all

execute_sortem.py

@@ -4,41 +4,24 @@ import numpy as np
 from super_clas_sortem import super_class
 from os.path import join,getsize,isdir,isfile,dirname,basename
 from os import listdir,rename
-from fac_sortem import DynAE
+import csv
+#from fac_sortem import DynAE
 from os import listdir,mkdir
 import glob
 from os.path import isabs
+from fac_sortem import DynAE
 class control_flow:
   
 
 
 
-        def __init__(self,num_gpus,gpu_list,num_cpus,num_clusters,star,deep_NN_batch_size,workdir,half_precision,max_particles,epochs,transfer_learning,verbose):
-       
-            self.workdir = workdir
-            self.verbose = verbose
-            if not isdir(workdir): # check if all dirs are their 
-                mkdir(workdir)
-            if not isdir(join(workdir,'gui_display')):
-                    mkdir(join(workdir,'gui_display'))
-            if not isdir(join(workdir,'particle_stack_dir')):
-                    mkdir(join(workdir,'particle_stack_dir'))
-            np.save(join(join(workdir,'gui_display'),'parameters.npy'),np.asarray([0.0,0.0,"preprocessing",0.0,0.0,0,0,'poul_nissen.jpg']))
+        def __init__(self,parameter_file_path):
             
-            self.epochs = epochs
 
-            if not isinstance(star, list)  and isdir(star):
-                star_files = glob.glob(star)
-         
-                if star_files == []:
-                    print("no star files in directory. You must point to atleast one star file to run Sortinator.")
-                    exit()
-                star_files = [join(star,i) for i in star_files]
-            else:
-   
-                star_files = star
-   
-            def get_star_file_parameters(star_files,path):
+            super_class.__init__(self
+                            ,parameter_file_path
+                            )
+            def get_star_file_parameters(star_files):
        
                 counter = []
                 f = 0
@@ -46,7 +29,9 @@ class control_flow:
                 star_files = np.unique(star_files)  
                 labels_list = []
                 count = 0
+                
                 for z in star_files:
+                    #print(z)
                     c = z[::-1].split('/',1)[1][::-1]
           
                     #c = '/emcc/au587640/cryosparc2_data/P139/'
@@ -78,8 +63,15 @@ class control_flow:
                                 take_this = i
                         header  = take_this
                         name = header.index('_rlnImageName')
-                    
-                    
+                        voltage = header.index('_rlnVoltage')
+                        defocusU = header.index('_rlnDefocusU')
+                        defocusV = header.index('_rlnDefocusV')
+                        defocusAngle = header.index('_rlnDefocusAngle')
+                        abberation = header.index('_rlnSphericalAberration')
+                        amp_contrast = header.index('_rlnAmplitudeContrast')
+                        phase_shift = header.index('_rlnPhaseShift')
+                        
+                        ctf_params = []
                         if self.verbose:
                             try: 
                                  class_num = header.index('_rlnClassNumber')
@@ -94,44 +86,79 @@ class control_flow:
                             if len(header)== len(row.split()):
                   
                                 f+=1
+                            
                                 current_name = row.split()[name].split('@')[1]
+                                if len(names) != 0:
+                                    if names[-1] != current_name:
+                                           names.append(join(c,current_name))
+                                else:
+                                     names.append(join(c,current_name))
                                 if self.verbose:
-                                 labels_list.append(int(row.split()[class_num]))
-                            
+                                      labels_list.append(int(row.split()[class_num]))
+                                V = float(row.split()[voltage])
+                                electron_volts  = (1.23*10**3)/np.sqrt(V*(V*10**(-7)*1.96+1))
+                                
+                                abberation_d  = float(row.split()[abberation])
+                                amp_contrast_d = float(row.split()[amp_contrast])
+                                ctf_params.append([float(row.split()[phase_shift]),float(row.split()[defocusU]),float(row.split()[defocusV]),float(row.split()[defocusAngle])])
+
                                 current_id = row.split()[name].split('@')[0]
-                                names.append(join(c,current_name))
+                             
              
