Python DataModel.getInstance方法代码示例

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
    def OnOpenImage(self, evt=None):
        # 1) Get the image key
        # Start with the table_id if there is one
        tblNum = None
        if p.table_id:
            dlg = wx.TextEntryDialog(self, p.table_id + ":", "Enter " + p.table_id)
            dlg.SetValue("0")
            if dlg.ShowModal() == wx.ID_OK:
                try:
                    tblNum = int(dlg.GetValue())
                except ValueError:
                    errdlg = wx.MessageDialog(
                        self, "Invalid value for %s!" % (p.table_id), "Invalid value", wx.OK | wx.ICON_EXCLAMATION
                    )
                    errdlg.ShowModal()
                    return
                dlg.Destroy()
            else:
                dlg.Destroy()
                return
        # Then get the image_id
        dlg = wx.TextEntryDialog(self, p.image_id + ":", "Enter " + p.image_id)
        dlg.SetValue("")
        if dlg.ShowModal() == wx.ID_OK:
            try:
                imgNum = int(dlg.GetValue())
            except ValueError:
                errdlg = wx.MessageDialog(
                    self, "Invalid value for %s!" % (p.image_id), "Invalid value", wx.OK | wx.ICON_EXCLAMATION
                )
                errdlg.ShowModal()
                return
            dlg.Destroy()
        else:
            dlg.Destroy()
            return
        # Build the imkey
        if p.table_id:
            imkey = (tblNum, imgNum)
        else:
            imkey = (imgNum,)

        dm = DataModel.getInstance()
        if imkey not in dm.GetAllImageKeys():
            errdlg = wx.MessageDialog(
                self, "There is no image with that key.", "Couldn't find image", wx.OK | wx.ICON_EXCLAMATION
            )
            errdlg.ShowModal()
            self.Destroy()
        else:
            # load the image
            self.img_key = imkey
            self.SetImage(imagetools.FetchImage(imkey), p.image_channel_colors)
            self.DoLayout()

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
 def get_image_keys_at_row(self, row):
     '''Returns a list of image keys at the given row or None if the column 
     names can't be found in col_labels
     '''
     if self.key_indices is None or self.grouping is None:
         return None
     else:
         if self.grouping.lower() == 'image':     
             return [tuple(self.data[self.row_order,:][row, self.key_indices])]
         elif self.grouping.lower() == 'object': 
             return [tuple(self.data[self.row_order,:][row, self.key_indices[:-1]])]
         else:
             dm = DataModel.getInstance()
             return dm.GetImagesInGroup(self.grouping, self.get_row_key(row))

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
    def PerImageCounts(self, filter_name=None, cb=None):
        # Clear the current perClassObjects storage
        for bin in self.classBins:
            self.perClassObjects[bin.label] = []

        # Retrieve a data model instance
        dm = DataModel.getInstance()

        # Retrieve image keys and initialize variables
        imageKeys = dm.GetAllImageKeys(filter_name)
        imageAmount = float(len(imageKeys))
        perImageData = []

        # Process all images
        for k_index, imKey in enumerate(imageKeys):
            try:
                # Retrieve the keys of the objects in the current image
                obKeys = dm.GetObjectsFromImage(imKey)
            except:
                raise ValueError('No such image: %s' % (imKey,))

            # Calculate the amount of hits for each of the classes in the current image
            classHits = {}
            objectCount = [imKey[0]]
            if obKeys:
                classObjects = self.FilterObjectsFromClassN(keys = [imKey])
                for clNum, bin in enumerate(self.classBins):
                    # Get the objects from the image which belong to the selected class
                    classHits[bin.label] = classObjects[float(clNum+1)]

                    # Store the total object count of this class for the current image
                    nrHits = len(classHits[bin.label])
                    objectCount.append(nrHits)

                    # Store the objects for the current class and image grouped
                    # by class if any are found for this class in the selected image
                    if nrHits > 0:
                        self.perClassObjects[bin.label] += classHits[bin.label]
            else:
                # If there are objects in the image, add zeros for all bins
                [objectCount.append(0) for bin in self.classBins]

