Exemple #1
0
    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
Exemple #2
0
    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
Exemple #4
0
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()
Exemple #5
0
        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
    '''
Exemple #6
0
 def __init__(self):
     self.IMG = Imager.Imager()
Exemple #7
0
def generatePotentialSpook():
    imager = Imager.Imager(IMG_WIDTH, IMG_HEIGHT)
    return imager.generateOneRGB()
Exemple #8
0
 def __init__(self, gridArray, freq, farFieldReference=None, fieldRes=None):
     Imager.__init__(self,
                     gridArray,
                     freq,
                     farFieldReference=farFieldReference,
                     fieldRes=fieldRes)
Exemple #9
0
 def __init__(self, root):
     super().__init__(root)
     self.img = Imager.Imager()
     self.FW = File_work.File_work()
     self.__init_main__()
     self.render_ui()