def main():
path = "../../resources/mmtf_reduced_sample/"
conf = SparkConf().setMaster("local[*]") \
.setAppName("FilterByDExperimentalMethods")
sc = SparkContext(conf=conf)
MmtfReader.readSequenceFile(path, sc) \
.filter(experimentalMethods(experimentalMethods.NEUTRON_DIFFRACTION, experimentalMethods.X_RAY_DIFFRACTION)) \
.keys() \
.foreach(lambda x: print(x))
sc.stop()
def main():
path = "../../resources/mmtf_reduced_sample/"
conf = SparkConf().setMaster("local[*]") \
.setAppName("FilterByPolymerChainType")
sc = SparkContext(conf=conf)
count = MmtfReader.readSequenceFile(path, sc) \
.filter(containsPolymerChainType(containsPolymerChainType.DNA_LINKING, containsPolymerChainType.RNA_LINKING)) \
.filter(notFilter(containsLProteinChain())) \
.filter(notFilter(containsDSaccharideChain()))
.count()
def main():
path = "../../resources/mmtf_reduced_sample/"
conf = SparkConf().setMaster("local[*]") \
.setAppName("filterByResolution")
sc = SparkContext(conf=conf)
count = MmtfReader.readSequenceFile(path, sc) \
.filter(rFree(0.0,2.0)) \
.count()
print("Number of structures : " + str(count))
sc.stop()
def main():
path = "../../resources/mmtf_reduced_sample/""
conf = SparkConf().setMaster("local[*]") \
.setAppName("FilterByGroup")
sc = SparkContext(conf = conf)
count = MmtfReader.readSequenceFile(path, sc) \
.filter(containsGroup("ATP", "MG")) \
.count()
print("Number of structure with ATP + MG : " +
str(count))
sc.stop()
def main():
path = "../../resources/mmtf_reduced_sample/""
conf = SparkConf().setMaster("local[*]") \
.setAppName("FilterByreleaseDate")
sc = SparkContext(conf = conf)
count = MmtfReader.readSequenceFile(path, sc) \
.filter(releaseDate("2000-01-28","2017-02-28")) \
.count()
print("Number of structure released between 2000-01-28 and 2017-02-28 is : " +
str(count))
sc.stop()
def main():
path = "../../resources/mmtf_full_sample/"
conf = SparkConf().setMaster("local[*]") \
.setAppName("keywordSearch")
sc = SparkContext(conf=conf)
pdb = MmtfReader.readSequenceFile(path, sc)
sql = "select pdbid, resolution, biol_species, db_uniprot, db_pfam, hit_score from keyword_search('porin') order by hit_score desc"
search = PdbjMine(sql)
count = pdb.filter(search).keys().count()
print(f"Number of entries using sql to filter: {count}")
dataset = PdbjMineService.getDataset(sql)
dataset.show(10)
search = PdbjMine(dataset=dataset)
count = pdb.filter(search).keys().count()
print(f"Number of entries using dataset to filter: {count}")
sc.stop()
def main():
start = time.time()
conf = SparkConf().setMaster("local[*]") \
.setAppName("secondaryStructureElementDemo")
sc = SparkContext(conf = conf)
pdb = MmtfReader.downloadMmtfFiles(["1STP"],sc).cache()
pdb = pdb.flatMap(structureToPolymerChains()) \
.filter(containsLProteinChain())
ds = secondaryStructureElementExtractor.getDataset(pdb,"E", 6)
ds.show(50, False)
end = time.time()
print("Time: %f sec." %(end-start))
sc.stop()