def zero_crossings(y_axis, x_axis=None, window=24):
"""
Algorithm to find zero crossings. Smoothens the curve and finds the
zero-crossings by looking for a sign change.
keyword arguments:
y_axis -- A list containg the signal over which to find zero-crossings
x_axis -- A x-axis whose values correspond to the 'y_axis' list and is used
in the return to specify the postion of the zero-crossings. If omitted
then the indice of the y_axis is used. (default: None)
window -- half of the dimension of the smoothing window;
(default: 24)
return -- the x_axis value or the indice for each zero-crossing
"""
# smooth the curve
length = len(y_axis)
if x_axis == None:
x_axis = range(length)
x_axis = np.asarray(x_axis)
y_axis = smooth(y_axis, window)
zero_crossings = np.where(np.diff(np.sign(y_axis)))[0]
times = [x_axis[indice] for indice in zero_crossings]
# check if zero-crossings are valid
diff = np.diff(times)
if diff.std() / diff.mean() > 0.1:
raise ValueError, "smoothing window too small, false zero-crossings found"
return times
def smooth_data(self):
with self.data_changed.hold_and_emit():
if self.smooth_degree > 0:
degree = int(self.smooth_degree)
self.data_y[:, 0] = smooth(self.data_y[:, 0], degree)
self.smooth_degree = 0.0
def smooth_pattern(pattern):
"""
Smooth the given pattern.
"""
return smooth(pattern, 15)
def smooth_pattern(pattern):
"""
Smooth the given pattern.
"""
return smooth(pattern, 15)
def plot_specimen(project, specimen, labels, marker_lbls, label_offset,
plot_left, offset, scale, marker_scale, axes):
"""
Plots a specimens patterns, markers and hatches using the given
offset and scale
"""
# Plot the patterns;
if specimen.display_experimental:
pattern = specimen.experimental_pattern
# plot the experimental pattern:
plot_pattern(pattern,
axes,
scale=scale,
offset=offset,
cap=pattern.cap_value)
#make_draggable(getattr(pattern, "__plot_line", None), drag_y_handler=specimen.on_pattern_dragged)
# get some common data for the next lines:
x_data, y_data = pattern.get_xy_data()
xmin, xmax = (np.min(x_data),
np.max(x_data)) if x_data.size > 0 else (0, 0)
ymin, ymax = (np.min(y_data),
np.max(y_data)) if y_data.size > 0 else (0, 0)
########################################################################
# plot the background pattern:
bg_line = getattr_or_create(
pattern, "__plot_bg_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="2", zorder=10)))
if pattern.bg_type == 0 and pattern._bg_position != 0.0:
bg_line.update(
dict(data=apply_transform(
([xmin, xmax], [pattern.bg_position, pattern.bg_position]),
scale=scale,
offset=offset),
visible=True))
elif pattern.bg_type == 1 and pattern.bg_pattern is not None:
bg_line.update(
dict(data=apply_transform(
(x_data, (pattern.bg_pattern * pattern.bg_scale) +
pattern.bg_position),
scale=scale,
offset=offset),
visible=True))
else:
bg_line.update(dict(data=([], []), visible=True))
if bg_line.get_visible() and not bg_line in axes.get_lines():
axes.add_line(bg_line)
elif not bg_line.get_visible():
try:
bg_line.remove()
except:
pass
pattern.__plot_bg_line = bg_line
########################################################################
########################################################################
# plot the smooth pattern:
smooth_line = getattr_or_create(
pattern, "__plot_smooth_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="2", zorder=10)))
if int(pattern.smooth_degree) > 1:
data = x_data, smooth(y_data, pattern.smooth_degree)
else:
data = [], []
smooth_line.update(
dict(data=apply_transform(data, scale=scale, offset=offset),
visible=bool(pattern.smooth_degree > 1)))
if smooth_line.get_visible() and not smooth_line in axes.get_lines():
axes.add_line(smooth_line)
elif not smooth_line.get_visible():
try:
smooth_line.remove()
except:
pass
pattern.__plot_smooth_line = smooth_line
########################################################################
########################################################################
# plot the noisified pattern:
noise_line = getattr_or_create(
pattern, "__plot_noise_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="2", zorder=10)))
if pattern.noise_fraction > 0.0:
data = x_data, add_noise(y_data, pattern.noise_fraction)
else:
data = [], []
noise_line.update(
dict(data=apply_transform(data, scale=scale, offset=offset),
visible=bool(pattern.noise_fraction > 0.0)))
if noise_line.get_visible() and not noise_line in axes.get_lines():
axes.add_line(noise_line)
elif not noise_line.get_visible():
try:
noise_line.remove()
except:
pass
