def train_partition(idx, iterator):
port = 50000 + idx % 256
main = SparkFiles.get("main.py")
architecture = SparkFiles.get("train_val.prototxt")
model = SparkFiles.get("deepq16.caffemodel")
solver = SparkFiles.get("solver.prototxt")
root = SparkFiles.getRootDirectory()
dset = os.path.join(root, "dset-%02d.hdf5" % idx)
flag_file = "flags/__BARISTA_READY__.%d" % port
if os.path.isfile(flag_file):
os.remove(flag_file)
# out = open(os.path.join(root, "barista.log"), 'w')
subprocess.Popen([
"python", main, architecture, model, "--dataset", dset, "--solver",
solver, "--dset-size", "30000", "--initial-replay", "20000", "--debug",
"--overwrite", "--port",
str(port)
])
while not os.path.isfile(flag_file):
pass
for step in iterator:
dc = DummyClient("127.0.0.1", port)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
yield response
def test_transform_data(self):
"""
Function that checks all the main process functionality based on check:
:num rows
:num of columns
:column names
"""
spark, sc = init_spark()
file_input_path = getcwd() + "/test_data.csv"
file_compare_path = getcwd() + "/output_test"
sc.addFile(file_input_path, True)
df = spark.read.option("header", "true").option(
"delimiter",
";").schema(get_schema()).load(SparkFiles.get(file_input_path),
format="csv")
df = df.withColumn("country", lit("country_test"))
df = process_logic(df)
sc.addFile(file_compare_path, True)
df_compare = spark.read.parquet(SparkFiles.get(file_compare_path))
expected_cols = len(df_compare.columns)
expected_rows = df_compare.count()
cols = len(df.columns)
rows = df.count()
self.assertEqual(expected_cols, cols)
self.assertEqual(expected_rows, rows)
self.assertTrue([col in df.columns for col in df_compare.columns])
def predict(self, X):
""" Assumes X is an RDD or a list of (data, label) minibatch tuples."""
if isinstance(X, RDD):
# Distribute files
X.context.addFile(self._solver_filename)
X.context.addFile(self._architecture_filename)
X.mapPartitions(self.predict)
solver_filename = \
SparkFiles.get(self._solver_filename.rsplit('/', 1)[-1])
architecture_filename = \
SparkFiles.get(self._architecture_filename.rsplit('/', 1)[-1])
# Might need to modify path to architecture file inside solver file.
# Maybe we should do this before shipping the file since all Spark
# tmp directories will be identically named.
net = SGDSolver(solver_filename).net
for minibatch_data, minibatch_label in X:
# TODO: update function call for latest Caffe
net.set_input_arrays(minibatch_data, minibatch_label,
self.input_index)
output = net.forward(end=self.score_blob)
scores = output[self.score_blob]
pred = np.argmax(scores, axis=1).squeeze()
yield pred
def start_spark(app_name='my_spark_app',
master='local[*]',
files=[],
spark_config={}):
spark_builder = (SparkSession.builder.master(master).config(
"spark.driver.extraClassPath",
"C:\\Users\\Saumya.Sahu\\Downloads\\Microsoft JDBC Driver 6.0 for SQL Server\\sqljdbc_6.0\\enu\\jre8\\sqljdbc42.jar"
).appName(app_name))
spark_files = ','.join(list(files))
spark_builder.config('spark.files', spark_files)
for key, val in spark_config.items():
spark_builder.config(key, val)
spark_sess = spark_builder.getOrCreate()
spark_files_dir = SparkFiles.getRootDirectory()
config_f = SparkFiles.get('Config/etl_config.json')
config_files = [
filename for filename in listdir(spark_files_dir)
if filename.endswith('config.json')
]
if config_files:
path_to_config_file = path.join(spark_files_dir, config_files[0])
with open(path_to_config_file, 'r') as config_file:
config_dict = json.load(config_file)
print(config_file)
else:
config_dict = None
return spark_sess, config_dict
def train_partition(idx, iterator):
port = 50000 + idx % 256
main = SparkFiles.get("main.py")
architecture = SparkFiles.get("train_val.prototxt")
model = SparkFiles.get("deepq16.caffemodel")
solver = SparkFiles.get("solver.prototxt")
root = SparkFiles.getRootDirectory()
dset = os.path.join(root, "dset-%02d.hdf5" % idx)
flag_file = "flags/__BARISTA_READY__.%d" % port
if os.path.isfile(flag_file):
os.remove(flag_file)
# out = open(os.path.join(root, "barista.log"), 'w')
subprocess.Popen(["python", main, architecture, model,
"--dataset", dset,
"--solver", solver,
"--dset-size", "30000",
"--initial-replay", "20000",
"--debug",
"--overwrite",
"--port", str(port)])
while not os.path.isfile(flag_file):
pass
for step in iterator:
dc = DummyClient("127.0.0.1", port)
dc.send(barista.GRAD_UPDATE)
response = dc.recv()
yield response
def spark_mapper(current_range):
"""
Gets the paths to the file(s) in the current executor, then
declares the headers found.
Args:
current_range (tuple): A pair that contains the starting and
ending values of the current range.
Returns:
function: The map function to be executed on each executor,
complete with all headers needed for the analysis.
"""
# Get and declare headers on each worker
headers_on_executor = [
SparkFiles.get(ntpath.basename(filepath))
for filepath in includes_headers
]
Utils.declare_headers(headers_on_executor)
# Get and declare shared libraries on each worker
shared_libs_on_ex = [
SparkFiles.get(ntpath.basename(filepath))
for filepath in includes_shared_libraries
]
Utils.declare_shared_libraries(shared_libs_on_ex)
return mapper(current_range)
def processTweets(_, lines):
import re
import pyproj
import rtree
import shapely.geometry as geom
separator = re.compile('\W+')
proj = pyproj.Proj(init="epsg:5070", preserve_units=True)
index, zones = createIndex(SparkFiles.get('500cities_tracts.geojson'))
counts = {}
drugs1 = SparkFiles.get('drug_illegal.txt')
drugs2 = SparkFiles.get('drug_sched2.txt')
terms = set(map(lambda x: x.strip(),
(open(drugs1, 'r').readlines()+
open(drugs2, 'r').readlines())))
for line in lines:
fields = line.split('|')
lat,lon,body = fields[1], fields[2], fields[-1]
words = separator.split(body.lower())
for words in terms:
if len(terms.intersection(words)) >= len(words):
p = geom.Point(proj(lon,lat))
match = None
try:
zone = findZone(p, index, zones)
except:
continue
if zone:
if zone[1] > 0:
counts[zone[0]] = counts.get(zone[0], 0) + (1.0 / zone[1])
return counts.items()
def predict(self, X):
""" Assumes X is an RDD or a list of (data, label) minibatch tuples."""
if isinstance(X, RDD):
# Distribute files
X.context.addFile(self._solver_filename)
X.context.addFile(self._architecture_filename)
X.mapPartitions(self.predict)
solver_filename = \
SparkFiles.get(self._solver_filename.rsplit('/', 1)[-1])
architecture_filename = \
SparkFiles.get(self._architecture_filename.rsplit('/', 1)[-1])
# Might need to modify path to architecture file inside solver file.
# Maybe we should do this before shipping the file since all Spark
# tmp directories will be identically named.
net = SGDSolver(solver_filename).net
for minibatch_data, minibatch_label in X:
# TODO: update function call for latest Caffe
net.set_input_arrays(minibatch_data,
minibatch_label,
self.input_index)
output = net.forward(end=self.score_blob)
scores = output[self.score_blob]
pred = np.argmax(scores, axis=1).squeeze()
yield pred
def crfexec(sc, inputFilename, outputDirectory,
limit=LIMIT, location='hdfs', outputFormat="text", partitions=None):
crfConfigDir = os.path.join(os.path.dirname(__file__), "data/config")
crfExecutable = "/usr/local/bin/crf_test"
crfModelFilename = os.path.join(crfConfigDir, "dig-hair-eye-train.model")
rdd_pipeinput = sc.textFile(inputFilename)
rdd_pipeinput.setName('rdd_pipeinput')
# rdd_pipeinput.persist()
# DON'T USE SparkFiles.get to fetch the crf_test or model
# This only works with local Spark (--master local[*])
if location == 'hdfs':
cmd = "%s -m %s" % (os.path.basename(crfExecutable), os.path.basename(crfModelFilename))
elif location == 'local':
cmd = "%s -m %s" % (SparkFiles.get(os.path.basename(crfExecutable)), SparkFiles.get(os.path.basename(crfModelFilename)))
print "### %s" % cmd
rdd_crf = rdd_pipeinput.pipe(cmd)
rdd_final = rdd_crf
if outputFormat == "sequence":
rdd_final.saveAsSequenceFile(outputDirectory)
elif outputFormat == "text":
rdd_final.saveAsTextFile(outputDirectory)
else:
raise RuntimeError("Unrecognized output format: %s" % outputFormat)
def main(
toxcast: str,
output: str,
adverse_events: str,
safety_risk: str,
log_file: Optional[str] = None,
):
"""
This module puts together data from different sources that describe target safety liabilities.
