def get_qscore_dataframe(run_folder: Path) -> pd.DataFrame:
"""
Returns a pandas DataFrame containing x (Q-score), y (Total in millions) values for the run
"""
df_dict = {}
x_vals = []
y_vals = []
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.Q] = 1
run_metrics.read(str(run_folder), valid_to_load)
bar_data = py_interop_plot.bar_plot_data()
boundary = 30
options = py_interop_plot.filter_options(
run_metrics.run_info().flowcell().naming_method())
py_interop_plot.plot_qscore_histogram(run_metrics, options, bar_data,
boundary)
for i in range(bar_data.size() - 1):
x = [bar_data.at(i).at(j).x() for j in range(bar_data.at(i).size())]
y = [bar_data.at(i).at(j).y() for j in range(bar_data.at(i).size())]
x_vals += x
y_vals += y
df_dict['x'] = x_vals
df_dict['y'] = y_vals
df = pd.DataFrame.from_dict(df_dict)
return df
def test_bad_format_exception(self):
"""
Test that exceptions can be caught and they have the expected message
"""
tmp = numpy.asarray([
0, 3, 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()
try:
py_interop_comm.read_interop_from_buffer(
tmp, run.extraction_metric_set())
self.fail("bad_format_exception should have been thrown")
except py_interop_comm.bad_format_exception as ex:
self.assertEqual(
str(ex).split('\n')[0],
"No format found to parse ExtractionMetricsOut.bin with version: 0 of 2"
)
def test_incomplete_file_exception(self):
"""
Test that exceptions can be caught and they have the expected message
"""
tmp = numpy.asarray([
3, 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()
try:
py_interop_comm.read_interop_from_buffer(
tmp, run.extraction_metric_set())
self.fail("incomplete_file_exception should have been thrown")
except py_interop_comm.incomplete_file_exception as ex:
self.assertEqual(
str(ex).split('\n')[0],
"Insufficient data read from the file, got: 13 != expected: 50 for Extraction v3"
)
def test_run_metrics_member(self):
"""
Test if the run metrics object is properly wrapped
"""
run_metrics = py_interop_run_metrics.run_metrics()
self.assertEqual(run_metrics.extraction_metric_set().size(), 0)
def run(self):
run_metrics = py_interop_run_metrics.run_metrics()
run_metrics.run_info()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount,
0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
run_metrics.read(self.runfolder, valid_to_load)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
lanes = summary.lane_count()
reads = self.get_non_index_reads(summary)
for lane in range(lanes):
# The interop library uses zero based indexing, however most people uses read 1/2
# to denote the different reads, this enumeration is used to transform from
# zero based indexing to this form. /JD 2017-10-27
for new_read_nbr, original_read_nbr in enumerate(reads):
read = summary.at(original_read_nbr).at(lane)
error_rate = read.error_rate().mean()
percent_q30 = read.percent_gt_q30()
self._send_to_subscribers(("error_rate", {
"lane": lane + 1,
"read": new_read_nbr + 1,
"error_rate": error_rate
}))
self._send_to_subscribers(("percent_q30", {
"lane": lane + 1,
"read": new_read_nbr + 1,
"percent_q30": percent_q30
}))
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_summary_run_metric_set_method(self):
"""
Test if the enum parsing is properly wrapped
"""
run = py_interop_run_metrics.run_metrics()
summary_run_metric_set = run.summary_run_metric_set()
def test_invalid_argument(self):
"""
Test that exceptions can be caught and they have the expected message
"""
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())
try:
buf = numpy.zeros(3, dtype=numpy.uint8)
py_interop_comm.write_interop_to_buffer(
run.extraction_metric_set(), buf)
self.fail("invalid_argument should have been thrown")
except py_interop_comm.invalid_argument as ex:
self.assertEqual(str(ex).split('\n')[0], "Buffer size too small")
def main():
""" Retrieve run folder paths from the command line
Ensure only metrics required for summary are loaded
Load the run metrics
Calculate the summary metrics
Display error by lane, read
"""
logging.basicConfig(level=logging.INFO)
run_metrics = py_interop_run_metrics.run_metrics()
summary = py_interop_summary.run_summary()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
for run_folder_path in sys.argv[1:]:
run_folder = os.path.basename(run_folder_path)
try:
run_metrics.read(run_folder_path, valid_to_load)
