def run_stage1(params_dict):
imzXMLPath = params_dict.get('imzxmlpath')
imzBinPath = params_dict.get('imzbinpath')
logs_dir = params_dict.get('logsdir')
output_rdd = params_dict.get('outpathrdd')
output_matrix = params_dict.get('outputmatrix')
conf = SparkConf().set('spark.eventLog.enabled', 'true').set('spark.eventLog.dir', logs_dir).set('spark.driver.maxResultSize', '2g')
sc = SparkContext(appName='Loader', conf=conf)
dataset = converter(sc, imzXMLPath, imzBinPath, output_rdd)
rdd = dataset.spectra
dataset.save(output_rdd)
data = MSIDataset.load(sc, output_rdd)
mat = MSIMatrix(data)
non_zer = mat.nonzeros
rdd = non_zer.map(lambda x: ' ,'.join(str(ele) for ele in x)) # str(x[0]) +',' + str(x[1]) + ',' + str(x[2]) )
rdd.saveAsTextFile(output_matrix)
def run_stage4(params_dict):
#logs_dir = params_dict.get('logsdir')
column_leverage_score = params_dict.get('leveragescores')
on_rows = params_dict.get('on_rows')
sc = params_dict.get('sc')
mappings = params_dict.get('mappingsfile')
raw_rdd = params_dict.get('raw_rdd')
output = params_dict.get('spatialvals') if on_rows else params_dict.get(
'columnmzvals')
column_leverage_scores = sc.textFile(column_leverage_score).map(
lambda x: float(str(x)))
zipped = column_leverage_scores.zipWithIndex().map(lambda x: (x[1], x[0]))
mappings = sc.textFile(mappings).map(lambda x: map(int, x.split(','))).map(
lambda x: (x[1], x[0], 'zip'))
unioned = zipped.union(mappings)
rows_grouped = unioned.map(lambda x: (x[0], (x))).groupByKey().map(
lambda x: (x[0], list(x[1])))
new_ids = rows_grouped.map(lambda x: replace(x[1]))
data = MSIDataset.load(sc, raw_rdd)
mz_range = data.mz_range
xlen, ylen, tlen, mzlen = data.shape
if on_rows:
save_rdd = new_ids.map(
lambda x: (get_x(x[0], xlen), get_y(x[0], xlen), x[0], x[1]))
sorted_rdd = save_rdd.sortBy(lambda x: x[3], ascending=False)
else:
def f(xs):
mz_axis = get_mz_axis(mz_range)
for x in xs:
yield (get_t(x[0], tlen), get_mz(x[0], tlen),
transform_tomz(x[0], mz_axis, tlen), x[0], x[1])
get_t_mz = new_ids.mapPartitions(f)
sorted_rdd = get_t_mz.sortBy(lambda x: x[4], ascending=False)
formatted_vals = sorted_rdd.map(lambda x: ', '.join(str(i) for i in x))
formatted_vals.saveAsTextFile(output)
def run_stage1(params_dict):
imzXMLPath = params_dict.get('imzxmlpath')
imzBinPath = params_dict.get('imzbinpath')
logs_dir = params_dict.get('logsdir')
output_rdd = params_dict.get('outpathrdd')
output_matrix = params_dict.get('outputmatrix')
conf = SparkConf().set('spark.eventLog.enabled', 'true').set(
'spark.eventLog.dir', logs_dir).set('spark.driver.maxResultSize', '2g')
sc = SparkContext(appName='Loader', conf=conf)
dataset = converter(sc, imzXMLPath, imzBinPath, output_rdd)
rdd = dataset.spectra
dataset.save(output_rdd)
data = MSIDataset.load(sc, output_rdd)
mat = MSIMatrix(data)
non_zer = mat.nonzeros
rdd = non_zer.map(lambda x: ' ,'.join(str(ele) for ele in x)
) # str(x[0]) +',' + str(x[1]) + ',' + str(x[2]) )
rdd.saveAsTextFile(output_matrix)
def run_stage4(params_dict):
#logs_dir = params_dict.get('logsdir')
column_leverage_score = params_dict.get('leveragescores')
on_rows = params_dict.get('on_rows')
sc = params_dict.get('sc')
mappings = params_dict.get('mappingsfile')
raw_rdd = params_dict.get('raw_rdd')
output = params_dict.get('spatialvals') if on_rows else params_dict.get('columnmzvals')
column_leverage_scores = sc.textFile(column_leverage_score).map(lambda x: float(str(x)))
zipped = column_leverage_scores.zipWithIndex().map(lambda x:(x[1],x[0]))
mappings = sc.textFile(mappings).map(lambda x: map(int ,x.split(','))).map(lambda x:(x[1],x[0],'zip'))
unioned = zipped.union(mappings)
rows_grouped = unioned.map(lambda x:(x[0], (x))).groupByKey().map(lambda x:(x[0], list(x[1])))
new_ids = rows_grouped.map(lambda x: replace(x[1]))
data = MSIDataset.load(sc, raw_rdd)
mz_range = data.mz_range
xlen,ylen,tlen,mzlen = data.shape
if on_rows:
save_rdd = new_ids.map(lambda x: (get_x(x[0], xlen), get_y(x[0], xlen), x[0], x[1]))
sorted_rdd = save_rdd.sortBy(lambda x:x[3], ascending=False)
else:
def f(xs):
mz_axis = get_mz_axis(mz_range)
for x in xs:
yield (get_t(x[0], tlen),get_mz(x[0], tlen), transform_tomz(x[0], mz_axis, tlen), x[0],x[1])
get_t_mz = new_ids.mapPartitions(f)
sorted_rdd = get_t_mz.sortBy(lambda x:x[4], ascending=False)
formatted_vals = sorted_rdd.map(lambda x: ', '.join(str(i) for i in x))
formatted_vals.saveAsTextFile(output)