-         
-           
-                np.save(join(join(self.workdir, 'particle_stack_dir'),'labels'),np.asarray(labels_list))
+                np.save(join(self.particle_stack_dir,'depth.npy'),f)
+                np.save(join(self.particle_stack_dir,'names.npy'),names)
+                np.save(join(self.particle_stack_dir,'electron_volts.npy'),electron_volts)
+                np.save(join(self.particle_stack_dir,'spherical_abberation.npy'),abberation_d)
+                np.save(join(self.particle_stack_dir,'amplitude_contrast.npy'),amp_contrast_d)
+                np.save(join(self.particle_stack_dir,'ctf_params.npy'),np.asarray(ctf_params))
+                np.save(join(self.particle_stack_dir,'labels.npy'),np.asarray(labels_list))
                 return f,np.unique(names)
+      
+
+
+            if not isinstance(self.star, list)  and isdir(star):
+                star_files = glob.glob(self.star)
+         
+                if star_files == []:
+                    print("no star files in directory. You must point to atleast one star file to run Sortinator.")
+                    exit()
+                star_files = [join(star,i) for i in star_files]
+            else:
+   
+                star_files = self.star
+     
+                      
+            if not isfile(join(self.particle_stack_dir,'depth.npy')):
+                depth,mrc_paths = get_star_file_parameters(star_files)
+            else:
+                depth = np.load(join(self.particle_stack_dir,'depth.npy'))
+                mrc_paths  = np.load(join(self.particle_stack_dir,'names.npy'))
+       
+            length,bytes_pr_record = self.get_parameters(mrc_paths[0])   
+            
+
         
-            depth,mrc_paths = get_star_file_parameters(star_files,star)
-    
-            length,bytes_pr_record = self.get_parameters(mrc_paths)   
-        
-          
+            self.add_params(parameter_file_path,'current_image.png',bytes_pr_record,depth,length)
            
-            DynAE(  mrc_paths,
-                    workdir,
-                    length,
-                    bytes_pr_record,
-                    num_cpus,
-                    num_gpus,
-                    depth,
-                    num_clusters,
-                    gpu_list,
-                    half_precision,
-                    deep_NN_batch_size,
-                    transfer_learning,
-                    max_particles,
-                    epochs,
-                    verbose)
+            DynAE(parameter_file_path,mrc_paths)
                                     
            
-          
-            self.write_star_file(star_files,labels)
-
+            final_labels = np.load(join(self.refined,'final_labels.npy'))
+            self.write_star_file(star_files,final_labels)
+        def add_params(self,parameter_file_path,current_image,binary,num_particles,width):
+           
+            with open(join(parameter_file_path,'parameters.csv'), 'r', newline='') as file:
+                writer = csv.reader(file, delimiter = '\t')
+                parameters = list(writer)[0]
+            parameters[-1] = width
+            parameters[-2] = num_particles
+            parameters[-3] = binary
+            parameters[-4] = current_image
+       
+            with open(join(parameter_file_path,'parameters.csv'), 'w', newline='') as file:
+                writer = csv.writer(file, delimiter = '\t')
+                writer.writerow(parameters)
+ 
 
         def write_star_file(self,star_files,labels):
                 names = []
@@ -150,7 +177,7 @@ class control_flow:
                               
                                  names.append(row.split())
                 for index,i in enumerate(np.unique(labels)):
-                    f = open(join(join(self.workdir,'output_star_files'),'cluster_%s.star' %index), 'w')
+                    f = open(join(self.star_files,'cluster_%s.star' %index), 'w')
                
                     with f:
 
@@ -185,7 +212,7 @@ class control_flow:
 
         def get_parameters(self,paths):
       
-            width,length,depth,record_size_new =   self.read(paths[0])
+            width,length,depth,record_size_new =   self.read(paths)
             return length,record_size_new
 
 

image_make.py

+import numpy as np
+
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+from matplotlib.colors import LogNorm
+from scipy.stats.kde import gaussian_kde
+
+a = np.random.multivariate_normal([0,0], [[ 1, 0], [0,  0.01]], check_valid='warn', tol=1e-8,size=[10000])
+
+b = np.random.multivariate_normal([0,0], [[ 1,  0], [0,  0.01]],  check_valid='warn', tol=1e-8,size=[10000])
+
+c = np.random.multivariate_normal([0,0], [[ 1, 0], [0, 0.01]],  check_valid='warn', tol=1e-8,size=[10000])
+
+s = np.concatenate([a,b,c],axis=0)
+
+x = s[:,0]
+
+y = s[:,1]
+
+H, xedges, yedges =np.histogram2d(x, y, bins=100)
+
+plt.pcolormesh(xedges, yedges , H ,alpha=0.9)