except ex:
logging.warn("Skipping - cannot read RunInfo.xml: %s - %s" %
(run_folder, str(ex)))
continue
py_interop_summary.summarize_run_metrics(run_metrics, summary)
error_rate_read_lane_surface = numpy.zeros(
(summary.size(), summary.lane_count(), summary.surface_count()))
for read_index in range(summary.size()):
for lane_index in range(summary.lane_count()):
for surface_index in range(summary.surface_count()):
error_rate_read_lane_surface[read_index, lane_index, surface_index] = \
summary.at(read_index).at(lane_index).at(surface_index).error_rate().mean()
logging.info("Run Folder: " + run_folder)
for read_index in range(summary.size()):
read_summary = summary.at(read_index)
logging.info("Read " + str(read_summary.read().number()))
def parse_single_json(run_folder_path, xrange=None):
run_folder = os.path.basename(run_folder_path)
run_metrics = py_interop_run_metrics.run_metrics()
summary = py_interop_summary.run_summary()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
percent_occupied_df = pd.DataFrame
if 'MyRun' not in str(run_folder_path):
percent_occupied_df = get_percent_occupied_by_lane(run_folder_path)
summary_dict = {}
try:
run_metrics.read(run_folder_path, valid_to_load)
py_interop_summary.summarize_run_metrics(run_metrics, summary)
logging.info("Read: {}, Lane: {}".format(summary.size(),
summary.lane_count()))
for read_index in range(summary.size()):
logging.info("Read {}".format(read_index + 1))
summary_dict.setdefault("run", run_folder)
for lane_index in range(summary.lane_count()):
read_summary = summary.at(read_index)
lane_summary = read_summary.at(lane_index)
summary_dict.setdefault("data", []).append({
"runname":
parse_runid(run_folder),
"read":
read_summary.read().number(),
"lane":
lane_summary.lane(),
"density":
parse_float(lane_summary.density().mean() / 1000),
"density_stddev":
parse_float(lane_summary.density().stddev() / 1000),
"clusterpf":
parse_float(lane_summary.percent_pf().mean()),
"clusterpf_stddev":
parse_float(lane_summary.percent_pf().stddev()),
"readsm":
parse_float(lane_summary.reads() / 1000000),
"readspfm":
parse_float(lane_summary.reads_pf() / 1000000),
"q30":
parse_float(lane_summary.percent_gt_q30()),
"aligned":
parse_float(lane_summary.percent_aligned().mean()),
"aligned_stddev":
parse_float(lane_summary.percent_aligned().stddev()),
"errorrate":
parse_float(lane_summary.error_rate().mean()),
"errorrate_stddev":
parse_float(lane_summary.error_rate().stddev()),
"percent_occupied":
parse_float(
parse_lane_occupancy(lane_index, percent_occupied_df))
})
return summary_dict
except Exception as ex:
logging.warn("Skipping - ERROR: %s - %s" % (run_folder, str(ex)))
def test_invalid_channel_exception(self):
"""
Test that exceptions can be caught and they have the expected message
"""
run_metrics = py_interop_run_metrics.run_metrics()
try:
run_metrics.finalize_after_load();
self.fail("invalid_channel_exception should have been thrown")
except py_interop_run_metrics.invalid_channel_exception as ex:
self.assertEqual(str(ex).split('\n')[0], "Channel names are missing from the RunInfo.xml, and RunParameters.xml does not contain sufficient information on the instrument run.")
def test_run_metrics_typedef_wrapping(self):
"""
Test that the metric typedefs in run_metrics are properly wrapped
"""
run_metrics = py_interop_run_metrics.run_metrics()
metric_set_getters = [method for method in dir(run_metrics) if method.endswith('metric_set') and not method.startswith('set_')]
for getter in metric_set_getters:
metric_set = getattr(run_metrics, getter)()
self.assertTrue(hasattr(metric_set, 'size'))
self.assertEqual(metric_set.size(),0)
def test_invalid_parameter(self):
"""
Test that exceptions can be caught and they have the expected message
"""
valid_to_load = py_interop_run.uchar_vector(2)
run_metrics = py_interop_run_metrics.run_metrics()
try:
run_metrics.read_metrics("", 3, valid_to_load, 1)
self.fail("invalid_parameter should have been thrown")
except py_interop_run_metrics.invalid_parameter as ex:
self.assertEqual(str(ex).split('\n')[0], "Boolean array valid_to_load does not match expected number of metrics: 2 != 23")
def get_run_metrics_handle(run_dir):
""" Load the goodies from the .bin files in the InterOp directory.
This black magic is copy-pasted straight out of the tutorial linked above!