Args:
adverse_events: Input TSV containing adverse events associated with targets that have been collected from relevant publications. Fetched from GitHub.
safety_risk: Input TSV containing cardiovascular safety liabilities associated with targets that have been collected from relevant publications. Fetched from GitHub.
toxcast: Input table containing biological processes associated with relevant targets that have been observed in toxicity assays.
output: Output gzipped json file following the target safety liabilities data model.
log_file: Destination of the logs generated by this script. Defaults to None.
"""
# Logger initializer. If no log_file is specified, logs are written to stderr
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
)
if log_file:
logging.config.fileConfig(filename=log_file)
else:
logging.StreamHandler(sys.stderr)
# Initialize spark context
global spark
spark = initialize_spark()
spark.sparkContext.addFile(adverse_events)
spark.sparkContext.addFile(safety_risk)
logging.info('Remote files successfully added to the Spark Context.')
# Load and process the input files into dataframes
ae_df = process_adverse_events(
SparkFiles.get(adverse_events.split('/')[-1]))
sr_df = process_safety_risk(SparkFiles.get(safety_risk.split('/')[-1]))
toxcast_df = process_toxcast(toxcast)
logging.info('Data has been processed. Merging...')
# Combine dfs and group evidence
safety_df = (
# dfs are combined; unionByName is used instead of union to address for the differences in the schemas
ae_df.unionByName(sr_df, allowMissingColumns=True).unionByName(
toxcast_df, allowMissingColumns=True))
# Write output
logging.info('Evidence strings have been processed. Saving...')
write_evidence_strings(safety_df, output)
logging.info(
f'{safety_df.count()} evidence of safety liabilities have been saved to {output}. Exiting.'
)
return 0
def ship_prototxt_to_data(self, rdd):
rdd.context.addFile(self._solver_filename)
rdd.context.addFile(self._architecture_filename)
solver_filename = \
SparkFiles.get(self._solver_filename.rsplit('/', 1)[-1])
architecture_filename = \
SparkFiles.get(self._architecture_filename.rsplit('/', 1)[-1])
return solver_filename, architecture_filename
def ship_prototxt_to_data(self, rdd):
rdd.context.addFile(self._solver_filename)
rdd.context.addFile(self._architecture_filename)
solver_filename = \
SparkFiles.get(self._solver_filename.rsplit('/', 1)[-1])
architecture_filename = \
SparkFiles.get(self._architecture_filename.rsplit('/', 1)[-1])
return solver_filename, architecture_filename
def start_spark(app_name='my_spark_app', master='local[*]', jar_packages=[],
files=[], spark_config={}):
# detect execution environment
flag_repl = not(hasattr(__main__, '__file__'))
flag_debug = 'DEBUG' in environ.keys()
if not (flag_repl or flag_debug):
spark_builder = (
SparkSession
.builder
.appName(app_name))
else:
spark_builder = (
SparkSession
.builder
.master(master)
.appName(app_name))
spark_files = ','.join(list(files))
spark_builder.config('spark.files', spark_files)
# add other config params
for key, val in spark_config.items():
spark_builder.config(key, val)
# create session and retrieve Spark logger object
spark_sess = spark_builder.getOrCreate()
spark_logger = logging.Log4j(spark_sess)
# get config file if sent to cluster with --files
spark_files_dir = SparkFiles.getRootDirectory()
spark_logger.warn('spark_files_dir' + str(spark_files_dir))
for filename in listdir(spark_files_dir):
spark_logger.warn('filename' + str(filename))
config_f = SparkFiles.get('configs/etl_config.json')
spark_logger.warn('config_f' + str(config_f))
config_files = [filename
for filename in listdir(spark_files_dir)
if filename.endswith('config.json')]
spark_logger.warn('config_files' + str(config_files))
if config_files:
path_to_config_file = path.join(spark_files_dir, config_files[0])
with open(path_to_config_file, 'r') as config_file:
config_dict = json.load(config_file)
spark_logger.warn('loaded config from ' + config_files[0])
else:
spark_logger.warn('no config file found')
config_dict = None
return spark_sess, spark_logger, config_dict
def compute_buried_area(pdb_complex):
chZ = "chZ"
sasa_complex = -1.0
sasa_rec = -1.0
sasa_lig = -1.0
buried_total = -1.0
base_name = get_name_model_pdb(pdb_complex)
ligand_name = get_ligand_from_receptor_ligand_model(base_name)
f_pdb_ligand_no_docking = os.path.join(pdb_ligand_path.value,ligand_name+".pdb")
f_ndx = os.path.join(path_analysis_pdb_complex_b.value,base_name+".ndx")
f_temp_sasa_complex = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_complex.xvg")
f_temp_sasa_rec = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_rec.xvg")
f_temp_sasa_lig = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_lig.xvg")
# Makes the index file with the ligand (chain z) and the rest (non chain z)
script_make_ndx = SparkFiles.get("make_ndx_buried_area_total.sh") #Getting bash script that was copied by addFile command
command = script_make_ndx + " " + gromacs_path.value + " "+ pdb_complex + " "+ f_ndx
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
command = gromacs_path.value +"gmx sasa -f " + pdb_complex + " -s " + pdb_complex + " -nopbc " + " -n " + f_ndx + " -surface System " + " -output System "+ " -xvg none " + " -o " + f_temp_sasa_complex
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
# Makes f_temp_sasa_rec file
script_make_sasa_rec = SparkFiles.get("make_sasa_rec_buried_area_total.sh") #Getting bash script that was copied by addFile command
command = script_make_sasa_rec + " " + gromacs_path.value + " "+ pdb_complex + " "+ f_ndx + " " + f_temp_sasa_rec
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
command = gromacs_path.value +"gmx sasa -f " + pdb_complex + " -s " + pdb_complex + " -nopbc " + " -n " + f_ndx + " -surface chZ " + " -output chZ "+ " -xvg none " + " -o " + f_temp_sasa_lig
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
sasa_complex = get_value_from_xvg_sasa(f_temp_sasa_complex)
sasa_rec = get_value_from_xvg_sasa(f_temp_sasa_rec)
sasa_lig = get_value_from_xvg_sasa(f_temp_sasa_lig)
buried_total = sasa_rec + sasa_lig - sasa_complex
#Generating result - See column sorting because resultaed file will be created based on this sorting
returned_list = (base_name, buried_total)
#Deleting files
os.remove(f_ndx)
os.remove(f_temp_sasa_complex)
os.remove(f_temp_sasa_rec)
os.remove(f_temp_sasa_lig)
return returned_list
class Dictionary(object):
automaton = Automaton()
with open(SparkFiles.get('dict_token.txt')) as f:
token_dict = f.read().split()
token_dictionary = [x.strip() for x in token_dict]
automaton.add_all(token_dictionary)
with open(SparkFiles.get('dict_garbage.txt')) as f:
garbage_dict = f.readlines()
garbage_dictionary = [x.strip() for x in garbage_dict]
garbage_dictionary.sort(key=lambda item: (-len(item), item))
def SparkFiles(self, file_path):
u""" 访问Spark作业文件
"""
# 获取添加文件
SparkFiles.get(file_path)
# 获取包含添加文件的根目录
SparkFiles.getRootDirectory()
with open(SparkFiles.get(file_path)) as f:
rows = f.readlines()
for row in rows:
print row
return rows
def start_spark(app_name="my_spark_app",
master="local[*]",
files=['etl_conf.json']):
flag_repl = not (hasattr(__main__, '__file__'))
flag_debug = 'DEBUG' in environ.keys()
if not (flag_repl or flag_debug):
spark_builder = (SparkSession.builder.appName(app_name))
else:
spark_builder = SparkSession.builder.appName(app_name).master(master)
spark_files = '.'.join(list(files))
spark_builder.config('spark.files', spark_files)
spark_builder.config(conf=SparkConf())
spark_sess = spark_builder.getOrCreate()
#spark_logger=logger.Log4j(spark_sess)
spark_files_dir = SparkFiles.getRootDirectory()
config_files = [
x for x in listdir(spark_files_dir) if x.endswith('conf.json')
]
if config_files:
path_to_config_file = path.join(spark_files_dir, config_files[0])
with open(path_to_config_file, 'r') as f:
config_dict = json.load(f)
else:
config_dict = None
return spark_sess, config_dict
def read_gene_burden_curation(curated_data: str, spark_instance: SparkSession) -> DataFrame:
"""Read manual gene burden curation from remote to a Spark DataFrame."""
