def create_table(self, title: str, column_titles: list, rows: list,
metrics: list):
array = numpy.array(rows)
data_frame = pandas.DataFrame(data=array, columns=column_titles)
data_frame = data_frame[column_titles]
data_frame.sort_values(by=metrics)
path = Utils.outputs_path(title)
Imager.data_frame_to_jpg(title, data_frame, path)
self.outputs[Utils.outputs_name(title)] = path
def operate(self, input_name, configs):
# (输出):
# 交易数据:
# (IMAGE):
# #(输入值): '/Users/LiXiangYu/Desktop/交易数据-(TODAY).jpg'
# (输出值): '交易数据-(TODAY).jpg'
# (EXCEL):
# #(输入值): '/Users/LiXiangYu/Desktop/交易数据-(TODAY).xls'
# (输出值): '交易数据-(TODAY).xls'
# 一个用于output的数据集
input_items = self.inputs[input_name]
# 校验是否空数据集
if len(input_items) == 0:
print('空数据集' + input_name)
return
keys = list(input_items[0].keys())
for output_type, config in configs.items():
output_name = config['(输出值)']
if len(output_name) == 0:
raise RuntimeError('输出值长度0')
output_name = Keywords.active_date(output_name)
# 如果有path，那么用path作为输出路径，否则自定义路径
if '(输入值)' in config.keys():
path = config['(输入值)']
path = Keywords.active_date(path)
else:
outputs_dir = os.path.join(os.path.dirname(__file__),
"outputs")
if os.path.exists(outputs_dir) is False:
os.makedirs(outputs_dir)
path = os.path.join(outputs_dir, output_name)
# 如果已经存在输出，则删除输出
if os.path.exists(path):
os.remove(path)
if output_type == '(IMAGE)':
Imager.data2jpg(input_items, keys, path)
self.inputs[output_name] = path
elif output_type == '(EXCEL)':
Exceler.data2sheet(input_items, keys, 'sheet1', path)
def addImage(self):
self.shiftImages()
self.addNames()
self.saveImages()
imageAgent = Imager.Imager(1.0, 1.0, 1.0, 1.0)
imageAgent.takePicture()
imageToAdd = cv2.imread(self.tempFile, 0)
self.imageArray.append(imageToAdd)
if self.size < 30:
self.size = self.size + 1
nt = datetime.datetime.now()
self.lastAccessed = datetime.datetime.strptime(
str(nt), '%Y-%m-%d %H:%M:%S.%f').strftime("%s")
if self.size == self.arrayMaxSize:
self.arrayFull = True
self.buildGif()
self.saveState()
return
f.addNoise(variance=1, bandwidth=15e3, mode='point', point=pos_ruido)
# 8x8 minimum missing lags array
xs = np.array([0, 1, 4, 9, 15, 22, 32, 34]) * 0.3 / 34
ys = xs
a = GridArray(xs, ys, sampleRate=44100, verbose=True)
#a.show() # plots array geometry
a.receiveSignal(f)
a.addNoise(snr=30) # 30 dB sampling noise
windowFFT = 44100
a.signal2FFT(windowFFT=windowFFT)
lookingFreq = 14037
imager = Imager(a, freq=lookingFreq, fieldRes=fieldRes)
imager.calculateRn()
imager.Rn_regularization = 1e-5 * np.mean(np.abs(imager.Rn))
imager.Rx_regularization = 1e-2 * np.mean(np.abs(imager.Rn))
t0 = time.time()
y_das = imager.beamform( #beamformer = "DAS")
beamformer="X-KAT")
#beamformer = "CSM-KAT")
#beamformer = "MPDR")
#beamformer = "MVDR")
t1 = time.time()
print("Beamformer {} in {}s".format(imager.beamformer, t1 - t0))
iterations = 2e2
t0 = time.time()
signal_plel.fill(self.S_restr_plel(model))
signal_perp = self.S_restr_perp(radii, model)
signal_ST = empty(500)
signal_ST.fill(self.ST(model))
figure()
plot(radii, signal_plel)
plot(radii, signal_perp)
plot(radii, signal_ST)
show()
if __name__ == '__main__':
imager = Imager()
model = Model()
c = ComputeVariables(imager, model)
num_vectors = 10
vector_list = c.genVectorsWithTheta(0, num_vectors)
axon_1_signal_list = []
for vector in vector_list:
c.imager.g = vector
c.updateVariables(model)
axon_1_signal_list.append(c.calcS(model))
print vector
c.plotSignal(model)
#print axon_1_signal_list
'''
def __init__(self):
self.IMG = Imager.Imager()
def generatePotentialSpook():
imager = Imager.Imager(IMG_WIDTH, IMG_HEIGHT)
return imager.generateOneRGB()
def __init__(self, gridArray, freq, farFieldReference=None, fieldRes=None):
Imager.__init__(self,
gridArray,
freq,
farFieldReference=farFieldReference,
fieldRes=fieldRes)
def __init__(self, root):
super().__init__(root)
self.img = Imager.Imager()
self.FW = File_work.File_work()
self.__init_main__()
self.render_ui()