def predict(model_file, data_file, image_prefix):
generator = make_generator_model(input_width=16,
input_height=22,
output_width=200,
output_height=128)
generator.load_weights(model_file)
input_array = np.load(data_file, allow_pickle=True)
input_array = np.concatenate([
np.expand_dims(i, axis=0) for i in input_array if i.shape == (22, 16)
],
axis=0)
input_array = input_array + 0.01
input_array = (input_array - np.expand_dims(
input_array.mean(axis=1), axis=1)) / np.expand_dims(
input_array.std(axis=1), axis=1)
input_tensor = tf.constant(input_array)
n_sample, w, h = input_tensor.shape
input_tensor = tf.reshape(input_tensor, shape=(n_sample, w * h))
generated_pic = generator(input_tensor, training=False)
generated_pic = np.array(generated_pic)
generated_pic = generated_pic * 127.5 + 127.5
generated_pic = np.round(generated_pic, 0).astype(np.int32)
for idx, pic in enumerate(generated_pic):
plt.imshow(np.flipud(np.rot90(pic)))
plt.savefig(image_prefix + str(idx) + ".png")
def run(train_data, valid_data, len_size, scale, EPOCHS, root_path='./', load_model_dir=None, saved_model_dir=None, log_dir=None, summary=False):
if log_dir is None:
log_dir = os.path.join(root_path, 'our_model', 'logs', 'model')
logging.info(train_data)
logging.info(valid_data)
# get generator model and discriminator model
Gen = model.make_generator_model(len_high_size=len_size, scale=scale)
Dis = model.make_discriminator_model(len_high_size=len_size, scale=scale)
if load_model_dir is not None:
#load_model_dir = os.path.join(root_path, 'our_model', 'saved_model')
file_path = os.path.join(load_model_dir, 'gen_model_'+str(len_size), 'gen_weights')
if os.path.exists(file_path):
Gen.load_weights(file_path)
else:
logging.info("generator doesn't exist. create a new one.")
file_path = os.path.join(load_model_dir, 'dis_model_'+str(len_size), 'dis_weights')
if os.path.exists(file_path):
Dis.load_weights(file_path)
else:
logging.info("discriminator model doesn't exist. create a new one")
if summary:
logging.info(Gen.summary())
tf.keras.utils.plot_model(Gen, to_file='G.png', show_shapes=True)
logging.info(Dis.summary())
tf.keras.utils.plot_model(Dis, to_file='D.png', show_shapes=True)
if saved_model_dir is None:
saved_model_dir = os.path.join(root_path, 'our_model', 'saved_model')
model.fit(Gen, Dis, train_data, EPOCHS, len_size, scale,
valid_data, log_dir=log_dir, saved_model_dir=saved_model_dir)
file_path = os.path.join(
saved_model_dir, 'gen_model_'+str(len_size), 'gen_weights')
Gen.save_weights(file_path)
file_path = os.path.join(
saved_model_dir, 'dis_model_'+str(len_size), 'dis_weights')
Dis.save_weights(file_path)
def main(input_array, input_width=16, input_height=22, output_width=100, output_height=64):
input_array = (input_array - 127.5) / 127.5
train_dataset = tf.data.Dataset.from_tensor_slices(input_array).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
generator = make_generator_model(input_width, input_height, output_width, output_height)
discriminator = make_discriminator_model(output_width, output_height)
generator_optimizer = tf.keras.optimizers.Adam(1e-4)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-4)
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
seed = tf.random.normal([num_examples_to_generate, noise_dim])
@tf.function
def train_step(images):
noise = tf.random.normal([BATCH_SIZE, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator(noise, training=True)
real_output = discriminator(images, training=True)
fake_output = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output)
disc_loss = discriminator_loss(real_output, fake_output)
gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))
def generate_and_save_images(model, epoch, test_input):
# Notice `training` is set to False.
# This is so all layers run in inference mode (batchnorm).
predictions = model(test_input, training=False)
fig = plt.figure(figsize=(4, 4))
for i in range(predictions.shape[0]):
plt.subplot(4, 4, i + 1)
plt.imshow(predictions[i, :, :, :3] * 127.5 + 127.5)
plt.axis('off')
plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
plt.show()
def train(dataset, epochs):
for epoch in range(epochs):
start = time.time()
for image_batch in dataset:
train_step(image_batch)
# Produce images for the GIF as we go
generate_and_save_images(generator,
epoch + 1,
seed)
# Save the model every 15 epochs
if (epoch + 1) % 20 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
generator.save_weights("model_epoch_"+ str(epoch) + ".h5")
print('Time for epoch {} is {} sec'.format(epoch + 1, time.time() - start))
# Generate after the final epoch
generate_and_save_images(generator,
epochs,
seed)
train(train_dataset, EPOCHS)
import tensorflow as tf
import model
import matplotlib.pyplot as plt
import datetime
import os
import time
import numpy as np
from define import *
# build model
generator = model.make_generator_model()
# checkpoint
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator=generator)
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
# make seed
seed = np.load("./cherry_pick.npy")
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
test_summary_writer = tf.summary.create_file_writer(
f'logs/{current_time}/vector_arithmetic/')
for i in range(10):
# seed1 = tf.random.normal([noise_dim])
# seed2 = tf.random.normal([noise_dim])
# seed3 = tf.random.normal([noise_dim])
def predict(path='./data',
raw_path='raw',
raw_file='Rao2014-GM12878-DpnII-allreps-filtered.10kb.cool',
model_path=None,
sr_path='output',
chromosome='22',
scale=4,
len_size=200,
genomic_distance=2000000,
start=None,
end=None,
draw_out=False):
sr_file = raw_file.split('-')[0] + '_' + raw_file.split(
'-')[1] + '_' + raw_file.split('-')[2] + '_' + raw_file.split('.')[1]
directory_sr = os.path.join(path, sr_path, sr_file, 'SR',
'chr' + chromosome)
if not os.path.exists(directory_sr):
os.makedirs(directory_sr)
# get generator model
if model_path is None:
gan_model_weights_path = './our_model/saved_model/gen_model_' + \
str(len_size)+'/gen_weights'
else:
gan_model_weights_path = model_path
Generator = model.make_generator_model(len_high_size=len_size, scale=4)
Generator.load_weights(gan_model_weights_path)
print(Generator)
name = os.path.join(path, raw_path, raw_file)
c = cooler.Cooler(name)
resolution = c.binsize
if 'chr' + chromosome not in c.chromnames:
return
mat = c.matrix(balance=True).fetch('chr' + chromosome)
[Mh, idx] = operations.remove_zeros(mat)
print('Shape HR: {}'.format(Mh.shape), end='\t')
if start is None:
start = 0
if end is None:
end = Mh.shape[0]
Mh = Mh[start:end, start:end]
print('MH: {}'.format(Mh.shape), end='\t')
Ml = operations.sampling_hic(Mh, scale**2, fix_seed=True)
print('ML: {}'.format(Ml.shape))
