qubx.hill

56 """Measures, displays, updates the dose-response curve for frontmost data, and fits to the Hill equation. 57 58 @ivar fit: L{qubx.fit_space.FitSpace} 59 @ivar controls: L{qubx.faces.Face} for QubX.About 60 @ivar measure: method for finding response; one of HILL_MEASURE_PEAK, HILL_MEASURE_MEAN, HILL_MEASURE_EQUIL 61 @ivar filter_kHz: if measure == HILL_MEASURE_PEAK, applies this filter to minimize measurement noise effect on the one-point peak 62 @ivar mean_dur: if measure == HILL_MEASURE_MEAN, number of milliseconds of data to average each segment 63 @ivar mean_from: if measure == HILL_MEASURE_MEAN, number of milliseconds to skip at the beginning of each segment 64 """ 65 66 __explore_featured = ['profile', 'fit', 'layNotebook', 'subNotebook', 'nbChart', 'nbPicture', 'nbParams', 'controls', 67 'measure', 'filter_kHz', 'mean_dur', 'mean_from'] 68

69 - def __init__(self, name='Hill', global_name='QubX.Figures.Hill'):

70 qubx.faces.Face.__init__(self, name, global_name) 71 self.__ref = Reffer() 72 cat = qubx.settings.SettingsMgr['Hill'] 73 self.profile = cat.active 74 cat.OnSet = self.__ref(self.__onUpdateProfile) 75 self.__measure = node_data_or_set_def(self.profile, 'Measure', HILL_MEASURE_PEAK) 76 self.__filter_kHz = node_data_or_set_def(self.profile, 'Filter_kHz', 1.0) 77 self.__mean_dur = node_data_or_set_def(self.profile, 'Mean_dur', 10.0) 78 self.__mean_from = node_data_or_set_def(self.profile, 'Mean_from', 500.0) 79 self.__dose = self.__model_Peq = None 80 self.fit = pack_item(qubx.fit_space.FitSpace(label=name, global_name='%s.fit' % global_name), self, expand=True) 81 self.fit.caption = "log10(Ligand) v. fraction open, fitted to the Hill equation" 82 self.fit.controls.robot.OnStats += self.__ref(self.__onStats) 83 self.fit.controls.robot.OnException += self.__ref(self.__onRobotException) 84 self.fit.controls.robot.OnEndFit += self.__ref(self.__onEndFit) 85 self.fit.controls.robot.set_expr('L**Hill / (L**Hill + EC50**Hill)') 86 self.fit.controls.robot.set_data(numpy.arange(10, dtype='float32'), numpy.zeros(shape=(10,), dtype='float32')) 87 self.fit.OnOverlay += self.__ref(self.__onOverlayFit) 88 self.layNotebook = qubx.toolspace.Layer(x=1, y=-5.5-qubx.fit_space.LINE_EMS, w=2, h=2, cBG=qubx.toolspace.COLOR_CLEAR) 89 self.subNotebook = qubx.notebookGTK.SubLayer_Notebook(x=0, y=0, w=2, h=2) 90 self.layNotebook.add_sublayer(self.subNotebook) 91 self.fit.add_layer(self.layNotebook) 92 self.nbChart = qubx.notebook.NbChart('Hill chart', 'QubX.Figures.Hill.nbChart', lambda: 'Hill', 93 self.fit.controls.nb_get_shape, self.fit.controls.nb_get_headers, 94 get_col=self.fit.controls.nb_get_col, get_type=lambda: float, 95 get_series=self.fit.nb_get_series) 96 self.subNotebook.items.append(self.nbChart) 97 self.nbPicture = qubx.notebookGTK.NbPicture('Hill picture', 'QubX.Figures.Hill.nbPicture', self.fit.nb_picture_get_shape, 98 self.fit.nb_picture_draw) 99 self.subNotebook.items.append(self.nbPicture) 100 self.nbParams = qubx.notebook.NbDynText('Parameters', 'QubX.Figures.Hill.nbParams', self.fit.controls.nb_get_param_text) 101 self.nbParams.std_err_est = None 102 self.subNotebook.items.append(self.nbParams) 103 self.__nb_next_stats = False 104 qubx.notebook.Notebook.register_auto('Hill_fit.Parameters', 'Hill Parameters, on Fit') 105 qubx.notebook.Notebook.register_auto('Hill_fit.Results', 'Hill Results, on Fit') 106 qubx.notebook.Notebook.register_auto('Hill_fit.Chart', 'Hill chart, on Fit') 107 qubx.notebook.Notebook.register_auto('Hill_fit.Picture', 'Hill picture, on Fit') 108 109 self.QubX = qubx.pyenv.env.globals['QubX'] 110 self.QubX.OnQuit += self.