            # Store the results for the current image and update the callback
            # function if available
            perImageData.append(objectCount)
            if cb:
                cb(min(1, k_index/imageAmount))

        return perImageData

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
 def get_object_keys_at_row(self, row):
     '''Returns a list of object keys at the given row or None if the column
     names can't be found in col_labels
     '''
     if self.key_indices is None or self.grouping is None:
         return None
     else:
         dm = DataModel.getInstance()
         # If the key index for the row is an object key, just return that key
         if self.grouping.lower() == 'object': 
             return [tuple(self.data[self.row_order,:][row, self.key_indices])]
         else: # Otherwise, return all object keys in the image
             imkeys = self.get_image_keys_at_row(row) 
             obkeys = []
             for imkey in imkeys:
                 obs = dm.GetObjectCountFromImage(imkey)
                 obkeys += [tuple(list(imkey)+[i]) for i in range(1,obs+1)]
             return obkeys

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
 def on_dclick_label(self, evt):
     '''Handle display of images and objects'''
     if evt.Row >= 0:
         obkeys = self.grid.Table.get_object_keys_at_row(evt.Row)
         if self.grid.Table.grouping is None:
             # We need to know how the table is grouped to know what to do
             logging.warn('CPA does not know how to link this table to your images. Can\'t launch ImageViewer.')
             return
         elif self.grid.Table.grouping.lower() == 'object':
             # For per-object grouping, show the objects in the image
             imview = imagetools.ShowImage(obkeys[0][:-1], 
                                               p.image_channel_colors,
                                               parent=self.Parent)
             if obkeys is not None:
                 for obkey in obkeys:
                     imview.SelectObject(obkey)
         elif self.grid.Table.grouping.lower() == 'image':
             # For per-image grouping just show the images.
             # If there is only one object, then highlight it
             if obkeys is not None and len(obkeys) == 1:
                 imview = imagetools.ShowImage(obkeys[0][:-1], 
                                               p.image_channel_colors,
                                               parent=self.Parent)
                 imview.SelectObject(obkeys[0])
             else:
                 imkeys = self.grid.Table.get_image_keys_at_row(evt.Row)
                 if imkeys:
                     #XXX: warn if there are a lot
                     for imkey in imkeys:
                         imagetools.ShowImage(imkey, p.image_channel_colors,
                                              parent=self.Parent)
         else:
             key_cols = self.grid.Table.get_row_key(evt.Row)
             if key_cols:
                 dm = DataModel.getInstance()
                 imkeys = dm.GetImagesInGroup(self.grid.Table.grouping, key_cols)
                 for imkey in imkeys:
                     imagetools.ShowImage(imkey, p.image_channel_colors,
                                          parent=self.Parent)

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
    def FilterObjectsFromClassN(self, classN = None, keys = None):
        '''
    	Filter the input objects to output the keys of those in classN, 
    	using a defined SVM model classifier.
    	'''
        # Retrieve instance of the database connection
        db = dbconnect.DBConnect.getInstance()
        object_data = {}
        if isinstance(keys, str):
            object_data[0] = db.GetCellDataForClassifier(keys)
        elif keys != []:
            if len(keys) == len(dbconnect.image_key_columns()):
                # Retrieve instance of the data model and retrieve objects in the requested image
                dm = DataModel.getInstance()
                obKeys = dm.GetObjectsFromImage(keys[0])
            else:
                obKeys = keys
            for key in obKeys:
                object_data[key] = db.GetCellDataForClassifier(key)

        sorted_keys = sorted(object_data.keys())
        values_array = np.array([object_data[key] for key in sorted_keys])
        scaled_values = self.ScaleData(values_array)
        pred_labels = self.model.predict(scaled_values)

        # Group the object keys per class
        classObjects = {}
        for index in range(1, len(self.classBins)+1):
            classObjects[float(index)] = []
        for index, label in enumerate(pred_labels):
            classObjects[np.int(label)+1].append(sorted_keys[index])

        # Return either a summary of all classes and their corresponding objects
        # or just the objects for a specific class
        if classN is None:
            return classObjects
        else:
            return classObjects[classN]