schema = StructType(
[
StructField('projectId', StringType(), True),
StructField('targetFromSource', StringType(), True),
StructField('targetFromSourceId', StringType(), True),
StructField('diseaseFromSource', StringType(), True),
StructField('diseaseFromSourceMappedId', StringType(), True),
StructField('resourceScore', DoubleType(), True),
StructField('pValueMantissa', DoubleType(), True),
StructField('pValueExponent', IntegerType(), True),
StructField('oddsRatio', DoubleType(), True),
StructField('ConfidenceIntervalLower', DoubleType(), True),
StructField('ConfidenceIntervalUpper', DoubleType(), True),
StructField('beta', DoubleType(), True),
StructField('ancestry', StringType(), True),
StructField('ancestryId', StringType(), True),
StructField('cohortId', StringType(), True),
StructField('studyId', StringType(), True),
StructField('studySampleSize', IntegerType(), True),
StructField('studyCases', IntegerType(), True),
StructField('studyCasesWithQualifyingVariants', IntegerType(), True),
StructField('allelicRequirements', StringType(), True),
StructField('statisticalMethod', StringType(), True),
StructField('statisticalMethodOverview', StringType(), True),
StructField('literature', StringType(), True),
StructField('url', StringType(), True),
]
)
spark.sparkContext.addFile(curated_data)
return spark_instance.read.csv(SparkFiles.get(curated_data.split('/')[-1]), sep='\t', header=True, schema=schema)
def read_source_data(self):
print("Log Step 1: Reading Source Data")
try:
if "json" in self.filename:
with open(self.filename, newline=''):
file_path = dir_path + "/" + self.filename
disticts_df = spark.read.json(SparkFiles.get(file_path))
return (disticts_df)
elif "log" in self.filename:
list_final = []
with open(self.filename) as f:
for line in f:
list_a = json.loads(line)
list_final.append(list_a)
drivers_df = pd.DataFrame(list_final)
drivers_df = spark.createDataFrame(drivers_df,
schema=driverSchema)
return (drivers_df)
except TestFailed as message:
print("Entered exception")
print(message)
email = Email(message)
email.send_email()
def transformAndMask(spark, df_valid, sensitive_words):
with open(SparkFiles.get(sensitive_words)) as f:
fieldnames = f.read().split('\n')
for fieldname in fieldnames:
if (fieldname) :
df_valid = func1(fieldname, df_valid)
return df_valid
def get_cached_dataset():
"""
Returns a dataset of missense mutations in dbSNP mapped to UniProt and PDB residue positions.
The dataset contains the following columns:
+---+---------+---------+------------+---------+----------+----------+----------+---------+-------+-------+ ...
|chr| pos| snp_id| master_acc|master_gi|master_pos|master_res|master_var| pdb_gi|pdb_res|pdb_pos|
+---+---------+---------+------------+---------+----------+----------+----------+---------+-------+-------+ ...
| 4| 79525461|764726341| NP_005130| 4826643| 274| R| *|157829892| R| 274|
| 4| 79525462|771966889| NP_005130| 4826643| 274| R| P|157829892| R| 274| ...
... +-----------+--------------------+----------+------+---------+---------+----------+
|blast_ident| clinsig|pdbChainId|tax_id|pdbResNum|uniprotId|uniprotNum|
... +-----------+--------------------+----------+------+---------+---------+----------+
| 100.0| null| 1AII.A| 9606| 275| P12429| 274|
... | 100.0| null| 1AII.A| 9606| 275| P12429| 274|
Reference:
dbSNP: https://www.ncbi.nlm.nih.gov/projects/SNP/
:return: dataset of missense mutations in dbSNP
"""
spark = SparkSession.builder.getOrCreate()
# download cached dataset
spark.sparkContext.addFile(CACHED_FILE_URL)
# read dataset
spark.conf.set("spark.sql.orc.impl", "native")
return spark.read.orc(SparkFiles.get(FILENAME))
def test_add_file_locally(self):
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
self.sc.addFile(path)
download_path = SparkFiles.get("hello.txt")
self.assertNotEqual(path, download_path)
with open(download_path) as test_file:
self.assertEqual("Hello World!\n", test_file.readline())
def start_spark(app, files=[], pyfiles=[]):
## Spark Context ##
#conf = SparkConf().setAppName("AppSparkContext").set("spark.files","etl_config.json").toDebugString()
#sc = SparkContext(conf=conf)
#warehouse_location = abspath('hdfs://localhost:9001/lake/files')
## Spark Session ##
spark_builder = SparkSession.builder.appName(app).master('local[*]')
spark_builder.config(
'spark.files',
"SparkFinal/configs/etl_config.json,/usr/local/Cellar/hive/2.1.0/libexec/conf/hive-site.xml"
)
#spark_builder.config('spark.logConf','true')
#spark_builder.config('spark.jars.repositories','/Users/kkartikgoel/dev/spark-2.1.0-bin-hadoop2.7/jars')
#spark_builder.config('spark.jars.packages','com.databricks:spark-avro_2.10:1.0.0')
#spark_builder.config('spark.jars.packages','com.databricks:spark-avro_2.10:1.0.0')
#spark_builder.config('hive.metastore.uris','thrift://localhost:9083')
spark_sess = spark_builder.enableHiveSupport().getOrCreate()
##properties
spark_conf_list = spark_sess.sparkContext.getConf().getAll()
for key, val in spark_conf_list:
print key + "=" + val
#spark_sess.sparkContext.getConf().contains("spark.files")
#spark_sess.conf.get("spark.files")
print "Spark WebURL= %s" % spark_sess.sparkContext.uiWebUrl
## Spark Files ##
spark_files_dir = SparkFiles.getRootDirectory()
print "spark_files_dir= %s" % spark_files_dir
print "file_in_Spark_dir= %s" % os.listdir(spark_files_dir)
spark_sess.sql("SET -v").show()
spark_logger = logging.Log4j(spark_sess)
return spark_sess, spark_files_dir, spark_logger
def run_etl(source, output_path, spark=None):
"""
Run Spark ETL of source file.
:params source (string) - name of source type (should be module in intake/sources/)
:param output_path (string) - where to write parquet output
:params spark - spark context
"""
if not spark:
spark = SparkSession.builder.getOrCreate()
config = yaml.safe_load(
pkg_resources.resource_stream(f'intake.sources.{source}',
f'{source}_config.yml'))
file_path = config['source']
src_type = file_path.split('.')[-1]
header_keys = config['header_keys']
ignore_symbol = config['ignore_symbol']
spark.sparkContext.addFile(file_path)
data_path = SparkFiles.get(file_path.split('/')[-1])
rdd = spark.sparkContext.textFile(data_path)
# Use mapPartitions for structuring rows to only load
# keys once per partition. Alternatively, we can consider
# broadcasting the header_keys to workers...
# TODO - refactor column renames/yyyymmdd index creation as add more data sources...
df = rdd.mapPartitions(lambda partition: filter_helper(partition, header=','.join(list(header_keys.keys())), ignore_symbol=ignore_symbol)) \
.mapPartitions(lambda partition: structure_as_row(partition, header_keys, src_type)) \
.map(lambda Row: create_yyyymmdd_index(Row.asDict())).toDF() \
df = column_rename_factory(df, source)
df.write.mode("overwrite").parquet(
output_path) # Always overwrite with latest dataset
def privatize_example(example, local_vocab, local_embedding_dims,
local_epsilon):
from annoy import AnnoyIndex
# Load files
local_index = AnnoyIndex(local_embedding_dims, 'euclidean')
local_index.load(SparkFiles.get("index.ann"))
sensitive_phrases = [
x.strip() for x in clean_example(example) if x.strip()
]
privatized_phrases = []
for sensitive_phrase in sensitive_phrases:
privatized_words = []
for sensitive_word in sensitive_phrase.split(' '):
privatized_word = replace_word(sensitive_word, local_vocab,
local_epsilon, local_index,
local_embedding_dims)
privatized_words.append(privatized_word)
# Flatten nested list of words
privatized_phrases.append(itertools.chain(*[privatized_words]))
privatized_review = " ".join(list(itertools.chain(*privatized_phrases)))
privatized_row = "\"{}\",{}".format(privatized_review, example.sentiment)
return privatized_row
def partition_handle(mons):
from geoip2 import database
etcd_key = get_etcd_key(cfg)
def ip2subdivision(monitor):
try:
province_name = reader.city(
monitor['pubip']).subdivisions.most_specific.name
monitor['province'] = province_name or 'None'
identity_code = IDENTITY_CODE.get(province_name)
# 获取地区分组类别
monitor['group'] = etcd_key.get(identity_code, 'default')
except:
monitor['province'] = 'None'
monitor['group'] = 'default'
return monitor
def rtt2level(monitor):
for info in monitor.get('req_rtt', []):
info['rtt_level'] = rtt_level_map.get(
bisect.bisect_right(rtt_level_list, info['rtt']),
rtt_level_map[1])
return monitor
reader = database.Reader(SparkFiles.get(geo_db_path))
ip_res = [ip2subdivision(mon) for mon in mons]
rtt_level_res = [rtt2level(mon) for mon in ip_res]
return rtt_level_res
def get_spark_session(keyfile=None,
chdir=False,
gcs_temp_bucket=None) -> SparkSession:
conf = SparkConf().setAppName("Metric Engine").set("spark.scheduler.mode",
"FAIR")
if gcs_temp_bucket is not None:
conf = conf.set('temporaryGcsBucket', gcs_temp_bucket)
conf = conf.set(
"spark.hadoop.fs.gs.impl",
"com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem")
conf = conf.set("spark.hadoop.fs.AbstractFileSystem.gs.impl",
"com.google.cloud.hadoop.fs.gcs.GoogleHadoopFS")
conf = conf.set('spark.hadoop.fs.gs.auth.service.account.enable',
'true')
if keyfile is not None:
conf = conf.set(
'spark.hadoop.google.cloud.auth.service.account.json.keyfile',
keyfile)
spark = SparkSession.builder.config(
conf=conf).enableHiveSupport().getOrCreate()
if chdir:
os.chdir(SparkFiles.getRootDirectory())
return spark
def collect_docs(p, lang_detection_model_name=None, lang='en'):
if lang_detection_model_name != None:
from pyfasttext import FastText
model_path = SparkFiles.get(lang_detection_model_name)
model = FastText(model_path)
regex = re.compile(
r'^(?:http|ftp)s?://' # http:// or https://
r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain...
