def process(filename):
print("Processing " + filename)
sequence_record_list = []
# print(os.getcwd())
for record in SeqIO.parse(filename, "fasta"):
sequence_record_list.append(record.seq)
print("Sequences Extracted!")
sequence_record = ''.join(str(e) for e in sequence_record_list)
atree = ATree()
print(str(len(sequence_record)) + "-->" + str(atree))
for subsequence_chunks in break_sequence(sequence_record, 8):
atree.process_subsequence(subsequence_chunks)
atree.dump_to_file(filename + "_TREE")
print("Ensuring correct File System Navigation: " + os.getcwd())
atree.pickle_into_file("GenomeDataset/Processing/" +
os.path.basename(filename) + "_pTREE")
subprocess.call([
"rsync", "-az", "GenomeDataset/Processing/",
"[email protected]:~/Documents/master-GSAFv2/gsaf-2.0/GenomeDataset/Processing/"
])
return len(sequence_record)
def process(filename):
print("Processing " + filename)
sequence_record_list = []
# print(os.getcwd())
for record in SeqIO.parse(filename, "fasta"):
sequence_record_list.append(record.seq)
print("Sequences Extracted!")
sequence_record = ''.join(str(e) for e in sequence_record_list)
atree = ATree()
print(str(len(sequence_record)) + "-->" + str(atree))
for subsequence_chunks in break_sequence(sequence_record, 8):
atree.process_subsequence(subsequence_chunks)
atree.dump_to_file(filename + "_TREE")
print("Ensuring correct File System Navigation: " + os.getcwd())
atree.pickle_into_file("GenomeDataset/Processing/" + os.path.basename(filename) + "_pTREE")
subprocess.call(["rsync", "-az", "GenomeDataset/Processing/",
"[email protected]:~/Documents/master-GSAFv2/gsaf-2.0/GenomeDataset/Processing/"])
return len(sequence_record)
def __init__(self):
self.eigen_exists = False
self.eigen_values = []
# statistical_inferences attribute used for storing mean, mode, median, min, max, average
self.statistical_inferences_exist = False
self.statistical_inferences = {
"mean": 0,
"mode": 0,
'median': 0,
'min': 0,
'max': 0,
'range': 0
}
self.has_been_analyzed = False
self.gmap_exists = False
self.gmap = np.zeros((340, 340), dtype=np.int)
self.lookup_table = []
self.new_tree = ATree()
def __init__(self):
self.eigen_exists = False
self.eigen_values = []
# statistical_inferences attribute used for storing mean, mode, median, min, max, average
self.statistical_inferences_exist = False
self.statistical_inferences = {"mean": 0, "mode": 0, "median": 0, "min": 0, "max": 0, "range": 0}
self.has_been_analyzed = False
self.gmap_exists = False
self.gmap = np.zeros((340, 340), dtype=np.int)
self.lookup_table = []
self.new_tree = ATree()
class Chromosome:
"""Chromosome class to handle analysis modules"""
_pattern_list = [
"".join(x) for i in range(1, 9) for x in product(*['ACGT'] * i)
]
_pattern_list.sort()
_pattern_list_half = [
"".join(x) for i in range(1, 5) for x in product(*['ACGT'] * i)
]
_pattern_list_half.sort()
def __init__(self):
self.eigen_exists = False
self.eigen_values = []
# statistical_inferences attribute used for storing mean, mode, median, min, max, average
self.statistical_inferences_exist = False
self.statistical_inferences = {
"mean": 0,
"mode": 0,
'median': 0,
'min': 0,
'max': 0,
'range': 0
}
self.has_been_analyzed = False
self.gmap_exists = False
self.gmap = np.zeros((340, 340), dtype=np.int)
self.lookup_table = []
self.new_tree = ATree()
def load_chromosome_tree(self, filename):
with open(filename, 'rb') as in_fh:
self.new_tree = pickle.load(in_fh)
print(filename + "Chromosome Tree Loaded!")
def analyze(self, chromosome_file):
print("Analyzing " + chromosome_file)
if not self.has_been_analyzed:
self.load_chromosome_tree(chromosome_file)
self.map(chromosome_file) if not self.gmap_exists else print(
'Chromosome is mapped!')
self.calculate_eigen_values() if not self.eigen_exists else print(
'EV(s) exist!')
