Source code for mesmerize.pyqtgraphCore.flowchart.library.Data

# -*- coding: utf-8 -*-
from ...Qt import QtGui, QtCore, QtWidgets
from spyder.widgets.variableexplorer.objecteditor import oedit
from .common import *
import traceback
from ....analysis import Transmission
from ....analysis.history_widget import HistoryTreeWidget
from ....common import get_project_manager
import os
from tslearn.preprocessing import TimeSeriesScalerMinMax
import pickle


[docs]class LoadProjDF(CtrlNode): """Load raw project DataFrames as Transmission""" nodeName = 'Load_Proj_DF' uiTemplate = [('DF_Name', 'combo'), ('Update', 'button', {'text': 'Update', 'toolTip': 'When clicked this node will update' ' from the project DataFrame'}), ('Apply', 'check', {'applyBox': True, 'checked': False}), ('PinDF', 'check', {'text': 'Yes', 'toolTip': 'Pin the DataFrame to the flowchart, this way\n' 'you can open another project and still propogate\n' 'the data from this node.'})] def __init__(self, name): CtrlNode.__init__(self, name, terminals={'Out': {'io': 'out'}}) self._loadNode = True self.t = None child_df_names = ['root'] + list(get_project_manager().child_dataframes.keys()) self.ctrls['DF_Name'].addItems(child_df_names) self.ctrls['Update'].clicked.connect(self.changed) # print('Node Refs:') # print(configuration.df_refs) def process(self): if self.ctrls['Apply'].isChecked() is False: return self.t # print('#######Weak Refs Dict########') # print(configuration.df_refs) if self.ctrls['PinDF'].isEnabled(): if self.ctrls['PinDF'].isChecked(): # self.t = self.t.copy() self.ctrls['PinDF'].setDisabled(True) self.ctrls['Update'].setDisabled(True) return {'Out': self.t} if self.ctrls['DF_Name'].currentText() == '': return child_df_name = self.ctrls['DF_Name'].currentText() if child_df_name == 'root': df = get_project_manager().dataframe filter_history = None else: df = get_project_manager().child_dataframes[child_df_name]['dataframe'] filter_history = get_project_manager().child_dataframes[child_df_name]['filter_history'] proj_path = get_project_manager().root_dir # print('*****************config df ref hex ID:*****************') # print(hex(id(df))) self.t = Transmission.from_proj(proj_path, df, sub_dataframe_name=child_df_name, dataframe_filter_history={'dataframe_filter_history': filter_history}) # print('Tranmission dataframe hexID:') # print(hex(id(self.t.df))) return {'Out': self.t}
[docs]class LoadFile(CtrlNode): """Load Transmission data object from pickled file""" nodeName = 'LoadFile' uiTemplate = [('load_trn', 'button', {'text': 'Open .trn File'}), ('fname', 'label', {'text': ''}), ('proj_path', 'button', {'text': 'Project Path'}), ('proj_path_label', 'label', {'text': ''}) ] def __init__(self, name): CtrlNode.__init__(self, name, terminals={'Out': {'io': 'out'}}) self.ctrls['load_trn'].clicked.connect(self.file_dialog_trn_file) self.ctrls['proj_path'].clicked.connect(self.dir_dialog_proj_path) self.t = None self._loadNode = True def file_dialog_trn_file(self): path = QtWidgets.QFileDialog.getOpenFileName(None, 'Import Transmission object', '', '(*.trn *.ptrn)') if path == '': return self.load_file(path[0]) def load_file(self, path: str): try: self.t = Transmission.from_hdf5(path) except: QtWidgets.QMessageBox.warning(None, 'File open Error!', 'Could not open the chosen file.\n' + traceback.format_exc()) return self.ctrls['fname'].setText(os.path.basename(path)) proj_path = get_project_manager().root_dir if proj_path is not None: self._set_proj_path(proj_path) # print(self.transmission) # self.update() self.changed() def _set_proj_path(self, path: str): self.ctrls['proj_path_label'].setText(os.path.basename(path)) self.t.set_proj_path(path) self.t.set_proj_config() def dir_dialog_proj_path(self): path = QtWidgets.QFileDialog.getExistingDirectory(None, 'Select Project Folder') if path == '': return try: self._set_proj_path(path) self.changed() except (FileNotFoundError, NotADirectoryError) as e: QtWidgets.QMessageBox.warning(None, 'Invalid Project Folder', 'This is not a valid Mesmerize project\n' + e) return def process(self): self.t.get_proj_path() return {'Out': self.t}
