def db(
meta: Metadata, folder: str, rank: str = "genus", threads: int = 1
) -> JSONDirectory:
"""Create a model database from a set of SBML files."""
meta = meta.to_dataframe()
meta.columns = meta.columns.str.lower()
if not REQ_FIELDS.isin(meta.columns).all():
raise ValueError(
"Metadata File needs to have the following "
"columns %s." % ", ".join(REQ_FIELDS)
)
meta["id"] = meta.index
files = os.listdir(folder)
meta["file"] = meta.id + ".xml"
bad = meta.file.apply(lambda x: x not in files)
if any(bad):
raise ValueError(
"The following models are in the Metadata but not "
"in the folder: %s" % meta.file[bad]
)
meta = meta.groupby(rank).apply(reduce_group).reset_index(drop=True)
meta.index = meta[rank]
json_dir = JSONDirectory()
args = [
(tid, row, str(json_dir.json_files.path_maker(model_id=tid)), folder)
for tid, row in meta.iterrows()
]
workflow(_summarize_models, args, threads)
meta["file"] = meta.index + ".json"
meta["id"] = meta.index
meta["summary_rank"] = rank
meta.to_csv(json_dir.manifest.path_maker(), index=False)
return json_dir
def draw_interactive_map(output_dir: str,
metadata: qiime2.Metadata,
column: str = None,
latitude: str = 'Latitude',
longitude: str = 'Longitude',
color_palette: str = 'rainbow',
discrete: bool = False,
missing_data: str = 'error'):
metadata = _load_and_validate(
metadata, [column, latitude, longitude],
['column', 'latitude', 'longitude'], missing_data)
lat_0, lat_1, lon_0, lon_1 = get_max_extent(
metadata[latitude], metadata[longitude])
loc_min, loc_max = [lon_0, lat_0], [lon_1, lat_1]
cmap = plt.get_cmap(color_palette)
data = []
# If column is numeric, color points by column
if np.issubdtype(metadata[column].dtype, np.number) and not discrete:
metadata[column] = metadata[column].astype(float)
normalize = mcolors.Normalize(
vmin=metadata[column].min(), vmax=metadata[column].max())
scalarmappaple = cm.ScalarMappable(
norm=normalize, cmap=cmap)
scalarmappaple.set_array(metadata[column])
fig, ax = plt.subplots()
plt.colorbar(scalarmappaple).set_label(column)
ax.remove()
metadata.sort_values(by=column, ascending=False, inplace=True)
for i, row in metadata.iterrows():
data.append({
'sample_id': i,
column: row[column],
'latitude': row[latitude],
'longitude': row[longitude],
'color': mcolors.to_hex(scalarmappaple.to_rgba(row[column]))
})
# if column is not numeric, color discretely
else:
groups = metadata[column].unique()
len_groups = len(groups)
colors = {g: mcolors.to_hex(c) for g, c in zip(
groups, cmap(np.linspace(0, 1, len(groups))))}
for i, row in metadata.iterrows():
data.append({
'sample_id': i,
column: row[column],
'latitude': row[latitude],
'longitude': row[longitude],
'color': colors[row[column]]
})
fig = plt.figure(figsize=[len_groups * 0.05, len_groups/2])
ax = fig.add_axes([0, 0, 1, 1])
for idx, (g, color) in enumerate(colors.items()):
r = mpatch.Rectangle((0, idx), 1, 1, color=color)
_ = ax.text(2, idx+.5, ' %s' % g, va='center', fontsize=10)
ax.add_patch(r)
ax.axhline(idx, color='k')
ax.set_xlim(0, 3)
ax.set_ylim(0, idx + 2)
ax.axis('off')
save_animated_map(output_dir, loc_min, loc_max, data, column)
