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plot_avg_org.py
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plot_avg_org.py
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import os
from coefficients import alignment, coefs
from warps import parameterize
os.chdir("C:/Users/trang.le/Desktop/2D_shape_space")
import numpy as np
from imageio import imwrite
from PIL import Image, ImageSequence
from matplotlib import pyplot as plt
from utils import plotting
from skimage.morphology import dilation, square, erosion
import pandas as pd
import seaborn as sns
LABEL_TO_ALIAS = {
0: 'Nucleoplasm',
1: 'NuclearM',
2: 'Nucleoli',
3: 'NucleoliFC',
4: 'NuclearS',
5: 'NuclearB',
6: 'EndoplasmicR',
7: 'GolgiA',
8: 'IntermediateF',
9: 'ActinF',
10: 'Microtubules',
11: 'MitoticS',
12: 'Centrosome',
13: 'PlasmaM',
14: 'Mitochondria',
15: 'Aggresome',
16: 'Cytosol',
17: 'VesiclesPCP',
#18: 'Negative',
#19:'Multi-Location',
}
all_locations = dict((v, k) for k,v in LABEL_TO_ALIAS.items())
COLORS = [
'#f44336', '#e91e63', '#9c27b0', '#673ab7', '#3f51b5',
'#2196f3', '#03a9f4', '#00bcd4', '#009688', '#4caf50',
'#8bc34a', '#cddc39', '#ffeb3b', '#ffc107', '#ff9800',
'#ff5722', '#795548', '#9e9e9e', '#607d8b', '#dddddd',
'#212121', '#ff9e80', '#ff6d00', '#ffff00', '#76ff03',
'#00e676', '#64ffda', '#18ffff',
]
COLORS_MAP = {
"Nucleoplasm":"Blues",
"GolgiA":"Greens",
"IntermediateF":"Oranges",
"Mitochondria":"Reds",
"PlasmaM":"Purples",
"Cytosol":"Greys"
}
def open_gif(gif_path):
animated_gif = Image.open(gif_path)#f"{organelle_dir}/{org}_PC1.gif")
frames = [f for f in ImageSequence.Iterator(animated_gif)]
frames = []
for frame in ImageSequence.Iterator(animated_gif):
fr = frame.copy()
fr.past()
def coordinates_to_image(x, y, intensity, binarize=False, shift_x = None, shift_y = None):
"""
Parameters
----------
x : np.array shape (n_rings, n_positions)
X coordiates of sampled points, each ring from nu_centroid-nu_membrane-cell_membrane has n_positions of x
y : list of np.array shape (n_rings, n_positions)
y coordiates of sampled points, each ring from nu_centroid-nu_membrane-cell_membrane has n_positions of y
intensity : sampled intensity at (n_rings, n_positions) points
DESCRIPTION.
binarize : whether to binarize intensity or not
binarize threshold = mean (TODO: udpate this to histogram normalization or other)
shift_x : distance to add to all x coordinates
If None, shift_y=min(x).
shift_y : distance to add to all y coordinates
If None, shift_y=min(y).
Returns
-------
img : np.array
output image.