"""
#print("Examining: {}".format(run_dir))
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
for v2l in (py_interop_run.Tile, py_interop_run.ExtendedTile):
valid_to_load[v2l] = 1
run_metrics = py_interop_run_metrics.run_metrics()
run_metrics.read(run_dir, valid_to_load)
return run_metrics
def get_qc_run_summary(ngs_folder_path): run_folder = ngs_folder_path run_metrics = py_interop_run_metrics.run_metrics() valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0) py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load) run_folder = run_metrics.read(run_folder, valid_to_load) summary = py_interop_summary.run_summary() py_interop_summary.summarize_run_metrics(run_metrics, summary) return summary
def test_plot_qscore_heatmap(self):
"""
Test that plot_flowcell_map is properly wrapped
"""
run = py_interop_run_metrics.run_metrics()
options = py_interop_plot.filter_options(run.run_info().flowcell().naming_method())
rows = py_interop_plot.count_rows_for_heatmap(run)
cols = py_interop_plot.count_columns_for_heatmap(run)
dataBuffer = numpy.zeros((rows, cols), dtype=numpy.float32)
data = py_interop_plot.heatmap_data()
try:
py_interop_plot.plot_qscore_heatmap(run, options, data, dataBuffer.ravel())
except py_interop_plot.invalid_filter_option: pass
def test_plot_flowcell_map(self):
"""
Test that plot_flowcell_map is properly wrapped
"""
run = py_interop_run_metrics.run_metrics()
options = py_interop_plot.filter_options(run.run_info().flowcell().naming_method())
bufferSize = py_interop_plot.calculate_flowcell_buffer_size(run, options)
dataBuffer = numpy.zeros(bufferSize, dtype=numpy.float32)
idBuffer = numpy.zeros(bufferSize, dtype=numpy.uint32)
data = py_interop_plot.flowcell_data()
try:
py_interop_plot.plot_flowcell_map2(run, py_interop_run.Intensity, options, data, dataBuffer, idBuffer)
except py_interop_plot.invalid_filter_option:
pass
def __init__(self, run_folder_path):
# Initialize interop objects
self.run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
# Read from run folder
self.run_metrics.read(run_folder_path, valid_to_load)
# Load up summary metrics
self.summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(self.run_metrics, self.summary)
# Cached result tables for subsequent calls
self.run_summary_df = None
self.read_summary_dfs = {}
def plot_percent_base(run_folder: str, output_svg="percent_base.svg"):
"""
Plots the base % across each cycle. Each line represents a different base.
Reference lines are added for each read.
Base %: The percentage of clusters for which the selected base (A, C, T, or G) has been called.
"""
# Initialize interop objects
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
# Read from the run folder
run_metrics.read(run_folder, valid_to_load)
logger.info('Generating % base plot')
plot_data = py_interop_plot.candle_stick_plot_data()
options = py_interop_plot.filter_options(run_metrics.run_info().flowcell().naming_method())
py_interop_plot.plot_by_cycle(run_metrics, "BasePercent", options, plot_data)
# Plot each base
for base_index in range(plot_data.size()):
line_data = plot_data.at(base_index)
x = [line_data.at(i).x() for i in range(line_data.size())]
y = [line_data.at(i).y() for i in range(line_data.size())]
plt.plot(x, y, color=line_data.color(), linewidth=0.5, label=line_data.title())
# Plot reference lines for reads
read_vector = run_metrics.run_info().reads()
for read_index in range(read_vector.size()):
read_name = f'R{read_vector[read_index].number()}'
cycle_start = read_vector[read_index].first_cycle()
plt.axvline(x=cycle_start, color='purple', linestyle='--', linewidth=0.35)
plt.text(cycle_start, plt.gca().get_ylim()[1], read_name, fontsize=8, color='purple')
# Plot settings
axes_data = plot_data.xyaxes()
plt.xlabel(axes_data.x().label(), fontsize=10)
plt.ylabel(axes_data.y().label(), fontsize=10)
plt.title(plot_data.title(), fontsize=10)
plt.legend()
plt.ylim([axes_data.y().min(), axes_data.y().max()])
plt.xlim([axes_data.x().min(), axes_data.x().max()])
# Save figure
plt.savefig(output_svg)
def parse(run_folder, dictionary):
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
run_folder = run_metrics.read(run_folder, valid_to_load)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
# Parse data from interop files -- % reads over Q30, cluster density, clusters passing filter
dictionary["Percent Q30"] = round(summary.total_summary().percent_gt_q30(),
2)
dictionary["Cluster density"] = round(
summary.at(0).at(0).density().mean() / 1000, 2)
dictionary["Percent PF"] = round(
summary.at(0).at(0).percent_pf().mean(), 2)
dictionary["Phasing"] = round(summary.at(0).at(0).phasing().mean(), 2)
dictionary["Prephasing"] = round(
summary.at(0).at(0).prephasing().mean(), 2)
dictionary["Error rate"] = round(summary.total_summary().error_rate(), 2)
dictionary["Aligned"] = round(summary.total_summary().percent_aligned(), 2)