r'localhost|' #localhost...
r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip
r'(?::\d+)?' # optional port
r'(?:/?|[/?]\S+)$',
re.IGNORECASE)
result = []
lines = list(p)
indices = [i for i, line in enumerate(lines) if regex.search(line.strip())]
for i in range(0, len(indices)):
idx = indices[i]
content = lines[idx + 1]
paras = re.findall('<PAR>(.*?)</PAR>', content, re.DOTALL)
if model:
#filter only english paras
langs = model.predict(paras)
en_paras = list(filter(lambda p: lang in p[1], zip(paras, langs)))
paras = list(map(lambda pair: pair[0], en_paras))
if paras:
url = lines[idx].strip()
result.append((url, paras))
return result
def test_add_file_locally(self):
path = os.path.join(SPARK_HOME, "python/test_support/hello/hello.txt")
self.sc.addFile(path)
download_path = SparkFiles.get("hello.txt")
self.assertNotEqual(path, download_path)
with open(download_path) as test_file:
self.assertEqual("Hello World!\n", test_file.readline())
def load_params():
try:
import yaml
params = yaml.load(open(SparkFiles.get('params.yaml')))
except ImportError:
from params import params
return params
def _install_libs(run_id):
current_dir = os.getcwd()
base_dir = os.path.join(current_dir, run_id)
lib_dir = os.path.join(base_dir, 'python_libs')
lib_zip = SparkFiles.get("lib.zip")
lock_name = os.path.join(base_dir, '__lock__')
os.makedirs(base_dir, exist_ok=True)
for i in range(0, 100):
try:
lock_fh = os.open(lock_name, os.O_CREAT | os.O_EXCL | os.O_WRONLY)
os.close(lock_fh)
try:
if not os.path.isdir(lib_dir):
print("_install_libs: install lib starts")
os.makedirs(lib_dir)
subprocess.check_call(['unzip', "-qq", lib_zip, "-d", lib_dir])
print("_install_libs: install lib done")
if lib_dir not in sys.path:
print(f"_install_libs: add {lib_dir} path")
sys.path.insert(0, lib_dir)
return
finally:
os.remove(lock_name)
except OSError as e:
if e.errno == errno.EEXIST:
time.sleep(random.randint(1, 10))
continue
raise
raise Exception("Failed to install libraries!")
def get_sentences(s, prop):
import nltk
nltk.data.path.append(SparkFiles.get("nltk_data"))
from nltk import ne_chunk, pos_tag, word_tokenize, sent_tokenize
from nltk.corpus import stopwords
text = s[prop]
if "_____" in text:
text = text.split("_____")[0]
if "Hello, users of CMV!" in text:
text = text.split("Hello, users of CMV!")[0]
sentences = sent_tokenize(text)
sentences_processed = []
for se in sentences:
stops = stopwords.words('english') + list(string.punctuation)
wo_stops = " ".join(
[word for word in re.findall('\w+', se) if word not in stops])
sentences_processed.append(wo_stops)
s['sentences'] = sentences_processed
entities = []
tokenized = word_tokenize(text)
chunks = ne_chunk(pos_tag(tokenized))
for c in chunks.subtrees():
if c.label() in ('PERSON', 'GPE'):
entities.append(" ".join(w for w, t in c))
s['ne'] = entities
return s
def send2monit(data):
"""
Helper function which wraps StompAMQ and incoming dataframe into
notification message. Then it sends it to AMQ end-point provided
by credentials file.
"""
if not StompAMQ:
return
# main function logic
with open(SparkFiles.get('amq_broker.json')) as istream:
creds = json.load(istream)
host, port = creds['host_and_ports'].split(':')
port = int(port)
amq = StompAMQ(creds['username'], creds['password'], \
creds['producer'], creds['topic'], \
validation_schema=None, \
host_and_ports=[(host, port)])
arr = []
for idx, row in enumerate(data):
# if not idx:
# print("### row", row, type(row))
doc = json.loads(row)
hid = doc.get("hash", 1)
arr.append(amq.make_notification(doc, hid))
amq.send(arr)
print("### Send %s docs to CERN MONIT" % len(arr))
def process(records):
import csv
reader = csv.reader(records)
counts = {}
streets_list = dict()
with open(SparkFiles.get("nyc_cscl.csv")) as csv_file:
tmp = csv.DictReader(csv_file, delimiter=',')
for item in tmp:
# print(item)
if item['FULL_STREE'] not in streets_list.keys():
streets_list[item['FULL_STREE']] = []
streets_list[item['FULL_STREE']].append([
item['PHYSICALID'], item['FULL_STREE'], item['ST_NAME'],
item['L_LOW_HN'], item['L_HIGH_HN'], item['R_LOW_HN'],
item['R_HIGH_HN']
])
print("!!!!!!!!!!!!!!")
print("!!!!!!!!!!!!!!")
print("!!!!!!!!!!!!!!")
print("!!!!!!!!!!!!!!")
for row in reader:
county = row[0]
num = row[1]
st = row[2]
zoneid = find_id(num, st, streets_list)
if zoneid:
counts[zoneid] = counts.get(zoneid, 0) + 1
return counts.items()
def start_spark(app_name='my_spark_app', master='local[*]', jar_packages=[],
files=[], spark_config={}):
spark_builder = (SparkSession.builder.appName(app_name))
# create Spark JAR packages string
spark_jars_packages = ','.join(list(jar_packages))
spark_builder.config('spark.jars.packages', spark_jars_packages)
spark_files = ','.join(list(files))
spark_builder.config('spark.files', spark_files)
# add other config params
for key, val in spark_config.items():
spark_builder.config(key, val)
# create session and retrieve Spark logger object
spark_sess = spark_builder.getOrCreate()
spark_logger = log.Log4j(spark_sess)
# get config file if sent to cluster with --files
spark_files_dir = SparkFiles.getRootDirectory()
config_files = [filename
for filename in listdir(spark_files_dir)
if filename.endswith('config.json')]
if config_files:
path_to_config_file = path.join(spark_files_dir, config_files[0])
with open(path_to_config_file, 'r') as config_file:
config_dict = json.load(config_file)
spark_logger.warn('loaded config from ' + config_files[0])
else:
spark_logger.warn('no config file found')
config_dict = None
return spark_sess, spark_logger, config_dict
def get_sentences(s, prop):
import nltk
nltk.data.path.append(SparkFiles.get("nltk_data"))
from nltk import ne_chunk, pos_tag, word_tokenize, sent_tokenize
from nltk.corpus import stopwords
try:
text = s[prop]
except Exception as e:
print(s)
s['sentences'] = []
return s
if "_____" in text:
text = text.split("_____")[0]
if "Hello, users of CMV!" in text:
text = text.split("Hello, users of CMV!")[0]
sentences = sent_tokenize(text)
sentences_processed = []
for se in sentences:
stops = stopwords.words('english') + list(string.punctuation)
wo_stops = " ".join(
[word for word in re.findall('\w+', se) if word not in stops])
sentences_processed.append(wo_stops)
s['sentences'] = sentences_processed
return s
def load_timestep(timestep):
path = data_path
if download or config.copy_local:
path = SparkFiles.get('pr_amon_BCSD_rcp26_r1i1p1_CONUS_bcc-csm1-1_202101-202512.nc')
data = Dataset(path)
pr = data.variables['pr']
step = pr[timestep]
# Return valid values
return (timestep, step[~step.mask])
def spawn_barista(partition):
main = SparkFiles.get("main.py")
architecture = SparkFiles.get("train_val.prototxt")
model = SparkFiles.get("deepq16.caffemodel")
solver = SparkFiles.get("solver.prototxt")
root = SparkFiles.getRootDirectory()
dset = os.path.join(root, "dset.hdf5")
flag_file = "flags/__BARISTA_READY__"
if os.path.isfile(flag_file):
os.remove("flags/__BARISTA_READY__")
out = open(os.path.join(root, "barista.log"), 'w')
subprocess.Popen(["python", main, architecture, model,
"--dataset", dset,
"--solver", solver],
stdout=out,
stderr=subprocess.STDOUT)
while not os.path.isfile("flags/__BARISTA_READY__"):
pass
def partitionIp2city(iter):
from geoip2 import database
def ip2city(ip):
try:
city = reader.city(ip).city.name
except:
city = 'not found'
return city
reader = database.Reader(SparkFiles.get(geoDBpath))
#return [ip2city(ip) for ip in iter]
return ip2city(iter)
def main(sc):
sqlContext = SQLContext(sc)
df = sqlContext.jsonFile(DATA_PATH)
#add the filter file
sc.addFile(FILTER_TERMS_FILE_PATH)
filter_terms = sc.textFile(SparkFiles.get("freebase-symptoms-just-terms.txt"))
global filter_terms_set_bc
filter_terms_set_bc = sc.broadcast(Set(filter_terms.collect()))
