ldm/plotting_utils.py

#based on https://github.com/CompVis/taming-transformers
 
import matplotlib.pyplot as plt
import seaborn as sns
import os
from pathlib import Path
import torchvision
import torch
import numpy as np
from PIL import Image
import json
import csv
import pandas as pd

from sklearn.metrics import ConfusionMatrixDisplay


def dump_to_json(dict, ckpt_path, name='results', get_fig_path=True):
    
    if get_fig_path:
        root = get_fig_pth(ckpt_path)
    else:
        root = ckpt_path
        if not os.path.exists(root):
            os.mkdir(root)

    with open(os.path.join(root, name+".json"), "w") as outfile:
        json.dump(dict, outfile)
        

def save_to_cvs(ckpt_path, postfix, file_name, list_of_created_sequence):
    if ckpt_path is not None:
        root = get_fig_pth(ckpt_path, postfix=postfix)
    else:
        root = postfix
        
    file = open(os.path.join(root, file_name), 'w')
    with file:  
        write = csv.writer(file)
        write.writerows(list_of_created_sequence)
        
def save_to_txt(arr, ckpt_path, name='results'):
    root = get_fig_pth(ckpt_path)
    with open(os.path.join(root, name+".txt"), "w") as outfile:
        outfile.write(str(arr))



def save_image_grid(torch_images, ckpt_path=None, subfolder=None, postfix="", nrow=10):
    if ckpt_path is not None:
        root = get_fig_pth(ckpt_path, postfix=subfolder)
    else:
        root = subfolder

    grid = torchvision.utils.make_grid(torch_images, nrow=nrow)
    grid = torch.clamp(grid, -1., 1.)

    grid = (grid+1.0)/2.0 # -1,1 -> 0,1; c,h,w
    grid = grid.transpose(0,1).transpose(1,2).squeeze(-1)
    grid = grid.cpu().numpy()
    grid = (grid*255).astype(np.uint8)
    filename = "code_changes_"+postfix+".png"
    path = os.path.join(root, filename)
    os.makedirs(os.path.split(path)[0], exist_ok=True)
    Image.fromarray(grid).save(path, bbox_inches='tight')


def unprocess_image(torch_image):
    torch_image = torch.clamp(torch_image, -1., 1.)

    torch_image = (torch_image+1.0)/2.0 # -1,1 -> 0,1; c,h,w
    torch_image = torch_image.transpose(0,1).transpose(1,2).squeeze(-1)
    torch_image = torch_image.cpu().numpy()
    torch_image = (torch_image*255).astype(np.uint8)
    return torch_image

def save_image(torch_image, image_name, ckpt_path=None, subfolder=None):
    if ckpt_path is not None:
        root = get_fig_pth(ckpt_path, postfix=subfolder)
    else:
        root = subfolder

    torch_image = unprocess_image(torch_image)
    
    filename = image_name+".png"
    path = os.path.join(root, filename)
    os.makedirs(os.path.split(path)[0], exist_ok=True)
    fig = plt.figure()
    plt.imshow(torch_image[0].squeeze())
    fig.savefig(path,bbox_inches='tight',dpi=300)
    
    

def get_fig_pth(ckpt_path, postfix=None):
    figs_postfix = 'figs'
    postfix = os.path.join(figs_postfix, postfix) if postfix is not None else figs_postfix
    parent_path = Path(ckpt_path).parent.parent.absolute()
    fig_path = Path(os.path.join(parent_path, postfix))
    os.makedirs(fig_path, exist_ok=True)
    return fig_path

def plot_heatmap(heatmap, ckpt_path=None, title='default', postfix=None):
    if ckpt_path is not None:
        path = get_fig_pth(ckpt_path, postfix=postfix)
    else:
        path = postfix
        
    # show
    fig = plt.figure()
    ax = plt.imshow(heatmap, cmap='hot', interpolation='nearest')
    plt.tick_params(left=False, bottom=False)
    # cbar = ax.collections[0].colorbar
    cbar = plt.colorbar(ax)
    cbar.ax.tick_params(labelsize=15)
    plt.axis('off')
    plt.show()
    fig.savefig(os.path.join(path, title+ " heat_map.png"),bbox_inches='tight',dpi=300)
    pd.DataFrame(heatmap.numpy()).to_csv(os.path.join(path, title+ " heat_map.csv"))

def plot_heatmap_at_path(heatmap, save_path, ckpt_path=None, title='default', postfix=None):
    if ckpt_path is not None:
        path = get_fig_pth(ckpt_path, postfix=postfix)
    else:
        path = postfix
        