def get_qc_run_summary(ngs_folder_path): """ Creates the InterOp summary object. Input: ngs_folder_path = The Illumina folder containing the QC data. Should contain the following: 1) InterOp/ containing *MetricsOut.bin files. 2) RunInfo.xml 3) RunParameters.xml Output: Returns False if an error occurs otherwise: summary = The InterOp simmary object. See https://github.com/Illumina/interop/blob/master/docs/src/Tutorial_01_Intro.ipynb """ try: run_folder = ngs_folder_path except: return False #An error has occured. run_metrics = py_interop_run_metrics.run_metrics() valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0) py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load) run_folder = run_metrics.read(run_folder, valid_to_load) summary = py_interop_summary.run_summary() py_interop_summary.summarize_run_metrics(run_metrics, summary) return summary
def interop_parse(self):
"""
Use interop to parse the files in the InterOp folder to extract the number of reads mapping to PhiX as well as
the error rate
"""
# Parse the files and load the data
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount,
0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
run_metrics.read(self.path, valid_to_load)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
# PhiX error rate for run over all "usable cycles"
errorrate = summary.total_summary().error_rate()
# Percent aligned PhiX
pctaligned = summary.total_summary().percent_aligned()
# Add the error rate and the percent of reads that align to PhiX to the metadata object
for sample in self.metadata:
sample.run.error_rate = '{:.2f}'.format(errorrate)
sample.run.phix_aligned = '{:.2f}'.format(pctaligned)
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 parse_illumina_interop(run_dir):
# Read interop
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
try:
run_metrics.read(run_dir, valid_to_load)
except Exception:
sys.stderr.write("Cannot parse information in InterOp")
sys.exit(2)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
lanes = summary.lane_count()
reads = summary.size()
# Parse the interop stats lane by lane for non-index reads
run_stats_summary = dict()
for lane in range(lanes):
lane_nbr = getattr(summary.at(0).at(lane), "lane")()
for read in range(reads):
if not summary.at(read).read().is_index():
stats = dict()
stats.update({"density" : getattr(summary.at(read).at(lane), "density")().mean()/1000})
stats.update({"error_rate" : getattr(summary.at(read).at(lane), "error_rate")().mean()})
stats.update({"first_cycle_intensity" : getattr(summary.at(read).at(lane), "first_cycle_intensity")().mean()})
stats.update({"percent_aligned" : getattr(summary.at(read).at(lane), "percent_aligned")().mean()})
stats.update({"percent_gt_q30" : getattr(summary.at(read).at(lane), "percent_gt_q30")()})
stats.update({"percent_pf" : getattr(summary.at(read).at(lane), "percent_pf")().mean()})
stats.update({"phasing" : getattr(summary.at(read).at(lane), "phasing")().mean()})
stats.update({"prephasing" : getattr(summary.at(read).at(lane), "prephasing")().mean()})
stats.update({"reads_pf" : getattr(summary.at(read).at(lane), "reads_pf")()})
stats.update({"yield_g" : getattr(summary.at(read).at(lane), "yield_g")()})
if lane_nbr not in list(run_stats_summary.keys()):
run_stats_summary.update({lane_nbr: {read: stats}})
else:
run_stats_summary[lane_nbr].update({read : stats})
return run_stats_summary
lane_artifacts[laneno] = art
art.name = "Lane {}:1".format(laneno)
# run_dir_all = glob.glob("/data/runScratch.boston/demultiplexed/*/*/{}".format(run_id))
run_dir_all = glob.glob("/data/runScratch.boston/nova-interop-temp-marius/{}".format(run_id))
if not run_dir_all:
print("No run folder for", run_id)
else:
run_dir = run_dir_all[0]
try: # Ignore parsing error, to not disturb the sequencer integrations
# Parse InterOp data
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
valid_to_load[py_interop_run.ExtendedTile] = 1
run_metrics = py_interop_run_metrics.run_metrics()
run_metrics.read(run_dir, valid_to_load)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
extended_tile_metrics = run_metrics.extended_tile_metric_set()
read_count = summary.size()
lane_count = summary.lane_count()
if lane_count != len(lane_artifacts):
raise RuntimeError("Error: Number of lanes in InterOp data: {}, does not match the number "
"of lanes in LIMS: {}.".format(lane_count, len(lane_artifacts)))
result = {}
phix_pct = [] # We report PhiX % per read R1 / R2 (non-index)
for lane_number, artifact in lane_artifacts.items():
def process_files(run_folders, channel, curve_type, **extra):
''' Read the InterOp data and plot the distortion curves for each input run folder
:param run_folders: list of run folders to process
:param output: output image name (optional)
:param extra: unused arguments
'''
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.OpticalModel] = 1