# Register the DataFrame as a table.
df.registerTempTable("tweet")
results = sqlContext.sql("SELECT id,user.id,user.lang,created_at, coordinates,text FROM tweet where user.lang='en'")
#filter tweets to find health related tweets
filter_health_tweets = results.rdd.filter(healthFilter)
filter_health_tweets.mapPartitions(writeRecords).saveAsTextFile("output/")
def compute_buried_area_ligand(pdb_complex):
chZ = "chZ"
buried_lig_rec_perc = -1.0
buried_lig_rec = -1.0
buried_lig_lig = -1.0
buried_lig_lig_perc = -1.0
base_name = get_name_model_pdb(pdb_complex)
ligand_name = get_ligand_from_receptor_ligand_model(base_name)
receptor_name = get_receptor_from_receptor_ligand_model(base_name)
pose = get_model_from_receptor_ligand_model(base_name)
pdb_before_vs = os.path.join(pdb_ligand_path.value,ligand_name+".pdb")
#ndx files
f_ndx = os.path.join(path_analysis_pdb_complex_b.value,base_name+".ndx")
#xvg files
xvg_temp_sasa_lig_pose = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_lig_pose"+".xvg")
xvg_temp_sasa_lig_complex = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_lig_complex"+".xvg")
xvg_temp_sasa_lig_min = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_sasa_lig_min"+".xvg")
# Creates a selection with the residues that are closer than 6A to the ligand
script_make_ndx_buried_area_ligand = SparkFiles.get("make_ndx_buried_area_ligand.sh") #Getting bash script that was copied by addFile command
command = script_make_ndx_buried_area_ligand + " " + gromacs_path.value + " "+ pdb_complex + " "+ f_ndx + " "+ xvg_temp_sasa_lig_pose + " "+ str(probe.value) + " "+ str(ndots.value) + " "+ xvg_temp_sasa_lig_complex + " "+ pdb_before_vs + " "+ xvg_temp_sasa_lig_min
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
try:
# SASA of the isolated ligand in the pose conformation
sasa_lig_pose = get_value_from_xvg_sasa(xvg_temp_sasa_lig_pose)
# SASA of the complexed ligand in the pose conformation
sasa_lig_complex = get_value_from_xvg_sasa(xvg_temp_sasa_lig_complex)
# SASA of the isolated ligand in its energy-minimized conformation. Only for carbohydrates!
sasa_lig_min = get_value_from_xvg_sasa(xvg_temp_sasa_lig_min)
# Area of the ligand which is buried in the receptor
buried_lig_rec = sasa_lig_pose - sasa_lig_complex
buried_lig_rec_perc = buried_lig_rec / sasa_lig_pose
# Area of the ligand in the pose conformation which is buried in itself when compared to the energy-minimized conformation
buried_lig_lig = sasa_lig_min - sasa_lig_pose
buried_lig_lig_perc = buried_lig_lig / sasa_lig_min
returned_list = (base_name, buried_lig_rec, buried_lig_rec_perc, buried_lig_lig, buried_lig_lig_perc)
#Deleting files
os.remove(f_ndx)
os.remove(xvg_temp_sasa_lig_pose)
os.remove(xvg_temp_sasa_lig_complex)
os.remove(xvg_temp_sasa_lig_min)
return returned_list
except:
return (base_name, float(0.0), float(0.0), float(0.0), float(0.0))
def partition_processor(partitionlinechunks):
"""
Partition logic for pyspark parallel processing
"""
model_pipe_object = joblib.load(SparkFiles.get("mmp_phase1_D2.clf"))
def set_predictions(x):
segment = model_pipe_object.predict_proba(x)
return segment
df_with_nan = build_dataframe(partitionlinechunks)
df_with_newline = df_with_nan.replace(u"NULL", pd.np.nan)
behaviour_df = df_with_newline.replace(u"\\N", pd.np.nan)
predictions_ser = set_predictions(behaviour_df)
predictions_list = [value for value in [zip(predictions_ser.index, predictions_ser.loc[:,'A'], predictions_ser.loc[:,'Y'], predictions_ser.loc[:,'segment'], predictions_ser.loc[:,'model_version'])]]
return iter(predictions_list)
def load_matrix(
filename,
sc,
num_users=NUM_USER,
num_items=NUM_SONG
):
global alpha
global total
global num_zeros
print 'Start to load matrix...'
t0 = time.time()
counts = np.zeros((num_users, num_items))
total = 0.0
num_zeros = num_users * num_items
url = "s3n://spark-mllib/fastcode/data/" + filename
# url = "hdfs://localhost:9000/data/" + filename
print 'loading... ' + url
# data = sc.textFile(url)
# data.map(lambda l: fill_maxtrix(l, counts))
sc.addFile(url)
with open(SparkFiles.get(filename)) as f:
for line in f:
fill_maxtrix(line, counts)
alpha = num_zeros / total
print 'alpha %.2f' % alpha
counts *= alpha
t1 = time.time()
print 'Finished loading matrix in %f seconds\n' % (t1 - t0)
print 'Total entry:', num_users * num_items
print 'Non-zeros:', num_users * num_items - num_zeros
counts = sparse.csr_matrix(counts)
return counts, num_users * num_items - num_zeros
distScript = os.getcwd()+"/src/R/finddistance.R"
distScriptName = "finddistance.R"
sc.addFile(distScript)
def hasDistInfo(call):
"""Verify that a call has the fields required to compute the distance"""
requiredFields = ["mylat", "mylong", "contactlat", "contactlong"]
return all(map(lambda f: call[f], requiredFields))
def formatCall(call):
"""Format a call so that it can be parsed by our R program"""
return "{0},{1},{2},{3}".format(
call["mylat"], call["mylong"],
call["contactlat"], call["contactlong"])
pipeInputs = contactsContactList.values().flatMap(
lambda calls: map(formatCall, filter(hasDistInfo, calls)))
distances = pipeInputs.pipe(SparkFiles.get(distScriptName))
print distances.collect()
# Convert our RDD of strings to numeric data so we can compute stats and
# remove the outliers.
distanceNumerics = distances.map(lambda string: float(string))
stats = distanceNumerics.stats()
stddev = stats.stdev()
mean = stats.mean()
reasonableDistances = distanceNumerics.filter(
lambda x: math.fabs(x - mean) < 3 * stddev)
print reasonableDistances.collect()
def driver(sc, inputFilename, outputDirectory,
crfExecutable, crfScript,
featureListFilename, crfModelFilename,
eyeColorRef, eyeColorConfig, hairRef, hairConfig,
limit=limit, location='hdfs', outputFormat="text", partitions=None):
dump = False
partitions = None
# Program to compute CRF++
c = crf_features.CrfFeatures(featureListFilename)
