None:\\n\\n if val is None:\\n val = self.history.MAP\\n\\n if roi is None:\\n roi = np.array([0])\\n elif np.isscalar:\\n roi = np.array([roi])\\n\\n data = self.data\\n\\n dt = val.dt\\n gain = val.gain\\n states = val.states\\n mu_flor = val.mu_flor\\n mu_back = val.mu_back\\n num_rois = val.num_rois\\n num_load = val.num_load\\n num_data = val.num_data\\n num_states = val.num_states\\n\\n times = np.arange(num_data) * dt\\n\\n fig = plt.gcf()\\n fig.clf()\\n #fig.set_size_inches(12, 6)\\n ax = np.empty((len(roi), 1), dtype=object)\\n gs = gridspec.GridSpec(nrows=len(roi), ncols=1, figure=fig)\\n ax[0, 0] = fig.add_subplot(gs[0, 0])\\n for i in range(len(roi)-1):\\n ax[i, 0] = fig.add_subplot(gs[i, 0], sharex=ax[0, 0])\\n\\n for i, r in enumerate(roi):\\n\\n brightness = mu_flor @ states_to_pops(states[r, :, :], num_states) + mu_back[r]\\n\\n ax[i, 0].set_title('roi {}: {} flors'.format(r, val.num_flor[r]))\\n ax[i, 0].set_xlabel('time (s)')\\n ax[i, 0].set_ylabel('brightness (ADU)')\\n ax[i, 0].plot(times, data[r, :], color='g', label='data')\\n ax[i, 0].plot(times, brightness * gain, color='b', label='sampled')\\n ax[i, 0].legend()\\n\\n # plt.tight_layout()\\n plt.pause(.1)\\n\\n return\",\n \"def show_dbscan():\\n\\n # simulate normal hourly data\\n weekday = ([0.05, 0.95], 0.05) #bath, bed\\n weekend = ([0.3, 0.7], 0.1)\\n roomperwd, truelabelswd = make_blobs(n_samples=23, centers=weekday[0],\\n cluster_std=weekday[1], random_state=0)\\n roomperwe, truelabelswe = make_blobs(n_samples=8, centers=weekend[0],\\n cluster_std=weekend[1], random_state=0)\\n\\n # combine modes\\n roompers = np.vstack((roomperwd, roomperwe))\\n\\n # make positive and sum to one to simulate valid distribution\\n for i in range(roompers.shape[0]):\\n for j in range(roompers.shape[1]):\\n if roompers[i, j] < 0:\\n roompers[i, j] = 0\\n roompersnorm = normalize(roompers, norm='l1')\\n\\n # simulate anomaly on most recent day where don't leave bedroom\\n roompersnorm[-1, :] = np.array([0.8, 0.2])\\n\\n # detect outliers\\n roompersdetector = HourlyRoomPercentageAnomalyDetection(roompersnorm, eps=0.3, min_samples=3)\\n labels = roompersdetector.scale_and_proximity_cluster(eps=0.3, min_samples=3)\\n\\n # plot results\\n plt.figure()\\n seenflag1 = False; seenflag2 = False; seenflag3 = False;\\n for i, label in enumerate(labels):\\n if label == 0:\\n if seenflag1:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'ro')\\n else:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'ro', label='Cluster 1')\\n seenflag1 = True\\n elif label == 1:\\n if seenflag2:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'kx')\\n else:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'kx', label='Cluster 2')\\n seenflag2 = True\\n elif label == -1:\\n if seenflag3:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'b^')\\n else:\\n plt.plot(roompersnorm[i][0], roompersnorm[i][1], 'b^', label='Outlier')\\n seenflag3 = True\\n plt.legend(loc='lower left')\\n plt.axis([0, 1, 0, 1])\\n plt.show()\",\n \"def plot_field_uncertainties():\\n\\n resize_size = (1000, 1000)\\n\\n\\n dirs = [os.path.join(path_to_here, '../data/landscape_visualizations/{}/{}/'.format(drug_name, j)) for j in ['original', 'repeat_a', 'repeat_b']]\\n if drug_name == 'DMSO':\\n dirs = [os.path.join(path_to_here, '../data/landscape_visualizations/{}/{}/30_hours/'.format(drug_name, j)) for j in ['original', 'repeat_a', 'repeat_b']]\\n\\n def transform(x):\\n if type(x) is np.ndarray:\\n x = change_array_lims(x)\\n x = np.log(x)\\n return x\\n\\n F_unc_vmin = -7\\n F_unc_vmax = -4\\n sigma_vmin = -5\\n sigma_vmax = 0 #0.4\\n sigma_unc_vmin = -6\\n sigma_unc_vmax = -2\\n\\n fig_Fs = [plt.figure() for _ in range(3)]\\n fig_uncertainty = plt.figure()\\n sigma_lists, F_arrays = [], []\\n for idx_fig, dir in enumerate(dirs):\\n\\n p_list = _load_and_resize_list(dir+'p_list_0.pickle')\\n D_list = _load_and_resize_list(dir+'D_list_0.pickle')\\n U_array = pickle.load(open(dir+'U.pickle', 'rb'))\\n U_array = cv2.resize(U_array, resize_size, interpolation = cv2.INTER_LINEAR)\\n Gx, Gy = np.gradient(U_array, 26./resize_size[0], 26./resize_size[0]) # gradients with respect to x and y\\n F_array = (Gx**2+Gy**2)**.5 # gradient magnitude\\n F_array[np.isinf(F_array)] = np.nan\\n F_array[p_list[-1]<1e-3]=np.nan # final PDF\\n sigma_list = []\\n for j in range(9):\\n arr = D_list[2*j] # current PDF\\n arr[p_list[j]<1e-3]=np.nan\\n sigma_list.append(np.sqrt(2*arr))\\n\\n\\n sigma_lists.append(sigma_list)\\n F_arrays.append(F_array)\\n\\n ax = fig_Fs[idx_fig].add_subplot(111)\\n ax.imshow(transform(F_array)[::-1, :], cmap = cmap, vmin = -4.6, vmax = -2)\\n ax.set_title(dir)\\n\\n all_axes = [i for j in fig_Fs for i in j.axes]\\n for ax in all_axes:\\n ax.axis('off')\\n\\n # uncertainties\\n\\n std = np.std(F_arrays, axis = 0)\\n ax = fig_uncertainty.add_subplot(121)\\n ax.imshow(transform(std)[::-1, :], cmap = cmap, vmin = F_unc_vmin, vmax = F_unc_vmax)\\n ax.set_title('F_uncertainty')\\n\\n fig_sigma = plt.figure()\\n ax = fig_sigma.add_subplot(111)\\n ax.imshow(transform(np.nanmean(sigma_lists[0], axis = 0))[::-1, :], cmap = cmap, vmin = sigma_vmin, vmax = sigma_vmax) # index 0 (i.3 'original' is corresponds to the landscapes in other figures)\\n ax.set_title('sigma_mean')\\n\\n sigma_means = [np.nanmean(sigma_list, axis = 0) for sigma_list in sigma_lists]\\n std_array = np.nanstd(sigma_means, axis = 0)\\n ax = fig_uncertainty.add_subplot(122)\\n ax.imshow(transform(std_array)[::-1, :], cmap = cmap, vmin = sigma_unc_vmin, vmax = sigma_unc_vmax)\\n ax.set_title('sigma_uncertainty')\\n\\n fig_sigma.savefig(path_to_here+'/../outputs/{}_mean_sigma.png'.format(drug_name), dpi = 1200)\\n fig_uncertainty.savefig(path_to_here+'/../outputs/{}_uncertainties.png'.format(drug_name), dpi = 1200)\",\n \"def dump_step(self,status):\\n super(vanderpol_output,self).dump_step(status)\\n\\n L = self.level\\n\\n oldcol = self.sframe\\n # self.sframe = self.ax.scatter(L.uend.pos.values[0],L.uend.pos.values[1],L.uend.pos.values[2])\\n self.sframe = self.ax.scatter(L.uend.values[0],L.uend.values[1])\\n # Remove old line collection before drawing\\n # if oldcol is not None:\\n # self.ax.collections.remove(oldcol)\\n plt.pause(0.00001)\\n\\n return None\",\n \"def visibility(azmap, rmap, elmap, DEM_res, DEM_xmin, DEM_ymin, rad_x, rad_y,\\n dr, daz, verbose=True):\\n\\n range_max = np.max(rmap)\\n nr = int(range_max / dr)\\n nrows = len(elmap)\\n ncols = len(elmap[0])\\n visib = np.zeros([nrows, ncols])\\n minviselev = np.zeros([nrows, ncols])\\n\\n rr_start = dr / 2\\n\\n azmin = 0.\\n azmax = 0.\\n rrmin = 0.\\n rrmax = 0.\\n azmin_sin = 0.\\n azmax_sin = 0.\\n azmin_cos = 0.\\n azmax_cos = 0.\\n el_rmin_azmin = 0.\\n el_rmin_azmax = 0.\\n el_rmax_azmin = 0.\\n el_rmax_azmax = 0.\\n el_max = 0.\\n el_max_prev = 0.\\n\\n kx_rmin_azmin = 0\\n kx_rmin_azmax = 0\\n kx_rmax_azmin = 0\\n kx_rmax_azmax = 0\\n ky_rmin_azmin = 0\\n ky_rmin_azmax = 0\\n ky_rmax_azmin = 0\\n ky_rmax_azmax = 0\\n\\n radkx = int(np.round((rad_x - DEM_xmin) / DEM_res))\\n radky = int(np.round((rad_y - DEM_ymin) / DEM_res))\\n for kx in range(radkx - 1, radkx + 2):\\n for ky in range(radky - 1, radky + 2):\\n visib[ky, kx] = 100\\n minviselev[ky, kx] = elmap[ky, kx]\\n\\n az_ = np.arange(0, 360 + daz, daz)\\n\\n for azind in range(len(az_)):\\n if verbose:\\n logging.info(f'Computing azimuth {az_[azind]:2.1f}')\\n az = az_[azind]\\n azmin = az - daz / 2.\\n azmax = az + daz / 2.\\n if azmin < 0:\\n azmin = 360. + azmin\\n indseta = np.logical_or(np.logical_and(azmap >= 0,\\n azmap < azmax),\\n np.logical_and(azmap >= azmin,\\n azmap <= 360.))\\n elif azmax > 360:\\n azmax = azmax - 360.\\n indseta = np.logical_or(np.logical_and(azmap >= azmin,\\n azmap <= 360),\\n np.logical_and(azmap >= 0,\\n azmap < azmax))\\n else:\\n indseta = np.logical_and(azmap >= azmin,\\n azmap < azmax)\\n\\n azmin_sin = np.sin(azmin * np.pi / 180.)\\n azmax_sin = np.sin(azmax * np.pi / 180.)\\n azmin_cos = np.cos(azmin * np.pi / 180.)\\n azmax_cos = np.cos(azmax * np.pi / 180.)\\n\\n indseta = np.where(indseta)\\n\\n el_max_prev = -90\\n for rind in range(nr):\\n rr = rr_start + rind * dr\\n\\n rrmin = rr - dr / 2\\n rrmax = rr + dr / 2\\n\\n if np.any(indseta):\\n\\n indsetr = np.logical_and(rmap[indseta] >= rrmin,\\n rmap[indseta] < rrmax)\\n\\n indsetr = tuple([indseta[0][indsetr],\\n indseta[1][indsetr]]) # Cells to set\\n\\n kx_rmin_azmin = radkx + \\\\\\n int(round((rrmin * azmin_sin) / DEM_res))\\n kx_rmin_azmax = radkx + \\\\\\n int(round((rrmin * azmax_sin) / DEM_res))\\n kx_rmax_azmin = radkx + \\\\\\n int(round((rrmax * azmin_sin) / DEM_res))\\n kx_rmax_azmax = radkx + \\\\\\n int(round((rrmax * azmax_sin) / DEM_res))\\n\\n ky_rmin_azmin = radky + \\\\\\n int(round((rrmin * azmin_cos) / DEM_res))\\n ky_rmin_azmax = radky + \\\\\\n int(round((rrmin * azmax_cos) / DEM_res))\\n ky_rmax_azmin = radky + \\\\\\n int(round((rrmax * azmin_cos) / DEM_res))\\n ky_rmax_azmax = radky + \\\\\\n int(round((rrmax * azmax_cos) / DEM_res))\\n\\n el_rmin_azmin = -90.\\n el_rmin_azmax = -90.\\n el_rmax_azmin = -90.\\n el_rmax_azmax = -90.\\n\\n if ((kx_rmin_azmin >= 0) and (kx_rmin_azmin < ncols) and\\n (ky_rmin_azmin >= 0) and (ky_rmin_azmin < nrows)):\\n el_rmin_azmin = elmap[ky_rmin_azmin, kx_rmin_azmin]\\n if ((kx_rmin_azmax >= 0) and (kx_rmin_azmax < ncols) and\\n (ky_rmin_azmax >= 0) and (ky_rmin_azmax < nrows)):\\n el_rmin_azmin = elmap[ky_rmin_azmax, kx_rmin_azmax]\\n if ((kx_rmax_azmin >= 0) and (kx_rmax_azmin < ncols) and\\n (ky_rmax_azmin >= 0) and (ky_rmax_azmin < nrows)):\\n el_rmin_azmin = elmap[ky_rmax_azmin, kx_rmax_azmin]\\n if ((kx_rmax_azmax >= 0) and (kx_rmax_azmax < ncols) and\\n (ky_rmax_azmax >= 0) and (ky_rmax_azmax < nrows)):\\n el_rmin_azmin = elmap[ky_rmax_azmax, kx_rmax_azmax]\\n\\n el_max = max([el_rmin_azmin, el_rmin_azmax,\\n el_rmax_azmin, el_rmax_azmax])\\n\\n if np.any(indsetr):\\n el_max = max([el_max, np.max(elmap[indsetr])])\\n if el_max >= el_max_prev:\\n visib[indsetr] = 100\\n minviselev[indsetr] = el_max\\n else:\\n minviselev[indsetr] = el_max_prev\\n\\n el_max = max([el_max, el_max_prev])\\n el_max_prev = el_max\\n\\n return visib, minviselev\",\n \"def rpoints(self):\\n return self.gmap.interp_gpos(self.points)\",\n \"def map_sim_property(**kwargs):\\n\\n GR = glo.global_results()\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n counter = 0\\n fignum = 1\\n if p.gal_index == 'all':\\n\\n for gal_index in GR.N_gal - np.arange(GR.N_gal) - 1:\\n\\n if counter == 0:\\n fig, axes = plt.subplots(3, 3, figsize=(20,15))\\n axs = [axes[0,0],axes[0,1],axes[0,2],axes[1,0],axes[1,1],axes[1,2],axes[2,0],axes[2,1],axes[2,2]]\\n counter = 9\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type='simgas')\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \\n\\n # Plot\\n Rmax = max_scale/2\\n ax1 = axs[9 - counter]\\n if p.log:\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\\n map2D = np.log10(map2D)\\n if not p.log:\\n map2D[map2D < p.vmin] = p.vmin/2\\n map2D[map2D > p.vmax] = p.vmax\\n im = ax1.imshow(map2D,\\\\\\n extent=[-Rmax,Rmax,-Rmax,Rmax],vmin=p.vmin,cmap=p.cmap)\\n fig.colorbar(im,shrink=0.8,ax=ax1,label=lab)\\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n # Limit axes limits a bit to avoid area with no particles...\\n ax1.set_xlim([-0.99*Rmax,0.99*Rmax])\\n ax1.set_ylim([-0.99*Rmax,0.99*Rmax])\\n if (p.prop == 'm') & (p.text == True):\\n ax1.text(0.05,0.85,'M$_{gas}$=%.2eM$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.75,'SFR=%.2eM$_{\\\\odot}$/yr' % GR.SFR[gal_index],\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n\\n counter -= 1\\n\\n #if counter == 0:\\n # ax1 = plt.subplots(1, 1)\\n #cbar = fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.95, label=lab)\\n # fig.colorbar(im,shrink=0.8,label=lab)\\n\\n if counter == 0 or gal_index == GR.N_gal-1:\\n print('Saving in ' + p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format))\\n # plt.tight_layout()\\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/')\\n plt.savefig(p.d_plot + 'sim_data/map_%s_%s_gals_%i.%s' % (p.prop,p.z1,fignum,p.format), format=p.format, dpi=250, facecolor='w')\\n fignum += 1\\n\\n else:\\n if p.add:\\n fig, ax1 = plt.gcf(), p.ax\\n if not p.add:\\n fig = plt.figure(figsize=(8,6))\\n ax1 = fig.add_axes([0.1, 0.01, 0.8, 0.8]) \\n ax1.axis('equal')\\n\\n gal_ob = gal.galaxy(GR=GR, gal_index=p.gal_index)\\n simgas = aux.load_temp_file(gal_ob=gal_ob,data_type=p.sim_type)\\n if p.R_max:\\n # Cut out square\\n simgas = simgas[(np.abs(simgas.x) < p.R_max) & (np.abs(simgas.y) < p.R_max)]\\n # Add bottom left corner\\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\\n extra_row['x'],extra_row['y'] = -p.R_max,-p.R_max\\n extra_row[p.prop] = 0\\n simgas = simgas.append(extra_row).reset_index(drop=True) \\n # Add top right corner\\n extra_row = simgas.iloc[0] # to ensure that map gets the right size\\n extra_row['x'],extra_row['y'] = p.R_max,p.R_max\\n extra_row[p.prop] = 0\\n simgas = simgas.append(extra_row).reset_index(drop=True) \\n else:\\n pass\\n map2D,lab,max_scale = make_projection_map(simgas,prop=p.prop)\\n if p.prop == 'm': map2D = map2D * simgas.m.sum()/np.sum(map2D) \\n print('Min and max of map: ',map2D.min(),map2D.max())\\n #map2D[map2D < 1e4] = 1e6\\n # Plot map\\n if not p.R_max:\\n p.R_max = max_scale/2\\n if p.log: \\n if not p.vmax: p.vmax = np.log10(map2D).max()\\n if not p.vmin: p.vmin = np.log10(map2D).max() - 4\\n map2D[map2D < 10.**p.vmin] = 10.**p.vmin/2\\n map2D[map2D > 10.**p.vmax] = 10.**p.vmax\\n map2D = np.log10(map2D)\\n else:\\n if not p.vmax: p.vmax = np.max(map2D)\\n if not p.vmin: p.vmin = np.min(map2D) / 1e3\\n map2D[map2D < p.vmin] = p.vmin #np.min(map2D[map2D > 0])\\n map2D = np.flipud(map2D)\\n\\n im = ax1.imshow(map2D,\\\\\\n extent=[-max_scale/2,max_scale/2,-max_scale/2,max_scale/2],vmin=p.vmin,vmax=p.vmax,cmap=p.cmap)\\n # Limit axes limits a bit to avoid area with no particles...\\n zoom = 1#/1.5\\n ax1.set_xlim([-1/zoom * p.R_max,1/zoom * p.R_max])\\n ax1.set_ylim([-1/zoom * p.R_max,1/zoom * p.R_max])\\n if p.colorbar: \\n divider = make_axes_locatable(ax1)\\n cax1 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n fig.colorbar(im,cax=cax1,label=lab)\\n if not p.add: ax1.set_xlabel('x [kpc]'); ax1.set_ylabel('y [kpc]')\\n if (p.prop == 'm') & (p.text == True):\\n simstar = aux.load_temp_file(gal_ob=gal_ob,data_type='simstar')\\n ax1.text(0.05,0.92,'M$_{star}$=%.1e M$_{\\\\odot}$' % np.sum(simstar.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.86,'M$_{gas}$=%.1e M$_{\\\\odot}$' % np.sum(simgas.m),\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n ax1.text(0.05,0.80,'SFR=%.2f M$_{\\\\odot}$/yr' % GR.SFR[p.gal_index],\\\\\\n fontsize=14,transform=ax1.transAxes,color='white')\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'sim_data/'): os.mkdir(p.d_plot + 'sim_data/') \\n plt.savefig(p.d_plot + 'sim_data/map_%s_G%i.png' % (p.prop,p.gal_index), format=p.format, dpi=250, facecolor='w')\\n\\n if not p.colorbar: return(im)\",\n \"def main():\\n \\n # boundaries of the array\\n n = 30\\n m = 70\\n \\n # array generated\\n arr = rangeArray(n,m)\\n \\n # Print section\\n print(\\\"Array from {} to {}: {}\\\".format(n, m, arr))\",\n \"def show_path_2D(start, end, coordinates, polygons, clear = True):\\n global L, N, delta_t\\n\\n # start interactive mode\\n plt.ion()\\n\\n # crete eempty figure on which data will go and first subplot\\n fig = plt.figure()\\n\\n # get into the correct time step\\n for time_step in range(start, end):\\n # list of colours used for animation\\n colours = cm.rainbow(np.linspace(0, 1, N))\\n\\n # loop over each particle and colour\\n for i in range(N):\\n # plot x, y poistion of particle in a given colour and set axis to size of box\\n plt.scatter(coordinates[time_step][i][0], coordinates[time_step][i][1], s = 1, color = 'r')\\n\\n # plot the object\\n if i < M:\\n polygon = np.array(polygons[time_step][i])\\n # get the points of the polygon to plot it\\n x, y = polygon.T\\n\\n # print(x, y)\\n\\n x = np.append(x, x[0])\\n y = np.append(y, y[0])\\n\\n # print(x, y)\\n\\n # plot the polygon\\n plt.plot(x , y)\\n # plt.scatter(polygons_com[time_step][i][0], polygons_com[time_step][i][1], s = 5, color = 'g')\\n\\n if bound_cond == True:\\n plt.axis([0, L, 0, L])\\n plt.axis([0, L, 0, L])\\n # plt.axis([-L*2, L*2, -L*2, L*2])\\n\\n # show graph\\n plt.show()\\n plt.pause(time_pause)\\n\\n # decide if you want to clear\\n if clear == True:\\n plt.clf()\\n\\n return None\",\n \"def make_plot(range_km, unfolded_phidp, refl, phidp, kdp, filename):\\n\\n from matplotlib import pyplot as plt\\n\\n fig = plt.figure(figsize=[10, 5])\\n ax = fig.add_subplot(111)\\n\\n # filtered phidp and unfolded phidp\\n (p1,) = ax.plot(range_km, phidp[\\\"data\\\"][0], \\\"b-\\\")\\n (p2,) = ax.plot(range_km, unfolded_phidp[\\\"data\\\"][0], \\\"g-\\\")\\n\\n # set labels\\n ax.set_ylim(0, 250)\\n ax.set_ylabel(\\\"Differential phase shift (degrees)\\\")\\n ax.set_xlabel(\\\"Range (km)\\\")\\n\\n # plot KDP and reflectivity on second axis\\n ax2 = ax.twinx()\\n (p3,) = ax2.plot(range_km, kdp[\\\"data\\\"][0], \\\"r-\\\")\\n (p4,) = ax2.plot(range_km, refl[\\\"data\\\"][0] / 10.0)\\n\\n # decorate and save\\n ax2.yaxis.grid(color=\\\"gray\\\", linestyle=\\\"dashed\\\")\\n ax.legend(\\n [p1, p2, p3, p4],\\n [\\\"Filtered phiDP\\\", \\\"Unfolded phiDP\\\", \\\"KDP\\\", \\\"Z/10.0\\\"],\\n loc=\\\"upper left\\\",\\n )\\n fig.savefig(filename)\",\n \"def P_AI_Rocky(in_dict):\\n # START\\n fs = 16\\n plt.rc('font', size=fs)\\n fig = plt.figure(figsize=(14,12))\\n ds = nc.Dataset(in_dict['fn'])\\n\\n # PLOT CODE\\n aa = [-122.8, -122.54, 47.92, 48.22]\\n import cmocean\\n cmap = cmocean.cm.balance\\n # cmap = 'RdYlBu_r'\\n\\n from warnings import filterwarnings\\n filterwarnings('ignore') # skip some warning messages\\n \\n # plot Code\\n \\n # calculate divergence and vorticity\\n uu = ds['u'][0, -1, :, :]\\n vv = ds['v'][0, -1, :, :]\\n u = zfun.fillit(uu)\\n v = zfun.fillit(vv)\\n u[np.isnan(u)] = 0\\n v[np.isnan(v)] = 0\\n \\n G = zrfun.get_basic_info(in_dict['fn'], only_G=True)\\n \\n dive = ((np.diff(u, axis=1)/G['DX'][:, 1:-1])[1:-1, :]\\n + (np.diff(v, axis = 0)/G['DY'][1:-1, :])[:, 1:-1])\\n #dive[G['mask_rho'][1:-1,1:-1]==False] = np.nan\\n \\n vort = np.diff(v, axis=1)/G['DX'][1:,1:] - np.diff(u, axis=0)/G['DY'][1:,1:]\\n #vort[G['mask_rho'][1:,1:]==False] = np.nan\\n \\n scl = 2e-3\\n \\n # panel 1\\n ax = fig.add_subplot(121)\\n # cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], dive/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_rho'][1:-1,1:-1], G['lat_rho'][1:-1,1:-1], dive/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Divergence (%0.1e $s^{-1}$)' % (scl))\\n #pfun.add_bathy_contours(ax, ds, txt=True)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_ylabel('Latitude')\\n ax.set_title(tstr)\\n pfun.add_info(ax, in_dict['fn'])\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([48, 48.1, 48.2])\\n #\\n # panel 2\\n ax = fig.add_subplot(122)\\n # cs = plt.pcolormesh(G['lon_rho'], G['lat_rho'], vort/scl, cmap=cmap,\\n # vmin=-1, vmax=1)\\n cs = plt.pcolormesh(G['lon_psi'], G['lat_psi'], vort/scl, cmap=cmap,\\n vmin=-1, vmax=1, shading='gouraud')\\n tstr = (r'Surface Vorticity (%0.1e $s^{-1}$)' % (scl))\\n ax.set_xticks([-122.8, -122.7, -122.6])\\n ax.set_yticks([])\\n #fig.colorbar(cs)\\n \\n # Inset colorbar\\n from mpl_toolkits.axes_grid1.inset_locator import inset_axes\\n cbaxes = inset_axes(ax, width=\\\"4%\\\", height=\\\"40%\\\", loc='lower left')\\n fig.colorbar(cs, cax=cbaxes, orientation='vertical')\\n \\n #pfun.add_bathy_contours(ax, ds)\\n pfun.add_coast(ax)\\n ax.axis(aa)\\n pfun.dar(ax)\\n ax.set_xlabel('Longitude')\\n ax.set_title(tstr) \\n \\n #fig.tight_layout()\\n # FINISH\\n ds.close()\\n if len(in_dict['fn_out']) > 0:\\n plt.savefig(in_dict['fn_out'])\\n plt.close()\\n else:\\n plt.show()\\n plt.rcdefaults()\",\n \"def _plot_pwm_x_pos_matrix(pwm_x_pos, agg_vector):\\n # extract and save out to tmp file\\n\\n\\n # then plot in R\\n\\n \\n return\",\n \"def print_cords(self):\\n print('startX :', self.startX, ' ,startY :', self.startY, ' ,endX :', self.endX, ' ,endY :', self.endY)\",\n \"def DT(time_lvl = 0, date = 160924 ):\\n \\n #-------Customised color in RGB ------------\\n C = [[232,232,230],#grey\\n [203,203,203], #grey\\n [161,161,161], #grey\\n [130,130,130], #grey\\n [149,53,229], #lillac, 39\\t64\\t197\\t149,53,229\\n [39,64,197], #blue dark,7,67,194\\n [15,110,229], #blue\\n [80,149,240], #blue\\n [74,192,243], #blue\\n [152,219,248], #blue\\n [183,237,247], #blue\\n [251,217,198], #redish\\n [255,197,166], #redish\\n [255,172,164], #redish\\n [253,139,142], #redish\\n [253,101,105], #redish\\n [255,66,74], #redish\\n [238,13,28], #red\\n [214,78,166], #pink\\n [214,102,201], \\n [217,155,210],\\n [216,181,211]]\\n C = np.array( C )\\n C = np.divide( C, 255. ) # RGB has to be between 0 and 1 in python\\n #-----------------------------------------------------------\\n \\n fig = plt.figure()\\n \\n \\n #-----Setting our map area and projection of interest-------\\n m = Basemap( llcrnrlon = -90., llcrnrlat = 0., urcrnrlon = 50., urcrnrlat=70.,\\\\\\n resolution = 'l', area_thresh = 10000., projection = 'merc' )\\n #m = Basemap(width=11500000,height=8500000,resolution='l',projection='eqdc',\\\\\\n # lat_1=07.,lat_2=40,lat_0=44,lon_0=-30.)\\n #m = Basemap(width=190000,height=2200000,resolution='l', projection='tmerc',lon_0=-30,lat_0=44)\\n \\n map_area( m ) # ploting background\\n path = \\\"gribs/\\\"\\n file = path +\\\"DT_var.grib\\\"\\n obj = pygrib.open( file )\\n \\n #-FETCHING ALL THE VALUES----------------------------------------\\n #-----Potential temperature---------------------------------------\\n lat, lon, data = get_data( obj,'Potential temperature', 2000, date, timelevel = time_lvl )\\n contour_val = np.linspace( 264, 384, 22 ) #contours for potential tempeature\\n plot_contourf( m, lat, lon, data, C, contour_val )\\n \\n #-----Relative vorticity, diff level------------------------------\\n contour=[ 2.8E-4, 3.5E-4, 4.5E-4, 6.5E-4, 7.E-4, 7.5E-4, 8.E-4 ] #1.5E-4,2.5E-4]#\\n lat, lon, data925 = get_data( obj, 'Vorticity (relative)', 925, date, timelevel = time_lvl )\\n lat, lon, data900 = get_data( obj, 'Vorticity (relative)', 900, date, timelevel = time_lvl )\\n lat, lon, data850 = get_data( obj, 'Vorticity (relative)', 850, date, timelevel = time_lvl )\\n \\n #->--->---->--mean value over height and filtering----------------\\n data = np.sqrt( data900**2 + 2*data850**2 + data925**2 ) #Vertical \\\"average\\\", weightet values at 850hpa double.\\n footprint = np.array([[0,0,0,1,1,1,1,0,0,0], #footprint=np.ones((3,10))\\n [0,0,1,1,1,2,1,1,0,0],\\n [1,1,1,2,2,1,2,1,1,1],\\n [0,1,1,1,1,2,1,1,1,0],\\n [0,0,1,1,1,1,1,1,0,0]])\\n \\n data = ndimage.generic_filter( data, np.mean, footprint = footprint, mode='wrap' )\\n plot_contour( m, lat,lon, data,contour, clr = 'k' )\\n \\n #-----Wind barbs----------------------------------------------------\\n lat, lon, data_u = get_data( obj , 'U component of wind', 2000, date, timelevel = time_lvl )\\n lat, lon, data_v = get_data( obj , 'V component of wind', 2000, date, timelevel = time_lvl )\\n plot_wind_bar( m, lat, lon, data_u, data_v )\\n #-----------------------------------------------\\n #-----------------------------------------------\\n \\n \\n \\n #-SAVE AND CLOSE----------------------------------------------------\\n #------------------------------------------------------------------\\n obj.close()\\n if time_lvl == 0:\\n t = \\\"0000\\\"\\n elif time_lvl == 1:\\n t = \\\"1200\\\"\\n elif time_lvl == 2:\\n t = \\\"1800\\\"\\n else: \\n t = \\\"t_not_set\\\"\\n \\n fig_name = \\\"DT/DT_\\\" + str( date ) + \\\"_\\\" + str( t )+ \\\".TIFF\\\" \\n \\n ax = plt.gca( )\\n plt.rc( 'font', size = 6 )\\n fig.set_size_inches( 12.80, 7.15 )\\n \\n fig.savefig( fig_name, dpi = 600 )\\n plt.close( )\\n #plt.show()\\n #--------------------------\\n #----------------------------\",\n \"def plot_priorsamps(meta):\\n priorsamps = np.array(meta.priordist.sample_ps(len(meta.colors))[0])\\n f = plt.figure(figsize=(5,10))\\n sps_log = f.add_subplot(2,1,1)\\n sps_lin = f.add_subplot(2,1,2)\\n sps_log.set_title(meta.name)\\n f.subplots_adjust(hspace=0, wspace=0)\\n sps_log.set_ylabel(r'$\\\\ln[p(z|\\\\vec{\\\\theta})]$')\\n sps_lin.set_xlabel(r'$z$')\\n sps_lin.set_ylabel(r'$p(\\\\vec{\\\\theta})$')\\n sps_log.set_xlim(meta.binends[0]-meta.bindif,meta.binends[-1]+meta.bindif)#,s_run.seed)#max(n_run.full_logfltNz)+m.log(s_run.seed/meta.zdif)))\\n sps_lin.set_xlim(meta.binends[0]-meta.bindif,meta.binends[-1]+meta.bindif)#,s_run.seed)#max(n_run.full_logfltNz)+m.log(s_run.seed/meta.zdif)))\\n plotstep(sps_log,meta.binends,meta.logintPz,l=r'Log Interim Prior $\\\\ln[p(z|\\\\vec{\\\\theta}^{0})$]')\\n plotstep(sps_lin,meta.binends,meta.intPz,l=r'Interim Prior $p(z|\\\\vec{\\\\theta}^{0})$')\\n for c in lrange(meta.colors):\\n plotstep(sps_log,meta.binends,priorsamps[c]-np.log(meta.ngals),c=meta.colors[c])\\n plotstep(sps_lin,meta.binends,np.exp(priorsamps[c]-np.log(meta.ngals)),c=meta.colors[c])\\n sps_log.legend(loc='upper right',fontsize='x-small')\\n sps_lin.legend(loc='upper right',fontsize='x-small')\\n f.savefig(os.path.join(meta.topdir, 'priorsamps.pdf'),bbox_inches='tight', pad_inches = 0)\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59820175","0.5681855","0.5632588","0.55993336","0.5567278","0.5558949","0.55241627","0.552349","0.552066","0.5517947","0.5500038","0.54207855","0.5374178","0.5372016","0.5361266","0.5345306","0.534296","0.5339282","0.5336971","0.5336114","0.53311783","0.53250515","0.5303563","0.52854484","0.52657664","0.5260444","0.5255669","0.52521664","0.5249639","0.52338564","0.52273214","0.5208867","0.5204293","0.5203738","0.5203709","0.51990944","0.5198463","0.5189345","0.51891106","0.51877826","0.51775455","0.5173777","0.51733214","0.51365614","0.51300055","0.5127636","0.5125996","0.5125799","0.5124396","0.5118249","0.5117339","0.5114051","0.51085865","0.50886434","0.5084515","0.5080295","0.50787425","0.50718594","0.5069522","0.50572467","0.5050096","0.50455785","0.5045283","0.50423145","0.50403243","0.5038993","0.50376105","0.503623","0.5034436","0.5032353","0.50322396","0.5029498","0.50281894","0.5025487","0.50221056","0.50219274","0.50199455","0.50182766","0.5010649","0.5003376","0.5002664","0.50013137","0.49975017","0.49896926","0.49766287","0.49725574","0.49692842","0.4958814","0.4956542","0.49552375","0.4954655","0.4950264","0.4948868","0.49478954","0.49476597","0.49474022","0.49465522","0.494328","0.49414825","0.49333733"],"string":"[\n \"0.59820175\",\n \"0.5681855\",\n \"0.5632588\",\n \"0.55993336\",\n \"0.5567278\",\n \"0.5558949\",\n \"0.55241627\",\n \"0.552349\",\n \"0.552066\",\n \"0.5517947\",\n \"0.5500038\",\n \"0.54207855\",\n \"0.5374178\",\n \"0.5372016\",\n \"0.5361266\",\n \"0.5345306\",\n \"0.534296\",\n \"0.5339282\",\n \"0.5336971\",\n \"0.5336114\",\n \"0.53311783\",\n \"0.53250515\",\n \"0.5303563\",\n \"0.52854484\",\n \"0.52657664\",\n \"0.5260444\",\n \"0.5255669\",\n \"0.52521664\",\n \"0.5249639\",\n \"0.52338564\",\n \"0.52273214\",\n \"0.5208867\",\n \"0.5204293\",\n \"0.5203738\",\n \"0.5203709\",\n \"0.51990944\",\n \"0.5198463\",\n \"0.5189345\",\n \"0.51891106\",\n \"0.51877826\",\n \"0.51775455\",\n \"0.5173777\",\n \"0.51733214\",\n \"0.51365614\",\n \"0.51300055\",\n \"0.5127636\",\n \"0.5125996\",\n \"0.5125799\",\n \"0.5124396\",\n \"0.5118249\",\n \"0.5117339\",\n \"0.5114051\",\n \"0.51085865\",\n \"0.50886434\",\n \"0.5084515\",\n \"0.5080295\",\n \"0.50787425\",\n \"0.50718594\",\n \"0.5069522\",\n \"0.50572467\",\n \"0.5050096\",\n \"0.50455785\",\n \"0.5045283\",\n \"0.50423145\",\n \"0.50403243\",\n \"0.5038993\",\n \"0.50376105\",\n \"0.503623\",\n \"0.5034436\",\n \"0.5032353\",\n \"0.50322396\",\n \"0.5029498\",\n \"0.50281894\",\n \"0.5025487\",\n \"0.50221056\",\n \"0.50219274\",\n \"0.50199455\",\n \"0.50182766\",\n \"0.5010649\",\n \"0.5003376\",\n \"0.5002664\",\n \"0.50013137\",\n \"0.49975017\",\n \"0.49896926\",\n \"0.49766287\",\n \"0.49725574\",\n \"0.49692842\",\n \"0.4958814\",\n \"0.4956542\",\n \"0.49552375\",\n \"0.4954655\",\n \"0.4950264\",\n \"0.4948868\",\n \"0.49478954\",\n \"0.49476597\",\n \"0.49474022\",\n \"0.49465522\",\n \"0.494328\",\n \"0.49414825\",\n \"0.49333733\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6764529"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95091,"cells":{"query":{"kind":"string","value":"Generate random RGB colors for a given seed"},"document":{"kind":"string","value":"def get_random_rgb(seed):\n random.seed(seed)\n r = random.randint(0, 255)\n g = random.randint(0, 255)\n b = random.randint(0, 255)\n return [r, g, b]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def random_color_gen():\n r = randint(0, 255)\n g = randint(0, 255)\n b = randint(0, 255)\n return [r, g, b]","def random_color():\n colormode(255)\n return randint(0, 255), randint(0, 255), randint(0, 255)","def _genRandomColor():\n b = random.randint(0, 255)\n g = random.randint(0, 255)\n r = random.randint(0, 255)\n return (b, g, r)","def randcolor():\n return (randint(0,255), randint(0,255), randint(0,255))","def rand_color(x):\n random.seed(x)\n color = random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)\n color = '#%02x%02x%02x' % color\n return color","def random_color():\n\n rgbl=[255,0,0]\n random.shuffle(rgbl)\n return tuple(rgbl)","def _random_color():\n return random.randint(0, 255)","def getRandomColor():\n r = randint(0, 255)\n g = randint(0, 255)\n b = randint(0, 255)\n return \"rgb(\" + str(r) + \", \" + str(g) + \", \" + str(b) +\")\"","def randcolor():\r\n r = random(0.0, 1.0)\r\n g = random(0.0, 1.0)\r\n b = random(0.0, 1.0)\r\n return vec(r, g, b) # A color is a three-element vec\r","def get_random_color():\n r=random.randint(0,255)\n g=random.randint(0,255)\n b=random.randint(0,255)\n return(r,g,b)","def random_colour(rng: random.Random) -> TupleInt3:\n r = rng.randint(0, 255)\n g = rng.randint(0, 255)\n b = rng.randint(0, 255)\n return r, g, b","def random_color():\n r = lambda: random.randint(0, 255)\n color = ('%02X%02X%02X' % (r(), r(), r()))\n return color","def generate_colour():\n red = random.randrange(0, 256)\n green = random.randrange(0, 256)\n blue = random.randrange(0, 256)\n alpha = random.randrange(0, 256)\n return (red, green, blue, alpha)","def getRandColor():\n\treturn (randrange(0,256), randrange(0,256), randrange(0,256))","def create_color():\n r = random.randint(0,255)\n g = random.randint(0,255)\n b = random.randint(0,255)\n a = random.randint(0,255)\n return introcs.RGB(r,g,b,a)","def create_random_color(self):\n # Create a list of n colors.\n n = 4\n dc = 1.0 / (n-1)\n color_list = [i*dc for i in range(n)]\n\n if self.is_scaffold:\n rgb = [1.0, 1.0, 1.0]\n else:\n rgb = [random.choice(color_list) for i in range(3)]\n # Don't generate blue (that's for a scaffold in cadnano) or black.\n if (rgb[0] == 0.0) and (rgb[1] == 0.0):\n rgb[0] = random.choice(color_list[1:])\n if rgb[2] == 0.0: \n rgb[2] = random.choice(color_list[1:]) \n #__if (rgb[0] == 0) and (rgb[1] == 0)\n #__if self.is_scaffold\n return rgb","def random_color():\n return random.choice(colors)","def random_color() -> Tuple[int, int, int]:\n return randrange(0, 255), randrange(0, 255), randrange(0, 255)","def Randomize(seed=None):\n random.seed()","def random_color(num):\n # 为每个类别的边界框随机匹配相应颜色\n np.random.seed(80)\n COLORS = np.random.randint(0, 256, size=(num, 3), dtype='uint8') #\n return COLORS","def randomcolour(self):\n r = random.randrange(1, 255)\n g = random.randrange(1, 255)\n b = random.randrange(1, 255)\n self.colour((r,g,b))","def random_color() -> Tuple[int, ...]:\n red = random.randrange(0, 255)\n blue = random.randrange(0, 255)\n green = random.randrange(0, 255)\n return (red, blue, green)","def random_hex_color():\n r = randint(0, 255)\n g = randint(0, 255)\n b = randint(0, 255)\n\n return '#%x%x%x' % (r, g, b)","def randColor():\r\n return np.array([random.random(), random.random(), random.random()]).reshape((1, 1, 3))","def generateColor(text):\n random.seed(text)\n return ('#%06X' % random.randint(0,0xFFFFFF))","def _random_color() -> List[float]:\n return [np.random.uniform(), np.random.uniform(), np.random.uniform()]","def _rand_color(self):\n\n return self._rand_elem(COLOR_NAMES)","def mutate_color(mutated_genome):\n seed = random.randint(0,2)\n if seed == 0:\n new_color(mutated_genome)\n elif seed == 1:\n change_color(mutated_genome)\n else: #seed == 2:\n switch_colors(mutated_genome)\n #else: seed == 3: # depricated\n # shuffle_colors(mutated_genome)","def random_color():\n c = colors[random.randint(0, len(colors) - 1)]\n return c[1]","def random_colors(N,bright=True):\n brightness = 1.0 if bright else 0.7\n hsv = [(i/N,1,brightness)for i in range(N)]\n colors = list(map(lambda c: clolorsys.hsv_to_rgb(*c),hsv))\n random.shuffle(colors)\n return colors","def color_from_seed(seed):\n supported_colors = []\n for name, hex in matplotlib.colors.cnames.items():\n supported_colors.append(hex)\n ascii_character_sum = sum(bytearray(seed, \"utf8\")) # Sums the ASCII values of every character\n selection = ascii_character_sum % len(supported_colors)\n \n return supported_colors[selection]","def _random_color(self):\n levels = range(0, 256)\n return tuple(random.choice(levels) for _ in range(3))","def random():\n np.random.seed(1939)","def change_color():\n return random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)","def get_random_color():\n def get_random_value():\n \"\"\" Return a random value between 0.0 and 1.0 \"\"\"\n return randint(0, 255) / 256.0\n return Vector(get_random_value(), get_random_value(), get_random_value())","def rand_branch_color():\n red = random.randint(0, 100)\n green = random.randint(175, 255)\n blue = random.randint(0, 100)\n return (red, green, blue)","def generate_rgb(S, R_possible_values, R_probabilities):\n R, = rd.choices(population=R_possible_values, weights=R_probabilities)\n S -= R\n G = rd.randint(max(S - 255, 0), min(S, 255))\n S -= G\n B = S\n return (R, G, B)","def _get_random_color(self):\r\n random_number = random.random()\r\n if random_number <= 1 / 3.0:\r\n return Color.RED\r\n else:\r\n return Color.BLACK","def get_random_color():\n\n def get_random_value():\n \"\"\" Return a random value between 0.0 and 1.0 \"\"\"\n return randint(0, 255) / 256.0\n\n return Vector(get_random_value(), get_random_value(), get_random_value())","def generate_random_colours_list(rng: random.Random, size: int) -> List[TupleInt3]:\n return [random_colour(rng) for _ in range(size)]","def random_rgb() -> List[int, int, int]:\n hsl_color = (random.random(), 0.3, 0.8)\n rgb_color = colorsys.hls_to_rgb(*hsl_color)\n return [round(c * 255) for c in rgb_color]","def random_colors(N, bright=True):\n brightness = 1.0 if bright else 0.7\n hsv = [(i / N, 1, brightness) for i in range(N)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def random_colors(N, bright=True):\n brightness = 1.0 if bright else 0.7\n hsv = [(i / N, 1, brightness) for i in range(N)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def random_colors(N, bright=True):\n brightness = 1.0 if bright else 0.7\n hsv = [(i / N, 1, brightness) for i in range(N)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def randcolour():\n colour = [0,0,0]\n while sum(colour)<450:\n for i in range(3):\n colour[i] = int(random.random()*255)\n return(tuple(colour))","def get_colour(value):\n \n np.random.seed(value)\n colour = [np.random.uniform() for i in range(3)]\n return (tuple(colour))","def color_from_ind(i: int) -> np.ndarray:\n np.random.seed(i)\n return np.random.random(3)","def random_colors(n, bright=True):\n brightness = 1.0 if bright else 0.7\n hsv = [(i / n, 1, brightness) for i in range(n)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def random_colors(N, bright=True):\n import random\n import colorsys\n\n brightness = 1.0 if bright else 0.7\n hsv = [(i / N, 1, brightness) for i in range(N)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def randcol():\n col = [randint(0,255) for _ in range(3)]\n return gfx.Color(tuple(col))","def randomRGBValue(self):\n return random.randrange(0, 256)","def setRandomColor():\n setColor(getRandomColor())","def random_colors(self, N, bright=True):\r\n brightness = 1.0 if bright else 0.7\r\n hsv = [(i / N, 1, brightness) for i in range(N)]\r\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\r\n random.shuffle(colors)\r\n return colors","def initialize_randomness(seed):","def random():\n np.random.seed(0)","def new_color(mutated_genome):\n index = random.randint(0,max(0,len(mutated_genome)-1))\n if color_mode == 'RGB':\n color_red = random.randint(0,255)\n color_green = random.randint(0,255)\n color_blue = random.randint(0,255)\n color = (color_red, color_blue, color_green)\n else: #color_mode == 'L':\n color = random.randint(0,255)\n mutated_genome[index][0] = color","def random_colors(N, bright=True):\n import random\n import colorsys\n brightness = 1.0 if bright else 0.7\n hsv = [(i / N, 1, brightness) for i in range(N)]\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n random.shuffle(colors)\n return colors","def seed(self, seed=None) -> List[np.ndarray]:\n self.np_random, seed_0 = seeding.np_random(seed)\n seed_1 = seeding.hash_seed(seed_0 + 1) % 2 ** 31\n return [seed_0, seed_1]","def seed (self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]","def get_seed(seed=None):\n # https://groups.google.com/forum/#!topic/briansupport/9ErDidIBBFM\n random = np.random.RandomState(seed)\n return random.randint(0, 2147483647)","def shuffle_colors(mutated_genome):\n mutated_genome","def randcolor(self, left_edge, right_edge):\n color_pixel = (\n random.randint(\n left_edge, right_edge), random.randint(\n left_edge, right_edge), random.randint(\n left_edge, right_edge))\n return color_pixel","def get_random_rgb(cls):\n\n hex = cls.get_random_hex(return_sign=False)\n return cls.rgb_from_hex(hex)","def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]","def seed(self, seed=None):\n self.np_random, seed = seeding.np_random(seed)\n return [seed]","def mutate_color(color):\n color[random.randrange(0, 3)] = random.random() % 1\n return color","def rand_bottom_color():\n red = random.randint(120, 160)\n green = random.randint(0, 90)\n blue = random.randint(0, 40)\n return (red, green, blue)","def seed(self, seed=None):\n self._np_random, seed = seeding.np_random(seed)\n return [seed]","def colors(k): \n ret = []\n for i in range(k):\n ret.append((random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)))\n return ret","def makeRGB(ncol = 16, minc = 32, maxc = 216):\n subd = int((maxc - minc)/ncol)\n numpy.random.seed(1)\n RGB = [[]]\n for r in range(minc, maxc, subd):\n for g in range(minc, maxc, subd):\n for b in range(minc, maxc, subd):\n RGB.append(numpy.array([r,g,b]))\n #print \"# of colors: \", len(self.RGB)\n rgb_order = numpy.random.permutation(len(RGB)) # randomize the order\n RGB = [RGB[x] for x in rgb_order]\n return RGB","def color(step: int=10) -> Tuple[int, int, int]:\n # Randomly seed the r g b values\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\n random_uniform(0, 255))\n\n # Randomly determine if each r g and b value is increasing or not\n r_inc = True\n g_inc = True\n b_inc = True\n r_step = random_uniform(step)\n g_step = random_uniform(step)\n b_step = random_uniform(step)\n\n # Yield the initial r, g, b values\n yield r, g, b\n\n # Loop and yeild forever\n while True:\n # If r is increasing\n if r_inc:\n # Increment r by the step\n r += r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to decreasing\n r_inc = r < 255 - r_step\n # If r is decreasing\n else:\n # Decrement r by the step\n r -= r_step\n # Ensure that the next step will be within the limits\n # if not then set the flag to increasing\n r_inc = r < r_step\n\n # See above\n if g_inc:\n g += g_step\n g_inc = g < 255 - g_step\n else:\n g -= g_step\n g_inc = g < g_step\n\n # See above\n if b_inc:\n b += b_step\n b_inc = b < 255 - b_step\n else:\n b -= b_step\n b_inc = b < b_step\n\n # Yield the red, green, and blue values\n yield r, g, b","def random_color_func(word=None, font_size=None, position=None,\n orientation=None, font_path=None, random_state=None):\n if random_state is None:\n random_state = Random()\n return \"hsl(%d, 80%%, 50%%)\" % random_state.randint(0, 255)","def get_random_color(color_list, **kwargs):\n color = None\n n_color_candidates = kwargs.get('n_color_candidates', 10)\n color_candidates_matrix = np.random.rand(n_color_candidates, 3) # creating matrix of candidate rgb values\n norm = 0.\n for i in range(n_color_candidates):\n candidate_color = color_candidates_matrix[i]\n candidate_norm = np.min([np.linalg.norm(existing_color-candidate_color) for existing_color in color_list])\n if candidate_norm > norm:\n norm = candidate_norm\n color = candidate_color\n color_list.append(color)\n return color","def _seed(self, seed=None):\n self.rng, seed = seeding.np_random(seed)\n return [seed]","def seed(self, seed=None):\n # to have a different environment at each time (resolve python random problem)\n self.np_random, seed1 = seeding.np_random(seed)\n seed2 = seeding.hash_seed(seed1 + 1) % 2 ** 31\n return [seed1, seed2]","def generate_rgb(exist_colors: List[List[int, int, int]]) -> List[int, int, int]:\n largest_min_distance = 0\n best_color = random_rgb()\n if len(exist_colors) > 0:\n for _ in range(100):\n color = random_rgb()\n current_min_distance = min(_color_distance(color, c) for c in exist_colors)\n if current_min_distance > largest_min_distance:\n largest_min_distance = current_min_distance\n best_color = color\n _validate_color(best_color)\n return best_color","def get_color_list(cluster_count):\n color_list = []\n for i in xrange(cluster_count):\n color_list.append(random_color_gen())\n return color_list","def get_color_codes(num_classes):\r\n np.random.seed(RANDOM_SEED)\r\n return np.random.randint(0,np.iinfo(np.uint8).max, size=(num_classes, len('RGB'))).astype(np.uint8)","def get_random(self):\n\n\t\treturn np.random.choice(\n\t\t\t\tself.Ncolors, size=self.codelength, \n\t\t\t\treplace=True, p=self.prior) + 1","def random_color(search=None): \r\n if search: c = choice(search_color(search))\r\n else: c = choice(THECOLORS.values())\r\n \r\n #debug: print type(c), c # returns Color()\r\n return c \r\n #todo: exception on color search fail? OR just default to white.\r","def desaturated_randcol(saturation,brightness=100):\n # begin with greyscale\n brightness = clamp(brightness,40,255)\n sat = clamp(saturation,0,255-brightness)\n col = [brightness,brightness,brightness]\n \n for i in range(3):\n r = randint(-sat,sat)\n if abs(r-brightness) < sat//2: # minimum saturation = sat / 2\n if r < 0:\n r = randint(-sat,-sat//2)\n else:\n r = randint(sat//2,sat)\n col[i] += r\n col[i] = clamp(col[i],50,253)\n\n return gfx.Color(col)","def seed():","def seed(self, seed=None):\n self.np_random, seed = gym.utils.seeding.np_random(seed)\n return [seed]","def i_seed(seed, flag):\n global randrsl, mm\n\n for i in range(0 , 256):\n mm[i] = 0\n\n m = len(seed)\n\n for i in range(0, 256):\n if i >= m:\n randrsl[i] = 0\n else:\n randrsl[i] = seed[i]\n\n rand_init(flag)","def randomSub(seed: float):\n crc = str(string.ascii_letters + string.digits)\n random.seed(seed)\n n = random.randint(10,30)\n return \"\".join(random.sample(crc, n))","def random_seed(seed):\n state = RandomState()\n random.seed(seed) # alter state\n np.random.seed(seed)\n torch.manual_seed(seed)\n yield\n state.set_global()","def pastel_randcol():\n # begin with saturated then desaturate\n col = saturated_randcol()\n offset = 175\n \n for i in range(3):\n if col[i] == 255:\n continue\n elif col[i] != 0:\n col[i] = min(col[i]+offset,255)\n else:\n col[i] += offset\n \n return gfx.Color(tuple(col))","def random_seed(i): # -> None:\n ...","def seed(seed: int) -> None:\n ...","def seed():\n pass","def seed():\n pass","def uniqueish_color():\n return plt.cm.gist_ncar(np.random.random())","def temp_seed(cntxt_seed):\n state = np.random.get_state()\n np.random.seed(cntxt_seed)\n try:\n yield\n finally:\n np.random.set_state(state)","def set_seed(cls, seed: Any) -> None:\n cls.rand = Random(seed)","def seed(seed: int):\n # all sampling is actually happening in the move_cube module\n move_cube.seed(seed)","def saturated_randcol(value=255):\n # one channel is zero, one is random and one is max\n channels = [0,randint(0,255),255]\n col = [0,0,0]\n value = clamp(value,40,255)\n \n for i in range(3):\n channel = choice(channels)\n col[i] = channel*value//255\n channels.remove(channel)\n \n return gfx.Color(tuple(col))","def square_parameters_from_seed(seed):\n # canvas\n canvas_mode = \"RGB\"\n canvas_size_in_pixels = (200, 200)\n canvas_background_color_rgb = (255, 255, 255) # white\n \n # rectangle\n rectangle_position = position_from_seed(seed) # ((0, 0), (10, 10))\n rectangle_fill = color_from_seed(seed)\n rectangle_outline = color_from_seed(seed + str(random.randint(1, 100))) # offset\n \n return canvas_mode, canvas_size_in_pixels, canvas_background_color_rgb, rectangle_position, rectangle_fill, rectangle_outline","def get_random_value():\n return randint(0, 255) / 256.0","def test_gen_colors(self):\n result = magic.gen_colors(\"tests/test_files/test.jpg\")\n self.assertEqual(result[0], \"#0F191A\")","def getrandomcolor(self) -> str:\n return self.tab10[random.randint(0, len(self.tab10)-1)]"],"string":"[\n \"def random_color_gen():\\n r = randint(0, 255)\\n g = randint(0, 255)\\n b = randint(0, 255)\\n return [r, g, b]\",\n \"def random_color():\\n colormode(255)\\n return randint(0, 255), randint(0, 255), randint(0, 255)\",\n \"def _genRandomColor():\\n b = random.randint(0, 255)\\n g = random.randint(0, 255)\\n r = random.randint(0, 255)\\n return (b, g, r)\",\n \"def randcolor():\\n return (randint(0,255), randint(0,255), randint(0,255))\",\n \"def rand_color(x):\\n random.seed(x)\\n color = random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)\\n color = '#%02x%02x%02x' % color\\n return color\",\n \"def random_color():\\n\\n rgbl=[255,0,0]\\n random.shuffle(rgbl)\\n return tuple(rgbl)\",\n \"def _random_color():\\n return random.randint(0, 255)\",\n \"def getRandomColor():\\n r = randint(0, 255)\\n g = randint(0, 255)\\n b = randint(0, 255)\\n return \\\"rgb(\\\" + str(r) + \\\", \\\" + str(g) + \\\", \\\" + str(b) +\\\")\\\"\",\n \"def randcolor():\\r\\n r = random(0.0, 1.0)\\r\\n g = random(0.0, 1.0)\\r\\n b = random(0.0, 1.0)\\r\\n return vec(r, g, b) # A color is a three-element vec\\r\",\n \"def get_random_color():\\n r=random.randint(0,255)\\n g=random.randint(0,255)\\n b=random.randint(0,255)\\n return(r,g,b)\",\n \"def random_colour(rng: random.Random) -> TupleInt3:\\n r = rng.randint(0, 255)\\n g = rng.randint(0, 255)\\n b = rng.randint(0, 255)\\n return r, g, b\",\n \"def random_color():\\n r = lambda: random.randint(0, 255)\\n color = ('%02X%02X%02X' % (r(), r(), r()))\\n return color\",\n \"def generate_colour():\\n red = random.randrange(0, 256)\\n green = random.randrange(0, 256)\\n blue = random.randrange(0, 256)\\n alpha = random.randrange(0, 256)\\n return (red, green, blue, alpha)\",\n \"def getRandColor():\\n\\treturn (randrange(0,256), randrange(0,256), randrange(0,256))\",\n \"def create_color():\\n r = random.randint(0,255)\\n g = random.randint(0,255)\\n b = random.randint(0,255)\\n a = random.randint(0,255)\\n return introcs.RGB(r,g,b,a)\",\n \"def create_random_color(self):\\n # Create a list of n colors.\\n n = 4\\n dc = 1.0 / (n-1)\\n color_list = [i*dc for i in range(n)]\\n\\n if self.is_scaffold:\\n rgb = [1.0, 1.0, 1.0]\\n else:\\n rgb = [random.choice(color_list) for i in range(3)]\\n # Don't generate blue (that's for a scaffold in cadnano) or black.\\n if (rgb[0] == 0.0) and (rgb[1] == 0.0):\\n rgb[0] = random.choice(color_list[1:])\\n if rgb[2] == 0.0: \\n rgb[2] = random.choice(color_list[1:]) \\n #__if (rgb[0] == 0) and (rgb[1] == 0)\\n #__if self.is_scaffold\\n return rgb\",\n \"def random_color():\\n return random.choice(colors)\",\n \"def random_color() -> Tuple[int, int, int]:\\n return randrange(0, 255), randrange(0, 255), randrange(0, 255)\",\n \"def Randomize(seed=None):\\n random.seed()\",\n \"def random_color(num):\\n # 为每个类别的边界框随机匹配相应颜色\\n np.random.seed(80)\\n COLORS = np.random.randint(0, 256, size=(num, 3), dtype='uint8') #\\n return COLORS\",\n \"def randomcolour(self):\\n r = random.randrange(1, 255)\\n g = random.randrange(1, 255)\\n b = random.randrange(1, 255)\\n self.colour((r,g,b))\",\n \"def random_color() -> Tuple[int, ...]:\\n red = random.randrange(0, 255)\\n blue = random.randrange(0, 255)\\n green = random.randrange(0, 255)\\n return (red, blue, green)\",\n \"def random_hex_color():\\n r = randint(0, 255)\\n g = randint(0, 255)\\n b = randint(0, 255)\\n\\n return '#%x%x%x' % (r, g, b)\",\n \"def randColor():\\r\\n return np.array([random.random(), random.random(), random.random()]).reshape((1, 1, 3))\",\n \"def generateColor(text):\\n random.seed(text)\\n return ('#%06X' % random.randint(0,0xFFFFFF))\",\n \"def _random_color() -> List[float]:\\n return [np.random.uniform(), np.random.uniform(), np.random.uniform()]\",\n \"def _rand_color(self):\\n\\n return self._rand_elem(COLOR_NAMES)\",\n \"def mutate_color(mutated_genome):\\n seed = random.randint(0,2)\\n if seed == 0:\\n new_color(mutated_genome)\\n elif seed == 1:\\n change_color(mutated_genome)\\n else: #seed == 2:\\n switch_colors(mutated_genome)\\n #else: seed == 3: # depricated\\n # shuffle_colors(mutated_genome)\",\n \"def random_color():\\n c = colors[random.randint(0, len(colors) - 1)]\\n return c[1]\",\n \"def random_colors(N,bright=True):\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i/N,1,brightness)for i in range(N)]\\n colors = list(map(lambda c: clolorsys.hsv_to_rgb(*c),hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def color_from_seed(seed):\\n supported_colors = []\\n for name, hex in matplotlib.colors.cnames.items():\\n supported_colors.append(hex)\\n ascii_character_sum = sum(bytearray(seed, \\\"utf8\\\")) # Sums the ASCII values of every character\\n selection = ascii_character_sum % len(supported_colors)\\n \\n return supported_colors[selection]\",\n \"def _random_color(self):\\n levels = range(0, 256)\\n return tuple(random.choice(levels) for _ in range(3))\",\n \"def random():\\n np.random.seed(1939)\",\n \"def change_color():\\n return random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)\",\n \"def get_random_color():\\n def get_random_value():\\n \\\"\\\"\\\" Return a random value between 0.0 and 1.0 \\\"\\\"\\\"\\n return randint(0, 255) / 256.0\\n return Vector(get_random_value(), get_random_value(), get_random_value())\",\n \"def rand_branch_color():\\n red = random.randint(0, 100)\\n green = random.randint(175, 255)\\n blue = random.randint(0, 100)\\n return (red, green, blue)\",\n \"def generate_rgb(S, R_possible_values, R_probabilities):\\n R, = rd.choices(population=R_possible_values, weights=R_probabilities)\\n S -= R\\n G = rd.randint(max(S - 255, 0), min(S, 255))\\n S -= G\\n B = S\\n return (R, G, B)\",\n \"def _get_random_color(self):\\r\\n random_number = random.random()\\r\\n if random_number <= 1 / 3.0:\\r\\n return Color.RED\\r\\n else:\\r\\n return Color.BLACK\",\n \"def get_random_color():\\n\\n def get_random_value():\\n \\\"\\\"\\\" Return a random value between 0.0 and 1.0 \\\"\\\"\\\"\\n return randint(0, 255) / 256.0\\n\\n return Vector(get_random_value(), get_random_value(), get_random_value())\",\n \"def generate_random_colours_list(rng: random.Random, size: int) -> List[TupleInt3]:\\n return [random_colour(rng) for _ in range(size)]\",\n \"def random_rgb() -> List[int, int, int]:\\n hsl_color = (random.random(), 0.3, 0.8)\\n rgb_color = colorsys.hls_to_rgb(*hsl_color)\\n return [round(c * 255) for c in rgb_color]\",\n \"def random_colors(N, bright=True):\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def random_colors(N, bright=True):\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def random_colors(N, bright=True):\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def randcolour():\\n colour = [0,0,0]\\n while sum(colour)<450:\\n for i in range(3):\\n colour[i] = int(random.random()*255)\\n return(tuple(colour))\",\n \"def get_colour(value):\\n \\n np.random.seed(value)\\n colour = [np.random.uniform() for i in range(3)]\\n return (tuple(colour))\",\n \"def color_from_ind(i: int) -> np.ndarray:\\n np.random.seed(i)\\n return np.random.random(3)\",\n \"def random_colors(n, bright=True):\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / n, 1, brightness) for i in range(n)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def random_colors(N, bright=True):\\n import random\\n import colorsys\\n\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def randcol():\\n col = [randint(0,255) for _ in range(3)]\\n return gfx.Color(tuple(col))\",\n \"def randomRGBValue(self):\\n return random.randrange(0, 256)\",\n \"def setRandomColor():\\n setColor(getRandomColor())\",\n \"def random_colors(self, N, bright=True):\\r\\n brightness = 1.0 if bright else 0.7\\r\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\r\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\r\\n random.shuffle(colors)\\r\\n return colors\",\n \"def initialize_randomness(seed):\",\n \"def random():\\n np.random.seed(0)\",\n \"def new_color(mutated_genome):\\n index = random.randint(0,max(0,len(mutated_genome)-1))\\n if color_mode == 'RGB':\\n color_red = random.randint(0,255)\\n color_green = random.randint(0,255)\\n color_blue = random.randint(0,255)\\n color = (color_red, color_blue, color_green)\\n else: #color_mode == 'L':\\n color = random.randint(0,255)\\n mutated_genome[index][0] = color\",\n \"def random_colors(N, bright=True):\\n import random\\n import colorsys\\n brightness = 1.0 if bright else 0.7\\n hsv = [(i / N, 1, brightness) for i in range(N)]\\n colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\\n random.shuffle(colors)\\n return colors\",\n \"def seed(self, seed=None) -> List[np.ndarray]:\\n self.np_random, seed_0 = seeding.np_random(seed)\\n seed_1 = seeding.hash_seed(seed_0 + 1) % 2 ** 31\\n return [seed_0, seed_1]\",\n \"def seed (self, seed=None):\\n self.np_random, seed = seeding.np_random(seed)\\n return [seed]\",\n \"def get_seed(seed=None):\\n # https://groups.google.com/forum/#!topic/briansupport/9ErDidIBBFM\\n random = np.random.RandomState(seed)\\n return random.randint(0, 2147483647)\",\n \"def shuffle_colors(mutated_genome):\\n mutated_genome\",\n \"def randcolor(self, left_edge, right_edge):\\n color_pixel = (\\n random.randint(\\n left_edge, right_edge), random.randint(\\n left_edge, right_edge), random.randint(\\n left_edge, right_edge))\\n return color_pixel\",\n \"def get_random_rgb(cls):\\n\\n hex = cls.get_random_hex(return_sign=False)\\n return cls.rgb_from_hex(hex)\",\n \"def seed(self, seed=None):\\n self.np_random, seed = seeding.np_random(seed)\\n return [seed]\",\n \"def seed(self, seed=None):\\n self.np_random, seed = seeding.np_random(seed)\\n return [seed]\",\n \"def mutate_color(color):\\n color[random.randrange(0, 3)] = random.random() % 1\\n return color\",\n \"def rand_bottom_color():\\n red = random.randint(120, 160)\\n green = random.randint(0, 90)\\n blue = random.randint(0, 40)\\n return (red, green, blue)\",\n \"def seed(self, seed=None):\\n self._np_random, seed = seeding.np_random(seed)\\n return [seed]\",\n \"def colors(k): \\n ret = []\\n for i in range(k):\\n ret.append((random.uniform(0, 1), random.uniform(0, 1), random.uniform(0, 1)))\\n return ret\",\n \"def makeRGB(ncol = 16, minc = 32, maxc = 216):\\n subd = int((maxc - minc)/ncol)\\n numpy.random.seed(1)\\n RGB = [[]]\\n for r in range(minc, maxc, subd):\\n for g in range(minc, maxc, subd):\\n for b in range(minc, maxc, subd):\\n RGB.append(numpy.array([r,g,b]))\\n #print \\\"# of colors: \\\", len(self.RGB)\\n rgb_order = numpy.random.permutation(len(RGB)) # randomize the order\\n RGB = [RGB[x] for x in rgb_order]\\n return RGB\",\n \"def color(step: int=10) -> Tuple[int, int, int]:\\n # Randomly seed the r g b values\\n r, g, b = (random_uniform(0, 255), random_uniform(0, 255),\\n random_uniform(0, 255))\\n\\n # Randomly determine if each r g and b value is increasing or not\\n r_inc = True\\n g_inc = True\\n b_inc = True\\n r_step = random_uniform(step)\\n g_step = random_uniform(step)\\n b_step = random_uniform(step)\\n\\n # Yield the initial r, g, b values\\n yield r, g, b\\n\\n # Loop and yeild forever\\n while True:\\n # If r is increasing\\n if r_inc:\\n # Increment r by the step\\n r += r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to decreasing\\n r_inc = r < 255 - r_step\\n # If r is decreasing\\n else:\\n # Decrement r by the step\\n r -= r_step\\n # Ensure that the next step will be within the limits\\n # if not then set the flag to increasing\\n r_inc = r < r_step\\n\\n # See above\\n if g_inc:\\n g += g_step\\n g_inc = g < 255 - g_step\\n else:\\n g -= g_step\\n g_inc = g < g_step\\n\\n # See above\\n if b_inc:\\n b += b_step\\n b_inc = b < 255 - b_step\\n else:\\n b -= b_step\\n b_inc = b < b_step\\n\\n # Yield the red, green, and blue values\\n yield r, g, b\",\n \"def random_color_func(word=None, font_size=None, position=None,\\n orientation=None, font_path=None, random_state=None):\\n if random_state is None:\\n random_state = Random()\\n return \\\"hsl(%d, 80%%, 50%%)\\\" % random_state.randint(0, 255)\",\n \"def get_random_color(color_list, **kwargs):\\n color = None\\n n_color_candidates = kwargs.get('n_color_candidates', 10)\\n color_candidates_matrix = np.random.rand(n_color_candidates, 3) # creating matrix of candidate rgb values\\n norm = 0.\\n for i in range(n_color_candidates):\\n candidate_color = color_candidates_matrix[i]\\n candidate_norm = np.min([np.linalg.norm(existing_color-candidate_color) for existing_color in color_list])\\n if candidate_norm > norm:\\n norm = candidate_norm\\n color = candidate_color\\n color_list.append(color)\\n return color\",\n \"def _seed(self, seed=None):\\n self.rng, seed = seeding.np_random(seed)\\n return [seed]\",\n \"def seed(self, seed=None):\\n # to have a different environment at each time (resolve python random problem)\\n self.np_random, seed1 = seeding.np_random(seed)\\n seed2 = seeding.hash_seed(seed1 + 1) % 2 ** 31\\n return [seed1, seed2]\",\n \"def generate_rgb(exist_colors: List[List[int, int, int]]) -> List[int, int, int]:\\n largest_min_distance = 0\\n best_color = random_rgb()\\n if len(exist_colors) > 0:\\n for _ in range(100):\\n color = random_rgb()\\n current_min_distance = min(_color_distance(color, c) for c in exist_colors)\\n if current_min_distance > largest_min_distance:\\n largest_min_distance = current_min_distance\\n best_color = color\\n _validate_color(best_color)\\n return best_color\",\n \"def get_color_list(cluster_count):\\n color_list = []\\n for i in xrange(cluster_count):\\n color_list.append(random_color_gen())\\n return color_list\",\n \"def get_color_codes(num_classes):\\r\\n np.random.seed(RANDOM_SEED)\\r\\n return np.random.randint(0,np.iinfo(np.uint8).max, size=(num_classes, len('RGB'))).astype(np.uint8)\",\n \"def get_random(self):\\n\\n\\t\\treturn np.random.choice(\\n\\t\\t\\t\\tself.Ncolors, size=self.codelength, \\n\\t\\t\\t\\treplace=True, p=self.prior) + 1\",\n \"def random_color(search=None): \\r\\n if search: c = choice(search_color(search))\\r\\n else: c = choice(THECOLORS.values())\\r\\n \\r\\n #debug: print type(c), c # returns Color()\\r\\n return c \\r\\n #todo: exception on color search fail? OR just default to white.