run_coeff_map = {}
channels = None
selected_channel_index = -1
for run_folder in run_folders:
_logger.info("Parsing coefficients for %s" % run_folder)
basename = os.path.basename(run_folder)
try:
run_metrics.read(run_folder, valid_to_load)
except:
_logger.warn("Skipping - cannot read RunInfo.xml: %s" % basename)
continue
if channels is None:
channels = [
name.lower() for name in run_metrics.run_info().channels()
]
try:
selected_channel_index = channels.index(channel.lower())
except:
selected_channel_index = -1
if channel != "All":
raise RuntimeError("Channel not found: %s not in %s" %
(channel, ",".join(channels)))
if basename == "":
basename = os.path.basename(os.path.dirname(run_folder))
if run_metrics.distortion_metric_set().empty():
_logger.info("No distortion coefficients found: Parsing log file")
try:
run_coeff_map[basename] = parse_log_file(
run_folder, run_metrics.run_info())
except:
_logger.warn("Skipping - cannot parse log file: %s" % basename)
continue
else:
run_coeff_map[basename] = create_plot(
run_metrics.distortion_metric_set())
if len(run_coeff_map) == 0:
raise Exception("No run folders read")
if selected_channel_index == -1:
raise NotImplementedError("All channels is not implemented")
colormap = cm.cool
lane_count = run_metrics.run_info().flowcell().lane_count()
surface_count = run_metrics.run_info().flowcell().surface_count()
lane_surface_norm = matplotlib.colors.Normalize(vmin=0,
vmax=surface_count *
lane_count)
for run_folder, (y_values, x_values) in run_coeff_map.iteritems():
_logger.info("Drawing curves for %s, %d" % (run_folder, len(y_values)))
if len(y_values) == 0:
_logger.warn("Skipping %s - missing curve" % run_folder)
continue
for fig in create_figure(run_folder, **extra):
pylab.title(run_folder)
pylab.xlabel("Distance from Center of Image")
pylab.ylabel("Distortion (Pixels)")
has_label = {}
for lane, tile, y_for_channel in y_values:
surface = int(str(tile)[0])
lane = int(lane)
lane_surface = (surface - 1) * lane_count + lane - 1
label = None
if lane_surface not in has_label:
has_label[lane_surface] = True
label = "%d_%d" % (lane, surface)
pylab.plot(x_values,
y_for_channel[selected_channel_index][curve_type],
color=colormap(lane_surface_norm(lane_surface)),
label=label)
pylab.legend(loc='upper center')
if extra['output'] == "": pylab.show()
def parse_interop_data(run_folder_dir, num_reads, num_lanes):
"""
Parses summary statistics out of interops data using the Illumina interops package
"""
# make empty dict to store output
interop_dict = {'read_summaries': {}}
# taken from illumina interops package documentation, all of this is required,
# even though only the summary variable is used further on
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
run_folder = run_metrics.read(run_folder_dir, valid_to_load)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
for read in range(num_reads):
new_read = read + 1
if new_read not in interop_dict['read_summaries']:
interop_dict['read_summaries'][new_read] = {}
for lane in range(num_lanes):
new_lane = lane + 1
if new_lane not in interop_dict['read_summaries'][new_read]:
interop_dict['read_summaries'][new_read][new_lane] = {}
interop_dict['read_summaries'][read + 1][
lane +
1]['percent_q30'] = summary.at(read).at(lane).percent_gt_q30()
interop_dict['read_summaries'][read + 1][
lane +
1]['density'] = summary.at(read).at(lane).density().mean()
interop_dict['read_summaries'][read + 1][lane + 1][
'density_pf'] = summary.at(read).at(lane).density_pf().mean()
interop_dict['read_summaries'][read + 1][
lane + 1]['cluster_count'] = summary.at(read).at(
lane).density_pf().mean()
interop_dict['read_summaries'][read + 1][
lane + 1]['cluster_count_pf'] = summary.at(read).at(
lane).cluster_count_pf().mean()
interop_dict['read_summaries'][read + 1][lane + 1][
'error_rate'] = summary.at(read).at(lane).error_rate().mean()
interop_dict['read_summaries'][read + 1][
lane + 1]['percent_aligned'] = summary.at(read).at(
lane).percent_aligned().mean()
interop_dict['read_summaries'][read + 1][lane + 1][
'percent_pf'] = summary.at(read).at(lane).percent_pf().mean()
interop_dict['read_summaries'][read + 1][
lane +
1]['phasing'] = summary.at(read).at(lane).phasing().mean()
interop_dict['read_summaries'][read + 1][lane + 1][
'prephasing'] = summary.at(read).at(lane).prephasing().mean()
interop_dict['read_summaries'][read + 1][
lane + 1]['reads'] = summary.at(read).at(lane).reads()
interop_dict['read_summaries'][read + 1][
lane + 1]['reads_pf'] = summary.at(read).at(lane).reads_pf()
interop_dict['read_summaries'][read + 1][
lane + 1]['yield_g'] = summary.at(read).at(lane).yield_g()
for key in interop_dict['read_summaries'][read + 1][lane + 1]:
if math.isnan(interop_dict['read_summaries'][read +
1][lane +
1][key]):
interop_dict['read_summaries'][read + 1][lane +
1][key] = None
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_index_metrics_to_load(valid_to_load)
