コード例 #1
0
def load():
   cmd.set("valence")
   r = 0
   list = glob("pdb/*/*")
#   while list[0]!="pdb/f8/pdb1f8u":
#      list.pop(0)
   for file in list:
      try:
         cmd.delete('pdb')
         cmd.load(file,'pdb')
         cmd.set_title('pdb',1,os.path.split(file)[-1])
         cmd.rewind()
         cmd.orient('pdb')
         cmd.refresh()
         cmd.zoom('center',16)
         cmd.label("polymer and (name ca or elem P)","'//%s/%s/%s`%s/%s'%(segi,chain,resn,resi,name)")
         cmd.refresh()
         sys.__stderr__.write(".")
         sys.__stderr__.flush()
         n = cmd.count_states()
         if n>1:
            cmd.rewind()
            sys.__stderr__.write(file+"\n")
            sys.__stderr__.flush()
            for a in range(1,n+1):
               cmd.forward()
               cmd.refresh()
      except:
         traceback.print_exc()
コード例 #2
0
ファイル: B03.py プロジェクト: dprada/pymol-open-source
def load():
   cmd.set("valence")
   r = 0
   list = glob("pdb/*/*")
#   while list[0]!="pdb/f8/pdb1f8u":
#      list.pop(0)
   for file in list:
      try:
         cmd.delete('pdb')
         cmd.load(file,'pdb')
         cmd.set_title('pdb',1,os.path.split(file)[-1])
         cmd.rewind()
         cmd.orient('pdb')
         cmd.refresh()
         cmd.show_as("ribbon")
         cmd.refresh()
         cmd.show_as("sticks")
         cmd.refresh()
         sys.__stderr__.write(".")
         sys.__stderr__.flush()
         n = cmd.count_states()
         if n>1:
            cmd.rewind()
            sys.__stderr__.write(file+"\n")
            sys.__stderr__.flush()
            for a in range(1,n+1):
               cmd.forward()
               cmd.refresh()
      except:
         traceback.print_exc()
コード例 #3
0
def load():
    cmd.set("valence")
    r = 0
    list = glob("pdb/*/*")
    #   while list[0]!="pdb/f8/pdb1f8u":
    #      list.pop(0)
    for file in list:
        try:
            cmd.delete('pdb')
            cmd.load(file, 'pdb')
            cmd.set_title('pdb', 1, os.path.split(file)[-1])
            cmd.rewind()
            cmd.orient('pdb')
            cmd.refresh()
            cmd.show_as("ribbon")
            cmd.refresh()
            cmd.show_as("sticks")
            cmd.refresh()
            sys.__stderr__.write(".")
            sys.__stderr__.flush()
            n = cmd.count_states()
            if n > 1:
                cmd.rewind()
                sys.__stderr__.write(file + "\n")
                sys.__stderr__.flush()
                for a in range(1, n + 1):
                    cmd.forward()
                    cmd.refresh()
        except:
            traceback.print_exc()
コード例 #4
0
ファイル: filter.py プロジェクト: xiaomeihu/pymol-open-source
 def accept(self):
     # accept compound and advance
     if self.object is None:
         print(" Filter-Error: Please choose an object first")
     else:
         state = cmd.get_object_state(self.object)
         ident = self.get_ident(self.object,state)
         print(" Filter: Accepting '%s'"%ident)
         self.count(ident,accept_str)
     cmd.forward()
     cmd.refresh_wizard()
コード例 #5
0
ファイル: filter.py プロジェクト: evonove/pymol
 def accept(self):
     # accept compound and advance
     if self.object==None:
         print " Filter-Error: Please choose an object first"
     else:
         state = cmd.get_state()
         ident = self.get_ident(self.object,state)
         print " Filter: Accepting '%s'"%ident
         self.count(ident,accept_str)
     cmd.forward()         
     cmd.refresh_wizard()
コード例 #6
0
ファイル: filter.py プロジェクト: xiaomeihu/pymol-open-source
 def defer(self):
     # defer compound and advance
     if self.object is None:
         print(" Filter-Error: Please choose an object first")
     else:
         state = cmd.get_object_state(self.object)
         ident = self.get_ident(self.object,state)
         print(" Filter: Deferring '%s'"%ident)
         self.check_object_dict()
         self.count(ident,defer_str)
     cmd.forward()
     cmd.refresh_wizard()
コード例 #7
0
ファイル: filter.py プロジェクト: jchodera/pymol
 def reject(self):
     # reject compound and advance
     if self.object == None:
         print " Filter-Error: Please choose an object first"
     else:
         state = cmd.get_state()
         ident = cmd.get_title(self.object, state)
         print " Filter: Rejecting '%s'" % ident
         self.check_object_dict()
         self.count(ident, reject_str)
     cmd.forward()
     cmd.refresh_wizard()
コード例 #8
0
ファイル: filter.py プロジェクト: evonove/pymol
 def defer(self):
     # defer compound and advance
     if self.object==None:
         print " Filter-Error: Please choose an object first"
     else:
         state = cmd.get_state()
         ident = self.get_ident(self.object,state)
         print " Filter: Deferring '%s'"%ident
         self.check_object_dict()
         self.count(ident,defer_str)
     cmd.forward()
     cmd.refresh_wizard()
コード例 #9
0
ファイル: particle02.py プロジェクト: jchodera/pymol
def simulation():
    state = 1
    import traceback
    try:
        while state < n_states:
            state = state + 1
            for part in particle:
                # simplistic Euler intergration