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
def FormatPlateMapData(keys_and_vals, categorical=False):
    '''
    keys_and_vals -- a list of lists of well-keys and values
                     eg: [['p1', 'A01', 0.2], 
                          ['p1', 'A02', 0.9], ...]
    returns a 2-tuple containing:
       -an array in the shape of the plate containing the given values with 
        NaNs filling empty slots. If multiple sites per-well are given, then
        the array will be shaped (rows, cols, sites)
       -an array in the shape of the plate containing the given keys with 
        (UnknownPlate, UnknownWell) filling empty slots
    '''
    from itertools import groupby
    keys_and_vals = np.array(keys_and_vals)
    nkeycols = len(dbconnect.well_key_columns())
    shape = list(p.plate_shape)
    if p.plate_type == '5600': 
        well_keys = keys_and_vals[:,:-1] # first column(s) are keys
        data = keys_and_vals[:,-1]       # last column is data
        assert data.ndim == 1
        if len(data) < 5600: raise Exception(
            '''The measurement you chose to plot was missing for some spots. 
            Because CPA doesn't know the well labelling convention used by this
            microarray, we can't be sure how to plot the data. If you are 
            plotting an object measurement, you may have some spots with 0 
            objects and therefore no entry in the table.''')
        assert len(data) == 5600
        data = np.array(list(meander(data.reshape(shape)))).reshape(shape)
        sort_indices = np.array(list(meander(np.arange(np.prod(shape)).reshape(shape)))).reshape(shape)
        well_keys = np.array(list(meander(well_keys.reshape(shape + [nkeycols] )))).reshape(shape + [nkeycols])
        return data, well_keys, sort_indices

    # compute the number of sites-per-well as the max number of rows with the same well-key
    nsites = max([len(list(grp))
                  for k, grp in groupby(keys_and_vals, 
                                        lambda row: tuple(row[:nkeycols]))
                  ])
    if nsites > 1:
        # add a sites dimension to the array shape if there's >1 site per well
        shape += [nsites]
    data = np.ones(shape) * np.nan
    if categorical:
        data = data.astype('object')
    if p.plate_id:
        dummy_key = ('UnknownPlate', 'UnknownWell')
    else:
        dummy_key = ('UnknownWell',)
    well_keys = np.array([dummy_key] * np.prod(shape), 
                         dtype=object).reshape(shape + [nkeycols])
    sort_indices = np.ones(data.shape)*np.nan
    
    dm = DataModel.getInstance()
    ind = keys_and_vals.argsort(axis=0)
    for i, (k, well_grp) in enumerate(groupby(keys_and_vals[ind[:,len(dummy_key)-1],:], 
                                              lambda row: tuple(row[:len(dummy_key)]))):
        (row, col) = dm.get_well_position_from_name(k[-1])
        well_data = np.array(list(well_grp))[:,-1]
        if len(well_data) == 1:
            data[row, col] = well_data[0]
            sort_indices[row,col] = ind[:,len(dummy_key)-1][i]
        else:
            data[row, col] = well_data
            sort_indices[row,col] = ind[:,len(dummy_key)-1][i*nsites + np.array(range(nsites))] 
        well_keys[row, col] = k
        
    return data, well_keys, sort_indices

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
def score_objects(properties, ts, gt, nRules, filter_name=None, group='Image',
          show_results=False, results_table=None, overwrite=False):
    '''
    Trains a Classifier on a training set and scores the experiment
    returns the table of scores as a numpy array.
        
    properties    -- Properties instance
    ts            -- TrainingSet instance
    gt            -- Ground Truth instance
    nRules        -- number of rules to use
    filter_name   -- name of a filter to use from the properties file
    group         -- name of a group to use from the properties file
    show_results  -- whether or not to show the results in TableViewer
    results_table -- table name to save results to or None.
    '''
    
    p = properties
    #db = DBConnect.getInstance() ## Removed writing to db.  Results_table should be 'None' anyway
    dm = DataModel.getInstance()

    #if group == None:
        #group = 'Image'
        
    if results_table:
        if db.table_exists(results_table) and not overwrite:
            print 'Table "%s" already exists. Delete this table before running scoreall.'%(results_table)
            return None

    print ''
    print 'properties:    ', properties
    print 'initial training set:  ', ts
    print 'ground truth training set:  ', gt
    print '# rules:       ', nRules
    print 'filter:        ', filter_name
    print 'grouping by:   ', group
    print 'show results:  ', show_results
    print 'results table: ', results_table
    print 'overwrite:     ', overwrite
    print ''
            
    nClasses = len(ts.labels)
    nKeyCols = len(image_key_columns())
    
    assert 200 > nRules > 0, '# of rules must be between 1 and 200.  Value was %s'%(nRules,)
    assert filter_name in p._filters.keys()+[None], 'Filter %s not found in properties file.  Valid filters are: %s'%(filter_name, ','.join(p._filters.keys()),)
    assert group in p._groups.keys()+['Image', 'None'], 'Group %s not found in properties file.  Valid groups are: %s'%(group, ','.join(p._groups.keys()),)
    
    output = StringIO()
    logging.info('Training classifier with %s rules...'%nRules)
    t0 = time()
    weaklearners = fastgentleboostingmulticlass.train(ts.colnames,
                                                      nRules, ts.label_matrix, 
                                                      ts.values, output)
    logging.info('Training done in %f seconds'%(time()-t0))
    
    t0 = time()
    #def update(frac): 
        #logging.info('%d%% '%(frac*100.,))