# Dump pickled data
self.store_to_file(chromosome_file + "_pChromosome",
filealso=False)
# Update has_been_analyzed attribute
self.has_been_analyzed = True
def map(self, chromosome_file):
print("Performing Mapping Logic on: " + chromosome_file)
# Mapping Logic Code
for pattern in self._pattern_list:
if len(pattern) > 1:
if len(pattern) == 2:
self.gmap[self._pattern_list_half.index(
pattern[:int(len(pattern) /
2)])][self._pattern_list_half.index(
pattern[int(len(pattern) / 2):]
)] = self.new_tree.count(pattern)
# print(pattern[:int(len(pattern) / 2)] + "-->" + pattern[int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[int(len(pattern) / 2):])]))
else:
if 1 + len(pattern) / 2 <= 4:
self.gmap[self._pattern_list_half.index(
pattern[:int(len(pattern) /
2)])][self._pattern_list_half.index(
pattern[int(len(pattern) / 2):]
)] = self.new_tree.count(pattern)
# print(pattern[:int(len(pattern) / 2)] + "-->" + pattern[int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[int(len(pattern) / 2):])]))
self.gmap[self._pattern_list_half.index(
pattern[:1 +
int(len(pattern) /
2)])][self._pattern_list_half.index(
pattern[1 + int(len(pattern) / 2):]
)] = self.new_tree.count(pattern)
# print(pattern[:1 + int(len(pattern) / 2)] + "-->" + pattern[1 + int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:1 + int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[1 + int(len(pattern) / 2):])]))
self.gmap_exists = True
print(str(chromosome_file) + " mapped successfully!")
# print(self.gmap)
with open('op', 'w') as outfile:
outfile.write(str(self.gmap))
# This is to check if the mapping is indeed correct. If correct, both the print statements will print the same numerical value.
# print(self.gmap[self._pattern_list_half.index('A')][self._pattern_list_half.index('AA')])
# print(self.gmap[self._pattern_list_half.index('AA')][self._pattern_list_half.index('A')])
def store_to_file(self, filename, filealso=False):
with open(filename, 'wb') as picklefile:
pickle.dump(self, picklefile)
print("Stored to " + filename + " successfully!")
if filealso:
self.gmap.tofile(filename + "MAP_MATRIX_")
def calculate_eigen_values(self):
# Eigen Value Calculation and Output Code
w, v = LA.eig(self.gmap)
# print(len(w))
# Populate statistical inferences
self.statistical_inferences['mean'] = np.mean(self.gmap)
# self.statistical_inferences['mode'] =
self.statistical_inferences['median'] = np.median(self.gmap)
self.statistical_inferences['min'] = np.amin(self.gmap)
self.statistical_inferences['max'] = np.amax(self.gmap)
self.statistical_inferences['range'] = np.ptp(self.gmap)
self.statistical_inferences_exist = True
# print(self.statistical_inferences)
# with open("map" + )
return w
class Chromosome:
"""Chromosome class to handle analysis modules"""
_pattern_list = ["".join(x) for i in range(1, 9) for x in product(*["ACGT"] * i)]
_pattern_list.sort()
_pattern_list_half = ["".join(x) for i in range(1, 5) for x in product(*["ACGT"] * i)]
_pattern_list_half.sort()
def __init__(self):
self.eigen_exists = False
self.eigen_values = []
# statistical_inferences attribute used for storing mean, mode, median, min, max, average
self.statistical_inferences_exist = False
self.statistical_inferences = {"mean": 0, "mode": 0, "median": 0, "min": 0, "max": 0, "range": 0}
self.has_been_analyzed = False
self.gmap_exists = False
self.gmap = np.zeros((340, 340), dtype=np.int)
self.lookup_table = []
self.new_tree = ATree()
def load_chromosome_tree(self, filename):
with open(filename, "rb") as in_fh:
self.new_tree = pickle.load(in_fh)
print(filename + "Chromosome Tree Loaded!")