# Normalization
# the input should not be type of np.matrix!
Ml = np.asarray(Ml)
Mh = np.asarray(Mh)
Ml, Dl = operations.scn_normalization(Ml, max_iter=3000)
print('Dl shape:{}'.format(Dl.shape))
Mh, Dh = operations.scn_normalization(Mh, max_iter=3000)
print('Dh shape:{}'.format(Dh.shape))
#Ml = np.divide((Ml-Ml.min()), (Ml.max()-Ml.min()), dtype=float, out=np.zeros_like(Ml), where=(Ml.max()-Ml.min()) != 0)
#Mh = np.divide((Mh-Mh.min()), (Mh.max()-Mh.min()), dtype=float, out=np.zeros_like(Mh), where=(Mh.max()-Mh.min()) != 0)
if genomic_distance is None:
max_boundary = None
else:
max_boundary = np.ceil(genomic_distance / (resolution))
residual = Mh.shape[0] % int(len_size / 2)
print('residual: {}'.format(residual))
hic_hr_front, index_1d_2d_front, index_2d_1d_front = operations.divide_pieces_hic(
Mh[0:-residual, 0:-residual],
block_size=len_size,
max_distance=max_boundary,
save_file=False)
hic_hr_front = np.asarray(hic_hr_front, dtype=np.float32)
print('shape hic_hr front: ', hic_hr_front.shape)
true_hic_hr_front = hic_hr_front
print('shape true hic_hr: ', true_hic_hr_front.shape)
hic_hr_offset, index_1d_2d_offset, index_2d_1d_offset = operations.divide_pieces_hic(
Mh[residual:, residual:],
block_size=len_size,
max_distance=max_boundary,
save_file=False)
hic_hr_offset = np.asarray(hic_hr_offset, dtype=np.float32)
print('shape hic_hr offset: ', hic_hr_offset.shape)
true_hic_hr_offset = hic_hr_offset
print('shape true hic_hr: ', true_hic_hr_offset.shape)
Ml_front = Ml[0:-residual, 0:-residual]
hic_lr_front, _, _ = operations.divide_pieces_hic(
Ml_front,
block_size=len_size,
max_distance=max_boundary,
save_file=False)
hic_lr_front = np.asarray(hic_lr_front, dtype=np.float32)
print('shape hic_lr: ', hic_lr_front.shape)
hic_lr_ds = tf.data.Dataset.from_tensor_slices(
hic_lr_front[..., np.newaxis]).batch(9)
predict_hic_hr_front = None
for i, input_data in enumerate(hic_lr_ds):
[_, _, tmp, _, _] = Generator(input_data, training=False)
if predict_hic_hr_front is None:
predict_hic_hr_front = tmp.numpy()
else:
predict_hic_hr_front = np.concatenate(
(predict_hic_hr_front, tmp.numpy()), axis=0)
predict_hic_hr_front = np.squeeze(predict_hic_hr_front, axis=3)
print('Shape of prediction front: ', predict_hic_hr_front.shape)
file_path = os.path.join(directory_sr, sr_file + '_chr' + chromosome)
np.savez_compressed(file_path + '_front.npz',
predict_hic=predict_hic_hr_front,
true_hic=true_hic_hr_front,
index_1D_2D=index_1d_2d_front,
index_2D_1D=index_2d_1d_front,
start_id=start,
end_id=end,
residual=0)
predict_hic_hr_merge_front = operations.merge_hic(
predict_hic_hr_front,
index_1D_2D=index_1d_2d_front,
max_distance=max_boundary)
print('Shape of merge predict hic HR front',
predict_hic_hr_merge_front.shape)
Ml_offset = Ml[residual:, residual:]
hic_lr_offset, _, _ = operations.divide_pieces_hic(
Ml_offset,
block_size=len_size,
max_distance=max_boundary,
save_file=False)
hic_lr_offset = np.asarray(hic_lr_offset, dtype=np.float32)