run_folder = run_metrics.read(run_folder_dir, valid_to_load)
return interop_dict
def plot_tile_intensity(run_folder: str, output_svg_prefix='max_intensity'):
# 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.Extraction] = 1
# Read from the run folder
run_metrics.read(run_folder, valid_to_load)
# Get the extraction metrics
extraction_metrics = run_metrics.extraction_metric_set()
extraction_metrics.rebuild_index(True)
# Format a DataFrame
df = []
for lane in extraction_metrics.lanes():
print(f"Processing lane {lane}")
for tile in extraction_metrics.tile_numbers_for_lane(lane):
if not isinstance(tile, int):
continue
print(f"Processing tile {tile}")
for cycle in range(extraction_metrics.max_cycle()):
print(f"Processing cycle {cycle}")
try:
extraction_metric = extraction_metrics.get_metric(
lane, tile, cycle)
except:
continue
for channel in range(extraction_metrics.channel_count()):
df.append(
dict(channel=channel,
lane=lane,
tile=tile,
cycle=cycle,
max_intensity=extraction_metric.max_intensity(
channel)))
df = pd.DataFrame(df)
# Iterate over lanes
for lane, lane_df in df.groupby('lane'):
# Plot the change in max intensity over cycles
sns.lineplot(
data=lane_df,
x='cycle',
y='max_intensity',
)
# Set a title
plt.title(f"Lane {lane}")
# Save to the PDF
plt.savefig(f"{output_svg_prefix}_{lane}.svg")
plt.close()
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()
def parsing_run_metrics_files(local_run_metric_folder, run_process_obj,
experiment_name):
'''
Description:
The function parse the information from the run metric files
Input:
local_run_metric_folder # local folder with the run metric files
run_process_obj # RunProcess object for this run
experiment_name # experiment name
Import:
py_interop_run
py_interop_run_metrics
Variables:
bin_run_stats_summary_list # list of dictionnary with the summary
information
run_stats_read_list # list of dictionnary with the read information
Return:
bin_run_stats_summary_list, run_stats_read_list
'''
logger = logging.getLogger(__name__)
logger.debug('%s : Starting function parsing_run_metrics', experiment_name)
run_param_obj = RunningParameters.objects.get(runName_id=run_process_obj)
# get the number of lanes for the run
number_of_lanes = int(run_param_obj.get_number_of_lanes())
# get number of reads for the run
num_of_reads = run_param_obj.get_number_of_reads()
logger.info('%s : Fetched run information needed for running metrics',
experiment_name)
run_metrics = py_interop_run_metrics.run_metrics()
valid_to_load = py_interop_run.uchar_vector(py_interop_run.MetricCount, 0)
py_interop_run_metrics.list_summary_metrics_to_load(valid_to_load)
run_metric_folder = run_metrics.read(local_run_metric_folder)
summary = py_interop_summary.run_summary()
py_interop_summary.summarize_run_metrics(run_metrics, summary)
bin_run_stats_summary_list = []
logger.info('%s : Starts collecting data for run metric ', experiment_name)
# get the Run Summary for each Read
for read_level in range(num_of_reads):
run_summary_stats_level = {}
# summary yield total
run_summary_stats_level['yieldTotal'] = format(
summary.at(read_level).summary().yield_g(), '.3f')
# summary projected total yield
run_summary_stats_level['projectedTotalYield'] = format(
summary.at(read_level).summary().projected_yield_g(), '.3f')
# percent yield
run_summary_stats_level['aligned'] = format(
summary.at(read_level).summary().percent_aligned(), '.3f')
# Error rate
run_summary_stats_level['errorRate'] = format(
summary.at(read_level).summary().error_rate(), '.3f')
# intensity cycle 1
run_summary_stats_level['intensityCycle'] = str(
round(summary.at(read_level).summary().first_cycle_intensity()))
# Q30
run_summary_stats_level['biggerQ30'] = format(
summary.at(read_level).summary().percent_gt_q30(), '.3f')
run_summary_stats_level['level'] = str(read_level + 1)
bin_run_stats_summary_list.append(run_summary_stats_level)
logger.info('%s : Parsed run Metrics on summary level ', experiment_name)
# get the run summary for Total
run_summary_stats_level = {}
# total summary
run_summary_stats_level['yieldTotal'] = format(
summary.total_summary().yield_g(), '.3f')
# total projected_yield_g
run_summary_stats_level['projectedTotalYield'] = format(
summary.total_summary().projected_yield_g(), '.3f')
# total percent aligned
run_summary_stats_level['aligned'] = format(
summary.total_summary().percent_aligned(), '.3f')
# total error rate
run_summary_stats_level['errorRate'] = format(
summary.total_summary().error_rate(), '.3f')
# total intensity cycle
run_summary_stats_level['intensityCycle'] = str(
round(summary.total_summary().first_cycle_intensity()))
# total Q 30
run_summary_stats_level['biggerQ30'] = format(
summary.total_summary().percent_gt_q30(), '.3f')
run_summary_stats_level['level'] = 'Total'
logger.info('%s : Parsed run Metrics on Total lane', experiment_name)