__ref(lambda: self.fit.dispose()) 111 self.controls = qubx.faces.Face(name, global_name='QubX.About.%s' % name) 112 self.controls.set_size_request(100, 100) 113 self.controls.show() 114 pack_label('Dose-Response:', self.controls) 115 self.lblVs = pack_label('log10(Ligand) v. fraction open', self.controls) 116 pack_label('Measure each level at:', self.controls) 117 line = pack_item(gtk.HBox(), self.controls) 118 self.chkPeak = pack_radio('Peak filtered at ', line, active=False, on_toggle=bind_with_args_before(self.__onToggleMeasure, HILL_MEASURE_PEAK)) 119 self.txtFilter = pack_item(qubx.GTK.NumEntry(self.__filter_kHz, acceptFloatGreaterThan(0.0), width_chars=4), line) 120 self.txtFilter.OnChange += self.__ref(self.__onChangeFilter) 121 pack_label('kHz', line) 122 line = pack_item(gtk.HBox(), self.controls) 123 self.chkMean = pack_radio('Mean of ', line, group=self.chkPeak, active=False, on_toggle=bind_with_args_before(self.__onToggleMeasure, HILL_MEASURE_MEAN)) 124 self.txtMeanDur = pack_item(qubx.GTK.NumEntry(self.__mean_dur, acceptFloatGreaterThan(0.0), width_chars=5), line) 125 self.txtMeanDur.OnChange += self.__ref(self.__onChangeMeanDur) 126 pack_label('ms', line) 127 line = pack_item(gtk.HBox(), self.controls) 128 pack_label('ms', line, at_end=True) 129 self.txtMeanFrom = pack_item(qubx.GTK.NumEntry(self.__mean_from, acceptFloatGreaterThan(0.0), width_chars=5), line, at_end=True) 130 self.txtMeanFrom.OnChange += self.__ref(self.__onChangeMeanFrom) 131 pack_label('starting at ', line, at_end=True) 132 self.chkEquil = pack_radio('Equilibrium', self.controls, group=self.chkPeak, active=False, on_toggle=bind_with_args_before(self.__onToggleMeasure, HILL_MEASURE_EQUIL)) 133 pack_label('The blue trace is occupancy prob.', pack_item(gtk.HBox(), self.controls)) 134 pack_label('of class 1 (red) calculated from', pack_item(gtk.HBox(), self.controls)) 135 pack_label('the model.', pack_item(gtk.HBox(), self.controls)) 136 self.datasrc = self.QubX.DataSource 137 self.datasrc.OnChangeSamples += self.__ref(self.__onChangeSamples) 138 self.models = ModelTracker() 139 for evt in (self.models.OnSwitch, self.models.OnChange, self.models.OnSetRate): 140 evt += self.__ref(bind(self.__onChangeModel)) 141 gobject.idle_add(self.__onChangeSamples) 142 self.__serial_data = 0 143 self.set_measure(self.__measure)

144 - def set_measure(self, x):

145 self.__measure = x 146 if self.__measure == HILL_MEASURE_PEAK: 147 self.chkPeak.set_active(True) 148 elif self.__measure == HILL_MEASURE_MEAN: 149 self.chkMean.set_active(True) 150 elif self.__measure == HILL_MEASURE_EQUIL: 151 self.chkEquil.set_active(True) 152 self.profile['Measure'].data = x 153 self.__onChangeSamples()

154 measure = property(lambda self: self.__measure, lambda self, x: self.set_measure(x))

155 - def set_filter_kHz(self, x):

156 self.__filter_kHz = x 157 self.txtFilter.value = x 158 self.profile['Filter_kHz'].data = x 159 self.__onChangeSamples()

160 filter_kHz = property(lambda self: self.__filter_kHz, lambda self, x: self.set_filter_kHz(x))

161 - def set_mean_dur(self, x):

162 self.__mean_dur = x 163 self.txtMeanDur.value = x 164 self.profile['Mean_dur'].data = x 165 self.__onChangeSamples()

