print('hazzah!')

shared_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.shared';

design_file_path = '/home/jlynch/gsoc2013/data/Stool.0.03.subsample.0.03.filter.mix.design';

shared_data = mothur_files.load_shared_file(shared_file_path)

design_data = mothur_files.load_design_file(design_file_path)

otu1 = 'Otu29878'

otu2 = 'Otu29552'

# where are Otu29741 and Otu29678

n_otu1 = shared_data.otu_column_names.index(otu1)

n_otu2 = shared_data.otu_column_names.index(otu2)

print('{} is on column {}'.format(otu1, n_otu1))

print('{} is on column {}'.format(otu2, n_otu2))

print('shape of design_data.class_number_for_row {}'.format(design_data.class_number_for_row.shape))

class_zero = design_data.class_number_for_row == 2.0

class_one = design_data.class_number_for_row == 1.0

print('class zero count: {}'.format(np.sum(class_zero)))

print('class one count: {}'.format(np.sum(class_one)))

two_labels = np.logical_or(class_zero, class_one)

print('shape of two_labels: {}'.format(two_labels.shape));

label_index = np.arange(design_data.class_number_for_row.shape[0])

reduced_label_index = label_index[two_labels[:,0]]

print('reduced_label_index: {}'.format(reduced_label_index))

two_labels_otu_frequency = shared_data.otu_frequency[reduced_label_index,:]

print('shape of two_labels_otu_frequency: {}'.format(two_labels_otu_frequency.shape))

reduced_otu_frequency = two_labels_otu_frequency[:,[n_otu1, n_otu2]]

print('shaped of reduced_otu_frequency: {}'.format(reduced_otu_frequency.shape))

#print('reduced_otu_frequency:\n{}'.format(reduced_otu_frequency))

scaler = sklearn.preprocessing.StandardScaler()

# the scaler returns a copy by default

#X = scaler.fit_transform(reduced_otu_frequency)

#exit()

# the next line is pretty good

# smo.smo(reduced_otu_frequency, design_data.class_number_for_row[two_labels], 0.5)

smo.smo(reduced_otu_frequency, design_data.class_number_for_row[two_labels], 0.5)

pl.xlabel(otu1)

pl.ylabel(otu2)

pl.gca().set_xticklabels([])

pl.gca().set_yticklabels([])