def test_calculate_msd(self):
track = Track.from_dict(TRACK_DATA['track'])
track.calculate_msd()
self.assertTrue(track.is_msd_calculated())
self.assertTrue(np.isclose(track.get_msd(), TRACK_DATA['msd']).all())
self.assertTrue(
np.isclose(track.get_msd_error(), TRACK_DATA['msd_error']).all())
def load_track(self,
filename: str,
id=None,
unit_length=None,
unit_time=None,
col_names=None):
if unit_length is None or unit_time is None or col_names is None:
unit_length, unit_time, col_names = self.show_import_dialog(
pd.read_csv(filename).columns)
if unit_length is None or unit_time is None or col_names is None:
return
try:
self.track = Track.from_file(filename,
id=id,
unit_length=unit_length,
unit_time=unit_time,
col_name_x=col_names[0],
col_name_y=col_names[1],
col_name_t=col_names[2],
col_name_id=col_names[3]).normalized(
normalize_t=True,
normalize_xy=False)
except LoadTrackMissingIdError:
id = self.show_trackid_dialog()
if id is None:
return
self.load_track(filename=filename,
id=id,
unit_length=unit_length,
unit_time=unit_time,
col_names=col_names)
except LoadTrackIdNotFoundError:
mb = QMessageBox()
mb.setText("There is no track with that ID!")
mb.setWindowTitle("Error")
mb.setIcon(QMessageBox.Critical)
mb.exec()
except KeyError as e:
mb = QMessageBox()
mb.setText("Key not found: {}!".format(str(e)))
mb.setWindowTitle("Error")
mb.setIcon(QMessageBox.Critical)
mb.exec()
if self.track == None:
return
self.sigTrackLoaded.emit()
self.statusbar.showMessage("Loaded track of length {}".format(
self.track.get_size()))
import numpy as np
class ModelBrownian(ModelBase):
lower = [0.0, 0.0]
upper = [np.inf, np.inf]
initial = [0.5e-12, 2.0e-9]
def __call__(self, t, D, delta):
return D + delta**2 / (2 * t * (1 - 2 * self.R * self.dt / t))
# %%
# After we've defined the model, we can simply add it to the :class:`trait2d.analysis.ModelDB`.
from trait2d.analysis import ModelDB
ModelDB().add_model(ModelBrownian)
# %%
# We can now run ADC analysis with the model.
from trait2d.analysis import Track
track = Track.from_file("track1.csv", unit_length='micrometres')
track.adc_analysis(fraction_fit_points=0.15)
track.plot_adc_analysis_results()
# %%
# It is a good idea to use `ModelDB().cleanup()` at the end of your notebooks to remove all models again. Otherwise they may carry over into other open notebooks.
ModelDB().cleanup()
from trait2d.analysis import ModelDB
from trait2d.analysis.models import ModelBrownian, ModelConfined, ModelHop
ModelDB().add_model(ModelBrownian)
ModelDB().add_model(ModelConfined)
ModelDB().add_model(ModelHop)
# %%
# Single tracks are stored in a ``Track`` object.
from trait2d.analysis import Track
# %%
# We can create a single track from our last simulation:
single_track = Track.from_dict(simulator_brownian.trajectory)
# %%
# We can now do ADC analysis on the track:
results = single_track.adc_analysis(fit_max_time=0.5e-1)
# %%
# Analysis results like the calculated values for :math:`D_{app}`, fit parameters and much more are returned in a dictionary. We can also retreive the dictionary of the last analysis at any time with ``get_adc_analysis_results``.
fit_results = results["fit_results"]
best_model = results["best_model"]
print(fit_results)
print(best_model)
single_track.plot_adc_analysis_results()
def test_msd_analysis(self):
track = Track.from_dict(TRACK_DATA['track'])
track.msd_analysis()
self.assertTrue(track.get_msd_analysis_results() != None)
def test_from_dict(self):
track = Track.from_dict(TRACK_DATA['track'])
self.assertEqual(track.get_size(), len(TRACK_DATA['track']['x']))