    ## Score Ground Truth using established classifier
    gt_predicted_scores = per_cell_scores(weaklearners, gt.values, gt.colnames)
    #plt.hist(gt_predicted_scores)
    #plt.show()
    gt_predicted_signs = np.sign(gt_predicted_scores)
    
    
    ## Compare Ground Truth score signs with the actual ground truth values
    numclasses = ts.labels.size
    gt_actual_signs = gt.label_matrix[:,0]
    cm_unrotated = metrics.confusion_matrix(gt_actual_signs,gt_predicted_signs)
    ## sklearn.metrics.confusion_matrix -- 2D confusion matrix is inverted from convention.
    ## https://github.com/scikit-learn/scikit-learn/issues/1664
    cm = np.rot90(np.rot90(cm_unrotated))
    fpr, sens, thresholds = metrics.roc_curve(gt_actual_signs,gt_predicted_signs)
    spec = 1-fpr
    s = np.sum(cm,axis=1)
    percent = [100*cm[i,i]/float(s[i]) for i in range(len(s))]
    avg = np.mean(percent)
    avgTotal = 100 * np.trace(cm) / float(np.sum(cm))    
    print 'accuracy = %f' % avgTotal
    print 'Confusion Matrix = ... '
    print cm
    my_sens = cm[0,0] / float(cm[0,0] + cm[0,1]) #TP/(TP+FN)
    my_spec = cm[1,1] / float(cm[1,1] + cm[1,0]) #TN/(TN+FP)
    print 'My_Sensitivity = %f' % my_sens
    print 'My_Specificity = %f' % my_spec
    print 'Sensitivity = ...'
    print sens
    print 'Specificity = ...'
    print spec
    print 'Done calculating'
    
    ############
    ## Confusion Matrix code from here: http://stackoverflow.com/questions/5821125/how-to-plot-confusion-matrix-with-string-axis-rather-than-integer-in-python
    conf_arr = cm
    norm_conf = []
    ## This normalizes each *row* to the color map, but I chose to normalize the whole confusion matrix to the same scale
    ##for i in conf_arr:
        ##a = 0
        ##tmp_arr = []
#.........这里部分代码省略.........

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
from sys import stderr
from tempfile import gettempdir
from time import ctime, time
#from wx.lib.embeddedimage import PyEmbeddedImage
import dbconnect
import imagetools
import csv
import logging
import numpy as np
import os
import sys
import weakref
import wx
import wx.grid

dm = DataModel.getInstance()
db = dbconnect.DBConnect.getInstance()
p = Properties.getInstance()

ID_LOAD_CSV = wx.NewId()
ID_SAVE_CSV = wx.NewId()
ID_EXIT = wx.NewId()

DO_NOT_LINK_TO_IMAGES = 'Do not link to images'
ROW_LABEL_SIZE = 30

# Icon to be used for row headers (difficult to implement) 
#img_icon = PyEmbeddedImage('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')

class HugeTable(wx.grid.PyGridTableBase):
    '''

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
def score(properties, ts, nRules, filter_name=None, group='Image',
          show_results=False, results_table=None, overwrite=False):
    '''
    Trains a Classifier on a training set and scores the experiment
    returns the table of scores as a numpy array.
        
    properties    -- Properties instance
    ts            -- TrainingSet instance
    nRules        -- number of rules to use
    filter_name   -- name of a filter to use from the properties file
    group         -- name of a group to use from the properties file
    show_results  -- whether or not to show the results in TableViewer
    results_table -- table name to save results to or None.
    '''
    
    p = properties
    db = DBConnect.getInstance()
    dm = DataModel.getInstance()

    if group == None:
        group = 'Image'
        
    if results_table:
        if db.table_exists(results_table) and not overwrite:
            print 'Table "%s" already exists. Delete this table before running scoreall.'%(results_table)
            return None

    print ''
    print 'properties:    ', properties
    print 'training set:  ', ts
    print '# rules:       ', nRules
    print 'filter:        ', filter_name
    print 'grouping by:   ', group
    print 'show results:  ', show_results
    print 'results table: ', results_table
    print 'overwrite:     ', overwrite
    print ''
            