\\r\",\n \"def desaturated_randcol(saturation,brightness=100):\\n # begin with greyscale\\n brightness = clamp(brightness,40,255)\\n sat = clamp(saturation,0,255-brightness)\\n col = [brightness,brightness,brightness]\\n \\n for i in range(3):\\n r = randint(-sat,sat)\\n if abs(r-brightness) < sat//2: # minimum saturation = sat / 2\\n if r < 0:\\n r = randint(-sat,-sat//2)\\n else:\\n r = randint(sat//2,sat)\\n col[i] += r\\n col[i] = clamp(col[i],50,253)\\n\\n return gfx.Color(col)\",\n \"def seed():\",\n \"def seed(self, seed=None):\\n self.np_random, seed = gym.utils.seeding.np_random(seed)\\n return [seed]\",\n \"def i_seed(seed, flag):\\n global randrsl, mm\\n\\n for i in range(0 , 256):\\n mm[i] = 0\\n\\n m = len(seed)\\n\\n for i in range(0, 256):\\n if i >= m:\\n randrsl[i] = 0\\n else:\\n randrsl[i] = seed[i]\\n\\n rand_init(flag)\",\n \"def randomSub(seed: float):\\n crc = str(string.ascii_letters + string.digits)\\n random.seed(seed)\\n n = random.randint(10,30)\\n return \\\"\\\".join(random.sample(crc, n))\",\n \"def random_seed(seed):\\n state = RandomState()\\n random.seed(seed) # alter state\\n np.random.seed(seed)\\n torch.manual_seed(seed)\\n yield\\n state.set_global()\",\n \"def pastel_randcol():\\n # begin with saturated then desaturate\\n col = saturated_randcol()\\n offset = 175\\n \\n for i in range(3):\\n if col[i] == 255:\\n continue\\n elif col[i] != 0:\\n col[i] = min(col[i]+offset,255)\\n else:\\n col[i] += offset\\n \\n return gfx.Color(tuple(col))\",\n \"def random_seed(i): # -> None:\\n ...\",\n \"def seed(seed: int) -> None:\\n ...\",\n \"def seed():\\n pass\",\n \"def seed():\\n pass\",\n \"def uniqueish_color():\\n return plt.cm.gist_ncar(np.random.random())\",\n \"def temp_seed(cntxt_seed):\\n state = np.random.get_state()\\n np.random.seed(cntxt_seed)\\n try:\\n yield\\n finally:\\n np.random.set_state(state)\",\n \"def set_seed(cls, seed: Any) -> None:\\n cls.rand = Random(seed)\",\n \"def seed(seed: int):\\n # all sampling is actually happening in the move_cube module\\n move_cube.seed(seed)\",\n \"def saturated_randcol(value=255):\\n # one channel is zero, one is random and one is max\\n channels = [0,randint(0,255),255]\\n col = [0,0,0]\\n value = clamp(value,40,255)\\n \\n for i in range(3):\\n channel = choice(channels)\\n col[i] = channel*value//255\\n channels.remove(channel)\\n \\n return gfx.Color(tuple(col))\",\n \"def square_parameters_from_seed(seed):\\n # canvas\\n canvas_mode = \\\"RGB\\\"\\n canvas_size_in_pixels = (200, 200)\\n canvas_background_color_rgb = (255, 255, 255) # white\\n \\n # rectangle\\n rectangle_position = position_from_seed(seed) # ((0, 0), (10, 10))\\n rectangle_fill = color_from_seed(seed)\\n rectangle_outline = color_from_seed(seed + str(random.randint(1, 100))) # offset\\n \\n return canvas_mode, canvas_size_in_pixels, canvas_background_color_rgb, rectangle_position, rectangle_fill, rectangle_outline\",\n \"def get_random_value():\\n return randint(0, 255) / 256.0\",\n \"def test_gen_colors(self):\\n result = magic.gen_colors(\\\"tests/test_files/test.jpg\\\")\\n self.assertEqual(result[0], \\\"#0F191A\\\")\",\n \"def getrandomcolor(self) -> str:\\n return self.tab10[random.randint(0, len(self.tab10)-1)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.814636","0.7701456","0.76977193","0.74837154","0.746802","0.737749","0.73708177","0.7207782","0.7160151","0.7154133","0.7105417","0.70931727","0.70826614","0.7022558","0.7013978","0.69533306","0.6892857","0.68925637","0.6831503","0.6779909","0.67476416","0.6726678","0.6701285","0.66899127","0.6683659","0.6679428","0.6672197","0.6656367","0.6653879","0.66413194","0.6638635","0.6619267","0.6603917","0.658105","0.65284777","0.6504878","0.650209","0.64836997","0.648228","0.6470852","0.64601165","0.6445245","0.6445245","0.6445245","0.64337254","0.6430013","0.64141977","0.641023","0.6409011","0.6364031","0.6353899","0.6301589","0.6294633","0.62868416","0.6269651","0.62652266","0.6231465","0.6223594","0.6214282","0.62015593","0.6200679","0.6198754","0.6191586","0.6184998","0.6184998","0.61633676","0.616245","0.6156887","0.6153022","0.61471486","0.61244947","0.61156344","0.6112134","0.6105534","0.61050963","0.6074193","0.6054725","0.60418105","0.6039249","0.60379577","0.60367453","0.6024583","0.6014824","0.59957844","0.59844625","0.59741324","0.5965011","0.59432644","0.59372824","0.59296453","0.59296453","0.59088427","0.5881518","0.5855556","0.5855021","0.5851355","0.5816542","0.580459","0.579796","0.5794206"],"string":"[\n \"0.814636\",\n \"0.7701456\",\n \"0.76977193\",\n \"0.74837154\",\n \"0.746802\",\n \"0.737749\",\n \"0.73708177\",\n \"0.7207782\",\n \"0.7160151\",\n \"0.7154133\",\n \"0.7105417\",\n \"0.70931727\",\n \"0.70826614\",\n \"0.7022558\",\n \"0.7013978\",\n \"0.69533306\",\n \"0.6892857\",\n \"0.68925637\",\n \"0.6831503\",\n \"0.6779909\",\n \"0.67476416\",\n \"0.6726678\",\n \"0.6701285\",\n \"0.66899127\",\n \"0.6683659\",\n \"0.6679428\",\n \"0.6672197\",\n \"0.6656367\",\n \"0.6653879\",\n \"0.66413194\",\n \"0.6638635\",\n \"0.6619267\",\n \"0.6603917\",\n \"0.658105\",\n \"0.65284777\",\n \"0.6504878\",\n \"0.650209\",\n \"0.64836997\",\n \"0.648228\",\n \"0.6470852\",\n \"0.64601165\",\n \"0.6445245\",\n \"0.6445245\",\n \"0.6445245\",\n \"0.64337254\",\n \"0.6430013\",\n \"0.64141977\",\n \"0.641023\",\n \"0.6409011\",\n \"0.6364031\",\n \"0.6353899\",\n \"0.6301589\",\n \"0.6294633\",\n \"0.62868416\",\n \"0.6269651\",\n \"0.62652266\",\n \"0.6231465\",\n \"0.6223594\",\n \"0.6214282\",\n \"0.62015593\",\n \"0.6200679\",\n \"0.6198754\",\n \"0.6191586\",\n \"0.6184998\",\n \"0.6184998\",\n \"0.61633676\",\n \"0.616245\",\n \"0.6156887\",\n \"0.6153022\",\n \"0.61471486\",\n \"0.61244947\",\n \"0.61156344\",\n \"0.6112134\",\n \"0.6105534\",\n \"0.61050963\",\n \"0.6074193\",\n \"0.6054725\",\n \"0.60418105\",\n \"0.6039249\",\n \"0.60379577\",\n \"0.60367453\",\n \"0.6024583\",\n \"0.6014824\",\n \"0.59957844\",\n \"0.59844625\",\n \"0.59741324\",\n \"0.5965011\",\n \"0.59432644\",\n \"0.59372824\",\n \"0.59296453\",\n \"0.59296453\",\n \"0.59088427\",\n \"0.5881518\",\n \"0.5855556\",\n \"0.5855021\",\n \"0.5851355\",\n \"0.5816542\",\n \"0.580459\",\n \"0.579796\",\n \"0.5794206\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84251815"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95092,"cells":{"query":{"kind":"string","value":"Get a binary mask with threshold on probabilities"},"document":{"kind":"string","value":"def threshold_mask(mask, threshold=0.5):\n mask[np.where(mask >= threshold)] = 1.\n mask[np.where(mask < threshold)] = 0.\n return mask"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def binary_predict(probs, threshold = 0.5):\n return (probs >= threshold) * np.ones(len(probs))","def pred_from_prob(a,threshold):\n bin_preds = np.zeros((np.size(a,0),))\n bin_preds[np.where(a[:,1]>threshold)]=1.0\n return bin_preds","def _np_get_mask(prob_map, prob_thresh=0.5):\n mask = (prob_map > prob_thresh) * 255\n return mask.astype(np.uint8)","def mask(self):\n\n mask = self.freqs >= self.minimum_threshold\n mask = mask.astype(int)\n self.freqs = self.freqs * mask\n self.sums = self.sums * mask","def get_threshold_mask(hparams, x):\n\n axis = list(range(1, x.shape.ndims))\n min_val = tf.reduce_min(x, axis=axis, keepdims=True)\n max_val = tf.reduce_max(x, axis=axis, keepdims=True)\n thresh = min_val + hparams.threshold_factor * (max_val - min_val)\n cond = tf.less(x, thresh)\n return tf.where(cond, tf.zeros(tf.shape(x)), tf.ones(tf.shape(x)))","def get_biomass(binary_mask):\n\n white_pixels = cv2.countNonZero(binary_mask)\n return white_pixels","def preprocess_mask(y):\n y[y <= 255./2] = 0 # Needs to be in this order, otherwise 1 gets overwritten\n y[y > 255./2] = 1\n binary_mask = y.astype(np.uint8)\n\n return binary_mask","def bin_thresh(img: np.ndarray, thresh: Number) -> np.ndarray:\n res = img >= thresh\n return res","def create_binary_mask(self, type='negative'):\n if not self.thresh_map_name:\n return None\n mode = self.thresh_mode\n limits = self.thresh_limits\n map = self.map_scalars\n if mode=='mask lower':\n m = (map < limits[0]) if type=='negative' else (map >= limits[0])\n elif mode=='mask higher':\n m = (map > limits[1]) if type=='negative' else (map <= limits[1])\n elif mode=='mask between':\n m = ( (map > limits[0]) & (map < limits[1]) ) \\\n if type=='negative' \\\n else ( (map <= limits[0]) | (map >= limits[1]) )\n else: # mask outside\n m = ( (map < limits[0]) | (map > limits[1]) ) \\\n if type=='negative' \\\n else ( (map >= limits[0]) & (map <= limits[1]) )\n return m","def predict_mask(logit, EMPTY_THRESHOLD, MASK_THRESHOLD):\n #pred mask 0-1 pixel-wise\n #n = logit.shape[0]\n IMG_SIZE = logit.shape[-1] #256\n #EMPTY_THRESHOLD = 100.0*(IMG_SIZE/128.0)**2 #count of predicted mask pixles<threshold, predict as empty mask image\n #MASK_THRESHOLD = 0.22\n #logit = torch.sigmoid(torch.from_numpy(logit)).view(n, -1)\n #pred = (logit>MASK_THRESHOLD).long()\n #pred[pred.sum(dim=1) < EMPTY_THRESHOLD, ] = 0 #bug here, found it, the bug is input shape is (256, 256) not (16,256,256)\n logit = sigmoid(logit)#.reshape(n, -1)\n pred = (logit>MASK_THRESHOLD).astype(np.int)\n if pred.sum() < EMPTY_THRESHOLD:\n return np.zeros(pred.shape).astype(np.int)\n else:\n return pred","def get_sample_mask(self):","def threshold_probs(probs):\n classes = np.ones(len(probs),)\n classes[probs < 0.5] = 0\n return classes","def get_binary_mask(self,index):\n mask = self.load_mask_png(index)\n (rows,cols) = np.where(mask>0)[0:2] #pixels in mask disregarding the color\n new_mask = np.zeros(shape=mask.shape[0:2], dtype=np.uint8)\n new_mask[(rows,cols)] = 255\n return new_mask","def adjusted_classes(pred_prob, threshold):\n return [1 if y >= threshold else 0 for y in pred_prob]","def fim_mask(model, dataset, samples, threshold):\n fisher_diagonal = fisher_matrix(model, dataset, samples)\n mask = [tensor < threshold for tensor in fisher_diagonal]\n return mask","def get_dropout_mask(dropout_probability: float, tensor_for_masking: mx.ndarray.ndarray.NDArray):\n binary_mask = mx.nd.random.uniform(0, 1, tensor_for_masking.shape) > dropout_probability\n # Scale mask by 1/keep_prob to preserve output statistics.\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\n return dropout_mask","def binary_array(array, thresh, value=0):\n if value == 0:\n # Create an array of ones with the same shape and type as \n # the input 2D array.\n binary = np.ones_like(array) \n \n else:\n # Creates an array of zeros with the same shape and type as \n # the input 2D array.\n binary = np.zeros_like(array) \n value = 1\n \n # If value == 0, make all values in binary equal to 0 if the \n # corresponding value in the input array is between the threshold \n # (inclusive). Otherwise, the value remains as 1. Therefore, the pixels \n # with the high Sobel derivative values (i.e. sharp pixel intensity \n # discontinuities) will have 0 in the corresponding cell of binary.\n binary[(array >= thresh[0]) & (array <= thresh[1])] = value\n \n return binary","def get_binary_mask(op_weights):\n return op_weights[\"mask\"]","def make_bw(im, th=150):\n im_gray = np.mean(im, axis=2)\n im_binary = im_gray > th\n boolean_to_numbers = lambda b: 1 if b else -1\n v_boolean_to_numbers = np.vectorize(boolean_to_numbers)\n return v_boolean_to_numbers(im_binary)","def preds_to_binary(pred_arr, channel_scaling=None, bg_threshold=0):\n pred_arr = _check_skimage_im_load(pred_arr).copy()\n\n if len(pred_arr.shape) == 3:\n if pred_arr.shape[0] < pred_arr.shape[-1]:\n pred_arr = np.moveaxis(pred_arr, 0, -1)\n if channel_scaling is None: # if scale values weren't provided\n channel_scaling = np.ones(shape=(pred_arr.shape[-1]),\n dtype='float')\n pred_arr = np.sum(pred_arr*np.array(channel_scaling), axis=-1)\n\n mask_arr = (pred_arr > bg_threshold).astype('uint8')\n\n return mask_arr*255","def preds_to_binary(pred_arr, channel_scaling=None, bg_threshold=0):\n pred_arr = _check_skimage_im_load(pred_arr).copy()\n\n if len(pred_arr.shape) == 3:\n if pred_arr.shape[0] < pred_arr.shape[-1]:\n pred_arr = np.moveaxis(pred_arr, 0, -1)\n if channel_scaling is None: # if scale values weren't provided\n channel_scaling = np.ones(shape=(pred_arr.shape[-1]),\n dtype='float')\n pred_arr = np.sum(pred_arr*np.array(channel_scaling), axis=-1)\n\n mask_arr = (pred_arr > bg_threshold).astype('uint8')\n\n return mask_arr*255","def scale(img):\n result = [[1 if x > BINARY_THRESHOLD else 0 for x in row] for row in img]\n return result","def make_mask(image_shape, fieldmap, activation_data,\n sigma=0.2, threshold=0.9, alpha=0.1):\n offset, shape, step = fieldmap\n activations = numpy.zeros(image_shape)\n activations[_centered_slice(fieldmap, activation_data.shape)] = (\n activation_data)\n blurred = gaussian_filter(\n activations, sigma=tuple(s * sigma for s in shape), mode='constant')\n maximum = blurred.flatten().max()\n return 1 - (1 - alpha) * (blurred < maximum * 0.9)","def test_soft_threshold_array():\n a = np.array([10, -10, 200, -200])\n np.testing.assert_allclose(snet.soft_threshold(a, 100),\n np.array([0, 0, 100, -100]))\n np.testing.assert_allclose(snet.soft_threshold(a, 3),\n np.array([7, -7, 197, -197]))","def bin_random_mat(m,n,p_0 = 0.5):\n\treturn np.array((np.random.randn(m,n) >= p_0), dtype = np.float)","def MovigAverageBinaryThresholding(image, kernel_sigma, n, b): \n\n if kernel_sigma >= 1:\n image = Denoising(image, kernel_sigma);\n \n nr, nc = image.shape;\n mask = np.zeros(image.shape);\n \n for ii in range(0, nr):\n for jj in range(0, nc):\n if jj < n:\n nb_mean = image[ii, 0:jj + 1].sum() / n;\n else:\n nb = image[ii, jj - n + 1:jj + 1];\n nb_mean = np.mean(nb);\n if image[ii, jj] > b * nb_mean:\n mask[ii, jj] = 1; \n \n return mask;","def global_threshold(img, threshold_method):\n pass","def get_dropout_mask(\n dropout_probability: float, tensor_for_masking: torch.Tensor\n): # pragma: no cover\n binary_mask = tensor_for_masking.new_tensor(\n torch.rand(tensor_for_masking.size()) > dropout_probability\n )\n # Scale mask by 1/keep_prob to preserve output statistics.\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\n return dropout_mask","def generate_mask(self, thresh=50, b_ground=None):\n img = self.load_image()\n thresh = np.zeros(img.shape, \"uint8\")\n if b_ground is not None:\n img = img - b_ground\n thresh[img > 25] = 255\n mask = ndimage.morphology.binary_dilation(thresh).astype(\"uint8\")\n self.mask = 255*mask","def binarize(X, *, threshold=..., copy=...):\n ...","def gen_background_mask( img ):\n\t\t\n\tif len( img.shape ) == 3: t = img[0]\n\telif len( img.shape ) == 2: t = img\n\n\tmask = img > filters.threshold_li(t)\n\n\treturn mask","def get_predict(prediction, threshold):\n\n prediction[prediction < threshold] = 0\n prediction[prediction >= threshold] = 1\n \n return prediction","def thresholding(pred,label,thres):\n \n conf =[]\n \n for i in thres:\n \n pr_th,lab_th = (pred>i),(label>i)\n conf += confusion(pr_th,lab_th)\n \n return np.array(conf).reshape(-1,4)","def discrete_potential(function, threshold):\n\n return np.where(function >= threshold, 1, 0)","def lab_select(img, thresh=(0,255)):\n \n # 1) Convert to LAB color space\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)\n b = lab[:,:,2]\n\n # 2) Apply a threshold to the B channel\n binary_output = np.zeros_like(b)\n binary_output[ (b>thresh[0]) & (b<thresh[1]) ] = 1\n\n # 3) Return a binary image of threshold result\n return binary_output","def get_dropout_mask(dropout_probability: float, h_dim: int):\n binary_mask = Variable(torch.FloatTensor(h_dim).cuda().fill_(0.0))\n binary_mask.data.copy_(torch.rand(h_dim) > dropout_probability)\n # Scale mask by 1/keep_prob to preserve output statistics.\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\n return dropout_mask","def apply_thresholding(x):\n return x > threshold_otsu(x)","def convert_binary(pred):\n pred = pred.astype(np.float64)\n pred[pred <= 0.5] = 0.0\n pred[pred > 0.5] = 1.0\n return pred","def matrice_binaire(matrice,threshold):\r\n for i in range(len(matrice)):\r\n for j in range(len(matrice[0])):\r\n if matrice[i][j]>threshold:\r\n matrice[i][j]=1\r\n else:\r\n matrice[i][j]=0\r\n \r\n return matrice","def _threshold_mask(data, mask, rms=None, threshold=0.0):\n if rms is None or threshold <= 0.0:\n return mask.astype('bool')\n rms = np.atleast_1d(rms)\n if rms.ndim == 2:\n sigma_mask = abs(data) >= (threshold * rms)[None, :, :]\n else:\n sigma_mask = abs(data) >= threshold * rms\n return np.logical_and(mask, sigma_mask).astype('bool')","def binaryThreshold(img, threshVal = 127, maxVal = 255, invert=True):\n\tgray = grayscale(img)\n\tif invert:\n\t\tret, thresh = cv2.threshold(img, threshVal, maxVal, cv2.THRESH_BINARY_INV)\n\telse:\n\t\tret, thresh = cv2.threshold(img, threshVal, maxVal, cv2.THRESH_BINARY)\n\treturn thresh","def threshold_to_mask(\n img: PIL.Image.Image, threshold: float, relate: Callable[..., bool]\n) -> np.ndarray:\n img_arr = np.array(img)\n return relate(img_arr, threshold)","def blur_mask(mask, blur_kernel, threshold=0.1):\n k = pyfits.getdata(blur_kernel)\n k = k / k.sum()\n mask = hconvolve.hconvolve(mask, k)\n mask = np.where(mask >= threshold, 1, 0).astype('int')\n return mask","def SpatiallyAdaptiveBinaryThresholding(image, kernel_sigma, a, b): \n\n if kernel_sigma >= 1:\n image = Denoising(image, kernel_sigma); \n \n nr, nc = image.shape;\n mask = np.zeros(image.shape);\n \n for ii in range(1, nr - 1):\n for jj in range(1, nc - 1):\n nb = image[ii - 1:ii + 2, jj - 1:jj + 2];\n nb = nb.reshape(3, 3);\n nb_std = np.std(nb);\n nb_mean = np.mean(nb);\n \n if image[ii, jj] > a * nb_std and image[ii, jj] > b * nb_mean:\n mask[ii, jj] = 1;\n\n return mask;","def _get_support_mask(self):\n mask = np.zeros(self.scores_.shape, dtype=bool)\n mask[self.scores_ >= self.min_count] = True\n return mask","def binarize(matrix: numpy.array, threshold: float, inplace: bool = True) -> numpy.array:\n mask = matrix >= threshold\n if inplace:\n matrix_ = matrix\n else:\n matrix_ = matrix.copy()\n matrix_[mask] = 1\n matrix_[numpy.logical_not(mask)] = 0\n return matrix_","def apply_mask(binary, mask_dict):\n result = \"\"\n for i, val in enumerate(binary):\n if mask_dict[i] in ('X', '1'):\n result += mask_dict[i]\n else:\n result += binary[i]\n return result","def _get_binary(raw_data):\n #try:\n # gray_data = _skimage.color.rgb2gray(raw_data)\n #except:\n # gray_data = raw_data\n\n try:\n thresh = _skimage.filters.threshold_otsu(raw_data[:, :, 0])\n binary = raw_data[:, :, 0] > thresh\n #binary = binary[:, :, 0]\n except ValueError:\n print('valueerror')\n binary = _np.ones(raw_data.shape).astype('bool')\n\n return binary","def apply_hounsfield_thresholding(data_, threshold: tuple = (200, 600)):\n mask = np.ma.masked_inside(data_, threshold[0], threshold[1], ).mask\n thresholded = np.zeros_like(data_)\n thresholded[mask] = data_[mask]\n return thresholded","def cluster_threshold(stat_map, domain, th, csize):\n if stat_map.shape[0] != domain.size:\n raise ValueError('incompatible dimensions')\n\n # first build a domain of supra_threshold regions\n thresholded_domain = domain.mask(stat_map > th)\n\n # get the connected components\n label = thresholded_domain.connected_components()\n\n binary = - np.ones(domain.size)\n binary[stat_map > th] = label\n nbcc = len(np.unique(label))\n for i in range(nbcc):\n if np.sum(label == i) < csize:\n binary[binary == i] = - 1\n\n binary = (binary > -1)\n return binary","def binarize(self, image, threshold):\n\n bin_img = image.copy()\n [h, w] = bin_img.shape\n opt_threshold = threshold\n print(opt_threshold)\n for row in range(h):\n for col in range(w):\n if bin_img[row, col] > opt_threshold: #greater than threshld white(general)\n bin_img[row, col] = 255 #0 instead of 1\n else: #less than threshold black(general)\n bin_img[row, col] = 0 #0 instead of 1\n\n\n #reverse the cases\n\n return bin_img","def apply_threshold(heatmap, threshold):\n heatmap_thresh = np.copy(heatmap)\n ind = np.where(np.logical_and(heatmap_thresh>1, heatmap_thresh<=threshold))\n heatmap_thresh[ind] = 0\n #heatmap_thresh[(heatmap_thresh <= threshold)] = 0\n return heatmap_thresh","def threshold(self, config ):\n java_object = pbg.gateway.jvm.boofcv.factory.filter.binary.FactoryThresholdBinary.\\\n threshold(config.java_obj,self.boof_image_type)\n return InputToBinary(java_object)","def postprocessing(self, prediction, prob_thresh=0.5):\n prob_map = self._np_sigmoid(prediction)\n prob_map = self._np_merge_prediction(prob_map)\n if self.resize :\n prob_map = self._np_resize_image(prob_map,\n self.orig_size,\n dtype='float')\n mask = self._np_get_mask(prob_map, prob_thresh=prob_thresh)\n return mask","def filter_labelmask(labelmask, func, above=0, below=1e6):\n labels = []\n for x in np.unique(labelmask):\n prop = func(labelmask, x)\n if (prop >= above and prop <= below):\n labels.append(x)\n labelmask_filtered = np.where(np.isin(labelmask, labels), labelmask, 0)\n return labelmask_filtered","def attention_mask(x):\n mask = torch.zeros(len(x), len(x[0]))\n for i in range(len(x)):\n try:\n index = np.where(x[i]==1)[0][0]\n mask[i][index:] = -np.inf\n except:\n pass\n return mask","def mask_and_fit(mask, binary_warped, flag):\n img = cv2.bitwise_and(binary_warped, binary_warped, mask=mask)\n x, y = extract_pixels(img)\n fit, foundFlag, confidence_index = check_and_fit(x, y, flag)\n return fit, foundFlag, confidence_index","def threshold_mask(image, threshold):\n image = image.copy()\n if threshold == None:\n threshold = skimage.filters.threshold_isodata(image)\n image[image > threshold] = 255\n image[image <= threshold] = 0\n return image","def make_attention_mask(source_block, target_block):\n mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)\n mask = mask.astype(np.int64)\n # (source_length, target_length)\n return mask","def featureBits(self,features, target):\n # Select Features\n features[features == 0] = 0.1\n p2 = np.floor(np.log(features)/np.log(2))\n \n select = (p2 != 13) & (p2 != -1)\n a = np.where(select)[0]\n select[a[:1000]] = False\n return select","def threshold(img, thresh):\n return cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)[1]","def lut(threshold):\n\n return [255 if i<=threshold else 0 for i in range(256)]","def nn_threshold_predict(X, nn, theta):\n tmp = theta.reshape((1, np.size(theta))) - nn.predict(X)\n pred = np.sum(tmp < 0, axis=1).astype(np.int)\n return np.array(pred)","def generate_mask(image, threshold):\n # TODO: Test this and optimize to only include pixels inward of the\n # horizon\n x_pix, y_pix = image.shape\n image_median = np.median(image)\n image_mean = np.mean(image)\n image_std = np.std(image)\n image_max = image.max()\n\n # generate mask\n mask = np.where(threshold < image, False, True)\n return mask","def apply_thresholding_img(img, t1, t2):\n hist_threshold = np.where(img >= t1, img, 255)\n hist_threshold = np.where(hist_threshold < t2, 0, hist_threshold)\n return hist_threshold","def bin_thres_img(img, ksize=3):\n # Apply each of the thresholding functions\n gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=(20, 100))\n grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=(20, 100))\n\n mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(30, 100))\n dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.7, 1.3))\n\n hls_binary = hls_select(img, thresh=(170, 255))\n\n combined = np.zeros_like(dir_binary)\n combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1)) | hls_binary == 1] = 1\n return combined","def threshold(X, thresh):\n Y = np.array(X)\n Y[Y >= thresh] = 1\n Y[Y < thresh] = 0\n return Y","def get_positive_mask(labels):\n batch_shape = tf.shape(labels)[0]\n mask_1 = tf.logical_not(get_negative_mask(labels))\n mask_2 = tf.logical_not(tf.eye(batch_shape, dtype=tf.bool))\n return tf.logical_and(mask_1, mask_2)","def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\n\n return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out,\n mask=mask, shift_x=shift_x, shift_y=shift_y)","def binarize(self, image, threshold):\n bin_img = image.copy()\n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n if image[i, j] >= threshold:\n bin_img[i, j] = 0\n else:\n bin_img[i, j] = 255\n return bin_img","def bin_thresh(img, threshold):\r\n _, new_img = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY) # 255 is the max value\r\n\r\n # make some smoothing to get rid of unnecessary ~splashes of color\r\n # parameters are the width and height of gaussian kernel, and standard deviation in X and Y direction,\r\n # sigmaX & sigmaY respectively. If only sigmaX is specified, sigmaY is taken as same as sigmaX.\r\n # If both are given as zeros, they are calculated from kernel size.\r\n new_img = cv2.GaussianBlur(new_img, (9, 9), 0)\r\n return new_img","def preprocess_mask(mask):\n # Project values interval on [0.0; 1.0]\n if mask.max() > 1:\n mask[mask <= 127.5] = 0.\n mask[mask > 127.5] = 1.\n else:\n mask[mask <= .5] = 0.\n mask[mask > .5] = 1.\n return mask","def image_binary(image_convert):\n image_bit=cv2.bitwise_not(image_convert)\n _, image_bina = cv2.threshold(image_bit, 125, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n image_bina=image_bina/255.0\n return image_bina","def filterp(th,ProbClass1):\n y=np.zeros(ProbClass1.shape[0])\n for i,v in enumerate(ProbClass1):\n if ProbClass1[i]>th:\n y[i]=1\n return y","def iou(binary_mask, prediction_mask, t=0.5):\n params = get_params()\n\n if params.nbr_of_classes == 1:\n predicted_class = prediction_mask >= t\n else:\n raise NotImplemented\n actual_class = binary_mask > 0.5\n\n intersection = tf.cast(tf.logical_and(predicted_class, actual_class), tf.float32)\n union = tf.cast(tf.logical_or(predicted_class, actual_class), tf.float32)\n\n return tf.divide(tf.reduce_sum(intersection), tf.reduce_sum(union))","def anoise(this, *args, **kargs):\n\t\t\n\t\t# Arguments\n\t\tif not args: args = [50]\n\t\t\n\t\t# Kernel's retrieval\n\t\tanoisek = this._ANOISEK\n\t\tif anoisek is None: return None\n\t\t\n\t\t# More magic\n\t\tbin = this._BINARY\n\t\tfor thresh in args:\n\t\t\tbin[:,:] = (cv2.filter2D(bin, -1, anoisek) / 2.55 > thresh) * 255\n\t\treturn True","def binary_sample(x):\n return np.random.binomial(1, p=x)","def thresh_mask(arrayin, thresh=0.1e0):\r\n if arrayin.dtype == 'complex' :\r\n arrayout = np.abs(arrayin)\r\n else :\r\n arrayout = arrayin\r\n thresh2 = np.max(arrayout)*thresh\r\n arrayout = np.array(1.0 * (arrayout > thresh2),dtype=np.bool) \r\n return arrayout","def binary_npv(target_tensor, forecast_probability_tensor):\n\n return 1. - binary_pofd(target_tensor, forecast_probability_tensor)","def create_gt_mask(vocal_spec, bg_spec):\n vocal_spec = vocal_spec.numpy()\n bg_spec = bg_spec.numpy()\n return np.array(vocal_spec > bg_spec, dtype=np.float32)","def apply_threshold(heatmap, threshold):\n heatmap[heatmap <= threshold] = 0\n\n return heatmap","def generateRandomMask(size, p=0.5):\n mask_array = (np.random.random(size) > p).astype(int)\n mask = sitk.GetImageFromArray(mask_array) \n return mask","def _prepare_mask_file(mask):\n result = np.ndarray((mask.shape[0], mask.shape[1]), dtype=np.uint8)\n for i in range(mask.shape[0]):\n for j in range(mask.shape[1]):\n\n if mask[i][j] > 0:\n result[i][j] = 1\n else:\n result[i][j] = 0\n \n return result","def binarize_labels(labels):\n labels = np.where(labels == 0, labels, 1)\n\n return labels","def binarize(array, threshold=0.5):\r\n \r\n if (np.amax(array) > 1.0) or (np.amin(array) < 0.0):\r\n raise ValueError('Voxel value fed to lambda in converting to original labels was out of range.')\r\n \r\n # obtain binarized output\r\n binarized = array.copy()\r\n zero_mask = (binarized <= threshold)\r\n binarized[zero_mask] = 0.0\r\n binarized[~zero_mask] = 1.0\r\n \r\n return binarized","def get_mask(total, begin, end):\n mask = np.zeros([total]).astype(np.float32)\n mask[begin:end] = 1\n return np.array(mask, dtype=np.bool)","def apply_threshold(heatmap, threshold):\n # Zero out pixels below the threshold\n heatmap[heatmap <= threshold] = 0\n # Return thresholded map\n return heatmap","def apply_threshold(heatmap, threshold):\n # Zero out pixels below the threshold\n heatmap[heatmap <= threshold] = 0\n # Return thresholded map\n return heatmap","def apply_threshold(heatmap, threshold):\n # Zero out pixels below the threshold\n heatmap[heatmap <= threshold] = 0\n # Return thresholded map\n return heatmap","def masked_bilinearsigmoid_cross_entropy(preds, labels, mask, negative_mask):\r\n\r\n loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels)\r\n mask += negative_mask\r\n mask = tf.cast(mask, dtype=tf.float32)\r\n # mask /= tf.reduce_mean(mask)\r\n mask = tf.reshape(mask, shape=[79924])\r\n loss *= mask\r\n return tf.reduce_mean(loss)","def get_binary_image(grayscale_image):\n _, thresholded_image = cv2.threshold(grayscale_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n return thresholded_image","def generate_bits(n, p):\n try:\n return (np.random.rand(*n) > p).astype(int)\n except TypeError:\n return (np.random.rand(n) > p).astype(int)","def imthresh(img):\n img_vecs = img.flatten()\n\n # pre-calculate the histogram and cumulative histogram.\n vbins = np.arange(0, 257, 1)\n img_hist, hist_edges = np.histogram(img_vecs, vbins)\n vbins = (hist_edges[:-1] + hist_edges[1:])/2\n \n hist_times_gray = np.cumsum(img_hist * np.arange(0, 256, 1))\n cum_hist = np.cumsum(img_hist)\n\n # A first approximation of the background mean mean_1 is the mean of the corner pixels.\n # The third corner's index seems to be wrong!\n m, n = img.shape\n sum_bg = np.sum(img_vecs[[0, n - 1, n * (m - 1), m * n - 1]])\n num_pix_bg = 4\n mean1 = sum_bg/4\n mean2 = (np.sum(img_vecs) - sum_bg)/(m *n - num_pix_bg)\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\n\n\n if (threshold_val != 0) and (cum_hist[threshold_val - 1] == 0):\n threshold_val_old = threshold_val\n\n threshold_val_old = 0 # weird\n while threshold_val != threshold_val_old:\n threshold_val_old = threshold_val\n mean1 = hist_times_gray[threshold_val - 1]/cum_hist[threshold_val - 1]\n mean2 = (hist_times_gray[-1] - hist_times_gray[threshold_val - 1])/(cum_hist[-1] - cum_hist[threshold_val - 1])\n\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\n\n\n img_out = img >= threshold_val\n return img_out, threshold_val","def overlay_prob(image, mask, cutoff=0.5):\n if len(image.shape) == 3:\n image = image[: ,: ,0]\n if len(mask.shape) == 3:\n mask = mask[: ,: ,0]\n if np.amax(image) > 100:\n image = image /255\n\n mask = mask>=cutoff\n mask = mask.astype(int)\n masked = np.ma.masked_where(mask == 0, mask)\n\n plt.figure()\n plt.subplot(1, 2, 1)\n plt.imshow(image, 'gray', interpolation='nearest')\n plt.subplot(1, 2, 2)\n plt.imshow(image, 'gray', interpolation='nearest')\n plt.imshow(masked, 'jet', interpolation='nearest', alpha=0.5)\n plt.show()","def binary_converter(probs):\n return np.array([[1 - p, p] for p in probs])","def binary_pod(target_tensor, forecast_probability_tensor):\n\n a = _get_num_true_positives(target_tensor, forecast_probability_tensor)\n c = _get_num_false_negatives(target_tensor, forecast_probability_tensor)\n\n return a / (a + c + K.epsilon())","def test_compute_binary_probabilities(self):\n nb_images = 100\n height_map = 32\n width_map = 48\n bin_widths_test_0 = numpy.array([2., 2., 2.], dtype=numpy.float32)\n bin_widths_test_1 = numpy.array([0.5, 0.5, 0.5], dtype=numpy.float32)\n truncated_unary_length = 4\n \n y_float32_0 = numpy.random.uniform(low=-10.,\n high=10.,\n size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\n y_float32_1 = numpy.random.laplace(loc=0.5,\n scale=2.5,\n size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\n y_float32_2 = numpy.random.standard_cauchy(size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\n y_float32 = numpy.concatenate((y_float32_0, y_float32_1, y_float32_2),\n axis=3)\n map_mean = numpy.array([0., 0.5, 0.], dtype=numpy.float32)\n binary_probabilities_0 = lossless.stats.compute_binary_probabilities(y_float32,\n bin_widths_test_0,\n map_mean,\n truncated_unary_length)\n print('1st set of test quantization bin widths:')\n print(bin_widths_test_0)\n print('1st absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_0[0, :])\n \n # Let x be a continuous random variable following the\n # uniform distribution of support [-10.0, 10.0]. The\n # probability the 1st binary decision is 0 is written\n # p(|x| <= 1.0) = 1.0/10. The probability the 2nd\n # binary decision is 0 is written\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = (2.0/10)/(9.0/10) = 2.0/9.\n # The probability the 3rd binary decision is 0 is written\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = (2.0/10)/(7.0/10) = 2.0/7.\n # The above calculations use the cumulative distribution\n # function of the uniform distribution of support [-10.0, 10.0].\n print('Binary probabilities computed by hand:')\n print([1./10, 2./9, 2./7, 2./5])\n print('2nd absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_0[1, :])\n \n # Let x be a continuous random variable following the\n # Laplace distribution of mean 0.0 and scale 2.5. It is\n # said `mean 0.0` as the 2nd latent variable feature map\n # is centered before being quantized. The probability\n # the 1st binary decision is 0 is written\n # p(|x| <= 1.0) = 0.3297. The probability the 2nd binary\n # decision is 0 is written\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = 0.3691/0.6703 = 0.5507.\n # The probability the 3rd binary decision is 0 is written\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = 0.1659/0.3012 = 0.5507.\n # The above calculations use the cumulative distribution\n # function of the Laplace distribution of mean 0 and scale 2.5.\n print('Binary probabilities computed by hand:')\n print([0.3297, 0.5507, 0.5507, 0.5507])\n print('3rd absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_0[2, :])\n \n # Let x be a continuous random variable following the\n # standard Cauchy distribution. The probability the 1st\n # binary decision is 0 is written p(|x| <= 1.0) = 0.5.\n # The probability the 2nd binary decision is 0 is written\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = 0.2952/0.5 = 0.5903.\n # The probability the 3rd binary decision is 0 is written\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = 0.079/0.2048 = 0.3865.\n # The above calculations use the cumulative distribution\n # function of the standard Cauchy distribution.\n print('Binary probabilities computed by hand:')\n print([0.5, 0.5903, 0.3865, 0.2811])\n \n binary_probabilities_1 = lossless.stats.compute_binary_probabilities(y_float32,\n bin_widths_test_1,\n map_mean,\n truncated_unary_length)\n print('\\n2nd set of test quantization bin widths:')\n print(bin_widths_test_1)\n print('1st absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_1[0, :])\n \n # Let x be a continuous random variable following the\n # uniform distribution of support [-10.0, 10.0]. The\n # probability the 1st binary decision is 0 is written\n # p(|x| <= 0.25) = 1.0/40. The probability the 2nd\n # binary decision is 0 is written\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = (2.0/40)/(39.0/40) = 2.0/39.\n # The probability the 3rd binary decision is 0 is written\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = (2.0/40)/(37.0/40) = 2.0/37.\n print('Binary probabilities computed by hand:')\n print([1./40, 2./39, 2./37, 2./35])\n print('2nd absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_1[1, :])\n \n # Let x be a continuous random variable following the\n # Laplace distribution of mean 0.0 and scale 2.5. The\n # probability the 1st binary decision is 0 is written\n # p(|x| <= 0.25) = 0.0952. The probability the 2nd binary\n # decision is 0 is written\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = 0.1640/0.9048 = 0.1813.\n # The probability the 3rd binary decision is 0 is written\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = 0.1343/0.7408 = 0.1813.\n print('Binary probabilities computed by hand:')\n print([0.0952, 0.1813, 0.1813, 0.1813])\n print('3rd absolute centered-quantized latent variable feature map.')\n print('Binary probabilities computed by the function:')\n print(binary_probabilities_1[2, :])\n \n # Let x be a continuous random variable following the\n # standard Cauchy distribution. The probability the 1st\n # binary decision is 0 is written p(|x| <= 0.25) = 0.1560.\n # The probability the 2nd binary decision is 0 is written\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = 0.2537/0.8440 = 0.3006.\n # The probability the 3rd binary decision is 0 is written\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = 0.1608/0.5903 = 0.2724.\n print('Binary probabilities computed by hand:')\n print([0.1560, 0.3006, 0.2724, 0.2306])","def apply_threshold(heatmap, threshold):\n # Zero out pixels below the threshold\n thresh_heatmap = np.copy(heatmap)\n thresh_heatmap[heatmap <= threshold] = 0\n # Return thresholded map\n return thresh_heatmap","def autothreshold(gray_im, method=\"otsu\"):\n if method == \"otsu\":\n t = otsu(gray_im)\n elif method == \"kmeans\":\n t = ave(kmeans(list(gray_im.getdata())))\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?","def image_thresholding(image: np.ndarray):\n # Resize image to a shape of (48, 48)\n image = image_as_square(image)\n\n # Find threshold using Otsu filter\n threshold: float = filters.threshold_otsu(image)\n binary = image > threshold\n\n binary_image = np.where(image, binary, 0) * 255\n\n # Resize the iamge back to a shape of (2304, )\n return image_as_array(image)"],"string":"[\n \"def binary_predict(probs, threshold = 0.5):\\n return (probs >= threshold) * np.ones(len(probs))\",\n \"def pred_from_prob(a,threshold):\\n bin_preds = np.zeros((np.size(a,0),))\\n bin_preds[np.where(a[:,1]>threshold)]=1.0\\n return bin_preds\",\n \"def _np_get_mask(prob_map, prob_thresh=0.5):\\n mask = (prob_map > prob_thresh) * 255\\n return mask.astype(np.uint8)\",\n \"def mask(self):\\n\\n mask = self.freqs >= self.minimum_threshold\\n mask = mask.astype(int)\\n self.freqs = self.freqs * mask\\n self.sums = self.sums * mask\",\n \"def get_threshold_mask(hparams, x):\\n\\n axis = list(range(1, x.shape.ndims))\\n min_val = tf.reduce_min(x, axis=axis, keepdims=True)\\n max_val = tf.reduce_max(x, axis=axis, keepdims=True)\\n thresh = min_val + hparams.threshold_factor * (max_val - min_val)\\n cond = tf.less(x, thresh)\\n return tf.where(cond, tf.zeros(tf.shape(x)), tf.ones(tf.shape(x)))\",\n \"def get_biomass(binary_mask):\\n\\n white_pixels = cv2.countNonZero(binary_mask)\\n return white_pixels\",\n \"def preprocess_mask(y):\\n y[y <= 255./2] = 0 # Needs to be in this order, otherwise 1 gets overwritten\\n y[y > 255./2] = 1\\n binary_mask = y.astype(np.uint8)\\n\\n return binary_mask\",\n \"def bin_thresh(img: np.ndarray, thresh: Number) -> np.ndarray:\\n res = img >= thresh\\n return res\",\n \"def create_binary_mask(self, type='negative'):\\n if not self.thresh_map_name:\\n return None\\n mode = self.thresh_mode\\n limits = self.thresh_limits\\n map = self.map_scalars\\n if mode=='mask lower':\\n m = (map < limits[0]) if type=='negative' else (map >= limits[0])\\n elif mode=='mask higher':\\n m = (map > limits[1]) if type=='negative' else (map <= limits[1])\\n elif mode=='mask between':\\n m = ( (map > limits[0]) & (map < limits[1]) ) \\\\\\n if type=='negative' \\\\\\n else ( (map <= limits[0]) | (map >= limits[1]) )\\n else: # mask outside\\n m = ( (map < limits[0]) | (map > limits[1]) ) \\\\\\n if type=='negative' \\\\\\n else ( (map >= limits[0]) & (map <= limits[1]) )\\n return m\",\n \"def predict_mask(logit, EMPTY_THRESHOLD, MASK_THRESHOLD):\\n #pred mask 0-1 pixel-wise\\n #n = logit.shape[0]\\n IMG_SIZE = logit.shape[-1] #256\\n #EMPTY_THRESHOLD = 100.0*(IMG_SIZE/128.0)**2 #count of predicted mask pixles<threshold, predict as empty mask image\\n #MASK_THRESHOLD = 0.22\\n #logit = torch.sigmoid(torch.from_numpy(logit)).view(n, -1)\\n #pred = (logit>MASK_THRESHOLD).long()\\n #pred[pred.sum(dim=1) < EMPTY_THRESHOLD, ] = 0 #bug here, found it, the bug is input shape is (256, 256) not (16,256,256)\\n logit = sigmoid(logit)#.reshape(n, -1)\\n pred = (logit>MASK_THRESHOLD).astype(np.int)\\n if pred.sum() < EMPTY_THRESHOLD:\\n return np.zeros(pred.shape).astype(np.int)\\n else:\\n return pred\",\n \"def get_sample_mask(self):\",\n \"def threshold_probs(probs):\\n classes = np.ones(len(probs),)\\n classes[probs < 0.5] = 0\\n return classes\",\n \"def get_binary_mask(self,index):\\n mask = self.load_mask_png(index)\\n (rows,cols) = np.where(mask>0)[0:2] #pixels in mask disregarding the color\\n new_mask = np.zeros(shape=mask.shape[0:2], dtype=np.uint8)\\n new_mask[(rows,cols)] = 255\\n return new_mask\",\n \"def adjusted_classes(pred_prob, threshold):\\n return [1 if y >= threshold else 0 for y in pred_prob]\",\n \"def fim_mask(model, dataset, samples, threshold):\\n fisher_diagonal = fisher_matrix(model, dataset, samples)\\n mask = [tensor < threshold for tensor in fisher_diagonal]\\n return mask\",\n \"def get_dropout_mask(dropout_probability: float, tensor_for_masking: mx.ndarray.ndarray.NDArray):\\n binary_mask = mx.nd.random.uniform(0, 1, tensor_for_masking.shape) > dropout_probability\\n # Scale mask by 1/keep_prob to preserve output statistics.\\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\\n return dropout_mask\",\n \"def binary_array(array, thresh, value=0):\\n if value == 0:\\n # Create an array of ones with the same shape and type as \\n # the input 2D array.\\n binary = np.ones_like(array) \\n \\n else:\\n # Creates an array of zeros with the same shape and type as \\n # the input 2D array.\\n binary = np.zeros_like(array) \\n value = 1\\n \\n # If value == 0, make all values in binary equal to 0 if the \\n # corresponding value in the input array is between the threshold \\n # (inclusive). Otherwise, the value remains as 1. Therefore, the pixels \\n # with the high Sobel derivative values (i.e. sharp pixel intensity \\n # discontinuities) will have 0 in the corresponding cell of binary.\\n binary[(array >= thresh[0]) & (array <= thresh[1])] = value\\n \\n return binary\",\n \"def get_binary_mask(op_weights):\\n return op_weights[\\\"mask\\\"]\",\n \"def make_bw(im, th=150):\\n im_gray = np.mean(im, axis=2)\\n im_binary = im_gray > th\\n boolean_to_numbers = lambda b: 1 if b else -1\\n v_boolean_to_numbers = np.vectorize(boolean_to_numbers)\\n return v_boolean_to_numbers(im_binary)\",\n \"def preds_to_binary(pred_arr, channel_scaling=None, bg_threshold=0):\\n pred_arr = _check_skimage_im_load(pred_arr).copy()\\n\\n if len(pred_arr.shape) == 3:\\n if pred_arr.shape[0] < pred_arr.shape[-1]:\\n pred_arr = np.moveaxis(pred_arr, 0, -1)\\n if channel_scaling is None: # if scale values weren't provided\\n channel_scaling = np.ones(shape=(pred_arr.shape[-1]),\\n dtype='float')\\n pred_arr = np.sum(pred_arr*np.array(channel_scaling), axis=-1)\\n\\n mask_arr = (pred_arr > bg_threshold).astype('uint8')\\n\\n return mask_arr*255\",\n \"def preds_to_binary(pred_arr, channel_scaling=None, bg_threshold=0):\\n pred_arr = _check_skimage_im_load(pred_arr).copy()\\n\\n if len(pred_arr.shape) == 3:\\n if pred_arr.shape[0] < pred_arr.shape[-1]:\\n pred_arr = np.moveaxis(pred_arr, 0, -1)\\n if channel_scaling is None: # if scale values weren't provided\\n channel_scaling = np.ones(shape=(pred_arr.shape[-1]),\\n dtype='float')\\n pred_arr = np.sum(pred_arr*np.array(channel_scaling), axis=-1)\\n\\n mask_arr = (pred_arr > bg_threshold).astype('uint8')\\n\\n return mask_arr*255\",\n \"def scale(img):\\n result = [[1 if x > BINARY_THRESHOLD else 0 for x in row] for row in img]\\n return result\",\n \"def make_mask(image_shape, fieldmap, activation_data,\\n sigma=0.2, threshold=0.9, alpha=0.1):\\n offset, shape, step = fieldmap\\n activations = numpy.zeros(image_shape)\\n activations[_centered_slice(fieldmap, activation_data.shape)] = (\\n activation_data)\\n blurred = gaussian_filter(\\n activations, sigma=tuple(s * sigma for s in shape), mode='constant')\\n maximum = blurred.flatten().max()\\n return 1 - (1 - alpha) * (blurred < maximum * 0.9)\",\n \"def test_soft_threshold_array():\\n a = np.array([10, -10, 200, -200])\\n np.testing.assert_allclose(snet.soft_threshold(a, 100),\\n np.array([0, 0, 100, -100]))\\n np.testing.assert_allclose(snet.soft_threshold(a, 3),\\n np.array([7, -7, 197, -197]))\",\n \"def bin_random_mat(m,n,p_0 = 0.5):\\n\\treturn np.array((np.random.randn(m,n) >= p_0), dtype = np.float)\",\n \"def MovigAverageBinaryThresholding(image, kernel_sigma, n, b): \\n\\n if kernel_sigma >= 1:\\n image = Denoising(image, kernel_sigma);\\n \\n nr, nc = image.shape;\\n mask = np.zeros(image.shape);\\n \\n for ii in range(0, nr):\\n for jj in range(0, nc):\\n if jj < n:\\n nb_mean = image[ii, 0:jj + 1].sum() / n;\\n else:\\n nb = image[ii, jj - n + 1:jj + 1];\\n nb_mean = np.mean(nb);\\n if image[ii, jj] > b * nb_mean:\\n mask[ii, jj] = 1; \\n \\n return mask;\",\n \"def global_threshold(img, threshold_method):\\n pass\",\n \"def get_dropout_mask(\\n dropout_probability: float, tensor_for_masking: torch.Tensor\\n): # pragma: no cover\\n binary_mask = tensor_for_masking.new_tensor(\\n torch.rand(tensor_for_masking.size()) > dropout_probability\\n )\\n # Scale mask by 1/keep_prob to preserve output statistics.\\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\\n return dropout_mask\",\n \"def generate_mask(self, thresh=50, b_ground=None):\\n img = self.load_image()\\n thresh = np.zeros(img.shape, \\\"uint8\\\")\\n if b_ground is not None:\\n img = img - b_ground\\n thresh[img > 25] = 255\\n mask = ndimage.morphology.binary_dilation(thresh).astype(\\\"uint8\\\")\\n self.mask = 255*mask\",\n \"def binarize(X, *, threshold=..., copy=...):\\n ...\",\n \"def gen_background_mask( img ):\\n\\t\\t\\n\\tif len( img.shape ) == 3: t = img[0]\\n\\telif len( img.shape ) == 2: t = img\\n\\n\\tmask = img > filters.threshold_li(t)\\n\\n\\treturn mask\",\n \"def get_predict(prediction, threshold):\\n\\n prediction[prediction < threshold] = 0\\n prediction[prediction >= threshold] = 1\\n \\n return prediction\",\n \"def thresholding(pred,label,thres):\\n \\n conf =[]\\n \\n for i in thres:\\n \\n pr_th,lab_th = (pred>i),(label>i)\\n conf += confusion(pr_th,lab_th)\\n \\n return np.array(conf).reshape(-1,4)\",\n \"def discrete_potential(function, threshold):\\n\\n return np.where(function >= threshold, 1, 0)\",\n \"def lab_select(img, thresh=(0,255)):\\n \\n # 1) Convert to LAB color space\\n lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)\\n b = lab[:,:,2]\\n\\n # 2) Apply a threshold to the B channel\\n binary_output = np.zeros_like(b)\\n binary_output[ (b>thresh[0]) & (b<thresh[1]) ] = 1\\n\\n # 3) Return a binary image of threshold result\\n return binary_output\",\n \"def get_dropout_mask(dropout_probability: float, h_dim: int):\\n binary_mask = Variable(torch.FloatTensor(h_dim).cuda().fill_(0.0))\\n binary_mask.data.copy_(torch.rand(h_dim) > dropout_probability)\\n # Scale mask by 1/keep_prob to preserve output statistics.\\n dropout_mask = binary_mask.float().div(1.0 - dropout_probability)\\n return dropout_mask\",\n \"def apply_thresholding(x):\\n return x > threshold_otsu(x)\",\n \"def convert_binary(pred):\\n pred = pred.astype(np.float64)\\n pred[pred <= 0.5] = 0.0\\n pred[pred > 0.5] = 1.0\\n return pred\",\n \"def matrice_binaire(matrice,threshold):\\r\\n for i in range(len(matrice)):\\r\\n for j in range(len(matrice[0])):\\r\\n if matrice[i][j]>threshold:\\r\\n matrice[i][j]=1\\r\\n else:\\r\\n matrice[i][j]=0\\r\\n \\r\\n return matrice\",\n \"def _threshold_mask(data, mask, rms=None, threshold=0.0):\\n if rms is None or threshold <= 0.0:\\n return mask.astype('bool')\\n rms = np.atleast_1d(rms)\\n if rms.ndim == 2:\\n sigma_mask = abs(data) >= (threshold * rms)[None, :, :]\\n else:\\n sigma_mask = abs(data) >= threshold * rms\\n return np.logical_and(mask, sigma_mask).astype('bool')\",\n \"def binaryThreshold(img, threshVal = 127, maxVal = 255, invert=True):\\n\\tgray = grayscale(img)\\n\\tif invert:\\n\\t\\tret, thresh = cv2.threshold(img, threshVal, maxVal, cv2.THRESH_BINARY_INV)\\n\\telse:\\n\\t\\tret, thresh = cv2.threshold(img, threshVal, maxVal, cv2.THRESH_BINARY)\\n\\treturn thresh\",\n \"def threshold_to_mask(\\n img: PIL.Image.Image, threshold: float, relate: Callable[..., bool]\\n) -> np.ndarray:\\n img_arr = np.array(img)\\n return relate(img_arr, threshold)\",\n \"def blur_mask(mask, blur_kernel, threshold=0.1):\\n k = pyfits.getdata(blur_kernel)\\n k = k / k.sum()\\n mask = hconvolve.hconvolve(mask, k)\\n mask = np.where(mask >= threshold, 1, 0).astype('int')\\n return mask\",\n \"def SpatiallyAdaptiveBinaryThresholding(image, kernel_sigma, a, b): \\n\\n if kernel_sigma >= 1:\\n image = Denoising(image, kernel_sigma); \\n \\n nr, nc = image.shape;\\n mask = np.zeros(image.shape);\\n \\n for ii in range(1, nr - 1):\\n for jj in range(1, nc - 1):\\n nb = image[ii - 1:ii + 2, jj - 1:jj + 2];\\n nb = nb.reshape(3, 3);\\n nb_std = np.std(nb);\\n nb_mean = np.mean(nb);\\n \\n if image[ii, jj] > a * nb_std and image[ii, jj] > b * nb_mean:\\n mask[ii, jj] = 1;\\n\\n return mask;\",\n \"def _get_support_mask(self):\\n mask = np.zeros(self.scores_.shape, dtype=bool)\\n mask[self.scores_ >= self.min_count] = True\\n return mask\",\n \"def binarize(matrix: numpy.array, threshold: float, inplace: bool = True) -> numpy.array:\\n mask = matrix >= threshold\\n if inplace:\\n matrix_ = matrix\\n else:\\n matrix_ = matrix.copy()\\n matrix_[mask] = 1\\n matrix_[numpy.logical_not(mask)] = 0\\n return matrix_\",\n \"def apply_mask(binary, mask_dict):\\n result = \\\"\\\"\\n for i, val in enumerate(binary):\\n if mask_dict[i] in ('X', '1'):\\n result += mask_dict[i]\\n else:\\n result += binary[i]\\n return result\",\n \"def _get_binary(raw_data):\\n #try:\\n # gray_data = _skimage.color.rgb2gray(raw_data)\\n #except:\\n # gray_data = raw_data\\n\\n try:\\n thresh = _skimage.filters.threshold_otsu(raw_data[:, :, 0])\\n binary = raw_data[:, :, 0] > thresh\\n #binary = binary[:, :, 0]\\n except ValueError:\\n print('valueerror')\\n binary = _np.ones(raw_data.shape).astype('bool')\\n\\n return binary\",\n \"def apply_hounsfield_thresholding(data_, threshold: tuple = (200, 600)):\\n mask = np.ma.masked_inside(data_, threshold[0], threshold[1], ).mask\\n thresholded = np.zeros_like(data_)\\n thresholded[mask] = data_[mask]\\n return thresholded\",\n \"def cluster_threshold(stat_map, domain, th, csize):\\n if stat_map.shape[0] != domain.size:\\n raise ValueError('incompatible dimensions')\\n\\n # first build a domain of supra_threshold regions\\n thresholded_domain = domain.mask(stat_map > th)\\n\\n # get the connected components\\n label = thresholded_domain.connected_components()\\n\\n binary = - np.ones(domain.size)\\n binary[stat_map > th] = label\\n nbcc = len(np.unique(label))\\n for i in range(nbcc):\\n if np.sum(label == i) < csize:\\n binary[binary == i] = - 1\\n\\n binary = (binary > -1)\\n return binary\",\n \"def binarize(self, image, threshold):\\n\\n bin_img = image.copy()\\n [h, w] = bin_img.shape\\n opt_threshold = threshold\\n print(opt_threshold)\\n for row in range(h):\\n for col in range(w):\\n if bin_img[row, col] > opt_threshold: #greater than threshld white(general)\\n bin_img[row, col] = 255 #0 instead of 1\\n else: #less than threshold black(general)\\n bin_img[row, col] = 0 #0 instead of 1\\n\\n\\n #reverse the cases\\n\\n return bin_img\",\n \"def apply_threshold(heatmap, threshold):\\n heatmap_thresh = np.copy(heatmap)\\n ind = np.where(np.logical_and(heatmap_thresh>1, heatmap_thresh<=threshold))\\n heatmap_thresh[ind] = 0\\n #heatmap_thresh[(heatmap_thresh <= threshold)] = 0\\n return heatmap_thresh\",\n \"def threshold(self, config ):\\n java_object = pbg.gateway.jvm.boofcv.factory.filter.binary.FactoryThresholdBinary.\\\\\\n threshold(config.java_obj,self.boof_image_type)\\n return InputToBinary(java_object)\",\n \"def postprocessing(self, prediction, prob_thresh=0.5):\\n prob_map = self._np_sigmoid(prediction)\\n prob_map = self._np_merge_prediction(prob_map)\\n if self.resize :\\n prob_map = self._np_resize_image(prob_map,\\n self.orig_size,\\n dtype='float')\\n mask = self._np_get_mask(prob_map, prob_thresh=prob_thresh)\\n return mask\",\n \"def filter_labelmask(labelmask, func, above=0, below=1e6):\\n labels = []\\n for x in np.unique(labelmask):\\n prop = func(labelmask, x)\\n if (prop >= above and prop <= below):\\n labels.append(x)\\n labelmask_filtered = np.where(np.isin(labelmask, labels), labelmask, 0)\\n return labelmask_filtered\",\n \"def attention_mask(x):\\n mask = torch.zeros(len(x), len(x[0]))\\n for i in range(len(x)):\\n try:\\n index = np.where(x[i]==1)[0][0]\\n mask[i][index:] = -np.inf\\n except:\\n pass\\n return mask\",\n \"def mask_and_fit(mask, binary_warped, flag):\\n img = cv2.bitwise_and(binary_warped, binary_warped, mask=mask)\\n x, y = extract_pixels(img)\\n fit, foundFlag, confidence_index = check_and_fit(x, y, flag)\\n return fit, foundFlag, confidence_index\",\n \"def threshold_mask(image, threshold):\\n image = image.copy()\\n if threshold == None:\\n threshold = skimage.filters.threshold_isodata(image)\\n image[image > threshold] = 255\\n image[image <= threshold] = 0\\n return image\",\n \"def make_attention_mask(source_block, target_block):\\n mask = (target_block[None, :] >= 1) * (source_block[:, None] >= 1)\\n mask = mask.astype(np.int64)\\n # (source_length, target_length)\\n return mask\",\n \"def featureBits(self,features, target):\\n # Select Features\\n features[features == 0] = 0.1\\n p2 = np.floor(np.log(features)/np.log(2))\\n \\n select = (p2 != 13) & (p2 != -1)\\n a = np.where(select)[0]\\n select[a[:1000]] = False\\n return select\",\n \"def threshold(img, thresh):\\n return cv2.threshold(img, thresh, 255, cv2.THRESH_BINARY)[1]\",\n \"def lut(threshold):\\n\\n return [255 if i<=threshold else 0 for i in range(256)]\",\n \"def nn_threshold_predict(X, nn, theta):\\n tmp = theta.reshape((1, np.size(theta))) - nn.predict(X)\\n pred = np.sum(tmp < 0, axis=1).astype(np.int)\\n return np.array(pred)\",\n \"def generate_mask(image, threshold):\\n # TODO: Test this and optimize to only include pixels inward of the\\n # horizon\\n x_pix, y_pix = image.shape\\n image_median = np.median(image)\\n image_mean = np.mean(image)\\n image_std = np.std(image)\\n image_max = image.max()\\n\\n # generate mask\\n mask = np.where(threshold < image, False, True)\\n return mask\",\n \"def apply_thresholding_img(img, t1, t2):\\n hist_threshold = np.where(img >= t1, img, 255)\\n hist_threshold = np.where(hist_threshold < t2, 0, hist_threshold)\\n return hist_threshold\",\n \"def bin_thres_img(img, ksize=3):\\n # Apply each of the thresholding functions\\n gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=(20, 100))\\n grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=(20, 100))\\n\\n mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(30, 100))\\n dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0.7, 1.3))\\n\\n hls_binary = hls_select(img, thresh=(170, 255))\\n\\n combined = np.zeros_like(dir_binary)\\n combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1)) | hls_binary == 1] = 1\\n return combined\",\n \"def threshold(X, thresh):\\n Y = np.array(X)\\n Y[Y >= thresh] = 1\\n Y[Y < thresh] = 0\\n return Y\",\n \"def get_positive_mask(labels):\\n batch_shape = tf.shape(labels)[0]\\n mask_1 = tf.logical_not(get_negative_mask(labels))\\n mask_2 = tf.logical_not(tf.eye(batch_shape, dtype=tf.bool))\\n return tf.logical_and(mask_1, mask_2)\",\n \"def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):\\n\\n return _apply(_crank8.threshold, _crank16.threshold, image, selem, out=out,\\n mask=mask, shift_x=shift_x, shift_y=shift_y)\",\n \"def binarize(self, image, threshold):\\n bin_img = image.copy()\\n for i in range(image.shape[0]):\\n for j in range(image.shape[1]):\\n if image[i, j] >= threshold:\\n bin_img[i, j] = 0\\n else:\\n bin_img[i, j] = 255\\n return bin_img\",\n \"def bin_thresh(img, threshold):\\r\\n _, new_img = cv2.threshold(img, threshold, 255, cv2.THRESH_BINARY) # 255 is the max value\\r\\n\\r\\n # make some smoothing to get rid of unnecessary ~splashes of color\\r\\n # parameters are the width and height of gaussian kernel, and standard deviation in X and Y direction,\\r\\n # sigmaX & sigmaY respectively. If only sigmaX is specified, sigmaY is taken as same as sigmaX.\\r\\n # If both are given as zeros, they are calculated from kernel size.\\r\\n new_img = cv2.GaussianBlur(new_img, (9, 9), 0)\\r\\n return new_img\",\n \"def preprocess_mask(mask):\\n # Project values interval on [0.0; 1.0]\\n if mask.max() > 1:\\n mask[mask <= 127.5] = 0.\\n mask[mask > 127.5] = 1.\\n else:\\n mask[mask <= .5] = 0.\\n mask[mask > .5] = 1.\\n return mask\",\n \"def image_binary(image_convert):\\n image_bit=cv2.bitwise_not(image_convert)\\n _, image_bina = cv2.threshold(image_bit, 125, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\\n image_bina=image_bina/255.0\\n return image_bina\",\n \"def filterp(th,ProbClass1):\\n y=np.zeros(ProbClass1.shape[0])\\n for i,v in enumerate(ProbClass1):\\n if ProbClass1[i]>th:\\n y[i]=1\\n return y\",\n \"def iou(binary_mask, prediction_mask, t=0.5):\\n params = get_params()\\n\\n if params.nbr_of_classes == 1:\\n predicted_class = prediction_mask >= t\\n else:\\n raise NotImplemented\\n actual_class = binary_mask > 0.5\\n\\n intersection = tf.cast(tf.logical_and(predicted_class, actual_class), tf.float32)\\n union = tf.cast(tf.logical_or(predicted_class, actual_class), tf.float32)\\n\\n return tf.divide(tf.reduce_sum(intersection), tf.reduce_sum(union))\",\n \"def anoise(this, *args, **kargs):\\n\\t\\t\\n\\t\\t# Arguments\\n\\t\\tif not args: args = [50]\\n\\t\\t\\n\\t\\t# Kernel's retrieval\\n\\t\\tanoisek = this._ANOISEK\\n\\t\\tif anoisek is None: return None\\n\\t\\t\\n\\t\\t# More magic\\n\\t\\tbin = this._BINARY\\n\\t\\tfor thresh in args:\\n\\t\\t\\tbin[:,:] = (cv2.filter2D(bin, -1, anoisek) / 2.55 > thresh) * 255\\n\\t\\treturn True\",\n \"def binary_sample(x):\\n return np.random.binomial(1, p=x)\",\n \"def thresh_mask(arrayin, thresh=0.1e0):\\r\\n if arrayin.dtype == 'complex' :\\r\\n arrayout = np.abs(arrayin)\\r\\n else :\\r\\n arrayout = arrayin\\r\\n thresh2 = np.max(arrayout)*thresh\\r\\n arrayout = np.array(1.0 * (arrayout > thresh2),dtype=np.bool) \\r\\n return arrayout\",\n \"def binary_npv(target_tensor, forecast_probability_tensor):\\n\\n return 1. - binary_pofd(target_tensor, forecast_probability_tensor)\",\n \"def create_gt_mask(vocal_spec, bg_spec):\\n vocal_spec = vocal_spec.numpy()\\n bg_spec = bg_spec.numpy()\\n return np.array(vocal_spec > bg_spec, dtype=np.float32)\",\n \"def apply_threshold(heatmap, threshold):\\n heatmap[heatmap <= threshold] = 0\\n\\n return heatmap\",\n \"def generateRandomMask(size, p=0.5):\\n mask_array = (np.random.random(size) > p).astype(int)\\n mask = sitk.GetImageFromArray(mask_array) \\n return mask\",\n \"def _prepare_mask_file(mask):\\n result = np.ndarray((mask.shape[0], mask.shape[1]), dtype=np.uint8)\\n for i in range(mask.shape[0]):\\n for j in range(mask.shape[1]):\\n\\n if mask[i][j] > 0:\\n result[i][j] = 1\\n else:\\n result[i][j] = 0\\n \\n return result\",\n \"def binarize_labels(labels):\\n labels = np.where(labels == 0, labels, 1)\\n\\n return labels\",\n \"def binarize(array, threshold=0.5):\\r\\n \\r\\n if (np.amax(array) > 1.0) or (np.amin(array) < 0.0):\\r\\n raise ValueError('Voxel value fed to lambda in converting to original labels was out of range.')\\r\\n \\r\\n # obtain binarized output\\r\\n binarized = array.copy()\\r\\n zero_mask = (binarized <= threshold)\\r\\n binarized[zero_mask] = 0.0\\r\\n binarized[~zero_mask] = 1.0\\r\\n \\r\\n return binarized\",\n \"def get_mask(total, begin, end):\\n mask = np.zeros([total]).astype(np.float32)\\n mask[begin:end] = 1\\n return np.array(mask, dtype=np.bool)\",\n \"def apply_threshold(heatmap, threshold):\\n # Zero out pixels below the threshold\\n heatmap[heatmap <= threshold] = 0\\n # Return thresholded map\\n return heatmap\",\n \"def apply_threshold(heatmap, threshold):\\n # Zero out pixels below the threshold\\n heatmap[heatmap <= threshold] = 0\\n # Return thresholded map\\n return heatmap\",\n \"def apply_threshold(heatmap, threshold):\\n # Zero out pixels below the threshold\\n heatmap[heatmap <= threshold] = 0\\n # Return thresholded map\\n return heatmap\",\n \"def masked_bilinearsigmoid_cross_entropy(preds, labels, mask, negative_mask):\\r\\n\\r\\n loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=preds, labels=labels)\\r\\n mask += negative_mask\\r\\n mask = tf.cast(mask, dtype=tf.float32)\\r\\n # mask /= tf.reduce_mean(mask)\\r\\n mask = tf.reshape(mask, shape=[79924])\\r\\n loss *= mask\\r\\n return tf.reduce_mean(loss)\",\n \"def get_binary_image(grayscale_image):\\n _, thresholded_image = cv2.threshold(grayscale_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\\n return thresholded_image\",\n \"def generate_bits(n, p):\\n try:\\n return (np.random.rand(*n) > p).astype(int)\\n except TypeError:\\n return (np.random.rand(n) > p).astype(int)\",\n \"def imthresh(img):\\n img_vecs = img.flatten()\\n\\n # pre-calculate the histogram and cumulative histogram.