"""
assert x.shape == y.shape == intensity.shape
x = np.array(np.round(x), dtype=int)
y = np.array(np.round(y), dtype=int)
if binarize:
thres = intensity.mean()
intensity = intensity > thres
if shift_x == None:
shift_x = x.min()
if shift_y == None:
shift_y = y.min()
x = x - shift_x
y = y - shift_y
img = np.zeros((x.max()-x.min() + 1, y.max()-y.min() +1), dtype='float64')
prev_x = []
prev_y = []
prev_int = []
for i_, (ix, iy, intensity_ring) in enumerate(zip(x,y, intensity)):
for ix_, iy_, intensity_ in zip(ix,iy, intensity_ring):
img[ix_-5:ix_+5, iy_-5:iy_+5] = intensity_
if i_ > 9:
for ix_, iy_, intensity_ in zip((ix+prev_x)/2,(iy+prev_y)/2, (intensity_ring+prev_int)/2):
img[int(ix_)-5:int(ix_)+5, int(iy_)-5:int(iy_)+5] = intensity_
prev_x = ix
prev_y = iy
prev_int = intensity_ring
return img
def main(plot=False):
shape_var_dir = "C:/Users/trang.le/Desktop/shapemode/U-2_OS/PCA_ratio8"
organelle_dir = "C:/Users/trang.le/Desktop/shapemode/organelle"
save_dir = "C:/Users/trang.le/Desktop/shapemode/avg_cell"
avg_coords = np.load(f"{shape_var_dir}/Avg_cell.npz")
print(avg_coords.files)
ix_n = avg_coords["ix_n"] #[avg_coords["ix_n"][i] for i in range(0,1280,5)]
iy_n = avg_coords["iy_n"] #[avg_coords["iy_n"][i] for i in range(0,1280,5)]
ix_c = avg_coords["ix_c"] #[avg_coords["ix_c"][i] for i in range(0,1280,5)]
iy_c = avg_coords["iy_c"] #[avg_coords["iy_c"][i] for i in range(0,1280,5)]
x_,y_ = parameterize.get_coordinates(
np.concatenate([ix_n, iy_n]),
np.concatenate([ix_c, iy_c]),
[0,0],
n_isos = [10,10],
plot=False)
avg_organelle_intensity = []
norm = plt.Normalize(vmin=0, vmax=1)
for org in all_locations.keys():
intensities = np.load(f"{organelle_dir}/{org}_PC1_intensity.npy")
intensities = intensities[4:6].sum(axis=0) #avg 2 slice in the middle
avg_organelle_intensity += [[org] + intensities.flatten().tolist()]
img = coordinates_to_image(np.asarray(x_), np.asarray(y_), intensities)
imwrite(f"{save_dir}/{org}.png",img)
#img = coordinates_to_image(np.asarray(x_), np.asarray(y_), intensities, binarize=True)
plt.imshow(dilation(erosion(img), square(7)))
plt.tight_layout()
plt.axis("off")
plt.savefig(f"{save_dir}/{org}.jpg", bbox_inches="tight")
plt.close()
avg_organelle_intensity = pd.DataFrame(avg_organelle_intensity)
avg_organelle_intensity.index = avg_organelle_intensity.iloc[:,0]
avg_organelle_intensity.drop([0], axis=1, inplace=True)
# No mitotic spindle in the average cells (which makes sense!) so remove the org
covar_mat = avg_organelle_intensity.transpose().drop(["MitoticS"],axis=1).corr()
sns.heatmap(covar_mat, cmap="RdBu", vmin=-1, vmax=1)
sns.clustermap(covar_mat, method="complete", cmap='RdBu', annot=True,
annot_kws={"size": 12}, vmin=-1, vmax=1, figsize=(15,15))
if plot:
for org,org_color in COLORS_MAP.items():
intensities = np.load(f"{organelle_dir}/{org}_PC1_intensity.npy")
intensities = intensities[4:6].sum(axis=0) #avg 2 slice in the middle
fig, ax = plt.subplots()
for i,(xi,yi,intensity) in enumerate(zip(x_,y_,intensities)):
ax.scatter(xi, yi,c=intensity, norm=norm, cmap= org_color)
ax.axis("scaled")
fig.tight_layout()
ax.axis("off")
ax.figure.savefig(f"{save_dir}/{org}.jpg", bbox_inches="tight")
def sample_covar_matrix(mat1, mat2):
"""
Parameters
----------
mat1 : TYPE
DESCRIPTION.
mat2 : TYPE
DESCRIPTION.
Returns
-------
corr :
"""
corr = np.cov(mat1)
return corr
def investigate_organell_pc_var():
"""
Sam
Returns
-------
None.
