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from matplotlib import pyplot as plt
from sunlab.common.data.shape_dataset import ShapeDataset
from sunlab.globals import DIR_ROOT
def get_nonphysical_masks(
model,
xrange=[-1, 1],
yrange=[-1, 1],
bins=[500, 500],
equivdiameter_threshold=10,
solidity_threshold=0.1,
area_threshold=100,
perimeter_threshold=10,
area_max_threshold=7000,
perimeter_max_threshold=350,
area_min_threshold=100,
perimeter_min_threshold=5,
consistency_check=False,
):
"""# Generate the Nonphysical Masks in Grid for Model
Hard Constraints:
- Non-negative values
- Ratios no greater than 1
Soft Constraints:
- Area/ Perimeter Thresholds"""
import numpy as np
x = np.linspace(xrange[0], xrange[1], bins[0])
y = np.linspace(yrange[0], yrange[1], bins[1])
X, Y = np.meshgrid(x, y)
X, Y = X.reshape((bins[0], bins[1], 1)), Y.reshape((bins[0], bins[1], 1))
XY = np.concatenate([X.reshape((-1, 1)), Y.reshape((-1, 1))], axis=-1)
dec_v = model.decoder(XY).numpy().reshape((bins[0] * bins[1], 13))
lXY = model.scaler.scaler.inverse_transform(dec_v).reshape((bins[0], bins[1], 13))
# Hard Limits
non_negative_mask = np.all(lXY > 0, axis=-1)
solidity_mask = np.abs(lXY[:, :, 6]) <= 1
extent_upper_bound_mask = lXY[:, :, 7] <= 1
# Soft Extremas
area_max_mask = lXY[:, :, 4] < area_max_threshold
perimeter_max_mask = lXY[:, :, 9] < perimeter_max_threshold
area_min_mask = lXY[:, :, 4] > area_min_threshold
perimeter_min_mask = lXY[:, :, 9] > perimeter_min_threshold
# Self-Consistency
man_solidity_mask = np.abs(lXY[:, :, 0] / lXY[:, :, 4]) <= 1
equivalent_diameter_mask = (
np.abs(lXY[:, :, 5] - np.sqrt(4 * np.abs(lXY[:, :, 0]) / np.pi))
< equivdiameter_threshold
)
convex_area_mask = lXY[:, :, 0] < lXY[:, :, 4] + area_threshold
convex_perimeter_mask = lXY[:, :, 9] < lXY[:, :, 8] + perimeter_threshold
mask_info = {
"non-negative": non_negative_mask,
"solidity": solidity_mask,
"extent-max": extent_upper_bound_mask,
#
"area-max": area_max_mask,
"perimeter-max": perimeter_max_mask,
"area-min": area_min_mask,
"perimeter-min": perimeter_min_mask,
#
"computed-solidity": man_solidity_mask,
"equivalent-diameter": equivalent_diameter_mask,
"convex-area": convex_area_mask,
"convex-perimeter": convex_perimeter_mask,
}
if not consistency_check:
mask_info = {
"non-negative": non_negative_mask,
"solidity": solidity_mask,
"extent-max": extent_upper_bound_mask,
#
"area-max": area_max_mask,
"perimeter-max": perimeter_max_mask,
"area-min": area_min_mask,
"perimeter-min": perimeter_min_mask,
}
mask_list = [mask_info[key] for key in mask_info.keys()]
return np.all(mask_list, axis=0), X, Y, mask_info
def excavate(input_2d_array):
"""# Return Boundaries for Masked Array
Use X, Y directions only"""
from copy import deepcopy as dc
from numpy import nan_to_num, zeros_like, abs
data_2d_array = dc(input_2d_array)
data_2d_array = nan_to_num(data_2d_array, nan=20)
# X-Gradient
x_grad = zeros_like(data_2d_array)