# Add files to be downloaded with this Spark job on every node.
sc.addFile(crfExecutable)
sc.addFile(crfScript)
sc.addFile(crfModelFilename)
# Map to reference sets
smEyeColor = HybridJaccard(ref_path=eyeColorRef, config_path=eyeColorConfig)
smHairColor = HybridJaccard(ref_path=hairRef, config_path=hairConfig)
if location == "hdfs":
print "We want to do hdfs dfs -rm -r %s" % outputDirectory
elif location == "local":
try:
shutil.rmtree(outputDirectory)
print "rmtree %s" % outputDirectory
except:
pass
else:
raise RuntimeError("No such location: %s" % location)
rdd_sequence_file_input = sc.sequenceFile(inputFilename)
rdd_sequence_file_input.setName('rdd_sequence_file_input')
# rdd_sequence_file_input.persist()
origSize = rdd_sequence_file_input.count()
# if limit:
# rdd = sc.parallelize(rdd_sequence_file_input.take(limit))
if partitions:
rdd_sequence_file_input = rdd_sequence_file_input.repartition(partitions)
print "### input %s: %d ads (orig %s, limit was %s), %d partitions" % (inputFilename, rdd_sequence_file_input.count(), origSize, limit, rdd_sequence_file_input.getNumPartitions())
rdd_json = rdd_sequence_file_input.mapValues(lambda x: json.loads(x))
rdd_json.setName('rdd_json')
# rdd_json.persist()
# all below should also be done for title
rdd_body = rdd_json.mapValues(lambda x: extract_body(x))
rdd_body.setName('rdd_body')
# rdd_body.persist()
if dump:
rdd_body.saveAsTextFile(ff("body"))
rdd_body_tokens = rdd_body.mapValues(lambda x: textTokens(x))
rdd_body_tokens.setName('rdd_body_tokens')
# rdd_body_tokens.persist()
if dump:
rdd_body_tokens.saveAsTextFile(ff("body_tokens"))
rdd_features = rdd_body_tokens.map(lambda x: (x[0], c.computeFeatMatrix(x[1], False, addLabels=[x[0]], addIndex=True)))
rdd_features.setName('rdd_features')
# rdd_features.persist()
if dump:
rdd_features.saveAsTextFile(ff("features"))
# rdd_pipeinput = rdd_features.mapValues(lambda x: base64.b64encode(vectorToString(x)))
rdd_pipeinput = rdd_features.mapValues(lambda x: vectorToString(x))
rdd_pipeinput.setName('rdd_pipeinput')
# rdd_pipeinput.persist()
if dump:
rdd_pipeinput.values().saveAsTextFile(ff("pi"))
# This caused a cannot concatenate string + None error
# rdd_pipeinput.saveAsTextFile(outputDirectory + "-pipeinput")
# DON'T USE SparkFiles.get to fetch the crf_test or model
# This only works with local Spark (--master local[*])
if location == 'hdfs':
cmd = "%s %s" % (os.path.basename(crfScript), os.path.basename(crfModelFilename))
elif location == 'local':
cmd = "%s %s" % (SparkFiles.get(os.path.basename(crfScript)), SparkFiles.get(os.path.basename(crfModelFilename)))
print "### %s" % cmd
rdd_pipeinput.saveAsTextFile(ff("before"))
exit(0)
rdd_crf_b64 = rdd_pipeinput.values().pipe(cmd)
rdd_crf_b64.setName('rdd_crf_b64')
# rdd_crf_b64.persist()
if dump:
rdd_crf_b64.saveAsTextFile(ff("po"))
# Go directly from base64 output to a reconstructed tuple format mapping URI to vector of vectors,
# with empty string suffix indicating blank line
# This is key for avoiding the groupBy step
rdd_restore = rdd_crf_b64.map(lambda x: restore(x))
rdd_restore.setName('rdd_restore')
# rdd_restore.persist()
if dump:
rdd_restore.saveAsTextFile(ff("restore"))
# ### WE NO LONGER HAVE TO GROUPBY
# ### BUT WE MUST TREAT EACH LINE INDIVIDUALLY NOW
# rdd_withuri = sc.parallelize(rdd_withuri.take(10))
rdd_harvested = rdd_restore.mapValues(lambda x: computeSpans(x, indexed=True)).filter(lambda p: p[1])
rdd_harvested.setName('rdd_harvested')
# rdd_harvested.persist()
if dump:
rdd_harvested.saveAsTextFile(ff("harvested"))
# This has the effect of generating 0, 1, 2, ... lines according to the number of spans
rdd_controlled = rdd_harvested.flatMapValues(lambda x: list(x))
rdd_controlled.setName('rdd_controlled')
# rdd_controlled.persist()
# map any eyeColor spans using smEyeColor, hairType spans using smHairColor
rdd_aligned = rdd_controlled.mapValues(lambda x: alignToControlledVocab(x, {"eyeColor": smEyeColor, "hairType": smHairColor}))
rdd_aligned.setName('rdd_aligned')
# rdd_aligned.persist()
if dump:
rdd_aligned.saveAsTextFile(ff("aligned"))
rdd_aligned_json = rdd_aligned.mapValues(lambda x: json.dumps(x))
rdd_aligned_json.setName('rdd_aligned_json')
# rdd_aligned_json.persist()
if dump:
rdd_aligned_json.saveAsTextFile(ff("aligned_json"))
rdd_final = rdd_aligned_json
empty = rdd_final.isEmpty()
if not empty:
l = "unknown>1"
print "### writing %s output (%s records) to %s" % (outputFormat, l, outputDirectory)
# print len(rdd_final.collect())
if outputFormat == "sequence":
rdd_final.saveAsSequenceFile(outputDirectory)
elif outputFormat == "text":
rdd_final.saveAsTextFile(outputDirectory)
else:
raise RuntimeError("Unrecognized output format: %s" % outputFormat)
else:
print "### No records: no output into %s" % (outputDirectory)
#X = sys.argv[1]
#normal
normalFilePath = '/home/worker/workspace/DeepDefense_dataStatistics' + '/csv' + '/topXraw.csv'
normalPath = os.path.join(normalFilePath)
sc.addFile(normalPath);
#attack
attackFilePath = '/home/worker/workspace/DeepDefense_dataStatistics' + '/csv' + '/topXraw.csv'
attackPath = os.path.join(attackFilePath)
sc.addFile(attackPath);
from pyspark import SparkFiles
normalRdd = sc.textFile(SparkFiles.get(normalFilePath)).cache()
attackRdd = sc.textFile(SparkFiles.get(attackFilePath)).cache()
# src, dst, data_length, protocol_name, protocol_number, arrival_time (len = 6)
normalRaw = normalRdd.map(lambda x: x.split(',')).filter(lambda x: len(x) == 6).cache()
attackRaw = attackRdd.map(lambda x: x.split(',')).filter(lambda x: len(x) == 6).cache()
#(ip, count)
normalTopXSrcIP = normalRaw.map(lambda x:(x[0], 1)).groupByKey().map(lambda (k,v):(k, sum(v))).takeOrdered(10, key = lambda (k,v): -v)
attackTopXSrcIP = attackRaw.map(lambda x:(x[0], 1)).groupByKey().map(lambda (k,v):(k, sum(v))).takeOrdered(10, key = lambda (k,v): -v)
#(ip, count)
normalTopXDstIP = normalRaw.map(lambda x:(x[1], 1)).groupByKey().map(lambda (k,v):(k, sum(v))).takeOrdered(10, key = lambda (k,v): -v)
attackTopXDstIP = attackRaw.map(lambda x:(x[1], 1)).groupByKey().map(lambda (k,v):(k, sum(v))).takeOrdered(10, key = lambda (k,v): -v)
#(ip, data_length)
def upload_file_job(context):
from pyspark import SparkFiles
with open(SparkFiles.get(upload_file_name)) as testFile:
file_val = testFile.readline()
return file_val
sc = SparkContext('local', 'testGeoSpark')
#X = sys.argv[1]
#normal
normalFilePath = '/home/worker/workspace/DeepDefense_dataStatistics' + '/csv' + '/topXraw.csv'
normalPath = os.path.join(normalFilePath)
sc.addFile(normalPath);
#attack
attackFilePath = '/home/worker/workspace/DeepDefense_dataStatistics' + '/csv' + '/topXraw.csv'
attackPath = os.path.join(attackFilePath)
sc.addFile(attackPath)
from pyspark import SparkFiles
normalRdd = sc.textFile(SparkFiles.get(normalFilePath))
attackRdd = sc.textFile(SparkFiles.get(attackFilePath))
import geoip2.database
geoDBpath = '/home/worker/workspace/geoDB/GeoLite2-City.mmdb'
geoPath = os.path.join(geoDBpath)
sc.addFile(geoPath)
#reader = geoip2.database.Reader(SparkFiles.get(geoPath))
#reader = geoip2.database.Reader('GeoLite2-City.mmdb')
# def ip2city(ip):
# try:
# city = reader.city(ip).city.name
# except:
# city = 'not found'
def crfprep(sc, inputFilename, outputDirectory,
limit=LIMIT, location='hdfs', outputFormat="text", partitions=None):
crfConfigDir = os.path.join(os.path.dirname(__file__), "data/config")
featureListFilename = os.path.join(crfConfigDir, "features.hair-eye")
crfConfigDir = os.path.join(os.path.dirname(__file__), "data/config")
crfExecutable = "/usr/local/bin/crf_test_filter.sh"
crfModelFilename = os.path.join(crfConfigDir, "dig-hair-eye-train.model")
rdd_sequence_file_input = sc.sequenceFile(inputFilename)
rdd_sequence_file_input.setName('rdd_sequence_file_input')
# rdd_sequence_file_input.persist()
if limit:
rdd_sequence_file_input = sc.parallelize(rdd_sequence_file_input.take(limit))
if partitions:
rdd_sequence_file_input = rdd_sequence_file_input.repartition(partitions)
rdd_json = rdd_sequence_file_input.mapValues(lambda x: json.loads(x))
rdd_json.setName('rdd_json')
# rdd_json.persist()
rdd_texts = rdd_json.mapValues(lambda x: (textTokens(extract_body(x)), textTokens(extract_title(x))))
rdd_texts.setName('rdd_texts')
# data format issue?
# rdd_texts.saveAsSequenceFile(outputDirectory + "_texts")
# This separator could have appeared in original text, and should serve to cleanly delimit the body from the title
# Not perfect, it could have appeared between real tokens
# Needs to have single labels+index feature
# former code was lost
c = crf_features.CrfFeatures(featureListFilename)
SEPARATOR = '&nbsp;',
def makeMatrix(c, uri, bodyTokens, titleTokens):
b = c.computeFeatMatrix(bodyTokens, False, addLabels=False, addIndex=False)
s = c.computeFeatMatrix([SEPARATOR, ""], False, addLabels=False, addIndex=False)
t = c.computeFeatMatrix(titleTokens, False, addLabels=False, addIndex=False)
idx = 1
for row in b:
if row == u"":
pass
else:
label = uri + "/%05d/%05d" % (0, idx)
row.append(label)
idx += 1
idx = 1
for row in s:
if row == u"":
pass
else:
label = uri + "/%05d/%05d" % (1, idx)
row.append(label)
idx += 1
idx = 1
for row in t:
if row == u"":
pass
else:
label = uri + "/%05d/%05d" % (2, idx)
row.append(label)