[docs]class Save(CtrlNode): """Save Transmission data object""" nodeName = 'Save' uiTemplate = [('saveBtn', 'button', {'text': 'OpenFileDialog'}), ('path', 'label', {'text' : ''}), ('Apply', 'check', {'checked': False, 'applyBox': True}) ] def __init__(self, name): # super(Save, self).__init__(name, terminals={'data': {'io': 'in'}}) CtrlNode.__init__(self, name, terminals={'In': {'io': 'in'}}) self._bypass = False self.bypassButton = None self.ctrls['saveBtn'].clicked.connect(self._fileDialog) self._saveNode = True self.saveValue = None def process(self, In, display=True): if In is not None: self._save(In) else: raise Exception('No incoming transmission to save!') def _fileDialog(self): path = QtWidgets.QFileDialog.getSaveFileName(None, 'Save Transmission as', '', '(*.trn)') if path == '': return if path[0].endswith('.trn'): path = path[0] else: path = path[0] + '.trn' self.ctrls['path'].setText(path) def _save(self, transmission): # self.ctrls['saveBtn'].clicked.connect(self._fileDialog) if self.ctrls['Apply'].isChecked is False: return if self.ctrls['path'].text() != '': try: transmission.to_hdf5(self.ctrls['path'].text()) except: QtWidgets.QMessageBox.warning(None, 'File save error', 'Could not save the transmission to file.\n' + traceback.format_exc())
[docs]class Merge(CtrlNode): """Merge transmissions""" nodeName = 'Merge' uiTemplate = [('no controls', 'label')] def __init__(self, name): CtrlNode.__init__(self, name, terminals={'In': {'io': 'in', 'multi': True}, 'Out': {'io': 'out'}}) def process(self, **kwargs): self.transmissions = kwargs['In'] self.transmissions_list = merge_transmissions(self.transmissions) self.t = Transmission.merge(self.transmissions_list) return {'Out': self.t}
[docs]class ViewTransmission(CtrlNode): """View transmission using the spyder object editor""" nodeName = 'ViewData' uiTemplate = [('no controls', 'label')] def __init__(self, name): CtrlNode.__init__(self, name, terminals={'In': {'io': 'in'}}) # self.edited_transmission = None def processData(self, transmission: Transmission): self.t = transmission.copy() oedit({'dataframe': self.t.df, 'history_trace': self.t.history_trace.history})
# if self.edited is not None: # self.edited.add_operation('all', 'object_editor', {}) # return self.edited # return self.t
[docs]class DropNa(CtrlNode): """Drop NaNs from the DataFrame""" nodeName = 'DropNaNs' uiTemplate = [('axis', 'combo', {'values': ['row', 'columns'], 'toolTip': 'Choose to drop NaNs from according to all ' 'rows, columns, or a specific column'}), ('how', 'combo', {'values': ['any', 'all'], 'toolTip': 'any: drop from chosen axis if any element is NaN\n' 'all: drop from chosen axis if all elements are NaN'}), ('Apply', 'check', {'checked': False, 'applyBox': True})] def __init__(self, *args, **kwargs): super(DropNa, self).__init__(*args, **kwargs) def processData(self, transmission: Transmission): items = ['row', 'columns'] + ['---------'] + transmission.df.columns.to_list() self.ctrls['axis'].setItems(items) if not self.ctrls['Apply'].isChecked(): return self.t = transmission.copy() axis = self.ctrls['axis'].currentText() if self.ctrls['axis'].currentIndex() < 2: if axis == 'row': axis = 0 elif axis == 'columns': axis = 1 how = self.ctrls['how'].currentText() self.t.df.dropna(axis=axis, how=how, inplace=True) else: how = None self.t.df = self.t.df[~self.t.df[axis].isna()] self.t.history_trace.add_operation('all', 'dropna', parameters={'axis': axis, 'how': how}) return self.t