image_restoration_sortem.py

+import matplotlib
+matplotlib.use('Agg')
+
+import tensorflow as tf
+from tensorflow.keras.layers import Conv2D, UpSampling2D,Activation,GlobalAveragePooling2D,PReLU,Conv2DTranspose,BatchNormalization,Flatten
+
+import matplotlib.pyplot as plt
+plt.switch_backend('agg')
+
+from mrc_loader_sortem import mrc_loader 
+import numpy as np
+from os.path import join
+from os.path import isfile
+from super_clas_sortem import super_class
+from models import Unet
+class Denoise(super_class):
+
+    def __init__(self,parameter_file_path,not_projected_star_files,projected_star_files,bytes_pr_record,validate=None,val_bytes=None):
+        super_class.__init__(self ,parameter_file_path)
+        self.not_projected_star_files = not_projected_star_files
+        self.validate = validate
+        self.val_bytes = val_bytes
+        self.projected_star_files     = projected_star_files
+        self.paths = [self.not_projected_star_files,self.projected_star_files]
+        self.bytes_pr_record = bytes_pr_record
+        num_steps = (self.max_particles/(self.batch_size*self.num_gpus))*self.epochs
+        self.steps = num_steps
+        self.opt = self.optimizer(self.steps)
+
+        self.unet = Unet()
+
+       
+        self.train()
+
+    @tf.function
+    def mask(self,image):
+        image = tf.squeeze(image)
+        x = tf.range(64)
+        s = tf.random.shuffle(x)
+        _,y = tf.meshgrid(x,x)
+        rs = tf.reduce_sum(tf.cast(tf.equal(y,tf.roll(s,int(64/2),axis=0)),self.precision)*image,axis=2)
+     
+        selected_pixels = tf.cast(tf.equal(y,s),self.precision)
+        not_selected_pixels = tf.cast(tf.not_equal(y,s),self.precision)
+        m_image = tf.transpose(tf.transpose(tf.stack([selected_pixels]*self.batch_size,axis=0),perm=[1,0,2])*rs,perm=[1,0,2])
+        return m_image+not_selected_pixels*image,selected_pixels
+
+
+
+    @tf.function
+    def loss(self,image,estimate,mask):
+        a = Flatten()(image)
+        b = Flatten()(estimate)
+        c = tf.reshape(mask,[-1])
+
+        mean = tf.reduce_mean(((a-b)*c)**2)
+        return mean
+
+
+    def plotlib(self,image,raw_image):
+
+        s = np.concatenate(np.split(image,image.shape[0]),axis=1)
+        t = np.concatenate(np.split(raw_image,raw_image.shape[0]),axis=1)
+        plt.imshow(np.squeeze(np.concatenate([s,t],axis=2)),cmap='gray')
+        plt.savefig(join(self.results,'image_signal.png'),)
+
+    @tf.function
+    def predict_net_low(self,raw_data):
+        
+      
+           stage3_img =   self.unet(raw_data)
+           return stage3_img
+
+    @tf.function
+    def train_net_L(self,raw_data_image):
+      
+        raw_data_image = tf.cast(raw_data_image,self.precision)
+        swaped_pixels,mask = self.mask(raw_data_image)
+        #plt.imshow(swaped_pixels[0])
+        #plt.savefig('test.png');exit()
+        swaped_pixels = tf.expand_dims(swaped_pixels,axis=-1)
+    
+        with tf.GradientTape() as tape:
+            estimate=   self.unet(swaped_pixels)
+
+            loss= self.loss(raw_data_image,estimate,mask)
+            variables = self.unet.trainable_weights
+            self.apply_grad(loss,variables,tape)
+        return loss
+    
+    
+    def train(self):
+        
+     
+        strategy,distribute = self.generator('contrastive',self.validate,self.val_bytes,self.batch_size)
+        dis = iter(distribute)
+        if self.validate != None:
+          strategy_validate,distribute_val = self.generator('predict',self.validate,self.val_bytes,self.predict_batch_size)
+        dis_val = iter(distribute_val)
+        pred_data = next(dis_val)
+
+        if self.verbose:
+            pred_data,y = pred_data
+        if not isfile(join(self.models,'unet.index')):
+
+                
+                    ite = 1
+                    while True:
+                        raw_data_image,perm = next(dis)
+                  
+
+
+                        
+                      
+                        loss = strategy.run(self.train_net_L,args=(perm,))
+                      
+                        if ite % self.validate_interval  == 0 and self.validate:
+                    
+                               validation_images  =     strategy_validate.run(self.predict_net_low,args=(pred_data,))
+                               self.plotlib(validation_images,pred_data)
+                               self.unet.save_weights(join(self.models,'unet'))
+                          
+                        print("step:%i of %i" %(ite,self.steps),loss.numpy())
+            
+
+                        ite +=1
+
+
+
+                        if self.steps > ite:
+                                break
+
+           
\ No newline at end of file

main_sortem.py

@@ -2,6 +2,10 @@
 
 import argparse
 from execute_sortem import control_flow
+import csv
+from os import mkdir
+from os.path import isdir,join
+
 
