code: """Calculates the Frechet Inception Distance (FID) to evalulate GANs
  
  The FID metric calculates the distance between two distributions of images.
  Typically, we have summary statistics (mean & covariance matrix) of one
  of these distributions, while the 2nd distribution is given by a GAN.
  
  When run as a stand-alone program, it compares the distribution of
  images that are stored as PNG/JPEG at a specified location with a
  distribution given by summary statistics (in pickle format).
  
  The FID is calculated by assuming that X_1 and X_2 are the activations of
  the pool_3 layer of the inception net for generated samples and real world
  samples respectively.
  
  See --help to see further details.
  
  Code apapted from https://github.com/bioinf-jku/TTUR to use PyTorch instead
  of Tensorflow
  
  Copyright 2018 Institute of Bioinformatics, JKU Linz
  
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at
  
     http://www.apache.org/licenses/LICENSE-2.0
  
  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License.
  """
  import os
  import pathlib
  from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
  from multiprocessing import cpu_count
  
  import numpy as np
  import torch
  import torchvision.transforms as TF
  from PIL import Image
  from scipy import linalg
  from torch.nn.functional import adaptive_avg_pool2d
  
  try:
      from tqdm import tqdm
  except ImportError:
      # If tqdm is not available, provide a mock version of it
      def tqdm(x):
          return x
  
  from utils.inception import InceptionV3
  
  print(InceptionV3.BLOCK_INDEX_BY_DIM)
  IMAGE_EXTENSIONS = {'bmp', 'jpg', 'jpeg', 'pgm', 'png', 'ppm',
                      'tif', 'tiff', 'webp'}
  
  
  class ImagePathDataset(torch.utils.data.Dataset):
      def __init__(self, files, transforms=None):
          self.files = files
          self.transforms = transforms
  
      def __len__(self):
          return len(self.files)
  
      def __getitem__(self, i):
          path = self.files[i]
          img = Image.open(path).convert('RGB')
          if self.transforms is not None:
              img = self.transforms(img)
          return img
  
  
  def get_activations(files, model, batch_size=50, dims=2048, device='cuda'):
      """Calculates the activations of the pool_3 layer for all images.
  
      Params:
      -- files       : List of image files paths
      -- model       : Instance of inception model
      -- batch_size  : Batch size of images for the model to process at once.
                       Make sure that the number of samples is a multiple of
                       the batch size, otherwise some samples are ignored. This
                       behavior is retained to match the original FID score
                       implementation.
      -- dims        : Dimensionality of features returned by Inception
      -- device      : Device to run calculations
  
      Returns:
      -- A numpy array of dimension (num images, dims) that contains the
         activations of the given tensor when feeding inception with the
         query tensor.
      """
      model.eval()
      print(len(files), batch_size)
  
      if batch_size > len(files):
          print(('Warning: batch size is bigger than the data size. '
                 'Setting batch size to data size'))
          batch_size = len(files)
  
      dataset = ImagePathDataset(files, transforms=TF.ToTensor())
  
      dataloader = torch.utils.data.DataLoader(dataset,
                                               batch_size=batch_size,
                                               shuffle=False,
                                               drop_last=False,
                                               num_workers=cpu_count())
  
      pred_arr = np.empty((len(files), dims))
  
      start_idx = 0
  
      for batch in tqdm(dataloader):
          batch = batch.to(device)
  
          with torch.no_grad():
              pred = model(batch)[0]
  
          # If model output is not scalar, apply global spatial average pooling.
          # This happens if you choose a dimensionality not equal 2048.
          if pred.size(2) != 1 or pred.size(3) != 1:
              pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
  
          pred = pred.squeeze(3).squeeze(2).cpu().numpy()
  
          pred_arr[start_idx:start_idx + pred.shape[0]] = pred
  
          start_idx = start_idx + pred.shape[0]
  
      return pred_arr
  
  
  def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
      """Numpy implementation of the Frechet Distance.
      The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1)
      and X_2 ~ N(mu_2, C_2) is
              d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)).
  
      Stable version by Dougal J. Sutherland.
  