print('Shape hic_lr_offset: ', hic_lr_offset.shape)
hic_lr_ds = tf.data.Dataset.from_tensor_slices(
hic_lr_offset[..., np.newaxis]).batch(9)
predict_hic_hr_offset = None
for i, input_data in enumerate(hic_lr_ds):
[_, _, tmp, _, _] = Generator(input_data, training=False)
if predict_hic_hr_offset is None:
predict_hic_hr_offset = tmp.numpy()
else:
predict_hic_hr_offset = np.concatenate(
(predict_hic_hr_offset, tmp.numpy()), axis=0)
predict_hic_hr_offset = np.squeeze(predict_hic_hr_offset, axis=3)
print('Shape of prediction offset: ', predict_hic_hr_offset.shape)
file_path = os.path.join(directory_sr, sr_file + '_chr' + chromosome)
np.savez_compressed(file_path + '_offset.npz',
predict_hic=predict_hic_hr_offset,
true_hic=true_hic_hr_offset,
index_1D_2D=index_1d_2d_offset,
index_2D_1D=index_2d_1d_offset,
start_id=start,
end_id=end,
residual=residual)
predict_hic_hr_merge_offset = operations.merge_hic(
predict_hic_hr_offset,
index_1D_2D=index_1d_2d_offset,
max_distance=max_boundary)
print('Shape of merge predict hic hr offset: ',
predict_hic_hr_merge_offset.shape)
predict_hic_hr_merge = np.zeros(Mh.shape)
predict_hic_hr_merge = addAtPos(predict_hic_hr_merge,
predict_hic_hr_merge_front, (0, 0))
predict_hic_hr_merge = addAtPos(predict_hic_hr_merge,
predict_hic_hr_merge_offset,
(residual, residual))
ave = np.ones_like(predict_hic_hr_merge)
twice = np.ones(shape=(Mh.shape[0] - 2 * residual,
Mh.shape[1] - 2 * residual))
ave = addAtPos(ave, twice, (residual, residual))
predict_hic_hr_merge = predict_hic_hr_merge / ave
true_hic_hr_merge_front = operations.merge_hic(
true_hic_hr_front,
index_1D_2D=index_1d_2d_front,
max_distance=max_boundary)
true_hic_hr_merge_offset = operations.merge_hic(
true_hic_hr_offset,
index_1D_2D=index_1d_2d_offset,
max_distance=max_boundary)
true_hic_hr_merge = np.zeros(Mh.shape)
true_hic_hr_merge = addAtPos(true_hic_hr_merge, true_hic_hr_merge_front,
(0, 0))
true_hic_hr_merge = addAtPos(true_hic_hr_merge, true_hic_hr_merge_offset,
(residual, residual))
true_hic_hr_merge = true_hic_hr_merge / ave
print('Shape of true merge hic hr: {}'.format(true_hic_hr_merge.shape))
'''# chrop Mh
residual = Mh.shape[0] % int(len_size/2)
print('residual: {}'.format(residual))
if residual > 0:
Mh = Mh[0:-residual, 0:-residual]
# true_hic_hr_merge = true_hic_hr_merge[0:-residual, 0:-residual]
Dh = Dh[0:-residual]
Dl = Dl[0:-residual]'''
# recover M from scn to origin
Mh = operations.scn_recover(Mh, Dh)
true_hic_hr_merge = operations.scn_recover(true_hic_hr_merge, Dh)
predict_hic_hr_merge = operations.scn_recover(predict_hic_hr_merge, Dh)
# remove diag and off diag
k = max_boundary.astype(int)
Mh = operations.filter_diag_boundary(Mh, diag_k=0, boundary_k=k)
true_hic_hr_merge = operations.filter_diag_boundary(true_hic_hr_merge,
diag_k=0,
boundary_k=k)
predict_hic_hr_merge = operations.filter_diag_boundary(
predict_hic_hr_merge, diag_k=0, boundary_k=k)
print('sum Mh:', np.sum(np.abs(Mh)))
print('sum true merge:', np.sum(np.abs(true_hic_hr_merge)))