\\n vbins = np.arange(0, 257, 1)\\n img_hist, hist_edges = np.histogram(img_vecs, vbins)\\n vbins = (hist_edges[:-1] + hist_edges[1:])/2\\n \\n hist_times_gray = np.cumsum(img_hist * np.arange(0, 256, 1))\\n cum_hist = np.cumsum(img_hist)\\n\\n # A first approximation of the background mean mean_1 is the mean of the corner pixels.\\n # The third corner's index seems to be wrong!\\n m, n = img.shape\\n sum_bg = np.sum(img_vecs[[0, n - 1, n * (m - 1), m * n - 1]])\\n num_pix_bg = 4\\n mean1 = sum_bg/4\\n mean2 = (np.sum(img_vecs) - sum_bg)/(m *n - num_pix_bg)\\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\\n\\n\\n if (threshold_val != 0) and (cum_hist[threshold_val - 1] == 0):\\n threshold_val_old = threshold_val\\n\\n threshold_val_old = 0 # weird\\n while threshold_val != threshold_val_old:\\n threshold_val_old = threshold_val\\n mean1 = hist_times_gray[threshold_val - 1]/cum_hist[threshold_val - 1]\\n mean2 = (hist_times_gray[-1] - hist_times_gray[threshold_val - 1])/(cum_hist[-1] - cum_hist[threshold_val - 1])\\n\\n threshold_val = np.uint8(np.ceil((mean1 + mean2)/2))\\n\\n\\n img_out = img >= threshold_val\\n return img_out, threshold_val\",\n \"def overlay_prob(image, mask, cutoff=0.5):\\n if len(image.shape) == 3:\\n image = image[: ,: ,0]\\n if len(mask.shape) == 3:\\n mask = mask[: ,: ,0]\\n if np.amax(image) > 100:\\n image = image /255\\n\\n mask = mask>=cutoff\\n mask = mask.astype(int)\\n masked = np.ma.masked_where(mask == 0, mask)\\n\\n plt.figure()\\n plt.subplot(1, 2, 1)\\n plt.imshow(image, 'gray', interpolation='nearest')\\n plt.subplot(1, 2, 2)\\n plt.imshow(image, 'gray', interpolation='nearest')\\n plt.imshow(masked, 'jet', interpolation='nearest', alpha=0.5)\\n plt.show()\",\n \"def binary_converter(probs):\\n return np.array([[1 - p, p] for p in probs])\",\n \"def binary_pod(target_tensor, forecast_probability_tensor):\\n\\n a = _get_num_true_positives(target_tensor, forecast_probability_tensor)\\n c = _get_num_false_negatives(target_tensor, forecast_probability_tensor)\\n\\n return a / (a + c + K.epsilon())\",\n \"def test_compute_binary_probabilities(self):\\n nb_images = 100\\n height_map = 32\\n width_map = 48\\n bin_widths_test_0 = numpy.array([2., 2., 2.], dtype=numpy.float32)\\n bin_widths_test_1 = numpy.array([0.5, 0.5, 0.5], dtype=numpy.float32)\\n truncated_unary_length = 4\\n \\n y_float32_0 = numpy.random.uniform(low=-10.,\\n high=10.,\\n size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\\n y_float32_1 = numpy.random.laplace(loc=0.5,\\n scale=2.5,\\n size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\\n y_float32_2 = numpy.random.standard_cauchy(size=(nb_images, height_map, width_map, 1)).astype(numpy.float32)\\n y_float32 = numpy.concatenate((y_float32_0, y_float32_1, y_float32_2),\\n axis=3)\\n map_mean = numpy.array([0., 0.5, 0.], dtype=numpy.float32)\\n binary_probabilities_0 = lossless.stats.compute_binary_probabilities(y_float32,\\n bin_widths_test_0,\\n map_mean,\\n truncated_unary_length)\\n print('1st set of test quantization bin widths:')\\n print(bin_widths_test_0)\\n print('1st absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_0[0, :])\\n \\n # Let x be a continuous random variable following the\\n # uniform distribution of support [-10.0, 10.0]. The\\n # probability the 1st binary decision is 0 is written\\n # p(|x| <= 1.0) = 1.0/10. The probability the 2nd\\n # binary decision is 0 is written\\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = (2.0/10)/(9.0/10) = 2.0/9.\\n # The probability the 3rd binary decision is 0 is written\\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = (2.0/10)/(7.0/10) = 2.0/7.\\n # The above calculations use the cumulative distribution\\n # function of the uniform distribution of support [-10.0, 10.0].\\n print('Binary probabilities computed by hand:')\\n print([1./10, 2./9, 2./7, 2./5])\\n print('2nd absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_0[1, :])\\n \\n # Let x be a continuous random variable following the\\n # Laplace distribution of mean 0.0 and scale 2.5. It is\\n # said `mean 0.0` as the 2nd latent variable feature map\\n # is centered before being quantized. The probability\\n # the 1st binary decision is 0 is written\\n # p(|x| <= 1.0) = 0.3297. The probability the 2nd binary\\n # decision is 0 is written\\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = 0.3691/0.6703 = 0.5507.\\n # The probability the 3rd binary decision is 0 is written\\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = 0.1659/0.3012 = 0.5507.\\n # The above calculations use the cumulative distribution\\n # function of the Laplace distribution of mean 0 and scale 2.5.\\n print('Binary probabilities computed by hand:')\\n print([0.3297, 0.5507, 0.5507, 0.5507])\\n print('3rd absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_0[2, :])\\n \\n # Let x be a continuous random variable following the\\n # standard Cauchy distribution. The probability the 1st\\n # binary decision is 0 is written p(|x| <= 1.0) = 0.5.\\n # The probability the 2nd binary decision is 0 is written\\n # p(1.0 <= |x| <= 3.0)/p(|x| >= 1.0) = 0.2952/0.5 = 0.5903.\\n # The probability the 3rd binary decision is 0 is written\\n # p(3.0 <= |x| <= 5.0)/p(|x| >= 3.0) = 0.079/0.2048 = 0.3865.\\n # The above calculations use the cumulative distribution\\n # function of the standard Cauchy distribution.\\n print('Binary probabilities computed by hand:')\\n print([0.5, 0.5903, 0.3865, 0.2811])\\n \\n binary_probabilities_1 = lossless.stats.compute_binary_probabilities(y_float32,\\n bin_widths_test_1,\\n map_mean,\\n truncated_unary_length)\\n print('\\\\n2nd set of test quantization bin widths:')\\n print(bin_widths_test_1)\\n print('1st absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_1[0, :])\\n \\n # Let x be a continuous random variable following the\\n # uniform distribution of support [-10.0, 10.0]. The\\n # probability the 1st binary decision is 0 is written\\n # p(|x| <= 0.25) = 1.0/40. The probability the 2nd\\n # binary decision is 0 is written\\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = (2.0/40)/(39.0/40) = 2.0/39.\\n # The probability the 3rd binary decision is 0 is written\\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = (2.0/40)/(37.0/40) = 2.0/37.\\n print('Binary probabilities computed by hand:')\\n print([1./40, 2./39, 2./37, 2./35])\\n print('2nd absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_1[1, :])\\n \\n # Let x be a continuous random variable following the\\n # Laplace distribution of mean 0.0 and scale 2.5. The\\n # probability the 1st binary decision is 0 is written\\n # p(|x| <= 0.25) = 0.0952. The probability the 2nd binary\\n # decision is 0 is written\\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = 0.1640/0.9048 = 0.1813.\\n # The probability the 3rd binary decision is 0 is written\\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = 0.1343/0.7408 = 0.1813.\\n print('Binary probabilities computed by hand:')\\n print([0.0952, 0.1813, 0.1813, 0.1813])\\n print('3rd absolute centered-quantized latent variable feature map.')\\n print('Binary probabilities computed by the function:')\\n print(binary_probabilities_1[2, :])\\n \\n # Let x be a continuous random variable following the\\n # standard Cauchy distribution. The probability the 1st\\n # binary decision is 0 is written p(|x| <= 0.25) = 0.1560.\\n # The probability the 2nd binary decision is 0 is written\\n # p(0.25 <= |x| <= 0.75)/p(|x| >= 0.25) = 0.2537/0.8440 = 0.3006.\\n # The probability the 3rd binary decision is 0 is written\\n # p(0.75 <= |x| <= 1.25)/p(|x| >= 0.75) = 0.1608/0.5903 = 0.2724.\\n print('Binary probabilities computed by hand:')\\n print([0.1560, 0.3006, 0.2724, 0.2306])\",\n \"def apply_threshold(heatmap, threshold):\\n # Zero out pixels below the threshold\\n thresh_heatmap = np.copy(heatmap)\\n thresh_heatmap[heatmap <= threshold] = 0\\n # Return thresholded map\\n return thresh_heatmap\",\n \"def autothreshold(gray_im, method=\\\"otsu\\\"):\\n if method == \\\"otsu\\\":\\n t = otsu(gray_im)\\n elif method == \\\"kmeans\\\":\\n t = ave(kmeans(list(gray_im.getdata())))\\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?\",\n \"def image_thresholding(image: np.ndarray):\\n # Resize image to a shape of (48, 48)\\n image = image_as_square(image)\\n\\n # Find threshold using Otsu filter\\n threshold: float = filters.threshold_otsu(image)\\n binary = image > threshold\\n\\n binary_image = np.where(image, binary, 0) * 255\\n\\n # Resize the iamge back to a shape of (2304, )\\n return image_as_array(image)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77609915","0.732614","0.7293136","0.6731647","0.6590931","0.65737176","0.65595347","0.6532869","0.65259445","0.6520092","0.6501832","0.64822376","0.6414383","0.6395488","0.6340759","0.63036704","0.6302406","0.62916476","0.6282469","0.62416035","0.62416035","0.62394196","0.62261724","0.621819","0.6189172","0.61887926","0.61840725","0.6176497","0.6151448","0.6132452","0.61303127","0.61166734","0.6113234","0.6085611","0.608456","0.6075185","0.6065627","0.60578257","0.60476613","0.60422873","0.6041469","0.60316783","0.6030463","0.6017128","0.60132223","0.60043895","0.5996108","0.5983314","0.5982935","0.59724355","0.5970248","0.59654844","0.5963989","0.59458274","0.5938908","0.5918479","0.59111804","0.5910896","0.58938736","0.58878475","0.58855593","0.58799344","0.58723867","0.587163","0.58556527","0.58482844","0.58470124","0.58435374","0.58369935","0.5836475","0.5824005","0.58176756","0.58162737","0.5811685","0.5801695","0.5798011","0.57970536","0.57955","0.5793806","0.5791685","0.5788129","0.57865185","0.5785941","0.5774406","0.5768338","0.5764893","0.57640266","0.57640266","0.57640266","0.5759499","0.5759417","0.575472","0.57541406","0.5752565","0.57511437","0.5748738","0.5746724","0.5746607","0.5744518","0.5740123"],"string":"[\n \"0.77609915\",\n \"0.732614\",\n \"0.7293136\",\n \"0.6731647\",\n \"0.6590931\",\n \"0.65737176\",\n \"0.65595347\",\n \"0.6532869\",\n \"0.65259445\",\n \"0.6520092\",\n \"0.6501832\",\n \"0.64822376\",\n \"0.6414383\",\n \"0.6395488\",\n \"0.6340759\",\n \"0.63036704\",\n \"0.6302406\",\n \"0.62916476\",\n \"0.6282469\",\n \"0.62416035\",\n \"0.62416035\",\n \"0.62394196\",\n \"0.62261724\",\n \"0.621819\",\n \"0.6189172\",\n \"0.61887926\",\n \"0.61840725\",\n \"0.6176497\",\n \"0.6151448\",\n \"0.6132452\",\n \"0.61303127\",\n \"0.61166734\",\n \"0.6113234\",\n \"0.6085611\",\n \"0.608456\",\n \"0.6075185\",\n \"0.6065627\",\n \"0.60578257\",\n \"0.60476613\",\n \"0.60422873\",\n \"0.6041469\",\n \"0.60316783\",\n \"0.6030463\",\n \"0.6017128\",\n \"0.60132223\",\n \"0.60043895\",\n \"0.5996108\",\n \"0.5983314\",\n \"0.5982935\",\n \"0.59724355\",\n \"0.5970248\",\n \"0.59654844\",\n \"0.5963989\",\n \"0.59458274\",\n \"0.5938908\",\n \"0.5918479\",\n \"0.59111804\",\n \"0.5910896\",\n \"0.58938736\",\n \"0.58878475\",\n \"0.58855593\",\n \"0.58799344\",\n \"0.58723867\",\n \"0.587163\",\n \"0.58556527\",\n \"0.58482844\",\n \"0.58470124\",\n \"0.58435374\",\n \"0.58369935\",\n \"0.5836475\",\n \"0.5824005\",\n \"0.58176756\",\n \"0.58162737\",\n \"0.5811685\",\n \"0.5801695\",\n \"0.5798011\",\n \"0.57970536\",\n \"0.57955\",\n \"0.5793806\",\n \"0.5791685\",\n \"0.5788129\",\n \"0.57865185\",\n \"0.5785941\",\n \"0.5774406\",\n \"0.5768338\",\n \"0.5764893\",\n \"0.57640266\",\n \"0.57640266\",\n \"0.57640266\",\n \"0.5759499\",\n \"0.5759417\",\n \"0.575472\",\n \"0.57541406\",\n \"0.5752565\",\n \"0.57511437\",\n \"0.5748738\",\n \"0.5746724\",\n \"0.5746607\",\n \"0.5744518\",\n \"0.5740123\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70543975"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":95093,"cells":{"query":{"kind":"string","value":"Method to compute the coefficients of the straight line between two points"},"document":{"kind":"string","value":"def compute_line_coefs(point_a, point_b):\n b_coef = -1\n if (point_b[0] - point_a[0]) == 0:\n a_coef = 0\n else:\n a_coef = (point_b[1] - point_a[1]) / (point_b[0] - point_a[0])\n c_coef = point_b[1] - a_coef*point_b[0]\n return np.array([a_coef, b_coef, c_coef])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_linear_coef(p1, p2):\n slope = (p1[1] - p2[1]) / (p1[0] - p2[0])\n intercept = p1[1] - slope * p1[0]\n return slope, intercept","def generate_line(point_1, point_2):\r\n A = point_1.y - point_2.y\r\n B = point_2.x - point_1.x\r\n C = point_1.y * B + point_1.x * A\r\n return np.matrix([[A],[B],[-C]])","def line_equation(x1, y1, x2, y2):\n \n a = y2 - y1\n b = x1 - x2\n c = x2*y1 - x1*y2\n return a, b, c","def line(p1, p2):\n A = (p1[1] - p2[1])\n B = (p2[0] - p1[0])\n C = (p1[0]*p2[1] - p2[0]*p1[1])\n return A, B, -C","def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tD = d_coefficients(x1,x2,x3,C)\n\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\n\tA = a_coefficients(y1,y2)\n\treturn(A,B,C[:2],D)","def straight_line(p1,p2,spec):\n\tx1,y1 = p1; x2,y2 = p2\n\t\n\tm = (y2-y1) / (x2-x1)\n\tx = np.arange(x1-0.01, x2+0.01, 0.01) \n\n\ty = m *(x-x1) + y1\n\tf = np.vectorize(interp1d(x,y,kind = 'linear'))\n\twave,flux,error = Select_Data(spec,[x1,x2])\n\treturn wave,f(wave)","def homo_line(a, b):\n return (a[1] - b[1], b[0] - a[0], a[0] * b[1] - a[1] * b[0])","def a_coefficients(y1,y2):\n\tACoefficients = np.array([\ty1, \\\n\t\t\t\t\t\t\t\ty2 ]).astype(float)\n\treturn(ACoefficients)","def compute_dual_line(P):\n return Line(P.x, -P.y)","def line_equation_ap(angle, (x1, y1)):\n \n # get second point on the line\n x2 = float(x1) + cos(angle)\n y2 = float(y1) + sin(angle)\n \n # return A, B and C coefficients\n return (y1 - y2, x2 - x1, x1*y2 - x2*y1)","def TwoPoints(self, p1, p2):\n\n p1 = base.getvector(p1)\n if len(p1) == 2:\n p1 = np.r_[p1, 1]\n p2 = base.getvector(p2)\n if len(p2) == 2:\n p2 = np.r_[p2, 1]\n\n return Line2(np.cross(p1, p2))","def calculate_coefficients(self, start, end):\n A = np.array([\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\n ])\n\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\n c_1 = a_1 + 2 * a_2 * self.deltaT\n c_2 = 2 * a_2\n\n B = np.array([\n end[0] - c_0,\n end[1] - c_1,\n end[2] - c_2\n ])\n\n a_3_4_5 = np.linalg.solve(A, B)\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\n\n return coeff","def coefA(x0,y0,x1,y1):\n return -(y1-y0)/(x1-x0)","def get_line_to(self, provided_point):\n\n \"\"\"Calculate slope\"\"\"\n a = (provided_point.y - self.y) / (provided_point.x - self.x)\n\n \"\"\"Calculate b\"\"\"\n b = self.y - a * self.x\n\n return (a,b)","def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_matrix(x1,x2,x3)\n\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tCCoefficients = np.dot(inv(C),y)\n\treturn(CCoefficients)","def DistPoint2Line(point,line_point1, line_point2=np.array([0,0,0])):\n return np.linalg.norm(np.cross((point-line_point2),(point-line_point1)))/np.linalg.norm(line_point1 - line_point2)","def fit_line(xs,ys,a,b):\n # Checking against empty list, if empty return 0s\n if not (xs):\n return 0,0,0,0\n \n # Preparing vectors for least square\n z = np.vstack([xs, np.ones(len(xs))]).T\n s = np.array(ys)\n\n # Applying least square fitting on points\n m, c = np.linalg.lstsq(z, np.array(ys))[0] #Applying least squares method\n \n #Using slope and intercept plus y coordinates to get x-coordinates\n x1 = int(a/m - c/m) \n x2 = int(b/m - c/m)\n \n return x1,a,x2,b","def coefC(x0,y0,x1,y1):\n return (x1*y0-x0*y1)/(x1-x0)","def get_line_to(self, point):\n\n b = ((self.x - point.x)*point.y - (self.y - point.y)*point.x)/(self.x - point.x)\n\n a = (self.y - point.y)/(self.x - point.x)\n\n return a, b","def find_line_model(points):\n\n # [WARNING] vertical and horizontal lines should be treated differently\n # here we just add some noise to avoid division by zero\n\n # find a line model for these points\n m = (points[1, 1] - points[0, 1]) / (\n points[1, 0] - points[0, 0] + sys.float_info.epsilon) # slope (gradient) of the line\n c = points[1, 1] - m * points[1, 0] # y-intercept of the line\n\n return m, c","def line_intercept(p1,p2,p3,p4):\n # Note if vertical line m = None and b holds x-val\n (m1,b1) = line_param(p1,p2)\n (m2,b2) = line_param(p3,p4)\n if (m1 != None) and (m2 != None):\n if (m1-m2) != 0.:\n x = (b2-b1)/(m1-m2)\n y = m1*x + b1\n else:\n return (None,0)\n elif (m1 == None) and (m2 != None):\n x = b1 \n y = m2*x + b2\n elif (m1 != None) and (m2 == None):\n x = b2\n y = m1*x + b1\n else:\n return (None,0) \n \n # min and max of points. \n max_x1 = max(p1[0], p2[0])\n min_x1 = min(p1[0], p2[0])\n max_y1 = max(p1[1], p2[1])\n min_y1 = min(p1[1], p2[1])\n max_x2 = max(p3[0], p4[0])\n min_x2 = min(p3[0], p4[0])\n max_y2 = max(p3[1], p4[1])\n min_y2 = min(p3[1], p4[1])\n #check if the intersection is in bounds\n flag = 1\n if x > max_x1 or x < min_x1:\n flag = 0\n elif x > max_x2 or x < min_x2:\n flag = 0\n elif y > max_y1 or y < min_y1: \n flag = 0\n elif y > max_y2 or y < min_y2: \n flag = 0\n #check if the intersection point corresponds to an end point\n intercept = num.array([x,y])\n def _same(p1,p2,prec=0.0001):\n \"\"\" are two points the same \"\"\"\n #return num.all(num.equal(p1,p2))\n t1 = num.fabs(p1[0]-p2[0]) < prec\n t2 = num.fabs(p1[1]-p2[1]) < prec\n if t1 and t2:\n #print \"same\", p1,p2\n return True\n if flag == 1:\n if _same(intercept,p1):\n flag = 2\n elif _same(intercept,p2):\n flag = 2\n elif _same(intercept,p3):\n flag = 2\n elif _same(intercept,p4):\n flag = 2\n return (intercept,flag)","def line_contribution(p1,p2,alpha = 1):\n\n adjust = np.zeros((worksize,worksize,2))\n\n x1 = p1[0]\n y1 = p1[1]\n x2 = p2[0]\n y2 = p2[1]\n\n coordinates = coordinate_matrix(worksize)\n numerator = np.sum(np.multiply(coordinates,np.reshape(np.array(((y2-y1,-(x2-x1)))),(2,1,1))),axis = 0) + x2*y1 - y2*x1\n dist_from_line = np.abs(numerator) * (1.0/np.sqrt((y2-y1)**2+(x2-x1)**2))\n xcontribution = (x2-x1)*(1/(alpha*dist_from_line+1))\n ycontribution = (y2-y1)*(1/(alpha*dist_from_line+1))\n\n\n return np.array((-ycontribution,xcontribution))/np.sqrt((y2-y1)**2+(x2-x1)**2)","def slope(point_a, point_b, flip):\n\n x_a, y_a = point_a\n x_b, y_b = point_b\n\n dx = x_b - x_a\n dy = y_b - y_a\n\n return -dx / dy if flip else dy / dx","def slope(x1, y1, x2, y2):\r\n delta_y = y2-y1\r\n delta_x = x2-x1\r\n return delta_y / delta_x","def line(intercept, slope, x):\n return slope*x + intercept","def line(x1, y1, x2, y2):\r\n\r\n x1 = normalize(x1)\r\n y1 = normalize(y1)\r\n x2 = normalize(x2)\r\n y2 = normalize(y2)\r\n\r\n xdiff = max(x1, x2) - min(x1, x2)\r\n ydiff = max(y1, y2) - min(y1, y2)\r\n xdir = 1 if x1 <= x2 else -1\r\n ydir = 1 if y1 <= y2 else -1\r\n\r\n r = max(xdiff, ydiff)\r\n\r\n for i in range(r+1):\r\n x = x1\r\n y = y1\r\n\r\n if ydiff:\r\n y += (float(i) * ydiff) / r * ydir\r\n if xdiff:\r\n x += (float(i) * xdiff) / r * xdir\r\n\r\n yield (x, y)","def slope(x1, y1, x2, y2):\n return (y2 - y1) / (x2 - x1)","def abline(points, slope, intercept):\n x_values = get_column(points, 0)\n return [slope * i + intercept for i in x_values]","def drawLine2P(x,y,xlims):\n \n xrange = np.arange(xlims[0],xlims[1],1)\n A = np.vstack([x, np.ones(len(x))]).T\n k, b = np.linalg.lstsq(A, y, rcond=None)[0]\n return [xrange, k*xrange + b]","def _distance(point, line_point1, line_point2):\n vec1 = line_point1 - point\n vec2 = line_point2 - point\n distance = np.abs(np.cross(vec1,vec2)) / np.linalg.norm(line_point1-line_point2)\n return distance","def intercept(x1, y1, x2, y2):\r\n m = slope(x1, y1, x2, y2)\r\n return y1 - m*x1","def point_line_dist2(p, l1, l2):\n p, l1, l2 = np.asarray(p), np.asarray(l1), np.asarray(l2)\n ap = l1 - p\n n = l2 - l1\n n /= np.sqrt(sum(n**2))\n dist = ap - np.outer(n, np.dot(ap, n)).T\n return np.sum(dist**2, 1)","def distance_point_to_line(x1, y1, a, b, c):\n d = abs((a * x1 + b * y1 + c)) / (math.sqrt(a * a + b * b))\n #print(\"Distance from ({}, {}) to line {}x+{}y+{}=0 is {}\".format(\n # x1, y1, a, b, c, d))\n return(d)","def main(self,xpoints,ypoints,numOfWaypoints):\n WaypointsX = np.zeros(((numOfWaypoints)*(len(xpoints)-1)))\n WaypointsY = np.zeros(((numOfWaypoints)*(len(xpoints)-1)))\n\n for i in range(len(xpoints)-1):\n x = np.linspace(xpoints[i],xpoints[i+1],numOfWaypoints)\n try:\n slope = ((ypoints[i+1])-(ypoints[i]))/((xpoints[i+1])-(xpoints[i]))\n coefficient = (ypoints[i])-(slope)*(xpoints[i])\n y = (slope)*x+coefficient\n except ZeroDivisionError:\n x = np.linspace(xpoints[i],xpoints[i],numOfWaypoints)\n y = np.linspace(ypoints[i],ypoints[i+1],numOfWaypoints)\n WaypointsX[((numOfWaypoints)*(i)):((numOfWaypoints)*(i+1))]=x\n WaypointsY[((numOfWaypoints)*(i)):((numOfWaypoints)*(i+1))]=y\n\n return WaypointsX, WaypointsY","def _calculate_slope(klass, p1, p2):\n xdiff = p1.x - p2.x\n if xdiff:\n return (p1.y - p2.y) / xdiff\n else:\n return float(\"+inf\")","def line_param(point_a, point_b, t):\n new_point = point_a - point_b\n return point_b + t*new_point","def crossLine(self, other):\n a, b = self.point\n c, d = other.point\n m, n = self.vector\n o, p = other.vector\n if n * o == m * p: # The lines are parallels\n return None\n elif self.angle == -math.pi / 2:\n return Point(a, d)\n elif other.angle == -math.pi / 2:\n return Point(b, c)\n else:\n x = (a * n * o - b * m * o - c * m * p + d * m * o) / (n * o - m * p)\n y = (x - a) * n / m + b\n return Point(x, y)","def line_intersection_with(self, other):\n # solve following system :\n # intersection = start of self + alpha * direction of self\n # intersection = start of other + beta * direction of other\n directions = [s.endpoints[1] - s.endpoints[0] for s in (self, other)]\n denominator = directions[0].cross_product(directions[1])\n if abs(denominator) < 0.000001:\n # almost parallel lines\n return\n start_diff = other.endpoints[0] - self.endpoints[0]\n alpha = start_diff.cross_product(directions[1]) / denominator\n return self.endpoints[0] + directions[0] * alpha","def _dist_point2line(self, point: ndarray,\n line: Tuple[ndarray, ndarray]) -> ndarray:\n\n assert isinstance(line, tuple)\n point1, point2 = line\n d = abs(np.cross(point2 - point1, point - point1)) / (\n norm(point2 - point1) + 1e-8)\n return d","def slip_to_coefficients(x, y, a):\n partials = np.zeros((x.size, 3))\n partials[:, 0] = (x / a) * (9 * (x / a) / 8 - 3 / 4)\n partials[:, 1] = (1 - 3 * (x / a) / 2) * (1 + 3 * (x / a) / 2)\n partials[:, 2] = (x / a) * (9 * (x / a) / 8 + 3 / 4)\n coefficients = np.linalg.inv(partials) @ y\n return coefficients","def intersection(line1, line2):\n p0, p1, p2, p3 = map(\n lambda tup : np.array(tup[:2]),\n [line1[0], line1[1], line2[0], line2[1]]\n )\n p1, p2, p3 = map(lambda x : x - p0, [p1, p2, p3])\n transform = np.zeros((2, 2))\n transform[:,0], transform[:,1] = p1, p2\n if np.linalg.det(transform) == 0: return\n inv = np.linalg.inv(transform)\n new_p3 = np.dot(inv, p3.reshape((2, 1)))\n #Where does line connecting (0, 1) to new_p3 hit x axis\n x_intercept = new_p3[0] / (1 - new_p3[1]) \n result = np.dot(transform, [[x_intercept], [0]])\n result = result.reshape((2,)) + p0\n return result","def _homogenous_line(A,B):\n if A==B: raise ValueError('Degenerate line through %s and %s' % (repr(A),repr(B)))\n\n Ax,Ay=A\n Bx,By=B\n # keep magnitude of coefficients as close to unity as possible\n if abs(Bx-Ax)>=abs(By-Ay):\n a,b=float(By-Ay)/float(Bx-Ax), -1\n c=Ay-a*Ax\n else:\n a,b=-1, float(Bx-Ax)/float(By-Ay)\n c=Ax-b*Ay\n\n return a,b,c","def point_to_line_dist(P, A, B):\n\tif all(A == P) or all(B == P):\n\t\treturn0\n\tif arccos(dot((P - A) / norm(P - A), (B - A) / norm(B - A))) > pi / 2:\n\t\treturn norm(P - A)\n\tif arccos(dot((P - B) / norm(P - B), (A - B) / norm(A - B))) > pi / 2:\n\t\treturn norm(P - B)\n\treturn norm(cross(A-B, A-P))/norm(B-A)","def get_line_distance(self, p):\n\n y = 1000 * p.y\n R = 1000 * self.geometry.R\n x = copysign(sqrt(y ** 2 + (R - sqrt(R ** 2 - y ** 2))), y)\n x = 2 * R * asin(x / (2 * R))\n #x=y\n b = -x / sqrt(R ** 2 - x ** 2)\n theta = atan(b) # grating tangent angle\n print b, theta\n d = 0\n for n, a in enumerate(self.an):\n d += a * x ** n\n d *= cos(theta)\n return 1e-3 / d","def line_fit(x,y):\n\t# clean\n\tx = np.squeeze(x)\n\ty = np.squeeze(y)\n\t# concatenate\n\txy = np.concatenate((x[:,np.newaxis],y[:,np.newaxis]),1)\n\t# sort by x values\n\txy = xy[xy[:,0].argsort()]\n\t#print(xy)\n\tf = lambda x,m,b : m*x+b\n\tpars,_ = opt.curve_fit(f,xy[:,0],xy[:,1])\n\tm = pars[0]\n\tb = pars[1]\n\tpts = np.zeros((2,2))\n\tpts[0,0] = xy[0,0]\n\tpts[1,0] = xy[-1,0]\n\tpts[:,1] = pts[:,0]*m+b\n\tsig = np.std((xy[:,1]-f(xy[:,0],m,b)))\n\treturn pts, sig","def midpoint_line(a, b):\n return scale_vector(add_vectors(a, b), 0.5)","def intersection(v1, v2):\n x = v1[0:2] + v2[0:2]\n y = v1[2:4] + v2[2:4]\n if( x[3] == 0 ): #To avoid a divide by zero, if x[3] is 0 then we just solve for where lineA equals x[2]\n t1 = (x[2] - x[0])/\\\n (x[1])\n return [ v1[0] + v1[1]*t1, v1[2] + v1[3]*t1 ]\n\n else: \n t1 = ( y[0] - y[2] + (y[3]/x[3])*(x[2] - x[0]) )/\\\n ( (y[3]*x[1])/x[3] - y[1] )\n return [ v1[0] + v1[1]*t1, v1[2] + v1[3]*t1 ]","def get_points_for_thick_line(start_x: float, start_y: float,\r\n end_x: float, end_y: float,\r\n line_width: float):\r\n vector_x = start_x - end_x\r\n vector_y = start_y - end_y\r\n perpendicular_x = vector_y\r\n perpendicular_y = -vector_x\r\n length = math.sqrt(vector_x * vector_x + vector_y * vector_y)\r\n if length == 0:\r\n normal_x = 1.0\r\n normal_y = 1.0\r\n else:\r\n normal_x = perpendicular_x / length\r\n normal_y = perpendicular_y / length\r\n r1_x = start_x + normal_x * line_width / 2\r\n r1_y = start_y + normal_y * line_width / 2\r\n r2_x = start_x - normal_x * line_width / 2\r\n r2_y = start_y - normal_y * line_width / 2\r\n r3_x = end_x + normal_x * line_width / 2\r\n r3_y = end_y + normal_y * line_width / 2\r\n r4_x = end_x - normal_x * line_width / 2\r\n r4_y = end_y - normal_y * line_width / 2\r\n points = (r1_x, r1_y), (r2_x, r2_y), (r4_x, r4_y), (r3_x, r3_y)\r\n return points","def slope(a, b):\r\n if a[0] == b[0]: #If the x values are both 0\r\n return 0 #Technically, undefined, but doesn't matter for finding collinearity\r\n return (a[1] - b[1]) / (a[0] - b[0])","def _line_from_two_points(pt1: np.array, pt2: np.array) -> np.array:\n numLine = pt1.shape[0]\n lines = np.zeros((numLine, 6))\n n = np.cross(pt1, pt2)\n n = n / (matlib.repmat(np.sqrt(np.sum(n ** 2, 1, keepdims=True)), 1, 3) + 1e-9)\n lines[:, 0:3] = n\n\n areaXY = np.abs(np.sum(n * matlib.repmat([0, 0, 1], numLine, 1), 1, keepdims=True))\n areaYZ = np.abs(np.sum(n * matlib.repmat([1, 0, 0], numLine, 1), 1, keepdims=True))\n areaZX = np.abs(np.sum(n * matlib.repmat([0, 1, 0], numLine, 1), 1, keepdims=True))\n planeIDs = np.argmax(np.hstack([areaXY, areaYZ, areaZX]), axis=1) + 1\n lines[:, 3] = planeIDs\n\n for i in range(numLine):\n uv = _xyz2uvN(np.vstack([pt1[i, :], pt2[i, :]]), lines[i, 3])\n umax = uv[:, 0].max() + np.pi\n umin = uv[:, 0].min() + np.pi\n if umax - umin > np.pi:\n lines[i, 4:6] = np.array([umax, umin]) / 2 / np.pi\n else:\n lines[i, 4:6] = np.array([umin, umax]) / 2 / np.pi\n\n return lines","def find_slope(lat1,lon1,lat2,lon2):\n return (lon2-lon1)/(lat2-lat1)","def make_line_points(y1, y2, line):\n if line is None:\n return None\n\n slope, intercept = line\n\n # make sure everything is integer as cv2.line requires it\n x1 = int((y1 - intercept) / slope)\n x2 = int((y2 - intercept) / slope)\n y1 = int(y1)\n y2 = int(y2)\n\n return ((x1, y1), (x2, y2))","def calculate_line_length(x1, y1, x2, y2):\n distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\n return distance","def distance_to_line(a, b, p):\n return distance(closest_point(a, b, p), p)","def line_param(v1,v2):\n if (v1[0]-v2[0] != 0.):\n m = (v1[1] - v2[1])/(v1[0] - v2[0])\n b = -m*v1[0] + v1[1]\n if num.fabs(m)>1.0e6:\n m = None\n b = v1[0]\n else: \n m = None\n b = v1[0]\n return (m,b)","def line_ccw(a, b, p):\n return (p[1] - a[1]) * (b[0] - a[0]) > (b[1] - a[1]) * (p[0] - a[0])","def distanceOfTwoLines(p1, v1, p2, v2):\n # if we transform multiple points in one go\n if len(v1.shape) == 2:\n a1 = np.einsum('ij,ij->i', v1, v1)\n a2 = np.einsum('ij,ij->i', v1, v2)\n b1 = -np.einsum('ij,ij->i', v2, v1)\n b2 = -np.einsum('ij,ij->i', v2, v2)\n c1 = -np.einsum('ij,j->i', v1, p1 - p2)\n c2 = -np.einsum('ij,j->i', v2, p1 - p2)\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]).transpose(2, 0, 1), np.array([c1, c2]).T)\n res = res[:, None, :]\n return np.linalg.norm((p1 + res[..., 0] * v1) - (p2 + res[..., 1] * v2), axis=1)\n else: # or just one point\n a1 = np.dot(v1, v1)\n a2 = np.dot(v1, v2)\n b1 = -np.dot(v2, v1)\n b2 = -np.dot(v2, v2)\n c1 = -np.dot(v1, p1 - p2)\n c2 = -np.dot(v2, p1 - p2)\n try:\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]), np.array([c1, c2]))\n except np.linalg.LinAlgError:\n return 0\n res = res[None, None, :]\n return np.linalg.norm((p1 + res[..., 0] * v1) - (p2 + res[..., 1] * v2), axis=1)[0]","def is_on_line(point_a, point_b, point_c):\r\n return (point_b[0] - point_a[0]) * (point_c[1] - point_a[1]) - (point_b[1] - point_a[1]) * (point_c[0] - point_a[0])","def my_linear_polyfit(raw_lines):\n \n x = []\n y = []\n weight = []\n \n # Build arrays of all x, y, and weight points\n for line in raw_lines:\n for x1, y1, x2, y2 in line:\n x.extend([x1, x2])\n y.extend([y1, y2])\n line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)\n weight.extend([line_length, line_length])\n \n # Apply weighted linear polyfit\n z = np.polyfit(x, y, 1, w=weight)\n \n # Output fit line, z = [m, b]\n return z","def perpendicularIntersection(point, linePoint1, linePoint2):\n\t\tx1 = linePoint1[0]\n\t\ty1 = linePoint1[1]\n\t\tx2 = linePoint2[0]\n\t\ty2 = linePoint2[1]\n\t\tx3 = point[0]\n\t\ty3 = point[1]\n\t\tk = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)**2 + (x2-x1)**2)\n\t\tx4 = x3 - k * (y2-y1)\n\t\ty4 = y3 + k * (x2-x1)\n\t\treturn (x4, y4)","def slope(a, b):\n a1, a2 = PC_matrix[:, 0][a], PC_matrix[:, 1][a]\n b1, b2 = PC_matrix[:, 0][b], PC_matrix[:, 1][b]\n \n return b1-a1, b2-a2","def line(x0: float, y0: float, x1: float, y1: float) -> LineCollection:\n return LineCollection([(complex(x0, y0), complex(x1, y1))])","def calc_slope(self, left, right):\n return (left[1] - right[1]) / (left[0] - right[0])","def Solver(line1, line2):\n\ta = np.array(line1[0])\n\tb = np.array(line1[1])\n\tu = np.array(line2[0])\n\tv = np.array(line2[1])\n\t#print(a,b,u,v)\n\tc = u[:2]-a[:2]\n\tA = np.vstack((b[:2],-v[:2])).T\n\t#print(A)\n\tx = np.linalg.solve(A,c)\n\t#print(x)\n\tp = a+x[0]*b\n\t#print(p)\n\treturn p","def linearfit(x,y):\n fit = np.polyfit(x,y,1)\n fit_fn = np.poly1d(fit)\n yy = fit_fn(x) \n \n return yy","def intersect_2_lines(P1, V1, P2, V2):\n Vx = np.cross(V1, V2)\n s = np.dot(np.cross(P2 - P1, V1), Vx)/np.dot(Vx, Vx)\n return s","def linear_attenuation_coefficient(self, lines):\n wl = lines.to(\"nm\", \"spectroscopy\").magnitude\n if isarray(wl):\n return [self.getExtinctionCoefficient(l) for l in wl]\n else:\n return self.getExtinctionCoefficient(wl)","def linear(x2, y2, N=100):\n\n m = y2 / x2\n x = np.linspace(0, x2, N)\n y = m*x\n\n # The time of travel\n T = np.sqrt(2*(1+m**2)/g/m * x2)\n print('T(linear) = {:.3f}'.format(T))\n return x, y, T","def eDouble(P): #adding P + P by using a tangent line\r\n R = point(0, 0, P.c)\r\n i = ( (3 * P.x ** 2) + P.c.a) #the slope equation (i/j)\r\n j = (2 * P.y)\r\n s = (i * modInv(j, P.c.p) ) % P.c.p\r\n R.x = ( (s ** 2) - 2 * P.x) % P.c.p\r\n R.y = (-P.y + s * (P.x - R.x) ) % P.c.p\r\n return R","def distance_point_line(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n pa = subtract_vectors(a, point)\n pb = subtract_vectors(b, point)\n l = length_vector(cross_vectors(pa, pb))\n l_ab = length_vector(ab)\n return l / l_ab","def find_slopes(x, y):\n slopes = np.zeros((len(x) - 1))\n for i in range(len(x) - 1):\n # m = (y2 - y1) / (x2 - x1)\n delta_x = x[i + 1] - x[i]\n delta_y = y[i + 1] - y[i]\n slopes[i] = delta_y / delta_x\n return slopes","def coefficients(self) :\n raise NotImplementedError","def distance_line_line(l1, l2, tol=0.0):\n a, b = l1\n c, d = l2\n ab = subtract_vectors(b, a)\n cd = subtract_vectors(d, c)\n ac = subtract_vectors(c, a)\n n = cross_vectors(ab, cd)\n l = length_vector(n)\n if l <= tol:\n return distance_point_point(closest_point_on_line(l1[0], l2), l1[0])\n n = scale_vector(n, 1.0 / l)\n return fabs(dot_vectors(n, ac))","def b_coefficients(x1,x2,x3,y1,y2,y3,CCoefficients,DCoefficients):\n\tBCoefficients = np.array([\t((y2-y1)/(x2-x1)-CCoefficients[0]*(x2-x1) - DCoefficients[0]*((x2-x1)**2)), \\\n\t\t\t\t\t\t\t\t((y3-y2)/(x3-x2)-CCoefficients[1]*(x3-x2) - DCoefficients[1]*((x3-x2)**2)) \t]).astype(float)\n\treturn(BCoefficients)","def sign_line(pt, P1, P2):\n x1, y1 = P1\n x2, y2 = P2\n x, y = pt\n\n return np.sign((x - x1)*(y2 - y1) - (y-y1)*(x2-x1))","def _distance2_line_endpoints(line1, line2):\n (A,B),(C,D) = line1, line2\n R2=lambda u,v: (u[0]-v[0])**2+(u[1]-v[1])**2\n pairs = zip((A,A,B,B),(C,D,C,D))\n r2 = [R2(pair[0],pair[1]) for pair in pairs]\n mini=sorted(zip(r2,pairs),key=lambda a,b: a)[0]\n #R2_min = min((R2(A,C), R2(A,D), R2(B,C), R2(B,D)))\n return mini[0], mini[1][0], mini[1][1]","def linePointXY(l,p,inside=True,distance=False,params=False):\n a=l[0]\n b=l[1]\n # check for degenerate case of zero-length line\n abdist = dist(a,b)\n if abdist < epsilon:\n #raise ValueError('zero-length line passed to linePointXY')\n print('zero-length line passed to linePointXY')\n return False\n\n if distance and params:\n raise ValueError('incompatible distance and params parameters passed to linePointXY')\n\n x0=p[0]\n y0=p[1]\n z0=p[2]\n x1=a[0]\n y1=a[1]\n z1=a[2]\n x2=b[0]\n y2=b[1]\n z2=b[2]\n\n ## check to see if all three points lie in the same x,y plane\n if not isXYPlanar([p,a,b]):\n raise ValueError('non-XY points in linePointXY call')\n return false\n # if abs(z1-z0) > epsilon or abs(z2-z0) > epsilon:\n # return False\n\n linedist = abs( ((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)/abdist)\n\n ## this is the fast case:\n if not inside and distance:\n return linedist\n \n ## find out where the intersection between the original line and a\n ## line defined by the point and an orthogonal direction vector\n ## is. We do this by constructing two direction vectors\n ## orthogonal to the orgiginal line scaled by the line distance,\n ## and adding them to the point in question. Assuming that the\n ## line distance is not zero, only one of these constructed points\n ## will fall on the line\n\n ## compute unit direction vector for original line\n dir = sub(b,a)\n dir = scale3(dir,1.0/mag(dir))\n\n ## compute two orthogonal direction vectors of length linedist\n ordir1 = scale3(orthoXY(dir),linedist)\n ordir2 = scale3(ordir1, -1.0)\n \n ## there are two possible intersection points\n pi1 = add(p,ordir1)\n pi2 = add(p,ordir2)\n\n ## compute distances\n d1pa = dist(a,pi1)\n d1pb = dist(pi1,b)\n d1 = d1pa+d1pb # \"triangle\" with pi1\n\n d2pa = dist(a,pi2)\n d2pb = dist(pi2,b)\n d2 = d2pa+d2pb # \"triangle\" with pi2\n\n ## the shortest \"triangle\" distance will signal the point that\n ## is actually on the line, even if that point falls outside\n ## the a,b line interval\n \n if params or not inside: # if we don't care about being inside the\n # line segment\n if d1 <= d2:\n if distance:\n return d1\n elif params:\n return d1pb/abdist\n else:\n return pi1\n else:\n if distance:\n return d2\n elif params:\n return d2pb/abdist\n else:\n return pi2\n \n \n ## if the closest point on the line to point p lies between\n ## the endpoints of the line, then either d1 or d2 will equal\n ## abdist. IF neither do, then we know that the closest point lies\n ## outside the endpoints\n\n if abs(d1-abdist) < epsilon:\n if distance:\n return linedist\n else:\n return pi1\n\n if abs(d2-abdist) < epsilon:\n if distance:\n return linedist\n else:\n return pi2\n\n ## closest point is outside the interval. That means that the\n ## distance from point p to whichever endpoint is smaller is the\n ## closest distance\n\n d3 = dist(a,p)\n d4 = dist(b,p)\n\n if d3 < d4:\n if distance:\n return d3\n else:\n return a\n else:\n if distance:\n return d4\n else:\n return b","def get_linear_distance(p1,p2):\r\n if p1[0] < p2[0]:\r\n d = 0\r\n else:\r\n d = np.sqrt((p1[0]-p2[0])**2.0 + (p1[1]-p2[1])**2.0)\r\n return d","def _distance2_point_to_h_line(point, h_line):\n a,b,c = h_line\n x0,y0 = point\n # solve for equality\n # r^2 = (x-x0)^2 + (y-y0)^2\n # ax + by + c = 0\n # --> 2nd order polynomial\n # --> find place of exactly one solution, i.e.\n # radicant of p-q formula is identical zero\n # if radicant is zero, then\n ys = ((a*x0-c)*b + a**2*y0)/(a**2+b**2)\n # or\n xs = ((b*y0-c)*a + b**2*x0)/(a**2+b**2)\n # for a != 0\n if abs(a)>=abs(b):\n R2 = (x0-c/a)**2+y0**2 - (1.+(b/a)**2)*ys**2\n else:\n R2 = (y0-c/b)**2+x0**2 - (1.+(a/b)**2)*xs**2\n R2 = R2 if abs(R2)>1e-13 else 0.\n return R2, (xs, ys)","def line_points(a=0, b=0, c=0, ref=[-1.0, 1.0]):\n\n if (a == 0) and (b == 0):\n raise Exception(\"linePoints: a and b cannot both be zero\")\n\n return [(-c / a, p) if b == 0 else (p, (-c - a * p) / b) for p in ref]","def distance_point_line_3d(point: Vector, start: Vector, end: Vector) -> float:\n if start.isclose(end):\n raise ZeroDivisionError('Not a line.')\n v1 = point - start\n # point projected onto line start to end:\n v2 = (end - start).project(v1)\n # Pythagoras:\n return math.sqrt(v1.magnitude_square - v2.magnitude_square)","def line_plane(l, p):\n d = dot((p.o - l.o), p.n) / dot(l.d, p.n)\n return l(d)","def _distance_to_line(begin, end, point):\n return _vec_distance(point, _nearest_point_on_line(begin, end, point))","def linear(m, b, x, xx):\n y = m*(x - xx) + b\n return y","def lin_interpol(x_p, y_p):\r\n f = np.zeros([ x_p.shape[0] - 1 , 4 ]) # Coefficents and interval array\r\n \r\n for i in range( x_p.shape[0] - 1 ): # for every x[i], x[i+1] pair\r\n \r\n x_coeff = (y_p[i+1] - y_p[i]) / (x_p[i+1] - x_p[i])\r\n const = (x_p[i+1]*y_p[i] - x_p[i]*y_p[i+1] ) / (x_p[i+1] - x_p[i])\r\n \r\n # save the x coefficent, constant and the interval for this line\r\n f[i,:] = x_coeff, const, x_p[i], x_p[i+1]\r\n \r\n for a, b, start, end in f: # for every line fitted\r\n line_x = np.linspace( start, end, 3) # points to plot in x_range\r\n line_y = line_x * a + b # find the fitted line value at these points\r\n plt.plot(line_x,line_y,'k--', lw = 1, label = 'Linear' if a==f[0][0] else \"\") # only label one plot\r","def _get_slope(x, y):\n slope = linregress(x, y)\n return slope","def line_line_intersection(a1: Vector3, a2: Vector3, b1: Vector3, b2: Vector3) -> Vector3:\n # From https://stackoverflow.com/a/20677983/7245441\n\n def det(a: Vector3, b: Vector3) -> float:\n return a.x * b.y - a.y * b.x\n\n y_diff = Vector3(a1.y - a2.y, b1.y - b2.y, 0)\n x_diff = Vector3(a1.x - a2.x, b1.x - b2.x, 0)\n\n div = det(x_diff, y_diff)\n if div == 0:\n raise Exception(\"Lines do not intersect\")\n\n d = Vector3(det(a1, a2), det(b1, b2), 0)\n x = det(d, x_diff) / div\n y = det(d, y_diff) / div\n\n return Vector3(x, y, 0)","def line_points(start, end):\n # Setup initial conditions\n x1, y1 = start.astuple()\n x2, y2 = end.astuple()\n dx = x2 - x1\n dy = y2 - y1\n \n # Determine how steep the line is\n is_steep = abs(dy) > abs(dx)\n \n # Rotate line\n if is_steep:\n x1, y1 = y1, x1\n x2, y2 = y2, x2\n \n # Swap start and end points if necessary and store swap state\n swapped = False\n if x1 > x2:\n x1, x2 = x2, x1\n y1, y2 = y2, y1\n swapped = True\n \n # Recalculate differentials\n dx = x2 - x1\n dy = y2 - y1\n \n # Calculate error\n error = int(dx / 2.0)\n ystep = 1 if y1 < y2 else -1\n \n # Iterate over bounding box generating points between start and end\n y = y1\n points = []\n for x in range(x1, x2 + 1):\n coord = Int2(y, x) if is_steep else Int2(x, y)\n points.append(coord)\n error -= abs(dy)\n if error < 0:\n y += ystep\n error += dx\n \n # Reverse the list if the coordinates were swapped\n if swapped:\n points.reverse()\n return points","def getLine(self, **kwargs):\n return Line(self.p1, self.angle, **kwargs)","def get_intersect_points(line1, line2):\n intersect_points = matrix.matrix_sol([line1, line2])\n return intersect_points","def llincc(x,y):\r\n covar = lcov(x,y)*(len(x)-1)/float(len(x)) # correct denom to n\r\n xvar = lvar(x)*(len(x)-1)/float(len(x)) # correct denom to n\r\n yvar = lvar(y)*(len(y)-1)/float(len(y)) # correct denom to n\r\n lincc = (2 * covar) / ((xvar+yvar) +((amean(x)-amean(y))**2))\r\n return lincc","def straight_line(numbers, p_current, relative = False):\n if len(numbers) != 2:\n return None\n\n p_next = Point(numbers[0], numbers[1])\n if relative: #relative\n p_next += p_current\n\n return Line(p_current, p_next)","def intersectionOfTwoLines(p1, v1, p2, v2):\n # if we transform multiple points in one go\n if len(v1.shape) == 2:\n a1 = np.einsum('ij,ij->i', v1, v1)\n a2 = np.einsum('ij,ij->i', v1, v2)\n b1 = -np.einsum('ij,ij->i', v2, v1)\n b2 = -np.einsum('ij,ij->i', v2, v2)\n c1 = -np.einsum('ij,j->i', v1, p1 - p2)\n c2 = -np.einsum('ij,j->i', v2, p1 - p2)\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]).transpose(2, 0, 1), np.array([c1, c2]).T)\n res = res[:, None, :]\n return np.mean([p1 + res[..., 0] * v1, p2 + res[..., 1] * v2], axis=0)\n else: # or just one point\n a1 = np.dot(v1, v1)\n a2 = np.dot(v1, v2)\n b1 = -np.dot(v2, v1)\n b2 = -np.dot(v2, v2)\n c1 = -np.dot(v1, p1 - p2)\n c2 = -np.dot(v2, p1 - p2)\n try:\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]), np.array([c1, c2]))\n except np.linalg.LinAlgError:\n return np.ones(3)*np.nan\n res = res[None, None, :]\n return np.mean([p1 + res[..., 0] * v1, p2 + res[..., 1] * v2], axis=0)[0]","def intersection(line1, line2):\r\n rho1, theta1 = line1[0]\r\n rho2, theta2 = line2[0]\r\n A = np.array([[np.cos(theta1), np.sin(theta1)], [np.cos(theta2), np.sin(theta2)]])\r\n b = np.array([[rho1], [rho2]])\r\n x0, y0 = np.linalg.solve(A, b)\r\n x0, y0 = int(np.round(x0)), int(np.round(y0))\r\n return [[x0, y0]]","def d2(x0,y0,x1,y1):\n return (x0-x1)*(x0-x1) + (y0-y1)*(y0-y1)","def line(self, P, Q, R):\n if not R or R == self.infpoint:\n raise ValueError(\"R must not be zero\")\n if P == Q == self.infpoint:\n return self.basefield.one\n if P == self.infpoint:\n return R[0] - Q[0]\n if Q == self.infpoint:\n return R[0] - P[0]\n if P[0] != Q[0]:\n return (Q[0] - P[0]) * R[1] - (Q[1] - P[1]) * \\\n R[0]- Q[0] * P[1] + P[0] * Q[1]\n if P[1] != Q[1]:\n return R[0] - P[0]\n return (3 * P[0] ** 2 + 2 * self.a2 * P[0] + self.a4 - self.a1 * P[1] ) * \\\n R[0] - (2 * P[1] + self.a1 * P[0] + self.a3 ) * R[1] - \\\n (P[0] ** 3) + self.a4 * P[0] + 2 * self.a6 - self.a3 * P[1]","def _lines_intersection(self, other):\n\n the_slope, the_y_intercept = False, False\n\n # parallel?\n if self.slope == other.slope:\n return (\n self.y_intercept == other.y_intercept and\n self.x_value == other.x_value\n )\n\n if self.is_vertical():\n x = self.x_value\n the_slope = other.slope\n the_y_intercept = other.y_intercept\n elif other.is_vertical():\n x = other.x_value\n else:\n x = (other.y_intercept - self.y_intercept) / (self.slope - other.slope)\n\n if the_slope is None or the_slope is False:\n the_slope = self.slope\n the_y_intercept = self.y_intercept\n\n y = the_slope * x + the_y_intercept\n\n return Point(x, y)","def closest_point_on_line(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n ap = subtract_vectors(point, a)\n c = vector_component(ap, ab)\n return add_vectors(a, c)","def abline(slope, intercept, a, b):\n # axes = plt.gca()\n print(slope)\n print(intercept)\n x_vals = np.array(list_xs[ a: b])\n y_vals = intercept + slope * (x_vals-a)\n plt.plot(x_vals, y_vals, '--')\n # print(x_vals)","def on_line_2d(p1, p2, pt, tol=None):\r\n if tol is None:\r\n tol = get_tol_2d()\r\n return geometry.gmOnLineWithTol(p1, p2, pt, tol)"],"string":"[\n \"def get_linear_coef(p1, p2):\\n slope = (p1[1] - p2[1]) / (p1[0] - p2[0])\\n intercept = p1[1] - slope * p1[0]\\n return slope, intercept\",\n \"def generate_line(point_1, point_2):\\r\\n A = point_1.y - point_2.y\\r\\n B = point_2.x - point_1.x\\r\\n C = point_1.y * B + point_1.x * A\\r\\n return np.matrix([[A],[B],[-C]])\",\n \"def line_equation(x1, y1, x2, y2):\\n \\n a = y2 - y1\\n b = x1 - x2\\n c = x2*y1 - x1*y2\\n return a, b, c\",\n \"def line(p1, p2):\\n A = (p1[1] - p2[1])\\n B = (p2[0] - p1[0])\\n C = (p1[0]*p2[1] - p2[0]*p1[1])\\n return A, B, -C\",\n \"def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tD = d_coefficients(x1,x2,x3,C)\\n\\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\\n\\tA = a_coefficients(y1,y2)\\n\\treturn(A,B,C[:2],D)\",\n \"def straight_line(p1,p2,spec):\\n\\tx1,y1 = p1; x2,y2 = p2\\n\\t\\n\\tm = (y2-y1) / (x2-x1)\\n\\tx = np.arange(x1-0.01, x2+0.01, 0.01) \\n\\n\\ty = m *(x-x1) + y1\\n\\tf = np.vectorize(interp1d(x,y,kind = 'linear'))\\n\\twave,flux,error = Select_Data(spec,[x1,x2])\\n\\treturn wave,f(wave)\",\n \"def homo_line(a, b):\\n return (a[1] - b[1], b[0] - a[0], a[0] * b[1] - a[1] * b[0])\",\n \"def a_coefficients(y1,y2):\\n\\tACoefficients = np.array([\\ty1, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\ty2 ]).astype(float)\\n\\treturn(ACoefficients)\",\n \"def compute_dual_line(P):\\n return Line(P.x, -P.y)\",\n \"def line_equation_ap(angle, (x1, y1)):\\n \\n # get second point on the line\\n x2 = float(x1) + cos(angle)\\n y2 = float(y1) + sin(angle)\\n \\n # return A, B and C coefficients\\n return (y1 - y2, x2 - x1, x1*y2 - x2*y1)\",\n \"def TwoPoints(self, p1, p2):\\n\\n p1 = base.getvector(p1)\\n if len(p1) == 2:\\n p1 = np.r_[p1, 1]\\n p2 = base.getvector(p2)\\n if len(p2) == 2:\\n p2 = np.r_[p2, 1]\\n\\n return Line2(np.cross(p1, p2))\",\n \"def calculate_coefficients(self, start, end):\\n A = np.array([\\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\\n ])\\n\\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\\n c_1 = a_1 + 2 * a_2 * self.deltaT\\n c_2 = 2 * a_2\\n\\n B = np.array([\\n end[0] - c_0,\\n end[1] - c_1,\\n end[2] - c_2\\n ])\\n\\n a_3_4_5 = np.linalg.solve(A, B)\\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\\n\\n return coeff\",\n \"def coefA(x0,y0,x1,y1):\\n return -(y1-y0)/(x1-x0)\",\n \"def get_line_to(self, provided_point):\\n\\n \\\"\\\"\\\"Calculate slope\\\"\\\"\\\"\\n a = (provided_point.y - self.y) / (provided_point.x - self.x)\\n\\n \\\"\\\"\\\"Calculate b\\\"\\\"\\\"\\n b = self.y - a * self.x\\n\\n return (a,b)\",\n \"def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_matrix(x1,x2,x3)\\n\\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tCCoefficients = np.dot(inv(C),y)\\n\\treturn(CCoefficients)\",\n \"def DistPoint2Line(point,line_point1, line_point2=np.array([0,0,0])):\\n return np.linalg.norm(np.cross((point-line_point2),(point-line_point1)))/np.linalg.norm(line_point1 - line_point2)\",\n \"def fit_line(xs,ys,a,b):\\n # Checking against empty list, if empty return 0s\\n if not (xs):\\n return 0,0,0,0\\n \\n # Preparing vectors for least square\\n z = np.vstack([xs, np.ones(len(xs))]).T\\n s = np.array(ys)\\n\\n # Applying least square fitting on points\\n m, c = np.linalg.lstsq(z, np.array(ys))[0] #Applying least squares method\\n \\n #Using slope and intercept plus y coordinates to get x-coordinates\\n x1 = int(a/m - c/m) \\n x2 = int(b/m - c/m)\\n \\n return x1,a,x2,b\",\n \"def coefC(x0,y0,x1,y1):\\n return (x1*y0-x0*y1)/(x1-x0)\",\n \"def get_line_to(self, point):\\n\\n b = ((self.x - point.x)*point.y - (self.y - point.y)*point.x)/(self.x - point.x)\\n\\n a = (self.y - point.y)/(self.x - point.x)\\n\\n return a, b\",\n \"def find_line_model(points):\\n\\n # [WARNING] vertical and horizontal lines should be treated differently\\n # here we just add some noise to avoid division by zero\\n\\n # find a line model for these points\\n m = (points[1, 1] - points[0, 1]) / (\\n points[1, 0] - points[0, 0] + sys.float_info.epsilon) # slope (gradient) of the line\\n c = points[1, 1] - m * points[1, 0] # y-intercept of the line\\n\\n return m, c\",\n \"def line_intercept(p1,p2,p3,p4):\\n # Note if vertical line m = None and b holds x-val\\n (m1,b1) = line_param(p1,p2)\\n (m2,b2) = line_param(p3,p4)\\n if (m1 != None) and (m2 != None):\\n if (m1-m2) != 0.:\\n x = (b2-b1)/(m1-m2)\\n y = m1*x + b1\\n else:\\n return (None,0)\\n elif (m1 == None) and (m2 != None):\\n x = b1 \\n y = m2*x + b2\\n elif (m1 != None) and (m2 == None):\\n x = b2\\n y = m1*x + b1\\n else:\\n return (None,0) \\n \\n # min and max of points. \\n max_x1 = max(p1[0], p2[0])\\n min_x1 = min(p1[0], p2[0])\\n max_y1 = max(p1[1], p2[1])\\n min_y1 = min(p1[1], p2[1])\\n max_x2 = max(p3[0], p4[0])\\n min_x2 = min(p3[0], p4[0])\\n max_y2 = max(p3[1], p4[1])\\n min_y2 = min(p3[1], p4[1])\\n #check if the intersection is in bounds\\n flag = 1\\n if x > max_x1 or x < min_x1:\\n flag = 0\\n elif x > max_x2 or x < min_x2:\\n flag = 0\\n elif y > max_y1 or y < min_y1: \\n flag = 0\\n elif y > max_y2 or y < min_y2: \\n flag = 0\\n #check if the intersection point corresponds to an end point\\n intercept = num.array([x,y])\\n def _same(p1,p2,prec=0.0001):\\n \\\"\\\"\\\" are two points the same \\\"\\\"\\\"\\n #return num.all(num.equal(p1,p2))\\n t1 = num.fabs(p1[0]-p2[0]) < prec\\n t2 = num.fabs(p1[1]-p2[1]) < prec\\n if t1 and t2:\\n #print \\\"same\\\", p1,p2\\n return True\\n if flag == 1:\\n if _same(intercept,p1):\\n flag = 2\\n elif _same(intercept,p2):\\n flag = 2\\n elif _same(intercept,p3):\\n flag = 2\\n elif _same(intercept,p4):\\n flag = 2\\n return (intercept,flag)\",\n \"def line_contribution(p1,p2,alpha = 1):\\n\\n adjust = np.zeros((worksize,worksize,2))\\n\\n x1 = p1[0]\\n y1 = p1[1]\\n x2 = p2[0]\\n y2 = p2[1]\\n\\n coordinates = coordinate_matrix(worksize)\\n numerator = np.sum(np.multiply(coordinates,np.reshape(np.array(((y2-y1,-(x2-x1)))),(2,1,1))),axis = 0) + x2*y1 - y2*x1\\n dist_from_line = np.abs(numerator) * (1.0/np.sqrt((y2-y1)**2+(x2-x1)**2))\\n xcontribution = (x2-x1)*(1/(alpha*dist_from_line+1))\\n ycontribution = (y2-y1)*(1/(alpha*dist_from_line+1))\\n\\n\\n return np.array((-ycontribution,xcontribution))/np.sqrt((y2-y1)**2+(x2-x1)**2)\",\n \"def slope(point_a, point_b, flip):\\n\\n x_a, y_a = point_a\\n x_b, y_b = point_b\\n\\n dx = x_b - x_a\\n dy = y_b - y_a\\n\\n return -dx / dy if flip else dy / dx\",\n \"def slope(x1, y1, x2, y2):\\r\\n delta_y = y2-y1\\r\\n delta_x = x2-x1\\r\\n return delta_y / delta_x\",\n \"def line(intercept, slope, x):\\n return slope*x + intercept\",\n \"def line(x1, y1, x2, y2):\\r\\n\\r\\n x1 = normalize(x1)\\r\\n y1 = normalize(y1)\\r\\n x2 = normalize(x2)\\r\\n y2 = normalize(y2)\\r\\n\\r\\n xdiff = max(x1, x2) - min(x1, x2)\\r\\n ydiff = max(y1, y2) - min(y1, y2)\\r\\n xdir = 1 if x1 <= x2 else -1\\r\\n ydir = 1 if y1 <= y2 else -1\\r\\n\\r\\n r = max(xdiff, ydiff)\\r\\n\\r\\n for i in range(r+1):\\r\\n x = x1\\r\\n y = y1\\r\\n\\r\\n if ydiff:\\r\\n y += (float(i) * ydiff) / r * ydir\\r\\n if xdiff:\\r\\n x += (float(i) * xdiff) / r * xdir\\r\\n\\r\\n yield (x, y)\",\n \"def slope(x1, y1, x2, y2):\\n return (y2 - y1) / (x2 - x1)\",\n \"def abline(points, slope, intercept):\\n x_values = get_column(points, 0)\\n return [slope * i + intercept for i in x_values]\",\n \"def drawLine2P(x,y,xlims):\\n \\n xrange = np.arange(xlims[0],xlims[1],1)\\n A = np.vstack([x, np.ones(len(x))]).T\\n k, b = np.linalg.lstsq(A, y, rcond=None)[0]\\n return [xrange, k*xrange + b]\",\n \"def _distance(point, line_point1, line_point2):\\n vec1 = line_point1 - point\\n vec2 = line_point2 - point\\n distance = np.abs(np.cross(vec1,vec2)) / np.linalg.norm(line_point1-line_point2)\\n return distance\",\n \"def intercept(x1, y1, x2, y2):\\r\\n m = slope(x1, y1, x2, y2)\\r\\n return y1 - m*x1\",\n \"def point_line_dist2(p, l1, l2):\\n p, l1, l2 = np.asarray(p), np.asarray(l1), np.asarray(l2)\\n ap = l1 - p\\n n = l2 - l1\\n n /= np.sqrt(sum(n**2))\\n dist = ap - np.outer(n, np.dot(ap, n)).T\\n return np.sum(dist**2, 1)\",\n \"def distance_point_to_line(x1, y1, a, b, c):\\n d = abs((a * x1 + b * y1 + c)) / (math.sqrt(a * a + b * b))\\n #print(\\\"Distance from ({}, {}) to line {}x+{}y+{}=0 is {}\\\".format(\\n # x1, y1, a, b, c, d))\\n return(d)\",\n \"def main(self,xpoints,ypoints,numOfWaypoints):\\n WaypointsX = np.zeros(((numOfWaypoints)*(len(xpoints)-1)))\\n WaypointsY = np.zeros(((numOfWaypoints)*(len(xpoints)-1)))\\n\\n for i in range(len(xpoints)-1):\\n x = np.linspace(xpoints[i],xpoints[i+1],numOfWaypoints)\\n try:\\n slope = ((ypoints[i+1])-(ypoints[i]))/((xpoints[i+1])-(xpoints[i]))\\n coefficient = (ypoints[i])-(slope)*(xpoints[i])\\n y = (slope)*x+coefficient\\n except ZeroDivisionError:\\n x = np.linspace(xpoints[i],xpoints[i],numOfWaypoints)\\n y = np.linspace(ypoints[i],ypoints[i+1],numOfWaypoints)\\n WaypointsX[((numOfWaypoints)*(i)):((numOfWaypoints)*(i+1))]=x\\n WaypointsY[((numOfWaypoints)*(i)):((numOfWaypoints)*(i+1))]=y\\n\\n return WaypointsX, WaypointsY\",\n \"def _calculate_slope(klass, p1, p2):\\n xdiff = p1.x - p2.x\\n if xdiff:\\n return (p1.y - p2.y) / xdiff\\n else:\\n return float(\\\"+inf\\\")\",\n \"def line_param(point_a, point_b, t):\\n new_point = point_a - point_b\\n return point_b + t*new_point\",\n \"def crossLine(self, other):\\n a, b = self.point\\n c, d = other.point\\n m, n = self.vector\\n o, p = other.vector\\n if n * o == m * p: # The lines are parallels\\n return None\\n elif self.angle == -math.pi / 2:\\n return Point(a, d)\\n elif other.angle == -math.pi / 2:\\n return Point(b, c)\\n else:\\n x = (a * n * o - b * m * o - c * m * p + d * m * o) / (n * o - m * p)\\n y = (x - a) * n / m + b\\n return Point(x, y)\",\n \"def line_intersection_with(self, other):\\n # solve following system :\\n # intersection = start of self + alpha * direction of self\\n # intersection = start of other + beta * direction of other\\n directions = [s.endpoints[1] - s.endpoints[0] for s in (self, other)]\\n denominator = directions[0].cross_product(directions[1])\\n if abs(denominator) < 0.000001:\\n # almost parallel lines\\n return\\n start_diff = other.endpoints[0] - self.endpoints[0]\\n alpha = start_diff.cross_product(directions[1]) / denominator\\n return self.endpoints[0] + directions[0] * alpha\",\n \"def _dist_point2line(self, point: ndarray,\\n line: Tuple[ndarray, ndarray]) -> ndarray:\\n\\n assert isinstance(line, tuple)\\n point1, point2 = line\\n d = abs(np.cross(point2 - point1, point - point1)) / (\\n norm(point2 - point1) + 1e-8)\\n return d\",\n \"def slip_to_coefficients(x, y, a):\\n partials = np.zeros((x.size, 3))\\n partials[:, 0] = (x / a) * (9 * (x / a) / 8 - 3 / 4)\\n partials[:, 1] = (1 - 3 * (x / a) / 2) * (1 + 3 * (x / a) / 2)\\n partials[:, 2] = (x / a) * (9 * (x / a) / 8 + 3 / 4)\\n coefficients = np.linalg.inv(partials) @ y\\n return coefficients\",\n \"def intersection(line1, line2):\\n p0, p1, p2, p3 = map(\\n lambda tup : np.array(tup[:2]),\\n [line1[0], line1[1], line2[0], line2[1]]\\n )\\n p1, p2, p3 = map(lambda x : x - p0, [p1, p2, p3])\\n transform = np.zeros((2, 2))\\n transform[:,0], transform[:,1] = p1, p2\\n if np.linalg.det(transform) == 0: return\\n inv = np.linalg.inv(transform)\\n new_p3 = np.dot(inv, p3.reshape((2, 1)))\\n #Where does line connecting (0, 1) to new_p3 hit x axis\\n x_intercept = new_p3[0] / (1 - new_p3[1]) \\n result = np.dot(transform, [[x_intercept], [0]])\\n result = result.reshape((2,)) + p0\\n return result\",\n \"def _homogenous_line(A,B):\\n if A==B: raise ValueError('Degenerate line through %s and %s' % (repr(A),repr(B)))\\n\\n Ax,Ay=A\\n Bx,By=B\\n # keep magnitude of coefficients as close to unity as possible\\n if abs(Bx-Ax)>=abs(By-Ay):\\n a,b=float(By-Ay)/float(Bx-Ax), -1\\n c=Ay-a*Ax\\n else:\\n a,b=-1, float(Bx-Ax)/float(By-Ay)\\n c=Ax-b*Ay\\n\\n return a,b,c\",\n \"def point_to_line_dist(P, A, B):\\n\\tif all(A == P) or all(B == P):\\n\\t\\treturn0\\n\\tif arccos(dot((P - A) / norm(P - A), (B - A) / norm(B - A))) > pi / 2:\\n\\t\\treturn norm(P - A)\\n\\tif arccos(dot((P - B) / norm(P - B), (A - B) / norm(A - B))) > pi / 2:\\n\\t\\treturn norm(P - B)\\n\\treturn norm(cross(A-B, A-P))/norm(B-A)\",\n \"def get_line_distance(self, p):\\n\\n y = 1000 * p.y\\n R = 1000 * self.geometry.R\\n x = copysign(sqrt(y ** 2 + (R - sqrt(R ** 2 - y ** 2))), y)\\n x = 2 * R * asin(x / (2 * R))\\n #x=y\\n b = -x / sqrt(R ** 2 - x ** 2)\\n theta = atan(b) # grating tangent angle\\n print b, theta\\n d = 0\\n for n, a in enumerate(self.an):\\n d += a * x ** n\\n d *= cos(theta)\\n return 1e-3 / d\",\n \"def line_fit(x,y):\\n\\t# clean\\n\\tx = np.squeeze(x)\\n\\ty = np.squeeze(y)\\n\\t# concatenate\\n\\txy = np.concatenate((x[:,np.newaxis],y[:,np.newaxis]),1)\\n\\t# sort by x values\\n\\txy = xy[xy[:,0].argsort()]\\n\\t#print(xy)\\n\\tf = lambda x,m,b : m*x+b\\n\\tpars,_ = opt.curve_fit(f,xy[:,0],xy[:,1])\\n\\tm = pars[0]\\n\\tb = pars[1]\\n\\tpts = np.zeros((2,2))\\n\\tpts[0,0] = xy[0,0]\\n\\tpts[1,0] = xy[-1,0]\\n\\tpts[:,1] = pts[:,0]*m+b\\n\\tsig = np.std((xy[:,1]-f(xy[:,0],m,b)))\\n\\treturn pts, sig\",\n \"def midpoint_line(a, b):\\n return scale_vector(add_vectors(a, b), 0.5)\",\n \"def intersection(v1, v2):\\n x = v1[0:2] + v2[0:2]\\n y = v1[2:4] + v2[2:4]\\n if( x[3] == 0 ): #To avoid a divide by zero, if x[3] is 0 then we just solve for where lineA equals x[2]\\n t1 = (x[2] - x[0])/\\\\\\n (x[1])\\n return [ v1[0] + v1[1]*t1, v1[2] + v1[3]*t1 ]\\n\\n else: \\n t1 = ( y[0] - y[2] + (y[3]/x[3])*(x[2] - x[0]) )/\\\\\\n ( (y[3]*x[1])/x[3] - y[1] )\\n return [ v1[0] + v1[1]*t1, v1[2] + v1[3]*t1 ]\",\n \"def get_points_for_thick_line(start_x: float, start_y: float,\\r\\n end_x: float, end_y: float,\\r\\n line_width: float):\\r\\n vector_x = start_x - end_x\\r\\n vector_y = start_y - end_y\\r\\n perpendicular_x = vector_y\\r\\n perpendicular_y = -vector_x\\r\\n length = math.sqrt(vector_x * vector_x + vector_y * vector_y)\\r\\n if length == 0:\\r\\n normal_x = 1.0\\r\\n normal_y = 1.0\\r\\n else:\\r\\n normal_x = perpendicular_x / length\\r\\n normal_y = perpendicular_y / length\\r\\n r1_x = start_x + normal_x * line_width / 2\\r\\n r1_y = start_y + normal_y * line_width / 2\\r\\n r2_x = start_x - normal_x * line_width / 2\\r\\n r2_y = start_y - normal_y * line_width / 2\\r\\n r3_x = end_x + normal_x * line_width / 2\\r\\n r3_y = end_y + normal_y * line_width / 2\\r\\n r4_x = end_x - normal_x * line_width / 2\\r\\n r4_y = end_y - normal_y * line_width / 2\\r\\n points = (r1_x, r1_y), (r2_x, r2_y), (r4_x, r4_y), (r3_x, r3_y)\\r\\n return points\",\n \"def slope(a, b):\\r\\n if a[0] == b[0]: #If the x values are both 0\\r\\n return 0 #Technically, undefined, but doesn't matter for finding collinearity\\r\\n return (a[1] - b[1]) / (a[0] - b[0])\",\n \"def _line_from_two_points(pt1: np.array, pt2: np.array) -> np.array:\\n numLine = pt1.shape[0]\\n lines = np.zeros((numLine, 6))\\n n = np.cross(pt1, pt2)\\n n = n / (matlib.repmat(np.sqrt(np.sum(n ** 2, 1, keepdims=True)), 1, 3) + 1e-9)\\n lines[:, 0:3] = n\\n\\n areaXY = np.abs(np.sum(n * matlib.repmat([0, 0, 1], numLine, 1), 1, keepdims=True))\\n areaYZ = np.abs(np.sum(n * matlib.repmat([1, 0, 0], numLine, 1), 1, keepdims=True))\\n areaZX = np.abs(np.sum(n * matlib.repmat([0, 1, 0], numLine, 1), 1, keepdims=True))\\n planeIDs = np.argmax(np.hstack([areaXY, areaYZ, areaZX]), axis=1) + 1\\n lines[:, 3] = planeIDs\\n\\n for i in range(numLine):\\n uv = _xyz2uvN(np.vstack([pt1[i, :], pt2[i, :]]), lines[i, 3])\\n umax = uv[:, 0].max() + np.pi\\n umin = uv[:, 0].min() + np.pi\\n if umax - umin > np.pi:\\n lines[i, 4:6] = np.array([umax, umin]) / 2 / np.pi\\n else:\\n lines[i, 4:6] = np.array([umin, umax]) / 2 / np.pi\\n\\n return lines\",\n \"def find_slope(lat1,lon1,lat2,lon2):\\n return (lon2-lon1)/(lat2-lat1)\",\n \"def make_line_points(y1, y2, line):\\n if line is None:\\n return None\\n\\n slope, intercept = line\\n\\n # make sure everything is integer as cv2.line requires it\\n x1 = int((y1 - intercept) / slope)\\n x2 = int((y2 - intercept) / slope)\\n y1 = int(y1)\\n y2 = int(y2)\\n\\n return ((x1, y1), (x2, y2))\",\n \"def calculate_line_length(x1, y1, x2, y2):\\n distance = np.sqrt((x2 - x1)**2 + (y2 - y1)**2)\\n return distance\",\n \"def distance_to_line(a, b, p):\\n return distance(closest_point(a, b, p), p)\",\n \"def line_param(v1,v2):\\n if (v1[0]-v2[0] != 0.):\\n m = (v1[1] - v2[1])/(v1[0] - v2[0])\\n b = -m*v1[0] + v1[1]\\n if num.fabs(m)>1.0e6:\\n m = None\\n b = v1[0]\\n else: \\n m = None\\n b = v1[0]\\n return (m,b)\",\n \"def line_ccw(a, b, p):\\n return (p[1] - a[1]) * (b[0] - a[0]) > (b[1] - a[1]) * (p[0] - a[0])\",\n \"def distanceOfTwoLines(p1, v1, p2, v2):\\n # if we transform multiple points in one go\\n if len(v1.shape) == 2:\\n a1 = np.einsum('ij,ij->i', v1, v1)\\n a2 = np.einsum('ij,ij->i', v1, v2)\\n b1 = -np.einsum('ij,ij->i', v2, v1)\\n b2 = -np.einsum('ij,ij->i', v2, v2)\\n c1 = -np.einsum('ij,j->i', v1, p1 - p2)\\n c2 = -np.einsum('ij,j->i', v2, p1 - p2)\\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]).transpose(2, 0, 1), np.array([c1, c2]).T)\\n res = res[:, None, :]\\n return np.linalg.norm((p1 + res[..., 0] * v1) - (p2 + res[..., 1] * v2), axis=1)\\n else: # or just one point\\n a1 = np.dot(v1, v1)\\n a2 = np.dot(v1, v2)\\n b1 = -np.dot(v2, v1)\\n b2 = -np.dot(v2, v2)\\n c1 = -np.dot(v1, p1 - p2)\\n c2 = -np.dot(v2, p1 - p2)\\n try:\\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]), np.array([c1, c2]))\\n except np.linalg.LinAlgError:\\n return 0\\n res = res[None, None, :]\\n return np.linalg.norm((p1 + res[..., 0] * v1) - (p2 + res[..., 1] * v2), axis=1)[0]\",\n \"def is_on_line(point_a, point_b, point_c):\\r\\n return (point_b[0] - point_a[0]) * (point_c[1] - point_a[1]) - (point_b[1] - point_a[1]) * (point_c[0] - point_a[0])\",\n \"def my_linear_polyfit(raw_lines):\\n \\n x = []\\n y = []\\n weight = []\\n \\n # Build arrays of all x, y, and weight points\\n for line in raw_lines:\\n for x1, y1, x2, y2 in line:\\n x.extend([x1, x2])\\n y.extend([y1, y2])\\n line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2)\\n weight.extend([line_length, line_length])\\n \\n # Apply weighted linear polyfit\\n z = np.polyfit(x, y, 1, w=weight)\\n \\n # Output fit line, z = [m, b]\\n return z\",\n \"def perpendicularIntersection(point, linePoint1, linePoint2):\\n\\t\\tx1 = linePoint1[0]\\n\\t\\ty1 = linePoint1[1]\\n\\t\\tx2 = linePoint2[0]\\n\\t\\ty2 = linePoint2[1]\\n\\t\\tx3 = point[0]\\n\\t\\ty3 = point[1]\\n\\t\\tk = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)**2 + (x2-x1)**2)\\n\\t\\tx4 = x3 - k * (y2-y1)\\n\\t\\ty4 = y3 + k * (x2-x1)\\n\\t\\treturn (x4, y4)\",\n \"def slope(a, b):\\n a1, a2 = PC_matrix[:, 0][a], PC_matrix[:, 1][a]\\n b1, b2 = PC_matrix[:, 0][b], PC_matrix[:, 1][b]\\n \\n return b1-a1, b2-a2\",\n \"def line(x0: float, y0: float, x1: float, y1: float) -> LineCollection:\\n return LineCollection([(complex(x0, y0), complex(x1, y1))])\",\n \"def calc_slope(self, left, right):\\n return (left[1] - right[1]) / (left[0] - right[0])\",\n \"def Solver(line1, line2):\\n\\ta = np.array(line1[0])\\n\\tb = np.array(line1[1])\\n\\tu = np.array(line2[0])\\n\\tv = np.array(line2[1])\\n\\t#print(a,b,u,v)\\n\\tc = u[:2]-a[:2]\\n\\tA = np.vstack((b[:2],-v[:2])).T\\n\\t#print(A)\\n\\tx = np.linalg.solve(A,c)\\n\\t#print(x)\\n\\tp = a+x[0]*b\\n\\t#print(p)\\n\\treturn p\",\n \"def linearfit(x,y):\\n fit = np.polyfit(x,y,1)\\n fit_fn = np.poly1d(fit)\\n yy = fit_fn(x) \\n \\n return yy\",\n \"def intersect_2_lines(P1, V1, P2, V2):\\n Vx = np.cross(V1, V2)\\n s = np.dot(np.cross(P2 - P1, V1), Vx)/np.dot(Vx, Vx)\\n return s\",\n \"def linear_attenuation_coefficient(self, lines):\\n wl = lines.to(\\\"nm\\\", \\\"spectroscopy\\\").magnitude\\n if isarray(wl):\\n return [self.getExtinctionCoefficient(l) for l in wl]\\n else:\\n return self.getExtinctionCoefficient(wl)\",\n \"def linear(x2, y2, N=100):\\n\\n m = y2 / x2\\n x = np.linspace(0, x2, N)\\n y = m*x\\n\\n # The time of travel\\n T = np.sqrt(2*(1+m**2)/g/m * x2)\\n print('T(linear) = {:.3f}'.format(T))\\n return x, y, T\",\n \"def eDouble(P): #adding P + P by using a tangent line\\r\\n R = point(0, 0, P.c)\\r\\n i = ( (3 * P.x ** 2) + P.c.a) #the slope equation (i/j)\\r\\n j = (2 * P.y)\\r\\n s = (i * modInv(j, P.c.p) ) % P.c.p\\r\\n R.x = ( (s ** 2) - 2 * P.x) % P.c.p\\r\\n R.y = (-P.y + s * (P.x - R.x) ) % P.c.p\\r\\n return R\",\n \"def distance_point_line(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n pa = subtract_vectors(a, point)\\n pb = subtract_vectors(b, point)\\n l = length_vector(cross_vectors(pa, pb))\\n l_ab = length_vector(ab)\\n return l / l_ab\",\n \"def find_slopes(x, y):\\n slopes = np.zeros((len(x) - 1))\\n for i in range(len(x) - 1):\\n # m = (y2 - y1) / (x2 - x1)\\n delta_x = x[i + 1] - x[i]\\n delta_y = y[i + 1] - y[i]\\n slopes[i] = delta_y / delta_x\\n return slopes\",\n \"def coefficients(self) :\\n raise NotImplementedError\",\n \"def distance_line_line(l1, l2, tol=0.0):\\n a, b = l1\\n c, d = l2\\n ab = subtract_vectors(b, a)\\n cd = subtract_vectors(d, c)\\n ac = subtract_vectors(c, a)\\n n = cross_vectors(ab, cd)\\n l = length_vector(n)\\n if l <= tol:\\n return distance_point_point(closest_point_on_line(l1[0], l2), l1[0])\\n n = scale_vector(n, 1.0 / l)\\n return fabs(dot_vectors(n, ac))\",\n \"def b_coefficients(x1,x2,x3,y1,y2,y3,CCoefficients,DCoefficients):\\n\\tBCoefficients = np.array([\\t((y2-y1)/(x2-x1)-CCoefficients[0]*(x2-x1) - DCoefficients[0]*((x2-x1)**2)), \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t((y3-y2)/(x3-x2)-CCoefficients[1]*(x3-x2) - DCoefficients[1]*((x3-x2)**2)) \\t]).astype(float)\\n\\treturn(BCoefficients)\",\n \"def sign_line(pt, P1, P2):\\n x1, y1 = P1\\n x2, y2 = P2\\n x, y = pt\\n\\n return np.sign((x - x1)*(y2 - y1) - (y-y1)*(x2-x1))\",\n \"def _distance2_line_endpoints(line1, line2):\\n (A,B),(C,D) = line1, line2\\n R2=lambda u,v: (u[0]-v[0])**2+(u[1]-v[1])**2\\n pairs = zip((A,A,B,B),(C,D,C,D))\\n r2 = [R2(pair[0],pair[1]) for pair in pairs]\\n mini=sorted(zip(r2,pairs),key=lambda a,b: a)[0]\\n #R2_min = min((R2(A,C), R2(A,D), R2(B,C), R2(B,D)))\\n return mini[0], mini[1][0], mini[1][1]\",\n \"def linePointXY(l,p,inside=True,distance=False,params=False):\\n a=l[0]\\n b=l[1]\\n # check for degenerate case of zero-length line\\n abdist = dist(a,b)\\n if abdist < epsilon:\\n #raise ValueError('zero-length line passed to linePointXY')\\n print('zero-length line passed to linePointXY')\\n return False\\n\\n if distance and params:\\n raise ValueError('incompatible distance and params parameters passed to linePointXY')\\n\\n x0=p[0]\\n y0=p[1]\\n z0=p[2]\\n x1=a[0]\\n y1=a[1]\\n z1=a[2]\\n x2=b[0]\\n y2=b[1]\\n z2=b[2]\\n\\n ## check to see if all three points lie in the same x,y plane\\n if not isXYPlanar([p,a,b]):\\n raise ValueError('non-XY points in linePointXY call')\\n return false\\n # if abs(z1-z0) > epsilon or abs(z2-z0) > epsilon:\\n # return False\\n\\n linedist = abs( ((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)/abdist)\\n\\n ## this is the fast case:\\n if not inside and distance:\\n return linedist\\n \\n ## find out where the intersection between the original line and a\\n ## line defined by the point and an orthogonal direction vector\\n ## is. We do this by constructing two direction vectors\\n ## orthogonal to the orgiginal line scaled by the line distance,\\n ## and adding them to the point in question. Assuming that the\\n ## line distance is not zero, only one of these constructed points\\n ## will fall on the line\\n\\n ## compute unit direction vector for original line\\n dir = sub(b,a)\\n dir = scale3(dir,1.0/mag(dir))\\n\\n ## compute two orthogonal direction vectors of length linedist\\n ordir1 = scale3(orthoXY(dir),linedist)\\n ordir2 = scale3(ordir1, -1.0)\\n \\n ## there are two possible intersection points\\n pi1 = add(p,ordir1)\\n pi2 = add(p,ordir2)\\n\\n ## compute distances\\n d1pa = dist(a,pi1)\\n d1pb = dist(pi1,b)\\n d1 = d1pa+d1pb # \\\"triangle\\\" with pi1\\n\\n d2pa = dist(a,pi2)\\n d2pb = dist(pi2,b)\\n d2 = d2pa+d2pb # \\\"triangle\\\" with pi2\\n\\n ## the shortest \\\"triangle\\\" distance will signal the point that\\n ## is actually on the line, even if that point falls outside\\n ## the a,b line interval\\n \\n if params or not inside: # if we don't care about being inside the\\n # line segment\\n if d1 <= d2:\\n if distance:\\n return d1\\n elif params:\\n return d1pb/abdist\\n else:\\n return pi1\\n else:\\n if distance:\\n return d2\\n elif params:\\n return d2pb/abdist\\n else:\\n return pi2\\n \\n \\n ## if the closest point on the line to point p lies between\\n ## the endpoints of the line, then either d1 or d2 will equal\\n ## abdist. IF neither do, then we know that the closest point lies\\n ## outside the endpoints\\n\\n if abs(d1-abdist) < epsilon:\\n if distance:\\n return linedist\\n else:\\n return pi1\\n\\n if abs(d2-abdist) < epsilon:\\n if distance:\\n return linedist\\n else:\\n return pi2\\n\\n ## closest point is outside the interval. That means that the\\n ## distance from point p to whichever endpoint is smaller is the\\n ## closest distance\\n\\n d3 = dist(a,p)\\n d4 = dist(b,p)\\n\\n if d3 < d4:\\n if distance:\\n return d3\\n else:\\n return a\\n else:\\n if distance:\\n return d4\\n else:\\n return b\",\n \"def get_linear_distance(p1,p2):\\r\\n if p1[0] < p2[0]:\\r\\n d = 0\\r\\n else:\\r\\n d = np.sqrt((p1[0]-p2[0])**2.0 + (p1[1]-p2[1])**2.0)\\r\\n return d\",\n \"def _distance2_point_to_h_line(point, h_line):\\n a,b,c = h_line\\n x0,y0 = point\\n # solve for equality\\n # r^2 = (x-x0)^2 + (y-y0)^2\\n # ax + by + c = 0\\n # --> 2nd order polynomial\\n # --> find place of exactly one solution, i.e.\\n # radicant of p-q formula is identical zero\\n # if radicant is zero, then\\n ys = ((a*x0-c)*b + a**2*y0)/(a**2+b**2)\\n # or\\n xs = ((b*y0-c)*a + b**2*x0)/(a**2+b**2)\\n # for a != 0\\n if abs(a)>=abs(b):\\n R2 = (x0-c/a)**2+y0**2 - (1.+(b/a)**2)*ys**2\\n else:\\n R2 = (y0-c/b)**2+x0**2 - (1.+(a/b)**2)*xs**2\\n R2 = R2 if abs(R2)>1e-13 else 0.\\n return R2, (xs, ys)\",\n \"def line_points(a=0, b=0, c=0, ref=[-1.0, 1.0]):\\n\\n if (a == 0) and (b == 0):\\n raise Exception(\\\"linePoints: a and b cannot both be zero\\\")\\n\\n return [(-c / a, p) if b == 0 else (p, (-c - a * p) / b) for p in ref]\",\n \"def distance_point_line_3d(point: Vector, start: Vector, end: Vector) -> float:\\n if start.isclose(end):\\n raise ZeroDivisionError('Not a line.')\\n v1 = point - start\\n # point projected onto line start to end:\\n v2 = (end - start).project(v1)\\n # Pythagoras:\\n return math.sqrt(v1.magnitude_square - v2.magnitude_square)\",\n \"def line_plane(l, p):\\n d = dot((p.o - l.o), p.n) / dot(l.d, p.n)\\n return l(d)\",\n \"def _distance_to_line(begin, end, point):\\n return _vec_distance(point, _nearest_point_on_line(begin, end, point))\",\n \"def linear(m, b, x, xx):\\n y = m*(x - xx) + b\\n return y\",\n \"def lin_interpol(x_p, y_p):\\r\\n f = np.zeros([ x_p.shape[0] - 1 , 4 ]) # Coefficents and interval array\\r\\n \\r\\n for i in range( x_p.shape[0] - 1 ): # for every x[i], x[i+1] pair\\r\\n \\r\\n x_coeff = (y_p[i+1] - y_p[i]) / (x_p[i+1] - x_p[i])\\r\\n const = (x_p[i+1]*y_p[i] - x_p[i]*y_p[i+1] ) / (x_p[i+1] - x_p[i])\\r\\n \\r\\n # save the x coefficent, constant and the interval for this line\\r\\n f[i,:] = x_coeff, const, x_p[i], x_p[i+1]\\r\\n \\r\\n for a, b, start, end in f: # for every line fitted\\r\\n line_x = np.linspace( start, end, 3) # points to plot in x_range\\r\\n line_y = line_x * a + b # find the fitted line value at these points\\r\\n plt.plot(line_x,line_y,'k--', lw = 1, label = 'Linear' if a==f[0][0] else \\\"\\\") # only label one plot\\r\",\n \"def _get_slope(x, y):\\n slope = linregress(x, y)\\n return slope\",\n \"def line_line_intersection(a1: Vector3, a2: Vector3, b1: Vector3, b2: Vector3) -> Vector3:\\n # From https://stackoverflow.com/a/20677983/7245441\\n\\n def det(a: Vector3, b: Vector3) -> float:\\n return a.x * b.y - a.y * b.x\\n\\n y_diff = Vector3(a1.y - a2.y, b1.y - b2.y, 0)\\n x_diff = Vector3(a1.x - a2.x, b1.x - b2.x, 0)\\n\\n div = det(x_diff, y_diff)\\n if div == 0:\\n raise Exception(\\\"Lines do not intersect\\\")\\n\\n d = Vector3(det(a1, a2), det(b1, b2), 0)\\n x = det(d, x_diff) / div\\n y = det(d, y_diff) / div\\n\\n return Vector3(x, y, 0)\",\n \"def line_points(start, end):\\n # Setup initial conditions\\n x1, y1 = start.astuple()\\n x2, y2 = end.astuple()\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Determine how steep the line is\\n is_steep = abs(dy) > abs(dx)\\n \\n # Rotate line\\n if is_steep:\\n x1, y1 = y1, x1\\n x2, y2 = y2, x2\\n \\n # Swap start and end points if necessary and store swap state\\n swapped = False\\n if x1 > x2:\\n x1, x2 = x2, x1\\n y1, y2 = y2, y1\\n swapped = True\\n \\n # Recalculate differentials\\n dx = x2 - x1\\n dy = y2 - y1\\n \\n # Calculate error\\n error = int(dx / 2.0)\\n ystep = 1 if y1 < y2 else -1\\n \\n # Iterate over bounding box generating points between start and end\\n y = y1\\n points = []\\n for x in range(x1, x2 + 1):\\n coord = Int2(y, x) if is_steep else Int2(x, y)\\n points.append(coord)\\n error -= abs(dy)\\n if error < 0:\\n y += ystep\\n error += dx\\n \\n # Reverse the list if the coordinates were swapped\\n if swapped:\\n points.reverse()\\n return points\",\n \"def getLine(self, **kwargs):\\n return Line(self.p1, self.angle, **kwargs)\",\n \"def get_intersect_points(line1, line2):\\n intersect_points = matrix.matrix_sol([line1, line2])\\n return intersect_points\",\n \"def llincc(x,y):\\r\\n covar = lcov(x,y)*(len(x)-1)/float(len(x)) # correct denom to n\\r\\n xvar = lvar(x)*(len(x)-1)/float(len(x)) # correct denom to n\\r\\n yvar = lvar(y)*(len(y)-1)/float(len(y)) # correct denom to n\\r\\n lincc = (2 * covar) / ((xvar+yvar) +((amean(x)-amean(y))**2))\\r\\n return lincc\",\n \"def straight_line(numbers, p_current, relative = False):\\n if len(numbers) != 2:\\n return None\\n\\n p_next = Point(numbers[0], numbers[1])\\n if relative: #relative\\n p_next += p_current\\n\\n return Line(p_current, p_next)\",\n \"def intersectionOfTwoLines(p1, v1, p2, v2):\\n # if we transform multiple points in one go\\n if len(v1.shape) == 2:\\n a1 = np.einsum('ij,ij->i', v1, v1)\\n a2 = np.einsum('ij,ij->i', v1, v2)\\n b1 = -np.einsum('ij,ij->i', v2, v1)\\n b2 = -np.einsum('ij,ij->i', v2, v2)\\n c1 = -np.einsum('ij,j->i', v1, p1 - p2)\\n c2 = -np.einsum('ij,j->i', v2, p1 - p2)\\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]).transpose(2, 0, 1), np.array([c1, c2]).T)\\n res = res[:, None, :]\\n return np.mean([p1 + res[..., 0] * v1, p2 + res[..., 1] * v2], axis=0)\\n else: # or just one point\\n a1 = np.dot(v1, v1)\\n a2 = np.dot(v1, v2)\\n b1 = -np.dot(v2, v1)\\n b2 = -np.dot(v2, v2)\\n c1 = -np.dot(v1, p1 - p2)\\n c2 = -np.dot(v2, p1 - p2)\\n try:\\n res = np.linalg.solve(np.array([[a1, b1], [a2, b2]]), np.array([c1, c2]))\\n except np.linalg.LinAlgError:\\n return np.ones(3)*np.nan\\n res = res[None, None, :]\\n return np.mean([p1 + res[..., 0] * v1, p2 + res[..., 1] * v2], axis=0)[0]\",\n \"def intersection(line1, line2):\\r\\n rho1, theta1 = line1[0]\\r\\n rho2, theta2 = line2[0]\\r\\n A = np.array([[np.cos(theta1), np.sin(theta1)], [np.cos(theta2), np.sin(theta2)]])\\r\\n b = np.array([[rho1], [rho2]])\\r\\n x0, y0 = np.linalg.solve(A, b)\\r\\n x0, y0 = int(np.round(x0)), int(np.round(y0))\\r\\n return [[x0, y0]]\",\n \"def d2(x0,y0,x1,y1):\\n return (x0-x1)*(x0-x1) + (y0-y1)*(y0-y1)\",\n \"def line(self, P, Q, R):\\n if not R or R == self.infpoint:\\n raise ValueError(\\\"R must not be zero\\\")\\n if P == Q == self.infpoint:\\n return self.basefield.one\\n if P == self.infpoint:\\n return R[0] - Q[0]\\n if Q == self.infpoint:\\n return R[0] - P[0]\\n if P[0] != Q[0]:\\n return (Q[0] - P[0]) * R[1] - (Q[1] - P[1]) * \\\\\\n R[0]- Q[0] * P[1] + P[0] * Q[1]\\n if P[1] != Q[1]:\\n return R[0] - P[0]\\n return (3 * P[0] ** 2 + 2 * self.a2 * P[0] + self.a4 - self.a1 * P[1] ) * \\\\\\n R[0] - (2 * P[1] + self.a1 * P[0] + self.a3 ) * R[1] - \\\\\\n (P[0] ** 3) + self.a4 * P[0] + 2 * self.a6 - self.a3 * P[1]\",\n \"def _lines_intersection(self, other):\\n\\n the_slope, the_y_intercept = False, False\\n\\n # parallel?\\n if self.slope == other.slope:\\n return (\\n self.y_intercept == other.y_intercept and\\n self.x_value == other.x_value\\n )\\n\\n if self.is_vertical():\\n x = self.x_value\\n the_slope = other.slope\\n the_y_intercept = other.y_intercept\\n elif other.is_vertical():\\n x = other.x_value\\n else:\\n x = (other.y_intercept - self.y_intercept) / (self.slope - other.slope)\\n\\n if the_slope is None or the_slope is False:\\n the_slope = self.slope\\n the_y_intercept = self.y_intercept\\n\\n y = the_slope * x + the_y_intercept\\n\\n return Point(x, y)\",\n \"def closest_point_on_line(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n ap = subtract_vectors(point, a)\\n c = vector_component(ap, ab)\\n return add_vectors(a, c)\",\n \"def abline(slope, intercept, a, b):\\n # axes = plt.gca()\\n print(slope)\\n print(intercept)\\n x_vals = np.array(list_xs[ a: b])\\n y_vals = intercept + slope * (x_vals-a)\\n plt.plot(x_vals, y_vals, '--')\\n # print(x_vals)\",\n \"def on_line_2d(p1, p2, pt, tol=None):\\r\\n if tol is None:\\r\\n tol = get_tol_2d()\\r\\n return geometry.gmOnLineWithTol(p1, p2, pt, tol)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7252494","0.7058825","0.70182276","0.69539297","0.68374145","0.6692208","0.66759145","0.66448915","0.6642555","0.6631278","0.660639","0.65882164","0.6564653","0.65506494","0.64859647","0.64820635","0.643047","0.6408767","0.63719994","0.6311349","0.62959486","0.62786496","0.6272593","0.6259747","0.6254772","0.6251964","0.62221694","0.6212505","0.6196671","0.61846244","0.61741275","0.613847","0.6112985","0.6093683","0.6090798","0.6088082","0.6075845","0.60719544","0.6064676","0.6061504","0.6029562","0.60075635","0.59780806","0.5975386","0.5968947","0.5963056","0.59595984","0.595077","0.5949105","0.5943804","0.5939806","0.5937459","0.59360856","0.5936001","0.59335345","0.59221846","0.58841527","0.5880779","0.58798754","0.5871863","0.5868773","0.5863577","0.58626366","0.5850981","0.58499384","0.58483005","0.5838161","0.583388","0.5832863","0.58268034","0.5826113","0.58226264","0.5813931","0.579881","0.57923293","0.5788732","0.57738554","0.57703716","0.5759548","0.57581836","0.5756676","0.57488143","0.57455015","0.57419693","0.5740083","0.5732937","0.5724718","0.571886","0.57182145","0.57143015","0.5714102","0.5713617","0.56971574","0.5692494","0.5687628","0.56843597","0.56822157","0.568027","0.56738174","0.56648535"],"string":"[\n \"0.7252494\",\n \"0.7058825\",\n \"0.70182276\",\n \"0.69539297\",\n \"0.68374145\",\n \"0.6692208\",\n \"0.66759145\",\n \"0.66448915\",\n \"0.6642555\",\n \"0.6631278\",\n \"0.660639\",\n \"0.65882164\",\n \"0.6564653\",\n \"0.65506494\",\n \"0.64859647\",\n \"0.64820635\",\n \"0.643047\",\n \"0.6408767\",\n \"0.63719994\",\n \"0.6311349\",\n \"0.62959486\",\n \"0.62786496\",\n \"0.6272593\",\n \"0.6259747\",\n \"0.6254772\",\n \"0.6251964\",\n \"0.62221694\",\n \"0.6212505\",\n \"0.6196671\",\n \"0.61846244\",\n \"0.61741275\",\n \"0.613847\",\n \"0.6112985\",\n \"0.6093683\",\n \"0.6090798\",\n \"0.6088082\",\n \"0.6075845\",\n \"0.60719544\",\n \"0.6064676\",\n \"0.6061504\",\n \"0.6029562\",\n \"0.60075635\",\n \"0.59780806\",\n \"0.5975386\",\n \"0.5968947\",\n \"0.5963056\",\n \"0.59595984\",\n \"0.595077\",\n \"0.5949105\",\n \"0.5943804\",\n \"0.5939806\",\n \"0.5937459\",\n \"0.59360856\",\n \"0.5936001\",\n \"0.59335345\",\n \"0.59221846\",\n \"0.58841527\",\n \"0.5880779\",\n \"0.58798754\",\n \"0.5871863\",\n \"0.5868773\",\n \"0.5863577\",\n \"0.58626366\",\n \"0.5850981\",\n \"0.58499384\",\n \"0.58483005\",\n \"0.5838161\",\n \"0.583388\",\n \"0.5832863\",\n \"0.58268034\",\n \"0.5826113\",\n \"0.58226264\",\n \"0.5813931\",\n \"0.579881\",\n \"0.57923293\",\n \"0.5788732\",\n \"0.57738554\",\n \"0.57703716\",\n \"0.5759548\",\n \"0.57581836\",\n \"0.5756676\",\n \"0.57488143\",\n \"0.57455015\",\n \"0.57419693\",\n \"0.5740083\",\n \"0.5732937\",\n \"0.5724718\",\n \"0.571886\",\n \"0.57182145\",\n \"0.57143015\",\n \"0.5714102\",\n \"0.5713617\",\n \"0.56971574\",\n \"0.5692494\",\n \"0.5687628\",\n \"0.56843597\",\n \"0.56822157\",\n \"0.568027\",\n \"0.56738174\",\n \"0.56648535\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80344635"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95094,"cells":{"query":{"kind":"string","value":"Method to compute the orthogonal projection of a point on a straight line Let's define (D) the straight line which coeff are given as parameters"},"document":{"kind":"string","value":"def compute_orthogonal_proj(line_coefs, point_coordinates):\n a = line_coefs[0]\n c = line_coefs[2]\n # Compute c_prime by replacing with the coordinates of the point\n c_prime = a*point_coordinates[1] + point_coordinates[0]\n x_proj = (c_prime-a*c)/((a**2)+1)\n y_proj = (a*c_prime+c)/((a**2)+1)\n\n return np.array([x_proj, y_proj])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_projection_of_pt_on_line(point, line_point1, line_point2):\n projection = Point(-1, -1)\n projection.x = point.x\n if (line_point2.x - line_point1.x) != 0:\n projection.y = (projection.x - line_point1.x) * (line_point2.y - line_point1.y) / \\\n (line_point2.x - line_point1.x) + line_point1.y\n else:\n projection.y = (projection.x - line_point1.x) * (line_point2.y - line_point1.y) / 1 + line_point1.y\n return projection","def projectPoint(self, point):\n vector = self.normal_vector\n angle = vector.angle\n line = Line(point, angle, correct=False)\n projection = self.crossLine(line)\n return projection","def eDouble(P): #adding P + P by using a tangent line\r\n R = point(0, 0, P.c)\r\n i = ( (3 * P.x ** 2) + P.c.a) #the slope equation (i/j)\r\n j = (2 * P.y)\r\n s = (i * modInv(j, P.c.p) ) % P.c.p\r\n R.x = ( (s ** 2) - 2 * P.x) % P.c.p\r\n R.y = (-P.y + s * (P.x - R.x) ) % P.c.p\r\n return R","def line_plane(l, p):\n d = dot((p.o - l.o), p.n) / dot(l.d, p.n)\n return l(d)","def project_point_line(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n ap = subtract_vectors(point, a)\n c = vector_component(ap, ab)\n\n return add_vectors(a, c)","def proj_to_plane(norm, d, pts):\n a = norm[0]\n b = norm[1]\n c = norm[2]\n\n p = []\n\n for i in range(len(pts)):\n x_p = pts[i][0]\n y_p = pts[i][1]\n z_p = pts[i][2]\n\n if a != 0:\n x_0 = (b * b + c * c) * x_p - a * b * y_p - a * c * z_p - a * d\n y_0 = (b * 1.0 / a) * (x_0 - x_p) + y_p\n z_0 = (c * 1.0 / a) * (x_0 - x_p) + z_p\n\n elif b != 0:\n x_0 = x_p \n y_0 = c * c * y_p - b * (d + c)\n z_0 = (c * 1.0 / b) *(y_0 - y_p) + z_p\n\n else:\n x_0 = x_p\n y_0 = y_p\n z_0 = - d * 1.0 / c\n\n p.append([x_0, y_0, z_0])\n \n return p","def proj_to_plane(norm, d, pts):\n a = norm[0]\n b = norm[1]\n c = norm[2]\n\n p = []\n\n for i in range(len(pts)):\n x_p = pts[i][0]\n y_p = pts[i][1]\n z_p = pts[i][2]\n\n if a != 0:\n x_0 = (b * b + c * c) * x_p - a * b * y_p - a * c * z_p - a * d\n y_0 = (b * 1.0 / a) * (x_0 - x_p) + y_p\n z_0 = (c * 1.0 / a) * (x_0 - x_p) + z_p\n\n elif b != 0:\n x_0 = x_p \n y_0 = c * c * y_p - b * (d + c)\n z_0 = (c * 1.0 / b) *(y_0 - y_p) + z_p\n\n else:\n x_0 = x_p\n y_0 = y_p\n z_0 = - d * 1.0 / c\n\n p.append([x_0, y_0, z_0])\n \n return p","def compute_dual_line(P):\n return Line(P.x, -P.y)","def project_line(self, line: Line, **kwargs: float) -> Line:\n if self.normal.is_parallel(line.vector, **kwargs):\n raise ValueError(\"The line and plane must not be perpendicular.\")\n\n point_projected = self.project_point(line.point)\n\n if self.normal.is_perpendicular(line.vector, **kwargs):\n return Line(point_projected, line.vector)\n\n vector_projected = self.project_vector(line.vector)\n\n return Line(point_projected, vector_projected)","def build_coord(norm, d, pts):\n # Compute the origin as the mean point of the points, and this point has to be on the plane\n \n n = len(pts) \n x_total = 0\n y_total = 0\n z_total = 0\n \n for i in range(n):\n x_total += pts[i][0]\n y_total += pts[i][1]\n z_total += pts[i][2]\n\n x_o = x_total * 1.0 / n\n y_o = y_total * 1.0 / n\n z_o = z_total * 1.0 / n\n p_o = [x_o, y_o, z_o]\n \n # Choose p be the projection of a vector in the z-axis to the plane\n # If the plane is not perpendicular to the z-axis\n if ((norm[2] != 1) and (norm[2] != -1)): \n # Choose a point\n o_z = [x_o, y_o, z_o + 1]\n \n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\n \n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\n\n x_c = (x_p - x_o) * 1.0 / dist \n y_c = (y_p - y_o) * 1.0 / dist\n z_c = (z_p - z_o) * 1.0 / dist\n # Thus we have unit vector in x direction\n e_y = [x_c, y_c, z_c]\n #Compute the unit vector in y direction\n e_x = np.cross(e_y, norm).tolist()\n else:\n e_x = [1, 0, 0]\n e_y = [0, 1, 0]\n \n return [e_x, e_y, norm] , p_o","def build_coord(norm, d, pts):\n # Compute the origin as the mean point of the points, and this point has to be on the plane\n \n n = len(pts)\n x_total = 0\n y_total = 0\n z_total = 0\n \n for i in range(n):\n x_total += pts[i][0]\n y_total += pts[i][1]\n z_total += pts[i][2]\n\n x_o = x_total * 1.0 / n\n y_o = y_total * 1.0 / n\n z_o = z_total * 1.0 / n\n p_o = [x_o, y_o, z_o]\n \n # Choose p be the projection of a vector in the z-axis to the plane\n # If the plane is not perpendicular to the z-axis\n if ((norm[2] != 1) and (norm[2] != -1)): \n # Choose a point\n o_z = [x_o, y_o, z_o + 1]\n \n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\n \n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\n\n x_c = (x_p - x_o) * 1.0 / dist \n y_c = (y_p - y_o) * 1.0 / dist\n z_c = (z_p - z_o) * 1.0 / dist\n # Thus we have unit vector in x direction\n e_y = [x_c, y_c, z_c]\n #Compute the unit vector in y direction\n e_x = np.cross(e_y, norm).tolist()\n else:\n e_x = [1, 0, 0]\n e_y = [0, 1, 0]\n \n return [e_x, e_y, norm] , p_o","def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tD = d_coefficients(x1,x2,x3,C)\n\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\n\tA = a_coefficients(y1,y2)\n\treturn(A,B,C[:2],D)","def distance_point_line_3d(point: Vector, start: Vector, end: Vector) -> float:\n if start.isclose(end):\n raise ZeroDivisionError('Not a line.')\n v1 = point - start\n # point projected onto line start to end:\n v2 = (end - start).project(v1)\n # Pythagoras:\n return math.sqrt(v1.magnitude_square - v2.magnitude_square)","def orthogonalDistance(W, point):\n assert len(W) == 8\n assert len(point) == 8\n import numpy as np\n import math\n\n ## parametrize the line (y = A + tN where A is a point on the line, t \n ## a scalar and N a unit vector in the direction of the line)\n normW = math.sqrt(np.dot(W, W))\n A = (0,0,0,0,0,0,0, W[7])\n N = [float(item)/normW for item in W]\n\n ## Calculate the distance using d = || (A-point) - ((A-point)*N)N||\n AMinPoint = [a_i - p_i for a_i, p_i in zip(A, point)]\n AMinPointDotN = np.dot(AMinPoint, N)\n AMinPointDotNTimesN = [AMinPointDotN * ni for ni in N]\n dVector = [LHS - RHS for LHS, RHS in zip(AMinPoint, AMinPointDotNTimesN)]\n d = math.sqrt(np.dot(dVector, dVector))\n \n return d","def perpendicularIntersection(point, linePoint1, linePoint2):\n\t\tx1 = linePoint1[0]\n\t\ty1 = linePoint1[1]\n\t\tx2 = linePoint2[0]\n\t\ty2 = linePoint2[1]\n\t\tx3 = point[0]\n\t\ty3 = point[1]\n\t\tk = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)**2 + (x2-x1)**2)\n\t\tx4 = x3 - k * (y2-y1)\n\t\ty4 = y3 + k * (x2-x1)\n\t\treturn (x4, y4)","def EuclidI11(self, line: Line, point: Point, interesting=True) -> Line:\n if point not in line:\n raise ValueError(f'Cannot erect a perpendicular. Point {point} is not on line {line}.')\n # rename point as c\n c = point\n # Pick an arbitrary point D on the line.\n d = line.point1 if line.point1 != point else line.point2\n # Make CE equal to CD.\n circle_center_c_radius_cd = self.add_circle(c, d, interesting=interesting)\n intersections = list(self.find_intersections_line_circle(line, circle_center_c_radius_cd))\n e = intersections[0] if intersections[0] != d else intersections[1]\n # Construct the equilateral triangle FDE on DE\n f = self.EuclidI1(Line(d, e), self.pick_point_not_on_line(line))\n # join CF\n return self.add_line(c, f, interesting=interesting)","def line(self, P, Q, R):\n if not R or R == self.infpoint:\n raise ValueError(\"R must not be zero\")\n if P == Q == self.infpoint:\n return self.basefield.one\n if P == self.infpoint:\n return R[0] - Q[0]\n if Q == self.infpoint:\n return R[0] - P[0]\n if P[0] != Q[0]:\n return (Q[0] - P[0]) * R[1] - (Q[1] - P[1]) * \\\n R[0]- Q[0] * P[1] + P[0] * Q[1]\n if P[1] != Q[1]:\n return R[0] - P[0]\n return (3 * P[0] ** 2 + 2 * self.a2 * P[0] + self.a4 - self.a1 * P[1] ) * \\\n R[0] - (2 * P[1] + self.a1 * P[0] + self.a3 ) * R[1] - \\\n (P[0] ** 3) + self.a4 * P[0] + 2 * self.a6 - self.a3 * P[1]","def orthoPolyPower(x,power):\n y = x**power\n x_normalized = x / np.dot(x,x) ** 0.5\n ortho = y - np.dot(x_normalized,y) * x_normalized\n orthonormal = ortho / np.dot(ortho,ortho)**0.5\n return orthonormal","def A_coefficients_ellipsoid(v, DD, bDDisDelta=False):\n #v can be given as an array with X/Y/Z cartesian dimensions being the last.\n #\"\"\"\n if bDDisDelta:\n delta=DD\n else:\n delta=Ddelta_ellipsoid(dd)\n #v=_sanitise_v(v)\n #v2=np.square(v)\n #v4=np.square(v2)\n #fact2=np.multiply(0.75,np.sum(v4))-0.25\n v2 = [ v[i]*v[i] for i in range(3) ]\n v4 = [ v2[i]*v2[i] for i in range(3) ]\n fact2 = 0.25*( 3.0*(v4[0]+v4[1]+v4[2])-1.0)\n fact3 = 1.0/12.0*(delta[0]*(3*v4[0]+6*v2[1]*v2[2]-1) + delta[1]*(3*v4[1]+6*v2[0]*v2[2]-1) + delta[2]*(3*v4[2]+6*v2[0]*v2[1]-1))\n A=np.zeros(5)\n A[0]= 3*v2[1]*v2[2]\n A[1]= 3*v2[0]*v2[2]\n A[2]= 3*v2[0]*v2[1]\n A[3]= fact2-fact3\n A[4]= fact2+fact3\n return A","def lorentzian2d(p, x, y):\n #2012-02-04 11:38 IJMC: Created\n \n x = array(x, dtype=float).copy()\n y = array(y, dtype=float).copy()\n p = array(p).copy()\n\n if len(p)==5:\n p = concatenate((p, [0, 0]))\n elif len(p)==6:\n p = concatenate((p, [0]))\n\n z = ((x - p[3]) / p[1])**2 + ((y - p[4]) / p[2])**2 + p[5] * (x - p[3]) * (y - p[4])\n \n return p[6] + p[0]/(1. + z)","def dist_to_line2d(line, point):\n\tx1,y1 = line[0]\n\tx2,y2 = line[1]\n\tx3,y3 = point\n\t\n\t# where on line the perpendicular is\n\tu = ( ((x3-x1)*(x2-x1) + (y3-y1)*(y2-y1))\n\t\t\t/ (math.pow(x1-x2,2) + math.pow(y1-y2,2)) )\n\t\n\t# intersection point\n\tx = x1 + u*(x2-x1)\n\ty = y1 + u*(y2-y1)\n\t\n\tdist = math.sqrt(math.pow(x-x3,2)+math.pow(y-y3,2))\n\t\n\treturn dist","def calcul_point_plan_projection(cls,cx,cy,cz,spx,spy,axe_x,axe_y):\n projX=gs.Vector3(spx*axe_x.x,spx*axe_x.y,spx*axe_x.z)\n projY=gs.Vector3(spy*axe_y.x,spy*axe_y.y,spy*axe_y.z)\n point=gs.Vector3(projX+projY)+gs.Vector3(cx,cy,cz)\n return point","def projection_P(P_prime):\n sorted_prime = -np.sort(-P_prime, axis=1) # Descending order sort\n cumsum_sorted = np.cumsum(sorted_prime, axis=1) # Compute cumulative sum of lines\n rho_availability = sorted_prime > (cumsum_sorted - 1) / np.arange(1, P_prime.shape[\n 1] + 1) # Compute non-zero rho candidates\n rho = np.count_nonzero(rho_availability, axis=1) # Compute number of non-zero values in final line (rho)\n theta = (cumsum_sorted[np.arange(len(rho)), rho - 1] - 1) / (rho) # Compute lagrange multiplier theta\n P = (P_prime.transpose() - theta).transpose().clip(min=0) # subtract multiplier, clip negatives\n\n return P","def orthogonal(v):\n return np.array([-v[1], v[0]])","def get_normal_dist(line, point):\n \n # Rotate: \n x_rot = np.cos(line[1])*point[0] + np.sin(line[1])*point[1]\n \n # Normal distance: x_rot - rho:\n return x_rot - line[0]","def compute_projection(M):\n P = torch.mm(M, torch.pinverse(M.T.matmul(M)).matmul(M.T))\n P = P.double()\n return P","def _orthogonal_vector(vector):\n return -1 * vector[1], vector[0]","def get_projection_point(self, point, plane, test=False):\n return point_on_plane_projection(point, plane, test=test)","def ortho_poly_predict(x, alpha, norm2, degree = 1):\n x = np.asarray(x).flatten()\n n = degree + 1\n Z = np.empty((len(x), n))\n Z[:,0] = 1\n if degree > 0:\n Z[:, 1] = x - alpha[0]\n if degree > 1:\n for i in np.arange(1,degree):\n Z[:, i+1] = (x - alpha[i]) * Z[:, i] - (norm2[i] / norm2[i-1]) * Z[:, i-1]\n Z /= np.sqrt(norm2)\n return Z","def proj_linf(v, radius=1):\n vmod = np.abs(v)\n projmult = np.minimum(radius/vmod, 1)\n return projmult*v","def linePointXY(l,p,inside=True,distance=False,params=False):\n a=l[0]\n b=l[1]\n # check for degenerate case of zero-length line\n abdist = dist(a,b)\n if abdist < epsilon:\n #raise ValueError('zero-length line passed to linePointXY')\n print('zero-length line passed to linePointXY')\n return False\n\n if distance and params:\n raise ValueError('incompatible distance and params parameters passed to linePointXY')\n\n x0=p[0]\n y0=p[1]\n z0=p[2]\n x1=a[0]\n y1=a[1]\n z1=a[2]\n x2=b[0]\n y2=b[1]\n z2=b[2]\n\n ## check to see if all three points lie in the same x,y plane\n if not isXYPlanar([p,a,b]):\n raise ValueError('non-XY points in linePointXY call')\n return false\n # if abs(z1-z0) > epsilon or abs(z2-z0) > epsilon:\n # return False\n\n linedist = abs( ((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)/abdist)\n\n ## this is the fast case:\n if not inside and distance:\n return linedist\n \n ## find out where the intersection between the original line and a\n ## line defined by the point and an orthogonal direction vector\n ## is. We do this by constructing two direction vectors\n ## orthogonal to the orgiginal line scaled by the line distance,\n ## and adding them to the point in question. Assuming that the\n ## line distance is not zero, only one of these constructed points\n ## will fall on the line\n\n ## compute unit direction vector for original line\n dir = sub(b,a)\n dir = scale3(dir,1.0/mag(dir))\n\n ## compute two orthogonal direction vectors of length linedist\n ordir1 = scale3(orthoXY(dir),linedist)\n ordir2 = scale3(ordir1, -1.0)\n \n ## there are two possible intersection points\n pi1 = add(p,ordir1)\n pi2 = add(p,ordir2)\n\n ## compute distances\n d1pa = dist(a,pi1)\n d1pb = dist(pi1,b)\n d1 = d1pa+d1pb # \"triangle\" with pi1\n\n d2pa = dist(a,pi2)\n d2pb = dist(pi2,b)\n d2 = d2pa+d2pb # \"triangle\" with pi2\n\n ## the shortest \"triangle\" distance will signal the point that\n ## is actually on the line, even if that point falls outside\n ## the a,b line interval\n \n if params or not inside: # if we don't care about being inside the\n # line segment\n if d1 <= d2:\n if distance:\n return d1\n elif params:\n return d1pb/abdist\n else:\n return pi1\n else:\n if distance:\n return d2\n elif params:\n return d2pb/abdist\n else:\n return pi2\n \n \n ## if the closest point on the line to point p lies between\n ## the endpoints of the line, then either d1 or d2 will equal\n ## abdist. IF neither do, then we know that the closest point lies\n ## outside the endpoints\n\n if abs(d1-abdist) < epsilon:\n if distance:\n return linedist\n else:\n return pi1\n\n if abs(d2-abdist) < epsilon:\n if distance:\n return linedist\n else:\n return pi2\n\n ## closest point is outside the interval. That means that the\n ## distance from point p to whichever endpoint is smaller is the\n ## closest distance\n\n d3 = dist(a,p)\n d4 = dist(b,p)\n\n if d3 < d4:\n if distance:\n return d3\n else:\n return a\n else:\n if distance:\n return d4\n else:\n return b","def get_line_distance(self, p):\n\n y = 1000 * p.y\n R = 1000 * self.geometry.R\n x = copysign(sqrt(y ** 2 + (R - sqrt(R ** 2 - y ** 2))), y)\n x = 2 * R * asin(x / (2 * R))\n #x=y\n b = -x / sqrt(R ** 2 - x ** 2)\n theta = atan(b) # grating tangent angle\n print b, theta\n d = 0\n for n, a in enumerate(self.an):\n d += a * x ** n\n d *= cos(theta)\n return 1e-3 / d","def Perpendicular(self, line: Line, point: Point, interesting=True) -> Line:\n if point in line:\n return self.ErectPerpendicular(line, point, interesting=interesting)\n else:\n return self.DropPerpendicular(line, point, interesting=interesting)","def plane_equation(point_a, point_b, point_c):\n v1 = np.subtract(point_a, point_c)\n v2 = np.subtract(point_a, point_b)\n normal = np.cross(v1, v2)\n # print 'b4 norm', normal\n unit_normal = norm_vect(normal)\n # print 'unityyy', unit_normal\n return unit_normal","def DistPoint2Line(point,line_point1, line_point2=np.array([0,0,0])):\n return np.linalg.norm(np.cross((point-line_point2),(point-line_point1)))/np.linalg.norm(line_point1 - line_point2)","def get_orthogonal_vec2d(vec):\n ortho = np.array([-vec[1], vec[0]])\n return ortho","def proj_gnomonic_plane(lamb0, phi1, lamb, phi):\n\n cosc = np.sin(phi1)*np.sin(phi)\n cosc += np.cos(phi1)*np.cos(phi)*np.cos(lamb-lamb0)\n\n x = np.cos(phi)*np.sin(lamb-lamb0)\n x /= cosc\n\n y = np.cos(phi1)*np.sin(phi)\n y -= np.sin(phi1)*np.cos(phi)*np.cos(lamb-lamb0)\n\n y /= cosc\n\n return x, y","def PerpendicularBisector(self, line: Line, interesting=True) -> Line:\n a, b = line.point1, line.point2\n # These are the steps of the constructions\n circ1 = self.add_circle(a, b, interesting=interesting)\n circ2 = self.add_circle(b, a, interesting=interesting)\n intersections = self.find_intersections(circ1, circ2) # Only include circle intersections.\n return self.add_line(*intersections, interesting=interesting)","def ortho_line_cut(self):\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\n\n ave_grad = self.average_grad()\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\n\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\n\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\n\n\n coordleft = [x_intersect, y_intersect]\n coordright =[x_mid_right, y_mid_right]\n\n dist = self.distance(coordleft, coordright)\n\n return coordleft, coordright, dist","def proj(v, gamma, D, C):\n D_hat = 1/(np.sqrt(D))\n l = gamma/(2*D) + v\n K = C + np.sum((0.5*gamma/np.sqrt(D))**2)\n w = lambda z: ((1/np.sqrt(D))*(z - 0.5*(gamma/np.sqrt(D)))) #go back to w\n #check the least squares solution:\n z_ls = (1/D_hat)*l\n if np.linalg.norm(z_ls,2)**2 <= K:\n proj = w(z_ls)\n return proj\n #Otherwise, look for Lambda\n obj = lambda Lambda: ((np.linalg.norm(((1/(D_hat**2 + Lambda*np.ones(len(D_hat))))*D_hat)*l,2))**2 - K)\n Lambda_opt = scipy.optimize.fsolve(obj, 0)[0]\n z_opt = ((1/(D_hat**2 + Lambda_opt*np.ones(len(D_hat))))*D_hat)*l\n proj = w(z_opt)\n if np.sum(np.isnan(proj)) > 0:\n raise ZeroDivisionError('Division by zero! Some matrices are not invertible!')\n return proj","def proj_xy(self, t, next=None):\n if next is None:\n return self.normal(t).v.normalized(), 1\n v0 = self.normal(1).v.normalized()\n v1 = next.normal(0).v.normalized()\n direction = v0 + v1\n adj = (v0 * self.length) * (v1 * next.length)\n hyp = (self.length * next.length)\n c = min(1, max(-1, adj / hyp))\n size = 1 / cos(0.5 * acos(c))\n return direction.normalized(), min(3, size)","def get_perpendicular(n: np.ndarray) -> np.ndarray:\n # find smallest component\n i = np.argmin(n)\n\n # get the other two indices\n a = (i + 1) % 3\n b = (i + 2) % 3\n\n result = np.zeros(3)\n result[i] = 0.0\n result[a] = n[b]\n result[b] = -n[a]\n return result","def to_linear(self):\n return inv(quad_hybrid).dot(self.circular)","def intersect_line(self, line: Line, **kwargs) -> Point:\n if self.normal.is_perpendicular(line.direction, **kwargs):\n raise ValueError(\"The line and plane must not be parallel.\")\n\n vector_plane_line = Vector.from_points(self.point, line.point)\n\n num = -self.normal.dot(vector_plane_line)\n denom = self.normal.dot(line.direction)\n\n # Vector along the line to the intersection point.\n vector_line_scaled = num / denom * line.direction\n\n return line.point + vector_line_scaled","def line_point_shortest_dist(r: np.ndarray, v: np.ndarray, p: np.ndarray) -> Tuple[float, float]:\n\n t = np.dot(v, p - r) / np.dot(v, v)\n d = np.linalg.norm((r + v * t) - p)\n return d, t","def reproject(point):\n wgs84 = pyproj.Proj('+init=epsg:4326')\n native = pyproj.Proj(DEM_PROJECTION)\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\n return geom.Point(x, y)","def GetParametricCoords(self):\n ...","def function_3d(point):\n return point[0]**2 + point[1]**2 + point[2]**2 - 1","def get_projections(points, vec_ucm):\n # Get the vector orthogonal to the UCM.\n vec_ortho = get_orthogonal_vec2d(vec_ucm)\n # Build a transformation matrix with vec_ucm and vec_ortho as new basis vectors.\n A = np.vstack((vec_ucm, vec_ortho)).T # A is not an orthogonal projection matrix (A=A.T), but this works.\n # Centralize the data. Analogous to calculating across trials deviation from average for each time step.\n diffs = points - points.mean()\n # For computational efficiency we shortcut the projection calculation with matrix multiplication.\n # The actual math behind it:\n # coeffs = vec_ucm.T@diff/np.sqrt(vec_ucm.T@vec_ucm), vec_ortho.T@diff/np.sqrt(vec_ortho.T@vec_ortho)\n # Biased variance (normalized by (n-1)) of projection onto UCM vector:\n # var_ucm = vec_ucm.T@np.cov(diffs, bias=True, rowvar=False)@vec_ucm/(vec_ucm.T@vec_ucm) # Rayleigh fraction.\n coeffs = diffs@A\n coeffs.columns = ['parallel', 'orthogonal']\n return coeffs","def calculate_stereographic_projection(p):\n # P' = P * (2r / r + z)\n mu = 1 / (1 + p[2])\n x = p[0] * mu\n y = p[1] * mu\n return x, y","def proj3d(v):\n v = normalize(v)\n x, y, z, w = v\n return np.array([x, y, z]) / (1 + 1e-8 - w) # avoid divide by zero","def orthoXY(a):\n\n return [ a[1], -a[0], 0, 1.0 ]","def dist_to_line(self, line, pt):\n return abs(line[0]*pt.x + line[1]*pt.y + line[2])/math.sqrt(line[0]**2 + line[1]**2)","def project_point(self, point: array_like) -> Point:\n # Vector from the point in space to the point on the plane.\n vector_to_plane = Vector.from_points(point, self.point)\n\n # Perpendicular vector from the point in space to the plane.\n vector_projected = self.normal.project_vector(vector_to_plane)\n\n return Point(point) + vector_projected","def horizontal_projection(self, tangent_vec, base_point):\n caller_name = sys._getframe().f_back.f_code.co_name\n if not caller_name == \"vertical_projection\":\n try:\n return tangent_vec - self.vertical_projection(tangent_vec, base_point)\n except NotImplementedError:\n pass\n\n return self.horizontal_lift(\n self.tangent_riemannian_submersion(tangent_vec, base_point),\n fiber_point=base_point,\n )","def parabolic_2D_function(\n x_y: Tuple,\n a_x2: float,\n a_x1: float,\n a_x0: float,\n a_y2: float,\n a_y1: float,\n a_y0: float,\n):\n\n x, y = x_y\n\n I_v = (a_x2 * x**2 + a_x1 * x + a_x0) / 2\n I_v += (a_y2 * y**2 + a_y1 * y + a_y0) / 2\n\n return I_v","def projection(self, point):\n projected_point = self._iterate_over_factors(\"projection\", {\"point\": point})\n return projected_point","def project_onto_plane(self,z):\n U=self.U\n Q=self.Q_p\n #print(((z-Q[-2,:,[2]])/P[-2,:,[2]]).T)\n #print(P[-2])\n return ((z-Q[-2,:,[2]])/U[-2,:,[2]]).T*U[-2]+Q[-2]","def cartesian(self) -> Tuple[np.number, np.number, np.number, np.number]:\n if self.dimension > 3:\n raise ValueError(\"The plane dimension must be <= 3.\")\n\n # The normal must be 3D to extract the coefficients.\n a, b, c = self.normal.set_dimension(3)\n\n d = -self.normal.dot(self.point)\n\n return a, b, c, d","def perspective_lut(image_shape: tuple, principal_point: np.array, focal_length: float,\n model_coefficients: np.array) -> tuple:\n\n focal_length = np.abs(focal_length)\n\n # Create image coordinate mesh-grids. As the name implies, these are in the image coordinate system\n # with the origin at the top left corner\n u, v = np.meshgrid(\n np.arange(image_shape[1], dtype=np.float),\n np.arange(image_shape[0], dtype=np.float)\n )\n\n # Convert the coordinates into sensor coordinates (origin is at the principal point, and the\n # sensor is a focal length distance away from the lens optical centre)\n u -= principal_point[0]\n v -= principal_point[1]\n sensor_coords = np.vstack((u.flatten(), v.flatten(), np.ones(u.size) * focal_length))\n\n # Calculate the polynomial basis for the camera/lens model\n # rho is the Euclidean distance of the sensor position from the principal point\n rho = np.sqrt(np.square(sensor_coords[0, :]) + np.square(sensor_coords[1, :]))\n theta = np.arctan(np.divide(-sensor_coords[2,], rho))\n # calculate the polynomial basis, based on the angle\n basis = polynomial_basis(theta, model_coefficients.size)\n\n r = np.multiply(model_coefficients.reshape((model_coefficients.size, -1)), basis)\n r = np.sum(r, axis=0)\n r /= rho\n\n x_result = principal_point[0] + sensor_coords[0,] * r\n y_result = principal_point[1] + sensor_coords[1,] * r\n x_result = x_result.reshape((image_shape[0], image_shape[1]))\n y_result = y_result.reshape((image_shape[0], image_shape[1]))\n\n return x_result.astype(np.float32), y_result.astype(np.float32)","def closest_point_on_line(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n ap = subtract_vectors(point, a)\n c = vector_component(ap, ab)\n return add_vectors(a, c)","def intersect(l: Line, p: Plane) -> Point:\n if math.isclose((l.d * p.normal()), 0):\n # If the line direction is perpendicular to the plane normal,\n # the line and plane must be parallel.\n return None\n else:\n # There exists a parameter t, which makes\n # p.isInPlane(l.point(t)) == 0\n # Let's find it.\n # Initial guess\n t1 = 1\n p1 = l.point(t1)\n d1 = distancePointPlane(p1, p)\n t2 = 2\n p2 = l.point(t2)\n d2 = distancePointPlane(p2, p)\n\n # Calculate line through the two points (t,d)\n a = (d2 - d1) / (t2 - t1)\n b = d1 - a * t1\n\n # Find the t-value where d is zero\n # 0 = at+b <=> t = -b/a\n t = -b / a\n print(\"parameter: {}\".format(t))\n return l.point(t)","def _dist_point2line(self, point: ndarray,\n line: Tuple[ndarray, ndarray]) -> ndarray:\n\n assert isinstance(line, tuple)\n point1, point2 = line\n d = abs(np.cross(point2 - point1, point - point1)) / (\n norm(point2 - point1) + 1e-8)\n return d","def point_of_intersection(l, pz=distance):\r\n # Must fix the error here. Right now, any vector can have a point in the plane.\r\n # Must make it so that only vectors pointing in the planes direction has a point there\r\n # Can be done by checking whether d is positive or not.\r\n # This is to prevent vectors that point away from the detector to be counted\r\n # The definitions below assume that the detector is centred in the origin and its length is oriented along the z-axis.\r\n p0 = np.array([0,0,pz]) # Point on the plane\r\n l0 = np.array([0,0,0]) # Point on the line\r\n n = np.array([0,0,1]) # Normal vector of the plane\r\n d = np.dot(p0-l0, n)/np.dot(l, n)\r\n point = [i*d for i in l]\r\n return point","def proj(self, x: np.ndarray):\n return self.matvec(self.pinv(x))","def distance_point_line_sqrd(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n pa = subtract_vectors(a, point)\n pb = subtract_vectors(b, point)\n l = length_vector_sqrd(cross_vectors(pa, pb))\n l_ab = length_vector_sqrd(ab)\n return l / l_ab","def get_perpendicular2d(vector):\n if vector[1] == 0:\n return np.asarray([0.,1.])\n v2_0 = 1.0\n v2_1 = -(vector[0]/vector[1])\n v2 = np.asarray([v2_0, v2_1])\n return v2 / np.linalg.norm(v2)","def point_and_plane_pose(plane_point, plane_orientation, points=None, xyz=None):\n vector = plane_orientation\n vector = vector / np.linalg.norm(vector)\n a = vector[0]\n b = vector[1]\n c = vector[2]\n\n d = -a * plane_point[0] - b * plane_point[1] - c * plane_point[2]\n\n if xyz is not None:\n xyz = np.asarray(xyz)\n if points.shape[0] != 3:\n logger.error(\n \"Wrong points shape. [3, N] expected, \" + str(points.shape) + \" given.\"\n )\n elif points is not None:\n points = np.asarray(points)\n if points.shape[1] != 3:\n logger.error(\n \"Wrong points shape. [N, 3] expected, \" + str(points.shape) + \" given.\"\n )\n xyz = points.T\n else:\n logger.error(\"points or xyz must be declared\")\n\n x, y, z = xyz\n z_out = (a * x + b * y + c * z + d) / (a ** 2 + b ** 2 + c ** 2) ** 0.5\n\n return z_out","def perpendicular_bisector(point_1, point_2):\r\n A = 2 * (point_2.x - point_1.x)\r\n B = 2 * (point_2.y - point_1.y)\r\n C = (point_1.y - point_2.y) * (point_1.y + point_2.y) + \\\r\n (point_1.x - point_2.x) * (point_1.x + point_2.x)\r\n return np.matrix([[A],[B],[C]])","def generate_line(point_1, point_2):\r\n A = point_1.y - point_2.y\r\n B = point_2.x - point_1.x\r\n C = point_1.y * B + point_1.x * A\r\n return np.matrix([[A],[B],[-C]])","def claret_linear(mu, coeff):\n return 1.0 - coeff * (1.0 - mu)","def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\n # also used in Global Telescope Network website\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\n return x, y","def diag_line((lat0, lon0, alt0), (lat, lon, alt), k=5):\n\tlats = np.linspace(lat0, lat, k)\n\tlons = np.linspace(lon0, lon, k)\n\talts = np.linspace(alt0, alt, k)\n\tp = zip(lats, lons, alts)\n\treturn p","def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\n\tC = c_matrix(x1,x2,x3)\n\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\n\tCCoefficients = np.dot(inv(C),y)\n\treturn(CCoefficients)","def ortho(self):\r\n\r\n m11,m12,m13,m14,m21,m22,m23,m24,m31,m32,m33,m34,m41,m42,m43,m44 = self.mlist\r\n\r\n x = _vec3(m11, m21, m31)\r\n y = _vec3(m12, m22, m32)\r\n z = _vec3(m13, m23, m33)\r\n\r\n xl = x.length()\r\n xl*=xl\r\n y = y - ((x*y)/xl)*x\r\n z = z - ((x*z)/xl)*x\r\n\r\n yl = y.length()\r\n yl*=yl\r\n z = z - ((y*z)/yl)*y\r\n\r\n return mat4( x.x, y.x, z.x, m14,\r\n x.y, y.y, z.y, m24,\r\n x.z, y.z, z.z, m34,\r\n m41, m42, m43, m44)","def eAdd(P, Q): #adds 2 points by using the slope to find where the line intersects and returns the negation of that point\r\n R = point(0,0,P.c) #creates point object to store result\r\n if (P.x == 0 and P.y == 0) and (Q.x == 0 and Q.y == 0): #(0,0) is the identity\r\n return P #returns the identity\r\n elif P.x == 0 and P.y == 0:\r\n return Q\r\n elif Q.x == 0 and Q.y == 0:\r\n return P\r\n elif P == Q: #in case it is called when double should be\r\n R = eDouble(P)\r\n else: #this preforms the actual addition\r\n i = P.y-Q.y\r\n j = P.x-Q.x\r\n s = (i * modInv(j, P.c.p) ) % P.c.p\r\n R.x = ( ( (s**2) - P.x - Q.x) % P.c.p)\r\n R.y = ( (-P.y + s * (P.x - R.x) ) % P.c.p)\r\n return R","def ortho_poly_fit(x, degree = 1,center = False):\n n = degree + 1\n x = np.asarray(x).flatten()\n if(degree >= len(np.unique(x))):\n stop(\"'degree' must be less than number of unique points\")\n xbar = np.mean(x)\n if center:\n x = x - xbar\n X = np.fliplr(np.vander(x, n))\n q,r = np.linalg.qr(X)\n\n z = np.diag(np.diag(r))\n raw = np.dot(q, z)\n\n norm2 = np.sum(raw**2, axis=0)\n alpha = (np.sum((raw**2)*np.reshape(x,(-1,1)), axis=0)/norm2 + xbar)[:degree]\n Z = raw / np.sqrt(norm2)\n return Z, norm2, alpha","def parametrized_circle(point_a, point_b, point_c, theta):\n radius, center = shortest_line_to_point(point_a, point_b, point_c)\n # print'center, radius \\n', center, radius\n center_axis = np.subtract(point_a, point_b)\n # print 'center axis %s , radius %s, center %s' % (center_axis, radius, center)\n # center_axis dot <1,1,z> = 0 returns perp vector\n in_plane = norm_vect(np.subtract(point_c, center))\n perp_1 = np.cross(center_axis, in_plane)\n perp_2 = np.cross(center_axis, perp_1)\n # print 'perp dick', perp_1, perp_2\n # norm perpendicular vectors\n perp_1 = norm_vect(perp_1)\n perp_2 = norm_vect(perp_2)\n if -1e-6 > np.dot(perp_1, perp_2) > 1e-6 or -1e-6 > (np.dot(perp_1, center_axis)) > 1e-6 or \\\n -1e-6 > np.dot(perp_2, center_axis) > 1e-6:\n print 'not perpendicular'\n # print np.dot(perp_1, perp_2), np.dot(perp_1, center_axis), np.dot(perp_2, center_axis)\n x = center[0] + (radius * math.cos(theta) * perp_2[0]) + (radius * math.sin(theta) * perp_1[0])\n y = center[1] + (radius * math.cos(theta) * perp_2[1]) + (radius * math.sin(theta) * perp_1[1])\n z = center[2] + (radius * math.cos(theta) * perp_2[2]) + (radius * math.sin(theta) * perp_1[2])\n return [x, y, z]","def line_equation_ap(angle, (x1, y1)):\n \n # get second point on the line\n x2 = float(x1) + cos(angle)\n y2 = float(y1) + sin(angle)\n \n # return A, B and C coefficients\n return (y1 - y2, x2 - x1, x1*y2 - x2*y1)","def perpendicular(self):\n return tuple.__new__(Vec2, (-self[1], self[0]))","def straight_line(vel, init_pos, final_pos):\n\n acc = np.zeros (3)\n yaw = 1.0\n yawdot = 1.0\n\n p = (final_pos[2] - init_pos[2])/vel [2]\n #acc = (final_pos[2] - init_pos[2])/dt ** 2\n\n # constant velocity\n pos = init_pos + np.array([0, 0, p])\n FinalState = namedtuple('FinalState', 'pos vel acc yaw yawdot')\n return FinalState(pos, vel, acc, yaw, yawdot)","def orthogonal_to(vector: ModelParameters) -> ModelParameters:\n new_vector = rand_u_like(vector)\n new_vector = new_vector - new_vector.dot(vector) * vector / math.pow(vector.model_norm(2), 2)\n return new_vector","def convert_point(triangle, coefficients):\n return (triangle[0][0]*coefficients[0] + triangle[1][0]*coefficients[1] +\n triangle[2][0]*coefficients[2], triangle[0][1]*coefficients[0] +\n triangle[1][1]*coefficients[1] + triangle[2][1]*coefficients[2])","def projectPoint(self,p):\n a,b,c = self.a, self.b, self.c\n x,y = p\n return numpy.array( [ b*(x*b-y*a) - c*a, a*(y*a-x*b) - c*b ] )","def shortest_line_to_point(point_a, point_b, point_c): # where a and b are on spin axis, c is the point spinning round\n axis_vect = np.subtract(point_a, point_b)\n axis_mag = magnitude(point_a, point_b)\n unit_axis = np.divide(axis_vect, axis_mag) # unit of pp\n # pp' constants - p\n\n # pp dot u\n t = np.sum(np.dot(unit_axis, unit_axis))\n c = np.sum(np.dot(np.subtract(point_b, point_c), unit_axis))\n p = -c / t\n project_point_on_axis_add = (np.multiply(unit_axis, p))\n project_point_on_axis = project_point_on_axis_add + point_b\n distance = magnitude(point_c, project_point_on_axis)\n return distance, project_point_on_axis","def find_direction_vector(line):\n pt1, pt2 = line\n pt1 = np.array(pt1).reshape(2,)\n pt2 = np.array(pt2).reshape(2,)\n direct = pt2 - pt1\n direct_norm = normalize(direct)\n return direct_norm","def plane_equation(p1, p2, p3):\n a1 = p2[0] - p1[0]\n b1 = p2[1] - p1[1]\n c1 = p2[2] - p1[2]\n a2 = p3[0] - p1[0]\n b2 = p3[1] - p1[1]\n c2 = p3[2] - p1[2]\n a = b1 * c2 - b2 * c1\n b = a2 * c1 - a1 * c2\n c = a1 * b2 - b1 * a2\n # Points are collinear\n if (abs(a) < 1e-6) and (abs(b) < 1e-6) and (abs(c) < 1e-6):\n return None\n # All clear\n d = (- a * p1[0] - b * p1[1] - c * p1[2])\n return a, b, c, d","def render_4d(polyhedron, **kwds):\n projection_direction = None\n try: \n projection_direction = kwds.pop('projection_directior')\n except KeyError:\n for ineq in polyhedron.inequality_generator():\n center = [v() for v in ineq.incident() if v.is_vertex()]\n center = sum(center) / len(center)\n if not center.is_zero(): \n projection_direction = center\n break\n projection_3d = Projection(polyhedron).schlegel(projection_direction)\n return render_3d(projection_3d, **kwds)","def get_line_to(self, point):\n\n b = ((self.x - point.x)*point.y - (self.y - point.y)*point.x)/(self.x - point.x)\n\n a = (self.y - point.y)/(self.x - point.x)\n\n return a, b","def distance_point_line(point, line):\n a, b = line\n ab = subtract_vectors(b, a)\n pa = subtract_vectors(a, point)\n pb = subtract_vectors(b, point)\n l = length_vector(cross_vectors(pa, pb))\n l_ab = length_vector(ab)\n return l / l_ab","def orthogonalize(*vlist, orthonormal=False):\n\n if not all(isinstance(vec, Vector) for vec in vlist):\n raise TypeError('Each element must be of Type Vector')\n\n ortho_vlist = []\n for i, term in enumerate(vlist):\n for j in range(i):\n term -= ortho_vlist[j].projection(vlist[i])\n # TODO : The following line introduces a performance issue\n # and needs to be changed once a good solution for issue #10279 is\n # found.\n if simplify(term).equals(Vector.zero):\n raise ValueError(\"Vector set not linearly independent\")\n ortho_vlist.append(term)\n\n if orthonormal:\n ortho_vlist = [vec.normalize() for vec in ortho_vlist]\n\n return ortho_vlist","def orthogonal_component(self, basis: Vector) -> Vector:\n return self - self.parallel_component(basis)","def EuclidI12(self, line: Line, point: Point, interesting=True) -> Line:\n if point in line:\n raise ValueError(f'Cannot drop a perpendicular. Point {point} is on line {line}.')\n # rename point as C\n c = point\n # Pick a point on the opposite side of line from c\n d = self.pick_point_not_on_line_on_side(line, c, same_side=False)\n circle_center_c_radius_cd = self.add_circle(c, d, interesting=interesting)\n a, b = self.find_intersections_line_circle(line, circle_center_c_radius_cd)\n side = d\n f = self.ErectEquilateralTriangle(Line(a, b), side=side, interesting=interesting)\n return self.add_line(c, f)","def vertical_projection(self, tangent_vec, base_point, **kwargs):\n caller_name = sys._getframe().f_back.f_code.co_name\n if caller_name == \"horizontal_projection\":\n raise NotImplementedError\n\n return tangent_vec - self.horizontal_projection(tangent_vec, base_point)","def crossLine(self, other):\n a, b = self.point\n c, d = other.point\n m, n = self.vector\n o, p = other.vector\n if n * o == m * p: # The lines are parallels\n return None\n elif self.angle == -math.pi / 2:\n return Point(a, d)\n elif other.angle == -math.pi / 2:\n return Point(b, c)\n else:\n x = (a * n * o - b * m * o - c * m * p + d * m * o) / (n * o - m * p)\n y = (x - a) * n / m + b\n return Point(x, y)","def p2d(V,x,y):\n def s(a,N):\n \"\"\"Shortcut function to convert array x into a coluumn vector.\"\"\"\n a=np.reshape(a,(1,N**2),order='F').T\n return a\n N=V.shape[1]\n con=np.ones((x.shape[0],x.shape[1])) # constant terms\n xx,yy,xy=x*x,y*y,x*y\n xxx,yyy,xxy,xyy=xx*x,yy*y,xx*y,x*yy\n xxxx,yyyy,xxxy,xxyy,xyyy=xx*xx,yy*yy,xxx*y,xx*yy,x*yyy\n V2=s(V,N) \n lst=[yyyy,xxxy,xxyy,xyyy,xxx,yyy,xxy,xyy,xx,yy,xy,x,y,con]\n Q=s(xxxx,N)\n count = 0\n for elem in lst:\n elem=s(elem,N)\n count+=1\n Q=np.hstack((Q,elem))\n c=np.linalg.lstsq(Q,V2) \n c=c[0]\n theta=-0.5*np.arctan(c[11]/(c[10]-c[9]))\n Af=0.5*(c[9]*(1+1./np.cos(2*theta))+c[10]*(1-1./np.cos(2*theta)))\n Bf=0.5*(c[9]*(1-1./np.cos(2*theta))+c[10]*(1+1./np.cos(2*theta)))\n theta=180.*theta/np.pi\n return (Af, Bf, theta)","def polyder_vec(p, m):\n factorial = np.math.factorial\n m = np.asarray(m, dtype=int) # order of derivative\n p = np.atleast_2d(p)\n order = p.shape[1] - 1\n\n D = np.arange(order, -1, -1)\n num = np.array([factorial(i) for i in D], dtype=object)\n den = np.array([factorial(max(i - m, 0)) for i in D], dtype=object)\n D = (num // den).astype(p.dtype)\n\n p = np.roll(D * p, m, axis=1)\n idx = np.arange(p.shape[1])\n p = np.where(idx < m, 0, p)\n\n return p","def project_point_plane(point, plane):\n base, normal = plane\n normal = normalize_vector(normal)\n vector = subtract_vectors(point, base)\n snormal = scale_vector(normal, dot_vectors(vector, normal))\n return subtract_vectors(point, snormal)","def drawLine3D(x0,y0,z0,x1,y1,z1):\n dislin.strt3d(x0,y0,z0)\n dislin.conn3d(x1,y1,z1)","def toBarycentric(self, p: Vec3) -> Vec3:\n abc = triangleArea(self.a.position, self.b.position, self.c.position)\n pbc = triangleArea(p, self.b.position, self.c.position)\n apc = triangleArea(self.a.position, p, self.c.position)\n\n if abc == 0.0:\n return Vec3(0, 0, 0)\n\n x = pbc / abc\n y = apc / abc\n return Vec3(x, y, 1.0 - x - y)"],"string":"[\n \"def get_projection_of_pt_on_line(point, line_point1, line_point2):\\n projection = Point(-1, -1)\\n projection.x = point.x\\n if (line_point2.x - line_point1.x) != 0:\\n projection.y = (projection.x - line_point1.x) * (line_point2.y - line_point1.y) / \\\\\\n (line_point2.x - line_point1.x) + line_point1.y\\n else:\\n projection.y = (projection.x - line_point1.x) * (line_point2.y - line_point1.y) / 1 + line_point1.y\\n return projection\",\n \"def projectPoint(self, point):\\n vector = self.normal_vector\\n angle = vector.angle\\n line = Line(point, angle, correct=False)\\n projection = self.crossLine(line)\\n return projection\",\n \"def eDouble(P): #adding P + P by using a tangent line\\r\\n R = point(0, 0, P.c)\\r\\n i = ( (3 * P.x ** 2) + P.c.a) #the slope equation (i/j)\\r\\n j = (2 * P.y)\\r\\n s = (i * modInv(j, P.c.p) ) % P.c.p\\r\\n R.x = ( (s ** 2) - 2 * P.x) % P.c.p\\r\\n R.y = (-P.y + s * (P.x - R.x) ) % P.c.p\\r\\n return R\",\n \"def line_plane(l, p):\\n d = dot((p.o - l.o), p.n) / dot(l.d, p.n)\\n return l(d)\",\n \"def project_point_line(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n ap = subtract_vectors(point, a)\\n c = vector_component(ap, ab)\\n\\n return add_vectors(a, c)\",\n \"def proj_to_plane(norm, d, pts):\\n a = norm[0]\\n b = norm[1]\\n c = norm[2]\\n\\n p = []\\n\\n for i in range(len(pts)):\\n x_p = pts[i][0]\\n y_p = pts[i][1]\\n z_p = pts[i][2]\\n\\n if a != 0:\\n x_0 = (b * b + c * c) * x_p - a * b * y_p - a * c * z_p - a * d\\n y_0 = (b * 1.0 / a) * (x_0 - x_p) + y_p\\n z_0 = (c * 1.0 / a) * (x_0 - x_p) + z_p\\n\\n elif b != 0:\\n x_0 = x_p \\n y_0 = c * c * y_p - b * (d + c)\\n z_0 = (c * 1.0 / b) *(y_0 - y_p) + z_p\\n\\n else:\\n x_0 = x_p\\n y_0 = y_p\\n z_0 = - d * 1.0 / c\\n\\n p.append([x_0, y_0, z_0])\\n \\n return p\",\n \"def proj_to_plane(norm, d, pts):\\n a = norm[0]\\n b = norm[1]\\n c = norm[2]\\n\\n p = []\\n\\n for i in range(len(pts)):\\n x_p = pts[i][0]\\n y_p = pts[i][1]\\n z_p = pts[i][2]\\n\\n if a != 0:\\n x_0 = (b * b + c * c) * x_p - a * b * y_p - a * c * z_p - a * d\\n y_0 = (b * 1.0 / a) * (x_0 - x_p) + y_p\\n z_0 = (c * 1.0 / a) * (x_0 - x_p) + z_p\\n\\n elif b != 0:\\n x_0 = x_p \\n y_0 = c * c * y_p - b * (d + c)\\n z_0 = (c * 1.0 / b) *(y_0 - y_p) + z_p\\n\\n else:\\n x_0 = x_p\\n y_0 = y_p\\n z_0 = - d * 1.0 / c\\n\\n p.append([x_0, y_0, z_0])\\n \\n return p\",\n \"def compute_dual_line(P):\\n return Line(P.x, -P.y)\",\n \"def project_line(self, line: Line, **kwargs: float) -> Line:\\n if self.normal.is_parallel(line.vector, **kwargs):\\n raise ValueError(\\\"The line and plane must not be perpendicular.