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)