idx += 1
# might be b[0:-1] + s[0:-1] + t?
return b[0:-1] + s[0:-1] + t
rdd_features = rdd_texts.map(lambda x: (x[0], makeMatrix(c, x[0], x[1][0], x[1][1])))
rdd_features.setName('rdd_features')
# rdd_features.persist()
rdd_pipeinput = rdd_features.mapValues(lambda x: vectorToUTF8(x)).values()
rdd_pipeinput.setName('rdd_pipeinput')
if location == 'hdfs':
cmd = "%s %s" % (os.path.basename(crfExecutable), os.path.basename(crfModelFilename))
elif location == 'local':
cmd = "%s %s" % (SparkFiles.get(os.path.basename(crfExecutable)), SparkFiles.get(os.path.basename(crfModelFilename)))
print "###CMD %s" % cmd
rdd_crfoutput = rdd_pipeinput.pipe(cmd)
rdd_crfoutput.setName('rdd_crfoutput')
# rdd_features.persist()
rdd_final = rdd_crfoutput
if outputFormat == "sequence":
rdd_final.saveAsSequenceFile(outputDirectory)
elif outputFormat == "text":
rdd_final.saveAsTextFile(outputDirectory)
else:
raise RuntimeError("Unrecognized output format: %s" % outputFormat)
h2oContext = H2OContext(sc).start()
# Define file names
chicagoAllWeather = "chicagoAllWeather.csv"
chicagoCensus = "chicagoCensus.csv"
chicagoCrimes10k = "chicagoCrimes10k.csv"
# Add files to Spark Cluster
sc.addFile(_locate(chicagoAllWeather))
sc.addFile(_locate(chicagoCensus))
sc.addFile(_locate(chicagoCrimes10k))
# Since we have already loaded files into spark, we have to use h2o.upload_file instead of h2o.import_file since
# h2o.import_file expects cluster-relative path (ie. the file on this path can be accessed from all the machines on the cluster)
# but SparkFiles.get(..) already give us relative path to the file on a current node which h2o.upload_file can handle ( it uploads file
# located on current node and distributes it to the H2O cluster)
f_weather = h2o.upload_file(SparkFiles.get(chicagoAllWeather))
f_census = h2o.upload_file(SparkFiles.get(chicagoCensus))
f_crimes = h2o.upload_file(SparkFiles.get(chicagoCrimes10k))
# Transform weather table
# Remove 1st column (date)
f_weather = f_weather[1:]
# Transform census table
# Remove all spaces from column names (causing problems in Spark SQL)
col_names = map(lambda s: s.strip().replace(' ', '_').replace('+', '_'), f_census.col_names)
# Update column names in the table
# f_weather.names = col_names
f_census.names = col_names
rdd_json = rdd.mapValues(lambda x: json.loads(x))
rdd_body = rdd_json.mapValues(lambda x: extract_body(x))
rdd_body_tokens = rdd_body.mapValues(lambda x: textTokens(x))
# TBD
# rdd_title = rdd_json.mapValues(lambda x: extract_title(x))
# rdd_title_tokens = rdd.title.mapValues(lambda x: textTokens(x))
# all below should also be done for title
# not a pair RDD?
rdd_features = rdd_body_tokens.map(lambda x: (x[0], c.computeFeatMatrix(x[1], False, addLabels=[x[0]], addIndex=True)))
rdd_pipeinput = rdd_features.mapValues(lambda x: vectorToString(x))
cmd = SparkFiles.get("crf_test") + " -m " + SparkFiles.get(crfModelFilename)
rdd_crf = rdd_pipeinput.values().pipe(cmd)
# not a pair RDD
# but we have the URI in the -3 position
# and the index in the -2 position
rdd_withuri = rdd_crf.map(lambda x: reconstructTuple(x))
rdd_grouped = rdd_withuri.groupByKey()
rdd_flat = rdd_grouped.mapValues(lambda x: [l[1:] for l in sorted(x, key=lambda r: int(r[0]))])
rdd_harvested = rdd_flat.mapValues(lambda x: computeSpans(x, indexed=True))
# This has the effect of generating 0, 1, 2, ... lines according to the number of spans
rdd_controlled = rdd_harvested.flatMapValues(lambda x: list(x))
# map any eyeColor spans using smEye, hairType spans using smHair
rdd_aligned = rdd_controlled.mapValues(lambda x: alignToControlledVocab(x, {"eyeColor": smEye, "hairType": smHair}))
def sparkFilePathMapper(self, path):
"""When Spark forwards files from the driver to worker nodes, it may be
necessary to map the filename path on a per-worker node basis."""
# Note the implication in this code that the feature list file and
# model file must have unique basenames.
return SparkFiles.get(os.path.basename(path))
def mapper(line, title, secfile, idsec):
post = mdb.posts
tokens = word_tokenize(line)
tagged = pos_tag(tokens)
ntities = chunk.ne_chunk(tagged)
newline = line.encode('utf-8')
posting = {"securitynow_id": idsec, "episode": secfile[3:6], "speaker": title, "original": line, "tokens": tokens,
"entities": ntities, "sentiment": classifier.classify(dict([(word, True) for word in newline]))}
post_id = post.insert(posting)
sc.addFile("/home/th3m4d0n3/NetBeansProjects/twAppDemo/data_dir/allSentimentData")
with open(SparkFiles.get("allSentimentData")) as f:
reader = csv.reader(f, delimiter=" ", quotechar='"')
jobs = bg.BackgroundJobManager()
map(parseForNltk, reader)
print("chezdata type DATA: {0} COUNT: {1}".format(type(chezdata), len(chezdata)))
map(getHighest, chezdata)
chezdataP = sc.parallelize(chezdata)
lowRatedP = sc.parallelize(lowRated)
highlyRatedP = sc.parallelize(highlyRated)
print("chezdataP type DATA: {0} COUNT: {1}".format(type(chezdataP), chezdataP.count()))
print("lowRatedP type DATA: {0} COUNT: {1}".format(type(lowRatedP), lowRatedP.count()))
def __ls(broadcast_vars, iterator):
"""
Get the list of files in the worker-local directory
"""
return [__get_hostname(), os.listdir(SparkFiles.getRootDirectory())]
# Can I perist a Caffe network object?
import copy
from pyspark import SparkContext, SparkConf
from pyspark import SparkFiles
from pyspark import StorageLevel
conf = SparkConf().setAppName("SparkCaffe Test")
conf.set("spark.executor.memory", "1g")
sc = SparkContext(conf=conf)
sc.addFile("models/solver.prototxt")
sc.addFile("models/train_val.prototxt")
solver = SparkFiles.get("solver.prototxt")
architecture = SparkFiles.get("train_val.prototxt")
def create_net(solver_filename):
from caffe import SGDSolver
net = SGDSolver(str(solver_filename)).net
return net
netRDD = sc.parallelize([solver] * 2, 2).map(create_net)
netRDD.persist(StorageLevel.MEMORY_ONLY)
def extract_unique_val(net):
return net.params["conv1"][0].data[0, 0, 0, 0]
def main():
conf = (SparkConf()
.setMaster("local[*]")
.setAppName("compare_engine"))
sc = SparkContext(conf = conf)
sc.setLogLevel('INFO')
sc.addFile(primary)
# rdd_primary = sc.textFile(primary, minPartitions=4, use_unicode=True).distinct()
rdd_primary = sc.textFile(SparkFiles.get(primary), minPartitions=4, use_unicode=True).distinct()
rdd_primary.partitionBy(10).cache()
os.system('rm -Rf collects_*')
os.system('rm -Rf holder.txt')
rdd_secondary = sc.textFile(secondary, minPartitions=4, use_unicode=True).distinct()
rdd_secondary.partitionBy(10).cache()
primary_count = rdd_primary.count()
primary_report['count'] = primary_count
print(primary_report)
secondary_count = rdd_secondary.count()
secondary_report['count'] = secondary_count
print(secondary_report)
# Return each Primary file line/record not contained in Secondary
not_in_primary = rdd_primary.subtract(rdd_secondary)
primary_diff = not_in_primary.count()
primary_report['diff'] = primary_diff
os.system('rm -Rf collects_*.csv')
primary_dir = 'collects_{}_primary'.format(run_date)
primary_report_name = 'collects_{}_primary_report.csv'.format(run_date)
not_in_primary.coalesce(1, True).saveAsTextFile(primary_dir)
# os.system('cat collects_{}_primary/part-0000* >> collects_{}_primary_report.csv'.format(run_date, run_date))
os.system('cat {}/part-0000* >> {}'.format(primary_dir, primary_report_name))
os.system('wc -l collects_{}_primary_report.csv'.format(run_date))
# Flip Primary Vs Secondary
# Return each Secondary file line/record not contained in Primary
not_in_secondary = rdd_secondary.subtract(rdd_primary)
secondary_diff = not_in_secondary.count()
secondary_report['diff'] = secondary_diff
not_in_secondary.coalesce(1,True).saveAsTextFile('collects_{}_secondary'.format(run_date))
os.system('cat collects_{}_secondary/part-0000* >> collects_{}_secondary_report.csv'.format(run_date, run_date))
os.system('wc -l collects_{}_secondary_report.csv'.format(run_date))
process_report['primary'] = primary_report
process_report['secondary'] = secondary_report
print("=" * 100)
print('\n')
print(process_report)
print('\n')
print("=" * 100)
spark_details(sc)
sc.stop()
def _getCountryByIP(ip):
citydb = geoIP.Reader(SparkFiles.get('GeoLite2-City.mmdb'))
return (citydb.city(ip).country.name or u'Unknown').encode()
def update_data(self):
self.arrays['data'][:] = np.random.randn(*self.arrays['data'].shape)
self.arrays['label'][:] = np.random.choice(
xrange(10),
size=self.arrays['label'].shape)
def process_model(self):
pass
# Create some dummy data
dataRDD = sc.parallelize(xrange(100))
# Create some barista instances
num_baristas = 2
start_script = 'python -m barista.start'
solver = SparkFiles.get("solver.prototxt")
interfaces = sc.parallelize([solver]*num_baristas, num_baristas) \
.pipe(start_script) \
.collect()
# Join the data
def train(interface, data):
solver_filename, pid = interface.split(',')
customer = MyCustomer(solver_filename)
customer.run_transaction()
grad_norm = np.linalg.norm(customer.arrays['conv1_dW'])
return grad_norm
grad_norms = dataRDD.map(lambda x: train(interfaces[0], x)).collect()
print grad_norms
def start_spark(app_name='my_spark_app', master='local[*]', jar_packages=[],
files=[], spark_config={}):
"""Start Spark session, get the Spark logger and load config files.
