def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
otu_table, headers, sample_labels = table.get_table_after_filtering_and_aggregation(user_request)
metadata = table.get_sample_metadata()
taxonomy_map = table.get_otu_metadata().get_taxonomy_map()
return self.analyse(user_request, otu_table, headers, sample_labels, metadata, taxonomy_map)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
metadata_vals = table.get_sample_metadata(
).get_metadata_column_table_order(sample_labels, user_request.catvar)
phylogenetic_tree = table.get_phylogenetic_tree()
return self.analyse(user_request, base, headers, sample_labels,
metadata_vals, phylogenetic_tree)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
metadata = table.get_sample_metadata().get_as_table()
return self.analyse(user_request, base, headers, sample_labels,
metadata)
def metadata_boxplots(self, user_request, yvals):
logger.info("Starting metadata_boxplots")
# This code path is used only when the user wants to draw boxplots from only the metadata data
table = OTUTable(user_request.user_id, user_request.pid)
metadata = table.get_sample_metadata().get_as_filtered_table(
user_request.sample_filter, user_request.sample_filter_role,
user_request.sample_filter_vals)
return self.process_metadata_boxplots(user_request, yvals, metadata)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(user_request)
metadata_column = []
catvar = user_request.catvar
if catvar != "none" and (user_request.get_custom_attr("rows") == "SampleID" or user_request.get_custom_attr("cols") == "SampleID"):
# Only if the sample ID is on the rows or the columns can we actually color the heatmap catvar indicator
metadata_column = table.get_sample_metadata().get_metadata_column_table_order(sample_labels, catvar)
return self.analyse(user_request, base, headers, sample_labels, metadata_column)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
otu_table, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
expvar = user_request.get_custom_attr("expvar")
metadata_vals = table.get_sample_metadata(
).get_metadata_column_table_order(sample_labels, expvar)
taxonomy_map = table.get_otu_metadata().get_taxonomy_map()
return self.analyse(user_request, otu_table, headers, metadata_vals,
taxonomy_map)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
metadata_col = []
if user_request.get_custom_attr(
"type") == "SampleID" and user_request.catvar != "none":
metadata_col = table.get_sample_metadata(
).get_metadata_column_table_order(sample_labels,
user_request.catvar)
return self.analyse(user_request, base, headers, sample_labels,
metadata_col)
def abundance_boxplots(self, user_request, yvals):
logger.info("Starting abundance_boxplots")
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
metadata = table.get_sample_metadata().get_as_table()
if user_request.get_custom_attr("colorvar") != "None":
color_metadata_values = table.get_sample_metadata(
).get_metadata_column_table_order(
sample_labels, user_request.get_custom_attr("colorvar"))
else:
color_metadata_values = []
return self.process_abundance_boxplots(user_request, yvals, base,
headers, sample_labels,
metadata, color_metadata_values)
def run(self, user_request):
logger.info("Starting TreeView analysis")
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
taxonomy_map = table.get_otu_metadata().get_taxonomy_map()
unique_meta_vals = table.get_sample_metadata(
).get_metadata_unique_vals(user_request.catvar)
sample_ids_to_metadata_map = table.get_sample_metadata(
).get_sample_id_to_metadata_map(user_request.catvar)
# If the sample_ids_to_metadata_map is empty, the user has not chosen to break down the visualization by
# any particular category. We will assume that there is just one "default" column
if len(sample_ids_to_metadata_map.keys()) == 0:
logger.info(
"No category breakdown selected - will generate a default column"
)
unique_meta_vals = ["Default"]
for sample_id in sample_labels:
sample_ids_to_metadata_map[sample_id] = "Default"
return self.analyse(user_request, base, headers, sample_labels,
taxonomy_map, unique_meta_vals,
sample_ids_to_metadata_map)
def create_project(self, pid, sampleFilter, sampleFilterRole,
sampleFilterVals):
map_file = Map(self.user_id, pid)
project_dir = os.path.join(ProjectManager.DATA_DIRECTORY, self.user_id)
project_dir = os.path.join(project_dir, pid)
try:
user_request = UserRequest(self.user_id, pid, "", "", "", "", [],
sampleFilter, sampleFilterRole,
sampleFilterVals, 0, "")
table = OTUTable(self.user_id, pid, use_raw=True, use_np=False)
orig_base = table.get_table()
orig_headers = table.get_headers()
orig_sample_labels = table.get_sample_labels()
base, headers, sample_labels = table.filter_otu_table_by_metadata(
orig_base, orig_headers, orig_sample_labels, user_request)
initial_samples_removed = list(
set(orig_sample_labels) - set(sample_labels))
new_base = []
i = 0
while i < len(base):
new_row = []
j = 0
while j < len(base[i]):
try:
if map_file.matrix_type == "float":
new_row.append(float(base[i][j]))
else:
new_row.append(int(base[i][j]))