 def main():
     parser = argparse.ArgumentParser(description='Run sortinator.')
@@ -13,7 +17,10 @@ def main():
     parser.add_argument('--star', type=str, nargs='+',
                         help='list of path to the star files, wild cards are accepted. The star file must refer to the .mrc files')
     parser.add_argument('--ab', type=int,default=200,
-                        help='deep learning model training batch')            
+                        help='deep learning model training batch')     
+    parser.add_argument('--pb', type=int,default=200,
+                        help='deep learning model training batch')  
+
     parser.add_argument('--o', type=str,default='./results',
                         help='output directory')   
     parser.add_argument('--f16', type=str,default="False",
@@ -26,14 +33,40 @@ def main():
 
     parser.add_argument('--log', type=str,default="False",help='log all possible values to file (loss, pca_components,NMI,Recall,false positives,false negatives.')   
         
-    parser.add_argument('--num_classes',type=int,default=4,help='the number subparts in the refinement process.')
+    parser.add_argument('--num_parts',type=int,default=2,help='the number subparts in the refinement process. This is to improve accuracy for highly similar proteins')
 
     parser.add_argument('--resize',type=int,default=256,help='Image to resize to for training and inference. If the image is less than 256 consider downsizing. Minimum size is 128')
 
     parser.add_argument('--part_resize',type=int,default=128,help='When refining the components the image size is usually 128')
-        
+
+    parser.add_argument('--early_stopping',type=float,default=0.001,help='when the model has converged. Stops early if max iter not reached.')
+
+    parser.add_argument('--dbscan',type=int,default=50000,help='The number of samples to use for dbscan. This scales a lot with memory. Default is 50000')
+
+    parser.add_argument('--lr',type=float,default=0.002,help='The learning rate of the model')
+
+    parser.add_argument('--vi',type=float,default=40,help='The validation interval')
+
+    parser.add_argument('--eps',type=float,default=0.01,help='The epsilon of the data.')
+
+    parser.add_argument('--nb',type=int,default=10,help='Nearest neighbours')
+
+    parser.add_argument('--pr',type=str,default='False',help='pretrain the signal model')
+
+    parser.add_argument('--mb',type=int,default=1,help='pretrain the signal model')
+
+    parser.add_argument('--ss',type=int,default=20,help='pretrain the signal model')
+
+    parser.add_argument('--rs',type=int,default=32,help='pretrain the signal model')
+   
+    parser.add_argument('--val', type=str, nargs='+',help='Use a validation set which is different from the training set.')
+
+    parser.add_argument('--rab', type=int, default=5,help='The Denoiser to Train for the image denoising.')
+   
+    parser.add_argument('--lam',type=int,default=0.2,help='The lambda weight value for the pretraining loss of the autoencoder')
     args = parser.parse_args()
-    num_gpus = args.num_gpus
+
+    
 
     
     if args.gpu_list == None:
@@ -42,52 +75,52 @@ def main():
         if len(args.gpu_list) != args.num_gpus:
             print("the list of gpus to use is not the same length as the number of gpus specified. The number of gpus will be changed to match list length")
             num_gpus = len(args.gpu_list)
-            gpu_list = args.gpu_list
+            gpu_list = []
+            for s in range(num_gpus):
+                gpu_list.append('/GPU:%i' %s)
+        
+        else:
+              gpu_list = []
+              num_gpus = len(args.gpu_list)
+              for s in range(num_gpus):
+                gpu_list.append('/GPU:%i' %s)
+        
 
 
+
+    batch_size = args.ab
+    pred_batch_size = args.pb
+
     num_cpus = args.num_cpus
     star = args.star
     train_light = args.tr
-    deep_NN_batch_size = args.ab
+
     epochs = args.epochs
     output = args.o
   
     half_precision = args.f16
     verbose = args.log
     max_particles = args.mp
-    num_clusters = args.num_classes
-
-
-    if train_light =='False':
-        train_light = False
-    else:
-        train_light = True
+    num_clusters = args.num_parts
 
+    binary = 0
+    num_particles = 0
+    width = 0
+    s1 = ' '.join(gpu_list)
+    
+    l = [args.ab,args.pb,args.num_parts,args.num_cpus,args.num_gpus,args.vi,' '.join(gpu_list),args.f16,args.tr,args.epochs,args.log,args.mp,args.eps,args.nb,' '.join(args.star),args.dbscan,args.lam,args.lr,args.val,args.mb,args.ss,args.rs,args.pr,'current_image',binary,num_particles,width]
+    if not isdir(args.o):
+         mkdir(args.o)
 
