def tile_lims(stack, num_std, bar=True, size=20):
from showit import tile
if type(stack) is list:
stack = np.array(stack)
stats = stack_describe(stack)
lims = [s['mean'] + num_std * s['std'] for s in stats]
tile(stack, bar=bar, clim=list(zip([0] * len(lims), lims)), size=size)
def show(args, print_help=False):
import matplotlib.pyplot as plt
from showit import tile
from .io import Stack
s = Stack()
s.read(args.in_json)
tile(s.image.squeeze(), size=args.sz, bar=True)
plt.show()
def show(args, print_help=False):
import matplotlib.pyplot as plt
from showit import tile
from .experiment import Experiment
s = Experiment()
s.read(args.in_json)
tile(s.image.squeeze(), size=args.sz, bar=True)
plt.show()
def show(in_json, sz):
import matplotlib.pyplot as plt
from showit import tile
from .experiment import Experiment
s = Experiment()
s.read(in_json)
tile(s.image.squeeze(), size=sz, bar=True)
plt.show()
def main():
# these samples will be automatically downloaded if they are not found locally
dataset = loader.load(samples=[
'4bad52d5ef5f68e87523ba40aa870494a63c318da7ec7609e486e62f7f7a25e8',
'a7e37600a431fa6d6023514df87cfc8bb5ec028fb6346a10c2ececc563cc5423',
'70a6300a00dbac92be9238252ee2a75c86faf4729f3ef267688ab859eed1cc60'
])
# this transform will cause the dataset to find the mean image of a movie
# anytime an item is requested from the dataset
transform = preprocess.Mean()
dataset.set_transform(transform)
mean_images = list()
for i in range(len(dataset)):
sample = dataset[i] # mean transform has already been applied!
mean_images.append(sample)
tile(mean_images)
plt.show()
def test_tile(): im = random.randn(5, 25, 25) tile(im)
# EPY: START markdown # ## Raw Data # # The raw data can be downloaded and formatted for analysis by running: ```python examples/get_iss_data.py ><raw data directory> <output directory> --d 1``` from the Starfish directory # EPY: END markdown # EPY: START code from starfish.io import Stack # replace <output directory> with where you saved the formatted data to with the above script in_json = '<output directory>/org.json' s = Stack() s.read(in_json) tile(s.squeeze(), size=10) # EPY: END code # EPY: START code image(s.aux_dict['dots'], size=10) # EPY: END code # EPY: START markdown # ## Register # EPY: END markdown # EPY: START code from starfish.registration._fourier_shift import compute_shift, shift_im upsample = 1000
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from showit import image, tile
import pprint
# EPY: ESCAPE %matplotlib inline
from starfish.io import Stack
from starfish.constants import Indices
s = Stack()
s.read(
'https://dmf0bdeheu4zf.cloudfront.net/20180611/ISS/fov_001/experiment.json'
)
# s.image.squeeze() simply converts the 4D tensor H*C*X*Y into a list of len(H*C) image planes for rendering by 'tile'
tile(s.image.squeeze())
# EPY: END code
# EPY: START markdown
# ## Show input file format that specifies how the tiff stack is organized
#
# The stack contains multiple single plane images, one for each color channel, 'ch', (columns in above image) and hybridization round, 'hyb', (rows in above image). This protocol assumes that genes are encoded with a length 4 quatenary barcode that can be read out from the images. Each hybridization encodes a position in the codeword. The maximum signal in each color channel (columns in the above image) corresponds to a letter in the codeword. The channels, in order, correspond to the letters: 'T', 'G', 'C', 'A'. The goal is now to process these image data into spatially organized barcodes, e.g., ACTG, which can then be mapped back to a codebook that specifies what gene this codeword corresponds to.
# EPY: END markdown
# EPY: START code
pp = pprint.PrettyPrinter(indent=2)
pp.pprint(s.org)
# EPY: END code
# EPY: START markdown
# The flat TIFF files are loaded into a 4-d tensor with dimensions corresponding to hybridization round, channel, x, and y. For other volumetric approaches that image the z-plane, this would be a 5-d tensor.
def test_tile():
im = random.randn(5, 25, 25)
tile(im)
import numpy as np
import matplotlib.pyplot as plt
import pickle
from pathlib import Path
from showit import tile
#%% Import training data
training_data = pd.read_pickle(
'/home/alex/Documents/DataProjects/Data/MBI/ProstateX/generated/train/dataframes/training.pkl'
)
#%% Plot grid of t2 significant findings
t2_train = training_data[(training_data['sequence_type'] == 't2')
& (training_data['ClinSig'] == True)]
t2_patches = t2_train['patch'].values
tile(t2_patches, grid=(50, 8), size=60)
#%% Plot grid of adc significant findings
adc_train = training_data[(training_data['sequence_type'] == 'adc')
& (training_data['ClinSig'] == True)]
adc_patches = adc_train['patch'].values
tile(adc_patches, grid=(50, 8), size=60)
#%% Plot grid of bval significant findings
bval_train = training_data[(training_data['sequence_type'] == 'bval')
& (training_data['ClinSig'] == True)]
bval_patches = bval_train['patch'].values
tile(bval_patches, grid=(50, 8), size=60)
#%% Plot grid of ktrans significant findings
ktrans_train = training_data[(training_data['sequence_type'] == 'ktrans')
# EPY: START markdown # ## Raw Data # # The raw data can be downloaded and formatted for analysis by running: ```python examples/get_iss_data.py ><raw data directory> <output directory> --d 1``` from the Starfish directory # EPY: END markdown # EPY: START code from starfish.io import Stack # replace <output directory> with where you saved the formatted data to with the above script in_json = '<output directory>/org.json' s = Stack() s.read(in_json) tile(s.image.squeeze(), size=10); # EPY: END code # EPY: START code image(s.auxiliary_images['dots'], size=10) # EPY: END code # EPY: START markdown # ## Register # EPY: END markdown # EPY: START code from starfish.pipeline.registration import Registration registration = Registration.fourier_shift(upsampling=1000) registration.register(s)
"""
Author: Alex Hamilton - https://github.com/alexhamiltonRN
Created: 2018-11-17
Description: A script to plot significant findings in numpy arrays.
"""
#%%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
from pathlib import Path
from showit import tile
#%% T2 Train
t2_train = np.load('/home/alex/Documents/DataProjects/Data/MBI/ProstateX/generated/train/numpy/t2/X_train.npy')
tile(t2_train, grid=(70,8), size = 60)
#%% ADC Train
adc_train = np.load('/home/alex/Documents/DataProjects/Data/MBI/ProstateX/generated/train/numpy/adc/X_train.npy')
tile(adc_train, grid=(70,8), size = 60)
#%% BVAL Train
bval_train = np.load('/home/alex/Documents/DataProjects/Data/MBI/ProstateX/generated/train/numpy/bval/X_train.npy')
tile(bval_train, grid=(70,8), size = 60)
#%% KTRANS Train
ktrans_train = np.load('/home/alex/Documents/DataProjects/Data/MBI/ProstateX/generated/train/numpy/ktrans/X_train.npy')
tile(ktrans_train, grid=(70,8), size = 60)