def plot_events(real,pred,meta,real_,pred_, label=None): from matplotlib.pylab import plt import random fig,ax = plt.subplots(figsize=(15, .8)) ax.set_title(label) plt.xlim(0,max(meta[1],10)) ax.set_xticks(np.arange(0,max(real[-1][1],10),.1),minor=True) maxsize=20 # random.random()/4 for i in range(min(maxsize,len(pred_))): d = pred_[i] plt.axvspan(d[0], d[1], 0, 0.6,linewidth=0,edgecolor='k',facecolor='#edb4b4', alpha=.6) plt.text((d[1] + d[0]) / 2, 0.2,f'{i}' , horizontalalignment='center', verticalalignment='center') for i in range(min(maxsize,len(pred))): d = pred[i] plt.axvspan(d[0], d[1], 0.0, 0.6,linewidth=0,edgecolor='k',facecolor='#a31f1f', alpha=.6) plt.text((d[1] + d[0]) / 2, 0.2,f'{i}' , horizontalalignment='center', verticalalignment='center') # maxsize=len(real) for i in range(min(maxsize,len(real_))): gt = real_[i] plt.axvspan(gt[0], gt[1], 0.4, 1,linewidth=0,edgecolor='k',facecolor='#d2f57a', alpha=.6) plt.text((gt[1] + gt[0]) / 2, 0.8,f'{i}' , horizontalalignment='center', verticalalignment='center') for i in range(min(maxsize,len(real))): gt = real[i] plt.axvspan(gt[0], gt[1], 0.4, 1,linewidth=0,edgecolor='k',facecolor='#1fa331', alpha=.6) plt.text((gt[1] + gt[0]) / 2, 0.8,f'{i}' , horizontalalignment='center', verticalalignment='center') # plt.grid(True) plt.minorticks_on() ax.set(yticks=[.25,.75], yticklabels=['P','R']) # plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2) plt.show()
def getGraph(): for i, clf in enumerate((svm, rbf_svc, rbf_svc_tunning)): # Se grafican las fronteras plt.subplot(2, 2, i + 1) plt.subplots_adjust(wspace=0.4, hspace=0.4) Z = clf.predict(np.c_[x_matrizSetEntrenamientoVect, y_clases]) #Color en las gráficas Z = Z.reshape(x_matrizSetEntrenamientoVect.shape) plt.contourf(x_matrizSetEntrenamientoVect, y_clases, Z, cmap=plt.cm.Paired, alpha=0.8) #Puntos de entrenamiento plt.scatter(x_matrizSetEntrenamientoVect[:, 0], x_matrizSetEntrenamientoVect[:, 1], c=y_clases, cmap=plt.cm.Paired) plt.xlabel('Longitud Sepal') plt.ylabel('Peso Sepal') plt.xlim(x_matrizSetEntrenamientoVect.min(), x_matrizSetEntrenamientoVect.max()) plt.ylim(y_clases.min(), y_clases.max()) plt.xticks(()) plt.yticks(()) plt.title(titles[i]) plt.show()
def plot_events(real, pred, real_, pred_, label=None): from matplotlib.pylab import plt import random fig, ax = plt.subplots(figsize=(10, 2)) ax.set_title(label) plt.xlim(0, max(real[-1][1], 10)) ax.set_xticks(np.arange(0, max(real[-1][1], 10), .1), minor=True) maxsize = 20 for i in range(min(maxsize, len(pred_))): d = pred_[i] plt.axvspan(d[0], d[1], 0, 0.4, linewidth=1, edgecolor='k', facecolor='m', alpha=.6) for i in range(min(maxsize, len(pred))): d = pred[i] plt.axvspan(d[0], d[1], 0.1, 0.5, linewidth=1, edgecolor='k', facecolor='r', alpha=.6) # maxsize=len(real) for i in range(min(maxsize, len(real_))): gt = real_[i] plt.axvspan(gt[0], gt[1], 0.6, 1, linewidth=1, edgecolor='k', facecolor='y', alpha=.6) for i in range(min(maxsize, len(real))): gt = real[i] plt.axvspan(gt[0], gt[1], 0.5, .9, linewidth=1, edgecolor='k', facecolor='g', alpha=.6) plt.grid(True) plt.minorticks_on() plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
