def __load_experiment_data__(): basename = sicsext.getBaseFilename() fullname = str(System.getProperty('sics.data.path') + '/' + basename) df.datasets.clear() ds = df[fullname] data = ds[str(data_name.value)] axis = ds[str(axis_name.value)] if data.size > axis.size: data = data[:axis.size] ds2 = Dataset(data, axes=[axis]) ds2.title = ds.id ds2.location = fullname Plot1.set_dataset(ds2) Plot1.x_label = axis_name.value Plot1.y_label = str(data_name.value) Plot1.title = str(data_name.value) + ' vs ' + axis_name.value Plot1.pv.getPlot().setMarkerEnabled(True)
def load_experiment_data(): basename = sicsext.getBaseFilename() fullname = str(System.getProperty('sics.data.path') + '/' + basename) df.datasets.clear() global __DATASOURCE__ try: sysname = fullname.replace('/', '\\') if __DATASOURCE__.getDataset(fullname) == None \ and __DATASOURCE__.getDataset(sysname) == None: __DATASOURCE__.addDataset(fullname, False) except: log('error in adding dataset: ' + fullname) ds = df[fullname] dname = str(data_name.value) data = SimpleData(ds[dname]) # data = ds[str(data_name.value)] axis = SimpleData(ds[str(axis_name.value)]) if data.size > axis.size: data = data[:axis.size] if normalise.value: if dname == 'bm1_counts': tname = 'bm1_time' elif dname == 'bm2_counts': tname = 'bm2_time' else: tname = 'detector_time' norm = ds[tname] if norm != None and hasattr(norm, '__len__'): avg = norm.sum() / len(norm) niter = norm.item_iter() if niter.next() <= 0: niter.set_curr(1) data = data / norm * avg ds2 = Dataset(data, axes=[axis]) ds2.title = ds.id ds2.location = fullname fit_min.value = axis.min() fit_max.value = axis.max() Plot1.set_dataset(ds2) Plot1.x_label = axis_name.value Plot1.y_label = str(data_name.value) Plot1.title = str(data_name.value) + ' vs ' + axis_name.value Plot1.pv.getPlot().setMarkerEnabled(True)
def load_experiment_data(): basename = sicsext.getBaseFilename() fullname = str(System.getProperty('sics.data.path') + '/' + basename) df.datasets.clear() global __DATASOURCE__ try: sysname = fullname.replace('/', '\\') if __DATASOURCE__.getDataset(fullname) == None \ and __DATASOURCE__.getDataset(sysname) == None: __DATASOURCE__.addDataset(fullname, False) except: log('error in adding dataset: ' + fullname) ds = df[fullname] dname = str(data_name.value) data = SimpleData(ds[dname]) # data = ds[str(data_name.value)] axis = SimpleData(ds[str(axis_name.value)]) if data.size > axis.size: data = data[:axis.size] if normalise.value : if dname == 'bm1_counts': tname = 'bm1_time' elif dname == 'bm2_counts': tname = 'bm2_time' else: tname = 'detector_time' norm = ds[tname] if norm != None and hasattr(norm, '__len__'): avg = norm.sum() / len(norm) niter = norm.item_iter() if niter.next() <= 0: niter.set_curr(1) data = data / norm * avg ds2 = Dataset(data, axes=[axis]) ds2.title = ds.id ds2.location = fullname fit_min.value = axis.min() fit_max.value = axis.max() Plot1.set_dataset(ds2) Plot1.x_label = axis_name.value Plot1.y_label = str(data_name.value) Plot1.title = str(data_name.value) + ' vs ' + axis_name.value Plot1.pv.getPlot().setMarkerEnabled(True)
def load_experiment_data(fullname = None): if fullname == None: basename = sicsext.getBaseFilename() fullname = str(System.getProperty('sics.data.path') + '/' + basename) df.datasets.clear() ds = df[fullname] dname = str(data_name.value) if dname == 'first_5_tubes': if ds.ndim == 4: data = ds[:,:,:,0:5].sum(0) elif ds.ndim == 3: data = ds[:,:,0:5].sum(0) elif ds.ndim == 2: data = ds[:,0:5].sum(0) else: raise Exception('invalid data dimension: {}'.format(ds.ndim)) elif dname == 'tube_9': if ds.ndim == 4: data = ds[:,:,:,9].sum(0) elif ds.ndim == 3: data = ds[:,:,9].sum(0) elif ds.ndim == 2: data = ds[:,9].sum(0) else: raise Exception('invalid data dimension: {}'.format(ds.ndim)) elif dname == 'tube_10': if ds.ndim == 4: data = ds[:,:,:,10].sum(0) elif ds.ndim == 3: data = ds[:,:,10].sum(0) elif ds.ndim == 2: data = ds[:,10].sum(0) else: raise Exception('invalid data dimension: {}'.format(ds.ndim)) else: data = SimpleData(ds[dname]) # data = ds[str(data_name.value)] if axis_name.value.strip() == '': axis = ds.axes[0] axis_name.value = axis.name # axis = ds.get_metadata(str(axis_name.value)) else: if fix_axis.value: axis = SimpleData(ds[str(axis_name.value)]) else: axis = ds.axes[0] axis_name.value = axis.name if not hasattr(axis, '__len__'): axis = SimpleData([axis], title = (axis_name.value)) if data.size > axis.size: data = data[:axis.size] if normalise.value : if dname == 'bm1_counts': tname = 'bm1_time' elif dname == 'bm2_counts': tname = 'bm2_time' else: tname = 'detector_time' norm = ds[tname] if norm != None and hasattr(norm, '__len__'): avg = norm.sum() / len(norm) niter = norm.item_iter() if niter.next() <= 0: niter.set_curr(1) data = data / norm * avg ds2 = Dataset(data, axes=[axis]) ds2.title = ds.id ds2.location = fullname fit_min.value = axis.min() fit_max.value = axis.max() Plot1.set_dataset(ds2) if dname == 'first_5_tubes' and show_individuals.value: for i in xrange(5): if ds.ndim == 4: di = ds[:,:,:,i].sum(0) if normalise.value : tname = 'detector_time' norm = ds[tname] if norm != None and hasattr(norm, '__len__'): avg = norm.sum() / len(norm) niter = norm.item_iter() if niter.next() <= 0: niter.set_curr(1) di = di / norm * avg di2 = Dataset(di, axes=[axis]) di2.title = 'tube {}'.format(i) Plot1.add_dataset(di2) Plot1.x_label = axis_name.value Plot1.y_label = str(data_name.value) Plot1.title = str(data_name.value) + ' vs ' + axis_name.value Plot1.pv.getPlot().setMarkerEnabled(True)