"""
shape_var_dir = "C:/Users/trang.le/Desktop/shapemode/U-2_OS/0"
organelle_dir = "C:/Users/trang.le/Desktop/shapemode/organelle"
save_dir = "C:/Users/trang.le/Desktop/shapemode/avg_cell"
# Plot all PCs for each organelle
for org in all_locations.keys():
fig,ax = plt.subplots(nrows=2, ncols=6)
for i in range(1,13):
intensities = np.load(f"{organelle_dir}/{org}_PC{i}_intensity.npy")
#coords = np.load(f"{organelle_dir}/{org}_PC{i}.npz")
ax[(i-1) //6, (i-1) % 6].imshow(intensities.mean(axis=2).T)
#plt.xticks(["nu_centroid","","","","","","","","","","nucleus","","","","","","","","","cell"])
#X = np.zeros((21,10))
ax[(i-1) //6, (i-1) % 6].set_title(f"PC{i}")
fig.suptitle(org)
plt.tight_layout()
plt.savefig(f"{save_dir}/allPC_{org}.png", bbox_inches="tight")
# Plot all organelles for each PC
for i in range(1,13):
fig,ax = plt.subplots(nrows=3, ncols=6, figsize=(25,20))
for org, k in all_locations.items():
intensities = np.load(f"{organelle_dir}/{org}_PC{i}_intensity.npy")
#coords = np.load(f"{organelle_dir}/{org}_PC{i}.npz")
ax[k //6, k % 6].imshow(intensities.mean(axis=2).T)
#plt.xticks(["nu_centroid","","","","","","","","","","nucleus","","","","","","","","","cell"])
#X = np.zeros((21,10))
ax[k //6, k % 6].set_title(f"{org}", fontsize=30)
fig.suptitle(f"PC{i}")
plt.axis("off")
plt.tight_layout()
plt.savefig(f"{save_dir}/allorg_PC{i}.png", bbox_inches="tight")
"""
plt.tight_layout()
fig, ax = plt.subplots(10,1)
for sp, intensity in enumerate(intensities):
ax[sp].imshow(intensity)
plt.axis("off")
plt.tight_layout()
img = plt.imread(f"{save_dir}/{org}.png")
"""
def cell_nu_ratio_cutoff():
organelle_dir = "C:/Users/trang.le/Desktop/shapemode/organelle"
shape_var_dir = "C:/Users/trang.le/Desktop/shapemode/U-2_OS/0"
n_cells_per_pc = pd.read_csv(f"{organelle_dir}/cells_per_bin.csv")
cell_nu_ratio = pd.read_csv(f"{shape_var_dir.rsplit('/',1)[0]}/cell_nu_ratio.txt", header=None)
cell_nu_ratio.columns=["path","name","ratio"]
plt.hist(cell_nu_ratio.ratio, bins=30, range=[0,25])
n_coef = 128
inverse_func = coefs.inverse_fft
# Different sample rate
d = "C:/Users/trang.le/Desktop/cellprows_mnt"
ids = ["410_E3_4_11","410_E3_4_12","410_E3_4_13","1377_F1_3_12"]
for im_id in ids:
#protein_path = imread(f"{d}/{im_id}_protein.png")
protein_path = f"{d}/{im_id}_protein.png"
cellshape_path = f"{d}/{im_id}.npy"
alignment.get_coefs_im(cellshape_path, save_dir, log_dir, n_coef=32, func=None, plot=False)
nuclei_coords =
cell_coords =
fcoef_n, e_n = coefs.fourier_coeffs(nuclei_coords, n=n_coef)
fcoef_c, e_c = coefs.fourier_coeffs(cell_coords, n=n_coef)
shifts = find_line(txt_path, specific_text)
intensity = plotting.get_protein_intensity(
pro_path = protein_path,
shift_dict = shifts[l],
ori_fft = ori_fft,
n_coef = n_coef,
inverse_func = inverse_func
)
def find_line(txt_path, specific_text):
"""
Find line containing specific text in a large txt file
Parameters
----------
txt_path : str
path to txt file.
specific_text : str
text to find.
Returns
-------
l : str
first line containing specific_text.
index : int
index of this line in the file
"""
with open(txt_path, "r") as fp:
lines = fp.readlines()
for l in lines:
if l.find(specific_text) != -1 :
return l, lines.index(l)
if __name__ == '__main__':
main()