def plot_spectrum(): # Plot telluric lines and fits plt.plot(spec['w'], spec['s'], label='Stellar Spectrum') plt.plot(spec['w'], mod, label='Telluric Model') plt.plot(spec['w'], spec['s'] - mod + 0.5, label='Residuals') plt.xlim(6275, 6305) plt.ylim(0.0, 1.05) plt.xlabel('Wavelength (A)') plt.ylabel('Intensity') plt.title('Telluric Lines')
def plot_spectrum(): # Plot telluric lines and fits plt.plot(spec['w'],spec['s'],label='Stellar Spectrum') plt.plot(spec['w'],mod,label='Telluric Model') plt.plot(spec['w'],spec['s']-mod+0.5,label='Residuals') plt.xlim(6275,6305) plt.ylim(0.0,1.05) plt.xlabel('Wavelength (A)') plt.ylabel('Intensity') plt.title('Telluric Lines')
def plot_score_dist(spacing, std_along, prob_miss, max_distance): from matplotlib.pylab import plt plt.close("Score Dist") plt.figure("Score Dist") d = np.linspace(0, max_distance, 500) plt.plot(d, [score_dist(di, spacing, std_along, prob_miss) for di in d]) plt.vlines(spacing, 0, 1) plt.vlines(spacing * 2, 0, 1, ls='--') plt.annotate("Miss-detect the next mine", (spacing * 2, 0.5), (12, 0), textcoords='offset points') plt.ylabel('$p(d)$') plt.xlabel('$d$') plt.grid() plt.xticks(np.arange(max_distance)) plt.xlim(0, max_distance) plt.savefig('score_dist.pdf')
def plot_piledspectra(): fig = plt.figure(figsize = (6,8)) plt.xlim(5000,9000) specindex = range(0,100,10) offset = np.arange(0,len(specindex)) * 0.5 ylim = [0.5, offset[-1] + 1.3] plt.ylim(ylim[0], ylim[1]) plt.rc('text', usetex=True) plt.rc('font', family='serif') plt.xlabel(r'Restrame Wavelength [ \AA\ ]') plt.ylabel(r'Flux') line_wave = [5175., 5892., 6562.8, 8498., 8542., 8662.] # ['Mgb', 'NaD', 'Halpha', 'CaT', 'CaT', 'CaT'] for line in line_wave: x = [line, line] y = [ylim[0], ylim[1]] plt.plot(x, y, c= 'gray', linewidth=1.0) plt.annotate(r'CaT', xy=(8540.0, ylim[1] + 0.05), xycoords='data', annotation_clip=False) plt.annotate(r'H$\alpha$', xy=(6562.8, ylim[1] + 0.05), xycoords='data', annotation_clip=False) plt.annotate(r'NaD', xy=(5892., ylim[1] + 0.05), xycoords='data', annotation_clip=False) plt.annotate(r'Mg$\beta$', xy=(5175., ylim[1] + 0.05), xycoords='data', annotation_clip=False) for i,j in zip(specindex,offset): iraf.noao.onedspec.continuum(input = GCssorted.ORIGINALFILE.iloc[i] + '[1]', output = '/Volumes/VINCE/OAC/continuum.fits', type = 'ratio', naverage = '3', function = 'spline3', order = '5', low_reject = '2.0', high_reject = '2.0', niterate = '10') data = fits.getdata('/Volumes/VINCE/OAC/continuum.fits', 0) hdu = fits.open(GCssorted.ORIGINALFILE.iloc[i]) header1 = hdu[1].header lamRange = header1['CRVAL1'] + np.array([0., header1['CD1_1'] * (header1['NAXIS1'] - 1)]) wavelength = np.linspace(lamRange[0],lamRange[1], header1['NAXIS1']) hdu.close() zp = 1. + (GCssorted.VREL.iloc[i] / 299792.458) plt.plot(wavelength/zp, gaussian_filter(data,2) + j, c = 'black', lw=1) os.remove('/Volumes/VINCE/OAC/continuum.fits')
