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Separate the calculate_threshold function out #15

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Separate the calculate_threshold function out #15

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41 changes: 38 additions & 3 deletions q2_katharoseq/_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,15 +97,15 @@ def fit_lm(table,
return lm, filtered, positive_controls


def read_count_threshold(
output_dir: str,
def calculate_threshold(
threshold: int,
positive_control_value: str,
positive_control_column: qiime2.CategoricalMetadataColumn,
cell_count_column: qiime2.NumericMetadataColumn,
table: pd.DataFrame,
control: str,
asv: str = None) -> None:
asv: str = None):

if control == 'asv':
if asv is None:
raise ValueError("Control type set to asv but no asv provided")
Expand Down Expand Up @@ -187,6 +187,41 @@ def read_count_threshold(
min_freq = get_threshold(katharo['log_asv_reads'],
katharo['correct_assign'],
threshold/100)
return min_freq, popt, pcov, katharo, max_inputT


def read_count_threshold(
output_dir: str,
threshold: int,
positive_control_value: str,
positive_control_column: qiime2.CategoricalMetadataColumn,
cell_count_column: qiime2.NumericMetadataColumn,
table: pd.DataFrame,
control: str,
asv: str = None) -> None:

min_freq, popt, pcov, katharo, max_inputT = \
calculate_threshold(threshold,
positive_control_value,
positive_control_column,
cell_count_column,
table,
control,
asv)


# PLOT
x = np.linspace(0, 5, 50)
y = allosteric_sigmoid(x, *popt)
plt.plot(katharo['log_asv_reads'],
katharo['correct_assign'],
'o', label='data')
plt.plot(x, y, label='fit')
plt.ylim(0, 1.05)
plt.legend(loc='best')
plt.savefig(os.path.join(output_dir, 'fit.svg'))
plt.close()


# VISUALIZER
max_input_html = q2templates.df_to_html(max_inputT.to_frame())
Expand Down
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