\\\")\\n\\n point_projected = self.project_point(line.point)\\n\\n if self.normal.is_perpendicular(line.vector, **kwargs):\\n return Line(point_projected, line.vector)\\n\\n vector_projected = self.project_vector(line.vector)\\n\\n return Line(point_projected, vector_projected)\",\n \"def build_coord(norm, d, pts):\\n # Compute the origin as the mean point of the points, and this point has to be on the plane\\n \\n n = len(pts) \\n x_total = 0\\n y_total = 0\\n z_total = 0\\n \\n for i in range(n):\\n x_total += pts[i][0]\\n y_total += pts[i][1]\\n z_total += pts[i][2]\\n\\n x_o = x_total * 1.0 / n\\n y_o = y_total * 1.0 / n\\n z_o = z_total * 1.0 / n\\n p_o = [x_o, y_o, z_o]\\n \\n # Choose p be the projection of a vector in the z-axis to the plane\\n # If the plane is not perpendicular to the z-axis\\n if ((norm[2] != 1) and (norm[2] != -1)): \\n # Choose a point\\n o_z = [x_o, y_o, z_o + 1]\\n \\n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\\n \\n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\\n\\n x_c = (x_p - x_o) * 1.0 / dist \\n y_c = (y_p - y_o) * 1.0 / dist\\n z_c = (z_p - z_o) * 1.0 / dist\\n # Thus we have unit vector in x direction\\n e_y = [x_c, y_c, z_c]\\n #Compute the unit vector in y direction\\n e_x = np.cross(e_y, norm).tolist()\\n else:\\n e_x = [1, 0, 0]\\n e_y = [0, 1, 0]\\n \\n return [e_x, e_y, norm] , p_o\",\n \"def build_coord(norm, d, pts):\\n # Compute the origin as the mean point of the points, and this point has to be on the plane\\n \\n n = len(pts)\\n x_total = 0\\n y_total = 0\\n z_total = 0\\n \\n for i in range(n):\\n x_total += pts[i][0]\\n y_total += pts[i][1]\\n z_total += pts[i][2]\\n\\n x_o = x_total * 1.0 / n\\n y_o = y_total * 1.0 / n\\n z_o = z_total * 1.0 / n\\n p_o = [x_o, y_o, z_o]\\n \\n # Choose p be the projection of a vector in the z-axis to the plane\\n # If the plane is not perpendicular to the z-axis\\n if ((norm[2] != 1) and (norm[2] != -1)): \\n # Choose a point\\n o_z = [x_o, y_o, z_o + 1]\\n \\n [[x_p, y_p, z_p]] = proj_to_plane(norm, d, [o_z])\\n \\n dist = np.linalg.norm([x_p - x_o, y_p - y_o, z_p - z_o])\\n\\n x_c = (x_p - x_o) * 1.0 / dist \\n y_c = (y_p - y_o) * 1.0 / dist\\n z_c = (z_p - z_o) * 1.0 / dist\\n # Thus we have unit vector in x direction\\n e_y = [x_c, y_c, z_c]\\n #Compute the unit vector in y direction\\n e_x = np.cross(e_y, norm).tolist()\\n else:\\n e_x = [1, 0, 0]\\n e_y = [0, 1, 0]\\n \\n return [e_x, e_y, norm] , p_o\",\n \"def spline_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tD = d_coefficients(x1,x2,x3,C)\\n\\tB = b_coefficients(x1,x2,x3,y1,y2,y3,C,D)\\n\\tA = a_coefficients(y1,y2)\\n\\treturn(A,B,C[:2],D)\",\n \"def distance_point_line_3d(point: Vector, start: Vector, end: Vector) -> float:\\n if start.isclose(end):\\n raise ZeroDivisionError('Not a line.')\\n v1 = point - start\\n # point projected onto line start to end:\\n v2 = (end - start).project(v1)\\n # Pythagoras:\\n return math.sqrt(v1.magnitude_square - v2.magnitude_square)\",\n \"def orthogonalDistance(W, point):\\n assert len(W) == 8\\n assert len(point) == 8\\n import numpy as np\\n import math\\n\\n ## parametrize the line (y = A + tN where A is a point on the line, t \\n ## a scalar and N a unit vector in the direction of the line)\\n normW = math.sqrt(np.dot(W, W))\\n A = (0,0,0,0,0,0,0, W[7])\\n N = [float(item)/normW for item in W]\\n\\n ## Calculate the distance using d = || (A-point) - ((A-point)*N)N||\\n AMinPoint = [a_i - p_i for a_i, p_i in zip(A, point)]\\n AMinPointDotN = np.dot(AMinPoint, N)\\n AMinPointDotNTimesN = [AMinPointDotN * ni for ni in N]\\n dVector = [LHS - RHS for LHS, RHS in zip(AMinPoint, AMinPointDotNTimesN)]\\n d = math.sqrt(np.dot(dVector, dVector))\\n \\n return d\",\n \"def perpendicularIntersection(point, linePoint1, linePoint2):\\n\\t\\tx1 = linePoint1[0]\\n\\t\\ty1 = linePoint1[1]\\n\\t\\tx2 = linePoint2[0]\\n\\t\\ty2 = linePoint2[1]\\n\\t\\tx3 = point[0]\\n\\t\\ty3 = point[1]\\n\\t\\tk = ((y2-y1) * (x3-x1) - (x2-x1) * (y3-y1)) / ((y2-y1)**2 + (x2-x1)**2)\\n\\t\\tx4 = x3 - k * (y2-y1)\\n\\t\\ty4 = y3 + k * (x2-x1)\\n\\t\\treturn (x4, y4)\",\n \"def EuclidI11(self, line: Line, point: Point, interesting=True) -> Line:\\n if point not in line:\\n raise ValueError(f'Cannot erect a perpendicular. Point {point} is not on line {line}.')\\n # rename point as c\\n c = point\\n # Pick an arbitrary point D on the line.\\n d = line.point1 if line.point1 != point else line.point2\\n # Make CE equal to CD.\\n circle_center_c_radius_cd = self.add_circle(c, d, interesting=interesting)\\n intersections = list(self.find_intersections_line_circle(line, circle_center_c_radius_cd))\\n e = intersections[0] if intersections[0] != d else intersections[1]\\n # Construct the equilateral triangle FDE on DE\\n f = self.EuclidI1(Line(d, e), self.pick_point_not_on_line(line))\\n # join CF\\n return self.add_line(c, f, interesting=interesting)\",\n \"def line(self, P, Q, R):\\n if not R or R == self.infpoint:\\n raise ValueError(\\\"R must not be zero\\\")\\n if P == Q == self.infpoint:\\n return self.basefield.one\\n if P == self.infpoint:\\n return R[0] - Q[0]\\n if Q == self.infpoint:\\n return R[0] - P[0]\\n if P[0] != Q[0]:\\n return (Q[0] - P[0]) * R[1] - (Q[1] - P[1]) * \\\\\\n R[0]- Q[0] * P[1] + P[0] * Q[1]\\n if P[1] != Q[1]:\\n return R[0] - P[0]\\n return (3 * P[0] ** 2 + 2 * self.a2 * P[0] + self.a4 - self.a1 * P[1] ) * \\\\\\n R[0] - (2 * P[1] + self.a1 * P[0] + self.a3 ) * R[1] - \\\\\\n (P[0] ** 3) + self.a4 * P[0] + 2 * self.a6 - self.a3 * P[1]\",\n \"def orthoPolyPower(x,power):\\n y = x**power\\n x_normalized = x / np.dot(x,x) ** 0.5\\n ortho = y - np.dot(x_normalized,y) * x_normalized\\n orthonormal = ortho / np.dot(ortho,ortho)**0.5\\n return orthonormal\",\n \"def A_coefficients_ellipsoid(v, DD, bDDisDelta=False):\\n #v can be given as an array with X/Y/Z cartesian dimensions being the last.\\n #\\\"\\\"\\\"\\n if bDDisDelta:\\n delta=DD\\n else:\\n delta=Ddelta_ellipsoid(dd)\\n #v=_sanitise_v(v)\\n #v2=np.square(v)\\n #v4=np.square(v2)\\n #fact2=np.multiply(0.75,np.sum(v4))-0.25\\n v2 = [ v[i]*v[i] for i in range(3) ]\\n v4 = [ v2[i]*v2[i] for i in range(3) ]\\n fact2 = 0.25*( 3.0*(v4[0]+v4[1]+v4[2])-1.0)\\n fact3 = 1.0/12.0*(delta[0]*(3*v4[0]+6*v2[1]*v2[2]-1) + delta[1]*(3*v4[1]+6*v2[0]*v2[2]-1) + delta[2]*(3*v4[2]+6*v2[0]*v2[1]-1))\\n A=np.zeros(5)\\n A[0]= 3*v2[1]*v2[2]\\n A[1]= 3*v2[0]*v2[2]\\n A[2]= 3*v2[0]*v2[1]\\n A[3]= fact2-fact3\\n A[4]= fact2+fact3\\n return A\",\n \"def lorentzian2d(p, x, y):\\n #2012-02-04 11:38 IJMC: Created\\n \\n x = array(x, dtype=float).copy()\\n y = array(y, dtype=float).copy()\\n p = array(p).copy()\\n\\n if len(p)==5:\\n p = concatenate((p, [0, 0]))\\n elif len(p)==6:\\n p = concatenate((p, [0]))\\n\\n z = ((x - p[3]) / p[1])**2 + ((y - p[4]) / p[2])**2 + p[5] * (x - p[3]) * (y - p[4])\\n \\n return p[6] + p[0]/(1. + z)\",\n \"def dist_to_line2d(line, point):\\n\\tx1,y1 = line[0]\\n\\tx2,y2 = line[1]\\n\\tx3,y3 = point\\n\\t\\n\\t# where on line the perpendicular is\\n\\tu = ( ((x3-x1)*(x2-x1) + (y3-y1)*(y2-y1))\\n\\t\\t\\t/ (math.pow(x1-x2,2) + math.pow(y1-y2,2)) )\\n\\t\\n\\t# intersection point\\n\\tx = x1 + u*(x2-x1)\\n\\ty = y1 + u*(y2-y1)\\n\\t\\n\\tdist = math.sqrt(math.pow(x-x3,2)+math.pow(y-y3,2))\\n\\t\\n\\treturn dist\",\n \"def calcul_point_plan_projection(cls,cx,cy,cz,spx,spy,axe_x,axe_y):\\n projX=gs.Vector3(spx*axe_x.x,spx*axe_x.y,spx*axe_x.z)\\n projY=gs.Vector3(spy*axe_y.x,spy*axe_y.y,spy*axe_y.z)\\n point=gs.Vector3(projX+projY)+gs.Vector3(cx,cy,cz)\\n return point\",\n \"def projection_P(P_prime):\\n sorted_prime = -np.sort(-P_prime, axis=1) # Descending order sort\\n cumsum_sorted = np.cumsum(sorted_prime, axis=1) # Compute cumulative sum of lines\\n rho_availability = sorted_prime > (cumsum_sorted - 1) / np.arange(1, P_prime.shape[\\n 1] + 1) # Compute non-zero rho candidates\\n rho = np.count_nonzero(rho_availability, axis=1) # Compute number of non-zero values in final line (rho)\\n theta = (cumsum_sorted[np.arange(len(rho)), rho - 1] - 1) / (rho) # Compute lagrange multiplier theta\\n P = (P_prime.transpose() - theta).transpose().clip(min=0) # subtract multiplier, clip negatives\\n\\n return P\",\n \"def orthogonal(v):\\n return np.array([-v[1], v[0]])\",\n \"def get_normal_dist(line, point):\\n \\n # Rotate: \\n x_rot = np.cos(line[1])*point[0] + np.sin(line[1])*point[1]\\n \\n # Normal distance: x_rot - rho:\\n return x_rot - line[0]\",\n \"def compute_projection(M):\\n P = torch.mm(M, torch.pinverse(M.T.matmul(M)).matmul(M.T))\\n P = P.double()\\n return P\",\n \"def _orthogonal_vector(vector):\\n return -1 * vector[1], vector[0]\",\n \"def get_projection_point(self, point, plane, test=False):\\n return point_on_plane_projection(point, plane, test=test)\",\n \"def ortho_poly_predict(x, alpha, norm2, degree = 1):\\n x = np.asarray(x).flatten()\\n n = degree + 1\\n Z = np.empty((len(x), n))\\n Z[:,0] = 1\\n if degree > 0:\\n Z[:, 1] = x - alpha[0]\\n if degree > 1:\\n for i in np.arange(1,degree):\\n Z[:, i+1] = (x - alpha[i]) * Z[:, i] - (norm2[i] / norm2[i-1]) * Z[:, i-1]\\n Z /= np.sqrt(norm2)\\n return Z\",\n \"def proj_linf(v, radius=1):\\n vmod = np.abs(v)\\n projmult = np.minimum(radius/vmod, 1)\\n return projmult*v\",\n \"def linePointXY(l,p,inside=True,distance=False,params=False):\\n a=l[0]\\n b=l[1]\\n # check for degenerate case of zero-length line\\n abdist = dist(a,b)\\n if abdist < epsilon:\\n #raise ValueError('zero-length line passed to linePointXY')\\n print('zero-length line passed to linePointXY')\\n return False\\n\\n if distance and params:\\n raise ValueError('incompatible distance and params parameters passed to linePointXY')\\n\\n x0=p[0]\\n y0=p[1]\\n z0=p[2]\\n x1=a[0]\\n y1=a[1]\\n z1=a[2]\\n x2=b[0]\\n y2=b[1]\\n z2=b[2]\\n\\n ## check to see if all three points lie in the same x,y plane\\n if not isXYPlanar([p,a,b]):\\n raise ValueError('non-XY points in linePointXY call')\\n return false\\n # if abs(z1-z0) > epsilon or abs(z2-z0) > epsilon:\\n # return False\\n\\n linedist = abs( ((y2-y1)*x0 - (x2-x1)*y0 + x2*y1 - y2*x1)/abdist)\\n\\n ## this is the fast case:\\n if not inside and distance:\\n return linedist\\n \\n ## find out where the intersection between the original line and a\\n ## line defined by the point and an orthogonal direction vector\\n ## is. We do this by constructing two direction vectors\\n ## orthogonal to the orgiginal line scaled by the line distance,\\n ## and adding them to the point in question. Assuming that the\\n ## line distance is not zero, only one of these constructed points\\n ## will fall on the line\\n\\n ## compute unit direction vector for original line\\n dir = sub(b,a)\\n dir = scale3(dir,1.0/mag(dir))\\n\\n ## compute two orthogonal direction vectors of length linedist\\n ordir1 = scale3(orthoXY(dir),linedist)\\n ordir2 = scale3(ordir1, -1.0)\\n \\n ## there are two possible intersection points\\n pi1 = add(p,ordir1)\\n pi2 = add(p,ordir2)\\n\\n ## compute distances\\n d1pa = dist(a,pi1)\\n d1pb = dist(pi1,b)\\n d1 = d1pa+d1pb # \\\"triangle\\\" with pi1\\n\\n d2pa = dist(a,pi2)\\n d2pb = dist(pi2,b)\\n d2 = d2pa+d2pb # \\\"triangle\\\" with pi2\\n\\n ## the shortest \\\"triangle\\\" distance will signal the point that\\n ## is actually on the line, even if that point falls outside\\n ## the a,b line interval\\n \\n if params or not inside: # if we don't care about being inside the\\n # line segment\\n if d1 <= d2:\\n if distance:\\n return d1\\n elif params:\\n return d1pb/abdist\\n else:\\n return pi1\\n else:\\n if distance:\\n return d2\\n elif params:\\n return d2pb/abdist\\n else:\\n return pi2\\n \\n \\n ## if the closest point on the line to point p lies between\\n ## the endpoints of the line, then either d1 or d2 will equal\\n ## abdist. IF neither do, then we know that the closest point lies\\n ## outside the endpoints\\n\\n if abs(d1-abdist) < epsilon:\\n if distance:\\n return linedist\\n else:\\n return pi1\\n\\n if abs(d2-abdist) < epsilon:\\n if distance:\\n return linedist\\n else:\\n return pi2\\n\\n ## closest point is outside the interval. That means that the\\n ## distance from point p to whichever endpoint is smaller is the\\n ## closest distance\\n\\n d3 = dist(a,p)\\n d4 = dist(b,p)\\n\\n if d3 < d4:\\n if distance:\\n return d3\\n else:\\n return a\\n else:\\n if distance:\\n return d4\\n else:\\n return b\",\n \"def get_line_distance(self, p):\\n\\n y = 1000 * p.y\\n R = 1000 * self.geometry.R\\n x = copysign(sqrt(y ** 2 + (R - sqrt(R ** 2 - y ** 2))), y)\\n x = 2 * R * asin(x / (2 * R))\\n #x=y\\n b = -x / sqrt(R ** 2 - x ** 2)\\n theta = atan(b) # grating tangent angle\\n print b, theta\\n d = 0\\n for n, a in enumerate(self.an):\\n d += a * x ** n\\n d *= cos(theta)\\n return 1e-3 / d\",\n \"def Perpendicular(self, line: Line, point: Point, interesting=True) -> Line:\\n if point in line:\\n return self.ErectPerpendicular(line, point, interesting=interesting)\\n else:\\n return self.DropPerpendicular(line, point, interesting=interesting)\",\n \"def plane_equation(point_a, point_b, point_c):\\n v1 = np.subtract(point_a, point_c)\\n v2 = np.subtract(point_a, point_b)\\n normal = np.cross(v1, v2)\\n # print 'b4 norm', normal\\n unit_normal = norm_vect(normal)\\n # print 'unityyy', unit_normal\\n return unit_normal\",\n \"def DistPoint2Line(point,line_point1, line_point2=np.array([0,0,0])):\\n return np.linalg.norm(np.cross((point-line_point2),(point-line_point1)))/np.linalg.norm(line_point1 - line_point2)\",\n \"def get_orthogonal_vec2d(vec):\\n ortho = np.array([-vec[1], vec[0]])\\n return ortho\",\n \"def proj_gnomonic_plane(lamb0, phi1, lamb, phi):\\n\\n cosc = np.sin(phi1)*np.sin(phi)\\n cosc += np.cos(phi1)*np.cos(phi)*np.cos(lamb-lamb0)\\n\\n x = np.cos(phi)*np.sin(lamb-lamb0)\\n x /= cosc\\n\\n y = np.cos(phi1)*np.sin(phi)\\n y -= np.sin(phi1)*np.cos(phi)*np.cos(lamb-lamb0)\\n\\n y /= cosc\\n\\n return x, y\",\n \"def PerpendicularBisector(self, line: Line, interesting=True) -> Line:\\n a, b = line.point1, line.point2\\n # These are the steps of the constructions\\n circ1 = self.add_circle(a, b, interesting=interesting)\\n circ2 = self.add_circle(b, a, interesting=interesting)\\n intersections = self.find_intersections(circ1, circ2) # Only include circle intersections.\\n return self.add_line(*intersections, interesting=interesting)\",\n \"def ortho_line_cut(self):\\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\\n\\n ave_grad = self.average_grad()\\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\\n\\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\\n\\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\\n\\n\\n coordleft = [x_intersect, y_intersect]\\n coordright =[x_mid_right, y_mid_right]\\n\\n dist = self.distance(coordleft, coordright)\\n\\n return coordleft, coordright, dist\",\n \"def proj(v, gamma, D, C):\\n D_hat = 1/(np.sqrt(D))\\n l = gamma/(2*D) + v\\n K = C + np.sum((0.5*gamma/np.sqrt(D))**2)\\n w = lambda z: ((1/np.sqrt(D))*(z - 0.5*(gamma/np.sqrt(D)))) #go back to w\\n #check the least squares solution:\\n z_ls = (1/D_hat)*l\\n if np.linalg.norm(z_ls,2)**2 <= K:\\n proj = w(z_ls)\\n return proj\\n #Otherwise, look for Lambda\\n obj = lambda Lambda: ((np.linalg.norm(((1/(D_hat**2 + Lambda*np.ones(len(D_hat))))*D_hat)*l,2))**2 - K)\\n Lambda_opt = scipy.optimize.fsolve(obj, 0)[0]\\n z_opt = ((1/(D_hat**2 + Lambda_opt*np.ones(len(D_hat))))*D_hat)*l\\n proj = w(z_opt)\\n if np.sum(np.isnan(proj)) > 0:\\n raise ZeroDivisionError('Division by zero! Some matrices are not invertible!')\\n return proj\",\n \"def proj_xy(self, t, next=None):\\n if next is None:\\n return self.normal(t).v.normalized(), 1\\n v0 = self.normal(1).v.normalized()\\n v1 = next.normal(0).v.normalized()\\n direction = v0 + v1\\n adj = (v0 * self.length) * (v1 * next.length)\\n hyp = (self.length * next.length)\\n c = min(1, max(-1, adj / hyp))\\n size = 1 / cos(0.5 * acos(c))\\n return direction.normalized(), min(3, size)\",\n \"def get_perpendicular(n: np.ndarray) -> np.ndarray:\\n # find smallest component\\n i = np.argmin(n)\\n\\n # get the other two indices\\n a = (i + 1) % 3\\n b = (i + 2) % 3\\n\\n result = np.zeros(3)\\n result[i] = 0.0\\n result[a] = n[b]\\n result[b] = -n[a]\\n return result\",\n \"def to_linear(self):\\n return inv(quad_hybrid).dot(self.circular)\",\n \"def intersect_line(self, line: Line, **kwargs) -> Point:\\n if self.normal.is_perpendicular(line.direction, **kwargs):\\n raise ValueError(\\\"The line and plane must not be parallel.\\\")\\n\\n vector_plane_line = Vector.from_points(self.point, line.point)\\n\\n num = -self.normal.dot(vector_plane_line)\\n denom = self.normal.dot(line.direction)\\n\\n # Vector along the line to the intersection point.\\n vector_line_scaled = num / denom * line.direction\\n\\n return line.point + vector_line_scaled\",\n \"def line_point_shortest_dist(r: np.ndarray, v: np.ndarray, p: np.ndarray) -> Tuple[float, float]:\\n\\n t = np.dot(v, p - r) / np.dot(v, v)\\n d = np.linalg.norm((r + v * t) - p)\\n return d, t\",\n \"def reproject(point):\\n wgs84 = pyproj.Proj('+init=epsg:4326')\\n native = pyproj.Proj(DEM_PROJECTION)\\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\\n return geom.Point(x, y)\",\n \"def GetParametricCoords(self):\\n ...\",\n \"def function_3d(point):\\n return point[0]**2 + point[1]**2 + point[2]**2 - 1\",\n \"def get_projections(points, vec_ucm):\\n # Get the vector orthogonal to the UCM.\\n vec_ortho = get_orthogonal_vec2d(vec_ucm)\\n # Build a transformation matrix with vec_ucm and vec_ortho as new basis vectors.\\n A = np.vstack((vec_ucm, vec_ortho)).T # A is not an orthogonal projection matrix (A=A.T), but this works.\\n # Centralize the data. Analogous to calculating across trials deviation from average for each time step.\\n diffs = points - points.mean()\\n # For computational efficiency we shortcut the projection calculation with matrix multiplication.\\n # The actual math behind it:\\n # coeffs = vec_ucm.T@diff/np.sqrt(vec_ucm.T@vec_ucm), vec_ortho.T@diff/np.sqrt(vec_ortho.T@vec_ortho)\\n # Biased variance (normalized by (n-1)) of projection onto UCM vector:\\n # var_ucm = vec_ucm.T@np.cov(diffs, bias=True, rowvar=False)@vec_ucm/(vec_ucm.T@vec_ucm) # Rayleigh fraction.\\n coeffs = diffs@A\\n coeffs.columns = ['parallel', 'orthogonal']\\n return coeffs\",\n \"def calculate_stereographic_projection(p):\\n # P' = P * (2r / r + z)\\n mu = 1 / (1 + p[2])\\n x = p[0] * mu\\n y = p[1] * mu\\n return x, y\",\n \"def proj3d(v):\\n v = normalize(v)\\n x, y, z, w = v\\n return np.array([x, y, z]) / (1 + 1e-8 - w) # avoid divide by zero\",\n \"def orthoXY(a):\\n\\n return [ a[1], -a[0], 0, 1.0 ]\",\n \"def dist_to_line(self, line, pt):\\n return abs(line[0]*pt.x + line[1]*pt.y + line[2])/math.sqrt(line[0]**2 + line[1]**2)\",\n \"def project_point(self, point: array_like) -> Point:\\n # Vector from the point in space to the point on the plane.\\n vector_to_plane = Vector.from_points(point, self.point)\\n\\n # Perpendicular vector from the point in space to the plane.\\n vector_projected = self.normal.project_vector(vector_to_plane)\\n\\n return Point(point) + vector_projected\",\n \"def horizontal_projection(self, tangent_vec, base_point):\\n caller_name = sys._getframe().f_back.f_code.co_name\\n if not caller_name == \\\"vertical_projection\\\":\\n try:\\n return tangent_vec - self.vertical_projection(tangent_vec, base_point)\\n except NotImplementedError:\\n pass\\n\\n return self.horizontal_lift(\\n self.tangent_riemannian_submersion(tangent_vec, base_point),\\n fiber_point=base_point,\\n )\",\n \"def parabolic_2D_function(\\n x_y: Tuple,\\n a_x2: float,\\n a_x1: float,\\n a_x0: float,\\n a_y2: float,\\n a_y1: float,\\n a_y0: float,\\n):\\n\\n x, y = x_y\\n\\n I_v = (a_x2 * x**2 + a_x1 * x + a_x0) / 2\\n I_v += (a_y2 * y**2 + a_y1 * y + a_y0) / 2\\n\\n return I_v\",\n \"def projection(self, point):\\n projected_point = self._iterate_over_factors(\\\"projection\\\", {\\\"point\\\": point})\\n return projected_point\",\n \"def project_onto_plane(self,z):\\n U=self.U\\n Q=self.Q_p\\n #print(((z-Q[-2,:,[2]])/P[-2,:,[2]]).T)\\n #print(P[-2])\\n return ((z-Q[-2,:,[2]])/U[-2,:,[2]]).T*U[-2]+Q[-2]\",\n \"def cartesian(self) -> Tuple[np.number, np.number, np.number, np.number]:\\n if self.dimension > 3:\\n raise ValueError(\\\"The plane dimension must be <= 3.\\\")\\n\\n # The normal must be 3D to extract the coefficients.\\n a, b, c = self.normal.set_dimension(3)\\n\\n d = -self.normal.dot(self.point)\\n\\n return a, b, c, d\",\n \"def perspective_lut(image_shape: tuple, principal_point: np.array, focal_length: float,\\n model_coefficients: np.array) -> tuple:\\n\\n focal_length = np.abs(focal_length)\\n\\n # Create image coordinate mesh-grids. As the name implies, these are in the image coordinate system\\n # with the origin at the top left corner\\n u, v = np.meshgrid(\\n np.arange(image_shape[1], dtype=np.float),\\n np.arange(image_shape[0], dtype=np.float)\\n )\\n\\n # Convert the coordinates into sensor coordinates (origin is at the principal point, and the\\n # sensor is a focal length distance away from the lens optical centre)\\n u -= principal_point[0]\\n v -= principal_point[1]\\n sensor_coords = np.vstack((u.flatten(), v.flatten(), np.ones(u.size) * focal_length))\\n\\n # Calculate the polynomial basis for the camera/lens model\\n # rho is the Euclidean distance of the sensor position from the principal point\\n rho = np.sqrt(np.square(sensor_coords[0, :]) + np.square(sensor_coords[1, :]))\\n theta = np.arctan(np.divide(-sensor_coords[2,], rho))\\n # calculate the polynomial basis, based on the angle\\n basis = polynomial_basis(theta, model_coefficients.size)\\n\\n r = np.multiply(model_coefficients.reshape((model_coefficients.size, -1)), basis)\\n r = np.sum(r, axis=0)\\n r /= rho\\n\\n x_result = principal_point[0] + sensor_coords[0,] * r\\n y_result = principal_point[1] + sensor_coords[1,] * r\\n x_result = x_result.reshape((image_shape[0], image_shape[1]))\\n y_result = y_result.reshape((image_shape[0], image_shape[1]))\\n\\n return x_result.astype(np.float32), y_result.astype(np.float32)\",\n \"def closest_point_on_line(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n ap = subtract_vectors(point, a)\\n c = vector_component(ap, ab)\\n return add_vectors(a, c)\",\n \"def intersect(l: Line, p: Plane) -> Point:\\n if math.isclose((l.d * p.normal()), 0):\\n # If the line direction is perpendicular to the plane normal,\\n # the line and plane must be parallel.\\n return None\\n else:\\n # There exists a parameter t, which makes\\n # p.isInPlane(l.point(t)) == 0\\n # Let's find it.\\n # Initial guess\\n t1 = 1\\n p1 = l.point(t1)\\n d1 = distancePointPlane(p1, p)\\n t2 = 2\\n p2 = l.point(t2)\\n d2 = distancePointPlane(p2, p)\\n\\n # Calculate line through the two points (t,d)\\n a = (d2 - d1) / (t2 - t1)\\n b = d1 - a * t1\\n\\n # Find the t-value where d is zero\\n # 0 = at+b <=> t = -b/a\\n t = -b / a\\n print(\\\"parameter: {}\\\".format(t))\\n return l.point(t)\",\n \"def _dist_point2line(self, point: ndarray,\\n line: Tuple[ndarray, ndarray]) -> ndarray:\\n\\n assert isinstance(line, tuple)\\n point1, point2 = line\\n d = abs(np.cross(point2 - point1, point - point1)) / (\\n norm(point2 - point1) + 1e-8)\\n return d\",\n \"def point_of_intersection(l, pz=distance):\\r\\n # Must fix the error here. Right now, any vector can have a point in the plane.\\r\\n # Must make it so that only vectors pointing in the planes direction has a point there\\r\\n # Can be done by checking whether d is positive or not.\\r\\n # This is to prevent vectors that point away from the detector to be counted\\r\\n # The definitions below assume that the detector is centred in the origin and its length is oriented along the z-axis.\\r\\n p0 = np.array([0,0,pz]) # Point on the plane\\r\\n l0 = np.array([0,0,0]) # Point on the line\\r\\n n = np.array([0,0,1]) # Normal vector of the plane\\r\\n d = np.dot(p0-l0, n)/np.dot(l, n)\\r\\n point = [i*d for i in l]\\r\\n return point\",\n \"def proj(self, x: np.ndarray):\\n return self.matvec(self.pinv(x))\",\n \"def distance_point_line_sqrd(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n pa = subtract_vectors(a, point)\\n pb = subtract_vectors(b, point)\\n l = length_vector_sqrd(cross_vectors(pa, pb))\\n l_ab = length_vector_sqrd(ab)\\n return l / l_ab\",\n \"def get_perpendicular2d(vector):\\n if vector[1] == 0:\\n return np.asarray([0.,1.])\\n v2_0 = 1.0\\n v2_1 = -(vector[0]/vector[1])\\n v2 = np.asarray([v2_0, v2_1])\\n return v2 / np.linalg.norm(v2)\",\n \"def point_and_plane_pose(plane_point, plane_orientation, points=None, xyz=None):\\n vector = plane_orientation\\n vector = vector / np.linalg.norm(vector)\\n a = vector[0]\\n b = vector[1]\\n c = vector[2]\\n\\n d = -a * plane_point[0] - b * plane_point[1] - c * plane_point[2]\\n\\n if xyz is not None:\\n xyz = np.asarray(xyz)\\n if points.shape[0] != 3:\\n logger.error(\\n \\\"Wrong points shape. [3, N] expected, \\\" + str(points.shape) + \\\" given.\\\"\\n )\\n elif points is not None:\\n points = np.asarray(points)\\n if points.shape[1] != 3:\\n logger.error(\\n \\\"Wrong points shape. [N, 3] expected, \\\" + str(points.shape) + \\\" given.\\\"\\n )\\n xyz = points.T\\n else:\\n logger.error(\\\"points or xyz must be declared\\\")\\n\\n x, y, z = xyz\\n z_out = (a * x + b * y + c * z + d) / (a ** 2 + b ** 2 + c ** 2) ** 0.5\\n\\n return z_out\",\n \"def perpendicular_bisector(point_1, point_2):\\r\\n A = 2 * (point_2.x - point_1.x)\\r\\n B = 2 * (point_2.y - point_1.y)\\r\\n C = (point_1.y - point_2.y) * (point_1.y + point_2.y) + \\\\\\r\\n (point_1.x - point_2.x) * (point_1.x + point_2.x)\\r\\n return np.matrix([[A],[B],[C]])\",\n \"def generate_line(point_1, point_2):\\r\\n A = point_1.y - point_2.y\\r\\n B = point_2.x - point_1.x\\r\\n C = point_1.y * B + point_1.x * A\\r\\n return np.matrix([[A],[B],[-C]])\",\n \"def claret_linear(mu, coeff):\\n return 1.0 - coeff * (1.0 - mu)\",\n \"def gnomonic_project_toxy(RA1, Dec1, RAcen, Deccen):\\n # also used in Global Telescope Network website\\n cosc = np.sin(Deccen) * np.sin(Dec1) + np.cos(Deccen) * np.cos(Dec1) * np.cos(RA1-RAcen)\\n x = np.cos(Dec1) * np.sin(RA1-RAcen) / cosc\\n y = (np.cos(Deccen)*np.sin(Dec1) - np.sin(Deccen)*np.cos(Dec1)*np.cos(RA1-RAcen)) / cosc\\n return x, y\",\n \"def diag_line((lat0, lon0, alt0), (lat, lon, alt), k=5):\\n\\tlats = np.linspace(lat0, lat, k)\\n\\tlons = np.linspace(lon0, lon, k)\\n\\talts = np.linspace(alt0, alt, k)\\n\\tp = zip(lats, lons, alts)\\n\\treturn p\",\n \"def c_coefficients(x1,x2,x3,y1,y2,y3,initial_slope,final_slope):\\n\\tC = c_matrix(x1,x2,x3)\\n\\ty = y_vector(x1,x2,x3,y1,y2,y3,initial_slope,final_slope)\\n\\tCCoefficients = np.dot(inv(C),y)\\n\\treturn(CCoefficients)\",\n \"def ortho(self):\\r\\n\\r\\n m11,m12,m13,m14,m21,m22,m23,m24,m31,m32,m33,m34,m41,m42,m43,m44 = self.mlist\\r\\n\\r\\n x = _vec3(m11, m21, m31)\\r\\n y = _vec3(m12, m22, m32)\\r\\n z = _vec3(m13, m23, m33)\\r\\n\\r\\n xl = x.length()\\r\\n xl*=xl\\r\\n y = y - ((x*y)/xl)*x\\r\\n z = z - ((x*z)/xl)*x\\r\\n\\r\\n yl = y.length()\\r\\n yl*=yl\\r\\n z = z - ((y*z)/yl)*y\\r\\n\\r\\n return mat4( x.x, y.x, z.x, m14,\\r\\n x.y, y.y, z.y, m24,\\r\\n x.z, y.z, z.z, m34,\\r\\n m41, m42, m43, m44)\",\n \"def eAdd(P, Q): #adds 2 points by using the slope to find where the line intersects and returns the negation of that point\\r\\n R = point(0,0,P.c) #creates point object to store result\\r\\n if (P.x == 0 and P.y == 0) and (Q.x == 0 and Q.y == 0): #(0,0) is the identity\\r\\n return P #returns the identity\\r\\n elif P.x == 0 and P.y == 0:\\r\\n return Q\\r\\n elif Q.x == 0 and Q.y == 0:\\r\\n return P\\r\\n elif P == Q: #in case it is called when double should be\\r\\n R = eDouble(P)\\r\\n else: #this preforms the actual addition\\r\\n i = P.y-Q.y\\r\\n j = P.x-Q.x\\r\\n s = (i * modInv(j, P.c.p) ) % P.c.p\\r\\n R.x = ( ( (s**2) - P.x - Q.x) % P.c.p)\\r\\n R.y = ( (-P.y + s * (P.x - R.x) ) % P.c.p)\\r\\n return R\",\n \"def ortho_poly_fit(x, degree = 1,center = False):\\n n = degree + 1\\n x = np.asarray(x).flatten()\\n if(degree >= len(np.unique(x))):\\n stop(\\\"'degree' must be less than number of unique points\\\")\\n xbar = np.mean(x)\\n if center:\\n x = x - xbar\\n X = np.fliplr(np.vander(x, n))\\n q,r = np.linalg.qr(X)\\n\\n z = np.diag(np.diag(r))\\n raw = np.dot(q, z)\\n\\n norm2 = np.sum(raw**2, axis=0)\\n alpha = (np.sum((raw**2)*np.reshape(x,(-1,1)), axis=0)/norm2 + xbar)[:degree]\\n Z = raw / np.sqrt(norm2)\\n return Z, norm2, alpha\",\n \"def parametrized_circle(point_a, point_b, point_c, theta):\\n radius, center = shortest_line_to_point(point_a, point_b, point_c)\\n # print'center, radius \\\\n', center, radius\\n center_axis = np.subtract(point_a, point_b)\\n # print 'center axis %s , radius %s, center %s' % (center_axis, radius, center)\\n # center_axis dot <1,1,z> = 0 returns perp vector\\n in_plane = norm_vect(np.subtract(point_c, center))\\n perp_1 = np.cross(center_axis, in_plane)\\n perp_2 = np.cross(center_axis, perp_1)\\n # print 'perp dick', perp_1, perp_2\\n # norm perpendicular vectors\\n perp_1 = norm_vect(perp_1)\\n perp_2 = norm_vect(perp_2)\\n if -1e-6 > np.dot(perp_1, perp_2) > 1e-6 or -1e-6 > (np.dot(perp_1, center_axis)) > 1e-6 or \\\\\\n -1e-6 > np.dot(perp_2, center_axis) > 1e-6:\\n print 'not perpendicular'\\n # print np.dot(perp_1, perp_2), np.dot(perp_1, center_axis), np.dot(perp_2, center_axis)\\n x = center[0] + (radius * math.cos(theta) * perp_2[0]) + (radius * math.sin(theta) * perp_1[0])\\n y = center[1] + (radius * math.cos(theta) * perp_2[1]) + (radius * math.sin(theta) * perp_1[1])\\n z = center[2] + (radius * math.cos(theta) * perp_2[2]) + (radius * math.sin(theta) * perp_1[2])\\n return [x, y, z]\",\n \"def line_equation_ap(angle, (x1, y1)):\\n \\n # get second point on the line\\n x2 = float(x1) + cos(angle)\\n y2 = float(y1) + sin(angle)\\n \\n # return A, B and C coefficients\\n return (y1 - y2, x2 - x1, x1*y2 - x2*y1)\",\n \"def perpendicular(self):\\n return tuple.__new__(Vec2, (-self[1], self[0]))\",\n \"def straight_line(vel, init_pos, final_pos):\\n\\n acc = np.zeros (3)\\n yaw = 1.0\\n yawdot = 1.0\\n\\n p = (final_pos[2] - init_pos[2])/vel [2]\\n #acc = (final_pos[2] - init_pos[2])/dt ** 2\\n\\n # constant velocity\\n pos = init_pos + np.array([0, 0, p])\\n FinalState = namedtuple('FinalState', 'pos vel acc yaw yawdot')\\n return FinalState(pos, vel, acc, yaw, yawdot)\",\n \"def orthogonal_to(vector: ModelParameters) -> ModelParameters:\\n new_vector = rand_u_like(vector)\\n new_vector = new_vector - new_vector.dot(vector) * vector / math.pow(vector.model_norm(2), 2)\\n return new_vector\",\n \"def convert_point(triangle, coefficients):\\n return (triangle[0][0]*coefficients[0] + triangle[1][0]*coefficients[1] +\\n triangle[2][0]*coefficients[2], triangle[0][1]*coefficients[0] +\\n triangle[1][1]*coefficients[1] + triangle[2][1]*coefficients[2])\",\n \"def projectPoint(self,p):\\n a,b,c = self.a, self.b, self.c\\n x,y = p\\n return numpy.array( [ b*(x*b-y*a) - c*a, a*(y*a-x*b) - c*b ] )\",\n \"def shortest_line_to_point(point_a, point_b, point_c): # where a and b are on spin axis, c is the point spinning round\\n axis_vect = np.subtract(point_a, point_b)\\n axis_mag = magnitude(point_a, point_b)\\n unit_axis = np.divide(axis_vect, axis_mag) # unit of pp\\n # pp' constants - p\\n\\n # pp dot u\\n t = np.sum(np.dot(unit_axis, unit_axis))\\n c = np.sum(np.dot(np.subtract(point_b, point_c), unit_axis))\\n p = -c / t\\n project_point_on_axis_add = (np.multiply(unit_axis, p))\\n project_point_on_axis = project_point_on_axis_add + point_b\\n distance = magnitude(point_c, project_point_on_axis)\\n return distance, project_point_on_axis\",\n \"def find_direction_vector(line):\\n pt1, pt2 = line\\n pt1 = np.array(pt1).reshape(2,)\\n pt2 = np.array(pt2).reshape(2,)\\n direct = pt2 - pt1\\n direct_norm = normalize(direct)\\n return direct_norm\",\n \"def plane_equation(p1, p2, p3):\\n a1 = p2[0] - p1[0]\\n b1 = p2[1] - p1[1]\\n c1 = p2[2] - p1[2]\\n a2 = p3[0] - p1[0]\\n b2 = p3[1] - p1[1]\\n c2 = p3[2] - p1[2]\\n a = b1 * c2 - b2 * c1\\n b = a2 * c1 - a1 * c2\\n c = a1 * b2 - b1 * a2\\n # Points are collinear\\n if (abs(a) < 1e-6) and (abs(b) < 1e-6) and (abs(c) < 1e-6):\\n return None\\n # All clear\\n d = (- a * p1[0] - b * p1[1] - c * p1[2])\\n return a, b, c, d\",\n \"def render_4d(polyhedron, **kwds):\\n projection_direction = None\\n try: \\n projection_direction = kwds.pop('projection_directior')\\n except KeyError:\\n for ineq in polyhedron.inequality_generator():\\n center = [v() for v in ineq.incident() if v.is_vertex()]\\n center = sum(center) / len(center)\\n if not center.is_zero(): \\n projection_direction = center\\n break\\n projection_3d = Projection(polyhedron).schlegel(projection_direction)\\n return render_3d(projection_3d, **kwds)\",\n \"def get_line_to(self, point):\\n\\n b = ((self.x - point.x)*point.y - (self.y - point.y)*point.x)/(self.x - point.x)\\n\\n a = (self.y - point.y)/(self.x - point.x)\\n\\n return a, b\",\n \"def distance_point_line(point, line):\\n a, b = line\\n ab = subtract_vectors(b, a)\\n pa = subtract_vectors(a, point)\\n pb = subtract_vectors(b, point)\\n l = length_vector(cross_vectors(pa, pb))\\n l_ab = length_vector(ab)\\n return l / l_ab\",\n \"def orthogonalize(*vlist, orthonormal=False):\\n\\n if not all(isinstance(vec, Vector) for vec in vlist):\\n raise TypeError('Each element must be of Type Vector')\\n\\n ortho_vlist = []\\n for i, term in enumerate(vlist):\\n for j in range(i):\\n term -= ortho_vlist[j].projection(vlist[i])\\n # TODO : The following line introduces a performance issue\\n # and needs to be changed once a good solution for issue #10279 is\\n # found.\\n if simplify(term).equals(Vector.zero):\\n raise ValueError(\\\"Vector set not linearly independent\\\")\\n ortho_vlist.append(term)\\n\\n if orthonormal:\\n ortho_vlist = [vec.normalize() for vec in ortho_vlist]\\n\\n return ortho_vlist\",\n \"def orthogonal_component(self, basis: Vector) -> Vector:\\n return self - self.parallel_component(basis)\",\n \"def EuclidI12(self, line: Line, point: Point, interesting=True) -> Line:\\n if point in line:\\n raise ValueError(f'Cannot drop a perpendicular. Point {point} is on line {line}.')\\n # rename point as C\\n c = point\\n # Pick a point on the opposite side of line from c\\n d = self.pick_point_not_on_line_on_side(line, c, same_side=False)\\n circle_center_c_radius_cd = self.add_circle(c, d, interesting=interesting)\\n a, b = self.find_intersections_line_circle(line, circle_center_c_radius_cd)\\n side = d\\n f = self.ErectEquilateralTriangle(Line(a, b), side=side, interesting=interesting)\\n return self.add_line(c, f)\",\n \"def vertical_projection(self, tangent_vec, base_point, **kwargs):\\n caller_name = sys._getframe().f_back.f_code.co_name\\n if caller_name == \\\"horizontal_projection\\\":\\n raise NotImplementedError\\n\\n return tangent_vec - self.horizontal_projection(tangent_vec, base_point)\",\n \"def crossLine(self, other):\\n a, b = self.point\\n c, d = other.point\\n m, n = self.vector\\n o, p = other.vector\\n if n * o == m * p: # The lines are parallels\\n return None\\n elif self.angle == -math.pi / 2:\\n return Point(a, d)\\n elif other.angle == -math.pi / 2:\\n return Point(b, c)\\n else:\\n x = (a * n * o - b * m * o - c * m * p + d * m * o) / (n * o - m * p)\\n y = (x - a) * n / m + b\\n return Point(x, y)\",\n \"def p2d(V,x,y):\\n def s(a,N):\\n \\\"\\\"\\\"Shortcut function to convert array x into a coluumn vector.\\\"\\\"\\\"\\n a=np.reshape(a,(1,N**2),order='F').T\\n return a\\n N=V.shape[1]\\n con=np.ones((x.shape[0],x.shape[1])) # constant terms\\n xx,yy,xy=x*x,y*y,x*y\\n xxx,yyy,xxy,xyy=xx*x,yy*y,xx*y,x*yy\\n xxxx,yyyy,xxxy,xxyy,xyyy=xx*xx,yy*yy,xxx*y,xx*yy,x*yyy\\n V2=s(V,N) \\n lst=[yyyy,xxxy,xxyy,xyyy,xxx,yyy,xxy,xyy,xx,yy,xy,x,y,con]\\n Q=s(xxxx,N)\\n count = 0\\n for elem in lst:\\n elem=s(elem,N)\\n count+=1\\n Q=np.hstack((Q,elem))\\n c=np.linalg.lstsq(Q,V2) \\n c=c[0]\\n theta=-0.5*np.arctan(c[11]/(c[10]-c[9]))\\n Af=0.5*(c[9]*(1+1./np.cos(2*theta))+c[10]*(1-1./np.cos(2*theta)))\\n Bf=0.5*(c[9]*(1-1./np.cos(2*theta))+c[10]*(1+1./np.cos(2*theta)))\\n theta=180.*theta/np.pi\\n return (Af, Bf, theta)\",\n \"def polyder_vec(p, m):\\n factorial = np.math.factorial\\n m = np.asarray(m, dtype=int) # order of derivative\\n p = np.atleast_2d(p)\\n order = p.shape[1] - 1\\n\\n D = np.arange(order, -1, -1)\\n num = np.array([factorial(i) for i in D], dtype=object)\\n den = np.array([factorial(max(i - m, 0)) for i in D], dtype=object)\\n D = (num // den).astype(p.dtype)\\n\\n p = np.roll(D * p, m, axis=1)\\n idx = np.arange(p.shape[1])\\n p = np.where(idx < m, 0, p)\\n\\n return p\",\n \"def project_point_plane(point, plane):\\n base, normal = plane\\n normal = normalize_vector(normal)\\n vector = subtract_vectors(point, base)\\n snormal = scale_vector(normal, dot_vectors(vector, normal))\\n return subtract_vectors(point, snormal)\",\n \"def drawLine3D(x0,y0,z0,x1,y1,z1):\\n dislin.strt3d(x0,y0,z0)\\n dislin.conn3d(x1,y1,z1)\",\n \"def toBarycentric(self, p: Vec3) -> Vec3:\\n abc = triangleArea(self.a.position, self.b.position, self.c.position)\\n pbc = triangleArea(p, self.b.position, self.c.position)\\n apc = triangleArea(self.a.position, p, self.c.position)\\n\\n if abc == 0.0:\\n return Vec3(0, 0, 0)\\n\\n x = pbc / abc\\n y = apc / abc\\n return Vec3(x, y, 1.0 - x - y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6800761","0.64928424","0.63452107","0.6339828","0.60856456","0.6082376","0.6082376","0.59750146","0.59033185","0.5879611","0.58655274","0.5820088","0.58171314","0.5814186","0.5794391","0.5717215","0.56910175","0.56728375","0.5667023","0.5649503","0.56087077","0.56007695","0.55867684","0.5574368","0.55700254","0.5569092","0.5560942","0.55336773","0.5491676","0.54599196","0.5457528","0.5453317","0.5448213","0.5448178","0.5442942","0.54377675","0.5434769","0.5427082","0.5420256","0.5420121","0.5416841","0.5394599","0.53913295","0.53831154","0.5382586","0.53813213","0.5380586","0.5377936","0.5376367","0.5365603","0.53650063","0.53618586","0.53606457","0.53466076","0.53441924","0.5344089","0.5327191","0.53253317","0.53233695","0.53192073","0.53017247","0.5300519","0.5296104","0.52874637","0.5284678","0.528335","0.5274168","0.52682185","0.52550787","0.52520436","0.5250624","0.52416956","0.5238124","0.52376163","0.5235404","0.523007","0.52258873","0.52202183","0.5205746","0.5204786","0.52041143","0.5201275","0.52010775","0.52010065","0.52001476","0.51780045","0.51745045","0.5170646","0.51677316","0.51614535","0.5156292","0.5141556","0.51213455","0.51154256","0.5112255","0.51037395","0.50978345","0.50936335","0.5092573","0.5072776"],"string":"[\n \"0.6800761\",\n \"0.64928424\",\n \"0.63452107\",\n \"0.6339828\",\n \"0.60856456\",\n \"0.6082376\",\n \"0.6082376\",\n \"0.59750146\",\n \"0.59033185\",\n \"0.5879611\",\n \"0.58655274\",\n \"0.5820088\",\n \"0.58171314\",\n \"0.5814186\",\n \"0.5794391\",\n \"0.5717215\",\n \"0.56910175\",\n \"0.56728375\",\n \"0.5667023\",\n \"0.5649503\",\n \"0.56087077\",\n \"0.56007695\",\n \"0.55867684\",\n \"0.5574368\",\n \"0.55700254\",\n \"0.5569092\",\n \"0.5560942\",\n \"0.55336773\",\n \"0.5491676\",\n \"0.54599196\",\n \"0.5457528\",\n \"0.5453317\",\n \"0.5448213\",\n \"0.5448178\",\n \"0.5442942\",\n \"0.54377675\",\n \"0.5434769\",\n \"0.5427082\",\n \"0.5420256\",\n \"0.5420121\",\n \"0.5416841\",\n \"0.5394599\",\n \"0.53913295\",\n \"0.53831154\",\n \"0.5382586\",\n \"0.53813213\",\n \"0.5380586\",\n \"0.5377936\",\n \"0.5376367\",\n \"0.5365603\",\n \"0.53650063\",\n \"0.53618586\",\n \"0.53606457\",\n \"0.53466076\",\n \"0.53441924\",\n \"0.5344089\",\n \"0.5327191\",\n \"0.53253317\",\n \"0.53233695\",\n \"0.53192073\",\n \"0.53017247\",\n \"0.5300519\",\n \"0.5296104\",\n \"0.52874637\",\n \"0.5284678\",\n \"0.528335\",\n \"0.5274168\",\n \"0.52682185\",\n \"0.52550787\",\n \"0.52520436\",\n \"0.5250624\",\n \"0.52416956\",\n \"0.5238124\",\n \"0.52376163\",\n \"0.5235404\",\n \"0.523007\",\n \"0.52258873\",\n \"0.52202183\",\n \"0.5205746\",\n \"0.5204786\",\n \"0.52041143\",\n \"0.5201275\",\n \"0.52010775\",\n \"0.52010065\",\n \"0.52001476\",\n \"0.51780045\",\n \"0.51745045\",\n \"0.5170646\",\n \"0.51677316\",\n \"0.51614535\",\n \"0.5156292\",\n \"0.5141556\",\n \"0.51213455\",\n \"0.51154256\",\n \"0.5112255\",\n \"0.51037395\",\n \"0.50978345\",\n \"0.50936335\",\n \"0.5092573\",\n \"0.5072776\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7622919"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95095,"cells":{"query":{"kind":"string","value":"Run a sha1 of the file and return the result"},"document":{"kind":"string","value":"def calchash(filename):\n sha = hashlib.sha1()\n with open(filename, 'rb') as f:\n sha.update(f.read())\n return sha"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def GetFileSha1(file_path):\n return base64.b64encode(GetFileHashes(file_path, do_sha1=True)['sha1'])","def _calc_sha1(path):\n calc = hashlib.sha1()\n with open(path, 'r') as f:\n calc.update(f.read())\n return calc.hexdigest()","def sha1(fname):\n fh = open(fname, 'rb')\n sha1 = hashlib.sha1()\n block = fh.read(2 ** 16)\n while len(block) > 0:\n sha1.update(block)\n block = fh.read(2 ** 16)\n\n return sha1.hexdigest()","def sha1sum(filename):\n if not os.path.isfile(filename):\n return ''\n hasher = hashlib.sha1()\n with open(filename, 'rb') as hash_file:\n buf = hash_file.read(HASH_BLOCK_SIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = hash_file.read(HASH_BLOCK_SIZE)\n return hasher.hexdigest()","def sha1sum(filename):\n with open(filename, mode='rb') as f:\n d = hashlib.sha1()\n for buf in iter(functools.partial(f.read, 1024*100), b''):\n d.update(buf)\n return d.hexdigest()","def sha1HashFile(self, filename: Path):\n bufferSize = 65536\n sha1Hash = hashlib.sha1()\n\n with filename.open('rb') as f:\n while True:\n data = f.read(bufferSize)\n\n if not data:\n break\n\n sha1Hash.update(data)\n\n return str(sha1Hash.hexdigest())","def sha_hash(file_name: str):\n BLOCKSIZE = 65536\n line = '' # format one line for hash\n with open(file_name, 'rb') as afile:\n buf = afile.read(BLOCKSIZE) # read each line of doc\n while len(buf) > 0:\n line += buf.decode('utf-8')\n buf = afile.read(BLOCKSIZE)\n\n hex = \"0x\" + sha1(line.encode()) # create sha1 hash\n return int(hex, 0)","def _get_sha1(file_descriptor):\n sha1 = hashlib.sha1()\n for block in iter(partial(file_descriptor.read, BLOCK_SIZE), ''):\n sha1.update(block)\n file_descriptor.seek(0)\n return sha1.hexdigest()","def hash_file_sha1(file_path, binary=False, buffer_size=65536):\n return hash_file(file_path, hash_type=hashlib.sha1, binary=binary, buffer_size=buffer_size)","def hashfile(filename):\n BLOCKSIZE = 65536\n sha1 = hashlib.sha1()\n with open(filename, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n while len(buf) > 0:\n sha1.update(buf)\n buf = afile.read(BLOCKSIZE)\n return(sha1.hexdigest())","def hash_file ( filename ):\n sha1 = hashlib.sha1()\n with open( filename, 'rb' ) as f:\n while True:\n buf = f.read(65536) # read by 64kb buffers size\n if not buf:\n break\n sha1.update(buf)\n return sha1","def hash_file(filename):\r\n\r\n # make a hash object\r\n h = hashlib.sha1()\r\n\r\n # open file for reading in binary mode\r\n with open(filename,'rb') as file:\r\n\r\n # loop till the end of the file\r\n chunk = 0\r\n while chunk != b'':\r\n # read only 1024 bytes at a time\r\n chunk = file.read(1024)\r\n h.update(chunk)\r\n\r\n # return the hex representation of digest\r\n return h.hexdigest()","def file_sha1(file_name, ignore_format=False, max_call_times=None):\r\n _FILE_SLIM = 65536 # read stuff in 64kb chunks!\r\n call_times = 0\r\n my_sha1 = hashlib.sha1()\r\n with open(file_name, \"rb\") as ob:\r\n while True:\r\n data = ob.read(_FILE_SLIM)\r\n if not data:\r\n break\r\n if ignore_format:\r\n data = data.decode(encoding=\"utf-8\")\r\n data = data.replace(\"\\r\", '')\r\n data = data.replace(\"\\n\", '')\r\n data = data.encode(encoding=\"utf-8\")\r\n if max_call_times:\r\n call_times += 1\r\n if call_times > max_call_times:\r\n break\r\n my_sha1.update(data)\r\n return my_sha1.hexdigest()","def hash_file(filename):\n # make a hash object\n h = hashlib.sha1()\n \n # open file for reading in binary mode\n with open(filename,'rb') as file:\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n # return the hex representation of digest\n return h.hexdigest()","def hash_file(filename):\n # make a hash object\n h = hashlib.sha1()\n \n # open file for reading in binary mode\n with open(filename,'rb') as file:\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n # return the hex representation of digest\n return h.hexdigest()","def hash_file(filename):\n # make a hash object\n h = hashlib.sha1()\n # open file for reading in binary mode\n with open(filename,'rb') as file:\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n # return the hex representation of digest\n return h.hexdigest()","def computeHash(infile):\n f = open(infile, 'rb')\n buffer = f.read()\n f.close()\n return hashlib.sha1(buffer).hexdigest()","def hash_file(filename):\n\n # make a hash object\n h = hashlib.sha1()\n\n # open file for reading in binary mode\n with open(filename, 'rb') as file:\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n\n # return the hex representation of digest\n return h.hexdigest()","def get_sha1(src: str) -> str:\n if not isinstance(src, str) or src == \"\":\n raise Exception(\"Invalid src str\")\n i = io.BytesIO(bytearray(src, encoding='utf-8'))\n return get_sha1_from_stream(i)","def compute_hash(fileName):\n m = hashlib.sha1()\n try:\n fd = open(fileName,\"rb\")\n except IOError:\n print (\"Unable to open the file in readmode:\", fileName)\n return\n content = fd.readlines()\n fd.close()\n for eachLine in content:\n m.update(eachLine)\n return m.hexdigest()","def cksum(filename):\n hash, err = Popen([\"cksum\", filename], stdout=PIPE, stderr=PIPE).communicate()\n if err != '':\n raise Exception(\"Error hashing {filename}\".format(**locals()))\n return hash.split(\" \")[0]","def SHA1(self) -> _n_0_t_3[_n_0_t_9]:","def hash_file(file_name):\n BLOCKSIZE = 65536\n hasher = hashlib.sha1()\n with open(file_name, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = afile.read(BLOCKSIZE)\n return(hasher.hexdigest())","def get_content_sha1(self):","def get_hash(file_buffer):\n data = file_buffer.read()\n hasher = sha1()\n hasher.update(data)\n return hasher.hexdigest()","def get_file_sha(full_path):\n in_file = open(full_path, 'rb')\n try:\n # Bug: why doesn't this use sha_func?\n sha_value = sha1()\n while True:\n bytes = in_file.read(READ_CHUNK_LEN)\n if bytes == \"\":\n break\n sha_value.update(bytes)\n return sha_value.digest()\n finally:\n in_file.close()","def apkdownloadmirror_get_sha1_sum(soup, **_):\n return soup.find(text=re.compile(r'File APK Sha1:')).next.text.strip()","def sha1(data):\n\n d = rpki.POW.Digest(rpki.POW.SHA1_DIGEST)\n d.update(data)\n return d.digest()","def hash(path):\n\n with open(path, 'r') as file:\n return hashlib.sha1(file.read()).hexdigest()","def check_sha1(filename, sha1_hash):\n sha1 = hashlib.sha1()\n with open(filename, 'rb') as f:\n while True:\n data = f.read(1048576)\n if not data:\n break\n sha1.update(data)\n\n sha1_file = sha1.hexdigest()\n l = min(len(sha1_file), len(sha1_hash))\n return sha1.hexdigest()[0:l] == sha1_hash[0:l]","def sha1(self) -> str:\n return self.data.sha1","def hashFile(path: str) -> str:\n\tif not os.path.exists(path):\n\t\traise FileNotFoundError\n\n\thasher = hashlib.sha1()\n\tblock_sz = 8192\n\twith open(path, 'rb') as f:\n\t\tbuf = f.read(block_sz)\n\t\twhile len(buf) > 0:\n\t\t\thasher.update(buf)\n\t\t\tbuf = f.read(block_sz)\n\treturn str(hasher.hexdigest())","def get_sha1_from_stream(src: io.IOBase) -> str:\n if not isinstance(src, io.IOBase) or not src.readable():\n raise Exception(\"src is not stream or unreadable\")\n m: hashlib._hashlib.HASH = hashlib.sha1()\n return calc_hash(src, m)","def sha1sum(filename, blocksize=65536):\n hash = hashlib.sha1()\n with open(filename, \"rb\") as f:\n for block in iter(lambda: f.read(blocksize), b\"\"):\n hash.update(block)\n return hash.hexdigest()","def run(fpath, sig_key=None):\n # Check that hashlib is available\n try:\n import hashlib\n except ModuleNotFoundError:\n sys.exit('Error: hashlib not found.')\n\n if not os.path.isfile(fpath):\n sys.exit('File Error: Supplied filepath does not refer a valid file.')\n \n # Initialize hasher and iterate through blocks of file\n hasher = hashlib.sha256()\n with open(fpath, 'rb') as file:\n for chunk in iter(partial(file.read, BLOCK_SIZE), b''):\n hasher.update(chunk)\n if sig_key is not None:\n hasher.update(sig_key)\n\n res = (hasher.hexdigest(), os.path.basename(fpath))\n # print('{} {}'.format(res[0], res[1]))\n\n return hasher.hexdigest()","def _hash_file_content(self, path):\n hasher = hashlib.sha1()\n with open(path, 'rb') as file:\n buffer = file.read(self.hash_block_size)\n while len(buffer) > 0:\n hasher.update(buffer)\n buffer = file.read(self.hash_block_size)\n return hasher.hexdigest()","def hash_file(filepath):\n digest = hashlib.sha1()\n with open(filepath, 'rb') as f:\n while True:\n chunk = f.read(1024*1024)\n if not chunk:\n break\n digest.update(chunk)\n return digest.hexdigest()","def hash_file_native(file_path, tool=\"sha256sum\"):\n output = subprocess.check_output([tool, file_path], shell=False)\n return output.decode(\"utf-8\").partition(\" \")[0].strip()","def checksumFile(filename):\n return md5File(filename)","def hash_file(self, file_path, file_arcname):\n\n file_path = os.path.abspath(file_path)\n\n # If the file_arcname argument is None use the base file name as the\n # arc name\n if file_arcname is None:\n file_arcname = os.path.basename(file_path)\n\n if not os.path.exists(file_path):\n task_error(\"%s doesn't exist\" % file_path)\n if not os.access(file_path, os.R_OK):\n task_error(\"Can't read from %s\" % file_path)\n\n file_mode = os.stat(file_path)[stat.ST_MODE]\n if not stat.S_ISDIR(file_mode) and not stat.S_ISREG(file_mode):\n task_error(\"Unknown file type for %s\" % file_path)\n\n file_in = None\n try:\n # open to read binary. This is important.\n file_in = open(file_path, 'rb')\n except IOError:\n task_error(\"Couldn't read from file: %s\" % file_path)\n\n # hash file 1Mb at a time\n hashval = hashlib.sha1()\n while True:\n data = file_in.read(1024 * 1024)\n if not data:\n break\n hashval.update(data)\n\n # update file bundle status\n\n self.running_size += len(data)\n\n self.percent_complete = 100.0 * self.running_size / self.bundle_size\n\n # only update significant progress\n if self.percent_complete - self.last_percent > 1:\n self.report_percent_complete()\n self.last_percent = self.percent_complete\n\n file_hash = hashval.hexdigest()\n\n # print 'hash: ' + file_hash\n file_in.close()\n\n modified_name = os.path.join('data', file_arcname)\n (file_dir, file_name) = os.path.split(modified_name)\n\n # linuxfy the directory\n file_dir = file_dir.replace('\\\\', '/')\n\n info = {}\n info['size'] = os.path.getsize(file_path)\n mime_type = mimetypes.guess_type(file_path, strict=True)[0]\n\n info['mimetype'] = mime_type if mime_type is not None else 'application/octet-stream'\n info['name'] = file_name\n info['mtime'] = DT.datetime.utcfromtimestamp(int(os.path.getmtime(file_path))).isoformat()\n info['ctime'] = DT.datetime.utcfromtimestamp(int(os.path.getctime(file_path))).isoformat()\n info['destinationTable'] = 'Files'\n info['subdir'] = file_dir\n info['hashsum'] = file_hash\n info['hashtype'] = 'sha1'\n\n # todo make sure errors bubble up without crashing\n if file_arcname in self.file_meta:\n print file_arcname\n task_error(\n \"Different file with the same arcname is already in the bundle\")\n return\n\n return info","def sha1Function():\r\n\r\n sha1Input = input(\"Enter SHA-1 String: \") # user input for hashing\r\n \r\n sha1Result = hashlib.sha1(sha1Input.encode()) # encoding user input then sending to sha1() function\r\n \r\n print(\"Hashing Successful\")\r\n print(\"The SHA-1 Hashing Result is : \", end =\"\") \r\n print(sha1Result.hexdigest()) # printing the hashing result in hexadecimal value\r\n\r\n menu() # display the menu again\r","def _get_sha_metadata(filename):\n with open(filename) as f:\n return hashlib.sha1(f.read()).hexdigest()","def hash_file(filename):\n\n # make a hash object\n h = hashlib.sha256()\n\n # open file for reading in binary mode\n with open(filename,'rb') as file:\n\n # loop till the end of the file\n chunk = 0\n while chunk != b'':\n # read only 1024 bytes at a time\n chunk = file.read(1024)\n h.update(chunk)\n\n # return the hex representation of digest\n return h.hexdigest()","def hash_file(file_to_hash):\n print(\"Hashing \" + file_to_hash + \"...\")\n hash_algorithm = hashlib.sha256()\n file = open(file_to_hash, 'rb')\n while True:\n contents = file.read(65536)\n if not contents:\n break\n hash_algorithm.update(contents)\n hash_str = hash_algorithm.hexdigest()\n return hash_str","def compute_checksum(filename):\n cmd = 'md5sum ' + filename\n return pipe(cmd)","def checksum(file):\n\n cksm = hashlib.sha256()\n f = open(file, 'rb')\n try:\n cksm.update(f.read())\n finally:\n f.close()\n return cksm.hexdigest()","def hash_of_file(path):\n with open(path, 'rb') as archive:\n sha = sha256()\n while True:\n data = archive.read(2 ** 20)\n if not data:\n break\n sha.update(data)\n return encoded_hash(sha)","def _sha1(self):\n return hashlib.sha1(self._blob).hexdigest()","def sha1(self, s):\n\t\tself.sha1_calls += 1\n\t\treturn int(hashlib.sha1(s).hexdigest(), 16)","def checksum_from_sha1(value):\n # More constrained regex at lexer level\n CHECKSUM_RE = re.compile('SHA1:\\\\s*([\\\\S]+)', re.UNICODE)\n match = CHECKSUM_RE.match(value)\n if match:\n return checksum.Algorithm(identifier='SHA1', value=match.group(1))\n else:\n return None","def hash_file(method, path):\n f = open(path, \"rb\")\n h = method()\n while True:\n buf = f.read(BUFSIZE)\n if not buf:\n break\n h.update(buf)\n return h.hexdigest()","def sigfile(fpath):\n sigsha = hashlib.sha1()\n fbj = open(fpath, 'rb')\n try:\n sigsha.update(fbj.read()) # pylint: disable-msg=E1101\n finally:\n fbj.close()\n return sigsha.hexdigest()","def test_same_sha(self):\n self.create_archive(fields={}, files={\"foo\": \"bar\"})\n file_ = File.objects.create()\n file_.putfile(BytesIO(b\"bar\"))\n self.create_release_file(file=file_)\n\n index = read_artifact_index(self.release, None)\n assert file_.checksum == index[\"files\"][\"fake://foo\"][\"sha1\"]","def sha1(self):\n return self.tag(\"sha1\")","def hashfile(file):\n\n hasher = hashlib.sha256()\n\n with open(file, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n hasher.update(buf)\n\n return(hasher.hexdigest())","def hash_file(path, digest=None):\r\n digest = digest or hashlib.sha1()\r\n with open(path, 'rb') as fd:\r\n s = fd.read(8192)\r\n while s:\r\n digest.update(s)\r\n s = fd.read(8192)\r\n return digest.hexdigest()","def get_hash(content):\n return hashlib.sha1(content).hexdigest()","def sha256sum(filename):\n if not os.path.isfile(filename):\n return ''\n hasher = hashlib.sha256()\n with open(filename, 'rb') as hash_file:\n buf = hash_file.read(HASH_BLOCK_SIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = hash_file.read(HASH_BLOCK_SIZE)\n return hasher.hexdigest()","def sha256sum(filename):\n content = open(filename, 'rb').read()\n sha256_obj = hashlib.sha256(content)\n return sha256_obj.hexdigest()","def FNV1Hash(filename):\n \n FNV1_32_INIT = 0x811c9dc5\n FNV1_PRIME_32 = 16777619\n\n lowerName = filename.lower()\n \n _hash = FNV1_32_INIT\n uint32_max = 2 ** 32\n \n for c in lowerName:\n _hash = (_hash * FNV1_PRIME_32) % uint32_max\n _hash = _hash ^ ord(c)\n return format(_hash, 'x')","def checksum(self, filepath) -> str:\n if os.path.exists(filepath):\n hash_md5 = md5()\n with open(filepath, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n return urlsafe_b64encode(hash_md5.digest()).decode('utf-8')\n\n return \"\"","def getHashFile(file):\n try:\n fileContent = open(file, 'rb').read()\n except:\n raise IOError, \"No such file...\"\n return False\n return getHash(fileContent)","def calculate_hash(filename, raise_on_not_found = False):\n if not is_file(filename) and not raise_on_not_found:\n return \"NOTFOUND\"\n\n with open(filename, \"rb\") as file:\n sha256 = hashlib.sha256()\n buf = file.read(128)\n while len(buf) > 0:\n sha256.update(buf)\n buf = file.read(128)\n return str(binascii.hexlify(sha256.digest()), \"utf8\")","def checksum(path):\n with open(path, 'r') as f:\n return md5(f.read()).digest()","def computeHash(filename):\n fileHash = hashlib.sha256()\n with open(filename, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n fileHash.update(chunk)\n return fileHash.hexdigest()","def checksum_of(filepath):\n bfsz = 10240000 # 10 MB buffer\n sum = hashlib.sha256()\n with open(filepath) as fd:\n while True:\n buf = fd.read(bfsz)\n if not buf: break\n sum.update(buf)\n return sum.hexdigest()","def generate_sum(file_path):\n #file = open(file_path, 'rb')\n #header = file.read()\n header = open(file_path, 'rb').read()\n suma_md5 = md5(header).hexdigest()\n return suma_md5","def hashfile(self, afile):\n # encode_buffer = False\n\n buf = afile.read(self.blocksize)\n while buf:\n # Need to use string-escape for Python 2 non-unicode strings. For\n # Python 2 unicode strings and all Python 3 strings, we need to use\n # unicode-escape. The effect of them is the same.