except ValueError:
new_row.append(0)
j += 1
new_base.append(new_row)
i += 1
logger.info("Subsampled file")
# Updates map.txt file
map_file.num_samples = len(sample_labels)
map_file.num_otus = len(headers)
map_file.save()
return pid, ""
except Exception as e:
print(e)
logger.exception("Error while processing the file format")
# Removes the project directory since the files in it are invalid
shutil.rmtree(project_dir, ignore_errors=True)
return GENERAL_ERROR, ""
def run(self, user_request):
# Rarefaction curves are only useful on the original raw data set
table = OTUTable(user_request.user_id, user_request.pid, True)
base = table.get_table()
headers = table.get_headers()
sample_labels = table.get_sample_labels()
if user_request.get_custom_attr("colorvar") != "None":
color_metadata_values = table.get_sample_metadata().get_metadata_column_table_order(sample_labels, user_request.get_custom_attr("colorvar"))
else:
color_metadata_values = []
return self.analyse(base, headers, sample_labels, color_metadata_values)
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
# No OTUs should be excluded for diversity analysis
otu_table, headers, sample_labels = table.get_table_after_filtering_and_aggregation(user_request)
metadata_values = table.get_sample_metadata().get_metadata_column_table_order(sample_labels, user_request.catvar)
if user_request.get_custom_attr("colorvar") != "None":
color_metadata_values = table.get_sample_metadata().get_metadata_column_table_order(sample_labels, user_request.get_custom_attr("colorvar"))
else:
color_metadata_values = []
strata_values = None
if user_request.get_custom_attr("strata").lower() != "none":
strata_values = table.get_sample_metadata().get_metadata_column_table_order(sample_labels, user_request.get_custom_attr("strata"))
sample_ids_to_metadata_map = table.get_sample_metadata().get_sample_id_to_metadata_map(user_request.catvar)
phylogenetic_tree = table.get_phylogenetic_tree()
if user_request.get_custom_attr("api").lower() == "beta":
return self.analyse(user_request, otu_table, headers, sample_labels, metadata_values, color_metadata_values, sample_ids_to_metadata_map, phylogenetic_tree)
else:
return self.analyse_permanova(user_request, otu_table, headers, sample_labels, metadata_values, strata_values, sample_ids_to_metadata_map)
def test_load_tsv(self):
# pass
# second_arg = {"a": 1}
# with Pool(5) as pool:
# iterable = [0] * 20
# func = partialmethod(f, second_arg)
# pool.map(func, iterable)
#
# print(second_arg)
user_request = AnalysisTestUtils.create_default_user_request()
user_request.level = 2
start = datetime.datetime.now()
otu_table = OTUTable("unit_tests", "large_biom")
end = datetime.datetime.now()
elapsed = end - start
print(elapsed)
start = datetime.datetime.now()
filtered_table, headers, sample_labels = otu_table.aggregate_otu_table_at_taxonomic_level(otu_table.get_table(), otu_table.headers,
otu_table.sample_labels, user_request)
filtered_table, headers, sample_labels = otu_table.filter_out_low_count_np(filtered_table, headers, sample_labels, user_request)
print(filtered_table.shape[0])
print(filtered_table.shape[1])
end = datetime.datetime.now()
elapsed = end - start
print(elapsed)
print("")
start = datetime.datetime.now()
filtered_table, headers, sample_labels = otu_table.aggregate_otu_table_at_taxonomic_level_np(otu_table.get_table(), otu_table.headers, otu_table.sample_labels, user_request)
filtered_table, headers, sample_labels = otu_table.filter_out_low_count_np(filtered_table, headers, sample_labels, user_request)
print(filtered_table.shape[0])
print(filtered_table.shape[1])
end = datetime.datetime.now()
elapsed = end - start
print(elapsed)
def get_filtering_info(self, pid, sampleFilter, sampleFilterRole,
sampleFilterVals):
"""
Returns information that will tell the user what samples will be removed and what the subsample value would be
:param pid:
:param sampleFilter:
:param sampleFilterRole:
:param sampleFilterVals:
:return:
"""
user_request = UserRequest(self.user_id, pid, "", "", "", "", [],
sampleFilter, sampleFilterRole,
sampleFilterVals, 0, "")
map = Map(self.user_id, pid)
table = OTUTable(self.user_id, pid, use_raw=True)
orig_base = table.get_table()
orig_headers = table.get_headers()
orig_sample_labels = table.get_sample_labels()
base, headers, sample_labels = table.filter_otu_table_by_metadata(
orig_base, orig_headers, orig_sample_labels, user_request)
initial_samples_removed = set(orig_sample_labels) - set(sample_labels)
has_float = map.matrix_type == "float"
orig_base = np.array(orig_base)
orig_row_sums = orig_base.sum(axis=1)
samples = {}
i = 0
while i < len(orig_base):
row_sum = orig_row_sums[i]
samples[orig_sample_labels[i]] = {
"row_sum": row_sum,
"removed": orig_sample_labels[i] in initial_samples_removed
}
i += 1
return {"samples": samples, "has_float": has_float}
def run(self, user_request):
table = OTUTable(user_request.user_id, user_request.pid)
base, headers, sample_labels = table.get_table_after_filtering_and_aggregation(
user_request)
return self.analyse(base, headers, sample_labels)