166 mean_dur = property(lambda self: self.__mean_dur, lambda self, x: self.set_mean_dur(x))

167 - def set_mean_from(self, x):

168 self.__mean_from = x 169 self.txtMeanFrom.value = x 170 self.profile['Mean_from'].data = x 171 self.__onChangeSamples()

172 mean_from = property(lambda self: self.__mean_from, lambda self, x: self.set_mean_from(x))

173 - def __onToggleMeasure(self, chk, val):

174 if chk.get_active(): 175 qubx.pyenv.env.OnScriptable('%s.measure = %s' % (self.global_name, repr(val))) 176 self.measure = val

177 - def __onChangeFilter(self, txt, val):

178 qubx.pyenv.env.OnScriptable('%s.filter_kHz = %s' % (self.global_name, repr(val))) 179 self.filter_kHz = val

180 - def __onChangeMeanDur(self, txt, val):

181 qubx.pyenv.env.OnScriptable('%s.mean_dur = %s' % (self.global_name, repr(val))) 182 self.mean_dur = val

183 - def __onChangeMeanFrom(self, txt, val):

184 qubx.pyenv.env.OnScriptable('%s.mean_from = %s' % (self.global_name, repr(val))) 185 self.mean_from = val

186 - def __onUpdateProfile(self, settings, updates):

187 if updates.find('Measure').data: 188 self.measure = updates['Measure'].data[0] 189 if updates.find('Filter_kHz').data: 190 self.filter_kHz = updates['Filter_kHz'].data[0] 191 if updates.find('Mean_dur').data: 192 self.mean_dur = updates['Mean_dur'].data[0] 193 if updates.find('Mean_from').data: 194 self.mean_from = updates['Mean_from'].data[0]

195

196 - def onShow(self, showing):

197 if showing: 198 self.QubX.About.append_face(self.controls) 199 self.QubX.About.show_face(self.face_name) 200 self.__onChangeSamples() 201 else: 202 self.QubX.About.remove_face( self.QubX.About.index(self.face_name) )

203 - def __onChangeModel(self):

204 if self.__dose is None: 205 return 206 from_mdl = qubx.modelGTK.calc_Pe_of_stim(self.models.view, {self.__dose_name : self.__dose}, 1) 207 peq = [fm['Peq'] for fm in from_mdl] 208 peq -= numpy.min(peq) 209 max_peq = numpy.max(peq) 210 peq /= max_peq 211 self.__model_Peq = peq 212 self.fit.redraw_canvas(False)

213 - def __onOverlayFit(self, context, w, h):

214 if not (self.__model_Peq is None): 215 self.fit.draw_extra_series(context, w, h, self.__model_Peq)

216 - def __onRobotException(self, robot, typ, val, trace):

217 traceback.print_exception(typ, val, trace)

218 - def __onEndFit(self):

219 self.__nb_next_stats = True

220 - def __onStats(self, correlation, is_pseudo, std_err_est, ssr, r2, runs_prob):

221 if self.showing and (self.__fit_fails < 2) and (numpy.isinf(r2) or numpy.isnan(r2) or (r2 < 0)): 222 self.__fit_fails += 1 223 self.fit.controls.robot.set_param('Hill', 1.0) 224 self.fit.controls.robot.set_param('EC50', 1.0) 225 self.fit.controls.robot.fit() 226 elif self.__nb_next_stats: 227 self.__nb_next_stats = False 228 qubx.notebook.Notebook.send(qubx.notebook.NbText("""Hill fit finished. 229 \tSSR: %(ssr).6g 230 \tR^2: %(r2).6g 231 \tWald-Wolfowitz runs probability: %(runs_prob).6g 232 \tCorrelation: 233 %(correlation)s 234 """ % locals()), auto_id='Hill_fit.Results') 235 self.nbParams.std_err_est = std_err_est 236 qubx.notebook.Notebook.send(self.nbParams, auto_id='Hill_fit.Parameters') 237 self.nbParams.std_err_est = None 238 qubx.notebook.Notebook.send(self.nbChart, auto_id='Hill_fit.Chart') 239 qubx.notebook.Notebook.send(self.nbPicture, auto_id='Hill_fit.Picture')

240 - def __onChangeSamples(self, *args):

241 self.on_change_data()

242 - def on_change_data(self):