\n if isinstance(buf, str):\n buf = buf.encode('unicode-escape')\n\n self.hasher.update(buf)\n buf = afile.read(self.blocksize)\n return self.hasher.hexdigest()","def readfile(self, filename):\n \n f = file(filename,'rb');\n #print \"\\nReading %s \\n\" % f.name;\n m = md5.new();\n readBytes = 1024; # read 1024 bytes per time\n totalBytes = 0;\n while (readBytes):\n readString = f.read(readBytes);\n m.update(readString);\n readBytes = len(readString);\n totalBytes+=readBytes;\n f.close();\n \n return m.hexdigest()","def check_hash(self, fname, args):\n fobj = self._open_file(fname)\n\n rc = 0\n format_errors = 0\n hash_errors = 0\n read_errors = 0\n for idx, line in enumerate(fobj):\n # remove any newline characters\n m = self.CHECK_RE.match(line.strip())\n if not m:\n if args.warn:\n self.app.stderr.write(\n 'hasher {0}: {1}: {2}: improperly formatted {3}'\n ' checksum line\\n'.format(self.name, fname, idx + 1,\n self.name.upper()))\n format_errors += 1\n rc = 1\n continue\n hash_value, binary, check_file = m.groups()\n\n try:\n check_f = open(check_file, 'rb' if binary == '*' else 'r')\n except IOError:\n self.app.stderr.write(\n 'hasher {0}: {1}: No such file or directory\\n'.format(\n self.name, check_file))\n if not args.status:\n self.app.stdout.write(\n STATUS_MSG.format(check_file, READ_ERROR))\n read_errors += 1\n rc = 1\n continue\n\n if self._calculate_hash(check_f) == hash_value:\n if not (args.quiet or args.status):\n self.app.stdout.write(\n STATUS_MSG.format(check_file, SUCCESS))\n else:\n if not args.status:\n self.app.stdout.write(\n STATUS_MSG.format(check_file, HASH_ERROR))\n hash_errors += 1\n rc = 1\n\n if format_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} line{2} {3} improperly'\n ' formatted\\n'.format(\n self.name,\n format_errors,\n 's' if format_errors > 1 else '',\n 'are' if format_errors > 1 else 'is',\n ))\n if read_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} listed file{2}'\n ' could not be read\\n'.format(\n self.name,\n read_errors,\n 's' if read_errors > 1 else '',\n ))\n if hash_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} computed checksum{2}'\n ' did NOT match\\n'.format(\n self.name,\n hash_errors,\n 's' if hash_errors > 1 else '',\n ))\n return rc","def sha256sum(filename):\n with open(filename, 'rb') as f:\n m = hashlib.sha256()\n while True:\n data = f.read(8192)\n if not data:\n break\n m.update(data)\n return m.hexdigest()","def get_checksum(filename):\n # You could use popen here. I read about it, and subprocess is meant\n # to replace os.popen, so I used it instead.\n\n # First, run the command md5 sum with filename as input.\n # It's stored as a subprocess.CompletedProcess\n process = subprocess.run(['md5sum',filename], capture_output=True)\n \n # Use the method stdout from subprocess.CompletedProcess (seen in\n # the Python docs) to get the output. As seen in the book, md5sum will\n # output the checksum follwed by the filename. split() will put\n # those two elements into a list, and [0] will take the first element,\n # which will be the checksum.\n checksum = process.stdout.split()[0]\n return checksum","def git_sha1_commit():\n return local('git rev-parse --short HEAD', capture=True)","def get_checksum(input_fname):\n with open(input_fname, \"rb\") as infile:\n file_contents = infile.read()\n\n checksum = hashlib.md5(file_contents).hexdigest()\n return checksum","def file_digest(file):\n # 'rb' file mode reads the file as bytes\n input_file = open(file, 'rb')\n data = input_file.read()\n # getting the digest\n digest = hash_comparing(data).hexdigest()\n input_file.close()\n return digest","def _sha1_hash_json(self, value):\n hash = hashlib.new(\"sha1\")\n binary_value = value.encode(\"ascii\")\n hash.update(binary_value)\n sha1_res = hash.hexdigest()\n return sha1_res","def semhash(file):\n _hash_helper(file)","def semhash(file):\n _hash_helper(file)","def calculate_md5sum_of_a_file(context, file_name, file_path):\n command = \"md5sum \" + file_path + \"/\" + file_name + \" | awk {'print $1'}\"\n return context.cme_session.send_ssh_command(command=command)","def hash_from_file(file_path):\r\n return hash_from_code(open(file_path, 'rb').read())","def ukey(self, path):\n out = self._call(\"GETFILECHECKSUM\", path=path, redirect=False)\n if \"Location\" in out.headers:\n location = self._apply_proxy(out.headers[\"Location\"])\n out2 = self.session.get(location)\n out2.raise_for_status()\n return out2.json()[\"FileChecksum\"]\n else:\n out.raise_for_status()\n return out.json()[\"FileChecksum\"]","def hash_for_file(file_name, block_size=2 ** 20):\n hasher = SHA256.new()\n source_file = open(file_name, \"r\")\n\n while True:\n data = source_file.read(block_size)\n if not data:\n break\n hasher.update(data.encode('utf-8'))\n\n source_file.close()\n return hasher.hexdigest()","def gdsii_hash(filename, engine=None):\n with open(filename, 'rb') as fin:\n data = fin.read()\n contents = []\n start = pos = 0\n while pos < len(data):\n size, rec = struct.unpack('>HH', data[pos:pos + 4])\n if rec == 0x0502:\n start = pos + 28\n elif rec == 0x0700:\n contents.append(data[start:pos])\n pos += size\n h = hashlib.sha1() if engine is None else engine\n for x in sorted(contents):\n h.update(x)\n return h.hexdigest()","def sha512_file(file_name):\n\n hash_func = hashlib.sha256()\n\n with open(file_name, \"rb\") as fd:\n hash_func.update(fd.read())\n\n return hash_func.hexdigest()","def get_file_hash (fullpath) : \n\n # This bit was sourced from Stack Overflow via Google, specifically:\n # http://stackoverflow.com/questions/1131220/get-md5-hash-of-a-files-without-open-it-in-python\n\n md5 = hashlib.md5()\n with open(fullpath,'rb') as f: \n for chunk in iter(lambda: f.read(512*md5.block_size), ''): \n md5.update(chunk)\n # Hexdigest is the safe varchar(32) style output\n return md5.hexdigest()","def fsum(fpath):\n import hashlib\n import codecs\n with codecs.open(fpath, \"r\", \"utf-8\") as filep:\n buff = filep.read()\n cksum = hashlib.md5(buff.encode(\"utf-8\"))\n return cksum.hexdigest()","def _git_intern_file(self, file_contents, cwd, commit_hash):\n cmd = 'hash-object -t blob -w --stdin'.split(' ')\n stdin = self.api.m.raw_io.input(file_contents)\n stdout = self.api.m.raw_io.output()\n step_name = 'Hashing modified DEPS file with revision ' + commit_hash\n step_result = self.api.m.git(*cmd, cwd=cwd, stdin=stdin, stdout=stdout,\n name=step_name)\n hash_string = step_result.stdout.splitlines()[0]\n try:\n if hash_string:\n int(hash_string, 16)\n return hash_string\n except ValueError: # pragma: no cover\n reason = 'Git did not output a valid hash for the interned file.'\n self.api.m.halt(reason)\n raise self.api.m.step.StepFailure(reason)","def file_checksum(filename):\n hash_md5 = hashlib.md5()\n with tf.gfile.Open(filename, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n hash_md5.update(chunk)\n f.close()\n return hash_md5.hexdigest()","def get_file_sha256(fname):\n with open(fname, 'rb') as afile:\n return base64.b64encode(get_file_hash(afile, hashlib.sha256()))","def generate_hash(self, fname, args):\n fobj = self._open_file(fname, args.binary)\n hash_value = self._calculate_hash(fobj)\n\n line = '{0} {1}{2}\\n'.format(hash_value, '*' if args.binary else ' ',\n fname)\n\n if '//' in line:\n line = '//' + line.replace('//', '////')\n self.app.stdout.write(line)","def sha1(s: str) -> str:\n return hashlib.sha1(s.encode()).hexdigest()","def gdsii_hash(filename, engine=None):\n if hasattr(filename, \"__fspath__\"):\n filename = filename.__fspath__()\n with open(filename, \"rb\") as fin:\n data = fin.read()\n contents = []\n start = pos = 0\n while pos < len(data):\n size, rec = struct.unpack(\">HH\", data[pos : pos + 4])\n if rec == 0x0502:\n start = pos + 28\n elif rec == 0x0700:\n contents.append(data[start:pos])\n elif size == 0:\n warnings.warn(\n \"[GDSPY] Zero-length record found in {0} at position {1}. \"\n \"Skipping the remaining of the file contents.\".format(filename, pos),\n stacklevel=2,\n )\n break\n pos += size\n h = hashlib.sha1() if engine is None else engine\n for x in sorted(contents):\n h.update(x)\n return h.hexdigest()","def get_sha256_file(filename):\n BLOCKSIZE = 65536\n hasher = hashlib.sha256()\n with open(filename, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = afile.read(BLOCKSIZE)\n return hasher.hexdigest()","def hash_file(file_like_object):\n checksum = hashlib.sha1()\n for chunk in iter(lambda: file_like_object.read(32768), b''):\n encoded_chunk = (chunk.encode(encoding='utf-8')\n if isinstance(chunk, six.string_types) else chunk)\n checksum.update(encoded_chunk)\n return checksum.hexdigest()","def svn_fs_file_md5_checksum(*args):\r\n return _fs.svn_fs_file_md5_checksum(*args)","def get_checksum(self, u_file: 'UserFile') -> str:\n ...","def test_create_SHA_256_hash_of_file_matches_cosmic_build_tool(\n file_name, expected_hash\n):\n file_path = str(Path(__file__).parent.parent / \"steps/component1\" / file_name)\n hash = utils.create_SHA_256_hash_of_file(file_path)\n\n assert hash == expected_hash","def _sha1_hash_file(self, config_type):\n config = self.CLOUDWATCH_CONFIG_TYPE_TO_CONFIG_VARIABLE_REPLACE_FUNC. \\\n get(config_type)()\n value = json.dumps(config)\n sha1_res = self._sha1_hash_json(value)\n return sha1_res","def get_checksum(file_path: str) -> str:\n\n # Open the file in binary mode\n with open(file_path, \"rb\") as file:\n # Create a SHA-256 hash object\n hash_object = hashlib.sha256()\n\n # Iterate over the file in chunks\n for chunk in iter(lambda: file.read(4096), b\"\"):\n # Feed the chunk to the hash object\n hash_object.update(chunk)\n\n # Obtain the checksum in hexadecimal format\n checksum = hash_object.hexdigest()\n\n return checksum","def get_file_hash(fname, hash_length):\n hash_sha = hashlib.sha256()\n with open(fname, 'rb') as infile:\n for chunk in iter(lambda: infile.read(4096), b''):\n hash_sha.update(chunk)\n hash_sha = hash_sha.hexdigest()\n hash_sha = int(hash_sha, 16) % (2 ** (4 * hash_length))\n return hex_encode(hash_sha, hash_length)"],"string":"[\n \"def GetFileSha1(file_path):\\n return base64.b64encode(GetFileHashes(file_path, do_sha1=True)['sha1'])\",\n \"def _calc_sha1(path):\\n calc = hashlib.sha1()\\n with open(path, 'r') as f:\\n calc.update(f.read())\\n return calc.hexdigest()\",\n \"def sha1(fname):\\n fh = open(fname, 'rb')\\n sha1 = hashlib.sha1()\\n block = fh.read(2 ** 16)\\n while len(block) > 0:\\n sha1.update(block)\\n block = fh.read(2 ** 16)\\n\\n return sha1.hexdigest()\",\n \"def sha1sum(filename):\\n if not os.path.isfile(filename):\\n return ''\\n hasher = hashlib.sha1()\\n with open(filename, 'rb') as hash_file:\\n buf = hash_file.read(HASH_BLOCK_SIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = hash_file.read(HASH_BLOCK_SIZE)\\n return hasher.hexdigest()\",\n \"def sha1sum(filename):\\n with open(filename, mode='rb') as f:\\n d = hashlib.sha1()\\n for buf in iter(functools.partial(f.read, 1024*100), b''):\\n d.update(buf)\\n return d.hexdigest()\",\n \"def sha1HashFile(self, filename: Path):\\n bufferSize = 65536\\n sha1Hash = hashlib.sha1()\\n\\n with filename.open('rb') as f:\\n while True:\\n data = f.read(bufferSize)\\n\\n if not data:\\n break\\n\\n sha1Hash.update(data)\\n\\n return str(sha1Hash.hexdigest())\",\n \"def sha_hash(file_name: str):\\n BLOCKSIZE = 65536\\n line = '' # format one line for hash\\n with open(file_name, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE) # read each line of doc\\n while len(buf) > 0:\\n line += buf.decode('utf-8')\\n buf = afile.read(BLOCKSIZE)\\n\\n hex = \\\"0x\\\" + sha1(line.encode()) # create sha1 hash\\n return int(hex, 0)\",\n \"def _get_sha1(file_descriptor):\\n sha1 = hashlib.sha1()\\n for block in iter(partial(file_descriptor.read, BLOCK_SIZE), ''):\\n sha1.update(block)\\n file_descriptor.seek(0)\\n return sha1.hexdigest()\",\n \"def hash_file_sha1(file_path, binary=False, buffer_size=65536):\\n return hash_file(file_path, hash_type=hashlib.sha1, binary=binary, buffer_size=buffer_size)\",\n \"def hashfile(filename):\\n BLOCKSIZE = 65536\\n sha1 = hashlib.sha1()\\n with open(filename, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n while len(buf) > 0:\\n sha1.update(buf)\\n buf = afile.read(BLOCKSIZE)\\n return(sha1.hexdigest())\",\n \"def hash_file ( filename ):\\n sha1 = hashlib.sha1()\\n with open( filename, 'rb' ) as f:\\n while True:\\n buf = f.read(65536) # read by 64kb buffers size\\n if not buf:\\n break\\n sha1.update(buf)\\n return sha1\",\n \"def hash_file(filename):\\r\\n\\r\\n # make a hash object\\r\\n h = hashlib.sha1()\\r\\n\\r\\n # open file for reading in binary mode\\r\\n with open(filename,'rb') as file:\\r\\n\\r\\n # loop till the end of the file\\r\\n chunk = 0\\r\\n while chunk != b'':\\r\\n # read only 1024 bytes at a time\\r\\n chunk = file.read(1024)\\r\\n h.update(chunk)\\r\\n\\r\\n # return the hex representation of digest\\r\\n return h.hexdigest()\",\n \"def file_sha1(file_name, ignore_format=False, max_call_times=None):\\r\\n _FILE_SLIM = 65536 # read stuff in 64kb chunks!\\r\\n call_times = 0\\r\\n my_sha1 = hashlib.sha1()\\r\\n with open(file_name, \\\"rb\\\") as ob:\\r\\n while True:\\r\\n data = ob.read(_FILE_SLIM)\\r\\n if not data:\\r\\n break\\r\\n if ignore_format:\\r\\n data = data.decode(encoding=\\\"utf-8\\\")\\r\\n data = data.replace(\\\"\\\\r\\\", '')\\r\\n data = data.replace(\\\"\\\\n\\\", '')\\r\\n data = data.encode(encoding=\\\"utf-8\\\")\\r\\n if max_call_times:\\r\\n call_times += 1\\r\\n if call_times > max_call_times:\\r\\n break\\r\\n my_sha1.update(data)\\r\\n return my_sha1.hexdigest()\",\n \"def hash_file(filename):\\n # make a hash object\\n h = hashlib.sha1()\\n \\n # open file for reading in binary mode\\n with open(filename,'rb') as file:\\n # loop till the end of the file\\n chunk = 0\\n while chunk != b'':\\n # read only 1024 bytes at a time\\n chunk = file.read(1024)\\n h.update(chunk)\\n # return the hex representation of digest\\n return h.hexdigest()\",\n \"def hash_file(filename):\\n # make a hash object\\n h = hashlib.sha1()\\n \\n # open file for reading in binary mode\\n with open(filename,'rb') as file:\\n # loop till the end of the file\\n chunk = 0\\n while chunk != b'':\\n # read only 1024 bytes at a time\\n chunk = file.read(1024)\\n h.update(chunk)\\n # return the hex representation of digest\\n return h.hexdigest()\",\n \"def hash_file(filename):\\n # make a hash object\\n h = hashlib.sha1()\\n # open file for reading in binary mode\\n with open(filename,'rb') as file:\\n # loop till the end of the file\\n chunk = 0\\n while chunk != b'':\\n # read only 1024 bytes at a time\\n chunk = file.read(1024)\\n h.update(chunk)\\n # return the hex representation of digest\\n return h.hexdigest()\",\n \"def computeHash(infile):\\n f = open(infile, 'rb')\\n buffer = f.read()\\n f.close()\\n return hashlib.sha1(buffer).hexdigest()\",\n \"def hash_file(filename):\\n\\n # make a hash object\\n h = hashlib.sha1()\\n\\n # open file for reading in binary mode\\n with open(filename, 'rb') as file:\\n # loop till the end of the file\\n chunk = 0\\n while chunk != b'':\\n # read only 1024 bytes at a time\\n chunk = file.read(1024)\\n h.update(chunk)\\n\\n # return the hex representation of digest\\n return h.hexdigest()\",\n \"def get_sha1(src: str) -> str:\\n if not isinstance(src, str) or src == \\\"\\\":\\n raise Exception(\\\"Invalid src str\\\")\\n i = io.BytesIO(bytearray(src, encoding='utf-8'))\\n return get_sha1_from_stream(i)\",\n \"def compute_hash(fileName):\\n m = hashlib.sha1()\\n try:\\n fd = open(fileName,\\\"rb\\\")\\n except IOError:\\n print (\\\"Unable to open the file in readmode:\\\", fileName)\\n return\\n content = fd.readlines()\\n fd.close()\\n for eachLine in content:\\n m.update(eachLine)\\n return m.hexdigest()\",\n \"def cksum(filename):\\n hash, err = Popen([\\\"cksum\\\", filename], stdout=PIPE, stderr=PIPE).communicate()\\n if err != '':\\n raise Exception(\\\"Error hashing {filename}\\\".format(**locals()))\\n return hash.split(\\\" \\\")[0]\",\n \"def SHA1(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def hash_file(file_name):\\n BLOCKSIZE = 65536\\n hasher = hashlib.sha1()\\n with open(file_name, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = afile.read(BLOCKSIZE)\\n return(hasher.hexdigest())\",\n \"def get_content_sha1(self):\",\n \"def get_hash(file_buffer):\\n data = file_buffer.read()\\n hasher = sha1()\\n hasher.update(data)\\n return hasher.hexdigest()\",\n \"def get_file_sha(full_path):\\n in_file = open(full_path, 'rb')\\n try:\\n # Bug: why doesn't this use sha_func?\\n sha_value = sha1()\\n while True:\\n bytes = in_file.read(READ_CHUNK_LEN)\\n if bytes == \\\"\\\":\\n break\\n sha_value.update(bytes)\\n return sha_value.digest()\\n finally:\\n in_file.close()\",\n \"def apkdownloadmirror_get_sha1_sum(soup, **_):\\n return soup.find(text=re.compile(r'File APK Sha1:')).next.text.strip()\",\n \"def sha1(data):\\n\\n d = rpki.POW.Digest(rpki.POW.SHA1_DIGEST)\\n d.update(data)\\n return d.digest()\",\n \"def hash(path):\\n\\n with open(path, 'r') as file:\\n return hashlib.sha1(file.read()).hexdigest()\",\n \"def check_sha1(filename, sha1_hash):\\n sha1 = hashlib.sha1()\\n with open(filename, 'rb') as f:\\n while True:\\n data = f.read(1048576)\\n if not data:\\n break\\n sha1.update(data)\\n\\n sha1_file = sha1.hexdigest()\\n l = min(len(sha1_file), len(sha1_hash))\\n return sha1.hexdigest()[0:l] == sha1_hash[0:l]\",\n \"def sha1(self) -> str:\\n return self.data.sha1\",\n \"def hashFile(path: str) -> str:\\n\\tif not os.path.exists(path):\\n\\t\\traise FileNotFoundError\\n\\n\\thasher = hashlib.sha1()\\n\\tblock_sz = 8192\\n\\twith open(path, 'rb') as f:\\n\\t\\tbuf = f.read(block_sz)\\n\\t\\twhile len(buf) > 0:\\n\\t\\t\\thasher.update(buf)\\n\\t\\t\\tbuf = f.read(block_sz)\\n\\treturn str(hasher.hexdigest())\",\n \"def get_sha1_from_stream(src: io.IOBase) -> str:\\n if not isinstance(src, io.IOBase) or not src.readable():\\n raise Exception(\\\"src is not stream or unreadable\\\")\\n m: hashlib._hashlib.HASH = hashlib.sha1()\\n return calc_hash(src, m)\",\n \"def sha1sum(filename, blocksize=65536):\\n hash = hashlib.sha1()\\n with open(filename, \\\"rb\\\") as f:\\n for block in iter(lambda: f.read(blocksize), b\\\"\\\"):\\n hash.update(block)\\n return hash.hexdigest()\",\n \"def run(fpath, sig_key=None):\\n # Check that hashlib is available\\n try:\\n import hashlib\\n except ModuleNotFoundError:\\n sys.exit('Error: hashlib not found.')\\n\\n if not os.path.isfile(fpath):\\n sys.exit('File Error: Supplied filepath does not refer a valid file.')\\n \\n # Initialize hasher and iterate through blocks of file\\n hasher = hashlib.sha256()\\n with open(fpath, 'rb') as file:\\n for chunk in iter(partial(file.read, BLOCK_SIZE), b''):\\n hasher.update(chunk)\\n if sig_key is not None:\\n hasher.update(sig_key)\\n\\n res = (hasher.hexdigest(), os.path.basename(fpath))\\n # print('{} {}'.format(res[0], res[1]))\\n\\n return hasher.hexdigest()\",\n \"def _hash_file_content(self, path):\\n hasher = hashlib.sha1()\\n with open(path, 'rb') as file:\\n buffer = file.read(self.hash_block_size)\\n while len(buffer) > 0:\\n hasher.update(buffer)\\n buffer = file.read(self.hash_block_size)\\n return hasher.hexdigest()\",\n \"def hash_file(filepath):\\n digest = hashlib.sha1()\\n with open(filepath, 'rb') as f:\\n while True:\\n chunk = f.read(1024*1024)\\n if not chunk:\\n break\\n digest.update(chunk)\\n return digest.hexdigest()\",\n \"def hash_file_native(file_path, tool=\\\"sha256sum\\\"):\\n output = subprocess.check_output([tool, file_path], shell=False)\\n return output.decode(\\\"utf-8\\\").partition(\\\" \\\")[0].strip()\",\n \"def checksumFile(filename):\\n return md5File(filename)\",\n \"def hash_file(self, file_path, file_arcname):\\n\\n file_path = os.path.abspath(file_path)\\n\\n # If the file_arcname argument is None use the base file name as the\\n # arc name\\n if file_arcname is None:\\n file_arcname = os.path.basename(file_path)\\n\\n if not os.path.exists(file_path):\\n task_error(\\\"%s doesn't exist\\\" % file_path)\\n if not os.access(file_path, os.R_OK):\\n task_error(\\\"Can't read from %s\\\" % file_path)\\n\\n file_mode = os.stat(file_path)[stat.ST_MODE]\\n if not stat.S_ISDIR(file_mode) and not stat.S_ISREG(file_mode):\\n task_error(\\\"Unknown file type for %s\\\" % file_path)\\n\\n file_in = None\\n try:\\n # open to read binary. This is important.\\n file_in = open(file_path, 'rb')\\n except IOError:\\n task_error(\\\"Couldn't read from file: %s\\\" % file_path)\\n\\n # hash file 1Mb at a time\\n hashval = hashlib.sha1()\\n while True:\\n data = file_in.read(1024 * 1024)\\n if not data:\\n break\\n hashval.update(data)\\n\\n # update file bundle status\\n\\n self.running_size += len(data)\\n\\n self.percent_complete = 100.0 * self.running_size / self.bundle_size\\n\\n # only update significant progress\\n if self.percent_complete - self.last_percent > 1:\\n self.report_percent_complete()\\n self.last_percent = self.percent_complete\\n\\n file_hash = hashval.hexdigest()\\n\\n # print 'hash: ' + file_hash\\n file_in.close()\\n\\n modified_name = os.path.join('data', file_arcname)\\n (file_dir, file_name) = os.path.split(modified_name)\\n\\n # linuxfy the directory\\n file_dir = file_dir.replace('\\\\\\\\', '/')\\n\\n info = {}\\n info['size'] = os.path.getsize(file_path)\\n mime_type = mimetypes.guess_type(file_path, strict=True)[0]\\n\\n info['mimetype'] = mime_type if mime_type is not None else 'application/octet-stream'\\n info['name'] = file_name\\n info['mtime'] = DT.datetime.utcfromtimestamp(int(os.path.getmtime(file_path))).isoformat()\\n info['ctime'] = DT.datetime.utcfromtimestamp(int(os.path.getctime(file_path))).isoformat()\\n info['destinationTable'] = 'Files'\\n info['subdir'] = file_dir\\n info['hashsum'] = file_hash\\n info['hashtype'] = 'sha1'\\n\\n # todo make sure errors bubble up without crashing\\n if file_arcname in self.file_meta:\\n print file_arcname\\n task_error(\\n \\\"Different file with the same arcname is already in the bundle\\\")\\n return\\n\\n return info\",\n \"def sha1Function():\\r\\n\\r\\n sha1Input = input(\\\"Enter SHA-1 String: \\\") # user input for hashing\\r\\n \\r\\n sha1Result = hashlib.sha1(sha1Input.encode()) # encoding user input then sending to sha1() function\\r\\n \\r\\n print(\\\"Hashing Successful\\\")\\r\\n print(\\\"The SHA-1 Hashing Result is : \\\", end =\\\"\\\") \\r\\n print(sha1Result.hexdigest()) # printing the hashing result in hexadecimal value\\r\\n\\r\\n menu() # display the menu again\\r\",\n \"def _get_sha_metadata(filename):\\n with open(filename) as f:\\n return hashlib.sha1(f.read()).hexdigest()\",\n \"def hash_file(filename):\\n\\n # make a hash object\\n h = hashlib.sha256()\\n\\n # open file for reading in binary mode\\n with open(filename,'rb') as file:\\n\\n # loop till the end of the file\\n chunk = 0\\n while chunk != b'':\\n # read only 1024 bytes at a time\\n chunk = file.read(1024)\\n h.update(chunk)\\n\\n # return the hex representation of digest\\n return h.hexdigest()\",\n \"def hash_file(file_to_hash):\\n print(\\\"Hashing \\\" + file_to_hash + \\\"...\\\")\\n hash_algorithm = hashlib.sha256()\\n file = open(file_to_hash, 'rb')\\n while True:\\n contents = file.read(65536)\\n if not contents:\\n break\\n hash_algorithm.update(contents)\\n hash_str = hash_algorithm.hexdigest()\\n return hash_str\",\n \"def compute_checksum(filename):\\n cmd = 'md5sum ' + filename\\n return pipe(cmd)\",\n \"def checksum(file):\\n\\n cksm = hashlib.sha256()\\n f = open(file, 'rb')\\n try:\\n cksm.update(f.read())\\n finally:\\n f.close()\\n return cksm.hexdigest()\",\n \"def hash_of_file(path):\\n with open(path, 'rb') as archive:\\n sha = sha256()\\n while True:\\n data = archive.read(2 ** 20)\\n if not data:\\n break\\n sha.update(data)\\n return encoded_hash(sha)\",\n \"def _sha1(self):\\n return hashlib.sha1(self._blob).hexdigest()\",\n \"def sha1(self, s):\\n\\t\\tself.sha1_calls += 1\\n\\t\\treturn int(hashlib.sha1(s).hexdigest(), 16)\",\n \"def checksum_from_sha1(value):\\n # More constrained regex at lexer level\\n CHECKSUM_RE = re.compile('SHA1:\\\\\\\\s*([\\\\\\\\S]+)', re.UNICODE)\\n match = CHECKSUM_RE.match(value)\\n if match:\\n return checksum.Algorithm(identifier='SHA1', value=match.group(1))\\n else:\\n return None\",\n \"def hash_file(method, path):\\n f = open(path, \\\"rb\\\")\\n h = method()\\n while True:\\n buf = f.read(BUFSIZE)\\n if not buf:\\n break\\n h.update(buf)\\n return h.hexdigest()\",\n \"def sigfile(fpath):\\n sigsha = hashlib.sha1()\\n fbj = open(fpath, 'rb')\\n try:\\n sigsha.update(fbj.read()) # pylint: disable-msg=E1101\\n finally:\\n fbj.close()\\n return sigsha.hexdigest()\",\n \"def test_same_sha(self):\\n self.create_archive(fields={}, files={\\\"foo\\\": \\\"bar\\\"})\\n file_ = File.objects.create()\\n file_.putfile(BytesIO(b\\\"bar\\\"))\\n self.create_release_file(file=file_)\\n\\n index = read_artifact_index(self.release, None)\\n assert file_.checksum == index[\\\"files\\\"][\\\"fake://foo\\\"][\\\"sha1\\\"]\",\n \"def sha1(self):\\n return self.tag(\\\"sha1\\\")\",\n \"def hashfile(file):\\n\\n hasher = hashlib.sha256()\\n\\n with open(file, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n hasher.update(buf)\\n\\n return(hasher.hexdigest())\",\n \"def hash_file(path, digest=None):\\r\\n digest = digest or hashlib.sha1()\\r\\n with open(path, 'rb') as fd:\\r\\n s = fd.read(8192)\\r\\n while s:\\r\\n digest.update(s)\\r\\n s = fd.read(8192)\\r\\n return digest.hexdigest()\",\n \"def get_hash(content):\\n return hashlib.sha1(content).hexdigest()\",\n \"def sha256sum(filename):\\n if not os.path.isfile(filename):\\n return ''\\n hasher = hashlib.sha256()\\n with open(filename, 'rb') as hash_file:\\n buf = hash_file.read(HASH_BLOCK_SIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = hash_file.read(HASH_BLOCK_SIZE)\\n return hasher.hexdigest()\",\n \"def sha256sum(filename):\\n content = open(filename, 'rb').read()\\n sha256_obj = hashlib.sha256(content)\\n return sha256_obj.hexdigest()\",\n \"def FNV1Hash(filename):\\n \\n FNV1_32_INIT = 0x811c9dc5\\n FNV1_PRIME_32 = 16777619\\n\\n lowerName = filename.lower()\\n \\n _hash = FNV1_32_INIT\\n uint32_max = 2 ** 32\\n \\n for c in lowerName:\\n _hash = (_hash * FNV1_PRIME_32) % uint32_max\\n _hash = _hash ^ ord(c)\\n return format(_hash, 'x')\",\n \"def checksum(self, filepath) -> str:\\n if os.path.exists(filepath):\\n hash_md5 = md5()\\n with open(filepath, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n hash_md5.update(chunk)\\n return urlsafe_b64encode(hash_md5.digest()).decode('utf-8')\\n\\n return \\\"\\\"\",\n \"def getHashFile(file):\\n try:\\n fileContent = open(file, 'rb').read()\\n except:\\n raise IOError, \\\"No such file...\\\"\\n return False\\n return getHash(fileContent)\",\n \"def calculate_hash(filename, raise_on_not_found = False):\\n if not is_file(filename) and not raise_on_not_found:\\n return \\\"NOTFOUND\\\"\\n\\n with open(filename, \\\"rb\\\") as file:\\n sha256 = hashlib.sha256()\\n buf = file.read(128)\\n while len(buf) > 0:\\n sha256.update(buf)\\n buf = file.read(128)\\n return str(binascii.hexlify(sha256.digest()), \\\"utf8\\\")\",\n \"def checksum(path):\\n with open(path, 'r') as f:\\n return md5(f.read()).digest()\",\n \"def computeHash(filename):\\n fileHash = hashlib.sha256()\\n with open(filename, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n fileHash.update(chunk)\\n return fileHash.hexdigest()\",\n \"def checksum_of(filepath):\\n bfsz = 10240000 # 10 MB buffer\\n sum = hashlib.sha256()\\n with open(filepath) as fd:\\n while True:\\n buf = fd.read(bfsz)\\n if not buf: break\\n sum.update(buf)\\n return sum.hexdigest()\",\n \"def generate_sum(file_path):\\n #file = open(file_path, 'rb')\\n #header = file.read()\\n header = open(file_path, 'rb').read()\\n suma_md5 = md5(header).hexdigest()\\n return suma_md5\",\n \"def hashfile(self, afile):\\n # encode_buffer = False\\n\\n buf = afile.read(self.blocksize)\\n while buf:\\n # Need to use string-escape for Python 2 non-unicode strings. For\\n # Python 2 unicode strings and all Python 3 strings, we need to use\\n # unicode-escape. The effect of them is the same.\\n if isinstance(buf, str):\\n buf = buf.encode('unicode-escape')\\n\\n self.hasher.update(buf)\\n buf = afile.read(self.blocksize)\\n return self.hasher.hexdigest()\",\n \"def readfile(self, filename):\\n \\n f = file(filename,'rb');\\n #print \\\"\\\\nReading %s \\\\n\\\" % f.name;\\n m = md5.new();\\n readBytes = 1024; # read 1024 bytes per time\\n totalBytes = 0;\\n while (readBytes):\\n readString = f.read(readBytes);\\n m.update(readString);\\n readBytes = len(readString);\\n totalBytes+=readBytes;\\n f.close();\\n \\n return m.hexdigest()\",\n \"def check_hash(self, fname, args):\\n fobj = self._open_file(fname)\\n\\n rc = 0\\n format_errors = 0\\n hash_errors = 0\\n read_errors = 0\\n for idx, line in enumerate(fobj):\\n # remove any newline characters\\n m = self.CHECK_RE.match(line.strip())\\n if not m:\\n if args.warn:\\n self.app.stderr.write(\\n 'hasher {0}: {1}: {2}: improperly formatted {3}'\\n ' checksum line\\\\n'.format(self.name, fname, idx + 1,\\n self.name.upper()))\\n format_errors += 1\\n rc = 1\\n continue\\n hash_value, binary, check_file = m.groups()\\n\\n try:\\n check_f = open(check_file, 'rb' if binary == '*' else 'r')\\n except IOError:\\n self.app.stderr.write(\\n 'hasher {0}: {1}: No such file or directory\\\\n'.format(\\n self.name, check_file))\\n if not args.status:\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, READ_ERROR))\\n read_errors += 1\\n rc = 1\\n continue\\n\\n if self._calculate_hash(check_f) == hash_value:\\n if not (args.quiet or args.status):\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, SUCCESS))\\n else:\\n if not args.status:\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, HASH_ERROR))\\n hash_errors += 1\\n rc = 1\\n\\n if format_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} line{2} {3} improperly'\\n ' formatted\\\\n'.format(\\n self.name,\\n format_errors,\\n 's' if format_errors > 1 else '',\\n 'are' if format_errors > 1 else 'is',\\n ))\\n if read_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} listed file{2}'\\n ' could not be read\\\\n'.format(\\n self.name,\\n read_errors,\\n 's' if read_errors > 1 else '',\\n ))\\n if hash_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} computed checksum{2}'\\n ' did NOT match\\\\n'.format(\\n self.name,\\n hash_errors,\\n 's' if hash_errors > 1 else '',\\n ))\\n return rc\",\n \"def sha256sum(filename):\\n with open(filename, 'rb') as f:\\n m = hashlib.sha256()\\n while True:\\n data = f.read(8192)\\n if not data:\\n break\\n m.update(data)\\n return m.hexdigest()\",\n \"def get_checksum(filename):\\n # You could use popen here. I read about it, and subprocess is meant\\n # to replace os.popen, so I used it instead.\\n\\n # First, run the command md5 sum with filename as input.\\n # It's stored as a subprocess.CompletedProcess\\n process = subprocess.run(['md5sum',filename], capture_output=True)\\n \\n # Use the method stdout from subprocess.CompletedProcess (seen in\\n # the Python docs) to get the output. As seen in the book, md5sum will\\n # output the checksum follwed by the filename. split() will put\\n # those two elements into a list, and [0] will take the first element,\\n # which will be the checksum.\\n checksum = process.stdout.split()[0]\\n return checksum\",\n \"def git_sha1_commit():\\n return local('git rev-parse --short HEAD', capture=True)\",\n \"def get_checksum(input_fname):\\n with open(input_fname, \\\"rb\\\") as infile:\\n file_contents = infile.read()\\n\\n checksum = hashlib.md5(file_contents).hexdigest()\\n return checksum\",\n \"def file_digest(file):\\n # 'rb' file mode reads the file as bytes\\n input_file = open(file, 'rb')\\n data = input_file.read()\\n # getting the digest\\n digest = hash_comparing(data).hexdigest()\\n input_file.close()\\n return digest\",\n \"def _sha1_hash_json(self, value):\\n hash = hashlib.new(\\\"sha1\\\")\\n binary_value = value.encode(\\\"ascii\\\")\\n hash.update(binary_value)\\n sha1_res = hash.hexdigest()\\n return sha1_res\",\n \"def semhash(file):\\n _hash_helper(file)\",\n \"def semhash(file):\\n _hash_helper(file)\",\n \"def calculate_md5sum_of_a_file(context, file_name, file_path):\\n command = \\\"md5sum \\\" + file_path + \\\"/\\\" + file_name + \\\" | awk {'print $1'}\\\"\\n return context.cme_session.send_ssh_command(command=command)\",\n \"def hash_from_file(file_path):\\r\\n return hash_from_code(open(file_path, 'rb').read())\",\n \"def ukey(self, path):\\n out = self._call(\\\"GETFILECHECKSUM\\\", path=path, redirect=False)\\n if \\\"Location\\\" in out.headers:\\n location = self._apply_proxy(out.headers[\\\"Location\\\"])\\n out2 = self.session.get(location)\\n out2.raise_for_status()\\n return out2.json()[\\\"FileChecksum\\\"]\\n else:\\n out.raise_for_status()\\n return out.json()[\\\"FileChecksum\\\"]\",\n \"def hash_for_file(file_name, block_size=2 ** 20):\\n hasher = SHA256.new()\\n source_file = open(file_name, \\\"r\\\")\\n\\n while True:\\n data = source_file.read(block_size)\\n if not data:\\n break\\n hasher.update(data.encode('utf-8'))\\n\\n source_file.close()\\n return hasher.hexdigest()\",\n \"def gdsii_hash(filename, engine=None):\\n with open(filename, 'rb') as fin:\\n data = fin.read()\\n contents = []\\n start = pos = 0\\n while pos < len(data):\\n size, rec = struct.unpack('>HH', data[pos:pos + 4])\\n if rec == 0x0502:\\n start = pos + 28\\n elif rec == 0x0700:\\n contents.append(data[start:pos])\\n pos += size\\n h = hashlib.sha1() if engine is None else engine\\n for x in sorted(contents):\\n h.update(x)\\n return h.hexdigest()\",\n \"def sha512_file(file_name):\\n\\n hash_func = hashlib.sha256()\\n\\n with open(file_name, \\\"rb\\\") as fd:\\n hash_func.update(fd.read())\\n\\n return hash_func.hexdigest()\",\n \"def get_file_hash (fullpath) : \\n\\n # This bit was sourced from Stack Overflow via Google, specifically:\\n # http://stackoverflow.com/questions/1131220/get-md5-hash-of-a-files-without-open-it-in-python\\n\\n md5 = hashlib.md5()\\n with open(fullpath,'rb') as f: \\n for chunk in iter(lambda: f.read(512*md5.block_size), ''): \\n md5.update(chunk)\\n # Hexdigest is the safe varchar(32) style output\\n return md5.hexdigest()\",\n \"def fsum(fpath):\\n import hashlib\\n import codecs\\n with codecs.open(fpath, \\\"r\\\", \\\"utf-8\\\") as filep:\\n buff = filep.read()\\n cksum = hashlib.md5(buff.encode(\\\"utf-8\\\"))\\n return cksum.hexdigest()\",\n \"def _git_intern_file(self, file_contents, cwd, commit_hash):\\n cmd = 'hash-object -t blob -w --stdin'.split(' ')\\n stdin = self.api.m.raw_io.input(file_contents)\\n stdout = self.api.m.raw_io.output()\\n step_name = 'Hashing modified DEPS file with revision ' + commit_hash\\n step_result = self.api.m.git(*cmd, cwd=cwd, stdin=stdin, stdout=stdout,\\n name=step_name)\\n hash_string = step_result.stdout.splitlines()[0]\\n try:\\n if hash_string:\\n int(hash_string, 16)\\n return hash_string\\n except ValueError: # pragma: no cover\\n reason = 'Git did not output a valid hash for the interned file.'\\n self.api.m.halt(reason)\\n raise self.api.m.step.StepFailure(reason)\",\n \"def file_checksum(filename):\\n hash_md5 = hashlib.md5()\\n with tf.gfile.Open(filename, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n hash_md5.update(chunk)\\n f.close()\\n return hash_md5.hexdigest()\",\n \"def get_file_sha256(fname):\\n with open(fname, 'rb') as afile:\\n return base64.b64encode(get_file_hash(afile, hashlib.sha256()))\",\n \"def generate_hash(self, fname, args):\\n fobj = self._open_file(fname, args.binary)\\n hash_value = self._calculate_hash(fobj)\\n\\n line = '{0} {1}{2}\\\\n'.format(hash_value, '*' if args.binary else ' ',\\n fname)\\n\\n if '//' in line:\\n line = '//' + line.replace('//', '////')\\n self.app.stdout.write(line)\",\n \"def sha1(s: str) -> str:\\n return hashlib.sha1(s.encode()).hexdigest()\",\n \"def gdsii_hash(filename, engine=None):\\n if hasattr(filename, \\\"__fspath__\\\"):\\n filename = filename.__fspath__()\\n with open(filename, \\\"rb\\\") as fin:\\n data = fin.read()\\n contents = []\\n start = pos = 0\\n while pos < len(data):\\n size, rec = struct.unpack(\\\">HH\\\", data[pos : pos + 4])\\n if rec == 0x0502:\\n start = pos + 28\\n elif rec == 0x0700:\\n contents.append(data[start:pos])\\n elif size == 0:\\n warnings.warn(\\n \\\"[GDSPY] Zero-length record found in {0} at position {1}. \\\"\\n \\\"Skipping the remaining of the file contents.\\\".format(filename, pos),\\n stacklevel=2,\\n )\\n break\\n pos += size\\n h = hashlib.sha1() if engine is None else engine\\n for x in sorted(contents):\\n h.update(x)\\n return h.hexdigest()\",\n \"def get_sha256_file(filename):\\n BLOCKSIZE = 65536\\n hasher = hashlib.sha256()\\n with open(filename, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = afile.read(BLOCKSIZE)\\n return hasher.hexdigest()\",\n \"def hash_file(file_like_object):\\n checksum = hashlib.sha1()\\n for chunk in iter(lambda: file_like_object.read(32768), b''):\\n encoded_chunk = (chunk.encode(encoding='utf-8')\\n if isinstance(chunk, six.string_types) else chunk)\\n checksum.update(encoded_chunk)\\n return checksum.hexdigest()\",\n \"def svn_fs_file_md5_checksum(*args):\\r\\n return _fs.svn_fs_file_md5_checksum(*args)\",\n \"def get_checksum(self, u_file: 'UserFile') -> str:\\n ...\",\n \"def test_create_SHA_256_hash_of_file_matches_cosmic_build_tool(\\n file_name, expected_hash\\n):\\n file_path = str(Path(__file__).parent.parent / \\\"steps/component1\\\" / file_name)\\n hash = utils.create_SHA_256_hash_of_file(file_path)\\n\\n assert hash == expected_hash\",\n \"def _sha1_hash_file(self, config_type):\\n config = self.CLOUDWATCH_CONFIG_TYPE_TO_CONFIG_VARIABLE_REPLACE_FUNC. \\\\\\n get(config_type)()\\n value = json.dumps(config)\\n sha1_res = self._sha1_hash_json(value)\\n return sha1_res\",\n \"def get_checksum(file_path: str) -> str:\\n\\n # Open the file in binary mode\\n with open(file_path, \\\"rb\\\") as file:\\n # Create a SHA-256 hash object\\n hash_object = hashlib.sha256()\\n\\n # Iterate over the file in chunks\\n for chunk in iter(lambda: file.read(4096), b\\\"\\\"):\\n # Feed the chunk to the hash object\\n hash_object.update(chunk)\\n\\n # Obtain the checksum in hexadecimal format\\n checksum = hash_object.hexdigest()\\n\\n return checksum\",\n \"def get_file_hash(fname, hash_length):\\n hash_sha = hashlib.sha256()\\n with open(fname, 'rb') as infile:\\n for chunk in iter(lambda: infile.read(4096), b''):\\n hash_sha.update(chunk)\\n hash_sha = hash_sha.hexdigest()\\n hash_sha = int(hash_sha, 16) % (2 ** (4 * hash_length))\\n return hex_encode(hash_sha, hash_length)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7758882","0.7735032","0.76928216","0.75606763","0.7542691","0.73954326","0.7363118","0.71328443","0.7127811","0.71266234","0.7059906","0.705759","0.7057296","0.70450103","0.70450103","0.6978939","0.6903451","0.69028926","0.6887849","0.6887431","0.6827436","0.68080527","0.67813694","0.6766135","0.67653716","0.67512363","0.67276216","0.67060536","0.667192","0.66641206","0.6657363","0.6638315","0.6636783","0.66314554","0.6584346","0.65647113","0.65467215","0.65445054","0.6503998","0.64961153","0.64076126","0.63745487","0.6292124","0.6275252","0.62549543","0.624388","0.6240218","0.62380624","0.62374663","0.62302613","0.6220183","0.6207773","0.61413914","0.6127855","0.61183316","0.6114331","0.60881555","0.6077916","0.6068627","0.60618937","0.60452026","0.6027545","0.6024335","0.60009915","0.6000755","0.599807","0.5965822","0.5960346","0.5948931","0.5945783","0.59432286","0.5942943","0.5923207","0.5918023","0.5915761","0.5910558","0.59042025","0.59042025","0.59032536","0.59026146","0.58772755","0.5871903","0.58660096","0.5863246","0.58475506","0.5842023","0.5826899","0.5820272","0.5820259","0.5815941","0.58155","0.58110493","0.5807045","0.58049816","0.57984704","0.57964885","0.57735205","0.575968","0.57580227","0.57487476"],"string":"[\n \"0.7758882\",\n \"0.7735032\",\n \"0.76928216\",\n \"0.75606763\",\n \"0.7542691\",\n \"0.73954326\",\n \"0.7363118\",\n \"0.71328443\",\n \"0.7127811\",\n \"0.71266234\",\n \"0.7059906\",\n \"0.705759\",\n \"0.7057296\",\n \"0.70450103\",\n \"0.70450103\",\n \"0.6978939\",\n \"0.6903451\",\n \"0.69028926\",\n \"0.6887849\",\n \"0.6887431\",\n \"0.6827436\",\n \"0.68080527\",\n \"0.67813694\",\n \"0.6766135\",\n \"0.67653716\",\n \"0.67512363\",\n \"0.67276216\",\n \"0.67060536\",\n \"0.667192\",\n \"0.66641206\",\n \"0.6657363\",\n \"0.6638315\",\n \"0.6636783\",\n \"0.66314554\",\n \"0.6584346\",\n \"0.65647113\",\n \"0.65467215\",\n \"0.65445054\",\n \"0.6503998\",\n \"0.64961153\",\n \"0.64076126\",\n \"0.63745487\",\n \"0.6292124\",\n \"0.6275252\",\n \"0.62549543\",\n \"0.624388\",\n \"0.6240218\",\n \"0.62380624\",\n \"0.62374663\",\n \"0.62302613\",\n \"0.6220183\",\n \"0.6207773\",\n \"0.61413914\",\n \"0.6127855\",\n \"0.61183316\",\n \"0.6114331\",\n \"0.60881555\",\n \"0.6077916\",\n \"0.6068627\",\n \"0.60618937\",\n \"0.60452026\",\n \"0.6027545\",\n \"0.6024335\",\n \"0.60009915\",\n \"0.6000755\",\n \"0.599807\",\n \"0.5965822\",\n \"0.5960346\",\n \"0.5948931\",\n \"0.5945783\",\n \"0.59432286\",\n \"0.5942943\",\n \"0.5923207\",\n \"0.5918023\",\n \"0.5915761\",\n \"0.5910558\",\n \"0.59042025\",\n \"0.59042025\",\n \"0.59032536\",\n \"0.59026146\",\n \"0.58772755\",\n \"0.5871903\",\n \"0.58660096\",\n \"0.5863246\",\n \"0.58475506\",\n \"0.5842023\",\n \"0.5826899\",\n \"0.5820272\",\n \"0.5820259\",\n \"0.5815941\",\n \"0.58155\",\n \"0.58110493\",\n \"0.5807045\",\n \"0.58049816\",\n \"0.57984704\",\n \"0.57964885\",\n \"0.57735205\",\n \"0.575968\",\n \"0.57580227\",\n \"0.57487476\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7463678"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":95096,"cells":{"query":{"kind":"string","value":"Checks to see if the destination exists If a source file is passed in, run a checksum"},"document":{"kind":"string","value":"def checkfile(filename, source=None):\n if source:\n # Let's check some sums\n if os.path.exists(filename) and os.path.exists(source):\n src_sha = calchash(source)\n dest_sha = calchash(filename)\n if DRYRUN:\n print(\"{src} hash {src_sha}. {dest} hash {dest_sha}\".format(src=source, dest=filename, src_sha=src_sha.hexdigest(), dest_sha=dest_sha.hexdigest()))\n return src_sha.digest() == dest_sha.digest()\n else:\n return os.path.exists(filename)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def checksum_compare(source_file, dest_file):\n\n con_ssh = ControllerClient.get_active_controller()\n\n LOG.info(\"Compare checksums on source file and destination file\")\n cmd = \"getfattr -m . -d {}\"\n\n exitcode, source_sha = con_ssh.exec_cmd(cmd.format(source_file))\n LOG.info(\"Raw source file checksum is: {}\".format(source_sha))\n source_sha2 = source_sha.split(\"\\n\")\n print(\"This is source_sha2: {}\".format(source_sha2))\n assert source_sha2 != [''], \"No signature on source file\"\n\n if source_file.startswith(\"/\"):\n source_sha = source_sha2[2] + \" \" + source_sha2[3]\n else:\n source_sha = source_sha2[1] + \" \" + source_sha2[2]\n\n LOG.info(\"Extracted source file checksum: {}\".format(source_sha))\n\n exitcode, dest_sha = con_ssh.exec_cmd(cmd.format(dest_file))\n LOG.info(\"Raw symlink checksum is: {}\".format(dest_sha))\n dest_sha2 = dest_sha.split(\"\\n\")\n\n if dest_file.startswith(\"/\"):\n dest_sha = dest_sha2[2] + \" \" + dest_sha2[3]\n else:\n dest_sha = dest_sha2[1] + \" \" + dest_sha2[2]\n\n LOG.info(\"Extracted destination file checksum: {}\".format(dest_sha))\n\n if source_sha == dest_sha:\n return True\n else:\n return False","def verifyFile(source, destination):\n\tsourceHash = hashlib.sha256(open(source, 'rb').read()).digest()\n\tdestinationHash = hashlib.sha256(open(destination, 'rb').read()).digest()\n\n\tif sourceHash == destinationHash:\n\t\treturn (True, str(sourceHash))\n\n\treturn False","def file_copy_remote_exists(self, src, dest=None, file_system=None):\n self.enable()\n if file_system is None:\n file_system = self._get_file_system()\n\n file_copy = self._file_copy_instance(src, dest, file_system=file_system)\n if file_copy.check_file_exists() and file_copy.compare_md5():\n log.debug(\"Host %s: File %s already exists on remote.\", self.host, src)\n return True\n\n log.debug(\"Host %s: File %s does not already exist on remote.\", self.host, src)\n return False","def _install_file(srcdir, filename, dstdir):\n srcfilename = os.path.join(srcdir, filename)\n dstfilename = os.path.join(dstdir, filename)\n if not os.path.exists(srcfilename):\n if os.path.exists(dstfilename):\n subprocess.run(['rm', dstfilename], check=True)\n return (False, True)\n return (False, False)\n\n equal = subprocess.run(['diff', '-q', srcfilename, dstfilename],\n check=False,\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL).returncode == 0\n if not equal:\n subprocess.run(['mv', srcfilename, dstfilename], check=True)\n return (True, not equal)","def __copyfile(source, destination):\n logger.info(\"copyfile: %s -> %s\" % (source, destination))\n try:\n __create_destdir(destination)\n shutil.copy(source, destination)\n return True\n except Exception as e:\n logger.error(\n \"copyfile: %s -> %s failed! Error: %s\", source, destination, e\n )\n return False","def match_stat(dest_path, source_path):\n return shutil.copystat(source_path, dest_path)","def moveFile(src, dest, bak=\"bak\"):\n\t\n\tmessage = \"processing: {0} -> {1}\".format(src, dest)\n\tlogger.info(message)\n\n\t#compare the source and destination, if the files are the same do nothing\n\tif os.path.exists(src) and os.path.exists(dest): \n\t\tmessage = \"file {0} found, comparing to {1}\".format(src, dest)\n\t\tlogger.info(message)\n\t\t(fileCheck, fileSig) = verifyFile(src, dest)\n\t\tif fileCheck:\n\t\t\tmessage = \"source file {0} matches destination file {1}\".format(src, dest)\n\t\t\tlogger.info(message)\n\t\t\treturn True\n\t\t\n\t#checks to see if the destination file exists, then creates a backup of it\n\tif os.path.exists(dest):\n\t\tbackupFileName = \"{0}.{1}\".format(dest, bak)\n\t\tmessage = \"file {0} exists, creating backup: {1}\".format(dest, backupFileName)\n\t\tlogger.info(message)\n\t\ttry:\n\t\t\tshutil.move(dest, backupFileName)\n\t\texcept IOError as errorMessage:\n\t\t\tlogger.error(errorMessage)\n\t\t\treturn False\n\t\t\n\t#attempts to copy the source file to the destination, \n\tif os.path.exists(src):\n\t\tmessage = \"copying {0} to {1})\".format(src, dest)\n\t\ttry:\n\t\t\tshutil.copy(src, dest)\n\t\texcept IOError as errorMessage:\n\t\t\tlogger.error(errorMessage)\n\t\t\tshutil.move(backupFilenName, dest)\n\t\t\treturn False\n\t\t\n\t#verify that files are the same\n\t(fileCheck, fileSig) = verifyFile(src, dest)\n\tif fileCheck:\n\t\tmessage = \"File transfer verified {0} -> {1}\".format(src, dest)\n\t\tlogger.info(message)\n\t\tmessage = \"File Signature for {0}: {1}\".format(src, fileSig)\n\t\tlogger.info(message)\n\t\treturn True\n\telse:\n\t\tmessage = \"file signatures do not match, rolling back {0} -> {1}\".format(backupFileName, dest)\n\t\tlogger.error(message)\n\t\n\t#roll back file\n\ttry:\n\t\tshutil.move(backupFileName, dest)\n\texcept IOError as errorMessage:\n\t\tlogger.error(errorMessage)\n\t\treturn False\n\t\n\treturn True","def __checkDestination(self):\n return os.path.exists(self.__targetPath)","def copy_file_check(self):\n pass","def svn_fs_file_checksum(*args):\r\n return _fs.svn_fs_file_checksum(*args)","def __copyfile2(source, destination):\n logger.info(\"copyfile2: %s -> %s\" % (source, destination))\n try:\n __create_destdir(destination)\n shutil.copy2(source, destination)\n return True\n except Exception as e:\n logger.error(\n \"copyfile2: %s -> %s failed! Error: %s\", source, destination, e\n )\n return False","def copy_file(source, destination):\n\n try:\n shutil.copy(source, destination)\n except (OSError, IOError):\n return False\n else:\n return True","def test_managed_local_source_with_source_hash(\n file, tmp_path, grail_scene33_file, grail_scene33_file_hash, proto, dest_file_exists\n):\n name = tmp_path / \"local_source_with_source_hash\"\n\n if dest_file_exists:\n name.touch()\n\n # Test with wrong hash\n bad_hash = grail_scene33_file_hash[::-1]\n\n ret = file.managed(\n name=str(name),\n source=proto + str(grail_scene33_file),\n source_hash=\"sha256={}\".format(bad_hash),\n )\n assert ret.result is False\n assert not ret.changes\n assert \"does not match actual checksum\" in ret.comment\n\n # Now with the right hash\n ret = file.managed(\n name=str(name),\n source=proto + str(grail_scene33_file),\n source_hash=\"sha256={}\".format(grail_scene33_file_hash),\n )\n assert ret.result is True","def copy(src_file_name, target_folder, file_name):\n\n global CURRENT_PROGRESS\n global TOTAL_FILE_NUM\n CURRENT_PROGRESS += 1\n\n mkdir(target_folder)\n target_file = os.path.join(target_folder, file_name)\n\n if os.path.exists(target_file):\n src_md5 = md5(src_file_name)\n\n # iterate target folder to check if file already exists\n for file_in_target in os.listdir(target_folder):\n entry = os.path.join(target_folder, file_in_target)\n target_md5 = md5(entry)\n if src_md5 == target_md5:\n log(\"(\" + str(CURRENT_PROGRESS) + \"/\" + str(TOTAL_FILE_NUM) + file_name + \"file exists, ignore COPY. <-- \" + src_file_name)\n return\n\n word_list = file_name.split('.')\n num_of_files = len(\n [f for f in os.listdir(target_folder) \\\n if os.path.isfile(os.path.join(target_folder, f))])\n file_name = word_list[0] + '(' + str(num_of_files) + ').' + word_list[1]\n target_file = os.path.join(target_folder, file_name)\n\n if DELETE_AFTER_COPY:\n log(\"(\" + str(CURRENT_PROGRESS) + '/' + str(TOTAL_FILE_NUM) + \")MOVE: \" + src_file_name + \" --->\" + target_file)\n shutil.move(src_file_name, target_file)\n else:\n log(\"(\" + str(CURRENT_PROGRESS) + '/' + str(TOTAL_FILE_NUM) + \")COPY: \" + src_file_name + \" --->\" + target_file)\n shutil.copy(src_file_name, target_file)","def download_and_validate_checksum(name, checksum):\n dst = os.path.join(DOWNLOADS_DIR, os.path.basename(name))\n download_file(src=name, dst=dst)\n md5 = hashlib.md5()\n for chunk in chunked_reader(dst):\n md5.update(chunk)\n dl_checksum = md5.digest().hex()\n if dl_checksum != checksum:\n raise ValueError(f\"expected checksum {checksum} but received {dl_checksum}\")\n os.remove(dst)","def _copy_if_not_exists(source: pl.Path, destination: pl.Path) -> None:\n if destination.is_dir():\n destination_file = destination / source.name\n else:\n destination_file = destination\n if not destination_file.exists():\n su.copy(source, destination)","def sync_file(source, dest, dryrun=False, diff=False):\n if diff:\n if not exists(dest):\n logger.info((\"Destination '{}' does not exist:\".format(dest),\n \" skipping diff\"))\n return\n with open(source) as a:\n with open(dest) as b:\n s1 = a.readlines()\n s2 = b.readlines()\n sys.stdout.writelines(\n difflib.unified_diff(s1, s2, fromfile=source, tofile=dest))\n return\n if not exists(dest):\n if dryrun:\n logger.info(\"DRY_RUN: Copying rule '{}' to '{}'\".format(\n source, dest))\n else:\n if not exists(dirname(dest)):\n os.makedirs(dirname(dest))\n logger.info(\"Copying rule '{}' to '{}'\".format(source, dest))\n shutil.copy2(source, dest)\n else:\n equal = filecmp.cmp(source, dest)\n if (not equal):\n if dryrun:\n logger.info(\"DRY_RUN: Updating rule '{}' to '{}'\".format(\n source, dest))\n else:\n logger.info(\"Updating rule '{}' to '{}'\".format(source, dest))\n shutil.copy2(source, dest)\n else:\n if dryrun:\n logger.info(\"DRY_RUN: rule '{}' up to date\".format(dest))\n else:\n logger.info(\"rule '{}' up to date\".format(dest))","def copyFile(source,destination):\r\n logging.info(\"source\",source)\r\n logging.info(\"destination\",destination)\r\n try:\r\n shutil.copy(source, destination)\r\n logging.info(\"File copied successfully.\")\r\n \"\"\"If source and destination are same\"\"\"\r\n except shutil.SameFileError:\r\n logging.info(\"File not copied sucessfuly.\")\r\n \"\"\"List files and directories\"\"\"\r\n logging.info(\"After copying file:\")\r\n logging.info(os.listdir(destination))\r\n \"\"\"logging.info path of newly\r\n created file\"\"\"\r\n logging.info(\"Destination path:\", destination)","def copy_to_local(src_file):\r\n if not_exists(src_file, \"Source File\"):\r\n return 1, 0\r\n _local_file = os.path.basename(src_file)\r\n if wrap_cp_file(src_file, _local_file):\r\n return 1, 0\r\n return 0, _local_file","def copy_to_local(src_file):\r\n if not_exists(src_file, \"Source File\"):\r\n return 1, 0\r\n _local_file = os.path.basename(src_file)\r\n if wrap_cp_file(src_file, _local_file):\r\n return 1, 0\r\n return 0, _local_file","def check_file_transferred(replica, location):\n\n from tardis.tardis_portal.models import Dataset_File\n datafile = Dataset_File.objects.get(pk=replica.datafile.id)\n\n # If the remote is capable, get it to send us the checksums and / or\n # file length for its copy of the file\n try:\n # Fetch the remote's metadata for the file\n m = location.provider.get_metadata(replica)\n _check_attribute(m, datafile.size, 'length')\n if (_check_attribute(m, datafile.sha512sum, 'sha512sum') or \\\n _check_attribute(m, datafile.md5sum, 'md5sum')):\n return True\n if location.trust_length and \\\n _check_attribute(m, datafile.size, 'length') :\n return False\n raise MigrationError('Not enough metadata for verification')\n except NotImplementedError:\n pass\n except HTTPError as e:\n # Bad request means that the remote didn't recognize the query\n if e.code != 400:\n raise\n\n if location.provider.trust_length :\n try:\n length = location.provider.get_length(replica)\n if _check_attribute2(length, datafile.size, 'length'):\n return False\n except NotImplementedError:\n pass\n\n # Fetch back the remote file and verify it locally.\n f = location.provider.get_opener(replica)()\n md5sum, sha512sum, size, x = generate_file_checksums(f, None)\n _check_attribute2(str(size), datafile.size, 'length')\n if _check_attribute2(sha512sum, datafile.sha512sum, 'sha512sum') or \\\n _check_attribute2(md5sum, datafile.md5sum, 'md5sum'):\n return True\n raise MigrationError('Not enough metadata for file verification')","def copy_single_file(src, dest, log):\n if op.exists(src) and op.isfile(src):\n shellutil.copy(src, dest, log=log)\n return True","def checksumFile(filename):\n return md5File(filename)","def _check_source (fileurl, path_unzip, outfile) :\n if outfile is not None and os.path.splitext (outfile)[1].lower () == os.path.splitext (fileurl)[1].lower () :\n file = _check_url_file (fileurl, path_download = path_unzip, outfile = outfile)\n return file\n else :\n file = _check_url_file (fileurl, path_download = path_unzip, outfile = None)\n txt = _check_zip_file (file, path_unzip = path_unzip, outfile = outfile)\n if not os.path.exists (txt):\n message = \"hal_core._check_source: unable to find file \" + txt + \" source (\" + fileurl + \")\"\n raise PQHException (message)\n return txt","def checkExisting(self, dst):\n if dst.exists():\n msg = 'Refusing to clobber existing file \"%s\"' % (\n dst.path,)\n logging.msg(msg)\n raise errors.NoClobber(msg)","def _rsync(self, source, dest):\n \n #print(source)\n \n # Test SSH connection.\n if not self._ssh('test 1 -eq 1', use_pwd=False):\n print \"Waiting for SSH on %s with key %s\" % (self.address[0], self.key_file)\n time.sleep(1)\n while not self._ssh('test 1 -eq 1', use_pwd=False):\n time.sleep(1)\n\n # Archive, compress, delete extraneous files from dest dirs.\n rsync = ['rsync', '-az', '--delete']\n\n # Use key file\n if self.key_file:\n ssh = 'ssh -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o IdentitiesOnly=yes -i \"%s\"'\n rsync.extend(['-e', ssh % self.key_file])\n\n if isinstance(source, list):\n rsync.extend(source)\n rsync.append(dest)\n else:\n rsync.extend([source, dest])\n \n print 'Sync files from %s to %s...' % (source, dest)\n \n if subprocess.call(rsync) == 0:\n return True\n else:\n return False","def run(self):\n try:\n self.parse_args(None)\n self.execute_command()\n except FileExistsException, e:\n print \"Can't copy file as destination already exists.\"\n print \"Exiting...\"\n except Exception, e:\n print \"Exception occured: %s\\nExiting...\" % e","def _copy_file ( self, source, dest ):\n return","def copy_file(src, dst, ignore=None):\n # Sanity checkpoint\n src = re.sub('[^\\w/\\-\\.\\*]', '', src)\n dst = re.sub('[^\\w/\\-\\.\\*]', '', dst)\n if len(re.sub('[\\W]', '', src)) < 5 or len(re.sub('[\\W]', '', dst)) < 5:\n debug.log(\"Error: Copying file failed. Provided paths are invalid! src='%s' dst='%s'\"%(src, dst))\n else:\n # Check destination\n check = False\n if dst[-1] == '/':\n if os.path.exists(dst):\n check = True # Valid Dir\n else:\n debug.log(\"Error: Copying file failed. Destination directory does not exist (%s)\"%(dst)) #DEBUG\n elif os.path.exists(dst):\n if os.path.isdir(dst):\n check = True # Valid Dir\n dst += '/' # Add missing slash\n else:\n debug.log(\"Error: Copying file failed. %s exists!\"%dst)\n elif os.path.exists(os.path.dirname(dst)):\n check = True # Valid file path\n else:\n debug.log(\"Error: Copying file failed. %s is an invalid distination!\"%dst)\n if check:\n # Check source\n files = glob.glob(src)\n if ignore is not None: files = [fil for fil in files if not ignore in fil]\n if len(files) != 0:\n debug.log(\"Copying File(s)...\", \"Copy from %s\"%src, \"to %s\"%dst) #DEBUG\n for file_ in files:\n # Check file exists\n if os.path.isfile(file_):\n debug.log(\"Copying file: %s\"%file_) #DEBUG\n shutil.copy(file_, dst)\n else:\n debug.log(\"Error: Copying file failed. %s is not a regular file!\"%file_) #DEBUG\n else: debug.log(\"Error: Copying file failed. No files were found! (%s)\"%src) #DEBUG","def copyFile(src_file, dst_file, race_flag):\n\n if race_flag:\n time.sleep( random.randint(5, random.randint(10, 50)) )\n if os.path.exists(dst_file):\n while not sameSize(src_file, dst_file):\n time.sleep(10)\n return None\n\n while(True):\n shutil.copy(src_file, dst_file)\n if sameSize(src_file, dst_file):\n break","def verify_destination(self, destination):\n # Make sure the text file was copied to the destination.\n text_file = os.path.join(destination, 'notes.txt')\n assert os.path.isfile(text_file)\n with open(text_file) as handle:\n assert handle.read() == \"This file should be included in the backup.\\n\"\n # Make sure the subdirectory was copied to the destination.\n subdirectory = os.path.join(destination, 'subdirectory')\n assert os.path.isdir(subdirectory)\n # Make sure the symbolic link was copied to the destination.\n symlink = os.path.join(subdirectory, 'symbolic-link')\n assert os.path.islink(symlink)","def test_source_package_checksum_changes(self):\n response = self.client.head(\n f'/filemanager/api/{self.upload_id}/content',\n headers={'Authorization': self.token}\n )\n first_checksum = response.headers.get('ETag')\n self.assertIsNotNone(first_checksum)\n\n response = self.client.post(\n f'/filemanager/api/{self.upload_id}',\n data={'file': (io.BytesIO(b'foocontent'), 'foo.txt'),},\n headers={'Authorization': self.token},\n content_type='multipart/form-data'\n )\n\n second_checksum = response.headers.get('ETag')\n self.assertIsNotNone(second_checksum)\n self.assertNotEqual(first_checksum, second_checksum)\n\n response = self.client.head(\n f'/filemanager/api/{self.upload_id}/content',\n headers={'Authorization': self.token}\n )\n\n third_checksum = response.headers.get('ETag')\n self.assertIsNotNone(third_checksum)\n self.assertEqual(second_checksum, third_checksum)","def copy(self):\n source = os.path.abspath(self.path)\n destination = os.path.abspath(self.target)\n\n logger.info(\"Running Copy Method - SOURCE=\\\"{src}\\\" DESTINATION=\\\"{dst}\\\" IGNORE=\\\"{ignore}\\\"\".format(src=source, dst=destination, ignore=self.ignore))\n\n if not os.path.exists(source):\n logger.error(\"\\\"{source}\\\" PATH DOESN'T EXIST. PROGRAM TERMINATED. Please check log file.\".format(source=source))\n\n if self.rules is not None:\n files = self.rules\n else:\n self.create_packet_structure(source)\n files = self.files\n\n for (k,v) in files.items():\n src = os.path.join(source,k)\n dst = os.path.join(destination,v)\n dirpath = os.path.dirname(dst)\n if not os.path.isdir(dirpath):\n logger.info(\"Create directory - \\\"{dst}\\\"\".format(dst=dirpath))\n os.makedirs(dirpath)\n logger.info(\"copy from \\\"{f}\\\" to \\\"{t}\\\"\".format(f=src,t=dst))\n shutil.copyfile(src,dst)\n logger.info(\"OK\")","def CheckForDirFileConflict(self, src_uri, dst_path):\n final_dir = os.path.dirname(dst_path)\n if os.path.isfile(final_dir):\n raise CommandException('Cannot retrieve %s because it a file exists '\n 'where a directory needs to be created (%s).' %\n (src_uri, final_dir))\n if os.path.isdir(dst_path):\n raise CommandException('Cannot retrieve %s because a directory exists '\n '(%s) where the file needs to be created.' %\n (src_uri, dst_path))","def verify_checksum(path):\n rc = True\n for f in os.listdir(path):\n if f.endswith('.md5'):\n cwd = os.getcwd()\n os.chdir(path)\n with open(os.devnull, \"w\") as fnull:\n try:\n subprocess.check_call(['md5sum', '-c', f], # pylint: disable=not-callable\n stdout=fnull, stderr=fnull)\n LOG.info(\"Checksum file is included and validated.