def test_populate_imaging_table(self):
"""
Test if imaging logic can be properly used
"""
tmp = numpy.asarray([2,38
,7,0,90,4,1,0,-12,-56,15,64,-98,35,12,64,0,0,0,0,0,0,0,0,46,1,17,1,0,0,0,0,96,-41,-104,36,122,-86,-46,-120
,7,0,-66,4,1,0,96,-43,14,64,-63,49,13,64,0,0,0,0,0,0,0,0,56,1,17,1,0,0,0,0,112,125,77,38,122,-86,-46,-120
,7,0,66,8,1,0,74,-68,6,64,-118,-7,8,64,0,0,0,0,0,0,0,0,93,1,46,1,0,0,0,0,-47,-104,2,40,122,-86,-46,-120],
dtype=numpy.uint8)
run = py_interop_run_metrics.run_metrics()
py_interop_comm.read_interop_from_buffer(tmp, run.extraction_metric_set())
self.assertEqual(run.extraction_metric_set().size(), 3)
reads = py_interop_run.read_info_vector()
reads.append(py_interop_run.read_info(1, 1, 26))
reads.append(py_interop_run.read_info(2, 27, 76))
run.run_info(py_interop_run.info(
py_interop_run.flowcell_layout(2, 2, 2, 16),
reads
))
run.legacy_channel_update(py_interop_run.HiSeq)
columns = py_interop_table.imaging_column_vector()
py_interop_table.create_imaging_table_columns(run, columns)
row_offsets = py_interop_table.map_id_offset()
py_interop_table.count_table_rows(run, row_offsets)
column_count = py_interop_table.count_table_columns(columns)
data = numpy.zeros((len(row_offsets), column_count), dtype=numpy.float32)
py_interop_table.populate_imaging_table_data(run, columns, row_offsets, data.ravel())
self.assertEqual(data[0, 0], 7)
def test_invalid_column_type(self):
"""
Test that exceptions can be caught and they have the expected message
"""
channels = py_interop_run.string_vector()
filled = py_interop_run.bool_vector()
columns = py_interop_table.imaging_column_vector()
try:
py_interop_table.create_imaging_table_columns(channels, filled, columns, 8, py_interop_run.SquareLayout)
self.fail("invalid_column_type should have been thrown")
except py_interop_table.invalid_column_type as ex:
self.assertEqual(str(ex).split('\n')[0], "Filled vector does not match number of column names")
def get_percent_occupied_by_lane(run_folder_path):
df = pd.DataFrame
for item in NOVASEQ:
if 'myrun' not in run_folder_path.lower() and item.lower(
) in run_folder_path.lower():
valid_to_load = py_interop_run.uchar_vector(
py_interop_run.MetricCount, 0)
valid_to_load[py_interop_run.ExtendedTile] = 1
valid_to_load[py_interop_run.Tile] = 1
valid_to_load[py_interop_run.Extraction] = 1
run_metrics = py_interop_run_metrics.run_metrics()
run_metrics.read(run_folder_path, valid_to_load)
columns = py_interop_table.imaging_column_vector()
py_interop_table.create_imaging_table_columns(run_metrics, columns)
headers = get_headers(columns, run_folder_path)
column_count = py_interop_table.count_table_columns(columns)
row_offsets = py_interop_table.map_id_offset()
py_interop_table.count_table_rows(run_metrics, row_offsets)
data = np.zeros((row_offsets.size(), column_count),
dtype=np.float32)
py_interop_table.populate_imaging_table_data(
run_metrics, columns, row_offsets, data.ravel())
header_subset = ["Lane", "Tile", "Cycle", "% Occupied"]
header_index = [(header, headers.index(header))
for header in header_subset]
ids = np.asarray(
[headers.index(header) for header in header_subset[:3]])
data_for_selected_header_subset = []
for label, col in header_index:
data_for_selected_header_subset.append(
(label,
pd.Series([val for val in data[:, col]],
index=[tuple(r) for r in data[:, ids]])))
df = pd.DataFrame.from_dict(dict(data_for_selected_header_subset))
return df
def plot_occupancy(run_folder: str, output_jpg_prefix="occupancy"):
"""
To optimize loading concentrations on the NovaSeq platform, the % Occupied and % Pass Filter
metrics can be plotted to determine if a run was underloaded, optimally loaded, or overloaded.
More information:
https://support.illumina.com/bulletins/2020/03/plotting---occupied-by---pass-filter-to-optimize-loading-concent.html
"""
# Initialize interop objects
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
valid_to_load[py_interop_run.ExtendedTile] = 1
valid_to_load[py_interop_run.Tile] = 1
valid_to_load[py_interop_run.Extraction] = 1
# Read from the run folder
run_metrics.read(run_folder, valid_to_load)
# Create the columns
columns = py_interop_table.imaging_column_vector()
py_interop_table.create_imaging_table_columns(run_metrics, columns)
headers = []
for i in range(columns.size()):
column = columns[i]
if column.has_children():
headers.extend(
[f"{column.name()} ({subname})" for subname in column.subcolumns()])
else:
headers.append(column.name())
column_count = py_interop_table.count_table_columns(columns)
row_offsets = py_interop_table.map_id_offset()
py_interop_table.count_table_rows(run_metrics, row_offsets)
data = np.zeros((row_offsets.size(), column_count), dtype=np.float32)
py_interop_table.populate_imaging_table_data(
run_metrics, columns, row_offsets, data.ravel()
)
# Make a DataFrame
df = pd.DataFrame(data, columns=headers)
# Skip if there is no data (% Occupied only available on NovaSeq)
if df.shape[0] == 0 or "% Occupied" not in df:
# Stop
print("Occupancy plot skipped, no data available")
return
x = "% Occupied"
y = "% Pass Filter"
hues = ["Tile", "Lane", "Cycle"]
# Make a few different types of plots
for hue in hues:
sns.scatterplot(
data=df,
x=x,
y=y,
hue=hue,
alpha=0.5,
linewidth=0,
)
plt.xlim([0, 100])
plt.ylim([0, 100])
plt.legend(title=hue, bbox_to_anchor=[1.2, 0.9])
plt.tight_layout()
plt.savefig(f"{output_jpg_prefix}_{hue.lower()}.jpg", dpi=600)
plt.close()