def analyze(self, chromosome_file):
print("Analyzing " + chromosome_file)
if not self.has_been_analyzed:
self.load_chromosome_tree(chromosome_file)
self.map(chromosome_file) if not self.gmap_exists else print("Chromosome is mapped!")
self.calculate_eigen_values() if not self.eigen_exists else print("EV(s) exist!")
# Dump pickled data
self.store_to_file(chromosome_file + "_pChromosome", filealso=False)
# Update has_been_analyzed attribute
self.has_been_analyzed = True
def map(self, chromosome_file):
print("Performing Mapping Logic on: " + chromosome_file)
# Mapping Logic Code
for pattern in self._pattern_list:
if len(pattern) > 1:
if len(pattern) == 2:
self.gmap[self._pattern_list_half.index(pattern[: int(len(pattern) / 2)])][
self._pattern_list_half.index(pattern[int(len(pattern) / 2) :])
] = self.new_tree.count(pattern)
# print(pattern[:int(len(pattern) / 2)] + "-->" + pattern[int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[int(len(pattern) / 2):])]))
else:
if 1 + len(pattern) / 2 <= 4:
self.gmap[self._pattern_list_half.index(pattern[: int(len(pattern) / 2)])][
self._pattern_list_half.index(pattern[int(len(pattern) / 2) :])
] = self.new_tree.count(pattern)
# print(pattern[:int(len(pattern) / 2)] + "-->" + pattern[int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[int(len(pattern) / 2):])]))
self.gmap[self._pattern_list_half.index(pattern[: 1 + int(len(pattern) / 2)])][
self._pattern_list_half.index(pattern[1 + int(len(pattern) / 2) :])
] = self.new_tree.count(pattern)
# print(pattern[:1 + int(len(pattern) / 2)] + "-->" + pattern[1 + int(len(pattern) / 2):] + ":::" + str(self.gmap[self._pattern_list_half.index(pattern[:1 + int(len(pattern) / 2)])][self._pattern_list_half.index(pattern[1 + int(len(pattern) / 2):])]))
self.gmap_exists = True
print(str(chromosome_file) + " mapped successfully!")
# print(self.gmap)
with open("op", "w") as outfile:
outfile.write(str(self.gmap))
# This is to check if the mapping is indeed correct. If correct, both the print statements will print the same numerical value.
# print(self.gmap[self._pattern_list_half.index('A')][self._pattern_list_half.index('AA')])
# print(self.gmap[self._pattern_list_half.index('AA')][self._pattern_list_half.index('A')])
def store_to_file(self, filename, filealso=False):
with open(filename, "wb") as picklefile:
pickle.dump(self, picklefile)
print("Stored to " + filename + " successfully!")
if filealso:
self.gmap.tofile(filename + "MAP_MATRIX_")
def calculate_eigen_values(self):
# Eigen Value Calculation and Output Code
w, v = LA.eig(self.gmap)
# print(len(w))
# Populate statistical inferences
self.statistical_inferences["mean"] = np.mean(self.gmap)
# self.statistical_inferences['mode'] =
self.statistical_inferences["median"] = np.median(self.gmap)
self.statistical_inferences["min"] = np.amin(self.gmap)
self.statistical_inferences["max"] = np.amax(self.gmap)
self.statistical_inferences["range"] = np.ptp(self.gmap)
self.statistical_inferences_exist = True
# print(self.statistical_inferences)
# with open("map" + )
return w
