def main():
umap = np.loadtxt(snakemake.input.umap, delimiter='\t')
chosen_k = int(snakemake.params.chosen_k)
chosen_res = float(snakemake.params.chosen_res)
if chosen_k == 0:
raise ValueError('No chosen number of neighbours - k - for louvain')
elif chosen_res == 0:
raise ValueError('No chosen resolution for louvain')
with open(snakemake.input.cluster_file, 'rb') as f:
clustering = pickle.load(f)
plot, no_clusters, no_singletons = plot_clusters_on_umap(umap,
clustering,
label=True)
plot.set_title('k_%s_res_%s__no_clusters__%s__no_singletons__%s' %
(chosen_k, chosen_res, no_clusters, no_singletons),
fontsize=20)
plt.savefig(snakemake.output.fig)
table = pd.read_csv(snakemake.input.filtered, sep='\t')
col_name = 'chosen_k_%s_res_%s' % (chosen_k, chosen_res)
table[col_name] = clustering
# just keep label id and clusters
if 'label_id' in table.columns:
table = table[['label_id', col_name]]
elif 'unique_id' in table.columns:
table = table[['unique_id', col_name]]
table.to_csv(snakemake.output.merged_table, index=False, sep='\t')
# make morph and viz tables
viz_table = pd.read_csv(snakemake.input.viz_table, sep='\t')
full_with_ids = pd.read_csv(snakemake.input.full_with_ids, sep='\t')
merged_morph = pd.read_csv(snakemake.input.merged_morph, sep='\t')
merged_morph = filter_texture_fails(merged_morph)
if 'unique' not in snakemake.params.gene_assign:
viz_table['clusters'] = clustering
# make table with label id & clusters for all cells
full_with_ids['clusters'] = clustering
table = full_with_ids[['label_id', 'clusters']]
else:
# make table with label id & clusters for all cells
cut = full_with_ids[['label_id', 'unique_id']]
table = cut.join(table.set_index('unique_id'),
on='unique_id',
how='left')
table = table[['label_id', col_name]]
table.columns = ['label_id', 'clusters']
viz_table = viz_table.join(table.set_index('label_id'),
on='label_id',
how='left')
make_binary_columns(viz_table, 'clusters', snakemake.output.viz_table)
morph_table = table.join(merged_morph.set_index('label_id'),
on='label_id',
how='inner')
morph_table.to_csv(snakemake.output.morph_table, index=False, sep='\t')
# and a normalised version
just_morph = morph_table.drop(columns=['label_id', 'clusters'])
col_names = just_morph.columns.tolist()
just_morph = StandardScaler().fit_transform(just_morph)
just_morph = pd.DataFrame(data=just_morph)
just_morph.columns = col_names
# can have issues with index matches producing nan values - reset indices here
just_morph.reset_index(drop=True, inplace=True)
morph_table.reset_index(drop=True, inplace=True)
just_morph.insert(0, 'clusters', morph_table['clusters'])
just_morph.insert(0, 'label_id', morph_table['label_id'])
just_morph.to_csv(snakemake.output.morph_table_normalised,
index=False,
sep='\t')
###############################################################################
# Backfill Volume data
###############################################################################
if 1:
# Get historical Volume data
volume, rat_volume = backfill_volume_with_singular(currencies, granularity,
_from, _to)
volume.reset_index(inplace=True)
volume = volume.rename({'index': 'timestamp'}, axis=1)
volume.to_pickle(volume_path)
# try standardized
ss = StandardScaler().fit_transform(volume.iloc[:, 1:])
ss = pd.DataFrame(ss, columns=volume.columns[1:], index=volume.index)
ss.insert(0, 'timestamp', volume.timestamp)
ss.drop('jpy', axis=1, inplace=True)
ss.drop('hkd', axis=1, inplace=True)
cu, ratios = backfill_with_singular(currencies, granularity, _from, _to)
cu.reset_index(inplace=True)
cu = cu.rename({'index': 'timestamp'}, axis=1)
cu.to_pickle(cu_small_path)
ratios.reset_index(inplace=True)
ratios = ratios.rename({'index': 'timestamp'}, axis=1)
###############################################################################
# Update Volume and Diff
###############################################################################
if 0:
