def adult_cohort_bins(complete_df, my_worms = None, bin_width_days = 2): ''' Compute bins of cohorts of worms. ''' if my_worms == None: my_worms = complete_df.worms adultspans = get_adultspans(complete_df)/24 max_adultspans = int(np.ceil(np.max(adultspans))) my_bins = [(i*bin_width_days, i*bin_width_days + bin_width_days) for i in range(0, max_adultspans//bin_width_days + 1)] life_cohorts = [[] for a_bin in my_bins] for i in range(0, adultspans.shape[0]): if complete_df.worms[i] in my_worms: my_lifespan = adultspans[i] my_bin = int(my_lifespan//bin_width_days) life_cohorts[my_bin].append(i) # Remove empty bins and cohorts. my_bins = [my_bins[i] for i in range(0, len(life_cohorts)) if len(life_cohorts[i]) > 0] life_cohorts = [a_cohort for a_cohort in life_cohorts if len(a_cohort) > 0] # Figure out the average lifespan of each bin. bin_lifes = np.zeros(len(my_bins)) for i in range(0, len(bin_lifes)): bin_lifes[i] = np.mean(adultspans[life_cohorts[i]]) # Color code the bins. my_colors = zplib_image_colorize.color_map(bin_lifes/np.max(bin_lifes[~np.isnan(bin_lifes)])) my_colors = my_colors/255 return (life_cohorts, bin_lifes, my_bins, my_colors)
def adult_cohort_bins(complete_df, my_worms=None, bin_width_days=2):
'''
Compute bins of cohorts of worms.
'''
if my_worms == None:
my_worms = complete_df.worms
adultspans = get_adultspans(complete_df) / 24
max_adultspans = int(np.ceil(np.max(adultspans)))
my_bins = [(i * bin_width_days, i * bin_width_days + bin_width_days)
for i in range(0, max_adultspans // bin_width_days + 1)]
life_cohorts = [[] for a_bin in my_bins]
for i in range(0, adultspans.shape[0]):
if complete_df.worms[i] in my_worms:
my_lifespan = adultspans[i]
my_bin = int(my_lifespan // bin_width_days)
life_cohorts[my_bin].append(i)
# Remove empty bins and cohorts.
my_bins = [
my_bins[i] for i in range(0, len(life_cohorts))
if len(life_cohorts[i]) > 0
]
life_cohorts = [a_cohort for a_cohort in life_cohorts if len(a_cohort) > 0]
# Figure out the average lifespan of each bin.
bin_lifes = np.zeros(len(my_bins))
for i in range(0, len(bin_lifes)):
bin_lifes[i] = np.mean(adultspans[life_cohorts[i]])
# Color code the bins.
my_colors = zplib_image_colorize.color_map(
bin_lifes / np.max(bin_lifes[~np.isnan(bin_lifes)]))
my_colors = my_colors / 255
return (life_cohorts, bin_lifes, my_bins, my_colors)
def plot(positions, values, radius, rw):
colors = colorize.color_map(colorize.scale(values, output_max=1))
circles = [
Circle(x, y, radius, color, metadata=(x, y, value))
for (x, y), color, value in zip(positions, colors, values)
]
for c in circles:
rw.image_scene.addItem(c)
return circles
def _feature_plot_data(worms, x_feature, y_feature, color_by='lifespan'):
x_feature_vals = worms.get_feature(x_feature)
y_feature_vals = worms.get_feature(y_feature)
color_vals = colorize.scale(worms.get_feature(color_by), output_max=1)
colors = colorize.color_map(color_vals, uint8=False)
out = []
for x, y, color in zip(x_feature_vals, y_feature_vals, colors):
out.append((x, y, color))
return out
def __init__(self, ris_widget, keypoint_names, colors=None, name='keypoints', worm_frame=True, auto_advance=False):
num_points = len(keypoint_names)
if colors is None:
colors = colorize.color_map(numpy.linspace(0, 1, num_points), spectrum_max=0.8)
self.colors = colors
self.keypoint_names = keypoint_names
if worm_frame:
self.ris_widget = split_view.split_ris_widget(ris_widget)
self.center_y_origin = True
# bounding rect change means that the image shape has changed in some way
self.ris_widget.image_scene.layer_stack_item.bounding_rect_changed.connect(self._new_image_shape)
else:
self.ris_widget = ris_widget