x_grad[:-1, :] = data_2d_array[1:, :] - data_2d_array[:-1, :]
x_grad[(abs(x_grad) > 10)] = 10
x_grad[(abs(x_grad) < 10) & (abs(x_grad) > 0)] = 1
x_grad[x_grad == 1] = 0.5
x_grad[x_grad > 1] = 1
# Y-Gradient
y_grad = zeros_like(data_2d_array)
y_grad[:, :-1] = data_2d_array[:, 1:] - data_2d_array[:, :-1]
y_grad[(abs(y_grad) > 10)] = 10
y_grad[(abs(y_grad) < 10) & (abs(y_grad) > 0)] = 1
y_grad[y_grad == 1] = 0.5
y_grad[y_grad > 1] = 1
return x_grad, y_grad
def excavate_extra(input_2d_array, N=1):
"""# Return Boundaries for Masked Array
Use all 8 directions"""
from copy import deepcopy as dc
from numpy import nan_to_num, zeros_like, abs
data_2d_array = dc(input_2d_array)
data_2d_array = nan_to_num(data_2d_array, nan=20)
# X-Gradient
x_grad = zeros_like(data_2d_array)
x_grad[:-N, :] = data_2d_array[N:, :] - data_2d_array[:-N, :]
x_grad[(abs(x_grad) > 10)] = 10
x_grad[(abs(x_grad) < 10) & (abs(x_grad) > 0)] = 1
x_grad[x_grad == 1] = 0.5
x_grad[x_grad > 1] = 1
# Y-Gradient
y_grad = zeros_like(data_2d_array)
y_grad[:, :-N] = data_2d_array[:, N:] - data_2d_array[:, :-N]
y_grad[(abs(y_grad) > 10)] = 10
y_grad[(abs(y_grad) < 10) & (abs(y_grad) > 0)] = 1
y_grad[y_grad == 1] = 0.5
y_grad[y_grad > 1] = 1
# XY-Gradient
xy_grad = zeros_like(data_2d_array)
xy_grad[:-N, :-N] = data_2d_array[N:, N:] - data_2d_array[:-N, :-N]
xy_grad[(abs(xy_grad) > 10)] = 10
xy_grad[(abs(xy_grad) < 10) & (abs(xy_grad) > 0)] = 1
xy_grad[xy_grad == 1] = 0.5
xy_grad[xy_grad > 1] = 1
# X(-Y)-Gradient
yx_grad = zeros_like(data_2d_array)
yx_grad[:-N, :-N] = data_2d_array[N:, :-N] - data_2d_array[:-N, N:]
yx_grad[(abs(yx_grad) > 10)] = 10
yx_grad[(abs(yx_grad) < 10) & (abs(yx_grad) > 0)] = 1
yx_grad[yx_grad == 1] = 0.5
yx_grad[yx_grad > 1] = 1
xyn_grad = dc(yx_grad)
# (-X)Y-Gradient
xny_grad = zeros_like(data_2d_array)
xny_grad[:-N, :-N] = data_2d_array[:-N, N:] - data_2d_array[N:, :-N]
xny_grad[(abs(xy_grad) > 10)] = 10
xny_grad[(abs(xy_grad) < 10) & (abs(xy_grad) > 0)] = 1
xny_grad[xy_grad == 1] = 0.5
xny_grad[xy_grad > 1] = 1
# (-X)(-Y)-Gradient
xnyn_grad = zeros_like(data_2d_array)
xnyn_grad[:-N, :-N] = data_2d_array[:-N, :-N] - data_2d_array[N:, N:]
xnyn_grad[(abs(yx_grad) > 10)] = 10
xnyn_grad[(abs(yx_grad) < 10) & (abs(yx_grad) > 0)] = 1
xnyn_grad[yx_grad == 1] = 0.5
xnyn_grad[yx_grad > 1] = 1
return x_grad, y_grad, xy_grad, xyn_grad, xny_grad, xnyn_grad
def excavate_outline(arr, thickness=1):
"""# Generate Transparency Mask with NaNs"""
from numpy import sum, abs, NaN
outline = sum(abs(excavate_extra(arr, thickness)), axis=0)
outline[outline == 0] = NaN
outline[outline > 0] = 0
return outline
def get_boundary_outline(
aae_model_object,
pixel_classification_file=None,
include_transition_regions=False,
border_thickness=3,
bin_count=800,
xrange=[-6.5, 6.5],
yrange=[-4.5, 4.5],
threshold=0.75,
):
"""# Get Boundary Outlines"""
from copy import deepcopy
import numpy as np
if pixel_classification_file is None:
pixel_classification_file = "../../extra_data/PhenotypePixels_65x45_800.npy"