GCs = pd.read_csv('/Volumes/VINCE/OAC/GCs_903.csv', dtype = {'ID': object}, comment = '#') # ---------------------------------- rep1 = GCs[GCs.Alt1.isin(GCs.ID)] df1 = pd.DataFrame() df2 = pd.DataFrame() for j in range(0,len(rep1)): df1.iloc[j] = rep1.iloc[j] x = VIMOS['VREL_helio'] xerr = VIMOS['VERR'] y = SchuberthMatch['HRV'] yerr = SchuberthMatch['e.1'] print 'rms (VIMOS - Schuberth) GCs = ', np.std(x-y) plt.close('all') plt.figure(figsize=(6,6)) plt.errorbar(x, y, yerr= yerr, xerr = xerr, fmt = 'o', c ='black', label = 'Schuberth et al.') plt.plot([-200, 2200], [-200, 2200], '--k') plt.xlim(-200,2200) plt.ylim(-200,2200) x = VIMOS['r_auto'] y = SchuberthMatch['Rmag'] plt.scatter(x, y, c ='black')
mask = (((result['g_auto'] - result['r_auto']) < (0.2 + 0.6 * (result['g_auto'] - result['i_auto']))) & ((result['g_auto'] - result['r_auto']) > (-0.2 + 0.6 * (result['g_auto'] - result['i_auto']))) & ((result['g_auto'] - result['i_auto']) > 0.5) & ((result['g_auto'] - result['i_auto']) < 1.3) & ((result['i_auto']) < 24)) subset = result[mask] subset = subset.sample(n=1000) plt.figure() plt.scatter(result['g_auto'] - result['i_auto'], result['g_auto'] - result['r_auto'], s=10, c='gray', edgecolor='none', alpha = 0.5) plt.scatter(subset['g_auto'] - subset['i_auto'], subset['g_auto'] - subset['r_auto'], s=20, c='blue', edgecolor='none') plt.scatter(GCs['g_auto'] - GCs['i_auto'], GCs['g_auto'] - GCs['r_auto'], s=10, c='red', edgecolor='none') plt.xlabel('(g - i)') plt.ylabel('(g - r)') plt.xlim(-1,4) plt.ylim(-1,4) plt.figure() plt.scatter(subset['g_auto'] - subset['r_auto'], subset['r_auto'], s=30, c='blue', edgecolor='none') plt.scatter(GCs['g_auto'] - GCs['r_auto'], GCs['i_auto'], s=8, c='red', edgecolor='none') plt.ylim(13,24) plt.gca().invert_yaxis() plt.xlabel('(g - i)') plt.ylabel('i') plt.figure() plt.scatter(result['g_auto'] - result['u_auto'], result['g_auto'] - result['r_auto'], s=10, c='gray', edgecolor='none', alpha = 0.5) plt.scatter(subset['g_auto'] - subset['u_auto'], subset['g_auto'] - subset['r_auto'], s=20, c='blue', edgecolor='none') plt.scatter(GCs['g_auto'] - GCs['u_auto'], GCs['g_auto'] - GCs['r_auto'], s=10, c='red', edgecolor='none') plt.scatter(galaxies['g_auto'] - galaxies['u_auto'], galaxies['g_auto'] - galaxies['r_auto'], s=10, c='green', edgecolor='none')
color='firebrick', linestyle='--') sns.kdeplot(APDF2, bw=1.5, linewidth=3, label='APDF2', color='mediumseagreen', linestyle='--') sns.kdeplot(APDF3, bw=1.5, linewidth=3, label='APDF3', color='goldenrod', linestyle='--') plt.xlabel('Temperature ($^{\circ}$C)', fontsize=18, **hfont) plt.xlim(1, 20) for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(16) for tick in ax.yaxis.get_major_ticks(): tick.label.set_fontsize(16) plt.title('(b)', fontsize=18, **hfont) #%% xL = 0 yL = 0 sigma = 6 bias = 20 res = 3 x1 = np.random.normal(xL + bias, sigma, 3000)[::res] x2 = np.append(np.random.normal(xL, sigma, 2000),