Start a Spark session on the worker node and register the Spark
application with the cluster. NOTE - only the app_name argument
will apply when this is called from a script sent to spark-submit
(i.e. when __name__ = '__main__'). All other arguments exist solely
for testing the script from within an interactive Python console.
This function also looks for a file ending in 'config.json' that
can be sent with the Spark job. If it is found, it is opened,
the contents parsed (assuming it contains valid JSON for the ETL job
configuration), into a dict of ETL job configuration parameters,
which are returned as the last element in the tuple returned by
this function. If the file cannot be found then the return tuple
only contains the Spark session and Spark logger objects.
:param app_name: Name of Spark app.
:param master: Cluster connection details (defaults to local[*].
:param jar_packages: List of Spark JAR package names.
:param files: List of files to send to Spark cluster (master and
workers).
:param spark_config: Dictionary of config key-value pairs.
:return: A tuple of references to the Spark session, logger and
config dict (only if available).
"""
if __name__ == '__main__':
# get Spark session factory
spark_builder = (
SparkSession
.builder
.appName(app_name))
else:
# get Spark session factory
spark_builder = (
SparkSession
.builder
.master(master)
.appName(app_name))
# create Spark JAR packages string
spark_jars_packages = ','.join(list(jar_packages))
spark_builder.config('spark.jars.packages', spark_jars_packages)
spark_files = ','.join(list(files))
spark_builder.config('spark.files', spark_files)
# add other config params
for key, val in spark_config.items():
spark_builder.config(key, val)
# create session and retrieve Spark logger object
spark_sess = spark_builder.getOrCreate()
spark_logger = logging.Log4j(spark_sess)
# get config file if sent to cluster with --files
spark_files_dir = SparkFiles.getRootDirectory()
config_files = [filename
for filename in listdir(spark_files_dir)
if filename.endswith('config.json')]
if len(config_files) != 0:
path_to_config_file = path.join(spark_files_dir, config_files[0])
with open(path_to_config_file, 'r') as config_file:
config_json = config_file.read().replace('\n', '')
config_dict = loads(config_json)
spark_logger.warn('loaded config from ' + config_files[0])
else:
config_dict = None
# build return tuple conditional on presence of config
if config_dict is not None:
return_tup = spark_sess, spark_logger, config_dict
else:
return_tup = spark_sess, spark_logger
return return_tup
def compute_buried_area_all_residues_and_receptor_area(pdb_complex):
chZ = "chZ"
res_buried_area_perc = -1
res_buried_area = -1
buried_receptor_system = -1
buried_receptor_res = -1
base_name = get_name_model_pdb(pdb_complex)
ligand_name = get_ligand_from_receptor_ligand_model(base_name)
receptor_name = get_receptor_from_receptor_ligand_model(base_name)
pose = get_model_from_receptor_ligand_model(base_name)
#output area receptor file
f_output_receptor_buried_area = os.path.join(path_analysis_pdb_complex_b.value,base_name+".outAreaRecep")
#ndx files
#f_ndx = os.path.join(path_analysis_pdb_complex_b.value,base_name+".ndx")
f_ndx_temporary_index_z = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_index_z"+".ndx")
f_ndx_temporary = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary"+".ndx")
f_ndx_temporary_sasa = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_sasa"+".ndx")
#xvg files
f_xvg_temporary_sasa_res_lig = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_sasa_res-lig"+".xvg")
f_xvg_temporary_sasa_res = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_sasa_res"+".xvg")
f_xvg_temporary_sasa_rec_lig = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_sasa_rec_lig"+".xvg")
f_xvg_temporary_sasa_rec = os.path.join(path_analysis_pdb_complex_b.value,base_name+"_temporary_sasa_rec"+".xvg")
# Creates a selection with the residues that are closer than 6A to the ligand
script_make_ndx_buried_area_receptor = SparkFiles.get("make_ndx_buried_area_receptor.sh") #Getting bash script that was copied by addFile command
command = script_make_ndx_buried_area_receptor + " " + gromacs_path.value + " "+ pdb_complex + " "+ f_ndx_temporary_index_z + " "+ f_ndx_temporary
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
#coping file
if os.path.exists(f_ndx_temporary):
shutil.copy(f_ndx_temporary, f_ndx_temporary_sasa)
#Get all residues for computing area receptor
all_res = get_residues_receptor_from_ndx_files(f_ndx_temporary)
returned_list = []
for res in all_res:
script_make_ndx_buried_area_receptor_res = SparkFiles.get("make_ndx_buried_area_receptor_res.sh") #Getting bash script that was copied by addFile command
command = script_make_ndx_buried_area_receptor_res + " " + gromacs_path.value + " "+ pdb_complex + " "+ f_ndx_temporary_sasa + " "+ str(res)
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
# compute surface of system - saved on xvg
command = gromacs_path.value +"gmx sasa -surface complex -output rec_"+str(res)+ " -o "+ f_xvg_temporary_sasa_res_lig + " -xvg none -f " + pdb_complex +" -s " + pdb_complex + " -n "+ f_ndx_temporary + " -nopbc "
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
# compute surface of receptor - save on xvg
command = gromacs_path.value +"gmx sasa -surface rec -output rec_"+str(res)+ " -o "+ f_xvg_temporary_sasa_res + " -xvg none -f " + pdb_complex +" -s " + pdb_complex + " -n "+ f_ndx_temporary + " -nopbc "
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
#calculate area
if os.path.exists(f_xvg_temporary_sasa_res_lig):
buried_receptor_system = get_value_from_xvg_sasa(f_xvg_temporary_sasa_res_lig)
else:
buried_receptor_system = 0
if os.path.exists(f_xvg_temporary_sasa_res):
buried_receptor_res = get_value_from_xvg_sasa(f_xvg_temporary_sasa_res)
else:
buried_receptor_res = 0
res_buried_area = buried_receptor_res - buried_receptor_system
if (res_buried_area > 0) and (buried_receptor_res > 0):
res_buried_area_perc = res_buried_area/buried_receptor_res
#Generating result
result = (base_name, res, res_buried_area, res_buried_area_perc)
returned_list.append(result)
#Deleting files
if os.path.exists(f_xvg_temporary_sasa_res_lig):
os.remove(f_xvg_temporary_sasa_res_lig)
if os.path.exists(f_xvg_temporary_sasa_res):
os.remove(f_xvg_temporary_sasa_res)
#Computing Receptor Area
command = gromacs_path.value +"gmx sasa -surface complex -output rec"+ " -o "+ f_xvg_temporary_sasa_rec_lig + " -xvg none -f " + pdb_complex +" -s " + pdb_complex + " -n "+ f_ndx_temporary + " -nopbc "
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
command = gromacs_path.value +"gmx sasa -surface rec -output rec"+ " -o "+ f_xvg_temporary_sasa_rec + " -xvg none -f " + pdb_complex +" -s " + pdb_complex + " -n "+ f_ndx_temporary + " -nopbc "
process = Popen(command,shell=True, stdout=PIPE, stderr=PIPE)
stdout, stderr = process.communicate()
if os.path.exists(f_xvg_temporary_sasa_rec_lig):
sasa_rec_lig = get_value_from_xvg_sasa(f_xvg_temporary_sasa_rec_lig)
else:
sasa_rec_lig = 0
if os.path.exists(f_xvg_temporary_sasa_rec):
sasa_rec = get_value_from_xvg_sasa(f_xvg_temporary_sasa_rec)
else:
sasa_rec = 0
receptor_area = sasa_rec - sasa_rec_lig
#Saving result file
output_receptor_buried_area = open(f_output_receptor_buried_area, "w")
output_receptor_buried_area.write(str(base_name)+" "+str(receptor_area) +"\n")
output_receptor_buried_area.close()
#Deleting all files
if os.path.exists(f_xvg_temporary_sasa_rec_lig):
os.remove(f_xvg_temporary_sasa_rec_lig)
if os.path.exists(f_xvg_temporary_sasa_rec):
os.remove(f_xvg_temporary_sasa_rec)
if os.path.exists(f_ndx_temporary):
os.remove(f_ndx_temporary)
if os.path.exists(f_ndx_temporary_sasa):
os.remove(f_ndx_temporary_sasa)
if os.path.exists(f_ndx_temporary_index_z):
os.remove(f_ndx_temporary_index_z)
return returned_list
else:
#Here means that some problem for computing area
return (base_name, "NAN", float(0), float(0))
