def getComposition(self):
composition = Composition()
# Star the collection
collection = self.__builder.getCollection()
composition.setCollection(collection)
# Then add bag
bag = self.__builder.getBag()
composition.setBag(bag)
# Finish wiht an extra
extra = self.__builder.getExtra()
composition.setExtra(extra)
return composition
from pydub.utils import make_chunks from pydub.effects import * from composition import Sound from composition import Composition from composition import open from composition import cut_tool import numpy as np from scipy.signal import butter, lfilter, freqz import matplotlib.pyplot as plt import array import IPython comp = Composition() lib = comp.library #make samp tool #make a beat #frequency limits/zones #16 to 32 #32 to 512 #512 to 2048 #2048 to 8192 #8192 to 16384 #C #16.351 #32.703
def read_file(self):
'''Reads File and parses it using other methods'''
file = open(self.file_name, "r")
current_line = file.readline()
self.header = current_line
header_parts = current_line.split(" ")
if header_parts[0] != "SAVELLYS":
raise CorruptedCompositionFileError("Unknown file type")
if header_parts[2].strip().lower() != 'tallennustiedosto':
raise CorruptedCompositionFileError("Unknown file type")
for line in file:
ref = False
count = 0
while line[0] == "#" or ref:
count += 1
if count > 10: break
if line[1:7].lower() == 'tiedot':
if self.comp != None:
raise CorruptedCompositionFileError("Monta tietoa")
else:
self.comp = Composition(
None, None, None, None, [],
[]) # Creating the composition object
line = self.parse_tiedot(file)
if line[1:10].lower() == 'kommentit':
for line in file:
if line[0] == "#":
ref = True
break
else:
ref = False
self.commentblock = self.commentblock + line
elif line[1:7].lower() == 'nuotit':
for line in file:
if line[0] == "#":
ref = True
break
else:
ref = False
self.parse_nuotit(line)
elif line[1:6].lower() == 'tauot':
for line in file:
if line[0] == "#":
ref = True
break
else:
ref = False
self.parse_tauot(line)
elif line[1:7].lower() == 'palkit':
for line in file:
if line[0] == "#":
ref = True
break
else:
ref = False
self.parse_palkit(line)
elif line[1:9].lower() == 'sanoitus':
for line in file:
if line[0] == "#":
ref = True
break
else:
ref = False
self.parse_sanoitus(line)
file.close()
CharGraphics(self.comp)
import graph_relations
import supervised
import test_lists
from composition import Composition
import sys
#print(lcs.process(example_list)) #test lcs
#print(relations.process(food_list50)) #test relations
#per liste molto lunghe nel graph_relations e' bene aumentare leggermente il parametro della similarita' per
#ridurre il numero di termini in output e quindi prendere un gruppo minore di nodi del grafo
#output1 = graph_relations.process(food_list50, 0.2) #test graph_relations
#print(output1)
#print(lcs.process(['pasta', 'vegetable', 'yogurt', 'meat', 'cheese', 'butter', 'chocolate', 'beef', 'seafood', 'bread', 'pork', 'fish']))
#print(supervised.process(" ".join(food_list50)))
summary1 = ['relations', 'graph_relations'] #riassuntivo 1
summary2 = ['relations', 'lcs', 'graph_relations'] #riassuntivo 2
description1 = ['graph_relations', 'lcs', 'relations'] #descrittivo 1
description2 = ['lcs', 'graph_relations', 'relations'] #descrittivo 2
#param Composition > stringlist, sequenza, similarita' minima per coppie di termini, numero di synset massimi per un termine
composition = Composition(test_lists.food_list50, summary2, 0.3, 10)
#creare una funzione che permuta automaticamente i metodi generando tutte le possibili
def basicMode(config, fasta_file, profilePath):
#create output folders
wDir = os.getcwd()
folders = ['pplacer', 'testing']
for folder in folders:
os.mkdir(os.path.join(wDir, folder))
#Instance Preprocessing class
window = Preprocessing(fasta_file, config['win_length'],
config['win_step'], "windows_sequence.fasta")
window.output_window()
reverseSeq = Preprocessing(fasta_file, config['win_length'],
config['win_step'], "reverse_windows.fasta")
reverseSeq.output_window()
print >> sys.stderr, "Creating windows_sequence.fasta"
#Instance Similarity and Composition class
sim = Similarity(fasta_file, config['score_adj'], wDir)
sim_matrix = sim.mcl_perform()
comp_results = Composition(config['kmer_len'])
comp_matrix = comp_results.joined()
#Join similarity and composition matrix for PCA
join = pd.concat([comp_matrix, sim_matrix], axis=1, join='inner')
print >> sys.stderr, "Calculating similarity and composition matrix"
#Instance Reduction class
pca = Reduction(join, config['pca_comp'])
pca_data = pca.perform_pca()
print >> sys.stderr, "Performing PCA"
#Instance Clustering class
cluster = Clustering(pca_data)
clust_obj = cluster.plot()
print >> sys.stderr, "Performing clustering plot"
#Instance ClusterReport class
report = ClusterReport(clust_obj)
file_name, querySeq = report.output_queryseq()
print >> sys.stderr, "Doing report of clusters"
#Instance Validate class
valid = Validate(file_name, fasta_file, wDir)
jfileComp, jfileMinus = valid.roundTwo()
print >> sys.stderr, "Validation of results"
#Instance ParseJplace Class
parsing = ParseJplace(jfileComp, jfileMinus)
corrMat = parsing.correlation()
print >> sys.stderr, "Doing profiles"
#Instance Profile Class
ttest = Profiles(corrMat, querySeq, wDir, profilePath)
bestWin = ttest.windowsAssigment()
print >> sys.stderr, "Doing permutations"
#Instance StatsBinom
finalResult = StatsBinom(fasta_file, config['win_length'], bestWin)
finalResult.binomial()
print >> sys.stderr, "Calculating p-value"
cleaning(file_name)