print('sum pred merge:', np.sum(np.abs(predict_hic_hr_merge)))
diff = np.abs(Mh - predict_hic_hr_merge)
print('sum Mh - pred square error: {:.5}'.format(np.sum(diff**2)))
diff = np.abs(true_hic_hr_merge - predict_hic_hr_merge)
print('sum true merge - pred square error: {:.5}'.format(np.sum(diff**2)))
diff = np.abs(Mh - true_hic_hr_merge)
print('sum Mh - true merge square error: {:.5}'.format(np.sum(diff**2)))
directory_sr = os.path.join(path, sr_path, sr_file, 'SR')
compact = idx[0:-residual]
file = 'predict_chr{}_{}.npz'.format(chromosome, resolution)
np.savez_compressed(os.path.join(directory_sr, file),
hic=predict_hic_hr_merge,
compact=compact)
print('Saving file: {}, at {}'.format(file, directory_sr))
directory_sr = os.path.join(path, sr_path, sr_file, 'SR',
'chr' + chromosome)
file = 'true_chr{}_{}.npz'.format(chromosome, resolution)
np.savez_compressed(os.path.join(directory_sr, file),
hic=Mh,
compact=compact)
print('Saving file: {}, at {}'.format(file, directory_sr))
'''file = 'truemerge_chr{}_{}.npz'.format(chromosome, resolution)
np.savez_compressed(os.path.join(directory_sr, file), hic=true_hic_hr_merge, compact=compact)
print('Saving file:', file)'''
if draw_out:
predict_hic_hr_merge = predict_hic_hr_merge[::10, ::10]
Mh = Mh[::10, ::10]
fig, axs = plt.subplots(1, 2, figsize=(8, 15))
# , cmap='RdBu_r'
ax = axs[0].imshow(np.log1p(predict_hic_hr_merge), cmap='RdBu')
axs[0].set_title('predict')
ax = axs[1].imshow(np.log1p(Mh), cmap='RdBu') # , cmap='RdBu_r'
axs[1].set_title('true')
plt.tight_layout()
plt.show()
def extract_features(path='./data',
raw_path='raw',
raw_file='Rao2014-GM12878-DpnII-allreps-filtered.10kb.cool',
model_path=None,
sr_path='output',
chromosome='22',
scale=4,
len_size=200,
genomic_distance=2000000,
start=None, end=None):
sr_file = raw_file.split('-')[0] + '_' + raw_file.split('-')[1] + '_' + raw_file.split('-')[2] + '_' + raw_file.split('.')[1]
directory_sr = os.path.join(path, sr_path, sr_file, 'extract_features')
if not os.path.exists(directory_sr):
os.makedirs(directory_sr)
# get generator model
if model_path is None:
gan_model_weights_path = './our_model/saved_model/gen_model_' + \
str(len_size)+'/gen_weights'
else:
gan_model_weights_path = model_path
Generator = model.make_generator_model(len_high_size=len_size, scale=4)
Generator.load_weights(gan_model_weights_path)
print(Generator)
name = os.path.join(path, raw_path, raw_file)
c = cooler.Cooler(name)
resolution = c.binsize
mat = c.matrix(balance=True).fetch('chr'+chromosome)
[Mh, idx] = operations.remove_zeros(mat)
nonzero_idx = np.array(np.where(idx)).flatten()
print(idx.shape)
print(nonzero_idx.shape)
print(nonzero_idx)
print(start, end, nonzero_idx[start:end])
print('Shape HR: {}'.format(Mh.shape), end='\t')
if start is None:
start = 0
if end is None:
end = Mh.shape[0]
Mh = Mh[start:end, start:end]
print('MH: {}'.format(Mh.shape), end='\t')
Ml = operations.sampling_hic(Mh, scale**2, fix_seed=True)
print('ML: {}'.format(Ml.shape))