    # show
    fig = plt.figure()
    ax = plt.imshow(heatmap, cmap='hot', interpolation='nearest')
    plt.tick_params(left=False, bottom=False)
    # cbar = ax.collections[0].colorbar
    cbar = plt.colorbar(ax)
    cbar.ax.tick_params(labelsize=15)
    plt.axis('off')
    plt.show()
    fig.savefig(os.path.join(save_path, title+ "_heat_map.png"),bbox_inches='tight',dpi=300)
    pd.DataFrame(heatmap.numpy()).to_csv(os.path.join(save_path, title+ "_heat_map.csv"))

def plot_confusionmatrix(preds, classes, classnames, ckpt_path, postfix=None, title="", get_fig_path=True):
    fig, ax = plt.subplots(figsize=(30,30))
    preds_max = np.argmax(preds.cpu().numpy(), axis=-1)
    disp = ConfusionMatrixDisplay.from_predictions(classes.cpu().numpy(), preds_max, display_labels=classnames, normalize='true', xticks_rotation='vertical', ax=ax)
    disp.plot()
    
    if get_fig_path:
        fig_path = get_fig_pth(ckpt_path, postfix=postfix)
    else:
        fig_path = ckpt_path
        if not os.path.exists(fig_path):
            os.mkdir(fig_path)
    
    print(fig_path)
    fig.savefig(os.path.join(fig_path, title+ " heat_map.png"))

def plot_confusionmatrix_colormap(preds, classes, classnames, ckpt_path, postfix=None, title="", get_fig_path=True):
    fig, ax = plt.subplots(figsize=(30,30))
    preds_max = np.argmax(preds.cpu().numpy(), axis=-1)
    class_labels = list(range(len(classnames)))
    disp = ConfusionMatrixDisplay.from_predictions(classes.cpu().numpy(), preds_max, display_labels=class_labels, normalize='true', xticks_rotation='vertical', ax=ax, cmap='coolwarm')
    disp.plot()
    
    if get_fig_path:
        fig_path = get_fig_pth(ckpt_path, postfix=postfix)
    else:
        fig_path = ckpt_path
        if not os.path.exists(fig_path):
            os.mkdir(fig_path)
    
    print(fig_path)
    fig.savefig(os.path.join(fig_path, title+ " heat_map_coolwarm.png"))
    

class Histogram_plotter:
    def __init__(self, codes_per_phylolevel, n_phylolevels, n_embed, 
                 converter, 
                 indx_to_label,
                 ckpt_path, directory):
        self.codes_per_phylolevel = codes_per_phylolevel
        self.n_phylolevels = n_phylolevels
        self.n_embed = n_embed
        self.converter = converter
        self.ckpt_path = ckpt_path
        self.directory = directory
        self.indx_to_label = indx_to_label
        
    def plot_histograms(self, histograms, species_indx, is_nonattribute=False, prefix="species"):
        fig, axs = plt.subplots(self.codes_per_phylolevel, self.n_phylolevels, figsize = (5*self.n_phylolevels,30))
        for i, ax in enumerate(axs.reshape(-1)):
            ax.hist(histograms[i], density=True, range=(0, self.n_embed-1), bins=self.n_embed)
            
            if not is_nonattribute:
                code_location, level = self.converter.get_code_reshaped_index(i)
                ax.set_title("code "+ str(code_location) + "/level " +str(level))
            else:
                ax.set_title("code "+ str(i))
        
        plt.show()
        sub_dir = 'attribute' if not is_nonattribute else 'non_attribute'
        fig.savefig(os.path.join(get_fig_pth(self.ckpt_path, postfix=self.directory+'/'+sub_dir), "{}_{}_{}_hostogram.png".format(prefix, species_indx, self.indx_to_label[species_indx])),bbox_inches='tight',dpi=300)
        plt.close(fig)