                # p = p + v

                part[1] = (half_box + part[1] + part[5]) % box_size - half_box
                part[2] = (half_box + part[2] + part[6]) % box_size - half_box
                part[3] = (half_box + part[3] + part[7]) % box_size - half_box

                # v = v + pseudo-gravitational acceleration

                factor = max(0.1 * box_size,
                             0.1 * (part[1]**2 + part[2]**2 + part[3]**2)**1.5)

                part[5] = part[5] - part[1] / factor
                part[6] = part[6] - part[2] / factor
                part[7] = part[7] - part[3] / factor

            # copy initial coordinates to a new state

            cmd.create("cloud", "cloud", 1, state)

            # update the new state coordinates
            cmd.alter_state(state,
                            "cloud",
                            "(x,y,z) = particle[int(resi)][1:4]",
                            space=globals())

            cmd.forward()
            cmd.refresh()

            # don't hog the CPU entirely
            sleep(0.01)

        cmd.mplay()
    except:
        traceback.print_exc()
コード例 #10
0
ファイル: particle02.py プロジェクト: aghozlane/pymol
def simulation():
    state = 1 
    import traceback
    try:
        while state < n_states:
            state = state + 1
            for part in particle:
                # simplistic Euler intergration

                # p = p + v
                
                part[1] = (half_box + part[1] + part[5]) % box_size - half_box
                part[2] = (half_box + part[2] + part[6]) % box_size - half_box
                part[3] = (half_box + part[3] + part[7]) % box_size - half_box

                # v = v + pseudo-gravitational acceleration
                
                factor = max(0.1*box_size, 0.1*(part[1]**2+part[2]**2+part[3]**2)**1.5)
                
                part[5] = part[5] - part[1] / factor
                part[6] = part[6] - part[2] / factor
                part[7] = part[7] - part[3] / factor

            # copy initial coordinates to a new state
            
            cmd.create("cloud","cloud",1,state) 

            # update the new state coordinates
            cmd.alter_state(state,"cloud","(x,y,z) = particle[int(resi)][1:4]",space=globals())

            cmd.forward()
            cmd.refresh()

            # don't hog the CPU entirely
            sleep(0.01)
            
        cmd.mplay()
    except:
        traceback.print_exc()
コード例 #11
0
    def testForward(self):
        self.prep_movie()
        cmd.forward()
        self.assertEquals(cmd.get_frame(), 2)
        cmd.forward()
        self.assertEquals(cmd.get_frame(), 3)
        cmd.frame(30)
        self.assertEquals(cmd.get_frame(), 30)
        cmd.frame(60)
        cmd.forward()
        self.assertEquals(cmd.get_frame(), 60)

        cmd.frame(60)
        cmd.forward()
        self.assertEquals(cmd.get_frame(), 60)
コード例 #12
0
ファイル: moving.py プロジェクト: schrodinger/pymol-testing
    def testForward(self):
        self.prep_movie()
        cmd.forward()
        self.assertEquals(cmd.get_frame(),2)
        cmd.forward()
        self.assertEquals(cmd.get_frame(),3)
        cmd.frame(30)
        self.assertEquals(cmd.get_frame(),30)
        cmd.frame(60)
        cmd.forward()
        self.assertEquals(cmd.get_frame(),60)

        cmd.frame(60)
        cmd.forward()
        self.assertEquals(cmd.get_frame(),60)
コード例 #13
0
ファイル: filter.py プロジェクト: xiaomeihu/pymol-open-source
 def forward(self):
     # go forward and update information
     cmd.forward()
     cmd.refresh_wizard()
コード例 #14
0
ファイル: viz_pymol.py プロジェクト: hopefulp/sandbox
  from dump import dump
  from pdbfile import pdbfile
  from pymol import cmd as pm

  d = dump("tmp.dump",0)
  p = pdbfile(d)
  d.next()
  d.unscale()
  p.single(ntimestep)
  pm.load("tmp.pdb")
  pm.show("spheres","tmp")
  
# run nfreq steps at a time w/out pre/post, read dump snapshot, display it

while ntimestep < nsteps:
  lmp.command("run %d pre no post no" % nfreq)
  ntimestep += nfreq
  if me == 0:
    d.next()
    d.unscale()
    p.single(ntimestep)
    pm.load("tmp.pdb")
    pm.forward()
    
lmp.command("run 0 pre no post yes")

