def test_3(): # checks that neighbor_smoothing works b = np.array([[np.arange(3), np.arange(3), np.arange(3)], [np.arange(3), np.arange(3), np.arange(3)], [np.arange(3), np.arange(3), np.arange(3)]]) a = b*-1 assert(len(a.shape) == 3) assert(a.shape == (3, 3, 3)) a_n = neighbor_smoothing(a, 3) assert_almost_equal(a_n, a) ones = np.ones((3, 3, 3)) rachel = np.ones((3, 3, 3)) rachel[1, 1, 1] = -1 rachel_n = neighbor_smoothing(rachel, 2) assert_array_almost_equal(rachel_n, ones)
def t_grouping_neighbor(t_3d,
mask,
cutoff,
neighbors=None,
prop=False,
abs_on=False,
binary=True,
off_value=0,
masked_value=.5):
"""
Masks a 3d array, does t_binary_grouping, and does neighboring
Parameters
----------
t_3d: t-value of the betas 3d numpy array
mask: a 3d numpy array of 0s and 1s that has the same shape as t_3d
cutoff: the limit for the false discovery rate
neighbors: number of neighbors for neighbor smoothing (must have binary be true)
prop: logical~ if the cutoff is a proportion or a value
abs_on: logical~ if we want to take absolute value of the t input
binary: if binary, then off_value is ignored and 0 is used as the
off_value, 1 as the on value
off_value: the value of those not selected
Returns
-------
output_3d: a 3d numpy array same size as the t_3d with either:
(1) binary on_off values for inside the mask and "masked_value"
for values outside mask or (2) t values to the accepted values,
and "off_values" for lost values, and "masked_value" for values
outside mask. MOREOVER, it can have had neighbor smoothing applied
the binary case
cutoff: the limit for the false discovery rate
"""
if neighbors != None and binary == False:
return False
t_1d = masking_reshape_start(t_3d, mask)
t_1d = np.ravel(t_1d)
zero_one, cutoff = t_binary_grouping(t_1d, cutoff, prop, abs_on)
if not binary:
t_1d = t_1d * zero_one + off_value * (1 - zero_one)
else:
t_1d = zero_one
output_3d = masking_reshape_end(t_1d, mask, masked_value)
if neighbors != None:
output_3d = neighbor_smoothing(output_3d, neighbors)
return output_3d, cutoff
def t_grouping_neighbor(t_3d, mask, cutoff, neighbors = None,
prop = False, abs_on = False, binary = True, off_value = 0, masked_value = .5):
"""
Masks a 3d array, does t_binary_grouping, and does neighboring
Parameters
----------
t_3d: t-value of the betas 3d numpy array
mask: a 3d numpy array of 0s and 1s that has the same shape as t_3d
cutoff: the limit for the false discovery rate
neighbors: number of neighbors for neighbor smoothing (must have binary be true)
prop: logical~ if the cutoff is a proportion or a value
abs_on: logical~ if we want to take absolute value of the t input
binary: if binary, then off_value is ignored and 0 is used as the
off_value, 1 as the on value
off_value: the value of those not selected
Returns
-------
output_3d: a 3d numpy array same size as the t_3d with either:
(1) binary on_off values for inside the mask and "masked_value"
for values outside mask or (2) t values to the accepted values,
and "off_values" for lost values, and "masked_value" for values
outside mask. MOREOVER, it can have had neighbor smoothing applied
the binary case
cutoff: the limit for the false discovery rate
"""
if neighbors != None and binary == False:
return False
t_1d = masking_reshape_start(t_3d, mask)
t_1d = np.ravel(t_1d)
zero_one, cutoff = t_binary_grouping(t_1d, cutoff, prop, abs_on)
if not binary:
t_1d = t_1d*zero_one + off_value*(1 - zero_one)
else:
t_1d = zero_one
output_3d = masking_reshape_end(t_1d, mask, masked_value)
if neighbors != None:
output_3d = neighbor_smoothing(output_3d, neighbors)
return output_3d, cutoff
# plot this
plt.imshow(present_3d(to_3d), interpolation='nearest', cmap='seismic')
plt.title(str(Q) + " with masked p-values")
plt.savefig(location_of_images + "masked_pval_" + str(Q) + ".png")
plt.figure()
plt.close()
print("# ==== END mask_phase_2_dimension_change plotting ==== #")
# Smoothing/clustering on the masked p-values
print("# ==== BEGIN smoothed masked p-value plotting ==== #")
# Where the neighbor_smoothing function should start
print("# == NEIGHBOR SMOOTHING 5")
smoothed = neighbor_smoothing(to_3d, 5)
plt.imshow(present_3d(smoothed), interpolation='nearest', cmap='seismic')
plt.title(str(Q) + " with neightbor-smoothing factor 5")
plt.savefig(location_of_images + "5neighbor_smooth_pval_" + str(Q) + ".png")
plt.figure()
plt.close()
print("# == NEIGHBOR SMOOTHING 10")
smoothed = neighbor_smoothing(to_3d, 10)
plt.imshow(present_3d(smoothed), interpolation='nearest', cmap='seismic')
plt.title(str(Q) + " with neightbor-smoothing factor 10")
plt.savefig(location_of_images + "10neighbor_smooth_pval_" + str(Q) + ".png")
plt.figure()
plt.close()
plt.imshow(present_3d(to_3d),interpolation='nearest', cmap='seismic')
plt.title(str(Q) + " with masked p-values")
#plt.colorbar()
plt.savefig(location_of_images + "masked_pval_" + str(Q) + ".png")
plt.figure()
plt.close()
print("# ==== END mask_phase_2_dimension_change plotting ==== #")
# Smoothing/clustering on the masked p-values
print("# ==== BEGIN smoothed masked p-value plotting ==== #")
#test = to_3d
# Where the neighbor_smoothing function should start
print("# == NEIGHBOR SMOOTHING 5")
smoothed = neighbor_smoothing(to_3d, 5)
#off = np.max(test)
#for i in 1 + np.arange(62):
# for j in 1 + np.arange(62):
# for k in 1 + np.arange(32):
# number of neighbors that need to be positivednm
# if np.sum(to_3d[(i - 1):(i + 2),(j - 1):(j + 2),(k - 1):(k + 2)] < 0) < 5 and to_3d[i, j, k] < 0:
# test[i, j, k] = off
#print(to_3d)
#print(test)
plt.imshow(present_3d(smoothed), interpolation='nearest', cmap='seismic')
plt.title(str(Q) + " with neightbor-smoothing factor 5")
#plt.colorbar()
plt.savefig(location_of_images + "5neighbor_smooth_pval_" + str(Q) + ".png")
plt.figure()
plt.close()