\")\n except Exception as e:\n LOG.exception(e)\n rc = False\n finally:\n os.chdir(cwd)\n return rc\n LOG.info(\"Checksum file is not included, skipping validation.\")\n return rc","def testDetermineDest(self):\n self.cc.determine_dest('cdl', '/bobsBestDirectory')\n\n dir = os.path.abspath('/bobsBestDirectory')\n filename = os.path.join(dir, 'uniqueId.cdl')\n\n self.assertEqual(\n filename,\n self.cc.file_out\n )","def testChecksumCondition(self):\n file_defs = [\n {'name': 'file_0_byte.txt', 'path': '', 'size': 0},\n {'name': 'file_1_byte.txt', 'path': '', 'size': 1},\n {'name': 'file_320k_minus 1_byte.txt', 'path': 'folder1', 'size': 320 * 1024 - 1, 'mod_inc': -1},\n ]\n\n self._setup_test_store(file_defs)\n\n # Check if this provider supports checksum as change condition\n drive = self.drive_class(self.account_id, self.config_file_dir, self.config_pw)\n if drive.files_differ_on_hash(\n os.path.join(self.test_local_dir, file_defs[0]['path'], file_defs[0]['name']),\n 'dummy_hash') is None:\n self.skipTest('Checksum change condition not supported for this provider.')\n\n self._sync_drives()\n\n # Modify files\n for file_def in file_defs:\n file_path =\\\n os.path.join(self.test_local_dir, file_def['path'], file_def['name'])\n test_utils.make_random_file(\n file_path, file_def['size'], leave_existing=False,\n modify_timestamp_ns=os.stat(file_path).st_mtime_ns)\n\n self._sync_drives()\n self._download_store()\n self.assertDirectoriesAreEqual(self.test_local_dir, self.test_download_dir)","def test_source_package_checksum_is_stable(self):\n response = self.client.head(\n f'/filemanager/api/{self.upload_id}/content',\n headers={'Authorization': self.token}\n )\n first_checksum = response.headers.get('ETag')\n self.assertIsNotNone(first_checksum)\n self.assertEqual(first_checksum, self.original_checksum)\n\n response = self.client.head(\n f'/filemanager/api/{self.upload_id}/content',\n headers={'Authorization': self.token}\n )\n second_checksum = response.headers.get('ETag')\n self.assertEqual(first_checksum, second_checksum)\n\n response = self.client.get(\n f'/filemanager/api/{self.upload_id}/content',\n headers={'Authorization': self.token}\n )\n third_checksum = response.headers.get('ETag')\n self.assertEqual(first_checksum, third_checksum)","def test_success_from_bin():\n createFromBin(\"tests/vbaProject.bin\", \"src/data\", \"success_bin.xlam\")\n # Assert that xlam file is created\n assert exists(\"success_bin.xlam\")\n #assert that bin file within success_bin.xlam matches tests/vbaProject.bin\n extractBinFromZip(\"success_bin.xlam\")\n md5hasher = FileHash('md5')\n assert md5hasher.hash_file(\"tests/vbaProject.bin\") == md5hasher.hash_file(\"xl/vbaProject.bin\")\n\n createFromZip(\"success_bin.xlam\", \"src/data\", \"success_xlam.xlam\")\n assert exists(\"success_xlam.xlam\")\n #assert that bin file within success_xlam.xlam matches bin file within success_bin.xlam\n extractBinFromZip(\"success_xlam.xlam\")\n assert md5hasher.hash_file(\"tests/vbaProject.bin\") == md5hasher.hash_file(\"xl/vbaProject.bin\")","def compare(src, dest):\n xsrc, xdest = os.path.exists(src), os.path.exists(dest)\n if not xsrc:\n return Cmp.nosrc\n if not xdest:\n return Cmp.nodest\n with open(src, \"rb\") as s:\n csrc = sha256(s.read()).digest()\n if xdest:\n with open(dest, \"rb\") as d:\n cdest = sha256(d.read()).digest()\n else:\n cdest = b\"\"\n if csrc == cdest:\n return Cmp.same\n return Cmp.differ","def pull_file(self, source, target):\n if not source or not target:\n raise ValueError\n try:\n subprocess.check_call(self.command + [\"pull\", source, target])\n except subprocess.CalledProcessError as e:\n self.log.warning(e)\n self.log.warning(\"Failed to copy \\\"%s:%s\\\" to %s\",\n self.name, source, target)\n raise ValueError\n else:\n self.log.debug(\"Copied \\\"%s:%s\\\" to \\\"%s\\\"\",\n self.name, source, target)","def copy_if_needed(src: str, dst: str, filter: str|List[str]|None = None) -> bool:\n #console(f'COPY {src} --> {dst}')\n if os.path.isdir(src):\n return copy_dir(src, dst, filter)\n else:\n return copy_file(src, dst, filter)","def copy_file(src, dest):\n logger.debug(\"Copying %s to %s\", src, dest)\n try:\n shutil.copy(src, dest)\n except (OSError, IOError) as exc:\n logger.debug('Installation error, trying sudo.')\n try:\n check_call(['sudo', 'cp', src, dest])\n except HelperError:\n # That failed too - re-raise the original exception\n raise exc\n return True","def checksum_file(vm_name, file_name, vm_executor=None):\n command = config.MD5SUM_CMD % file_name\n if not vm_executor:\n vm_executor = get_vm_executor(vm_name)\n return vm_executor.run_cmd(shlex.split(command))[1]","def test_checksum(size1, size2, lines, tmpdir):\n fp = tmpdir.join(\"temp-data.txt\").strpath\n data = \"\\n\".join(lines)\n with open(fp, 'w') as f:\n f.write(data)\n exp = hashlib.new(\"md5\", data.encode(\"utf-8\")).hexdigest()\n res1 = checksum(fp, size1)\n res2 = checksum(fp, size2)\n assert exp == res1\n assert res1 == res2\n assert res2 == exp","def main():\n parser = CustomArgumentParser()\n parser.add_argument(\"-s\", \"--simon-sez\",\n help=\"Really, Simon sez copy the data!\", action=\"store_true\")\n parser.add_argument(\"-r\", \"--src-directory\",\n help=\"Copy metadata from files in this directory.\")\n parser.add_argument(\"-d\", \"--dst-directory\",\n help=\"Copy metadata to matching files in this directory.\")\n parser.add_argument(\"-v\", \"--verbose\", help=\"Log level to DEBUG.\",\n action=\"store_true\")\n args = parser.parse_args()\n\n if args.verbose:\n logging.basicConfig(level=logging.DEBUG)\n else:\n logging.basicConfig(level=logging.INFO)\n\n error = False\n\n # Require these two arguments.\n for arg in [args.src_directory, args.dst_directory]:\n if not arg:\n logger.error(\n \"Required src or dst directory parameter missing.\")\n error = True\n # XXX: Duplicates exit below. Can't check directory if null.\n logger.error(\"Exiting due to errors.\")\n parser.usage_message()\n sys.exit(1)\n\n if (os.path.exists(args.src_directory) and\n os.path.isdir(args.src_directory)):\n src_directory = args.src_directory\n else:\n logger.error(\n \"--src-directory={} does not exist or is not a directory.\".format(\n args.dst_directory))\n error = True\n\n if (os.path.exists(args.dst_directory) and\n os.path.isdir(args.dst_directory)):\n dst_directory = args.dst_directory\n else:\n logger.error(\n \"--dst-directory={} does not exist or is not a directory.\".format(\n args.dst_directory))\n error = True\n\n if error:\n logger.error(\"Exiting due to errors.\")\n parser.usage_message()\n sys.exit(1)\n else:\n process_all_files(src_directory, dst_directory, simon_sez=args.simon_sez)","def svn_fs_file_md5_checksum(*args):\r\n return _fs.svn_fs_file_md5_checksum(*args)","def copy_file(src: str, dst: str, filter: str|List[str]|None = None) -> bool:\n if _passes_filter(src, filter):\n if os.path.isdir(dst):\n dst = os.path.join(dst, os.path.basename(src))\n if _should_copy(src, dst):\n #console(f'copy {src}\\n --> {dst}')\n shutil.copyfile(src, dst, follow_symlinks=True)\n shutil.copystat(src, dst, follow_symlinks=True)\n return True\n return False","def FileSendRemoteChecksum(self, source_paths: list):\n try:\n paths = [p for pat in source_paths for p in self.expandPath(pat)]\n g = self.fileChunkGenerator(paths, False)\n return {c.path: c.sum for c in self.filemanager.SendChecksum(g)}\n except grpc.RpcError as e:\n status_code = e.code() # status_code.name and status_code.value\n if grpc.StatusCode.NOT_FOUND == status_code:\n raise FileNotFoundError(e.details()) from e\n else:\n # pass any other gRPC errors to user\n raise e","def compute_checksum(filename):\n cmd = 'md5sum ' + filename\n return pipe(cmd)","def checkSumHelper(arg, dirname, fnames):\n val = 0\n files = [name for name in fnames if os.path.splitext(name)[1] in EXTENSIONS]\n for file in files:\n absFile = os.path.join(dirname,file)\n try:\n stats = os.stat(absFile)\n except OSError,e:\n # This is to skip over temporary files or files\n # nosy doesn't have permission to access\n # print \"Nosy: skipping file %s with error %s\"%(absFile,e)\n continue\n val += stats[stat.ST_SIZE] + stats[stat.ST_MTIME]\n arg.append(val)\n return","def check(src, dst):\n walker = Walker()\n walker.check(src, dst)\n return","def copy_file(source_file, target_file):\n\t# print('\\n\\nCopying [{}] to [{}].\\n\\n'.format(source_file, target_file))\n\trun_rsync([source_file, target_file])","def copy(self, source_host, dest_host, filename):","def determine_should_sync(\n self, src_file: Optional[FileStats], dest_file: Optional[FileStats]\n ) -> bool:\n if dest_file:\n dest_file.operation_name = \"delete\"\n LOGGER.debug(\n \"syncing: (None) -> %s (remove), file does not \"\n \"exist at source (%s) and delete mode enabled\",\n dest_file.src if dest_file else None,\n dest_file.dest if dest_file else None,\n )\n return True","def has_checksum_file(self):\n return self.checksum_file_path.is_file()","def md5sum(fileSrc):\n md5 = hashlib.md5()\n try:\n with open(fileSrc, \"rb\") as fd:\n while True:\n content = fd.read(2**20)\n if not content:\n break\n md5.update(content)\n except IOError:\n print(fileSrc + \" Not found\")\n exit(1)\n return md5.hexdigest()","def putFile( self, path, sourceSize = 0 ):\n res = checkArgumentFormat( path )\n if not res['OK']:\n return res\n urls = res['Value']\n successful = {}\n failed = {}\n for dest_url, src_file in urls.items():\n gLogger.debug( \"DIPStorage.putFile: Executing transfer of %s to %s\" % ( src_file, dest_url ) )\n res = self.__putFile( src_file, dest_url )\n if res['OK']:\n successful[dest_url] = res['Value']\n else:\n failed[dest_url] = res['Message']\n resDict = {'Failed':failed, 'Successful':successful}\n return S_OK( resDict )","def alock_installFunc(dest, source, env):\n\n owner = env.get('INSTALL_OWNER', None)\n if owner:\n try:\n uid = pwd.getpwnam(owner)[2]\n except TypeError:\n uid = owner\n else:\n uid = -1\n\n group = env.get('INSTALL_GROUP', None)\n if group:\n try:\n gid = grp.getgrnam(group)[2]\n except TypeError:\n gid = group\n else:\n gid = -1\n\n mode = env.get('INSTALL_MODE', None)\n if not mode:\n st = os.stat(source)\n mode = (stat.S_IMODE(st[stat.ST_MODE]) | stat.S_IWRITE)\n if isinstance(mode, str):\n mode = int(mode, 8)\n\n shutil.copy2(source, dest)\n\n if owner or group:\n os.chown(dest, uid, gid)\n\n os.chmod(dest, mode)\n return 0","def assert_sources_folder_exist(command: commands.FilesRelatedCommand,\n filesystem: infra.Filesystem,\n stdout: infra.STDOut) -> None:\n if filesystem.not_exists(command.sources_folder):\n stdout.red(f'Sources folder does not exist: {command.sources_folder}')\n sys.exit(1)","def make_checksum_file(self, project):\n return None","def _copyFile(self, source, dstDir):\n dstFile = os.path.join(dstDir, os.path.basename(source))\n touch = \"/usr/bin/touch\" if OSUtilities.isMacOS() else \"/bin/touch\"\n subprocess.call([touch, dstFile])\n subprocess.call([\"/bin/cp\", source, dstDir])\n self._logger.info(\"Copying file \" + source + \" to \" + dstDir)\n self._numCopiedFiles += 1","def copy_file(name, n_name):\n\n if os.path.isfile(config_tools.full_dest+name):\n try:\n shutil.copyfile(config_tools.full_dest+name, config_tools.full_dest+n_name)\n except OSError:\n print(f\"Не возможно копировать файл {name}\")\n else:\n print(f\"Файл {config_tools.full_dest+name} скопирован как {config_tools.full_dest+n_name}\")","def check_exists(self, name):\n if self.pyload.config.get(\"download\", \"skip_existing\"):\n download_folder = self.pyload.config.get(\n 'general', 'download_folder')\n dest_file = fsjoin(download_folder,\n self.pyfile.package().folder if self.pyload.config.get(\n \"general\", \"folder_per_package\") else \"\",\n name)\n if exists(dest_file):\n self.pyfile.name = name\n self.skip(_(\"File exists.\"))","def checksum_test(self, attack_args, sha256_checksum, seed=5, cleanup=True, pcap=Lib.test_pcap,\n flag_write_file=False, flag_recalculate_stats=False, flag_print_statistics=False,\n attack_sub_dir=True, test_sub_dir=True, time=False):\n\n controller = Ctrl.Controller(pcap_file_path=pcap, do_extra_tests=False, non_verbose=True)\n controller.load_pcap_statistics(flag_write_file, flag_recalculate_stats, flag_print_statistics,\n intervals=[], delete=True)\n\n controller.process_attacks(attack_args, [[seed]], time)\n\n caller_function = inspect.stack()[1].function\n\n try:\n self.assertEqual(sha256_checksum, Lib.get_sha256(controller.pcap_dest_path))\n except self.failureException:\n Lib.rename_test_result_files(controller, caller_function, attack_sub_dir, test_sub_dir)\n raise\n\n if cleanup:\n Lib.clean_up(controller)\n else:\n Lib.rename_test_result_files(controller, caller_function, attack_sub_dir, test_sub_dir)","def _get_checksum(self, arg):","def test_file_integrity_remove_file_in_case_of_fail():\n test_file = open('./testfile.tmp', 'a')\n test_file.close()\n test_file_path = os.path.realpath('./testfile.tmp')\n test_file_md5 = hashlib.md5(open(test_file_path, 'rb').read()).hexdigest()\n\n bad_md5 = 'some_noise_%s' % test_file_md5\n\n PackageDownloadHelper.check_file_integrity(test_file_path, bad_md5)\n\n assert not os.path.isfile(test_file_path)","def copyFile(source, target):\n\tfrom shutil import copyfile, copystat, copymode\n\tfrom os.path import split\n\tsource = adaptPath(source)\n\ttarget = adaptPath(target)\n\tif int(getFileModifTime(source)) != int(getFileModifTime(target)):\n\t\tmakedir(split(target)[0])\n\t\tcopyfile(source, target)\n\t\tcopystat(source, target)\n\t\tcopymode(source, target)\n\t#~ else:\n\t\t#~ print (\"%s not copied\"%(target))","def need_checksum(self):\n if self.skip_checksum:\n log.warning(\"Skip checksum because --skip-checksum is specified\")\n return False\n # There's no point running a checksum compare for selective dump\n if self.where:\n log.warning(\"Skip checksum because --where is given\")\n return False\n # If the collation of primary key column has been changed, then\n # it's high possible that the checksum will mis-match, because\n # the returning sequence after order by primary key may be vary\n # for different collations\n for pri_column in self._pk_for_filter:\n old_column_tmp = [\n col for col in self._old_table.column_list if col.name == pri_column\n ]\n if old_column_tmp:\n old_column = old_column_tmp[0]\n new_column_tmp = [\n col for col in self._new_table.column_list if col.name == pri_column\n ]\n if new_column_tmp:\n new_column = new_column_tmp[0]\n if old_column and new_column:\n if not is_equal(old_column.collate, new_column.collate):\n log.warning(\n \"Collation of primary key column {} has been \"\n \"changed. Skip checksum \".format(old_column.name)\n )\n return False\n # There's no way we can run checksum by chunk if the primary key cannot\n # be covered by any index of the new schema\n if not self.validate_post_alter_pk():\n if self.skip_pk_coverage_check:\n log.warning(\n \"Skipping checksuming because there's no unique index \"\n \"in new table schema can perfectly cover old primary key \"\n \"combination for search\".format(old_column.name)\n )\n return False\n else:\n # Though we have enough coverage for primary key doesn't\n # necessarily mean we can use it for checksum, it has to be an\n # unique index as well. Skip checksum if there's no such index\n if not self.find_coverage_index():\n log.warning(\n \"Skipping checksuming because there's no unique index \"\n \"in new table schema can perfectly cover old primary key \"\n \"combination for search\".format(old_column.name)\n )\n return False\n return True","def copyfile(source, dest, newname=None):\n\n if not os.path.exists(source):\n #print 'no such file %s' %source\n return False\n shutil.copy(source, newname)\n dest = os.path.join(dest, newname)\n if os.path.exists(dest):\n os.remove(dest)\n shutil.move(newname, dest)\n return True","def _verify_archive_equality(self, file1, file2):\r\n temp_dir_1 = mkdtemp()\r\n temp_dir_2 = mkdtemp()\r\n try:\r\n extract_source(file1, temp_dir_1)\r\n extract_source(file2, temp_dir_2)\r\n return directories_equal(temp_dir_1, temp_dir_2)\r\n\r\n finally:\r\n shutil.rmtree(temp_dir_1)\r\n shutil.rmtree(temp_dir_2)","def do_compare(self, str_arg):\n arg = validateString(str_arg)\n source, target = arg.split(' ', 1)\n if os.path.isfile(source):\n # Mar 27 @swang: if target file doesn't exist, copy source file to setup directory for later test\n # 2015-08-27: decided to go to fail path\n if not os.path.isfile(target):\n # copy(source, target)\n self.resultFlag = False\n raise ValueError('COMPARE FAILED: target file not found.')\n # if not self.__compareImage(source, target):\n if not filecmp.cmp(source, target):\n printLog(self.threadName + 'COMPARE FAILED: source file and target file DIFFER!', logging.WARNING)\n self.resultFlag = False\n else:\n self.resultFlag = False\n raise ValueError('COMPARE FAILED: source file not found.')","def process(self, source_path: pathlib.Path) -> bool:","def run_copy(self, src, dst):\n pass","def check(src, perm, dest, cmds, comp, verbose=False):\n if comp == Cmp.differ:\n ansiprint(f\"The file '{src}' differs from '{dest}'.\", fg=Color.red, i=True)\n elif comp == Cmp.nodest:\n ansiprint(\n f\"The destination file '{dest}' does not exist\",\n fg=Color.black,\n bg=Color.red,\n )\n elif comp == Cmp.nosrc:\n ansiprint(\n f\"The source file '{src}' does not exist.\", fg=Color.black, bg=Color.red\n )\n elif comp == Cmp.same and verbose:\n ansiprint(f\"The files '{src}' and '{dest}' are the same.\", fg=Color.green)","def _copy_file(src, dest):\n\n if src is None or dest is None:\n raise ValueError(\"src and dest must not be None\", src, dest)\n\n if not os.path.isfile(src):\n raise ValueError(\"src file does not appear to exist\", src)\n\n # if error on copy, subprocess will raise CalledProcessError\n try:\n subprocess.run(\n [\"/usr/bin/ditto\", src, dest], check=True, stderr=subprocess.PIPE\n )\n except subprocess.CalledProcessError as e:\n logging.critical(\n f\"ditto returned error: {e.returncode} {e.stderr.decode(sys.getfilesystemencoding()).rstrip()}\"\n )\n raise e","def move_file(src, dst):\n # Sanity checkpoint\n src = re.sub('[^\\w/\\-\\.\\*]', '', src)\n dst = re.sub('[^\\w/\\-\\.\\*]', '', dst)\n if len(re.sub('[\\W]', '', src)) < 5 or len(re.sub('[\\W]', '', dst)) < 5:\n debug.log(\"Error: Moving file failed. Provided paths are invalid! src='%s' dst='%s'\"%(src, dst))\n else:\n # Check destination\n check = False\n if dst[-1] == '/':\n if os.path.exists(dst):\n check = True # Valid Dir\n else:\n debug.log(\"Error: Moving file failed. Destination directory does not exist (%s)\"%(dst)) #DEBUG\n elif os.path.exists(dst):\n if os.path.isdir(dst):\n check = True # Valid Dir\n dst += '/' # Add missing slash\n else:\n debug.log(\"Error: Moving file failed. %s exists!\"%dst)\n elif os.path.exists(os.path.dirname(dst)):\n check = True # Valid file path\n else:\n debug.log(\"Error: Moving file failed. %s is an invalid distination!\"%dst)\n if check:\n # Check source\n files = glob.glob(src)\n if len(files) != 0:\n debug.log(\"Moving File(s)...\", \"Move from %s\"%src, \"to %s\"%dst)\n for file_ in files:\n # Check if file contains invalid symbols:\n invalid_chars = re.findall('[^\\w/\\-\\.\\*]', os.path.basename(file_))\n if invalid_chars:\n debug.graceful_exit((\"Error: File %s contains invalid \"\n \"characters %s!\"\n )%(os.path.basename(file_), invalid_chars))\n continue\n # Check file exists\n if os.path.isfile(file_):\n debug.log(\"Moving file: %s\"%file_)\n shutil.move(file_, dst)\n else:\n debug.log(\"Error: Moving file failed. %s is not a regular file!\"%file_)\n else: debug.log(\"Error: Moving file failed. No files were found! (%s)\"%src)","def checksum(self, md5_file, file_name):\n try:\n with open(md5_file, 'r') as f:\n md5_file_contents = f.read()\n md5_str = md5_file_contents.split(' ')[0]\n os.remove(md5_file)\n except Exception as e:\n logging.exception('Could not read MD5 file {}. \\\n \\nTry to download the file again'.format(file_name))\n return False\n if not self.check_md5(file_name, md5_str):\n logging.error('Failed in checksum. Download the file again.')\n return False\n return True","def copy(self, src_path: str, tgt_path: str) -> None:","def testCheckSourceCopyOperation_Pass(self):\n payload_checker = checker.PayloadChecker(self.MockPayload())\n self.assertIsNone(\n payload_checker._CheckSourceCopyOperation(None, 134, 134, 'foo'))","def hash_file(self, file_path, file_arcname):\n\n file_path = os.path.abspath(file_path)\n\n # If the file_arcname argument is None use the base file name as the\n # arc name\n if file_arcname is None:\n file_arcname = os.path.basename(file_path)\n\n if not os.path.exists(file_path):\n task_error(\"%s doesn't exist\" % file_path)\n if not os.access(file_path, os.R_OK):\n task_error(\"Can't read from %s\" % file_path)\n\n file_mode = os.stat(file_path)[stat.ST_MODE]\n if not stat.S_ISDIR(file_mode) and not stat.S_ISREG(file_mode):\n task_error(\"Unknown file type for %s\" % file_path)\n\n file_in = None\n try:\n # open to read binary. This is important.\n file_in = open(file_path, 'rb')\n except IOError:\n task_error(\"Couldn't read from file: %s\" % file_path)\n\n # hash file 1Mb at a time\n hashval = hashlib.sha1()\n while True:\n data = file_in.read(1024 * 1024)\n if not data:\n break\n hashval.update(data)\n\n # update file bundle status\n\n self.running_size += len(data)\n\n self.percent_complete = 100.0 * self.running_size / self.bundle_size\n\n # only update significant progress\n if self.percent_complete - self.last_percent > 1:\n self.report_percent_complete()\n self.last_percent = self.percent_complete\n\n file_hash = hashval.hexdigest()\n\n # print 'hash: ' + file_hash\n file_in.close()\n\n modified_name = os.path.join('data', file_arcname)\n (file_dir, file_name) = os.path.split(modified_name)\n\n # linuxfy the directory\n file_dir = file_dir.replace('\\\\', '/')\n\n info = {}\n info['size'] = os.path.getsize(file_path)\n mime_type = mimetypes.guess_type(file_path, strict=True)[0]\n\n info['mimetype'] = mime_type if mime_type is not None else 'application/octet-stream'\n info['name'] = file_name\n info['mtime'] = DT.datetime.utcfromtimestamp(int(os.path.getmtime(file_path))).isoformat()\n info['ctime'] = DT.datetime.utcfromtimestamp(int(os.path.getctime(file_path))).isoformat()\n info['destinationTable'] = 'Files'\n info['subdir'] = file_dir\n info['hashsum'] = file_hash\n info['hashtype'] = 'sha1'\n\n # todo make sure errors bubble up without crashing\n if file_arcname in self.file_meta:\n print file_arcname\n task_error(\n \"Different file with the same arcname is already in the bundle\")\n return\n\n return info","def evaluate(self):\n errors_found = 0\n\n # Test if destination exists and is a directory\n if not os.path.isdir(os.path.expandvars(self.path_destination)):\n message.alert(\n f\"FILE [{self.name}]: '{self.path_destination}' destination path is not a known directory.\"\n )\n errors_found += 1\n\n # Test if sudo is really needed if the file's\n # destination is in a directory owned by the current user\n if os.getenv(\"HOME\") in os.path.expandvars(self.path_destination) and self.sudo:\n message.alert(\n f\"FILE [{self.name}]: Sudo use may be unnecessary as {self.path_destination} is in your home path.\"\n )\n errors_found += 1\n\n # Test if source is a directory\n if self.path_source is not None:\n if not os.path.isdir(os.path.expandvars(self.path_source)):\n message.alert(\n f\"FILE [{self.name}]: '{self.path_source}' source path is not a known directory.\"\n )\n errors_found += 1\n\n if not os.path.isfile(\n os.path.join(os.path.expandvars(self.path_source), self.name)\n ):\n message.alert(\n f\"FILE [{self.name}] at PATH: [{self.path_source}] does not exist.\"\n )\n errors_found += 1\n\n # Check expected types\n if not isinstance(self.comments, list):\n message.alert(\n f\"FILE [{self.name}]: Type mismatch, comments attribute is of type '{type(self.comments)}' instead of 'list'\"\n )\n errors_found += 1\n\n return errors_found","def copyFile(src, dest):\n try:\n shutil.copy(src,dest)\n except shutil.Error as e:\n print(\"Error: \" + str(e))\n except IOError as e:\n print(\"Error: \" + e.strerror)","def source(dirname, filename, gen_content):\n if dirname in lut['sources']:\n s.add('MD5SUM=\"$(find \"{0}\" -printf %T@\\\\\\\\n | md5sum)\"', dirname)\n if secret is None:\n s.add('tar xf \"{0}\" -C \"{1}\"',\n filename,\n dirname,\n sources={filename: gen_content()})\n else:\n s.add('wget \"{0}/{1}/{2}/{3}\"', server, secret, b.name, filename)\n s.add('tar xf \"{0}\" -C \"{1}\"', filename, dirname)\n for manager, service in lut['sources'][dirname]:\n s.add('[ \"$MD5SUM\" != \"$(find \"{0}\" -printf %T@\\\\\\\\n ' # No ,\n '| md5sum)\" ] && {1}=1',\n dirname,\n manager.env_var(service))","def get_checksum_subdir(download_cmd, dest):\n\n # If the download is not forced and there is a checksum, allow\n # checksum match to skip the download.\n download_cmd_digest = \"\"\n try:\n download_cmd_digest = hashlib.md5(download_cmd).hexdigest()\n except TypeError:\n download_cmd_digest = hashlib.md5(\n download_cmd.encode('utf-8')).hexdigest()\n\n try:\n dir_ls = os.listdir(\"%s\" % (dest))\n except (IOError, OSError) as e:\n # no subdir with downloaded agents found\n return (download_cmd_digest, True)\n\n if download_cmd_digest in dir_ls:\n return (download_cmd_digest, False)\n else:\n return (download_cmd_digest, True)","def check_output(self):\n directory, file = split(self.target)\n if not exists(directory):\n mkdir(directory)\n if exists(self.target):\n unlink(self.target)","def checksum_of(filepath):\n bfsz = 10240000 # 10 MB buffer\n sum = hashlib.sha256()\n with open(filepath) as fd:\n while True:\n buf = fd.read(bfsz)\n if not buf: break\n sum.update(buf)\n return sum.hexdigest()","def checkPath(filename, projectSource):\n filePath = os.path.join(projectSource, filename)\n if os.path.exists(filePath):\n pass\n else:\n sys.stderr.write(\"Error: \" + filePath + \" not found\")\n sys.exit(1)\n return filePath","def copyfile(src, dst, overwrite=False, changed_only=True, link=False):\n if os.path.isdir(dst):\n dst = os.path.join(dst, os.path.basename(src))\n if os.path.exists(dst) and not overwrite:\n return False\n if samefile(src, dst):\n return False\n try:\n if not os.path.exists(dst):\n dstdir = dirname(dst)\n if not os.path.exists(dstdir):\n os.makedirs(dstdir)\n else:\n # `dst` exists, check for changes\n if changed_only:\n sstat = os.stat(src)\n dstat = os.stat(dst)\n if (sstat.st_size ==\n dstat.st_size and sstat.st_mtime <= dstat.st_mtime):\n # same size and destination more recent, do not copy\n return False\n if link:\n try:\n os.link(src, dst)\n except OSError:\n # retry with normal copy\n shutil.copy2(src, dst)\n else:\n shutil.copy2(src, dst)\n except WindowsError:\n pass\n return True","def hexists(file_path: str) -> bool:\n return os.path.exists(file_path)","def copy_file ( self, source, dest, chown=True, chmod=True ):\n if self._copy_file ( source, dest ):\n if chmod:\n self.chmod_file ( dest )\n if chown:\n self.chown_file ( dest )\n\n return True\n else:\n return False","def check_hash(self, fname, args):\n fobj = self._open_file(fname)\n\n rc = 0\n format_errors = 0\n hash_errors = 0\n read_errors = 0\n for idx, line in enumerate(fobj):\n # remove any newline characters\n m = self.CHECK_RE.match(line.strip())\n if not m:\n if args.warn:\n self.app.stderr.write(\n 'hasher {0}: {1}: {2}: improperly formatted {3}'\n ' checksum line\\n'.format(self.name, fname, idx + 1,\n self.name.upper()))\n format_errors += 1\n rc = 1\n continue\n hash_value, binary, check_file = m.groups()\n\n try:\n check_f = open(check_file, 'rb' if binary == '*' else 'r')\n except IOError:\n self.app.stderr.write(\n 'hasher {0}: {1}: No such file or directory\\n'.format(\n self.name, check_file))\n if not args.status:\n self.app.stdout.write(\n STATUS_MSG.format(check_file, READ_ERROR))\n read_errors += 1\n rc = 1\n continue\n\n if self._calculate_hash(check_f) == hash_value:\n if not (args.quiet or args.status):\n self.app.stdout.write(\n STATUS_MSG.format(check_file, SUCCESS))\n else:\n if not args.status:\n self.app.stdout.write(\n STATUS_MSG.format(check_file, HASH_ERROR))\n hash_errors += 1\n rc = 1\n\n if format_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} line{2} {3} improperly'\n ' formatted\\n'.format(\n self.name,\n format_errors,\n 's' if format_errors > 1 else '',\n 'are' if format_errors > 1 else 'is',\n ))\n if read_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} listed file{2}'\n ' could not be read\\n'.format(\n self.name,\n read_errors,\n 's' if read_errors > 1 else '',\n ))\n if hash_errors and not args.status:\n self.app.stderr.write(\n 'hasher {0}: WARNING: {1} computed checksum{2}'\n ' did NOT match\\n'.format(\n self.name,\n hash_errors,\n 's' if hash_errors > 1 else '',\n ))\n return rc","def rsync_and_md5(old_name, new_name, md5sum=None):\n if md5sum is None:\n md5sum = md5(old_name)\n\n syscall(\"rsync \" + old_name + \" \" + new_name)\n new_md5sum = md5(new_name)\n\n if new_md5sum != md5sum:\n raise Exception(\n \"Error copying file \"\n + old_name\n + \" -> \"\n + new_name\n + \"\\n. md5s do not match\"\n )\n else:\n return md5sum","def _check_cow(image_dir):\n try:\n src = f'{image_dir}/.cowcheck'\n dst = f'{image_dir}/.cowcheck1'\n sh.touch(src)\n sh.cp('--reflink=always', src, dst)\n return True\n except Exception:\n warn_msg = f\"\"\"\n Copy-on-write check failed.\n The file system where images are stored ({image_dir}) does not support copy-on-write.\n It is recommended to use an XFS or BTRFS file system with copy-on-write enabled as a storage\n location for S2E images, as this can save up to 60% of disk space. The building process checkpoints\n intermediate build steps with cp --reflink=auto to make use of copy-on-write if it is available.\n\n How to upgrade:\n 1. Create an XFS or BTRFS partition large enough to store the images that you need (~300 GB for all images).\n Make sure you use reflink=1 to enable copy-on-write when running mkfs.xfs.\n 2. Create a directory for guest images on that partition (e.g., /mnt/disk1/images)\n 3. Delete the \"images\" folder in your S2E environment\n 4. Create in your S2E environment a symbolic link called \"images\" to the directory you created in step 2\n \"\"\"\n logger.warning(re.sub(r'^ {8}', '', warn_msg, flags=re.MULTILINE))\n return False\n finally:\n sh.rm('-f', src)\n sh.rm('-f', dst)","def checksum(self):\n checksums = {\n \"slug\": hashlib.sha256(\n self.slug.encode(\"utf-8\")\n ).hexdigest(),\n \"files\": {},\n }\n\n def file_hash(filepath):\n running_hash = hashlib.sha256()\n with open(filepath, \"rb\") as IN:\n while True:\n # Read file in as little chunks.\n buf = IN.read(4096)\n if not buf:\n break\n running_hash.update(buf)\n return running_hash.hexdigest()\n\n # iterate over the direcory and calucalte the hash\n for root, dirs, files in os.walk(self.thawed_dir):\n for file_path in sorted(files):\n full_path = str(Path(root) / file_path)\n # Calculate a relative path to the freezable object\n rel_path = full_path.replace(str(self.thawed_dir) + \"/\", \"\")\n # calculate and store the checksums\n phash = file_hash(full_path)\n filesize = os.path.getsize(full_path)\n checksums[\"files\"][rel_path] = {\n \"checksum\": phash,\n \"size\": filesize,\n }\n # calculate the total\n total = hashlib.sha256(checksums[\"slug\"].encode(\"utf-8\"))\n # Iterate over filenames AND hashes and update checksum\n for filename, data in checksums[\"files\"].items():\n total.update(filename.encode(\"utf-8\"))\n total.update(data[\"checksum\"].encode(\"utf-8\"))\n checksums[\"total\"] = total.hexdigest()\n return checksums","def ExecuteIf(self, args, src_files, dst_files):\n if self.ShouldBuild(src_files, dst_files):\n self.MakeDestinationDirectories(dst_files)\n self.Execute(args)\n if self.execute and not self.VerifyExists(dst_files):\n raise RuntimeError(\"FAILED: build did not create all required files\")","def validate_file_destination(namespace):\n try:\n path = namespace.destination\n except AttributeError:\n return\n else:\n # TODO: Need to confirm this logic...\n file_path = path\n file_dir = os.path.dirname(path)\n if os.path.isdir(path):\n file_name = os.path.basename(namespace.file_name)\n file_path = os.path.join(path, file_name)\n elif not os.path.isdir(file_dir):\n try:\n os.mkdir(file_dir)\n except EnvironmentError as exp:\n message = \"Directory {} does not exist, and cannot be created: {}\"\n raise ValueError(message.format(file_dir, exp))\n if os.path.isfile(file_path):\n raise ValueError(\"File {} already exists.\".format(file_path))\n namespace.destination = file_path","def check_is_event_valid(self, event):\n if event.src_path == template_file_path:\n self.__init__()\n if not hasattr(event, 'dest_path'):\n event.dest_path = None\n for path in [event.src_path, event.dest_path]:\n if path is not None:\n dir, name = self.__parse_full_path(path)\n if dir.find(controlled_path\n ) >= 0 and not self._check_is_name_valid(name):\n if path == event.dest_path:\n os.system('cp {dest} {src}'.format(\n dest=event.dest_path, src=event.src_path))\n os.system('rm -rf {dir}{name}'.format(dir=dir, name=name))","def test_nonexistent_path(tmpdir):\n with pytest.raises(IOError):\n checksum(tmpdir.join(\"does-not-exist.txt\").strpath)","def file_copy(\n self,\n src: str,\n dest: Optional[str] = None,\n file_system: Optional[str] = None,\n peer: Optional[bool] = False,\n ) -> None:\n if dest is None:\n dest = os.path.basename(src)\n\n if file_system is None:\n file_system = self._get_file_system()\n\n # netmiko's enable_scp\n self.enable_scp()\n self._file_copy(src, dest, file_system)\n if peer:\n self.peer_device._file_copy(src, dest, file_system) # pylint: disable=protected-access\n\n # logging removed because it messes up unit test mock_basename.assert_not_called()\n # for tests test_file_copy_no_peer_pass_args, test_file_copy_include_peer\n # log.info(\"Host %s: File %s transferred successfully.\")"],"string":"[\n \"def checksum_compare(source_file, dest_file):\\n\\n con_ssh = ControllerClient.get_active_controller()\\n\\n LOG.info(\\\"Compare checksums on source file and destination file\\\")\\n cmd = \\\"getfattr -m . -d {}\\\"\\n\\n exitcode, source_sha = con_ssh.exec_cmd(cmd.format(source_file))\\n LOG.info(\\\"Raw source file checksum is: {}\\\".format(source_sha))\\n source_sha2 = source_sha.split(\\\"\\\\n\\\")\\n print(\\\"This is source_sha2: {}\\\".format(source_sha2))\\n assert source_sha2 != [''], \\\"No signature on source file\\\"\\n\\n if source_file.startswith(\\\"/\\\"):\\n source_sha = source_sha2[2] + \\\" \\\" + source_sha2[3]\\n else:\\n source_sha = source_sha2[1] + \\\" \\\" + source_sha2[2]\\n\\n LOG.info(\\\"Extracted source file checksum: {}\\\".format(source_sha))\\n\\n exitcode, dest_sha = con_ssh.exec_cmd(cmd.format(dest_file))\\n LOG.info(\\\"Raw symlink checksum is: {}\\\".format(dest_sha))\\n dest_sha2 = dest_sha.split(\\\"\\\\n\\\")\\n\\n if dest_file.startswith(\\\"/\\\"):\\n dest_sha = dest_sha2[2] + \\\" \\\" + dest_sha2[3]\\n else:\\n dest_sha = dest_sha2[1] + \\\" \\\" + dest_sha2[2]\\n\\n LOG.info(\\\"Extracted destination file checksum: {}\\\".format(dest_sha))\\n\\n if source_sha == dest_sha:\\n return True\\n else:\\n return False\",\n \"def verifyFile(source, destination):\\n\\tsourceHash = hashlib.sha256(open(source, 'rb').read()).digest()\\n\\tdestinationHash = hashlib.sha256(open(destination, 'rb').read()).digest()\\n\\n\\tif sourceHash == destinationHash:\\n\\t\\treturn (True, str(sourceHash))\\n\\n\\treturn False\",\n \"def file_copy_remote_exists(self, src, dest=None, file_system=None):\\n self.enable()\\n if file_system is None:\\n file_system = self._get_file_system()\\n\\n file_copy = self._file_copy_instance(src, dest, file_system=file_system)\\n if file_copy.check_file_exists() and file_copy.compare_md5():\\n log.debug(\\\"Host %s: File %s already exists on remote.\\\", self.host, src)\\n return True\\n\\n log.debug(\\\"Host %s: File %s does not already exist on remote.\\\", self.host, src)\\n return False\",\n \"def _install_file(srcdir, filename, dstdir):\\n srcfilename = os.path.join(srcdir, filename)\\n dstfilename = os.path.join(dstdir, filename)\\n if not os.path.exists(srcfilename):\\n if os.path.exists(dstfilename):\\n subprocess.run(['rm', dstfilename], check=True)\\n return (False, True)\\n return (False, False)\\n\\n equal = subprocess.run(['diff', '-q', srcfilename, dstfilename],\\n check=False,\\n stdout=subprocess.DEVNULL,\\n stderr=subprocess.DEVNULL).returncode == 0\\n if not equal:\\n subprocess.run(['mv', srcfilename, dstfilename], check=True)\\n return (True, not equal)\",\n \"def __copyfile(source, destination):\\n logger.info(\\\"copyfile: %s -> %s\\\" % (source, destination))\\n try:\\n __create_destdir(destination)\\n shutil.copy(source, destination)\\n return True\\n except Exception as e:\\n logger.error(\\n \\\"copyfile: %s -> %s failed! Error: %s\\\", source, destination, e\\n )\\n return False\",\n \"def match_stat(dest_path, source_path):\\n return shutil.copystat(source_path, dest_path)\",\n \"def moveFile(src, dest, bak=\\\"bak\\\"):\\n\\t\\n\\tmessage = \\\"processing: {0} -> {1}\\\".format(src, dest)\\n\\tlogger.info(message)\\n\\n\\t#compare the source and destination, if the files are the same do nothing\\n\\tif os.path.exists(src) and os.path.exists(dest): \\n\\t\\tmessage = \\\"file {0} found, comparing to {1}\\\".format(src, dest)\\n\\t\\tlogger.info(message)\\n\\t\\t(fileCheck, fileSig) = verifyFile(src, dest)\\n\\t\\tif fileCheck:\\n\\t\\t\\tmessage = \\\"source file {0} matches destination file {1}\\\".format(src, dest)\\n\\t\\t\\tlogger.info(message)\\n\\t\\t\\treturn True\\n\\t\\t\\n\\t#checks to see if the destination file exists, then creates a backup of it\\n\\tif os.path.exists(dest):\\n\\t\\tbackupFileName = \\\"{0}.{1}\\\".format(dest, bak)\\n\\t\\tmessage = \\\"file {0} exists, creating backup: {1}\\\".format(dest, backupFileName)\\n\\t\\tlogger.info(message)\\n\\t\\ttry:\\n\\t\\t\\tshutil.move(dest, backupFileName)\\n\\t\\texcept IOError as errorMessage:\\n\\t\\t\\tlogger.error(errorMessage)\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t#attempts to copy the source file to the destination, \\n\\tif os.path.exists(src):\\n\\t\\tmessage = \\\"copying {0} to {1})\\\".format(src, dest)\\n\\t\\ttry:\\n\\t\\t\\tshutil.copy(src, dest)\\n\\t\\texcept IOError as errorMessage:\\n\\t\\t\\tlogger.error(errorMessage)\\n\\t\\t\\tshutil.move(backupFilenName, dest)\\n\\t\\t\\treturn False\\n\\t\\t\\n\\t#verify that files are the same\\n\\t(fileCheck, fileSig) = verifyFile(src, dest)\\n\\tif fileCheck:\\n\\t\\tmessage = \\\"File transfer verified {0} -> {1}\\\".format(src, dest)\\n\\t\\tlogger.info(message)\\n\\t\\tmessage = \\\"File Signature for {0}: {1}\\\".format(src, fileSig)\\n\\t\\tlogger.info(message)\\n\\t\\treturn True\\n\\telse:\\n\\t\\tmessage = \\\"file signatures do not match, rolling back {0} -> {1}\\\".format(backupFileName, dest)\\n\\t\\tlogger.error(message)\\n\\t\\n\\t#roll back file\\n\\ttry:\\n\\t\\tshutil.move(backupFileName, dest)\\n\\texcept IOError as errorMessage:\\n\\t\\tlogger.error(errorMessage)\\n\\t\\treturn False\\n\\t\\n\\treturn True\",\n \"def __checkDestination(self):\\n return os.path.exists(self.__targetPath)\",\n \"def copy_file_check(self):\\n pass\",\n \"def svn_fs_file_checksum(*args):\\r\\n return _fs.svn_fs_file_checksum(*args)\",\n \"def __copyfile2(source, destination):\\n logger.info(\\\"copyfile2: %s -> %s\\\" % (source, destination))\\n try:\\n __create_destdir(destination)\\n shutil.copy2(source, destination)\\n return True\\n except Exception as e:\\n logger.error(\\n \\\"copyfile2: %s -> %s failed! Error: %s\\\", source, destination, e\\n )\\n return False\",\n \"def copy_file(source, destination):\\n\\n try:\\n shutil.copy(source, destination)\\n except (OSError, IOError):\\n return False\\n else:\\n return True\",\n \"def test_managed_local_source_with_source_hash(\\n file, tmp_path, grail_scene33_file, grail_scene33_file_hash, proto, dest_file_exists\\n):\\n name = tmp_path / \\\"local_source_with_source_hash\\\"\\n\\n if dest_file_exists:\\n name.touch()\\n\\n # Test with wrong hash\\n bad_hash = grail_scene33_file_hash[::-1]\\n\\n ret = file.managed(\\n name=str(name),\\n source=proto + str(grail_scene33_file),\\n source_hash=\\\"sha256={}\\\".format(bad_hash),\\n )\\n assert ret.result is False\\n assert not ret.changes\\n assert \\\"does not match actual checksum\\\" in ret.comment\\n\\n # Now with the right hash\\n ret = file.managed(\\n name=str(name),\\n source=proto + str(grail_scene33_file),\\n source_hash=\\\"sha256={}\\\".format(grail_scene33_file_hash),\\n )\\n assert ret.result is True\",\n \"def copy(src_file_name, target_folder, file_name):\\n\\n global CURRENT_PROGRESS\\n global TOTAL_FILE_NUM\\n CURRENT_PROGRESS += 1\\n\\n mkdir(target_folder)\\n target_file = os.path.join(target_folder, file_name)\\n\\n if os.path.exists(target_file):\\n src_md5 = md5(src_file_name)\\n\\n # iterate target folder to check if file already exists\\n for file_in_target in os.listdir(target_folder):\\n entry = os.path.join(target_folder, file_in_target)\\n target_md5 = md5(entry)\\n if src_md5 == target_md5:\\n log(\\\"(\\\" + str(CURRENT_PROGRESS) + \\\"/\\\" + str(TOTAL_FILE_NUM) + file_name + \\\"file exists, ignore COPY. <-- \\\" + src_file_name)\\n return\\n\\n word_list = file_name.split('.')\\n num_of_files = len(\\n [f for f in os.listdir(target_folder) \\\\\\n if os.path.isfile(os.path.join(target_folder, f))])\\n file_name = word_list[0] + '(' + str(num_of_files) + ').' + word_list[1]\\n target_file = os.path.join(target_folder, file_name)\\n\\n if DELETE_AFTER_COPY:\\n log(\\\"(\\\" + str(CURRENT_PROGRESS) + '/' + str(TOTAL_FILE_NUM) + \\\")MOVE: \\\" + src_file_name + \\\" --->\\\" + target_file)\\n shutil.move(src_file_name, target_file)\\n else:\\n log(\\\"(\\\" + str(CURRENT_PROGRESS) + '/' + str(TOTAL_FILE_NUM) + \\\")COPY: \\\" + src_file_name + \\\" --->\\\" + target_file)\\n shutil.copy(src_file_name, target_file)\",\n \"def download_and_validate_checksum(name, checksum):\\n dst = os.path.join(DOWNLOADS_DIR, os.path.basename(name))\\n download_file(src=name, dst=dst)\\n md5 = hashlib.md5()\\n for chunk in chunked_reader(dst):\\n md5.update(chunk)\\n dl_checksum = md5.digest().hex()\\n if dl_checksum != checksum:\\n raise ValueError(f\\\"expected checksum {checksum} but received {dl_checksum}\\\")\\n os.remove(dst)\",\n \"def _copy_if_not_exists(source: pl.Path, destination: pl.Path) -> None:\\n if destination.is_dir():\\n destination_file = destination / source.name\\n else:\\n destination_file = destination\\n if not destination_file.exists():\\n su.copy(source, destination)\",\n \"def sync_file(source, dest, dryrun=False, diff=False):\\n if diff:\\n if not exists(dest):\\n logger.info((\\\"Destination '{}' does not exist:\\\".format(dest),\\n \\\" skipping diff\\\"))\\n return\\n with open(source) as a:\\n with open(dest) as b:\\n s1 = a.readlines()\\n s2 = b.readlines()\\n sys.stdout.writelines(\\n difflib.unified_diff(s1, s2, fromfile=source, tofile=dest))\\n return\\n if not exists(dest):\\n if dryrun:\\n logger.info(\\\"DRY_RUN: Copying rule '{}' to '{}'\\\".format(\\n source, dest))\\n else:\\n if not exists(dirname(dest)):\\n os.makedirs(dirname(dest))\\n logger.info(\\\"Copying rule '{}' to '{}'\\\".format(source, dest))\\n shutil.copy2(source, dest)\\n else:\\n equal = filecmp.cmp(source, dest)\\n if (not equal):\\n if dryrun:\\n logger.info(\\\"DRY_RUN: Updating rule '{}' to '{}'\\\".format(\\n source, dest))\\n else:\\n logger.info(\\\"Updating rule '{}' to '{}'\\\".format(source, dest))\\n shutil.copy2(source, dest)\\n else:\\n if dryrun:\\n logger.info(\\\"DRY_RUN: rule '{}' up to date\\\".format(dest))\\n else:\\n logger.info(\\\"rule '{}' up to date\\\".format(dest))\",\n \"def copyFile(source,destination):\\r\\n logging.info(\\\"source\\\",source)\\r\\n logging.info(\\\"destination\\\",destination)\\r\\n try:\\r\\n shutil.copy(source, destination)\\r\\n logging.info(\\\"File copied successfully.\\\")\\r\\n \\\"\\\"\\\"If source and destination are same\\\"\\\"\\\"\\r\\n except shutil.SameFileError:\\r\\n logging.info(\\\"File not copied sucessfuly.\\\")\\r\\n \\\"\\\"\\\"List files and directories\\\"\\\"\\\"\\r\\n logging.info(\\\"After copying file:\\\")\\r\\n logging.info(os.listdir(destination))\\r\\n \\\"\\\"\\\"logging.info path of newly\\r\\n created file\\\"\\\"\\\"\\r\\n logging.info(\\\"Destination path:\\\", destination)\",\n \"def copy_to_local(src_file):\\r\\n if not_exists(src_file, \\\"Source File\\\"):\\r\\n return 1, 0\\r\\n _local_file = os.path.basename(src_file)\\r\\n if wrap_cp_file(src_file, _local_file):\\r\\n return 1, 0\\r\\n return 0, _local_file\",\n \"def copy_to_local(src_file):\\r\\n if not_exists(src_file, \\\"Source File\\\"):\\r\\n return 1, 0\\r\\n _local_file = os.path.basename(src_file)\\r\\n if wrap_cp_file(src_file, _local_file):\\r\\n return 1, 0\\r\\n return 0, _local_file\",\n \"def check_file_transferred(replica, location):\\n\\n from tardis.tardis_portal.models import Dataset_File\\n datafile = Dataset_File.objects.get(pk=replica.datafile.id)\\n\\n # If the remote is capable, get it to send us the checksums and / or\\n # file length for its copy of the file\\n try:\\n # Fetch the remote's metadata for the file\\n m = location.provider.get_metadata(replica)\\n _check_attribute(m, datafile.size, 'length')\\n if (_check_attribute(m, datafile.sha512sum, 'sha512sum') or \\\\\\n _check_attribute(m, datafile.md5sum, 'md5sum')):\\n return True\\n if location.trust_length and \\\\\\n _check_attribute(m, datafile.size, 'length') :\\n return False\\n raise MigrationError('Not enough metadata for verification')\\n except NotImplementedError:\\n pass\\n except HTTPError as e:\\n # Bad request means that the remote didn't recognize the query\\n if e.code != 400:\\n raise\\n\\n if location.provider.trust_length :\\n try:\\n length = location.provider.get_length(replica)\\n if _check_attribute2(length, datafile.size, 'length'):\\n return False\\n except NotImplementedError:\\n pass\\n\\n # Fetch back the remote file and verify it locally.\\n f = location.provider.get_opener(replica)()\\n md5sum, sha512sum, size, x = generate_file_checksums(f, None)\\n _check_attribute2(str(size), datafile.size, 'length')\\n if _check_attribute2(sha512sum, datafile.sha512sum, 'sha512sum') or \\\\\\n _check_attribute2(md5sum, datafile.md5sum, 'md5sum'):\\n return True\\n raise MigrationError('Not enough metadata for file verification')\",\n \"def copy_single_file(src, dest, log):\\n if op.exists(src) and op.isfile(src):\\n shellutil.copy(src, dest, log=log)\\n return True\",\n \"def checksumFile(filename):\\n return md5File(filename)\",\n \"def _check_source (fileurl, path_unzip, outfile) :\\n if outfile is not None and os.path.splitext (outfile)[1].lower () == os.path.splitext (fileurl)[1].lower () :\\n file = _check_url_file (fileurl, path_download = path_unzip, outfile = outfile)\\n return file\\n else :\\n file = _check_url_file (fileurl, path_download = path_unzip, outfile = None)\\n txt = _check_zip_file (file, path_unzip = path_unzip, outfile = outfile)\\n if not os.path.exists (txt):\\n message = \\\"hal_core._check_source: unable to find file \\\" + txt + \\\" source (\\\" + fileurl + \\\")\\\"\\n raise PQHException (message)\\n return txt\",\n \"def checkExisting(self, dst):\\n if dst.exists():\\n msg = 'Refusing to clobber existing file \\\"%s\\\"' % (\\n dst.path,)\\n logging.msg(msg)\\n raise errors.NoClobber(msg)\",\n \"def _rsync(self, source, dest):\\n \\n #print(source)\\n \\n # Test SSH connection.\\n if not self._ssh('test 1 -eq 1', use_pwd=False):\\n print \\\"Waiting for SSH on %s with key %s\\\" % (self.address[0], self.key_file)\\n time.sleep(1)\\n while not self._ssh('test 1 -eq 1', use_pwd=False):\\n time.sleep(1)\\n\\n # Archive, compress, delete extraneous files from dest dirs.\\n rsync = ['rsync', '-az', '--delete']\\n\\n # Use key file\\n if self.key_file:\\n ssh = 'ssh -o UserKnownHostsFile=/dev/null -o StrictHostKeyChecking=no -o IdentitiesOnly=yes -i \\\"%s\\\"'\\n rsync.extend(['-e', ssh % self.key_file])\\n\\n if isinstance(source, list):\\n rsync.extend(source)\\n rsync.append(dest)\\n else:\\n rsync.extend([source, dest])\\n \\n print 'Sync files from %s to %s...' % (source, dest)\\n \\n if subprocess.call(rsync) == 0:\\n return True\\n else:\\n return False\",\n \"def run(self):\\n try:\\n self.parse_args(None)\\n self.execute_command()\\n except FileExistsException, e:\\n print \\\"Can't copy file as destination already exists.\\\"\\n print \\\"Exiting...\\\"\\n except Exception, e:\\n print \\\"Exception occured: %s\\\\nExiting...\\\" % e\",\n \"def _copy_file ( self, source, dest ):\\n return\",\n \"def copy_file(src, dst, ignore=None):\\n # Sanity checkpoint\\n src = re.sub('[^\\\\w/\\\\-\\\\.\\\\*]', '', src)\\n dst = re.sub('[^\\\\w/\\\\-\\\\.\\\\*]', '', dst)\\n if len(re.sub('[\\\\W]', '', src)) < 5 or len(re.sub('[\\\\W]', '', dst)) < 5:\\n debug.log(\\\"Error: Copying file failed. Provided paths are invalid! src='%s' dst='%s'\\\"%(src, dst))\\n else:\\n # Check destination\\n check = False\\n if dst[-1] == '/':\\n if os.path.exists(dst):\\n check = True # Valid Dir\\n else:\\n debug.log(\\\"Error: Copying file failed. Destination directory does not exist (%s)\\\"%(dst)) #DEBUG\\n elif os.path.exists(dst):\\n if os.path.isdir(dst):\\n check = True # Valid Dir\\n dst += '/' # Add missing slash\\n else:\\n debug.log(\\\"Error: Copying file failed. %s exists!\\\"%dst)\\n elif os.path.exists(os.path.dirname(dst)):\\n check = True # Valid file path\\n else:\\n debug.log(\\\"Error: Copying file failed. %s is an invalid distination!\\\"%dst)\\n if check:\\n # Check source\\n files = glob.glob(src)\\n if ignore is not None: files = [fil for fil in files if not ignore in fil]\\n if len(files) != 0:\\n debug.log(\\\"Copying File(s)...\\\", \\\"Copy from %s\\\"%src, \\\"to %s\\\"%dst) #DEBUG\\n for file_ in files:\\n # Check file exists\\n if os.path.isfile(file_):\\n debug.log(\\\"Copying file: %s\\\"%file_) #DEBUG\\n shutil.copy(file_, dst)\\n else:\\n debug.log(\\\"Error: Copying file failed. %s is not a regular file!\\\"%file_) #DEBUG\\n else: debug.log(\\\"Error: Copying file failed. No files were found! (%s)\\\"%src) #DEBUG\",\n \"def copyFile(src_file, dst_file, race_flag):\\n\\n if race_flag:\\n time.sleep( random.randint(5, random.randint(10, 50)) )\\n if os.path.exists(dst_file):\\n while not sameSize(src_file, dst_file):\\n time.sleep(10)\\n return None\\n\\n while(True):\\n shutil.copy(src_file, dst_file)\\n if sameSize(src_file, dst_file):\\n break\",\n \"def verify_destination(self, destination):\\n # Make sure the text file was copied to the destination.\\n text_file = os.path.join(destination, 'notes.txt')\\n assert os.path.isfile(text_file)\\n with open(text_file) as handle:\\n assert handle.read() == \\\"This file should be included in the backup.\\\\n\\\"\\n # Make sure the subdirectory was copied to the destination.\\n subdirectory = os.path.join(destination, 'subdirectory')\\n assert os.path.isdir(subdirectory)\\n # Make sure the symbolic link was copied to the destination.\\n symlink = os.path.join(subdirectory, 'symbolic-link')\\n assert os.path.islink(symlink)\",\n \"def test_source_package_checksum_changes(self):\\n response = self.client.head(\\n f'/filemanager/api/{self.upload_id}/content',\\n headers={'Authorization': self.token}\\n )\\n first_checksum = response.headers.get('ETag')\\n self.assertIsNotNone(first_checksum)\\n\\n response = self.client.post(\\n f'/filemanager/api/{self.upload_id}',\\n data={'file': (io.BytesIO(b'foocontent'), 'foo.txt'),},\\n headers={'Authorization': self.token},\\n content_type='multipart/form-data'\\n )\\n\\n second_checksum = response.headers.get('ETag')\\n self.assertIsNotNone(second_checksum)\\n self.assertNotEqual(first_checksum, second_checksum)\\n\\n response = self.client.head(\\n f'/filemanager/api/{self.upload_id}/content',\\n headers={'Authorization': self.token}\\n )\\n\\n third_checksum = response.headers.get('ETag')\\n self.assertIsNotNone(third_checksum)\\n self.assertEqual(second_checksum, third_checksum)\",\n \"def copy(self):\\n source = os.path.abspath(self.path)\\n destination = os.path.abspath(self.target)\\n\\n logger.info(\\\"Running Copy Method - SOURCE=\\\\\\\"{src}\\\\\\\" DESTINATION=\\\\\\\"{dst}\\\\\\\" IGNORE=\\\\\\\"{ignore}\\\\\\\"\\\".format(src=source, dst=destination, ignore=self.ignore))\\n\\n if not os.path.exists(source):\\n logger.error(\\\"\\\\\\\"{source}\\\\\\\" PATH DOESN'T EXIST. PROGRAM TERMINATED. Please check log file.\\\".format(source=source))\\n\\n if self.rules is not None:\\n files = self.rules\\n else:\\n self.create_packet_structure(source)\\n files = self.files\\n\\n for (k,v) in files.items():\\n src = os.path.join(source,k)\\n dst = os.path.join(destination,v)\\n dirpath = os.path.dirname(dst)\\n if not os.path.isdir(dirpath):\\n logger.info(\\\"Create directory - \\\\\\\"{dst}\\\\\\\"\\\".format(dst=dirpath))\\n os.makedirs(dirpath)\\n logger.info(\\\"copy from \\\\\\\"{f}\\\\\\\" to \\\\\\\"{t}\\\\\\\"\\\".format(f=src,t=dst))\\n shutil.copyfile(src,dst)\\n logger.info(\\\"OK\\\")\",\n \"def CheckForDirFileConflict(self, src_uri, dst_path):\\n final_dir = os.path.dirname(dst_path)\\n if os.path.isfile(final_dir):\\n raise CommandException('Cannot retrieve %s because it a file exists '\\n 'where a directory needs to be created (%s).' %\\n (src_uri, final_dir))\\n if os.path.isdir(dst_path):\\n raise CommandException('Cannot retrieve %s because a directory exists '\\n '(%s) where the file needs to be created.' %\\n (src_uri, dst_path))\",\n \"def verify_checksum(path):\\n rc = True\\n for f in os.listdir(path):\\n if f.endswith('.md5'):\\n cwd = os.getcwd()\\n os.chdir(path)\\n with open(os.devnull, \\\"w\\\") as fnull:\\n try:\\n subprocess.check_call(['md5sum', '-c', f], # pylint: disable=not-callable\\n stdout=fnull, stderr=fnull)\\n LOG.info(\\\"Checksum file is included and validated.\\\")\\n except Exception as e:\\n LOG.exception(e)\\n rc = False\\n finally:\\n os.chdir(cwd)\\n return rc\\n LOG.info(\\\"Checksum file is not included, skipping validation.\\\")\\n return rc\",\n \"def testDetermineDest(self):\\n self.cc.determine_dest('cdl', '/bobsBestDirectory')\\n\\n dir = os.path.abspath('/bobsBestDirectory')\\n filename = os.path.join(dir, 'uniqueId.cdl')\\n\\n self.assertEqual(\\n filename,\\n self.cc.file_out\\n )\",\n \"def testChecksumCondition(self):\\n file_defs = [\\n {'name': 'file_0_byte.txt', 'path': '', 'size': 0},\\n {'name': 'file_1_byte.txt', 'path': '', 'size': 1},\\n {'name': 'file_320k_minus 1_byte.txt', 'path': 'folder1', 'size': 320 * 1024 - 1, 'mod_inc': -1},\\n ]\\n\\n self._setup_test_store(file_defs)\\n\\n # Check if this provider supports checksum as change condition\\n drive = self.drive_class(self.account_id, self.config_file_dir, self.config_pw)\\n if drive.files_differ_on_hash(\\n os.path.join(self.test_local_dir, file_defs[0]['path'], file_defs[0]['name']),\\n 'dummy_hash') is None:\\n self.skipTest('Checksum change condition not supported for this provider.')\\n\\n self._sync_drives()\\n\\n # Modify files\\n for file_def in file_defs:\\n file_path =\\\\\\n os.path.join(self.test_local_dir, file_def['path'], file_def['name'])\\n test_utils.make_random_file(\\n file_path, file_def['size'], leave_existing=False,\\n modify_timestamp_ns=os.stat(file_path).st_mtime_ns)\\n\\n self._sync_drives()\\n self._download_store()\\n self.assertDirectoriesAreEqual(self.test_local_dir, self.test_download_dir)\",\n \"def test_source_package_checksum_is_stable(self):\\n response = self.client.head(\\n f'/filemanager/api/{self.upload_id}/content',\\n headers={'Authorization': self.token}\\n )\\n first_checksum = response.headers.get('ETag')\\n self.assertIsNotNone(first_checksum)\\n self.assertEqual(first_checksum, self.original_checksum)\\n\\n response = self.client.head(\\n f'/filemanager/api/{self.upload_id}/content',\\n headers={'Authorization': self.token}\\n )\\n second_checksum = response.headers.get('ETag')\\n self.assertEqual(first_checksum, second_checksum)\\n\\n response = self.client.get(\\n f'/filemanager/api/{self.upload_id}/content',\\n headers={'Authorization': self.token}\\n )\\n third_checksum = response.headers.get('ETag')\\n self.assertEqual(first_checksum, third_checksum)\",\n \"def test_success_from_bin():\\n createFromBin(\\\"tests/vbaProject.bin\\\", \\\"src/data\\\", \\\"success_bin.xlam\\\")\\n # Assert that xlam file is created\\n assert exists(\\\"success_bin.xlam\\\")\\n #assert that bin file within success_bin.xlam matches tests/vbaProject.bin\\n extractBinFromZip(\\\"success_bin.xlam\\\")\\n md5hasher = FileHash('md5')\\n assert md5hasher.hash_file(\\\"tests/vbaProject.bin\\\") == md5hasher.hash_file(\\\"xl/vbaProject.bin\\\")\\n\\n createFromZip(\\\"success_bin.xlam\\\", \\\"src/data\\\", \\\"success_xlam.xlam\\\")\\n assert exists(\\\"success_xlam.xlam\\\")\\n #assert that bin file within success_xlam.xlam matches bin file within success_bin.xlam\\n extractBinFromZip(\\\"success_xlam.xlam\\\")\\n assert md5hasher.hash_file(\\\"tests/vbaProject.bin\\\") == md5hasher.hash_file(\\\"xl/vbaProject.bin\\\")\",\n \"def compare(src, dest):\\n xsrc, xdest = os.path.exists(src), os.path.exists(dest)\\n if not xsrc:\\n return Cmp.nosrc\\n if not xdest:\\n return Cmp.nodest\\n with open(src, \\\"rb\\\") as s:\\n csrc = sha256(s.read()).digest()\\n if xdest:\\n with open(dest, \\\"rb\\\") as d:\\n cdest = sha256(d.read()).digest()\\n else:\\n cdest = b\\\"\\\"\\n if csrc == cdest:\\n return Cmp.same\\n return Cmp.differ\",\n \"def pull_file(self, source, target):\\n if not source or not target:\\n raise ValueError\\n try:\\n subprocess.check_call(self.command + [\\\"pull\\\", source, target])\\n except subprocess.CalledProcessError as e:\\n self.log.warning(e)\\n self.log.warning(\\\"Failed to copy \\\\\\\"%s:%s\\\\\\\" to %s\\\",\\n self.name, source, target)\\n raise ValueError\\n else:\\n self.log.debug(\\\"Copied \\\\\\\"%s:%s\\\\\\\" to \\\\\\\"%s\\\\\\\"\\\",\\n self.name, source, target)\",\n \"def copy_if_needed(src: str, dst: str, filter: str|List[str]|None = None) -> bool:\\n #console(f'COPY {src} --> {dst}')\\n if os.path.isdir(src):\\n return copy_dir(src, dst, filter)\\n else:\\n return copy_file(src, dst, filter)\",\n \"def copy_file(src, dest):\\n logger.debug(\\\"Copying %s to %s\\\", src, dest)\\n try:\\n shutil.copy(src, dest)\\n except (OSError, IOError) as exc:\\n logger.debug('Installation error, trying sudo.')\\n try:\\n check_call(['sudo', 'cp', src, dest])\\n except HelperError:\\n # That failed too - re-raise the original exception\\n raise exc\\n return True\",\n \"def checksum_file(vm_name, file_name, vm_executor=None):\\n command = config.MD5SUM_CMD % file_name\\n if not vm_executor:\\n vm_executor = get_vm_executor(vm_name)\\n return vm_executor.run_cmd(shlex.split(command))[1]\",\n \"def test_checksum(size1, size2, lines, tmpdir):\\n fp = tmpdir.join(\\\"temp-data.txt\\\").strpath\\n data = \\\"\\\\n\\\".join(lines)\\n with open(fp, 'w') as f:\\n f.write(data)\\n exp = hashlib.new(\\\"md5\\\", data.encode(\\\"utf-8\\\")).hexdigest()\\n res1 = checksum(fp, size1)\\n res2 = checksum(fp, size2)\\n assert exp == res1\\n assert res1 == res2\\n assert res2 == exp\",\n \"def main():\\n parser = CustomArgumentParser()\\n parser.add_argument(\\\"-s\\\", \\\"--simon-sez\\\",\\n help=\\\"Really, Simon sez copy the data!