend(handles, labels, loc='best')
ax.set_xlabel(xtitle, fontsize=16)
ax.set_ylabel("Intensity [counts]", fontsize=16)
ax.tick_params(axis='both', which='major', labelsize=14)
ax.tick_params(axis='x', which='minor', bottom=True)
# add peak annotation if names and cfg provided
if cfg is not None:
# x axis of plot is in Energy (keV)
# determine peak energy values (Rayl energy = cfg[2])
from PyMca5.PyMcaPhysics.xrf import Elements
for n in names:
if n != 'Rayl' and n != 'Compt':
if n.split(" ")[1][0] == 'K':
energy = Elements.getxrayenergy(
n.split(" ")[0],
n.split(" ")[1] + 'L3') #Ka1
elif n.split(" ")[1][0] == 'L':
energy = Elements.getxrayenergy(
n.split(" ")[0],
n.split(" ")[1] + '3M5') #La1
elif n.split(" ")[1][0] == 'M':
energy = Elements.getxrayenergy(
n.split(" ")[0],
n.split(" ")[1] + '5N7') #Ma1
elif n == 'Rayl':
energy = cfg[2]
else:
energy = None
# if energy not None, plot label at this energy value
if energy is not None:
idx = max(np.where(handles[0].get_xdata() <= energy)[-1])
yval = 10**(
np.log10(max([hand.get_ydata()[idx]
for hand in handles])) * 1.025)
# plot the text label X% above this value
plt.text(energy,
yval,
n,
horizontalalignment='center',
fontsize=14)
plt.show()
plt.savefig(savename) #, bbox_inches='tight', pad_inches=0)
plt.close()