Alexander/P-unit_model
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print medians of percent errors

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a.ott 2020-07-28 09:34:05 +02:00
parent 2f76cefd3c
commit d100524e84
1 changed files with 15 additions and 14 deletions
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29
analysis.py
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@ -42,6 +42,19 @@ def main():
pass pass
def get_fit_info(folder):
fits_info = {}
for item in os.listdir(folder):
cell_folder = os.path.join(folder, item)
results = get_best_fit(cell_folder, use_comparable_error=True)
cell_behaviour, model_behaviour = results.get_behaviour_values()
fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]
return fits_info
def calculate_percent_errors(fits_info): def calculate_percent_errors(fits_info):
errors = {} errors = {}
@ -129,19 +142,6 @@ def parameter_correlations(fits_info):
return labels, corr_values, corrected_p_values return labels, corr_values, corrected_p_values
def get_fit_info(folder):
fits_info = {}
for item in os.listdir(folder):
cell_folder = os.path.join(folder, item)
results = get_best_fit(cell_folder)
cell_behaviour, model_behaviour = results.get_behaviour_values()
fits_info[item] = [results.get_final_parameters(), model_behaviour, cell_behaviour]
return fits_info
def create_correlation_plot(labels, correlations, p_values): def create_correlation_plot(labels, correlations, p_values):
cleaned_cors = np.zeros(correlations.shape) cleaned_cors = np.zeros(correlations.shape)
@ -181,7 +181,8 @@ def create_correlation_plot(labels, correlations, p_values):
def create_boxplots(errors): def create_boxplots(errors):
labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())] labels = ["{}_n:{}".format(k, len(errors[k])) for k in sorted(errors.keys())]
for k in sorted(errors.keys()):
print("{}: median %-error: {:.2f}".format(k, np.median(errors[k])))
y_values = [errors[k] for k in sorted(errors.keys())] y_values = [errors[k] for k in sorted(errors.keys())]
plt.boxplot(y_values) plt.boxplot(y_values)