bin_run_stats_summary_list.append(run_summary_stats_level)
# get the run summary for non index
run_summary_stats_level = {}
# non index yield
run_summary_stats_level['yieldTotal'] = format(
summary.nonindex_summary().yield_g(), '.3f')
# non index projected yield
run_summary_stats_level['projectedTotalYield'] = format(
summary.nonindex_summary().projected_yield_g(), '.3f')
# non index percent aligned
run_summary_stats_level['aligned'] = format(
summary.nonindex_summary().percent_aligned(), '.3f')
# non index percent error rate
run_summary_stats_level['errorRate'] = format(
summary.nonindex_summary().error_rate(), '.3f')
# non index intensity cycle
run_summary_stats_level['intensityCycle'] = str(
round(summary.nonindex_summary().first_cycle_intensity()))
# non index Q 30
run_summary_stats_level['biggerQ30'] = format(
summary.nonindex_summary().percent_gt_q30(), '.3f')
run_summary_stats_level['level'] = 'Non Index'
logger.info('%s : Parsed run metric for Non Index lane', experiment_name)
bin_run_stats_summary_list.append(run_summary_stats_level)
### information per reads
run_stats_read_list = []
#lan_summary= py_interop_summary.lane_summary()
# Tiles
for read_number in range(num_of_reads):
for lane_number in range(number_of_lanes):
logger.info(
'%s : Processing run metrics stats on Read %s and on Lane %s',
experiment_name, read_number, lane_number)
run_read_stats_level = {}
run_read_stats_level['tiles'] = str(
int(summary.at(read_number).at(lane_number).tile_count()) * 2)
# Density (k/mm2) divide the value by 1000 to have it K/mm2
# get the +/- with the steddev
try:
read_lane_density_mean = str(
round(
float(
summary.at(read_number).at(
lane_number).density().mean()) / 1000))
read_lane_density_stddev = str(
round(
float(
summary.at(read_number).at(
lane_number).density().stddev()) / 1000))
except:
read_lane_density_mean = 'NaN'
read_lane_density_stddev = 'NaN'
string_message = experiment_name + ' : Unable to convert to float '
logging_warnings(string_message, False)
run_read_stats_level[
'density'] = read_lane_density_mean + ' ' + chr(
177) + ' ' + read_lane_density_stddev
# cluster _pf in %
try:
read_lane_percent_pf_mean = format(
summary.at(read_number).at(
lane_number).percent_pf().mean(), '.3f')
read_lane_percent_pf_stddev = format(
summary.at(read_number).at(
lane_number).percent_pf().stddev(), '.3f')
except:
read_lane_percent_pf_mean = 'NaN'
read_lane_percent_pf_stddev = 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_percent_pf'
logging_warnings(string_message, False)
run_read_stats_level[
'cluster_PF'] = read_lane_percent_pf_mean + ' ' + chr(
177) + ' ' + read_lane_percent_pf_stddev
# phas/ prepas in %
try:
read_lane_phasing_mean = format(
summary.at(read_number).at(lane_number).phasing().mean(),
'.3f')
read_lane_phasing_dev = format(
summary.at(read_number).at(lane_number).phasing().stddev(),
'.1f')
read_lane_prephasing_mean = format(
summary.at(read_number).at(
lane_number).prephasing().mean(), '.3f')
read_lane_prephasing_stddev = format(
summary.at(read_number).at(
lane_number).prephasing().stddev(), '.3f')
except:
read_lane_phasing_mean, read_lane_phasing_dev, read_lane_prephasing_mean, read_lane_prephasing_stddev = 'NaN', 'NaN', 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_phasing'
logging_warnings(string_message, False)
run_read_stats_level['phas_prephas'] = read_lane_phasing_mean + ' ' + chr(
177
) + ' ' + read_lane_phasing_dev + ' / ' + read_lane_prephasing_mean + ' ' + chr(
177) + ' ' + read_lane_prephasing_stddev
# reads (M)
try:
run_read_stats_level['reads'] = format(
float(summary.at(read_number).at(lane_number).reads()) /
1000000, '.3f')
except:
run_read_stats_level['reads'] = 'NaN'
string_message = experiment_name + ' : Unable to format to float run_read_stats_level[reads]'
logging_warnings(string_message, False)
#reads PF (M)
try:
run_read_stats_level['reads_PF'] = format(
float(summary.at(read_number).at(lane_number).reads_pf()) /
1000000, '.3f')
except:
run_read_stats_level['reads_PF'] = 'NaN'
string_message = experiment_name + ' : Unable to format to float run_read_stats_level[reads_PF]'
logging_warnings(string_message, False)
# percent q30
try:
run_read_stats_level['q30'] = format(
summary.at(read_number).at(lane_number).percent_gt_q30(),
'.3f')
except:
run_read_stats_level['q30'] = 'NaN'
string_message = experiment_name + ' : Unable to format to float run_read_stats_level[q30]'
logging_warnings(string_message, False)
# yield _g
try:
run_read_stats_level['yields'] = format(
summary.at(read_number).at(lane_number).yield_g(), '.3f')
except:
run_read_stats_level['yields'] = 'NaN'
string_message = experiment_name + ' : Unable to format to float run_read_stats_level[yields]'
logging_warnings(string_message, False)
# cycles err Rate
try:
run_read_stats_level['cyclesErrRated'] = str(
summary.at(read_number).at(lane_number).cycle_state().