    nClasses = len(ts.labels)
    nKeyCols = len(image_key_columns())
    
    assert 200 > nRules > 0, '# of rules must be between 1 and 200.  Value was %s'%(nRules,)
    assert filter_name in p._filters.keys()+[None], 'Filter %s not found in properties file.  Valid filters are: %s'%(filter_name, ','.join(p._filters.keys()),)
    assert group in p._groups.keys()+['Image'], 'Group %s not found in properties file.  Valid groups are: %s'%(group, ','.join(p._groups.keys()),)
    
    output = StringIO()
    logging.info('Training classifier with %s rules...'%nRules)
    t0 = time()
    weaklearners = fastgentleboostingmulticlass.train(ts.colnames,
                                                      nRules, ts.label_matrix, 
                                                      ts.values, output)
    logging.info('Training done in %f seconds'%(time()-t0))
    
    logging.info('Computing per-image class counts...')
    t0 = time()
    def update(frac): 
        logging.info('%d%% '%(frac*100.,))
    keysAndCounts = multiclasssql.PerImageCounts(weaklearners, filter_name=(filter_name or None), cb=update)
    keysAndCounts.sort()
    logging.info('Counts found in %f seconds'%(time()-t0))
        
    if not keysAndCounts:
        logging.error('No images are in filter "%s". Please check the filter definition in your properties file.'%(filter_name))
        raise Exception('No images are in filter "%s". Please check the filter definition in your properties file.'%(filter_name))
        
    # AGGREGATE PER_IMAGE COUNTS TO GROUPS IF NOT GROUPING BY IMAGE
    if group != 'Image':
        logging.info('Grouping %s counts by %s...' % (p.object_name[0], group))
        t0 = time()
        imData = {}
        for row in keysAndCounts:
            key = tuple(row[:nKeyCols])
            imData[key] = np.array([float(v) for v in row[nKeyCols:]])
        
        groupedKeysAndCounts = np.array([list(k)+vals.tolist() for k, vals in dm.SumToGroup(imData, group).items()], dtype=object)
        nKeyCols = len(dm.GetGroupColumnNames(group))
        logging.info('Grouping done in %f seconds'%(time()-t0))
    else:
        groupedKeysAndCounts = np.array(keysAndCounts, dtype=object)
    
    # FIT THE BETA BINOMIAL
    logging.info('Fitting beta binomial distribution to data...')
    counts = groupedKeysAndCounts[:,-nClasses:]
    alpha, converged = polyafit.fit_betabinom_minka_alternating(counts)
    logging.info('   alpha = %s   converged = %s'%(alpha, converged))
    logging.info('   alpha/Sum(alpha) = %s'%([a/sum(alpha) for a in alpha]))
                
    # CONSTRUCT ARRAY OF TABLE DATA
    logging.info('Computing enrichment scores for each group...')
    t0 = time()
    tableData = []
    for i, row in enumerate(groupedKeysAndCounts):
        # Start this row with the group key: 
        tableRow = list(row[:nKeyCols])
        
        if group != 'Image':
            tableRow += [len(dm.GetImagesInGroup(group, tuple(row[:nKeyCols])))]
        # Append the counts:
        countsRow = [int(v) for v in row[nKeyCols:nKeyCols+nClasses]]
        tableRow += [sum(countsRow)]
#.........这里部分代码省略.........

# 需要导入模块: from datamodel import DataModel [as 别名]
# 或者: from datamodel.DataModel import getInstance [as 别名]
    def __init__(self, properties=None, parent=None, id=ID_IMAGE_GALLERY, **kwargs):

        if properties is not None:
            global p
            p = properties
            global db
            db = dbconnect.DBConnect.getInstance()

        wx.Frame.__init__(self, parent, id=id, title='CPA/ImageGallery - %s' % \
                                                     (os.path.basename(p._filename)), size=(800, 600), **kwargs)
        if parent is None and not sys.platform.startswith('win'):
            self.tbicon = wx.TaskBarIcon()
            self.tbicon.SetIcon(icons.get_cpa_icon(), 'CPA/ImageGallery')
        else:
            self.SetIcon(icons.get_cpa_icon())
        self.SetName('ImageGallery')

        db.register_gui_parent(self)

        global dm
        dm = DataModel.getInstance()

        if not p.is_initialized():
            logging.critical('ImageGallery requires a properties file. Exiting.')
            raise Exception('ImageGallery requires a properties file. Exiting.')

