def generate_data_make_html(bs_lines): """Parses the beta significance file and returns the info in a list of dicts Inputs: bs_lines: beta significance results open file object Returns list of dicts of: { LD_NAME: plot_name, LD_HEADERS: {LD_HEADERS_VER:[], LD_HEADERS_HOR:[]}, LD_MATRIX : list of lists containing the float values to plot LD_TRANSFORM_VALUES: {(val1, val2) : (plot_value, label)} must have a key of form (None, None) Is a dictionary which allows to transform the continue matrix values into a discrete values to plot. LD_TABLE_TITLE: table_title } Contains all the needed information to generate the html file. """ result = [] dict_data, test_name = parse_beta_significance_output_pairwise(bs_lines) raw_headers, raw_matrix = generate_headers_and_matrix(dict_data, 0) corr_headers, corr_matrix = generate_headers_and_matrix(dict_data, 1) result.append( generate_dict_data("Raw values", raw_headers, raw_matrix, test_name)) result.append( generate_dict_data("Corrected values", corr_headers, corr_matrix, test_name)) return result
def generate_data_make_html(bs_lines): """Parses the beta significance file and returns the info in a list of dicts Inputs: bs_lines: beta significance results open file object Returns list of dicts of: { LD_NAME: plot_name, LD_HEADERS: {LD_HEADERS_VER:[], LD_HEADERS_HOR:[]}, LD_MATRIX : list of lists containing the float values to plot LD_TRANSFORM_VALUES: {(val1, val2) : (plot_value, label)} must have a key of form (None, None) Is a dictionary which allows to transform the continue matrix values into a discrete values to plot. LD_TABLE_TITLE: table_title } Contains all the needed information to generate the html file. """ result = [] dict_data, test_name = parse_beta_significance_output_pairwise(bs_lines) raw_headers, raw_matrix = generate_headers_and_matrix(dict_data, 0) corr_headers, corr_matrix = generate_headers_and_matrix(dict_data, 1) result.append(generate_dict_data("Raw values", raw_headers, raw_matrix, test_name)) result.append(generate_dict_data("Corrected values", corr_headers, corr_matrix, test_name)) return result
def test_parse_beta_significance_output_pairwise(self): """The pairwise beta significance parser works""" out = open(self.input_file, 'w') out.write(bs_lines_pairwise) out.close() self._paths_to_clean_up = [self.input_file] bs_lines = open(self.input_file, 'U') obs_dict, obs_test_name = \ parse_beta_significance_output_pairwise(bs_lines) exp_dict = { ('s1', 's2'): (0.01, 0.15), ('s1', 's3'): (0.0, 0.01), ('s1', 's4'): (0.02, 0.3), ('s2', 's3'): (0.82, 1.0), ('s2', 's4'): (0.4, 1.0), ('s3', 's4'): (0.0, 0.01) } exp_test_name = "Comment with the name of the test realized" self.assertEqual(obs_dict, exp_dict) self.assertEqual(obs_test_name, exp_test_name)
def test_parse_beta_significance_output_pairwise(self): """The pairwise beta significance parser works""" out = open(self.input_file, "w") out.write(bs_lines_pairwise) out.close() self._paths_to_clean_up = [self.input_file] bs_lines = open(self.input_file, "U") obs_dict, obs_test_name = parse_beta_significance_output_pairwise(bs_lines) exp_dict = { ("s1", "s2"): (0.01, 0.15), ("s1", "s3"): (0.0, 0.01), ("s1", "s4"): (0.02, 0.3), ("s2", "s3"): (0.82, 1.0), ("s2", "s4"): (0.4, 1.0), ("s3", "s4"): (0.0, 0.01), } exp_test_name = "Comment with the name of the test realized" self.assertEqual(obs_dict, exp_dict) self.assertEqual(obs_test_name, exp_test_name)