+    
+    with open(join(args.o,'parameters.csv'), 'w', newline='') as file:
+         writer = csv.writer(file, delimiter = '\t')
+         writer.writerow(l)
 
-    if verbose == 'True':
-        verbose = True
-    else:
-        verbose = False
 
-    if half_precision == 'False':
-        half_precision = False
-    else:
-        half_precision = True
-
-    control_flow(num_gpus,
-                gpu_list,
-                num_cpus,
-                num_clusters,
-                star,
-                deep_NN_batch_size,
-                output,
-                half_precision,
-                max_particles,
-                epochs,
-                train_light,
-                verbose)
 
+    control_flow(args.o)
+    exit()
 
 
 #if __name__ == "__main__":

models.py

+
+
+from super_clas_sortem import MaxBlurPooling2D,super_class
+from tensorflow.keras import Model
+import tensorflow as tf
+from tensorflow.keras.layers import Layer, Flatten, Lambda, InputSpec, Input,LeakyReLU,Conv2DTranspose, Dense,Conv2D,GlobalAveragePooling2D,BatchNormalization,Activation,UpSampling2D,Conv3D,Conv3DTranspose,LeakyReLU,Dense,UpSampling3D,MaxPool3D,MaxPool2D
+from utils_sortem import   Spectral_norm,Instance_norm,transform_3D
+
+
+
+def AdaIn_3D(inputs,s1,b1):
+   b=  tf.shape(inputs)[0]
+   w=  tf.shape(inputs)[1]
+   h=  tf.shape(inputs)[2]
+   d=  tf.shape(inputs)[3] 
+   c=  tf.shape(inputs)[4] 
+
+   tmp = Flatten()(inputs)
+  
+   mean = tf.reduce_mean(tmp,axis=1)
+   std =        tf.math.reduce_std(tmp,axis=1)
+ 
+   out = s1*((inputs - mean)/std) + b1
+   
+
+   out = tf.reshape(out,[b,w,h,d,c])
+  
+   return out 
+def AdaIn_2D(inputs,s1,b1):
+   b=  tf.shape(inputs)[0]
+   w=  tf.shape(inputs)[1]
+   h=  tf.shape(inputs)[2]
+   c=  tf.shape(inputs)[3] 
+
+   inputs = Flatten()(inputs)
+   mean = tf.reduce_mean(inputs,axis=1)
+   std =        tf.math.reduce_std(inputs,axis=1)
+
+   out = s1*((inputs - mean)/std) + b1
+   out = tf.reshape(out,[b,w,h,c])
+
+class Double_Dense(Model):
+
+    def __init__(self,channels):
+       super(Double_Dense,self).__init__()
+       self.dense_1 = Dense(channels)
+       self.dense_2 = Dense(channels)
+  
+
+    def call(self,x):
+        x = Flatten()(x)
+        s = self.dense_1(x)
+        c = self.dense_2(x)
+        return s,c
+
+
+class Generator_AdaIN_Noise(Model):
+
+    def __init__(self,gf_dim=32):
+
+        self.gf_dim = gf_dim
+        super(Generator_AdaIN_Noise,self).__init__()
+
+  
+
+        self.zmap_0 = Double_Dense( self.gf_dim * 32)
+        self.h0 = AdaIn_2D
+        self.h0_a = LeakyReLU()
+
+        self.h1= Conv2DTranspose( self.gf_dim * 32,3,strides=2)
+        self.z_map_1= Double_Dense(self.gf_dim * 32)
+        self.h1_aI = AdaIn_2D
+        self.h1 = LeakyReLU()
+
+        self.h2 = Conv2DTranspose(self.gf_dim * 16,3,strides=2)
+        self.h2_a = LeakyReLU()
+        self.z_map_2= Double_Dense(self.gf_dim * 16)
+        self.h2_aI = AdaIn_2D
+        self.h2 = LeakyReLU()
+
+        self.h5 = Conv2DTranspose(self.gf_dim*4,4,strides=2)
+        self.z_map_4 = Double_Dense(self.gf_dim*4)
+        self.h6 = AdaIn_2D
+        self.h6_a = LeakyReLU()
+
+        self.h7 = Conv2DTranspose(self.gf_dim*2,4,strides=2)
+        self.z_map_5 = Double_Dense(self.gf_dim*2)
+        self.h7_in = AdaIn_2D
+        self.h7_a = LeakyReLU()
+
+        
+        self.h8 = Conv2DTranspose(self.gf_dim,4,strides=2)
+        self.z_map_6 = Double_Dense( self.gf_dim)
+        self.h8_in = AdaIn_2D