error_cycle_range().first_cycle())
except:
run_read_stats_level['cyclesErrRated'] = 'NaN'
string_message = experiment_name + ' : Unable to format to float run_read_stats_level[cyclesErrRated]'
logging_warnings(string_message, False)
#percent_aligned
try:
read_lane_percent_aligned_mean = format(
summary.at(read_number).at(
lane_number).percent_aligned().mean(), '.3f')
read_lane_percent_aligned_stddev = format(
summary.at(read_number).at(
lane_number).percent_aligned().stddev(), '3f')
except:
read_lane_percent_aligned_mean, read_lane_percent_aligned_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_percent_aligned_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'aligned'] = read_lane_percent_aligned_mean + ' ' + chr(
177) + ' ' + read_lane_percent_aligned_stddev
#error rate
try:
read_lane_error_rate_mean = format(
summary.at(read_number).at(
lane_number).error_rate().mean(), '.3f')
read_lane_error_rate_stddev = format(
summary.at(read_number).at(
lane_number).error_rate().stddev(), '.3f')
except:
read_lane_error_rate_mean, read_lane_error_rate_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_error_rate_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'errorRate'] = read_lane_error_rate_mean + ' ' + chr(
177) + ' ' + read_lane_error_rate_stddev
#error rate_35
try:
read_lane_error_rate_35_mean = format(
summary.at(read_number).at(
lane_number).error_rate_35().mean(), '.3f')
read_lane_error_rate_35_stddev = format(
summary.at(read_number).at(
lane_number).error_rate_35().stddev(), '.3f')
except:
read_lane_error_rate_35_mean, read_lane_error_rate_35_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_error_rate_35_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'errorRate35'] = read_lane_error_rate_35_mean + ' ' + chr(
177) + ' ' + read_lane_error_rate_35_stddev
#error rate 50
try:
read_lane_error_rate_50_mean = format(
summary.at(read_number).at(
lane_number).error_rate_50().mean(), '.3f')
read_lane_error_rate_50_stddev = format(
summary.at(read_number).at(
lane_number).error_rate_50().stddev(), '.3f')
except:
read_lane_error_rate_50_mean, read_lane_error_rate_50_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_error_rate_50_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'errorRate50'] = read_lane_error_rate_50_mean + ' ' + chr(
177) + ' ' + read_lane_error_rate_50_stddev
#error rate 75
try:
read_lane_error_rate_75_mean = format(
summary.at(read_number).at(
lane_number).error_rate_75().mean(), '.3f')
read_lane_error_rate_75_stddev = format(
summary.at(read_number).at(
lane_number).error_rate_75().stddev(), '.3f')
except:
read_lane_error_rate_75_mean, read_lane_error_rate_75_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_error_rate_75_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'errorRate75'] = read_lane_error_rate_75_mean + ' ' + chr(
177) + ' ' + read_lane_error_rate_75_stddev
#error rate 100
try:
read_lane_error_rate_100_mean = format(
summary.at(read_number).at(
lane_number).error_rate_100().mean(), '.3f')
read_lane_error_rate_100_stddev = format(
summary.at(read_number).at(
lane_number).error_rate_100().stddev(), '.3f')
except:
read_lane_error_rate_100_mean, read_lane_error_rate_100_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_error_rate_100_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'errorRate100'] = read_lane_error_rate_100_mean + ' ' + chr(
177) + ' ' + read_lane_error_rate_100_stddev
# intensity cycle 1
try:
read_lane_intensity_cycle_mean = format(
summary.at(read_number).at(
lane_number).first_cycle_intensity().mean(),
'.3f') # get tiles for read 1 and lane 1
read_lane_intensity_cycle_stddev = format(
summary.at(read_number).at(
lane_number).first_cycle_intensity().stddev(), '.3f')
except:
read_lane_intensity_cycle_mean, read_lane_intensity_cycle_stddev = 'NaN', 'NaN'
string_message = experiment_name + ' : Unable to format to float read_lane_intensity_cycle_mean'
logging_warnings(string_message, False)
run_read_stats_level[
'intensityCycle'] = read_lane_intensity_cycle_mean + ' ' + chr(
177) + ' ' + read_lane_intensity_cycle_stddev
run_read_stats_level['read'] = str(read_number + 1)
run_read_stats_level['lane'] = str(lane_number + 1)
# append run_read_stats_level information to run_stats_read_list
run_stats_read_list.append(run_read_stats_level)
logger.debug('%s : End function parsing_run_metrics', experiment_name)
return bin_run_stats_summary_list, run_stats_read_list