def exportAction(self, event):
"""Export everything in the table to a file."""
lastDir = ""
try:
# load the last used directory
# this will probably change as we will use a base64 encoded json as the complete config?
lastDir = self.callbacks.loadExtensionSetting("lastDir")
except:
# if there is not a last used directory in the settings, continue
pass
from Utils import saveFileDialog, writeFile
selectedFile, usedDirectory = saveFileDialog(parent=self.panel,
startingDir=lastDir,
title="Export Issues",
extension="json")
if selectedFile is not None:
# write to the file
writeFile(selectedFile.getAbsolutePath(),
self.tableIssue.exportIssues())
if usedDirectory is not None:
# overwrite the last used directory
self.callbacks.saveExtensionSetting("lastDir", usedDirectory)
def randWeightInitial(Num, FilePath):
if os.path.exists(FilePath):
os.remove(FilePath)
W = dict()
for i in range(Num):
W[i + 1] = np.mat(np.diag(np.random.randint(1, 10, i + 1)))
writeFile(W, FilePath)
return W
def Operation(self, file_path=None):
if self.Encoder:
if file_path is not None and os.path.exists(file_path):
self.ASCII_Dict = loadFile(file_path)
ASCII_List = self._ASCIIEncoder(self.PW)
HEX_STR = ''
for ASCII_Num in ASCII_List:
HEX_STR += self._HexEncoder(ASCII_Num) + '-'
DES_STR = self._DESEncoder(HEX_STR)
DES_STR = base64.b64encode(DES_STR).decode()
while True:
random_id = random.randint(1000, 9999)
if str(random_id) not in self.ASCII_Dict.keys():
random_id = str(random_id)
break
self.ASCII_Dict[random_id] = (DES_STR[:-self.K])
writeFile(self.ASCII_Dict, file_path)
ASCII_STR = DES_STR[-self.K:]
X = np.array([self._ASCIIEncoder(ASCII_STR)])
Y = self._MatrixEncoder(X, self.W[self.K])
return self._GenerateGDMM(Y, random_id)
else:
'''Do Something...'''
X = self._MatrixDecoder(self.Y, self.W[self.K].I)
X = X.tolist()[0]
X = [int(i) for i in X]
ASCII_STR = self._ASCIIDecoder(X)
ASCII_Dict = loadFile(file_path)
DES_STR = ASCII_Dict[self.ID] + ASCII_STR
DES_STR = base64.b64decode(DES_STR)
HEX_STR = self._DESDecoder(DES_STR)
HEX_STR_List = HEX_STR.split('-')[:-1]
ASCII_List = []
for hex_str in HEX_STR_List:
ASCII_List.append(self._HexDecoder(hex_str))
return self._ASCIIDecoder(ASCII_List)
def main(gffPath, gplexPath, nalPath, minCov=0.5, maxDist=0):
"""Generate a GFF file of genes that overlap at least one gplex and at least one non-alignment.
:param gffPath: path to the GFF3-formatted gene annotation file
:param gplexPath: path to the GFF3-formatted gplex file
:param nalPath: path to the GFF3-formatted non-alignment file
:param minCov: minimum overlap required of non-aligned region (default=0.5)
:param maxDist: max number of base pairs separating a gplex and gene (default=0)
:return: filters the three inputted files for entries that overlap each other into separate files
"""
import os
from Utils import load, writeFile
print('\nGenerating gene overlaps...')
print('Loading files...')
gplexData = load(gplexPath)[2]
nalsData = load(nalPath)[2]
tempGeneData = load(gffPath)
headers = tempGeneData[0] + tempGeneData[1]
geneData = []
for line in tempGeneData[2]:
if line[2] == 'gene': geneData.append(line)
# Finds overlaps for each ORF
print('Calculating overlaps...')
genes = []
nals = []
gplexes = []
l = len(geneData)
for i, gene in enumerate(geneData):
print('\tCalculating ' + str(i + 1) + ' of ' + str(l) + '...')