# uncomment if running in parallel via Pypar
#print("Proc %d out of %d procs has" % (me,nprocs), lmp)
#pypar.finalize()
コード例 #15
0
ファイル: cli.py プロジェクト: Mingchenchen/openprotein
def train_model(data_set_identifier, train_file, val_file, learning_rate,
                minibatch_size):
    set_experiment_id(data_set_identifier, learning_rate, minibatch_size)

    train_loader = contruct_dataloader_from_disk(train_file, minibatch_size)
    validation_loader = contruct_dataloader_from_disk(val_file, minibatch_size)
    validation_dataset_size = validation_loader.dataset.__len__()

    model = ExampleModel(21, minibatch_size,
                         use_gpu=use_gpu)  # embed size = 21

    # TODO: is soft_to_angle.parameters() included here?
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    sample_num = list()
    train_loss_values = list()
    validation_loss_values = list()
    rmsd_avg_values = list()
    drmsd_avg_values = list()

    best_model_loss = 1.1
    best_model_minibatch_time = None
    best_model_path = None
    stopping_condition_met = False
    minibatches_proccesed = 0

    while not stopping_condition_met:
        optimizer.zero_grad()
        model.zero_grad()
        loss_tracker = np.zeros(0)
        for minibatch_id, training_minibatch in enumerate(train_loader, 0):
            minibatches_proccesed += 1
            primary_sequence, tertiary_positions, mask = training_minibatch
            start_compute_loss = time.time()
            loss = model.compute_loss(primary_sequence, tertiary_positions)
            write_out("Train loss:", float(loss))
            start_compute_grad = time.time()
            loss.backward()
            loss_tracker = np.append(loss_tracker, float(loss))
            end = time.time()
            write_out("Loss time:", start_compute_grad - start_compute_loss,
                      "Grad time:", end - start_compute_grad)
            optimizer.step()
            optimizer.zero_grad()
            model.zero_grad()

            # for every eval_interval samples, plot performance on the validation set
            if minibatches_proccesed % args.eval_interval == 0:

                train_loss = loss_tracker.mean()
                loss_tracker = np.zeros(0)
                validation_loss, data_total, rmsd_avg, drmsd_avg = evaluate_model(
                    validation_loader, model)
                prim = data_total[0][0]
                pos = data_total[0][1]
                (aa_list, phi_list, psi_list,
                 omega_list) = calculate_dihedral_angels(prim, pos)
                write_to_pdb(
                    get_structure_from_angles(aa_list, phi_list[1:],
                                              psi_list[:-1], omega_list[:-1]),
                    "test")
                cmd.load("output/protein_test.pdb")
                write_to_pdb(data_total[0][3], "test_pred")
                cmd.load("output/protein_test_pred.pdb")
                cmd.forward()
                cmd.orient()
                if validation_loss < best_model_loss:
                    best_model_loss = validation_loss
                    best_model_minibatch_time = minibatches_proccesed
                    best_model_path = write_model_to_disk(model)

                write_out("Validation loss:", validation_loss, "Train loss:",
                          train_loss)
                write_out("Best model so far (label loss): ", validation_loss,
                          "at time", best_model_minibatch_time)
                write_out("Best model stored at " + best_model_path)
                write_out("Minibatches processed:", minibatches_proccesed)
                sample_num.append(minibatches_proccesed)
                train_loss_values.append(train_loss)
                validation_loss_values.append(validation_loss)
                rmsd_avg_values.append(rmsd_avg)
                drmsd_avg_values.append(drmsd_avg)
                if args.live_plot:
                    data = {}
                    data["validation_dataset_size"] = validation_dataset_size
                    data["sample_num"] = sample_num
                    data["train_loss_values"] = train_loss_values
                    data["validation_loss_values"] = validation_loss_values
                    data["phi_actual"] = list(
                        [math.degrees(float(v)) for v in phi_list[1:]])
                    data["psi_actual"] = list(
                        [math.degrees(float(v)) for v in psi_list[:-1]])
                    data["phi_predicted"] = list([
                        math.degrees(float(v)) for v in data_total[0]
                        [2].detach().transpose(0, 1)[0][1:]
                    ])
                    data["psi_predicted"] = list([
                        math.degrees(float(v)) for v in data_total[0]
                        [2].detach().transpose(0, 1)[1][:-1]
                    ])
                    data["drmsd_avg"] = drmsd_avg_values
                    data["rmsd_avg"] = rmsd_avg_values
                    res = requests.post('http://localhost:5000/graph',
                                        json=data)
                    if res.ok:
                        print(res.json())

                if minibatches_proccesed > args.minimum_updates and minibatches_proccesed > best_model_minibatch_time * 2:
                    stopping_condition_met = True
                    break
    write_result_summary(best_model_loss)
    return best_model_path
コード例 #16
0
ファイル: filter.py プロジェクト: evonove/pymol
 def forward(self):
     # go forward and update information
     cmd.forward()
     cmd.refresh_wizard()