self.center_y_origin = False
qcolors = [Qt.QColor(*c) for c in colors]
pen = Qt.QPen(Qt.QColor(255, 255, 255, 84))
pen.setWidth(3)
pen.setCosmetic(True)
self.point_set = identified_point_set.IdentifiedPointSet(self.ris_widget, num_points, qcolors, pen)
self.point_set.geometry_change_callbacks.append(self.on_geometry_change)
self._auto_advance = auto_advance
super().__init__(name, default={name: None for name in keypoint_names})
def _timecourse_plot_data(worms,
feature,
min_age=-numpy.inf,
max_age=numpy.inf,
age_feature='age',
color_by='lifespan',
color_map=None):
if color_map is None:
color_map = lambda array: colorize.color_map(array, uint8=False)
time_ranges = worms.get_time_range(feature, min_age, max_age, age_feature)
if color_by is None:
color_vals = [0] * len(time_ranges)
else:
color_vals = colorize.scale(worms.get_feature(color_by), output_max=1)
colors = color_map(color_vals)
out = []
for time_range, color in zip(time_ranges, colors):
x, y = time_range.T
out.append((x, y, color))
return out
def spatial_distribution(worms,
feature,
ax_h=None,
fig_h=None,
source='gradient'):
import matplotlib.pyplot as plt
INTERSLOT_SPACING = 30 # spacing between slots on stage insert (mm)
ax_provided = ax_h is not None
if not ax_provided:
fig_h, ax_h = plt.subplots()
ax_h.set_title(f'{worms[0].name.split()[0]}')
else:
assert fig_h is not None
feature_vals = worms.get_feature(feature)
nan_mask = numpy.isnan(feature_vals)
if all(nan_mask):
print(f'feature not found for any individuals')
return
elif any(nan_mask):
print(f'Warning: nan\'s found for {numpy.array(worms)[nan_mask]}')
good_worms = numpy.array(worms)[~nan_mask]
feature_vals = feature_vals[~nan_mask]
# color_vals = colorize.scale(feature_vals,output_max=1,gamma=0.5)
# color_vals = colorize.scale(feature_vals,output_max=1,gamma=0.5)
# color_vals = plotting_tools.build_gradient_palette(
# base_color = numpy.array(plotting_tools.qual_colors[numpy.random.randint(len(plotting_tools.qual_colors))]),
# num_colors=len(feature_vals)
# )
if source == 'gradient':
color_vals = plotting_tools.build_gradient_palette(
base_color=numpy.array(
plotting_tools.qual_colors[numpy.random.randint(
len(plotting_tools.qual_colors))]),
num_colors=256)
elif source == 'zplib':
color_vals = colorize.color_map(numpy.linspace(0, 1, 257))
median_x = numpy.median([worm.stage_x for worm in good_worms])
ref_x = (median_x // INTERSLOT_SPACING
) * INTERSLOT_SPACING # Adjusts to the nearest slide position
cmap = mpl_colors.ListedColormap(
[numpy.append(val, 1) for val in color_vals])
norm = mpl.colors.Normalize(vmin=feature_vals.min(),
vmax=feature_vals.max())
sm = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([])
rect = ax_h.add_artist(
plt.Rectangle((-5, -15),
35,
90,
linewidth=1,
edgecolor='k',
facecolor='none'))
# for worm, color in zip(good_worms,color_vals):
# ax_h.add_artist(
# plt.Circle((worm.stage_x-ref_x+(median_x-ref_x), worm.stage_y), 1, edgecolor = [0,0,0], facecolor=color))
for worm, feature_val in zip(good_worms, feature_vals):
ax_h.add_artist(
plt.Circle(
(worm.stage_x - ref_x + (median_x - ref_x), worm.stage_y),
1,
edgecolor=[0, 0, 0],
facecolor=sm.to_rgba(feature_val)))
for worm in worms.filter(lambda worm: worm not in good_worms):
ax_h.add_artist(
plt.Circle(
(worm.stage_x - ref_x + (median_x - ref_x), worm.stage_y),
1,
edgecolor=[0, 0, 0],
facecolor=[0, 0, 0]))
ax_h.set_xlim([-5, 30])
ax_h.set_ylim([-15, 75])
ax_h.set_xlim([-10, 35])
ax_h.set_ylim([-20, 80])
ax_h.spines['bottom'].set_visible(False)
ax_h.spines['left'].set_visible(False)
ax_h.set_aspect('equal')
# Show the colorbar for these values
fig_h.colorbar(sm, ax=ax_h)
if not ax_provided:
return fig_h, ax_h