pattern.__plot_noise_line = noise_line
########################################################################
########################################################################
# plot the shift & reference lines:
shifted_line = getattr_or_create(
pattern, "__plot_shifted_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="2", zorder=10)))
reference_line = getattr_or_create(
pattern, "__plot_reference_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="2", ls="--", zorder=10)))
if pattern.shift_value != 0.0:
shifted_line.update(
dict(data=apply_transform(
(x_data - pattern._shift_value, y_data.copy()),
scale=scale,
offset=offset),
visible=True))
position = specimen.goniometer.get_2t_from_nm(
pattern.shift_position)
reference_line.update(
dict(data=apply_transform(([position, position], [0, ymax]),
scale=scale,
offset=offset),
visible=True))
if not shifted_line in axes.get_lines():
axes.add_line(shifted_line)
if not reference_line in axes.get_lines():
axes.add_line(reference_line)
else:
shifted_line.set_data([], [])
shifted_line.set_visible(False)
try:
shifted_line.remove()
except:
pass
reference_line.set_data([], [])
reference_line.set_visible(False)
try:
reference_line.remove()
except:
pass
pattern.__plot_shifted_line = shifted_line
pattern.__plot_reference_line = reference_line
########################################################################
########################################################################
# plot the pattern after peak stripping:
stripped_line = getattr_or_create(
pattern, "__plot_stripped_line",
(matplotlib.lines.Line2D,
([], []), dict(c="#660099", lw="1", zorder=10)))
if pattern.strip_startx != 0.0 and pattern.strip_endx != 0.0:
strip_xdata, strip_ydata = pattern.stripped_pattern
stripped_line.update(
dict(data=apply_transform(
(strip_xdata.copy(), strip_ydata.copy()),
scale=scale,
offset=offset),
visible=True))
if not stripped_line in axes.get_lines():
axes.add_line(stripped_line)
else:
stripped_line.set_data([], [])
stripped_line.set_visible(False)
try:
stripped_line.remove()
except:
pass
pattern.__plot_stripped_line = stripped_line
########################################################################
########################################################################
# plot the pattern after peak stripping:
peak_area = getattr(specimen, "__plot_peak_area", None)
if peak_area is not None and peak_area in axes.get_children():
peak_area.remove()
if pattern.area_startx != 0.0 and pattern.area_endx != 0.0 and pattern.area_pattern is not None:
area_xdata, area_bg, area_ydata = pattern.area_pattern
_, area_bg = apply_transform((area_xdata.copy(), area_bg.copy()),
scale=scale,
offset=offset)
area_xdata, area_ydata = apply_transform(
(area_xdata.copy(), area_ydata.copy()),
scale=scale,
offset=offset)
peak_area = axes.fill_between(area_xdata,
area_bg,
area_ydata,
interpolate=True,
facecolor="#660099",
zorder=10)
setattr(specimen, "__plot_peak_area", peak_area)
if specimen.display_calculated:
pattern = specimen.calculated_pattern
plot_pattern(pattern, axes, scale=scale, offset=offset)
#if not specimen.display_experimental:
# make_draggable(getattr(pattern, "__plot_line", None), drag_y_handler=specimen.on_pattern_dragged)
# setup or update the calculated lines (phases)
if specimen.display_phases:
phase_lines = getattr(specimen, "__plot_phase_lines", [])
# Clear previous phase lines:
for phase_line in phase_lines:
if phase_line in axes.get_lines():
axes.remove_line(phase_line)
# Update & add phase lines:
for i in range(2, pattern.num_columns):
phase_data = pattern.get_xy_data(i)
# Get the line object or create it:
try:
phase_line = phase_lines[i - 2]
except IndexError:
phase_line = matplotlib.lines.Line2D(*phase_data)
phase_lines.append(phase_line)
# Get the phase color or use a default color:
try:
phase_color = pattern.phase_colors[i - 2]
except IndexError:
phase_color = pattern.color
# Update the line object properties:
phase_line.update(
dict(data=apply_transform(phase_data,
scale=scale,
offset=offset),
color=phase_color,
linewidth=pattern.lw))
# Add to axes:
axes.add_line(phase_line)
specimen.__plot_phase_lines = phase_lines
# mineral preview sticks
if hasattr(specimen,
"mineral_preview") and specimen.mineral_preview is not None:
name, peaks = specimen.mineral_preview
lines = getattr(specimen, "__plot_mineral_preview", [])
for line in lines:
try:
line.remove()
except:
pass
lines = []
for position, intensity in peaks:
position = specimen.goniometer.get_2t_from_nm(position / 10.)
intensity /= 100.