assert data.shape == (240, len(headers)) # In[100]: get_ipython().run_cell_magic( 'latex', '', '$\\textbf{Visualize the Correlations}: $\n$\\text{Cor}(X_i,Y_j) = \\frac{\\text{Cov}(X_i,Y_j)}{\\sigma_{X_i}\\sigma_{Y_j}}$' ) # In[101]: R = np.corrcoef(data.T) plt.figure(figsize=(10, 8)) plt.pcolor(R) plt.colorbar() plt.xlim([0, len(headers)]) plt.ylim([0, len(headers)]) plt.xticks(np.arange(32) + 0.5, np.array(headers), rotation='vertical') plt.yticks(np.arange(32) + 0.5, np.array(headers)) plt.show() # In[108]: #Lets fit both the models using PCA/FA down to two dimensions. #construct a function implementing the factor analysis which returns a vector of n_components largest # variances and the corresponding components (as column vectors in a matrix). You can # check your work by using decomposition.FactorAnalysis from sklearn #### ~THIS FUNCTION IS WAS A STAB, NEW CODE HERE: ###########
y = SchuberthMatch['HRV'] yerr = SchuberthMatch['e.1'] print 'rms (VIMOS - Schuberth) GCs = ', np.std(x - y) plt.close('all') plt.figure(figsize=(6, 6)) plt.errorbar(x, y, yerr=yerr, xerr=xerr, fmt='o', c='black', label='Schuberth et al.') plt.plot([-200, 2200], [-200, 2200], '--k') plt.xlim(-200, 2200) plt.ylim(-200, 2200) x = VIMOS['r_auto'] y = SchuberthMatch['Rmag'] plt.scatter(x, y, c='black') # ---------------------------------- # ---------------------------------- cat1 = coords.SkyCoord(stars['RA_g'], stars['DEC_g'], unit=(u.degree, u.degree)) cat2 = coords.SkyCoord(S_Stars['RA'], S_Stars['DEC'],
train_scores_std = np.std(train_scores, axis=1) test_scores_mean = np.mean(test_scores, axis=1) test_scores_std = np.std(test_scores, axis=1) plt.grid() plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std, alpha=0.1, color="r") plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std, alpha=0.1, color="g") plt.plot(train_sizes, train_scores_mean, '-', color="r", label="Training score") plt.plot(train_sizes, test_scores_mean, '-', color="g", label="Cross-validation score") plt.ylim(0,1.2) plt.xlim(0,200) plt.legend(loc="best") plt.xlabel("Train test size") plt.savefig("learning_curves.png") plt.close("all") # plot the model vs the predictions for what_plot in [0,1,2,3]: fig=plt.figure(figsize=(16,8)) # ax1 = fig.add_subplot(1,2,1); ax2 = fig.add_subplot(1,2,2) ax1.tick_params(labelsize=20); ax2.tick_params(labelsize=20) # if(what_plot==3):
recorded_h = df['EOG_H'].as_matrix() classifier = cal_mod.OrbitClassify(300, 200, 0.6, 0.5) classifier.setLowPassfilter(2, 0.17) rec_v = classifier.LowPassFilter(df['EOG_V'].as_matrix()) plt.subplot(3, 1, 1) plt.plot(rec_v) maxtab, mintab = peak_example.peakdet(rec_v, .3) plt.subplot(3, 1, 2) plt.plot(rec_v) plt.scatter(np.array(maxtab)[:, 0], np.array(maxtab)[:, 1], color='blue') plt.scatter(np.array(mintab)[:, 0], np.array(mintab)[:, 1], color='red') plt.xlim(0, 900) """ buffer Murtaza """ # buffer_vv = np.array([]) # for i in range(0,len(rec_v)): # if len(buffer_vv) < 50: # buffer_vv = np.append(buffer_vv,rec_v) # else: # buffer_vv = buffer_vv[1:] # buffer_vv = np.append(buffer_vv,rec_v) # if len(buffer_vv) > 1: # rec_v = rec_v - np.mean(buffer_vv) """ Simple Moving Average """ Nma1 = 20 A1 = np.ones(Nma1) / 20.0
def drawErrorPlot(self, error_list): plt.xlim(0, len(error_list)) plt.xlabel('Squared Error') plt.plot(error_list) plt.show()
