def write_to_movie_file(self, output_folder, verbose=False): """ Write an MPG movie to output folder :param output_folder: output directory :param verbose: verbose output, default = False """ individuals = [] for treatment in self.Treatments: for curr_subject in treatment["individuals"]: individuals.append(curr_subject) #print("individuals:",individuals) for individual in individuals: individual.MovementProcesses["x"].History.pop(0) individual.MovementProcesses["y"].History.pop(0) individual.MovementProcesses["z"].History.pop(0) for item in individuals: print(vars(item)) visualization.save_simulation_movie(individuals, output_folder, len(individuals), self.NTimepoints, black_background=True, data_o=True, verbose=self.verbose)
def run(self): "Run the experiment, simulating timesteps" visualization.save_simulation_figure(individuals, opts.output, n_individuals, n_timepoints, perturbation_timepoint) visualization.save_simulation_movie(individuals, opts.output, n_individuals, n_timepoints, perturbation_timepoint, verbose)
def visualization(output_dir: str, pcoa: str, metadata: str, individual_col: str, timepoint_col: str, treatment_col: str): """ Connect to karenina.visualization and save output :param output_dir: output directory :param pcoa: pcoa qza file :param metadata: metadata file location :param individual_col: individual column identifiers :param timepoint_col: timepoint column identifier :param treatment_col: treatment column identifier """ # Parse in pcoa and metadata as dataframes and inject to k_visualization ind = [] site = _parse_pcoa(pcoa) df = _parse_metadata(metadata, individual_col, timepoint_col, treatment_col, site) i = 0 colors = ['fuchsia', 'cyan', 'darkorange', 'blue', 'yellow'] tx = treatment_col treatments = df[tx].unique() while len(colors) < len(treatments): colors.append('lightgray') for row in df.iterrows(): curr_subject_id = "%s_%i" % (df[individual_col], i) j = 0 while row[1][3] != treatments[j]: j += 1 color = colors[j] params = { 'lambda': 0.2, 'delta': 0.25, 'interindividual_variation': 0.01 } params['color'] = color curr_subject = Individual(subject_id = curr_subject_id, params = params, \ metadata = {treatment_col: df[treatment_col]}, \ interindividual_variation = .01) ind.append(curr_subject) i += 1 k_visualization.save_simulation_figure(individuals=ind, output_folder=output_dir, n_timepoints=50, perturbation_timepoint=25, n_individuals=50) k_visualization.save_simulation_movie(individuals=ind, output_folder=output_dir, n_timepoints=50, n_individuals=50)