def deserialize_result(serialized): return deserialize_exp_result(serialized)
ip.calibrate({0: 0, 1: 0, 2: 0, 3: 0, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}) ip.categoryMap = {4: 'fred', 5: 'fred', 6: 'fred', 8: 'henry', 9: 'henry'} result = ip.measure() print('calibration data: ', result.calibration_data) print('concentrations: ', result.concentrations) print('categorized: ', result.categorized) print('negative controls', result.negative_controls_indices) print('background intensity: ', result.background_intensity) print('background concentration: ', result.intensity_to_concentration(result.background_intensity)) print('LoD', result.limit_of_detection) print('LoQ', result.limit_of_quantification) print( 'fitted curve', result._intensity_to_concentration(1) - result._intensity_to_concentration(0), result._intensity_to_concentration(0)) ser = result.serialize result = deserialize_exp_result(ser) print(result.asCSV) fig = plot.plot_overview(result, encoding=None) print(fig) #plot.plot_standard_curve(result, encoding=None, show=True) #plot.plot_signal_to_noise(result, encoding=None, show=True)
# calibration with dict ip.calibrate({0: 0, 1: 0, 2: 0, 3: 0, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8}) ip.categoryMap = {4: "fred", 5: "fred", 6: "fred", 8: "henry", 9: "henry"} result = ip.measure() print("calibration data: ", result.calibration_data) print("concentrations: ", result.concentrations) print("categorized: ", result.categorized) print("negative controls", result.negative_controls_indices) print("background intensity: ", result.background_intensity) print("background concentration: ", result.intensity_to_concentration(result.background_intensity)) print("LoD", result.limit_of_detection) print("LoQ", result.limit_of_quantification) print( "fitted curve", result._intensity_to_concentration(1) - result._intensity_to_concentration(0), result._intensity_to_concentration(0), ) ser = result.serialize result = deserialize_exp_result(ser) print(result.asCSV) fig = plot.plot_overview(result, encoding=None) print(fig) # plot.plot_standard_curve(result, encoding=None, show=True) # plot.plot_signal_to_noise(result, encoding=None, show=True)