[docs]class ViewHistory(CtrlNode): """View History Trace of the input Transmission""" nodeName = 'ViewHistory' uiTemplate = [('Show', 'button')] def __init__(self, *args, **kwargs): super(ViewHistory, self).__init__(*args, **kwargs) self.history_widget = HistoryTreeWidget() self.ctrls['Show'].clicked.connect(self.history_widget.show) def processData(self, transmission: Transmission): self.history_widget.fill_widget(transmission.history_trace)
[docs]class iloc(CtrlNode): """Pass only one or multiple DataFrame Indices""" nodeName = 'iloc' uiTemplate = [('Index', 'intSpin', {'min': 0, 'step': 1, 'value': 0}) # ('Indices', 'lineEdit', {'text': '0', 'toolTip': 'Index numbers separated by commas'}) ] def processData(self, transmission): self.ctrls['Index'].setMaximum(transmission.df.index.size - 1) # self.ctrls['Index'].valueChanged.connect( # partial(self.ctrls['Indices'].setText, str(self.ctrls['Index'].value()))) # indices = [int(ix.strip()) for ix in self.ctrls['Indices'].text().split(',')] i = self.ctrls['Index'].value() self.t = transmission.copy() self.t.df = self.t.df.iloc[i] # self.t.src.append({'iloc': {'index': i}}) return self.t
[docs]class SpliceArrays(CtrlNode): """Splice 1-D numpy arrays in a particular column.""" nodeName = 'SpliceArrays' uiTemplate = [('Apply', 'check', {'checked': True, 'applyBox': True}), ('data_column', 'combo', {}), ('indices', 'lineEdit', {'text': '', 'placeHolder': 'start_ix:end_ix'})] output_column = '_SPLICE_ARRAYS' def processData(self, transmission: Transmission): self.t = transmission self.set_data_column_combo_box() if self.ctrls['Apply'].isChecked() is False: return self.t = transmission.copy() indices = self.ctrls['indices'].text() if indices == '': return if ':' not in indices: return else: indices = indices.split(':') start_ix = int(indices[0]) end_ix = int(indices[1]) data_column = self.ctrls['data_column'].currentText() self.t.df[self.output_column] = self.t.df[data_column].apply(lambda a: a[start_ix:end_ix]) params = {'data_column': data_column, 'start_ix': start_ix, 'end_ix': end_ix, 'units': self.t.last_unit } self.t.history_trace.add_operation(data_block_id='all', operation='splice_arrays', parameters=params) self.t.last_output = self.output_column return self.t
[docs]class PadArrays(CtrlNode): """Pad 1-D numpy arrays in a particular column""" nodeName = 'PadArrays' uiTemplate = [('data_column', 'combo', {}), ('output_size', 'intSpin', {'min': -1, 'max': 9999999, 'step': 100, 'value': -1}), ('method', 'combo', {'items': ['fill-size', 'random']}), ('mode', 'combo', {'items': ['minimum', 'constant', 'edge', 'maximum', 'mean', 'median', 'reflect', 'symmetric', 'wrap'], 'toolTip': 'Passed to numpy.pad "mode" parameter'}), ('constant', 'doubleSpin', {'min': -9999999.9, 'max': 9999999.9, 'value': 1.0, 'step': 10.0, 'tooltip': 'Value to use if "mode" is set to "constant"'}), ('Apply', 'check', {'checked': False, 'applyBox': True}) ] def processData(self, transmission: Transmission): self.t = transmission self.set_data_column_combo_box() if self.ctrls['Apply'].isChecked(): return self.t = transmission.copy() def _pad(self): pass
[docs]class SelectRows(CtrlNode): pass
[docs]class SelectColumns(CtrlNode): pass