243 if (not self.QubX.Data.view) or (not self.showing): return 244 self.__fit_fails = 0 245 self.__serial_data += 1 246 self.fit.controls.robot.do(self.robot_read_data, self.__serial_data)

247 - def robot_read_data(self, serial):

248 if serial < self.__serial_data: return 249 with self.fit.controls.robot.main_hold: 250 rates = self.models.model.rates 251 ligands = [rates.get(i, 'Ligand') for i in xrange(rates.size)] 252 volts = [rates.get(i, 'Voltage') for i in xrange(rates.size)] 253 names = [nm for nm in chain(ligands, volts) if nm] 254 data = self.QubX.Data.view 255 series = [data.file.signals.get(i, 'Name') for i in xrange(data.file.signals.size)] 256 self.stim_ix = None 257 self.is_log = True 258 for i,nm in enumerate(series): 259 if nm in ligands: 260 self.stim_ix = i 261 self.is_log = True 262 break 263 elif nm in volts: 264 self.stim_ix = i 265 self.is_log = False 266 break 267 if not (self.stim_ix is None): 268 gobject.idle_add(self.lblVs.set_label, '%s v. fraction open' % ((self.is_log and 'log10(%s)' or '%s') % series[self.stim_ix])) 269 gobject.idle_add(self.fit.set_caption, '%s v. fraction open, fitted to the Hill equation' % ((self.is_log and 'log10(%s)' or '%s') % series[self.stim_ix])) 270 measure = self.__measure 271 filter_kHz = self.__filter_kHz 272 segments = self.datasrc.get_segmentation() 273 Nseg = len(segments) 274 Ntail = 1 275 if segments: 276 if self.measure == HILL_MEASURE_MEAN: 277 Ntail = segments[0].n 278 n_sub = int(round(self.mean_dur * 1e-3 / data.file.sampling)) 279 off_sub = int(round(self.mean_from * 1e-3 / data.file.sampling)) 280 segments = [data.get_segmentation_indexed(seg.f + off_sub, seg.f + off_sub + n_sub - 1, signal=seg.signal)[0] 281 for seg in segments] 282 else: 283 Ntail = min(100, segments[0].n / 5) 284 sign = 1 285 if self.models.model: 286 classes = self.models.model.classes 287 sign = classes.get(1, 'Amp') - classes.get(0, 'Amp') 288 if (self.stim_ix is None) or (not Nseg): 289 self.__dose = None 290 return 291 if measure == HILL_MEASURE_PEAK: 292 dr = qubx.fast.data.DoseResponse_Peak(segments[0].sampling, filter_kHz, sign) 293 else: 294 dr = qubx.fast.data.DoseResponse_Equil(Ntail) 295 for seg in reversed(segments): 296 for dose, response in izip(self.datasrc.gen_samples(reversed(seg.chunks), self.fit.controls.robot.main_hold, self.stim_ix), 297 self.datasrc.gen_samples(reversed(seg.chunks), self.fit.controls.robot.main_hold)): 298 if dose.included: 299 dr.add_data(dose.samples, response.samples) 300 if measure == HILL_MEASURE_PEAK: 301 dose, response = dr.result(HILL_MAX_RESULTS) 302 else: 303 dose, response, resp_std = dr.result(HILL_MAX_RESULTS) 304 small_doses = dose[dose>0.0] 305 zero_ish = min(small_doses) / numpy.e if len(small_doses) else 1e-8 306 if self.is_log: 307 logdose = numpy.log10(dose) 308 logdose[dose <= 0.0] = numpy.log10(zero_ish) 309 else: 310 logdose = dose 311 if sign > 0: 312 response -= numpy.min(response) 313 max_resp = numpy.max(response) 314 response /= max_resp 315 else: 316 response -= numpy.max(response) 317 max_resp = numpy.min(response) 318 response /= max_resp 319 if measure != HILL_MEASURE_PEAK: 320 resp_std /= max_resp 321 self.fit.controls.robot.set_data(logdose, response, [response, dose], ['I', 'L']) 322 self.fit.controls.robot.set_param('EC50', numpy.mean(dose)) 323 self.__dose = dose 324 self.__dose_name = series[self.stim_ix] 325 gobject.idle_add(self.__onChangeModel) 326 gobject.idle_add(self.fit.controls.robot.fit)

Source Code for Module qubx.hill