trans = transforms.blended_transform_factory(
axes.transData, axes.transAxes)
ymin, ymax = axes.get_ybound()
style = "solid"
color = "#FF00FF"
y0 = (offset - ymin) / (ymax - ymin)
y1 = y0 + (intensity - ymin) / (ymax - ymin)
line = matplotlib.lines.Line2D([position, position], [y0, y1],
transform=trans,
color=color,
ls=style)
axes.add_line(line)
lines.append(line)
setattr(specimen, "__plot_mineral_preview", lines)
# exclusion ranges;
plot_hatches(project, specimen, offset, scale, axes)
# markers;
plot_markers(project, specimen, marker_lbls, offset, scale, marker_scale,
axes)
# & label:
plot_label(specimen, labels, label_offset, plot_left, axes)
def plot_specimen(project, specimen, labels, marker_lbls, label_offset, plot_left,
offset, scale, marker_scale, axes):
"""
Plots a specimens patterns, markers and hatches using the given
offset and scale
"""
# Plot the patterns;
if specimen.display_experimental:
pattern = specimen.experimental_pattern
# plot the experimental pattern:
plot_pattern(pattern, axes, scale=scale, offset=offset, cap=pattern.cap_value)
#make_draggable(getattr(pattern, "__plot_line", None), drag_y_handler=specimen.on_pattern_dragged)
# get some common data for the next lines:
x_data, y_data = pattern.get_xy_data()
xmin, xmax = (np.min(x_data), np.max(x_data)) if x_data.size > 0 else (0, 0)
ymin, ymax = (np.min(y_data), np.max(y_data)) if y_data.size > 0 else (0, 0)
########################################################################
# plot the background pattern:
bg_line = getattr_or_create(pattern, "__plot_bg_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="2", zorder=10)))
if pattern.bg_type == 0 and pattern._bg_position != 0.0:
bg_line.update(dict(
data=apply_transform(([xmin, xmax], [pattern.bg_position, pattern.bg_position]), scale=scale, offset=offset),
visible=True
))
elif pattern.bg_type == 1 and pattern.bg_pattern is not None:
bg_line.update(dict(
data=apply_transform((x_data, (pattern.bg_pattern * pattern.bg_scale) + pattern.bg_position), scale=scale, offset=offset),
visible=True
))
else:
bg_line.update(dict(
data=([], []),
visible=True
))
if bg_line.get_visible() and not bg_line in axes.get_lines():
axes.add_line(bg_line)
elif not bg_line.get_visible():
try: bg_line.remove()
except: pass
pattern.__plot_bg_line = bg_line
########################################################################
########################################################################
# plot the smooth pattern:
smooth_line = getattr_or_create(pattern, "__plot_smooth_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="2", zorder=10)))
if int(pattern.smooth_degree) > 1:
data = x_data, smooth(y_data, pattern.smooth_degree)
else:
data = [], []
smooth_line.update(dict(
data=apply_transform(data, scale=scale, offset=offset),
visible=bool(pattern.smooth_degree > 1)
))
if smooth_line.get_visible() and not smooth_line in axes.get_lines():
axes.add_line(smooth_line)
elif not smooth_line.get_visible():
try: smooth_line.remove()
except: pass
pattern.__plot_smooth_line = smooth_line
########################################################################
########################################################################
# plot the noisified pattern:
noise_line = getattr_or_create(pattern, "__plot_noise_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="2", zorder=10)))
if pattern.noise_fraction > 0.0:
data = x_data, add_noise(y_data, pattern.noise_fraction)
else:
data = [], []
noise_line.update(dict(
data=apply_transform(data, scale=scale, offset=offset),
visible=bool(pattern.noise_fraction > 0.0)
))
if noise_line.get_visible() and not noise_line in axes.get_lines():
axes.add_line(noise_line)
elif not noise_line.get_visible():
try: noise_line.remove()
except: pass
pattern.__plot_noise_line = noise_line
########################################################################
########################################################################
# plot the shift & reference lines:
shifted_line = getattr_or_create(pattern, "__plot_shifted_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="2", zorder=10)))
reference_line = getattr_or_create(pattern, "__plot_reference_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="2", ls="--", zorder=10)))
if pattern.shift_value != 0.0:
shifted_line.update(dict(
data=apply_transform((x_data - pattern._shift_value, y_data.copy()), scale=scale, offset=offset),
visible=True
))
position = specimen.goniometer.get_2t_from_nm(pattern.shift_position)
reference_line.update(dict(
data=apply_transform(([position, position], [0, ymax]), scale=scale, offset=offset),