[docs]class TextFilter(CtrlNode): """Simple string filtering in a specified column""" nodeName = 'TextFilter' uiTemplate = [('Column', 'combo', {'toolTip': 'Filter according to this column'}), ('filter', 'lineEdit', {'toolTip': 'Filter to apply in selected column'}), ('Include', 'radioBtn', {'checked': True}), ('Exclude', 'radioBtn', {'checked': False}), ('Apply', 'check', {'checked': False, 'applyBox': True})] # def __init__(self, name): # CtrlNode.__init__(self, name, terminals={'In': {'io': 'in'}, 'Out': {'io': 'out', 'bypass': 'In'}}) # self.ctrls['ROI_Type'].returnPressed.connect(self._setAvailTags) def processData(self, transmission: Transmission): ccols = organize_dataframe_columns(transmission.df.columns.to_list())[1] self.ctrls['Column'].setItems(ccols) col = self.ctrls['Column'].currentText() completer = QtWidgets.QCompleter(list(map(str, transmission.df[col].unique()))) self.ctrls['filter'].setCompleter(completer) if self.ctrls['Apply'].isChecked() is False: return filt = self.ctrls['filter'].text() self.t = transmission.copy() include = self.ctrls['Include'].isChecked() exclude = self.ctrls['Exclude'].isChecked() params = {'column': col, 'filter': filt, 'include': include, 'exclude': exclude } if include: self.t.df = self.t.df[self.t.df[col].astype(str) == filt] elif exclude: self.t.df = self.t.df[self.t.df[col].astype(str) != filt] self.t.df = self.t.df.reset_index(drop=True) self.t.history_trace.add_operation('all', operation='text_filter', parameters=params) return self.t
[docs]class NormRaw(CtrlNode): """Normalize between raw min and max values.""" nodeName = 'NormRaw' uiTemplate = [('option', 'combo', {'items': ['top_5', 'top_10', 'top_5p', 'top_10p', 'top_25p', 'full_mean']}), ('Apply', 'check', {'applyBox': True, 'checked': False}) ] def processData(self, transmission: Transmission): t = transmission dcols = organize_dataframe_columns(t.df.columns)[0] if not self.ctrls['Apply'].isChecked(): return self.t = transmission.copy() output_column = '_NORMRAW' self.option = self.ctrls['option'].currentText() params = {'data_column': '_RAW_CURVE', 'option': self.option, 'output_column': output_column} self.proj_path = self.t.get_proj_path() tqdm().pandas() self.excluded = 0 self.t.df[output_column] = self.t.df.progress_apply(lambda r: self._func(r['_RAW_CURVE'], r['ImgInfoPath'], r['ROI_State']), axis=1) self.t.history_trace.add_operation('all', 'normrawminmax', params) self.t.last_output = output_column if self.excluded > 0: QtWidgets.QMessageBox.warning(None, 'Curves excluded', f'The following number of curves were excluded because ' f'the raw min value was larger than the max\n{self.excluded}') self.t.df = self.t.df[~self.t.df[output_column].isna()] return self.t def _func(self, data: np.ndarray, img_info_path: str, roi_state: dict) -> np.ndarray: if 'raw_min_max' in roi_state.keys(): raw_min_max = roi_state['raw_min_max'] else: cnmf_idx = roi_state['cnmf_idx'] img_info_path = os.path.join(self.proj_path, img_info_path) roi_states = pickle.load(open(img_info_path, 'rb'))['roi_states'] idx_components = roi_states['cnmf_output']['idx_components'] list_ix = np.argwhere(idx_components == cnmf_idx).ravel().item() state = roi_states['states'][list_ix] if not state['cnmf_idx'] == cnmf_idx: raise ValueError('cnmf_idx from ImgInfoPath dict and DataFrame ROI_State dict do not match.') raw_min_max = state['raw_min_max'] raw_min = raw_min_max['raw_min'][self.option] raw_max = raw_min_max['raw_max'][self.option] if raw_min >= raw_max: self.excluded += 1 return np.NaN return TimeSeriesScalerMinMax(value_range=(raw_min, raw_max)).fit_transform(data).ravel()