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import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import BoundaryNorm
from mcstasscript.instrument_diagnostics.diagnostics_instrument import DiagnosticsInstrument
from mcstasscript.interface.functions import name_search
from mcstasscript.data.data import McStasDataBinned
from mcstasscript.helper.plot_helper import _plot_fig_ax
class IntensityDiagnostics(DiagnosticsInstrument):
def __init__(self, instr):
super().__init__(instr)
self.data = None
self.data_dim = None
self.monitors = None
def add_monitor(self, before, options):
name = "I_before_" + before.name
mon = self.instr.add_component(name, "Monitor_nD", before=before)
mon.set_parameters(restore_neutron=1,
xwidth=100, yheight=100,
options=options,
filename='"' + name + ".diag" + '"')
mon.set_AT(before.AT_data, RELATIVE=before.AT_reference)
if before.ROTATED_specified:
mon.set_ROTATED(before.ROTATED_data, RELATIVE=before.ROTATED_reference)
return name
def run_general(self, variable=None, limits=None):#, start=None, end=None):
self.reset_instr()
self.remove_previous_use()
if limits is None:
limit_string = "auto"
else:
if not isinstance(limits, list) or len(limits) != 2:
raise TypeError("limits has to be a list of length 2.")
limit_string = f"limits=[{limits[0]},{limits[1]}]"
if variable is None:
options = f'"square boarders intensity"'
self.data_dim = 0
else:
options = f'"square {variable} {limit_string} bins=300"'
self.data_dim = 1
self.monitors = []
for comp in self.component_list[1:]:
mon_name = self.add_monitor(before=comp, options=options)
self.monitors.append((mon_name, comp.name))
self.correct_target_index()
self.data = self.instr.backengine()
def run(self):#, start=None, end=None):
self.reset_instr()
self.remove_previous_use()
options = f'"square boarders intensity"'
self.monitors = []
for comp in self.component_list[1:]:
mon_name = self.add_monitor(before=comp, options=options)
self.monitors.append((mon_name, comp.name))
self.correct_target_index()
self.data = self.instr.backengine()
def plot(self, figsize=None, ax=None, fig=None, show_comp_names=True,
y_tick_positions=None, ylimits=None):
if self.data_dim == 0:
self.plot_0D(figsize=figsize, ax=ax, fig=fig,
show_comp_names=show_comp_names,
y_tick_positions=y_tick_positions,
ylimits=ylimits)
elif self.data_dim == 1:
self.plot_1D(figsize=figsize, ax=ax, fig=fig,
show_comp_names=show_comp_names,
y_tick_positions=y_tick_positions,
ylimits=ylimits)
def plot_1D(self, figsize=None, ax=None, fig=None, show_comp_names=True,
y_tick_positions=None, ylimits=None):
if figsize is None:
figsize = (8, len(self.component_list) / 5 + 1)
data_sets = []
for I_monitor_name, component_name in self.monitors:
mon_data = name_search(I_monitor_name, self.data)
intensity = mon_data.Intensity
axis = mon_data.xaxis
data_sets.append({"name": component_name, "I": intensity, "axis": axis})
if y_tick_positions is None:
y_positions = np.linspace(1, len(data_sets), len(data_sets))
y_sep = y_positions[1] - y_positions[0]
y_positions = np.append(y_positions, y_positions[-1] + y_sep)
else:
#y_tick_positions.reverse()
y_positions = y_tick_positions
# Find min and max for axis
for index, data_set in enumerate(data_sets):
if index == 0:
max_axis = max(data_set["axis"])
min_axis = min(data_set["axis"])
else:
max_axis = max(max(data_set["axis"]), max_axis)
min_axis = min(min(data_set["axis"]), min_axis)
# New axis from min to max
n_bins = 300
axis = np.linspace(min_axis, max_axis, n_bins)
sep = axis[1] - axis[0]
bin_axis = np.append(axis - 0.5 * sep, axis[-1] + sep)
intensities = np.zeros((len(data_sets), len(axis)))
for index, data_set in enumerate(data_sets):
new_bins = np.digitize(data_set["axis"], bin_axis)
boarder_bins = np.where(new_bins == n_bins)
new_bins[boarder_bins] -= 1
intensities[index, new_bins] += data_set["I"]
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize)
component_names = [x.name for x in self.original_instr.make_component_subset()]
component_names.reverse()
if not show_comp_names:
component_names = [""] * len(component_names)
metadata = mon_data.metadata
metadata.dimension = [len(axis), len(y_positions) - 1]
metadata.limits = [0, 0, 0, 0]
metadata.limits[0] = min(axis)
metadata.limits[1] = max(axis)
metadata.limits[2] = min(y_positions)
metadata.limits[3] = max(y_positions)
display_data = np.flip(intensities, 0)
data = McStasDataBinned(metadata, display_data, np.zeros((1, 1)), np.zeros((1, 1)))
data.set_plot_options(show_colorbar=False, log=True, orders_of_mag=5)
data.set_ylabel("")
_plot_fig_ax(data, fig, ax)
ax.set_yticks(y_positions)
ax.set_yticklabels(component_names, fontsize=18)
ax.set_xlabel(mon_data.metadata.xlabel, fontsize=18)
def plot_0D(self, figsize=None, ax=None, fig=None, show_comp_names=True,
y_tick_positions=None, ylimits=None):
if figsize is None:
figsize = (8, len(self.component_list)/5 + 1)
intensities = []
ray_counts = []
component_names = []
indicies = []
index = 0
for I_monitor_name, component_name in self.monitors:
mon_data = name_search(I_monitor_name, self.data)
values = mon_data.metadata.info["values"].split()
# values contain strings of: Intensity, Error, Ncount
intensities.append(float(values[0]))
ray_counts.append(float(values[2]))
component_names.append(component_name)
indicies.append(index)
index += 1
# Extend with the last one
intensities.append(intensities[-1])
ray_counts.append(ray_counts[-1])
indicies.append(index)
component_names = [self.component_list[0].name] + component_names
if not show_comp_names:
component_names = [""] * len(component_names)
if ax is None:
fig, ax = plt.subplots(1, 1, figsize=figsize)
intensities.reverse()
ray_counts.reverse()
component_names.reverse()
if y_tick_positions is None:
y_positions = indicies
else:
y_tick_positions.reverse()
y_positions = y_tick_positions
# Ensure x scale for intensity and n count share same tick marks
I_limits, N_limits = common_range_limits(intensities, ray_counts)
ax.step(intensities, y_positions, where="post", color="k", zorder=3.5)
ax.set_yticks(y_positions)
ax.set_yticklabels(component_names, fontsize=18)
ax.set_xlabel("Intensity [n/s]", fontsize=18, color="k")
ax.set_xscale("log", nonpositive='clip')
ax.xaxis.set_tick_params(labelsize=16)
ax.set_xlim(I_limits)
if ylimits is None:
ax.set_ylim([-0.5, index + 0.5])
else:
ax.set_ylim(ylimits)
ax.grid(True)
ax2 = ax.twiny()
ax2.step(ray_counts, y_positions, where="post", color="g", linestyle="--", zorder=3.6)
ax2.set_xlabel("Ray count", fontsize=18, color="g")
ax2.set_xscale("log", nonpositive='clip')
ax2.xaxis.set_tick_params(labelsize=16, colors="g")
ax2.set_xlim(N_limits)
def common_range_limits(data_I, data_N):
# Convert to numpy
data_I = np.array(data_I)
data_I_nonzero = data_I[np.nonzero(data_I)]
data_N = np.array(data_N)
data_N_nonzero = data_N[np.nonzero(data_N)]
max_I = max(data_I_nonzero)
min_I = min(data_I_nonzero)
I_orders_of_mag = np.log10(max_I) - np.log10(min_I)
max_N = max(data_N_nonzero)
min_N = min(data_N_nonzero)
N_orders_of_mag = np.log10(max_N) - np.log10(min_N)
I_is_largest = I_orders_of_mag > N_orders_of_mag
if I_is_largest:
# Use intensity scale
max_large = max_I
min_large = min_I
max_small = max_N
else:
max_large = max_N
min_large = min_N
max_small = max_I
log_extra = 0.1 # Extra scale to avoid having data just at the edge
# Round I scale up
log_large_scale_max = np.ceil(np.log10(max_large)) + log_extra
log_large_scale_min = np.floor(np.log10(min_large))
large_limits = [10 ** log_large_scale_min, 10 ** log_large_scale_max]
# Find how many orders of mag this cover
large_orders_of_mag = log_large_scale_max - log_large_scale_min
# Apply same to ray count
log_small_scale_max = np.ceil(np.log10(max_small)) + log_extra
log_small_scale_min = log_small_scale_max - large_orders_of_mag
small_limits = [10 ** log_small_scale_min, 10 ** log_small_scale_max]
if I_is_largest:
I_limits = large_limits
N_limits = small_limits
else:
I_limits = small_limits
N_limits = large_limits
return I_limits, N_limits
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