# Normalization
# the input should not be type of np.matrix!
Ml = np.asarray(Ml)
Mh = np.asarray(Mh)
Ml, Dl = operations.scn_normalization(Ml, max_iter=3000)
print('Dl shape:{}'.format(Dl.shape))
Mh, Dh = operations.scn_normalization(Mh, max_iter=3000)
print('Dh shape:{}'.format(Dh.shape))
if genomic_distance is None:
max_boundary = None
else:
max_boundary = np.ceil(genomic_distance/(resolution))
# residual = Mh.shape[0] % int(len_size/2)
# print('residual: {}'.format(residual))
hic_hr, index_1d_2d, index_2d_1d = operations.divide_pieces_hic( Mh, block_size=len_size, max_distance=max_boundary, save_file=False)
hic_hr = np.asarray(hic_hr, dtype=np.float32)
print('shape hic_hr front: ', hic_hr.shape)
true_hic_hr = hic_hr
print('shape true hic_hr: ', true_hic_hr.shape)
hic_lr, _, _ = operations.divide_pieces_hic( Ml, block_size=len_size, max_distance=max_boundary, save_file=False)
hic_lr = np.asarray(hic_lr, dtype=np.float32)
print('shape hic_lr: ', hic_lr.shape)
hic_lr_ds = tf.data.Dataset.from_tensor_slices( hic_lr[..., np.newaxis]).batch(9)
predict_hic_hr = None
for i, input_data in enumerate(hic_lr_ds):
[out_low_x2, out_low_x4, tmp, low_x2, low_x4] = Generator(input_data, training=False)
if predict_hic_hr is None:
predict_hic_hr = tmp.numpy()
else:
predict_hic_hr = np.concatenate( (predict_hic_hr, tmp.numpy()), axis=0)
layer_name = 'dsd_x2'
for i, data in enumerate(hic_lr_ds):
intermediate_layer_model = keras.Model(inputs=Generator.get_layer(layer_name).input,
outputs=Generator.get_layer(layer_name).output)
intermediate_x2 = intermediate_layer_model(data)
layer_name = 'dsd_x4'
for i, data in enumerate(hic_lr_ds):
intermediate_layer_model = keras.Model(inputs=Generator.get_layer(layer_name).input,
outputs=Generator.get_layer(layer_name).output)
intermediate_x4 = intermediate_layer_model(data)
predict_hic_hr = np.squeeze(predict_hic_hr, axis=3)
print('Shape of prediction front: ', predict_hic_hr.shape)
file_path = os.path.join(directory_sr, sr_file+'_chr'+chromosome)
np.savez_compressed(file_path+'.npz', predict_hic=predict_hic_hr, true_hic=true_hic_hr,
index_1D_2D=index_1d_2d, index_2D_1D=index_2d_1d,
start_id=start, end_id=end, residual=0)
predict_hic_hr_merge = operations.merge_hic(predict_hic_hr, index_1D_2D=index_1d_2d, max_distance=max_boundary)
print('Shape of merge predict hic HR', predict_hic_hr_merge.shape)
true_hic_hr_merge = operations.merge_hic( true_hic_hr, index_1D_2D=index_1d_2d, max_distance=max_boundary)
print('Shape of merge true hic HR: {}'.format(true_hic_hr_merge.shape))
# recover M from scn to origin
Mh = operations.scn_recover(Mh, Dh)
true_hic_hr_merge = operations.scn_recover(true_hic_hr_merge, Dh)
predict_hic_hr_merge = operations.scn_recover(predict_hic_hr_merge, Dh)
# remove diag and off diag
k = max_boundary.astype(int)
Mh = operations.filter_diag_boundary(Mh, diag_k=1, boundary_k=None)
true_hic_hr_merge = operations.filter_diag_boundary(true_hic_hr_merge, diag_k=1, boundary_k=None)
predict_hic_hr_merge = operations.filter_diag_boundary(predict_hic_hr_merge, diag_k=1, boundary_k=None)
print('sum Mh:', np.sum(np.abs(Mh)))
print('sum true merge:', np.sum(np.abs(true_hic_hr_merge)))
print('sum pred merge:', np.sum(np.abs(predict_hic_hr_merge)))
diff = np.abs(Mh-predict_hic_hr_merge)
print('sum Mh - pred square error: {:.5}'.format(np.sum(diff**2)))
diff = np.abs(true_hic_hr_merge-predict_hic_hr_merge)
print('sum true merge - pred square error: {:.5}'.format(np.sum(diff**2)))
diff = np.abs(Mh-true_hic_hr_merge)
print('sum Mh - true merge square error: {:.5}'.format(np.sum(diff**2)))
compact = idx