        self.pmb = None
        self.worker = None
        self.trainingSet = None
        self.classBins = []
        self.binsCreated = 0
        self.chMap = p.image_channel_colors[:]
        self.toggleChMap = p.image_channel_colors[
                           :]  # used to store previous color mappings when toggling colors on/off with ctrl+1,2,3...
        self.brightness = 1.0
        self.scale = 1.0 
        
        self.contrast = 'Linear'
        self.defaultTSFileName = None
        self.defaultModelFileName = None
        self.lastScoringFilter = None

        self.menuBar = wx.MenuBar()
        self.SetMenuBar(self.menuBar)
        self.CreateMenus()

        self.CreateStatusBar()

        #### Create GUI elements
        # Top level - three split windows
        self.splitter = wx.SplitterWindow(self, style=wx.NO_BORDER | wx.SP_3DSASH)
        self.fetch_and_rules_panel = wx.Panel(self.splitter)
        self.bins_splitter = wx.SplitterWindow(self.splitter, style=wx.NO_BORDER | wx.SP_3DSASH)

        # fetch & rules
        self.fetch_panel = wx.Panel(self.fetch_and_rules_panel)
        self.find_rules_panel = wx.Panel(self.fetch_and_rules_panel)

        # sorting bins
        self.gallery_panel = wx.Panel(self.bins_splitter)
        o_label = p.object_name[0] if p.classification_type == 'image' else '' + ' image gallery'
        self.gallery_box = wx.StaticBox(self.gallery_panel, label=o_label)
        self.gallery_sizer = wx.StaticBoxSizer(self.gallery_box, wx.VERTICAL)
        self.galleryBin = sortbin.SortBin(parent=self.gallery_panel,
                                               classifier=self,
                                               label='image gallery',
                                               parentSizer=self.gallery_sizer)
        self.gallery_sizer.Add(self.galleryBin, proportion=1, flag=wx.EXPAND)
        self.gallery_panel.SetSizer(self.gallery_sizer)
        self.objects_bin_panel = wx.Panel(self.bins_splitter)

        # fetch objects interface
        self.startId = wx.TextCtrl(self.fetch_panel, id=-1, value='1', size=(60, -1), style=wx.TE_PROCESS_ENTER)
        self.endId = wx.TextCtrl(self.fetch_panel, id=-1, value='100', size=(60, -1), style=wx.TE_PROCESS_ENTER)
        self.fetchChoice = wx.Choice(self.fetch_panel, id=-1, choices=['range','all','individual'])
        self.fetchChoice.SetSelection(0)
        self.filterChoice = wx.Choice(self.fetch_panel, id=-1,
                                      choices=['experiment'] + p._filters_ordered + p._groups_ordered + [
                                          CREATE_NEW_FILTER])
        self.fetchFromGroupSizer = wx.BoxSizer(wx.HORIZONTAL)
        self.fetchBtn = wx.Button(self.fetch_panel, -1, 'Fetch!')

        #### Create Sizers
        self.fetchSizer = wx.BoxSizer(wx.HORIZONTAL)
        self.find_rules_sizer = wx.BoxSizer(wx.HORIZONTAL)
        self.fetch_and_rules_sizer = wx.BoxSizer(wx.VERTICAL)
        self.classified_bins_sizer = wx.BoxSizer(wx.HORIZONTAL)

        #### Add elements to sizers and splitters
        # fetch panel
        self.fetchSizer.AddStretchSpacer()
        self.fetchSizer.Add(wx.StaticText(self.fetch_panel, -1, 'Fetch '), flag=wx.ALIGN_CENTER_VERTICAL)
        self.fetchSizer.AddSpacer((5, 20))
        self.fetchSizer.Add(self.fetchChoice, flag=wx.ALIGN_CENTER_VERTICAL)
        self.fetchSizer.AddSpacer((5, 20))
        self.fetchTxt = wx.StaticText(self.fetch_panel, -1, label='of image IDs:')
        self.fetchSizer.Add(self.fetchTxt, flag=wx.ALIGN_CENTER_VERTICAL)
        self.fetchSizer.AddSpacer((5, 20))
        self.fetchSizer.Add(self.startId, flag=wx.ALIGN_CENTER_VERTICAL)
        self.fetchSizer.AddSpacer((5, 20))
#.........这里部分代码省略.........