#graph 2 (contour graph) x = np.arange(-5, 5, 0.01) y = np.arange(-5, 5, 0.01) X, Y = np.meshgrid(x, y) Z = function_1(np.array([X, Y])) idx = 1 plt.subplot(2, 2, idx) idx += 1 plt.plot( x_process[:,0], x_process[:,1], '.-', color="blue") #수렴과정 plt.contour(X, Y, Z) plt.ylim(-5, 5) plt.xlim(-5, 5) plt.plot(0, 0, '+', color = "red") #극소점 plt.show() #graph 3 (3 dimensional graph) x, x_process = gradient_descent_process(function_1, np.array([-3.0, 4.0]), L = 0.1, I = 50 ) x = np.arange(-5, 5, 0.01) y = np.arange(-5, 5, 0.01) x, y = np.meshgrid(x, y) z = function_1(np.array([X, Y])) fig = plt.figure()
import numpy as np from matplotlib.pylab import plt fig, ax = plt.subplots() plt.legend() ax.spines['left'].set_position('zero') ax.spines['right'].set_color('none') ax.spines['bottom'].set_position('zero') ax.spines['top'].set_color('none') plt.grid() plt.ylim(-4.5,4.5) plt.xlim(-5.5,5.5) ax.xaxis.set_ticks([-5,-4,-3,-2,-1,0,1,2,3,4,5]) plt.show()
color='k', alpha=0.8) plt.scatter(x1, y1, color=firstcloudcolor, alpha=1, label='first distribution') plt.scatter(x2, y2, color=secondcloudcolor, alpha=1, marker='+', s=60, label='second distribution') plt.scatter(xL, yL, color='k', marker='P', s=135, label='release location') plt.xlabel('$^{\circ}$E', fontsize=18) ax.tick_params(labelbottom=False, labelleft=False) plt.title('(c)', fontsize=18) plt.xlim(-40, 40) plt.ylim(-40, 40) plt.legend(bbox_to_anchor=(1.92, 1.05)) step = 8 xs, ys = np.mgrid[-44:48:step, -44:48:step] vs = np.ones(xs.shape, dtype=bool) xss = xs[:, 0] yss = ys[0] boxes = [] for i in range(len(x1)): nex = find_nearest_index(xss, x1[i]) ney = find_nearest_index(yss, y1[i])
import numpy as np from matplotlib.pylab import plt def relu(_x): '''nn一个激活函数''' return np.maximum(0, _x) if __name__ == "__main__": x = np.arange(-5.0, 5.0, 0.1) y = relu(x) plt.plot(x, y) plt.xlim(-6, 6) plt.show()
Y = np.arange(-10, 10, 0.01) X, Y = np.meshgrid(X, Y) Z = function_1(np.array([X, Y])) # 외곽선 단순화 mask = Z > 10 Z[mask] = 0 plt.subplot(2, 3, idx) idx += 1 plt.plot(x_process[:, 0], x_process[:, 1], '.-', color="blue") plt.contour(X, Y, Z) plt.ylim(-10, 10) plt.xlim(-10, 10) plt.plot(0, 0, '+', color="red") # 극소점 plt.title(key) plt.show() #graph comparision _2 (2 dimensional) idx = 1 for key in optimizers: optimizer = optimizers[key] def process(f, init_x, I=100): x = init_x process = []
# data_name = "blinkfrequency.csv" # data_name = "jun_video.csv" df = pd.DataFrame.from_csv(data_path + data_name) # df.index = pd.to_datetime(df['DATE'], unit='s') recorded_v = df['EOG_V'].as_matrix() recorded_h = df['EOG_H'].as_matrix() """Show Original Data""" plt.plot(recorded_v, label="Vertical EOG") plt.plot(recorded_h, label="Horizental EOG") plt.legend() plt.title("Original Data", fontsize=25) plt.xlabel("Time", fontsize=20) plt.ylabel("EOG Level ( Range in $\mu$$V$ )", fontsize=20) plt.xlim(0, 1200) plt.show() """Simple Moving Average""" Nma1 = 10 A1 = np.ones(Nma1) / 10.0 Vv_SMA = np.convolve(recorded_v, A1, 'valid') Vh_SMA = np.convolve(recorded_h, A1, 'valid') plt.plot(Vv_SMA, label="Vertical EOG") plt.plot(Vh_SMA, label="Horizental EOG") plt.legend() plt.title("SMA Data", fontsize=25) plt.xlabel("Time", fontsize=20) plt.ylabel("EOG Level ( Range in $\mu$$V$ )", fontsize=20) plt.xlim(0, 1200) plt.show() """Sampling windows size 10 , 0.1 sec"""