def bandPowerHistogram(cls, df, figure): figure.canvas.flush_events() ax = figure.add_subplot(111) ax.clear() ax.plot(df) ax.set_title('Band Power (dB)') ax.set_ylim([0, 80]) figure.canvas.draw() plt.pause(0.001) plt.show()
def draw_graph(self, country, lastdays): api = basic.Retriever() dates, cases = basic.Retriever.get_sinceDayone(api, country) lastdays = -1 * lastdays dates = dates[lastdays:] cases = cases[lastdays:] plt.figure(figsize=(1, 1)) data = {'Dates': dates, 'Cases': cases} df = DataFrame(data, columns=['Dates', 'Cases']) figure = plt.Figure(figsize=(6, 6), dpi=70) ax = figure.add_subplot(111) chart_type = FigureCanvasTkAgg(figure, root) chart_type.get_tk_widget().grid(row=6, column=0, padx=0, pady=0) df = df[['Dates', 'Cases']].groupby('Dates').sum() df.plot(kind='line', legend=True, ax=ax) ax.set_title('Cases in the last %s days in %s' % (-lastdays, country))
def plotRawEEG(self, figure=None, chunk=None, offset=200, plotTitle='eeg'): """ this version spits out a figure window for use in the UI :param title: title of the figure :param offset: DC offset between eeg channels :return: plot with all 14 eeg channels """ if chunk is None: chunk = self.chunk # define time axis tAxis = np.arange(0, len(chunk)) # create time axis w same length as the data matrix tAxis = tAxis / self.sampleRate # adjust time axis to 256 sample rate # use eeg matrix as y axis yAxis = chunk + offset * (self.nchannels-1) # add offset to display all channels for i in range(0, len(chunk[0, :])): yAxis[:, i] -= offset * i # plot figure if figure is None: figure = Figure() figure.canvas.flush_events() ax = figure.add_subplot(111) ax.clear() ax.set_title(plotTitle) ax.set_ylim(-300, offset * 20) ax.legend(self.eegChannels) ax.set_xlabel('time') ax.plot(tAxis, yAxis) figure.canvas.draw() plt.pause(0.001) return figure
parser = ArgumentParser(description="generate random coordinates for the lighthouse problem and find the original a,b parameters by using the Metropolis(-Hastings) algorithm") parser.add_argument('-a', dest='a', metavar='x', action='store', type=float, default=-0.5, help="x-coordinate of the lighthouse (default: %(default)s)") parser.add_argument('-b', dest='b', metavar='y', action='store', type=float, default=0.7, help="y-coordinate of the lighthouse (default: %(default)s)") parser.add_argument('-s', '--samples', metavar='N', dest='samples', action='store', type=int, default=100, help="the number of measurement samples to generate (default: %(default)s)") parser.add_argument('-m', '--minsteps', metavar='N', dest='minsteps', action='store', type=int, default=100, help="the minimal number of steps in each of the random-walks in the Metropolis algorithm (default: %(default)s)") parser.add_argument('-w', '--walks', metavar='N', dest='walks', action='store', type=int, default=100, help="the number of random walks in the Metropolis algorithm (default: %(default)s)") args = parser.parse_args() # generate random x x = randomX(args.a, args.b, args.samples) ab_s = map(lambda ab: metropolisWalk(x, ab, args.minsteps), [1j]*args.walks) from matplotlib.pyplot import hist, show, figure figure = figure() subp = figure.add_subplot(311) subp.hist(x, histtype='step', bins=40) subp.set_xlabel("simulated measurements") subp = figure.add_subplot(312) subp.hist(real(ab_s), histtype='step', bins=40) subp.axvline(x=args.a, color='g', linewidth=2) subp.set_xlabel("recovered x-coordinate of the lighthouse") subp = figure.add_subplot(313) subp.hist(imag(ab_s), histtype='step', bins=40) subp.axvline(x=args.b, color='g', linewidth=2) subp.set_xlabel("recovered y-coordinate of the lighthouse") show()
def friedman_func(y, a, omega): return [-1 / sqrt(omega / a + (1 - omega) * a ** 2), y[0] / a] if __name__ == "__main__": from argparse import ArgumentParser parser = ArgumentParser(description="integrate and plot the Friedman equations") parser.add_argument( "-o", "--omega", dest="omega", action="store", type=float, default=1.0, help="the omega parameter" ) parser.add_argument("-t", "--time", dest="time", action="store", type=float, default=1.0, help="initial time") parser.add_argument("-r", "--radius", dest="radius", action="store", type=float, default=1.0, help="initial radius") args = parser.parse_args() from scipy import arange from scipy.integrate import odeint from matplotlib.pyplot import plot, show, figure a = arange(0.1, 1.0, 0.001) y = odeint(friedman_func, [args.time, args.radius], a, (args.omega,)) figure = figure() subp = figure.add_subplot(2, 1, 1) subp.plot(a, y[:, 0]) subp = figure.add_subplot(2, 1, 2) subp.plot(a, y[:, 1]) show()
pred_error = pred - y pred_z_error = np.multiply(pred, (1 - pred)) squarebracket = np.multiply(pred_error, pred_z_error) dW = np.zeros(W.shape) # Gradient of MSE with respect to W for i in range(x.shape[0]): dW = np.add(dW, np.outer(squarebracket[i], x[i])) dw_0 = np.sum(squarebracket, axis=0) return ((W - alpha * dW), (w_0 - alpha * dw_0)) figure = plt.figure() axis = figure.add_subplot(1, 1, 1) # Arrays for storing plot values plot_iteration = [] train_errors = [] test_errors = [] error_rate = 1 def get_error_rate(x, y, W, w_0): pred = prediction(x, W, w_0) mistakes = 0 for i in range(pred.shape[0]):
bottomframe = Frame(window) bottomframe.grid( row=6, column=1 ) displayframe = Frame(window) displayframe.grid( row = 11, column= 1) tkvar = StringVar(window) choices = Locations tkvar.set("United States") popupMenu = OptionMenu(bottomframe, tkvar, *choices) popupMenu.grid(row = 1,column=2) popupMenu.configure(width=25, bg = "LightSalmon2") figure = plt.Figure(figsize=(8,7), dpi=100) ax1 = figure.add_subplot(111) chart_type = FigureCanvasTkAgg(figure, frame) chart_type.get_tk_widget().grid(row=3, column = 1) df1 = data[['Location','Cases per 1 million people']].groupby('Location').sum() df1.plot(kind='barh', legend=True, ax=ax1, fontsize = 7, color = "red") ax1.set_title('Cases Per Million People') figure = plt.Figure(figsize=(8,7), dpi=100) ax2 = figure.add_subplot(111) chart_type = FigureCanvasTkAgg(figure, frame) chart_type.get_tk_widget().grid(row=3, column = 3) df2 = data[['Location','Confirmed']].groupby('Location').sum() df2.plot(kind='bar', legend=True, ax=ax2, fontsize = 6) ax2.set_title('Confirmed Cases')