visible=True
))
if not shifted_line in axes.get_lines():
axes.add_line(shifted_line)
if not reference_line in axes.get_lines():
axes.add_line(reference_line)
else:
shifted_line.set_data([], [])
shifted_line.set_visible(False)
try: shifted_line.remove()
except: pass
reference_line.set_data([], [])
reference_line.set_visible(False)
try: reference_line.remove()
except: pass
pattern.__plot_shifted_line = shifted_line
pattern.__plot_reference_line = reference_line
########################################################################
########################################################################
# plot the pattern after peak stripping:
stripped_line = getattr_or_create(pattern, "__plot_stripped_line", (matplotlib.lines.Line2D, ([], []), dict(c="#660099", lw="1", zorder=10)))
if pattern.strip_startx != 0.0 and pattern.strip_endx != 0.0:
strip_xdata, strip_ydata = pattern.stripped_pattern
stripped_line.update(dict(
data=apply_transform((strip_xdata.copy(), strip_ydata.copy()), scale=scale, offset=offset),
visible=True
))
if not stripped_line in axes.get_lines():
axes.add_line(stripped_line)
else:
stripped_line.set_data([], [])
stripped_line.set_visible(False)
try: stripped_line.remove()
except: pass
pattern.__plot_stripped_line = stripped_line
########################################################################
########################################################################
# plot the pattern after peak stripping:
peak_area = getattr(specimen, "__plot_peak_area", None)
if peak_area is not None and peak_area in axes.get_children():
peak_area.remove()
if pattern.area_startx != 0.0 and pattern.area_endx != 0.0 and pattern.area_pattern is not None:
area_xdata, area_bg, area_ydata = pattern.area_pattern
_, area_bg = apply_transform((area_xdata.copy(), area_bg.copy()), scale=scale, offset=offset)
area_xdata, area_ydata = apply_transform((area_xdata.copy(), area_ydata.copy()), scale=scale, offset=offset)
peak_area = axes.fill_between(area_xdata, area_bg, area_ydata, interpolate=True, facecolor="#660099", zorder=10)
setattr(specimen, "__plot_peak_area", peak_area)
if specimen.display_calculated:
pattern = specimen.calculated_pattern
plot_pattern(pattern, axes, scale=scale, offset=offset)
#if not specimen.display_experimental:
# make_draggable(getattr(pattern, "__plot_line", None), drag_y_handler=specimen.on_pattern_dragged)
# setup or update the calculated lines (phases)
if specimen.display_phases:
phase_lines = getattr(specimen, "__plot_phase_lines", [])
# Clear previous phase lines:
for phase_line in phase_lines:
if phase_line in axes.get_lines():
axes.remove_line(phase_line)
# Update & add phase lines:
for i in xrange(2, pattern.num_columns):
phase_data = pattern.get_xy_data(i)
# Get the line object or create it:
try:
phase_line = phase_lines[i - 2]
except IndexError:
phase_line = matplotlib.lines.Line2D(*phase_data)
phase_lines.append(phase_line)
# Get the phase color or use a default color:
try:
phase_color = pattern.phase_colors[i - 2]
except IndexError:
phase_color = pattern.color
# Update the line object properties:
phase_line.update(dict(
data=apply_transform(phase_data, scale=scale, offset=offset),
color=phase_color,
linewidth=pattern.lw
))
# Add to axes:
axes.add_line(phase_line)
specimen.__plot_phase_lines = phase_lines
# mineral preview sticks
if hasattr(specimen, "mineral_preview") and specimen.mineral_preview is not None:
name, peaks = specimen.mineral_preview
lines = getattr(specimen, "__plot_mineral_preview", [])
for line in lines:
try: line.remove()
except: pass
lines = []
for position, intensity in peaks:
position = specimen.goniometer.get_2t_from_nm(position / 10.)
intensity /= 100.
trans = transforms.blended_transform_factory(axes.transData, axes.transAxes)
ymin, ymax = axes.get_ybound()
style = "solid"
color = "#FF00FF"
y0 = (offset - ymin) / (ymax - ymin)
y1 = y0 + (intensity - ymin) / (ymax - ymin)
line = matplotlib.lines.Line2D(
[position, position], [y0, y1],
transform=trans, color=color, ls=style
)
axes.add_line(line)
lines.append(line)
setattr(specimen, "__plot_mineral_preview", lines)
# exclusion ranges;
plot_hatches(project, specimen, offset, scale, axes)
# markers;
plot_markers(project, specimen, marker_lbls, offset, scale, marker_scale, axes)
# & label:
plot_label(specimen, labels, label_offset, plot_left, axes)
def smooth_data(self):
with self.data_changed.hold_and_emit():
if self.smooth_degree > 0:
degree = int(self.smooth_degree)
self.data_y[:, 0] = smooth(self.data_y[:, 0], degree)
self.smooth_degree = 0.0