      Params:
      -- mu1   : Numpy array containing the activations of a layer of the
                 inception net (like returned by the function 'get_predictions')
                 for generated samples.
      -- mu2   : The sample mean over activations, precalculated on an
                 representative data set.
      -- sigma1: The covariance matrix over activations for generated samples.
      -- sigma2: The covariance matrix over activations, precalculated on an
                 representative data set.
  
      Returns:
      --   : The Frechet Distance.
      """
  
      mu1 = np.atleast_1d(mu1)
      mu2 = np.atleast_1d(mu2)
  
      sigma1 = np.atleast_2d(sigma1)
      sigma2 = np.atleast_2d(sigma2)
  
      assert mu1.shape == mu2.shape, \
          'Training and test mean vectors have different lengths'
      assert sigma1.shape == sigma2.shape, \
          'Training and test covariances have different dimensions'
  
      diff = mu1 - mu2
  
      # Product might be almost singular
      covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
      if not np.isfinite(covmean).all():
          msg = ('fid calculation produces singular product; '
                 'adding %s to diagonal of cov estimates') % eps
          print(msg)
          offset = np.eye(sigma1.shape[0]) * eps
          covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
  
      # Numerical error might give slight imaginary component
      if np.iscomplexobj(covmean):
          if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
              m = np.max(np.abs(covmean.imag))
              raise ValueError('Imaginary component {}'.format(m))
          covmean = covmean.real
  
      tr_covmean = np.trace(covmean)
  
      return (diff.dot(diff) + np.trace(sigma1)
              + np.trace(sigma2) - 2 * tr_covmean)
  
  
  def calculate_activation_statistics(files, model, batch_size=50, dims=2048,
                                      device='cuda'):
      """Calculation of the statistics used by the FID.
      Params:
      -- files       : List of image files paths
      -- model       : Instance of inception model
      -- batch_size  : The images numpy array is split into batches with
                       batch size batch_size. A reasonable batch size
                       depends on the hardware.
      -- dims        : Dimensionality of features returned by Inception
      -- device      : Device to run calculations
  
      Returns:
      -- mu    : The mean over samples of the activations of the pool_3 layer of
                 the inception model.
      -- sigma : The covariance matrix of the activations of the pool_3 layer of
                 the inception model.
      """
      act = get_activations(files, model, batch_size, dims, device)
      mu = np.mean(act, axis=0)
      sigma = np.cov(act, rowvar=False)
      return mu, sigma
  
  
  def compute_statistics_of_path(path, model, batch_size, dims, device):
      if path.endswith('.npz'):
          with np.load(path) as f:
              m, s = f['mu'][:], f['sigma'][:]
      else:
          path = pathlib.Path(path)
          files = sorted([file for ext in IMAGE_EXTENSIONS
                          for file in path.glob('*.{}'.format(ext))])
          m, s = calculate_activation_statistics(files, model, batch_size,
                                                 dims, device)
  
      return m, s
  
  
  def calculate_fid_given_paths(paths, batch_size, device, dims):
      """Calculates the FID of two paths"""
      print('paths is :', paths)
      for p in paths:
          if not os.path.exists(p):
              raise RuntimeError('Invalid path: %s' % p)
  
      block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
  
      model = InceptionV3([block_idx]).to(device)
  
      m1, s1 = compute_statistics_of_path(paths[0], model, batch_size,
                                          dims, device)
      m2, s2 = compute_statistics_of_path(paths[1], model, batch_size,
                                          dims, device)
      fid_value = calculate_frechet_distance(m1, s1, m2, s2)
  
      return fid_value
  
  
  def return_fid(path1, path2):
      device = torch.device('cuda' if (torch.cuda.is_available()) else 'cpu')
  
      fid_value = calculate_fid_given_paths(paths=[path1, path2],
                                            batch_size=50,
                                            device=device,
                                            dims=2048)
      return fid_value
   #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Sun Sep 26 17:33:03 2021
  