tempNals = []
tempGplexes = []
sumCov = 0
gStart = int(gene[3])
gEnd = int(gene[4])
# Iterates over each non-alignment
for nline in nalsData:
if nline[0] == gene[0]:
start = max(gStart, int(nline[3]))
end = min(gEnd, int(nline[4]))
if end > start:
tempNals.append(nline)
sumCov += (end - start)
# Iterates over each gplex; gplex is included if its distance to the orf doesn't exceed 'maxDist'
for gplex in gplexData:
if gplex[0] == gene[0]:
start = max(gStart, int(gplex[3]))
end = min(gEnd, int(gplex[4]))
if (start - end) <= int(maxDist): tempGplexes.append(gplex)
# If coverage is at least 'minCov' and there exists at least one gplex, add to data
if (sumCov /
(gEnd - gStart) > float(minCov)) and (len(tempGplexes) > 0):
genes.append(gene)
nals.extend(tempNals)
gplexes.extend(tempGplexes)
# Write everything
print('Writing to output files...')
output = os.path.dirname(gffPath) + '/overlaps/'
writeFile(output + 'genes.gff', headers, genes)
writeFile(output + 'nals.gff', headers, nals)
writeFile(output + 'gplexes.gff', headers, gplexes)
print('Finished writing output to ' + output + '\nFinished!')
def getCorr():
"""
根据用户选择的振动参数做相关性分析
:return:
"""
vibName = request.args.get("vibName")
progress_data['getCorr'] = 0 # 将进度条置零
# 判断所选择的振动参数是否做过相关性分析
if vibName + "_FP_col" in os.listdir(
"Savedfile") and vibName + "_FP_corr" in os.listdir("Savedfile"):
vib_FP_highCorr_FP = loadFile('Savedfile/' + vibName + '_FP_corr')
vib_FP_highCol_FP = loadFile('Savedfile/' + vibName + '_FP_col')
progress_data['getCorr'] = float(100) # 进度条直接到100%
else:
repeat = []
corr_list = [] # 初始化相关性list
col_list = [] # 初始化变量名list
zeroCor_191207 = loadFile("Savedfile/zeroCor_191207")
vibFolder = baseURL + findKey(VIBParamsTrain,
vibName)[0] # 查找需要做相关性分析的参数所在文件路径
vibData, vibTime = readData(vibFolder, vibName) # 读取振动数据及相应时间
tempFPParams = FPParamsTrain
# 先遍历一次，统计飞参个数
FpNumber = 0
for file in FPParamsTrain.keys():
FpNumber += len(FPParamsTrain[file])
i = 1 # 初始化计数器
for file in tempFPParams.keys():
df = pd.read_table(baseURL + file, sep='\t')
time_i = timeProcess(df['TIME'].tolist())
for FPParam in tempFPParams[file]:
if (FPParam in zeroCor_191207): # 如果某个变量在zeroCor_191207中
print(FPParam + " Break!") # 告知并跳过该变量的计算
elif ("Accel"
in FPParam): # 如果某个变量包括"Accel"在变量名称中，该变量为加速度传感器数据
print(FPParam + " Break!") # 告知并跳过该变量的计算
elif (FPParam in repeat): # 如果有重复变量且已经做过相关性分析
print(FPParam + " Break!") # 告知并跳过该变量的计算
else:
files = findKey(FPParamsTrain, FPParam)
# 如果有重复的飞参，则进行拼接
if len(files) > 1:
repeat.append(FPParam) # 记录处理过的重复飞参
jCol = df[FPParam].fillna(0, inplace=True).tolist()
jCol_matched = matchData(vibTime, time_i,
jCol) # 将飞参数据与振动数据时间轴匹配
corrJ = scs.pearsonr(jCol_matched,
vibData)[0] # 计算该飞参数据与振动响应数据的相关性
corr_list.append(corrJ) # 将计算的相关性加入corr_list中
col_list.append(FPParam) # 将变量名称加入col_list
print("Correlation between " + FPParam + " and " +
str(vibName) + " is " + str(corrJ)) # 告知变量名与相关性
num_progress = round(i * 100 / FpNumber, 2)
i += 1
progress_data['getCorr'] = num_progress # 更新进度条
print(" Fetching complete!") # 告知飞参变量的读取完毕
# 按绝对值从大到小进行排序
corr = np.array(corr_list)
col = np.array(col_list)
vib_FP_highCorr_FP = corr[np.argsort(np.abs(corr))[::-1]].tolist()
vib_FP_highCol_FP = col[np.argsort(np.abs(corr))[::-1]].tolist()
# 按绝对值从大到小进行保存
writeFile("Savedfile/" + vibName + "_FP_corr", vib_FP_highCorr_FP)
writeFile("Savedfile/" + vibName + "_FP_col", vib_FP_highCol_FP)
result = {"corr_list": vib_FP_highCorr_FP, "col_list": vib_FP_highCol_FP}
return Response(json.dumps(result), mimetype='application/json')