base_classification = np.loadtxt(pixel_classification_file)
base_classification = base_classification.reshape((bin_count, bin_count, 4))
max_classification_probability = np.zeros((bin_count, bin_count, 1))
max_classification_probability[:, :, 0] = (
np.max(base_classification, axis=-1) < threshold
)
classes_with_include_transition_regions = np.concatenate(
[base_classification, max_classification_probability], axis=-1
)
if include_transition_regions:
phenotype_probabilities = deepcopy(
np.argsort(classes_with_include_transition_regions[:, :, :], axis=-1)[
:, :, -1
]
).astype(np.float32)
else:
phenotype_probabilities = deepcopy(
np.argsort(classes_with_include_transition_regions[:, :, :-1], axis=-1)[
:, :, -1
]
).astype(np.float32)
nonphysical_mask, _, _, _ = get_nonphysical_masks(
aae_model_object, xrange=xrange, yrange=yrange, bins=[bin_count, bin_count]
)
nonphysical_mask = nonphysical_mask.astype(np.float32)
nonphysical_mask[nonphysical_mask == 0] = np.NaN
nonphysical_mask[nonphysical_mask == 1] = 0
nonphysical_mask = nonphysical_mask.T
phenotype_regions = deepcopy(phenotype_probabilities.T + nonphysical_mask.T)
outline = excavate_outline(phenotype_regions, border_thickness)
return outline
def apply_boundary(
model_loc=DIR_ROOT + "models/current_model/",
border_thickness=3,
include_transition_regions=False,
threshold=0.7,
alpha=1,
_plt=None,
):
"""# Apply Boundary to Plot
Use Pregenerated Boundary by Default for Speed"""
from ..models import load_aae
from sunlab.common.scaler import MaxAbsScaler
import numpy as np
if _plt is None:
_plt = plt
if (model_loc == model_loc) and (border_thickness == 3) and (threshold == 0.7):
XYM = np.load(DIR_ROOT + "extra_data/OutlineXYM.npy")
XY = XYM[:2, :, :]
if include_transition_regions:
outline = XYM[3, :, :]
else:
outline = XYM[2, :, :]
_plt.pcolor(XY[0, :, :], XY[1, :, :], outline, cmap="gray", alpha=alpha)
return
model = load_aae(model_loc, MaxAbsScaler)
bin_count = 800
xrange = [-6.5, 6.5]
yrange = [-4.5, 4.5]
rng = [xrange, yrange]
X = np.linspace(rng[0][0], rng[0][1], bin_count)
Y = np.linspace(rng[1][0], rng[1][1], bin_count)
XY = np.array(np.meshgrid(X, Y))
kwparams = {
"bin_count": bin_count,
"xrange": xrange,
"yrange": yrange,
}
include_tregions = include_transition_regions
outline = get_boundary_outline(
model,
border_thickness=border_thickness,
include_transition_regions=include_tregions,
threshold=threshold,
**kwparams
)
_plt.pcolor(XY[0, :, :], XY[1, :, :], outline, cmap="gray", alpha=alpha)
plt.apply_boundary = apply_boundary
def plot_shape_dataset(self, model, *args, **kwargs):
"""# Plot Shape Dataset"""
if self.labels is None:
plt.scatter2d(model.encoder(self.dataset), *args, **kwargs)
else:
plt.scatter2d(model.encoder(self.dataset), self.labels, *args, **kwargs)
ShapeDataset.plot = lambda model, *args, **kwargs: plot_shape_dataset(
model, *args, **kwargs
)
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