file = 'predict_chr{}_{}.npz'.format(chromosome, resolution)
np.savez_compressed(os.path.join(directory_sr, file), hic=predict_hic_hr_merge, compact=compact)
print('Saving file: {}, at {}'.format(file, directory_sr))
file = 'true_chr{}_{}.npz'.format(chromosome, resolution)
np.savez_compressed(os.path.join(directory_sr, file), hic=Mh, compact=compact)
print('Saving file: {}, at {}'.format(file, directory_sr))
# predict_hic_hr_merge = predict_hic_hr_merge[::10, ::10]
# Mh = Mh[::10, ::10]
"""fig, axs = plt.subplots(1, 2, figsize=(15, 8))
# , cmap='RdBu_r'
ax = axs[0].imshow(np.log1p(predict_hic_hr_merge), cmap='OrRd')
axs[0].set_title('predict')
ax = axs[1].imshow(np.log1p(Mh), cmap='OrRd') # , cmap='RdBu_r'
axs[1].set_title('true')
plt.tight_layout()
fig.colorbar(ax, ax=axs, shrink=0.3)
'''cmap = mpl.cm.OrRd
norm = mpl.colors.Normalize(vmin=0, vmax=0.7)
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=axs, shrink=0.3)'''
output = os.path.join(directory_sr, 'prediction_chr{}_{}_{}.jpg'.format(chromosome, start, end))
plt.savefig(output, format='jpg')"""
nr,nc = 6,8
fig, axs = plt.subplots(nrows=nr, ncols=nc, figsize=(25, 20))
interm = intermediate_x2.numpy()
interm = np.squeeze(interm, axis=0)
interm = (interm-interm.min())/(interm.max()-interm.min())
sum_interm = np.sum(interm, axis=(0,1))
interm = interm[:,:, sum_interm.argsort()]
interm = interm[:,:,::-1]
print(interm.shape)
for i in np.arange(0, nr):
for j in np.arange(0, nc):
idx = 40 + (i*nc+j)*2
if idx > interm.shape[2]:
continue
m = interm[:,:, idx]
m = np.squeeze(m)
pcm = axs[i, j].imshow(np.log1p(m), cmap='OrRd')
plt.tight_layout()
fig.colorbar(pcm, ax=axs, shrink=0.3)
output = os.path.join(directory_sr, 'features_x2_chr{}_{}_{}.jpg'.format(chromosome, start, end))
plt.savefig(output, format='jpg')
nr,nc = 5,7
fig, axs = plt.subplots(nrows=nr, ncols=nc, figsize=(25, 20))
interm = intermediate_x4.numpy()
interm = np.squeeze(interm, axis=0)
interm = (interm-interm.min())/(interm.max()-interm.min())
sum_interm = np.sum(interm, axis=(0,1))
interm = interm[:,:, sum_interm.argsort()]
interm = interm[:,:,::-1]
for i in np.arange(0, nr):
for j in np.arange(0, nc):
idx = 10 + (i*nc+j)
if idx > interm.shape[2]:
continue
m = interm[:,:, idx]
m = np.squeeze(m)
pcm = axs[i, j].imshow(np.log1p(m), cmap='OrRd')
plt.tight_layout()
"""cmap = mpl.cm.seismic
norm = mpl.colors.Normalize(vmin=0, vmax=0.7)
fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), ax=axs, shrink=0.3)"""
fig.colorbar(pcm, ax=axs, shrink=0.3)
output = os.path.join(directory_sr, 'features_x4_chr{}_{}_{}.jpg'.format(chromosome, start, end))
plt.savefig(output, format='jpg')
name = 'prediection_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(predict_hic_hr_merge, directory_sr, name, title='Prediction Hi-C (10kb)')
name = 'prediction_x2_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(out_low_x2, directory_sr, name, 'Prediction Hi-C (20kb)')
name = 'prediction_x4_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(out_low_x4, directory_sr, name, 'Prediction Hi-C (40kb)')
name = 'HR_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(Mh, directory_sr, name, 'True Hi-C (10kb)')
name = 'true_x2_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(low_x2, directory_sr, name, 'True Hi-C (40kb, by average pooling)')
name = 'true_x4_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(low_x4, directory_sr, name, 'True Hi-C (40kb, by average pooling)')
name = 'LR_chr{}_{}_{}'.format(chromosome, start, end)
plot_hic_matrix(Ml, directory_sr, name, title = 'True Hi-C (40kb, x16 downsampling)')