  @author: ubuntu204
  """
  import numpy as np
  from scipy import stats
  import statsmodels.stats.multitest as multitest
  import matplotlib.pyplot as plt
  import os
  
  import pandas as pd
  from pandas import Series,DataFrame
  # import seaborn as sns
  # import palettable
  from sklearn import datasets
  from tqdm import tqdm
  
  plt.rcParams['font.sans-serif']=['SimHei']
  # plt.rcParams['axes.unicode_mnius']=False
  epsilon=1e-10
  
  def volcano_mine(data1,data2,method='hs',flag_output_src=0,flag_plot=0):
      data1=data1+epsilon
      data2=data2+epsilon
      
      mdata1=data1.mean(axis=0)
      mdata2=data2.mean(axis=0)    
      fold_change=(mdata2)/(mdata1)
      log2_fold_change=np.log2(fold_change)
      
      p_values=np.zeros_like(mdata1)
      for i in tqdm(range(len(p_values))):
          t,p=stats.ttest_ind(data1[:,i],data2[:,i])
          p_values[i]=p
      rejects,pvals_corrected,alphaSidak,alphaBonf=multitest.multipletests(p_values,method=method)
      log10_pvals_corrected=np.log10(pvals_corrected+epsilon)*(-1)
      
      return log2_fold_change,log10_pvals_corrected
      
  def plot_volume(log2_fold_change,log10_pvals_corrected,title=None,saved_name=None):
      npt=len(log2_fold_change)
      colors=list(['grey']*npt)
      idx_green=(log2_fold_change>=np.log2(1.2))&(log10_pvals_corrected>(-np.log10(0.05)))
      for i in range(len(idx_green)):
          if idx_green[i]:
              colors[i]='green'
      idx_red=(log2_fold_change<=-np.log2(1.2))&(log10_pvals_corrected>(-np.log10(0.05)))
      for i in range(len(idx_red)):
          if idx_red[i]:
              colors[i]='red'
      # colors[idx_red]='red'
      
      plt.figure()
      plt.style.use('seaborn-whitegrid')
      plt.scatter(log2_fold_change, log10_pvals_corrected, color=colors)
      plt.xlabel('Log2 Fold Change')
      plt.ylabel('-Log10 P-Value')
      if title:
          plt.title(title)
      if saved_name:
          plt.savefig(saved_name,bbox_inches='tight',dpi=300)
      return
  
  # def plot_heatmap(data,row_c=None,dpi=300,figsize=(8/2.54,16/2.54),saved_name=None):
  #     # plt.figure(dpi=dpi)
  #     data_show=data.copy()
  #     # data_show=data.drop(['class'],axis=1)
  #     if row_c:
  #         row_colors=data['class'].map(row_c)
  #     sns.clustermap(data=data_show,method='single',metric='euclidean',
  #                    figsize=figsize,row_cluster=False,col_cluster=False,
  #                    cmap='rainbow')
  #     sns.set(font_scale=1.5)
  #     if saved_name:
  #         plt.savefig(saved_name,bbox_inches='tight',dpi=dpi)
      
      
  if __name__=='__main__':    
      # data1=np.random.rand(5, 10)
      # data2=np.random.rand(5, 10)
      # data2[:,0]=data1[:,0]*2.5
      # data2[:,1]=data1[:,1]*10
      # data2[:,2]=data1[:,2]/2.5
      # data2[:,3]=data1[:,3]/10
      # logFC,logP=volcano_mine(data1, data2)
      # plot_volume(logFC,logP)
      
      iris=datasets.load_iris()
      x,y=iris.data,iris.target
      data=np.hstack((x,y.reshape(150,1)))
      pd_iris=pd.DataFrame(data,columns=['sepal length(cm)','sepal width(cm)','petal length(cm)','petal width(cm)','class'])
      row_c=dict(zip(pd_iris['class'].unique(),['green','yellow','pink']))
      # plot_heatmap(pd_iris,row_c=row_c)
apis: numpy.eye numpy.load numpy.atleast_2d numpy.cov torch.no_grad numpy.abs numpy.atleast_1d numpy.trace torch.nn.functional.adaptive_avg_pool2d torch.cuda.is_available numpy.isfinite numpy.iscomplexobj numpy.diagonal numpy.mean
apis: numpy.log2 matplotlib.pyplot.style.use numpy.zeros_like matplotlib.pyplot.figure scipy.stats.ttest_ind pandas.DataFrame matplotlib.pyplot.savefig matplotlib.pyplot.title matplotlib.pyplot.ylabel numpy.log10 matplotlib.pyplot.xlabel matplotlib.pyplot.scatter sklearn.datasets.load_iris