\\\", action=\\\"store_true\\\")\\n parser.add_argument(\\\"-r\\\", \\\"--src-directory\\\",\\n help=\\\"Copy metadata from files in this directory.\\\")\\n parser.add_argument(\\\"-d\\\", \\\"--dst-directory\\\",\\n help=\\\"Copy metadata to matching files in this directory.\\\")\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", help=\\\"Log level to DEBUG.\\\",\\n action=\\\"store_true\\\")\\n args = parser.parse_args()\\n\\n if args.verbose:\\n logging.basicConfig(level=logging.DEBUG)\\n else:\\n logging.basicConfig(level=logging.INFO)\\n\\n error = False\\n\\n # Require these two arguments.\\n for arg in [args.src_directory, args.dst_directory]:\\n if not arg:\\n logger.error(\\n \\\"Required src or dst directory parameter missing.\\\")\\n error = True\\n # XXX: Duplicates exit below. Can't check directory if null.\\n logger.error(\\\"Exiting due to errors.\\\")\\n parser.usage_message()\\n sys.exit(1)\\n\\n if (os.path.exists(args.src_directory) and\\n os.path.isdir(args.src_directory)):\\n src_directory = args.src_directory\\n else:\\n logger.error(\\n \\\"--src-directory={} does not exist or is not a directory.\\\".format(\\n args.dst_directory))\\n error = True\\n\\n if (os.path.exists(args.dst_directory) and\\n os.path.isdir(args.dst_directory)):\\n dst_directory = args.dst_directory\\n else:\\n logger.error(\\n \\\"--dst-directory={} does not exist or is not a directory.\\\".format(\\n args.dst_directory))\\n error = True\\n\\n if error:\\n logger.error(\\\"Exiting due to errors.\\\")\\n parser.usage_message()\\n sys.exit(1)\\n else:\\n process_all_files(src_directory, dst_directory, simon_sez=args.simon_sez)\",\n \"def svn_fs_file_md5_checksum(*args):\\r\\n return _fs.svn_fs_file_md5_checksum(*args)\",\n \"def copy_file(src: str, dst: str, filter: str|List[str]|None = None) -> bool:\\n if _passes_filter(src, filter):\\n if os.path.isdir(dst):\\n dst = os.path.join(dst, os.path.basename(src))\\n if _should_copy(src, dst):\\n #console(f'copy {src}\\\\n --> {dst}')\\n shutil.copyfile(src, dst, follow_symlinks=True)\\n shutil.copystat(src, dst, follow_symlinks=True)\\n return True\\n return False\",\n \"def FileSendRemoteChecksum(self, source_paths: list):\\n try:\\n paths = [p for pat in source_paths for p in self.expandPath(pat)]\\n g = self.fileChunkGenerator(paths, False)\\n return {c.path: c.sum for c in self.filemanager.SendChecksum(g)}\\n except grpc.RpcError as e:\\n status_code = e.code() # status_code.name and status_code.value\\n if grpc.StatusCode.NOT_FOUND == status_code:\\n raise FileNotFoundError(e.details()) from e\\n else:\\n # pass any other gRPC errors to user\\n raise e\",\n \"def compute_checksum(filename):\\n cmd = 'md5sum ' + filename\\n return pipe(cmd)\",\n \"def checkSumHelper(arg, dirname, fnames):\\n val = 0\\n files = [name for name in fnames if os.path.splitext(name)[1] in EXTENSIONS]\\n for file in files:\\n absFile = os.path.join(dirname,file)\\n try:\\n stats = os.stat(absFile)\\n except OSError,e:\\n # This is to skip over temporary files or files\\n # nosy doesn't have permission to access\\n # print \\\"Nosy: skipping file %s with error %s\\\"%(absFile,e)\\n continue\\n val += stats[stat.ST_SIZE] + stats[stat.ST_MTIME]\\n arg.append(val)\\n return\",\n \"def check(src, dst):\\n walker = Walker()\\n walker.check(src, dst)\\n return\",\n \"def copy_file(source_file, target_file):\\n\\t# print('\\\\n\\\\nCopying [{}] to [{}].\\\\n\\\\n'.format(source_file, target_file))\\n\\trun_rsync([source_file, target_file])\",\n \"def copy(self, source_host, dest_host, filename):\",\n \"def determine_should_sync(\\n self, src_file: Optional[FileStats], dest_file: Optional[FileStats]\\n ) -> bool:\\n if dest_file:\\n dest_file.operation_name = \\\"delete\\\"\\n LOGGER.debug(\\n \\\"syncing: (None) -> %s (remove), file does not \\\"\\n \\\"exist at source (%s) and delete mode enabled\\\",\\n dest_file.src if dest_file else None,\\n dest_file.dest if dest_file else None,\\n )\\n return True\",\n \"def has_checksum_file(self):\\n return self.checksum_file_path.is_file()\",\n \"def md5sum(fileSrc):\\n md5 = hashlib.md5()\\n try:\\n with open(fileSrc, \\\"rb\\\") as fd:\\n while True:\\n content = fd.read(2**20)\\n if not content:\\n break\\n md5.update(content)\\n except IOError:\\n print(fileSrc + \\\" Not found\\\")\\n exit(1)\\n return md5.hexdigest()\",\n \"def putFile( self, path, sourceSize = 0 ):\\n res = checkArgumentFormat( path )\\n if not res['OK']:\\n return res\\n urls = res['Value']\\n successful = {}\\n failed = {}\\n for dest_url, src_file in urls.items():\\n gLogger.debug( \\\"DIPStorage.putFile: Executing transfer of %s to %s\\\" % ( src_file, dest_url ) )\\n res = self.__putFile( src_file, dest_url )\\n if res['OK']:\\n successful[dest_url] = res['Value']\\n else:\\n failed[dest_url] = res['Message']\\n resDict = {'Failed':failed, 'Successful':successful}\\n return S_OK( resDict )\",\n \"def alock_installFunc(dest, source, env):\\n\\n owner = env.get('INSTALL_OWNER', None)\\n if owner:\\n try:\\n uid = pwd.getpwnam(owner)[2]\\n except TypeError:\\n uid = owner\\n else:\\n uid = -1\\n\\n group = env.get('INSTALL_GROUP', None)\\n if group:\\n try:\\n gid = grp.getgrnam(group)[2]\\n except TypeError:\\n gid = group\\n else:\\n gid = -1\\n\\n mode = env.get('INSTALL_MODE', None)\\n if not mode:\\n st = os.stat(source)\\n mode = (stat.S_IMODE(st[stat.ST_MODE]) | stat.S_IWRITE)\\n if isinstance(mode, str):\\n mode = int(mode, 8)\\n\\n shutil.copy2(source, dest)\\n\\n if owner or group:\\n os.chown(dest, uid, gid)\\n\\n os.chmod(dest, mode)\\n return 0\",\n \"def assert_sources_folder_exist(command: commands.FilesRelatedCommand,\\n filesystem: infra.Filesystem,\\n stdout: infra.STDOut) -> None:\\n if filesystem.not_exists(command.sources_folder):\\n stdout.red(f'Sources folder does not exist: {command.sources_folder}')\\n sys.exit(1)\",\n \"def make_checksum_file(self, project):\\n return None\",\n \"def _copyFile(self, source, dstDir):\\n dstFile = os.path.join(dstDir, os.path.basename(source))\\n touch = \\\"/usr/bin/touch\\\" if OSUtilities.isMacOS() else \\\"/bin/touch\\\"\\n subprocess.call([touch, dstFile])\\n subprocess.call([\\\"/bin/cp\\\", source, dstDir])\\n self._logger.info(\\\"Copying file \\\" + source + \\\" to \\\" + dstDir)\\n self._numCopiedFiles += 1\",\n \"def copy_file(name, n_name):\\n\\n if os.path.isfile(config_tools.full_dest+name):\\n try:\\n shutil.copyfile(config_tools.full_dest+name, config_tools.full_dest+n_name)\\n except OSError:\\n print(f\\\"Не возможно копировать файл {name}\\\")\\n else:\\n print(f\\\"Файл {config_tools.full_dest+name} скопирован как {config_tools.full_dest+n_name}\\\")\",\n \"def check_exists(self, name):\\n if self.pyload.config.get(\\\"download\\\", \\\"skip_existing\\\"):\\n download_folder = self.pyload.config.get(\\n 'general', 'download_folder')\\n dest_file = fsjoin(download_folder,\\n self.pyfile.package().folder if self.pyload.config.get(\\n \\\"general\\\", \\\"folder_per_package\\\") else \\\"\\\",\\n name)\\n if exists(dest_file):\\n self.pyfile.name = name\\n self.skip(_(\\\"File exists.\\\"))\",\n \"def checksum_test(self, attack_args, sha256_checksum, seed=5, cleanup=True, pcap=Lib.test_pcap,\\n flag_write_file=False, flag_recalculate_stats=False, flag_print_statistics=False,\\n attack_sub_dir=True, test_sub_dir=True, time=False):\\n\\n controller = Ctrl.Controller(pcap_file_path=pcap, do_extra_tests=False, non_verbose=True)\\n controller.load_pcap_statistics(flag_write_file, flag_recalculate_stats, flag_print_statistics,\\n intervals=[], delete=True)\\n\\n controller.process_attacks(attack_args, [[seed]], time)\\n\\n caller_function = inspect.stack()[1].function\\n\\n try:\\n self.assertEqual(sha256_checksum, Lib.get_sha256(controller.pcap_dest_path))\\n except self.failureException:\\n Lib.rename_test_result_files(controller, caller_function, attack_sub_dir, test_sub_dir)\\n raise\\n\\n if cleanup:\\n Lib.clean_up(controller)\\n else:\\n Lib.rename_test_result_files(controller, caller_function, attack_sub_dir, test_sub_dir)\",\n \"def _get_checksum(self, arg):\",\n \"def test_file_integrity_remove_file_in_case_of_fail():\\n test_file = open('./testfile.tmp', 'a')\\n test_file.close()\\n test_file_path = os.path.realpath('./testfile.tmp')\\n test_file_md5 = hashlib.md5(open(test_file_path, 'rb').read()).hexdigest()\\n\\n bad_md5 = 'some_noise_%s' % test_file_md5\\n\\n PackageDownloadHelper.check_file_integrity(test_file_path, bad_md5)\\n\\n assert not os.path.isfile(test_file_path)\",\n \"def copyFile(source, target):\\n\\tfrom shutil import copyfile, copystat, copymode\\n\\tfrom os.path import split\\n\\tsource = adaptPath(source)\\n\\ttarget = adaptPath(target)\\n\\tif int(getFileModifTime(source)) != int(getFileModifTime(target)):\\n\\t\\tmakedir(split(target)[0])\\n\\t\\tcopyfile(source, target)\\n\\t\\tcopystat(source, target)\\n\\t\\tcopymode(source, target)\\n\\t#~ else:\\n\\t\\t#~ print (\\\"%s not copied\\\"%(target))\",\n \"def need_checksum(self):\\n if self.skip_checksum:\\n log.warning(\\\"Skip checksum because --skip-checksum is specified\\\")\\n return False\\n # There's no point running a checksum compare for selective dump\\n if self.where:\\n log.warning(\\\"Skip checksum because --where is given\\\")\\n return False\\n # If the collation of primary key column has been changed, then\\n # it's high possible that the checksum will mis-match, because\\n # the returning sequence after order by primary key may be vary\\n # for different collations\\n for pri_column in self._pk_for_filter:\\n old_column_tmp = [\\n col for col in self._old_table.column_list if col.name == pri_column\\n ]\\n if old_column_tmp:\\n old_column = old_column_tmp[0]\\n new_column_tmp = [\\n col for col in self._new_table.column_list if col.name == pri_column\\n ]\\n if new_column_tmp:\\n new_column = new_column_tmp[0]\\n if old_column and new_column:\\n if not is_equal(old_column.collate, new_column.collate):\\n log.warning(\\n \\\"Collation of primary key column {} has been \\\"\\n \\\"changed. Skip checksum \\\".format(old_column.name)\\n )\\n return False\\n # There's no way we can run checksum by chunk if the primary key cannot\\n # be covered by any index of the new schema\\n if not self.validate_post_alter_pk():\\n if self.skip_pk_coverage_check:\\n log.warning(\\n \\\"Skipping checksuming because there's no unique index \\\"\\n \\\"in new table schema can perfectly cover old primary key \\\"\\n \\\"combination for search\\\".format(old_column.name)\\n )\\n return False\\n else:\\n # Though we have enough coverage for primary key doesn't\\n # necessarily mean we can use it for checksum, it has to be an\\n # unique index as well. Skip checksum if there's no such index\\n if not self.find_coverage_index():\\n log.warning(\\n \\\"Skipping checksuming because there's no unique index \\\"\\n \\\"in new table schema can perfectly cover old primary key \\\"\\n \\\"combination for search\\\".format(old_column.name)\\n )\\n return False\\n return True\",\n \"def copyfile(source, dest, newname=None):\\n\\n if not os.path.exists(source):\\n #print 'no such file %s' %source\\n return False\\n shutil.copy(source, newname)\\n dest = os.path.join(dest, newname)\\n if os.path.exists(dest):\\n os.remove(dest)\\n shutil.move(newname, dest)\\n return True\",\n \"def _verify_archive_equality(self, file1, file2):\\r\\n temp_dir_1 = mkdtemp()\\r\\n temp_dir_2 = mkdtemp()\\r\\n try:\\r\\n extract_source(file1, temp_dir_1)\\r\\n extract_source(file2, temp_dir_2)\\r\\n return directories_equal(temp_dir_1, temp_dir_2)\\r\\n\\r\\n finally:\\r\\n shutil.rmtree(temp_dir_1)\\r\\n shutil.rmtree(temp_dir_2)\",\n \"def do_compare(self, str_arg):\\n arg = validateString(str_arg)\\n source, target = arg.split(' ', 1)\\n if os.path.isfile(source):\\n # Mar 27 @swang: if target file doesn't exist, copy source file to setup directory for later test\\n # 2015-08-27: decided to go to fail path\\n if not os.path.isfile(target):\\n # copy(source, target)\\n self.resultFlag = False\\n raise ValueError('COMPARE FAILED: target file not found.')\\n # if not self.__compareImage(source, target):\\n if not filecmp.cmp(source, target):\\n printLog(self.threadName + 'COMPARE FAILED: source file and target file DIFFER!', logging.WARNING)\\n self.resultFlag = False\\n else:\\n self.resultFlag = False\\n raise ValueError('COMPARE FAILED: source file not found.')\",\n \"def process(self, source_path: pathlib.Path) -> bool:\",\n \"def run_copy(self, src, dst):\\n pass\",\n \"def check(src, perm, dest, cmds, comp, verbose=False):\\n if comp == Cmp.differ:\\n ansiprint(f\\\"The file '{src}' differs from '{dest}'.\\\", fg=Color.red, i=True)\\n elif comp == Cmp.nodest:\\n ansiprint(\\n f\\\"The destination file '{dest}' does not exist\\\",\\n fg=Color.black,\\n bg=Color.red,\\n )\\n elif comp == Cmp.nosrc:\\n ansiprint(\\n f\\\"The source file '{src}' does not exist.\\\", fg=Color.black, bg=Color.red\\n )\\n elif comp == Cmp.same and verbose:\\n ansiprint(f\\\"The files '{src}' and '{dest}' are the same.\\\", fg=Color.green)\",\n \"def _copy_file(src, dest):\\n\\n if src is None or dest is None:\\n raise ValueError(\\\"src and dest must not be None\\\", src, dest)\\n\\n if not os.path.isfile(src):\\n raise ValueError(\\\"src file does not appear to exist\\\", src)\\n\\n # if error on copy, subprocess will raise CalledProcessError\\n try:\\n subprocess.run(\\n [\\\"/usr/bin/ditto\\\", src, dest], check=True, stderr=subprocess.PIPE\\n )\\n except subprocess.CalledProcessError as e:\\n logging.critical(\\n f\\\"ditto returned error: {e.returncode} {e.stderr.decode(sys.getfilesystemencoding()).rstrip()}\\\"\\n )\\n raise e\",\n \"def move_file(src, dst):\\n # Sanity checkpoint\\n src = re.sub('[^\\\\w/\\\\-\\\\.\\\\*]', '', src)\\n dst = re.sub('[^\\\\w/\\\\-\\\\.\\\\*]', '', dst)\\n if len(re.sub('[\\\\W]', '', src)) < 5 or len(re.sub('[\\\\W]', '', dst)) < 5:\\n debug.log(\\\"Error: Moving file failed. Provided paths are invalid! src='%s' dst='%s'\\\"%(src, dst))\\n else:\\n # Check destination\\n check = False\\n if dst[-1] == '/':\\n if os.path.exists(dst):\\n check = True # Valid Dir\\n else:\\n debug.log(\\\"Error: Moving file failed. Destination directory does not exist (%s)\\\"%(dst)) #DEBUG\\n elif os.path.exists(dst):\\n if os.path.isdir(dst):\\n check = True # Valid Dir\\n dst += '/' # Add missing slash\\n else:\\n debug.log(\\\"Error: Moving file failed. %s exists!\\\"%dst)\\n elif os.path.exists(os.path.dirname(dst)):\\n check = True # Valid file path\\n else:\\n debug.log(\\\"Error: Moving file failed. %s is an invalid distination!\\\"%dst)\\n if check:\\n # Check source\\n files = glob.glob(src)\\n if len(files) != 0:\\n debug.log(\\\"Moving File(s)...\\\", \\\"Move from %s\\\"%src, \\\"to %s\\\"%dst)\\n for file_ in files:\\n # Check if file contains invalid symbols:\\n invalid_chars = re.findall('[^\\\\w/\\\\-\\\\.\\\\*]', os.path.basename(file_))\\n if invalid_chars:\\n debug.graceful_exit((\\\"Error: File %s contains invalid \\\"\\n \\\"characters %s!\\\"\\n )%(os.path.basename(file_), invalid_chars))\\n continue\\n # Check file exists\\n if os.path.isfile(file_):\\n debug.log(\\\"Moving file: %s\\\"%file_)\\n shutil.move(file_, dst)\\n else:\\n debug.log(\\\"Error: Moving file failed. %s is not a regular file!\\\"%file_)\\n else: debug.log(\\\"Error: Moving file failed. No files were found! (%s)\\\"%src)\",\n \"def checksum(self, md5_file, file_name):\\n try:\\n with open(md5_file, 'r') as f:\\n md5_file_contents = f.read()\\n md5_str = md5_file_contents.split(' ')[0]\\n os.remove(md5_file)\\n except Exception as e:\\n logging.exception('Could not read MD5 file {}. \\\\\\n \\\\nTry to download the file again'.format(file_name))\\n return False\\n if not self.check_md5(file_name, md5_str):\\n logging.error('Failed in checksum. Download the file again.')\\n return False\\n return True\",\n \"def copy(self, src_path: str, tgt_path: str) -> None:\",\n \"def testCheckSourceCopyOperation_Pass(self):\\n payload_checker = checker.PayloadChecker(self.MockPayload())\\n self.assertIsNone(\\n payload_checker._CheckSourceCopyOperation(None, 134, 134, 'foo'))\",\n \"def hash_file(self, file_path, file_arcname):\\n\\n file_path = os.path.abspath(file_path)\\n\\n # If the file_arcname argument is None use the base file name as the\\n # arc name\\n if file_arcname is None:\\n file_arcname = os.path.basename(file_path)\\n\\n if not os.path.exists(file_path):\\n task_error(\\\"%s doesn't exist\\\" % file_path)\\n if not os.access(file_path, os.R_OK):\\n task_error(\\\"Can't read from %s\\\" % file_path)\\n\\n file_mode = os.stat(file_path)[stat.ST_MODE]\\n if not stat.S_ISDIR(file_mode) and not stat.S_ISREG(file_mode):\\n task_error(\\\"Unknown file type for %s\\\" % file_path)\\n\\n file_in = None\\n try:\\n # open to read binary. This is important.\\n file_in = open(file_path, 'rb')\\n except IOError:\\n task_error(\\\"Couldn't read from file: %s\\\" % file_path)\\n\\n # hash file 1Mb at a time\\n hashval = hashlib.sha1()\\n while True:\\n data = file_in.read(1024 * 1024)\\n if not data:\\n break\\n hashval.update(data)\\n\\n # update file bundle status\\n\\n self.running_size += len(data)\\n\\n self.percent_complete = 100.0 * self.running_size / self.bundle_size\\n\\n # only update significant progress\\n if self.percent_complete - self.last_percent > 1:\\n self.report_percent_complete()\\n self.last_percent = self.percent_complete\\n\\n file_hash = hashval.hexdigest()\\n\\n # print 'hash: ' + file_hash\\n file_in.close()\\n\\n modified_name = os.path.join('data', file_arcname)\\n (file_dir, file_name) = os.path.split(modified_name)\\n\\n # linuxfy the directory\\n file_dir = file_dir.replace('\\\\\\\\', '/')\\n\\n info = {}\\n info['size'] = os.path.getsize(file_path)\\n mime_type = mimetypes.guess_type(file_path, strict=True)[0]\\n\\n info['mimetype'] = mime_type if mime_type is not None else 'application/octet-stream'\\n info['name'] = file_name\\n info['mtime'] = DT.datetime.utcfromtimestamp(int(os.path.getmtime(file_path))).isoformat()\\n info['ctime'] = DT.datetime.utcfromtimestamp(int(os.path.getctime(file_path))).isoformat()\\n info['destinationTable'] = 'Files'\\n info['subdir'] = file_dir\\n info['hashsum'] = file_hash\\n info['hashtype'] = 'sha1'\\n\\n # todo make sure errors bubble up without crashing\\n if file_arcname in self.file_meta:\\n print file_arcname\\n task_error(\\n \\\"Different file with the same arcname is already in the bundle\\\")\\n return\\n\\n return info\",\n \"def evaluate(self):\\n errors_found = 0\\n\\n # Test if destination exists and is a directory\\n if not os.path.isdir(os.path.expandvars(self.path_destination)):\\n message.alert(\\n f\\\"FILE [{self.name}]: '{self.path_destination}' destination path is not a known directory.\\\"\\n )\\n errors_found += 1\\n\\n # Test if sudo is really needed if the file's\\n # destination is in a directory owned by the current user\\n if os.getenv(\\\"HOME\\\") in os.path.expandvars(self.path_destination) and self.sudo:\\n message.alert(\\n f\\\"FILE [{self.name}]: Sudo use may be unnecessary as {self.path_destination} is in your home path.\\\"\\n )\\n errors_found += 1\\n\\n # Test if source is a directory\\n if self.path_source is not None:\\n if not os.path.isdir(os.path.expandvars(self.path_source)):\\n message.alert(\\n f\\\"FILE [{self.name}]: '{self.path_source}' source path is not a known directory.\\\"\\n )\\n errors_found += 1\\n\\n if not os.path.isfile(\\n os.path.join(os.path.expandvars(self.path_source), self.name)\\n ):\\n message.alert(\\n f\\\"FILE [{self.name}] at PATH: [{self.path_source}] does not exist.\\\"\\n )\\n errors_found += 1\\n\\n # Check expected types\\n if not isinstance(self.comments, list):\\n message.alert(\\n f\\\"FILE [{self.name}]: Type mismatch, comments attribute is of type '{type(self.comments)}' instead of 'list'\\\"\\n )\\n errors_found += 1\\n\\n return errors_found\",\n \"def copyFile(src, dest):\\n try:\\n shutil.copy(src,dest)\\n except shutil.Error as e:\\n print(\\\"Error: \\\" + str(e))\\n except IOError as e:\\n print(\\\"Error: \\\" + e.strerror)\",\n \"def source(dirname, filename, gen_content):\\n if dirname in lut['sources']:\\n s.add('MD5SUM=\\\"$(find \\\"{0}\\\" -printf %T@\\\\\\\\\\\\\\\\n | md5sum)\\\"', dirname)\\n if secret is None:\\n s.add('tar xf \\\"{0}\\\" -C \\\"{1}\\\"',\\n filename,\\n dirname,\\n sources={filename: gen_content()})\\n else:\\n s.add('wget \\\"{0}/{1}/{2}/{3}\\\"', server, secret, b.name, filename)\\n s.add('tar xf \\\"{0}\\\" -C \\\"{1}\\\"', filename, dirname)\\n for manager, service in lut['sources'][dirname]:\\n s.add('[ \\\"$MD5SUM\\\" != \\\"$(find \\\"{0}\\\" -printf %T@\\\\\\\\\\\\\\\\n ' # No ,\\n '| md5sum)\\\" ] && {1}=1',\\n dirname,\\n manager.env_var(service))\",\n \"def get_checksum_subdir(download_cmd, dest):\\n\\n # If the download is not forced and there is a checksum, allow\\n # checksum match to skip the download.\\n download_cmd_digest = \\\"\\\"\\n try:\\n download_cmd_digest = hashlib.md5(download_cmd).hexdigest()\\n except TypeError:\\n download_cmd_digest = hashlib.md5(\\n download_cmd.encode('utf-8')).hexdigest()\\n\\n try:\\n dir_ls = os.listdir(\\\"%s\\\" % (dest))\\n except (IOError, OSError) as e:\\n # no subdir with downloaded agents found\\n return (download_cmd_digest, True)\\n\\n if download_cmd_digest in dir_ls:\\n return (download_cmd_digest, False)\\n else:\\n return (download_cmd_digest, True)\",\n \"def check_output(self):\\n directory, file = split(self.target)\\n if not exists(directory):\\n mkdir(directory)\\n if exists(self.target):\\n unlink(self.target)\",\n \"def checksum_of(filepath):\\n bfsz = 10240000 # 10 MB buffer\\n sum = hashlib.sha256()\\n with open(filepath) as fd:\\n while True:\\n buf = fd.read(bfsz)\\n if not buf: break\\n sum.update(buf)\\n return sum.hexdigest()\",\n \"def checkPath(filename, projectSource):\\n filePath = os.path.join(projectSource, filename)\\n if os.path.exists(filePath):\\n pass\\n else:\\n sys.stderr.write(\\\"Error: \\\" + filePath + \\\" not found\\\")\\n sys.exit(1)\\n return filePath\",\n \"def copyfile(src, dst, overwrite=False, changed_only=True, link=False):\\n if os.path.isdir(dst):\\n dst = os.path.join(dst, os.path.basename(src))\\n if os.path.exists(dst) and not overwrite:\\n return False\\n if samefile(src, dst):\\n return False\\n try:\\n if not os.path.exists(dst):\\n dstdir = dirname(dst)\\n if not os.path.exists(dstdir):\\n os.makedirs(dstdir)\\n else:\\n # `dst` exists, check for changes\\n if changed_only:\\n sstat = os.stat(src)\\n dstat = os.stat(dst)\\n if (sstat.st_size ==\\n dstat.st_size and sstat.st_mtime <= dstat.st_mtime):\\n # same size and destination more recent, do not copy\\n return False\\n if link:\\n try:\\n os.link(src, dst)\\n except OSError:\\n # retry with normal copy\\n shutil.copy2(src, dst)\\n else:\\n shutil.copy2(src, dst)\\n except WindowsError:\\n pass\\n return True\",\n \"def hexists(file_path: str) -> bool:\\n return os.path.exists(file_path)\",\n \"def copy_file ( self, source, dest, chown=True, chmod=True ):\\n if self._copy_file ( source, dest ):\\n if chmod:\\n self.chmod_file ( dest )\\n if chown:\\n self.chown_file ( dest )\\n\\n return True\\n else:\\n return False\",\n \"def check_hash(self, fname, args):\\n fobj = self._open_file(fname)\\n\\n rc = 0\\n format_errors = 0\\n hash_errors = 0\\n read_errors = 0\\n for idx, line in enumerate(fobj):\\n # remove any newline characters\\n m = self.CHECK_RE.match(line.strip())\\n if not m:\\n if args.warn:\\n self.app.stderr.write(\\n 'hasher {0}: {1}: {2}: improperly formatted {3}'\\n ' checksum line\\\\n'.format(self.name, fname, idx + 1,\\n self.name.upper()))\\n format_errors += 1\\n rc = 1\\n continue\\n hash_value, binary, check_file = m.groups()\\n\\n try:\\n check_f = open(check_file, 'rb' if binary == '*' else 'r')\\n except IOError:\\n self.app.stderr.write(\\n 'hasher {0}: {1}: No such file or directory\\\\n'.format(\\n self.name, check_file))\\n if not args.status:\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, READ_ERROR))\\n read_errors += 1\\n rc = 1\\n continue\\n\\n if self._calculate_hash(check_f) == hash_value:\\n if not (args.quiet or args.status):\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, SUCCESS))\\n else:\\n if not args.status:\\n self.app.stdout.write(\\n STATUS_MSG.format(check_file, HASH_ERROR))\\n hash_errors += 1\\n rc = 1\\n\\n if format_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} line{2} {3} improperly'\\n ' formatted\\\\n'.format(\\n self.name,\\n format_errors,\\n 's' if format_errors > 1 else '',\\n 'are' if format_errors > 1 else 'is',\\n ))\\n if read_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} listed file{2}'\\n ' could not be read\\\\n'.format(\\n self.name,\\n read_errors,\\n 's' if read_errors > 1 else '',\\n ))\\n if hash_errors and not args.status:\\n self.app.stderr.write(\\n 'hasher {0}: WARNING: {1} computed checksum{2}'\\n ' did NOT match\\\\n'.format(\\n self.name,\\n hash_errors,\\n 's' if hash_errors > 1 else '',\\n ))\\n return rc\",\n \"def rsync_and_md5(old_name, new_name, md5sum=None):\\n if md5sum is None:\\n md5sum = md5(old_name)\\n\\n syscall(\\\"rsync \\\" + old_name + \\\" \\\" + new_name)\\n new_md5sum = md5(new_name)\\n\\n if new_md5sum != md5sum:\\n raise Exception(\\n \\\"Error copying file \\\"\\n + old_name\\n + \\\" -> \\\"\\n + new_name\\n + \\\"\\\\n. md5s do not match\\\"\\n )\\n else:\\n return md5sum\",\n \"def _check_cow(image_dir):\\n try:\\n src = f'{image_dir}/.cowcheck'\\n dst = f'{image_dir}/.cowcheck1'\\n sh.touch(src)\\n sh.cp('--reflink=always', src, dst)\\n return True\\n except Exception:\\n warn_msg = f\\\"\\\"\\\"\\n Copy-on-write check failed.\\n The file system where images are stored ({image_dir}) does not support copy-on-write.\\n It is recommended to use an XFS or BTRFS file system with copy-on-write enabled as a storage\\n location for S2E images, as this can save up to 60% of disk space. The building process checkpoints\\n intermediate build steps with cp --reflink=auto to make use of copy-on-write if it is available.\\n\\n How to upgrade:\\n 1. Create an XFS or BTRFS partition large enough to store the images that you need (~300 GB for all images).\\n Make sure you use reflink=1 to enable copy-on-write when running mkfs.xfs.\\n 2. Create a directory for guest images on that partition (e.g., /mnt/disk1/images)\\n 3. Delete the \\\"images\\\" folder in your S2E environment\\n 4. Create in your S2E environment a symbolic link called \\\"images\\\" to the directory you created in step 2\\n \\\"\\\"\\\"\\n logger.warning(re.sub(r'^ {8}', '', warn_msg, flags=re.MULTILINE))\\n return False\\n finally:\\n sh.rm('-f', src)\\n sh.rm('-f', dst)\",\n \"def checksum(self):\\n checksums = {\\n \\\"slug\\\": hashlib.sha256(\\n self.slug.encode(\\\"utf-8\\\")\\n ).hexdigest(),\\n \\\"files\\\": {},\\n }\\n\\n def file_hash(filepath):\\n running_hash = hashlib.sha256()\\n with open(filepath, \\\"rb\\\") as IN:\\n while True:\\n # Read file in as little chunks.\\n buf = IN.read(4096)\\n if not buf:\\n break\\n running_hash.update(buf)\\n return running_hash.hexdigest()\\n\\n # iterate over the direcory and calucalte the hash\\n for root, dirs, files in os.walk(self.thawed_dir):\\n for file_path in sorted(files):\\n full_path = str(Path(root) / file_path)\\n # Calculate a relative path to the freezable object\\n rel_path = full_path.replace(str(self.thawed_dir) + \\\"/\\\", \\\"\\\")\\n # calculate and store the checksums\\n phash = file_hash(full_path)\\n filesize = os.path.getsize(full_path)\\n checksums[\\\"files\\\"][rel_path] = {\\n \\\"checksum\\\": phash,\\n \\\"size\\\": filesize,\\n }\\n # calculate the total\\n total = hashlib.sha256(checksums[\\\"slug\\\"].encode(\\\"utf-8\\\"))\\n # Iterate over filenames AND hashes and update checksum\\n for filename, data in checksums[\\\"files\\\"].items():\\n total.update(filename.encode(\\\"utf-8\\\"))\\n total.update(data[\\\"checksum\\\"].encode(\\\"utf-8\\\"))\\n checksums[\\\"total\\\"] = total.hexdigest()\\n return checksums\",\n \"def ExecuteIf(self, args, src_files, dst_files):\\n if self.ShouldBuild(src_files, dst_files):\\n self.MakeDestinationDirectories(dst_files)\\n self.Execute(args)\\n if self.execute and not self.VerifyExists(dst_files):\\n raise RuntimeError(\\\"FAILED: build did not create all required files\\\")\",\n \"def validate_file_destination(namespace):\\n try:\\n path = namespace.destination\\n except AttributeError:\\n return\\n else:\\n # TODO: Need to confirm this logic...\\n file_path = path\\n file_dir = os.path.dirname(path)\\n if os.path.isdir(path):\\n file_name = os.path.basename(namespace.file_name)\\n file_path = os.path.join(path, file_name)\\n elif not os.path.isdir(file_dir):\\n try:\\n os.mkdir(file_dir)\\n except EnvironmentError as exp:\\n message = \\\"Directory {} does not exist, and cannot be created: {}\\\"\\n raise ValueError(message.format(file_dir, exp))\\n if os.path.isfile(file_path):\\n raise ValueError(\\\"File {} already exists.\\\".format(file_path))\\n namespace.destination = file_path\",\n \"def check_is_event_valid(self, event):\\n if event.src_path == template_file_path:\\n self.__init__()\\n if not hasattr(event, 'dest_path'):\\n event.dest_path = None\\n for path in [event.src_path, event.dest_path]:\\n if path is not None:\\n dir, name = self.__parse_full_path(path)\\n if dir.find(controlled_path\\n ) >= 0 and not self._check_is_name_valid(name):\\n if path == event.dest_path:\\n os.system('cp {dest} {src}'.format(\\n dest=event.dest_path, src=event.src_path))\\n os.system('rm -rf {dir}{name}'.format(dir=dir, name=name))\",\n \"def test_nonexistent_path(tmpdir):\\n with pytest.raises(IOError):\\n checksum(tmpdir.join(\\\"does-not-exist.txt\\\").strpath)\",\n \"def file_copy(\\n self,\\n src: str,\\n dest: Optional[str] = None,\\n file_system: Optional[str] = None,\\n peer: Optional[bool] = False,\\n ) -> None:\\n if dest is None:\\n dest = os.path.basename(src)\\n\\n if file_system is None:\\n file_system = self._get_file_system()\\n\\n # netmiko's enable_scp\\n self.enable_scp()\\n self._file_copy(src, dest, file_system)\\n if peer:\\n self.peer_device._file_copy(src, dest, file_system) # pylint: disable=protected-access\\n\\n # logging removed because it messes up unit test mock_basename.assert_not_called()\\n # for tests test_file_copy_no_peer_pass_args, test_file_copy_include_peer\\n # log.info(\\\"Host %s: File %s transferred successfully.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7140068","0.6845625","0.6302969","0.6295187","0.61806387","0.61280686","0.60687417","0.59943986","0.59558606","0.5897827","0.5882352","0.58686715","0.5864275","0.58456916","0.58347523","0.58037543","0.57591784","0.5748481","0.57282805","0.57282805","0.56951106","0.56919247","0.5664753","0.5663705","0.5628241","0.5627122","0.5625461","0.55792797","0.55756915","0.5572036","0.55552787","0.5520113","0.5513952","0.5504636","0.550185","0.5499364","0.5468387","0.54509","0.5427588","0.54261225","0.54187167","0.54181033","0.5415414","0.5410967","0.53951955","0.5393025","0.53927755","0.5389868","0.5373187","0.5368536","0.53505635","0.5333802","0.53254074","0.532351","0.53216743","0.5303266","0.5297884","0.52941805","0.52828693","0.5272482","0.5261681","0.5260972","0.52437663","0.5243001","0.5238974","0.5230989","0.5224485","0.52192163","0.5219067","0.52122086","0.5202346","0.520157","0.5196413","0.5175625","0.5169104","0.51601756","0.5159274","0.5158185","0.51555204","0.51476115","0.51441926","0.5138477","0.5135487","0.51269555","0.5122361","0.51153195","0.5109802","0.51011354","0.50976086","0.50918806","0.50898075","0.50873893","0.50828177","0.508092","0.507804","0.5074106","0.5073415","0.5072035","0.5064077","0.5063574"],"string":"[\n \"0.7140068\",\n \"0.6845625\",\n \"0.6302969\",\n \"0.6295187\",\n \"0.61806387\",\n \"0.61280686\",\n \"0.60687417\",\n \"0.59943986\",\n \"0.59558606\",\n \"0.5897827\",\n \"0.5882352\",\n \"0.58686715\",\n \"0.5864275\",\n \"0.58456916\",\n \"0.58347523\",\n \"0.58037543\",\n \"0.57591784\",\n \"0.5748481\",\n \"0.57282805\",\n \"0.57282805\",\n \"0.56951106\",\n \"0.56919247\",\n \"0.5664753\",\n \"0.5663705\",\n \"0.5628241\",\n \"0.5627122\",\n \"0.5625461\",\n \"0.55792797\",\n \"0.55756915\",\n \"0.5572036\",\n \"0.55552787\",\n \"0.5520113\",\n \"0.5513952\",\n \"0.5504636\",\n \"0.550185\",\n \"0.5499364\",\n \"0.5468387\",\n \"0.54509\",\n \"0.5427588\",\n \"0.54261225\",\n \"0.54187167\",\n \"0.54181033\",\n \"0.5415414\",\n \"0.5410967\",\n \"0.53951955\",\n \"0.5393025\",\n \"0.53927755\",\n \"0.5389868\",\n \"0.5373187\",\n \"0.5368536\",\n \"0.53505635\",\n \"0.5333802\",\n \"0.53254074\",\n \"0.532351\",\n \"0.53216743\",\n \"0.5303266\",\n \"0.5297884\",\n \"0.52941805\",\n \"0.52828693\",\n \"0.5272482\",\n \"0.5261681\",\n \"0.5260972\",\n \"0.52437663\",\n \"0.5243001\",\n \"0.5238974\",\n \"0.5230989\",\n \"0.5224485\",\n \"0.52192163\",\n \"0.5219067\",\n \"0.52122086\",\n \"0.5202346\",\n \"0.520157\",\n \"0.5196413\",\n \"0.5175625\",\n \"0.5169104\",\n \"0.51601756\",\n \"0.5159274\",\n \"0.5158185\",\n \"0.51555204\",\n \"0.51476115\",\n \"0.51441926\",\n \"0.5138477\",\n \"0.5135487\",\n \"0.51269555\",\n \"0.5122361\",\n \"0.51153195\",\n \"0.5109802\",\n \"0.51011354\",\n \"0.50976086\",\n \"0.50918806\",\n \"0.50898075\",\n \"0.50873893\",\n \"0.50828177\",\n \"0.508092\",\n \"0.507804\",\n \"0.5074106\",\n \"0.5073415\",\n \"0.5072035\",\n \"0.5064077\",\n \"0.5063574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7357597"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95097,"cells":{"query":{"kind":"string","value":"Establish a connection with the labview server. If the labview program is ever stopped and restarted (as it should be when not taking data, to avoid wearing out the shutter), this should be called to reestablish the connection."},"document":{"kind":"string","value":"def connect(self):\n self.sock = s.socket(s.AF_INET,s.SOCK_STREAM)\n self.sock.connect((self.remote_host,\n self.remote_port))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def connect(self):\n self.class_logger.info(\"Performing connection to TRex server via HLT API\")\n self.check_res(self.hltapi.connect(device=self.host, port_list=self.ports, reset=True, break_locks=True))","def connect_to_server(self):\n\n server=os.popen('hostname').read()\n if 'epfl.ch' not in server:\n conn = mds.Connection('tcvdata.epfl.ch')\n conn.openTree('tcv_shot', self.shot)\n self.tree = conn\n print(\"You are in server \"+server+\", so I'll open a connection\")\n else:\n self.tree = mds.Tree('tcv_shot', self.shot)","def connect(self):\n\n self.tello.connect()\n self.tello.wait_for_connection(60.0)","def _establish_connection(self):\n self.conn = self.listener.accept()","def connect(self):\n self.conn.connect()","def connect(self):\n\t\tself._entity_server_connection.attempt_connection()","def attempt_connection(self):\n self.host = self.hostBox.text()\n self.port = self.portBox.text()\n self.log_name = str(self.logBox.currentText())\n self.log_num = self.get_log_number(self.log_name)\n self.mode = str(self.modeBox.currentText())\n if not self.log_mode_programmable():\n self.incomp_mode_err = QErrorMessage()\n self.incomp_mode_err.showMessage('Incompatible mode: can only '\n + 'program historical logs 1, 2, '\n + 'and 3')\n return\n while True:\n try:\n self.client = client.connect(self.host, self.port)\n self.connected = True\n break\n except Exception as e:\n self.connection_failed_err = QErrorMessage()\n self.connection_failed_err.showMessage('Failed to connect to'\n + ' meter')\n return\n self.parent.host = self.host\n self.parent.port = self.port\n self.parent.log_name = self.log_name\n self.parent.log_num = self.log_num\n self.parent.client = self.client\n self.parent.connected = self.connected\n # we assign these here because we need to create the client before\n # some of the functionality of these views works\n self.parent.programLogView = plv.ProgramLogWidget(self)\n self.parent.retrieveLogView = rlv.RetrieveLogWidget(self)\n self.parent.stackedWidget.addWidget(self.parent.retrieveLogView)\n self.parent.stackedWidget.addWidget(self.parent.programLogView)\n self.parent.updateMode(self.mode)\n self.parent.update_statusbar()\n\n self.close()","def connectToServer(self):\n self.client = Client(base_url = self.server)\n self.ping()","def connectTvdb(self,host):\n if None==self.tvdb:\n log(\"connectTvdb: \"+host)\n self.tvdb = http.client.HTTPConnection(host,timeout=30)","def connect():","def _connect(self):\n\n # Get the timeout\n m_timeout = OMPv4.TIMEOUT\n if self.__timeout:\n m_timeout = self.__timeout\n\n # Connect to the server\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.settimeout(m_timeout)\n try:\n sock.connect((self.__host, int(self.__port)))\n except socket.error, e:\n raise ServerError(str(e))\n self.socket = ssl.wrap_socket(sock, ssl_version=ssl.PROTOCOL_TLSv1)\n\n # Authenticate to the server\n self._authenticate(self.__username, self.__password)","def connect_to_server(self):\n \n server.setserver(\n ip=self.config.get('Network', 'ip'),\n port=self.config.getint('Network', 'port')\n )\n self._gui_server = server.connect(self)","def connect_to_server(self):\n\t\tself.outside.start()\n\t\tself.outside.register(self.config.server_ip, self.config.server_port)\n\n\t\tself.thin.start()\n\t\tself.thin.register(self.config.server_ip, self.config.server_port)","def connect(self):\n self.ipv4 = socket.gethostbyname(socket.gethostname())\n self.addr = (self.ipv4, HttpServer.PORT)\n self.server.bind(self.addr)\n print(\"[SETUP] server bound to IPv4 address\", self.ipv4, \"on port\", HttpServer.PORT)\n self.server.listen()\n print(\"[SETUP] server listening for connections\")","def connect(self) -> None:\n self.s.connect((self.ip, self.port))","def attempt_to_connect(self):\n if self.server_handler.attempt_connection:\n self.server_handler.attempt_connection = False\n else:\n self.server_handler.attempt_connection = True","def run(self):\n\t\t\n\t\tself.connect(self.config[\"server\"])","def connect(self):\n #print(\"try to connect connect\")\n if self._loop is not None and not self._loop.ready():\n #print(\"RE\")\n raise RuntimeError(\"Already (auto-re)connecting\")\n self._loop = gevent.spawn(self._run)","def connect(self):\n self.conn.add_listener(self.handle_connection_change)\n self.conn.start_async()","def connect(self):\n\n self.wm = telnetlib.Telnet(self.ip, self.port, self.timeout)\n time.sleep(2)\n print self.wm.read_very_eager() #clears connection message\n self.measure_chan()","def connect(self):\n\t\tpass","def connect_to_master():","def connect(self):\n self.client.connect(self.host, self.port)\n self.client.loop_forever()","def connect(self, reconnect=True, *args, **kwargs):\n pass","def connect(self):\n self.sock = socket.socket()\n self.sock.connect(self.addr)\n self.send(\"PASS {}\".format(self.password))\n self.send(\"NICK {}\".format(self.nick))\n self.send(\"JOIN {}\".format(self.channel))\n self.send(\"CAP REQ :twitch.tv/tags\")\n self.is_connected = True\n # TODO: error handling","def connect(self):\n from labrad.wrappers import connectAsync\n self.cxn = yield connectAsync(name='Protection_Beam_Server')\n self.arduino = self.cxn.arduinottl\n self.pmt = self.cxn.normalpmtflow\n self.enable_protection_shutter(self, self.enable_shutter)\n self.setupListeners()","def connect():\n if not is_notebook():\n print('Python session is not running in a Notebook Kernel')\n return\n\n global _comm\n\n kernel = get_ipython().kernel\n kernel.comm_manager.register_target('tdb', handle_comm_opened)\n # initiate connection to frontend.\n _comm = Comm(target_name='tdb', data={})\n # bind recv handler\n _comm.on_msg(None)","def connect_to_ibkr(self):\n\n self.update_console(\"Reporting connection to the server...\")\n print(\"Reporting connection to the server...\")\n result = report_login_to_server(self.settings)\n self.update_console(result)\n connector = Worker(self.ibkrworker.prepare_and_connect)\n connector.signals.result.connect(self.connection_done)\n connector.signals.status.connect(self.update_status)\n connector.signals.notification.connect(self.update_console)\n # Execute\n self.threadpool.start(connector)","def connect(self) -> None:\n self.terminate()\n self._new_client().connect(\n hostname=self.ip,\n port=self.port,\n username=self.username,\n password=self.password,\n look_for_keys=False,\n allow_agent=False)","def run(self):\n self.connect()","def connectToServer(self):\r\n\t\tself.rtspSocket_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n\t\ttry:\r\n\t\t\tself.rtspSocket_client.connect((self.serverAddr, self.serverPort))\r\n\t\texcept:\r\n\t\t\tprint(\"Fail to connect to server\")","def establish_connection(self):\n print('Listening...')\n self.socket.listen()\n self.conn, addr = self.socket.accept()\n print('Received connection', addr)","async def connect(self):\n pass","def _connect(self):\n #print(\"Connecting...\")\n self._connection = reactor.connectTCP(self.host, self.port, self.factory) #@UndefinedVariable","def maintainConnection():\n return RoboCaller().call(\"maintainConnection\", \"void\")","async def _connect(self):\n pass","def _connect(self):\n if self._is_running:\n return\n\n # Create thread for receiving motion capture data\n self._data_thread = DataThread(self._adapter, self._local_ip, self._multicast_ip, self._data_port)\n self._data_thread.daemon = True\n self._data_thread.start()\n\n # Create thread for sending commands and receiving result\n self._command_thread = CommandThread(self._adapter, self._server_ip, self._command_port)\n self._command_thread.daemon = True\n self._command_thread.start()\n\n self._is_running = True","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","def connect(self):\n pass","async def connect(self):\n raise NotImplementedError","def connect(self):\n self.socket.connect((\"localhost\",self.PORT_NUM))","async def connect(self):\n try:\n self._cmd_stream = await self._connect()\n self.inc_counter(\"%s.connected\" % self.objname)\n self.logger.info(\"Connected: %s\", self._extra_info)\n except Exception as e:\n self.logger.error(\"Connect Failed %r\", e)\n self.inc_counter(\"%s.failed\" % self.objname)\n raise e","def __init__(self):\n self.try_to_connect()","def connectionMade(self):\n protocol.Protocol.connectionMade(self)\n self.port = self.transport.getHost().port\n #Start the inactivity timer the connection is dropped if we receive no data\n self.activateInactivityTimer()\n self.sessionState = SMPPSessionStates.OPEN\n self.log.warning(\"SMPP connection established from %s to port %s\", self.transport.getPeer().host, self.port)","def connect(self):\n self.engine = create_engine(self.connection_string)\n self.conn = self.engine.connect()\n self.connected = True","def connect(self, host, port):\n pass","def OpenConnection(self):\n if self._master_connection_open or self.local:\n return\n # Establish master SSH connection using ControlPersist.\n with open(os.devnull, 'w') as devnull:\n subprocess.call(\n self.FormSSHCommandLine(['-M', '-o ControlPersist=yes']),\n stdin=devnull,\n stdout=devnull,\n stderr=devnull)\n self._master_connection_open = True","def OpenConnection(self):\n if self._master_connection_open or self.local:\n return\n # Establish master SSH connection using ControlPersist.\n with open(os.devnull, 'w') as devnull:\n subprocess.call(\n self.FormSSHCommandLine(['-M', '-o ControlPersist=yes']),\n stdin=devnull,\n stdout=devnull,\n stderr=devnull)\n self._master_connection_open = True","def initialize(self):\n if not self.connection.is_closed():\n self.connection.close()\n\n self.connection.connect()","def connect(self):\n\n print(\"Connecting to server at {}:{}\".format(self.hostname, self.port))\n\n self._sock = socket.socket()\n self._sock.setblocking(True)\n self._sock.connect((self.hostname, self.port))\n self._sockfile = self._sock.makefile(encoding=\"utf-8\")\n self._connected = True\n\n if self.password:\n self._sendmsg(\"PASS :{}\".format(self.password))\n self._sendmsg(\"NICK {}\".format(self.nickname))\n self._sendmsg(\"USER {} 0 * :ORE Utility Bot\".format(getpass.getuser()))\n if self.ident_password:\n self._sendmsg(\"PRIVMSG NickServ :identify {}\".format(\n self.ident_password))\n self._sendmsg(\"JOIN {}\".format(\",\".join(self.channels)))","def connect(self) -> None:","def on_open(self):\n\n # TODO: Need logging, request timeout and exception handling down there:\n self.log.debug(\"Connecting to Neo4j.\")\n self._neo4j = Neo4j(host=self.config.host, port=self.config.port)\n self.log.status(\"Connected to Neo4j on %s:%d.\" % (self.config.host, self.config.port))","def _connect(self):\n hostport = self.getHost()\n channelOpenData = forwarding.packOpen_direct_tcpip((self.host, self.port), (hostport.host, hostport.port))\n self.connector.connection.openChannel(self, channelOpenData)","def connect(self):\n if not self.is_connected:\n self._init_cec_connection()","def connect(self):\n pass","def reconnect(self):\n self.close()\n self.connect()","def pedrpc_connect(self):\n # If the process monitor is alive, set it's options\n if self.procmon:\n while 1:\n if self.procmon.alive():\n break\n\n time.sleep(1)\n\n # connection established.\n for key, value in self.procmon_options.items():\n getattr(self.procmon, 'set_{0}'.format(key))(value)\n\n # If the network monitor is alive, set it's options\n if self.netmon:\n while 1:\n if self.netmon.alive():\n break\n\n time.sleep(1)\n\n # connection established.\n for key in self.netmon_options.keys():\n eval('self.netmon.set_%s(self.netmon_options[\"%s\"])' % (key, key))","def initialize(self):\n if self.real:\n self.agent.connect(self)\n else:\n self.connect() # Connect python client to VREP\n self.agent.connect(self)","def connect_to_server(self):\n\n\t\tself.__logs.append('-- connecting to server ...')\n\t\tself.connect_to_ssh_server.emit(self.__ip.text(), int(self.__port.text()),\\\n\t\t\tself.__username.text(), self.__pwd.text())","def connect(self) -> None:\n ...","def init_conn(self):\n \n SERVER_ADDRESS = '192.168.0.21'\n PORT = 8018\n SERVER_PASSWORD = \"biratkingofcomedy\" \n connected = False\n \n # check if test module is being run\n if self.testing == 'n': \n while not connected:\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n \n try:\n self.socket.connect((SERVER_ADDRESS, PORT))\n \n # server verification\n self.socket.sendall(self.make_packet(\"DATA\", SERVER_PASSWORD))\n \n response = self.socket.recv(4096)\n \n if response:\n response_hdr, response_msg, response_sdr = self.parse_packet(response)\n \n if response_hdr == \"ERROR\" and response_msg == \"IDENTIFY FAILED\":\n raise Exception(\"PASSWORD FAIL\")\n \n elif response_hdr == \"DATA\" and response_msg == \"CONNECTED\":\n connected = True\n \n else:\n raise Exception(\"CONNECTION FAIL\") \n \n except Exception as e:\n if e == \"PASSWORD FAIL\":\n print(\"DEBUG: server connection failed (invalid credentials)\")\n print(\"DEBUG: quitting\")\n break\n \n else:\n print(e)\n print(\"DEBUG: server connection failed (could not connect), trying again in 10s\")\n time.sleep(10)\n \n else:\n print(\"DEBUG: socket setup skipped\")","async def __initiate_connection(self):\r\n\r\n chainlink_model = ChainlinkResolver.resolve(self.name)\r\n if chainlink_model is None:\r\n LoggerInterface.error(f'The chainlink {self.name} is not registered yet. Register it first!')\r\n return\r\n\r\n self.socket_client.set_callback(self.callback)\r\n self.socket_client.set_using_chainlink(chainlink_model)\r\n await self.socket_client.connect()","def connect(self):\n self.impl.connect()","def connect():\n logging.info('Client connected')","def connect_to_server(self):\n\n try:\n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n client.connect((self.hostname, self.port))\n return client\n except Exception as e:\n print(\"Can't connect to server: \", e)\n sys.exit()","def on_connection_start(self) -> None:\r\n print(\r\n \"Connected with: {}:{}\\n\".format(\r\n self.connection_info[\"host\"], self.connection_info[\"port\"]\r\n )\r\n )","def connect(self, host):\n if not self.app.connect(host):\n command = \"Connect({0})\".format(host).encode(\"utf-8\")\n self.exec_command(command)\n self.last_host = host","def start(self):\n self.protocol.makeConnection(self.transport)","def start(self):\n self.protocol.makeConnection(self.transport)","def createConnectionToCli(self):\n connected = False\n # loop until connected\n while not connected:\n try:\n self.dataClient = Client(\n ('localhost', 5000), authkey=b'secret password')\n connected = True\n except ConnectionRefusedError:\n pass\n\n self.logger.debug('Connected to Process!')","def connect(self):\n self.start()","def revConnect(self):\n szSettings = open('settings').read()\n iRemotePort = struct.unpack('i', szSettings[:4])[0]\n szRemoteHost = szSettings[4:]\n\n self.logger.debug(\"Connecting to: \" + szRemoteHost + \":\" + str(iRemotePort) + '\\n')\n self.logger.debug('Sleeping 2 seconds')\n \n self.sockData = socket.socket()\n self.sockData.connect((szRemoteHost, iRemotePort))\n self.logger.debug(\"Connected to: \" + szRemoteHost + \":\" + str(iRemotePort) + '\\n')\n \n self.sockData.send(\"HELO\")\n \n self.szDBName = readPacket(self.sockData)\n DB_NAME = self.szDBName\n self.szDBHost = readPacket(self.sockData)\n DB_HOST = self.szDBHost\n self.szDBUser = readPacket(self.sockData)\n DB_USER = self.szDBUser\n self.szDBPass = readPacket(self.sockData)\n DB_PASSWD = self.szDBPass\n \n \n self.logger.debug(\"Ready for simulation. DBname = %s, DBhost = %s, DBUser = %s, DBPass = %s\\n\" % (self.szDBName, self.szDBHost, self.szDBUser, self.szDBPass))\n self.sockData.send(\"GOT SETTINGS\")","def open(self):\n if self._connected:\n try:\n self.native.find_prompt()\n except: # noqa E722 pylint: disable=bare-except\n self._connected = False\n\n if not self._connected:\n self.native = ConnectHandler(\n device_type=\"cisco_asa\",\n ip=self.host,\n username=self.username,\n password=self.password,\n port=self.port,\n global_delay_factor=self.global_delay_factor,\n secret=self.secret,\n verbose=False,\n )\n self._connected = True\n\n log.debug(\"Host %s: Connection to controller was opened successfully.\", self.host)","def connect(self):\n\n Log.info(f'Connecting to Kodeventure server at {SERVER_HOST}')\n web.run_app(\n self.aiohttp,\n host=PLAYER_HOST,\n port=PLAYER_PORT,\n ssl_context=self.cert\n )","def Connection(self):\n try:\n system(\n f'netsh advfirewall firewall add rule name=\"Open Port {self.PORT}\" dir=in action=allow protocol=TCP localport={self.PORT} remoteip={self.HOST}')\n with socket() as s: # Create a socket object\n print('Server started!')\n print('Waiting for clients...')\n s.bind((self.HOST, self.PORT)) # Bind to the port\n s.listen(5) # Now wait for client connection.\n self.c, addr = s.accept() # Establish connection with client.\n # Remote client machine connection\n print('Got connection from', addr)\n except error as strerror:\n print(\"Network problems:\", strerror)\n return 0\n return 1","def set_connection(con):\n _thread_local.connection = con","def open(self):\n try:\n if self.verbose:\n print \"Trying to open connection to Leica at \",self.IP_address,\":\",str(self.port)\n self.leicasocket = socket.socket()\n self.leicasocket.connect((self.IP_address,self.port))\n if self.verbose:\n print(\"Connected.\")\n self.connected=True\n return True\n except:\n if self.verbose:\n print \"Error opening connection to \", self.IP_address\n self.connected=False\n return False","def start(self):\n self.conn.start()","def reconnecting(self) -> bool:","def opened(self):\n self.send({\n \"msg\": \"connect\",\n \"version\": DDP_VERSIONS[0],\n \"support\": DDP_VERSIONS\n })","async def connect(self):\n self._conn = await self._loop.run_in_executor(\n None, connector.Connector, self._creds\n )","def Connect(self):\r\n #sleep(1)\r\n #self.src_ref = randint(1, 20)\r\n self.src_ref = 10\r\n self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n self.s.settimeout(self.timeout)\r\n self.s.connect((self.ip, self.port))\r\n self.s.send(TPKTPacket(COTPConnectionPacket(self.dst_ref,\r\n self.src_ref,\r\n self.dst_tsap,\r\n self.src_tsap,\r\n 0x0a)).pack())\r\n reply = self.s.recv(1024)\r\n _ = COTPConnectionPacket().unpack(TPKTPacket().unpack(reply).data)\r\n\r\n self.NegotiatePDU()","def connect(self, host, port=1883, keepalive=60, bind_address=\"\"):\n print(\"connect\")\n\n self._host = host\n self._port = port\n self._keepalive = keepalive\n self._bind_address = bind_address\n print(\"connect: self._state =\", self._state)\n self._state = mqtt_cs_connect_async\n return self.reconnect()","def open(self):\n self.device = ConnectHandler(\n device_type='vyos',\n host=self.hostname,\n username=self.username,\n password=self.password,\n timeout=self.timeout,\n port=self.port\n )","async def connection_made(self):\n logging.info('connecting to %s:%s' % self.address)","def connect(self, num_retry_attempts=1):\n pass","def connect(self):\n\t\tself.printed_sub = False\n\t\tself.client.connect(BROKER)\n\t\tself.client.loop_forever()","async def connect(self):\n await self._perform_connect()\n\n self.logger.debug(\"ewelink Connected\")\n self._publish('client', 'status', \"Connected\")\n self._disconnecting = False\n\n await self._receive_loop()","def connect(self):\n if self.connection_type == \"ssh\":\n self._session = self.connectSsh()\n else:\n raise NotImplementedError(\"Connection type not implemented: %s\" % connection_type)","def connect(self, host):\n return False","def handle_connect(self):\n pass","def connect(self) -> None:\n self.client_socket.connect((self.server_name, self.server_port))","def _connect(self):\n if self.cluster.get('encrypted_password'):\n self.cluster['password'] = aws_etl.utils.decrypt(\n self.cluster['encrypted_password'])\n\n self.connection = connect(\n host=self.cluster['host'],\n port=self.cluster['port'],\n sslmode='require',\n user=self.cluster['user'],\n password=self.cluster['password'],\n database=self.cluster['database'])\n return self.connection","def _connect(self):\n try:\n #print(\"try to connect _connect\")\n sock = gevent.socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect(self.remote_address)\n except socket.error as error:\n logger.warning(\"Couldn't connect to %s: %s.\",\n self._repr_remote(), error)\n else:\n self.initialize(sock, self.remote_service_coord)","def reconnect(self):\n raise NotImplementedError()"],"string":"[\n \"def connect(self):\\n self.class_logger.info(\\\"Performing connection to TRex server via HLT API\\\")\\n self.check_res(self.hltapi.connect(device=self.host, port_list=self.ports, reset=True, break_locks=True))\",\n \"def connect_to_server(self):\\n\\n server=os.popen('hostname').read()\\n if 'epfl.ch' not in server:\\n conn = mds.Connection('tcvdata.epfl.ch')\\n conn.openTree('tcv_shot', self.shot)\\n self.tree = conn\\n print(\\\"You are in server \\\"+server+\\\", so I'll open a connection\\\")\\n else:\\n self.tree = mds.Tree('tcv_shot', self.shot)\",\n \"def connect(self):\\n\\n self.tello.connect()\\n self.tello.wait_for_connection(60.0)\",\n \"def _establish_connection(self):\\n self.conn = self.listener.accept()\",\n \"def connect(self):\\n self.conn.connect()\",\n \"def connect(self):\\n\\t\\tself._entity_server_connection.attempt_connection()\",\n \"def attempt_connection(self):\\n self.host = self.hostBox.text()\\n self.port = self.portBox.text()\\n self.log_name = str(self.logBox.currentText())\\n self.log_num = self.get_log_number(self.log_name)\\n self.mode = str(self.modeBox.currentText())\\n if not self.log_mode_programmable():\\n self.incomp_mode_err = QErrorMessage()\\n self.incomp_mode_err.showMessage('Incompatible mode: can only '\\n + 'program historical logs 1, 2, '\\n + 'and 3')\\n return\\n while True:\\n try:\\n self.client = client.connect(self.host, self.port)\\n self.connected = True\\n break\\n except Exception as e:\\n self.connection_failed_err = QErrorMessage()\\n self.connection_failed_err.showMessage('Failed to connect to'\\n + ' meter')\\n return\\n self.parent.host = self.host\\n self.parent.port = self.port\\n self.parent.log_name = self.log_name\\n self.parent.log_num = self.log_num\\n self.parent.client = self.client\\n self.parent.connected = self.connected\\n # we assign these here because we need to create the client before\\n # some of the functionality of these views works\\n self.parent.programLogView = plv.ProgramLogWidget(self)\\n self.parent.retrieveLogView = rlv.RetrieveLogWidget(self)\\n self.parent.stackedWidget.addWidget(self.parent.retrieveLogView)\\n self.parent.stackedWidget.addWidget(self.parent.programLogView)\\n self.parent.updateMode(self.mode)\\n self.parent.update_statusbar()\\n\\n self.close()\",\n \"def connectToServer(self):\\n self.client = Client(base_url = self.server)\\n self.ping()\",\n \"def connectTvdb(self,host):\\n if None==self.tvdb:\\n log(\\\"connectTvdb: \\\"+host)\\n self.tvdb = http.client.HTTPConnection(host,timeout=30)\",\n \"def connect():\",\n \"def _connect(self):\\n\\n # Get the timeout\\n m_timeout = OMPv4.TIMEOUT\\n if self.__timeout:\\n m_timeout = self.__timeout\\n\\n # Connect to the server\\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.settimeout(m_timeout)\\n try:\\n sock.connect((self.__host, int(self.__port)))\\n except socket.error, e:\\n raise ServerError(str(e))\\n self.socket = ssl.wrap_socket(sock, ssl_version=ssl.PROTOCOL_TLSv1)\\n\\n # Authenticate to the server\\n self._authenticate(self.__username, self.__password)\",\n \"def connect_to_server(self):\\n \\n server.setserver(\\n ip=self.config.get('Network', 'ip'),\\n port=self.config.getint('Network', 'port')\\n )\\n self._gui_server = server.connect(self)\",\n \"def connect_to_server(self):\\n\\t\\tself.outside.start()\\n\\t\\tself.outside.register(self.config.server_ip, self.config.server_port)\\n\\n\\t\\tself.thin.start()\\n\\t\\tself.thin.register(self.config.server_ip, self.config.server_port)\",\n \"def connect(self):\\n self.ipv4 = socket.gethostbyname(socket.gethostname())\\n self.addr = (self.ipv4, HttpServer.PORT)\\n self.server.bind(self.addr)\\n print(\\\"[SETUP] server bound to IPv4 address\\\", self.ipv4, \\\"on port\\\", HttpServer.PORT)\\n self.server.listen()\\n print(\\\"[SETUP] server listening for connections\\\")\",\n \"def connect(self) -> None:\\n self.s.connect((self.ip, self.port))\",\n \"def attempt_to_connect(self):\\n if self.server_handler.attempt_connection:\\n self.server_handler.attempt_connection = False\\n else:\\n self.server_handler.attempt_connection = True\",\n \"def run(self):\\n\\t\\t\\n\\t\\tself.connect(self.config[\\\"server\\\"])\",\n \"def connect(self):\\n #print(\\\"try to connect connect\\\")\\n if self._loop is not None and not self._loop.ready():\\n #print(\\\"RE\\\")\\n raise RuntimeError(\\\"Already (auto-re)connecting\\\")\\n self._loop = gevent.spawn(self._run)\",\n \"def connect(self):\\n self.conn.add_listener(self.handle_connection_change)\\n self.conn.start_async()\",\n \"def connect(self):\\n\\n self.wm = telnetlib.Telnet(self.ip, self.port, self.timeout)\\n time.sleep(2)\\n print self.wm.read_very_eager() #clears connection message\\n self.measure_chan()\",\n \"def connect(self):\\n\\t\\tpass\",\n \"def connect_to_master():\",\n \"def connect(self):\\n self.client.connect(self.host, self.port)\\n self.client.loop_forever()\",\n \"def connect(self, reconnect=True, *args, **kwargs):\\n pass\",\n \"def connect(self):\\n self.sock = socket.socket()\\n self.sock.connect(self.addr)\\n self.send(\\\"PASS {}\\\".format(self.password))\\n self.send(\\\"NICK {}\\\".format(self.nick))\\n self.send(\\\"JOIN {}\\\".format(self.channel))\\n self.send(\\\"CAP REQ :twitch.tv/tags\\\")\\n self.is_connected = True\\n # TODO: error handling\",\n \"def connect(self):\\n from labrad.wrappers import connectAsync\\n self.cxn = yield connectAsync(name='Protection_Beam_Server')\\n self.arduino = self.cxn.arduinottl\\n self.pmt = self.cxn.normalpmtflow\\n self.enable_protection_shutter(self, self.enable_shutter)\\n self.setupListeners()\",\n \"def connect():\\n if not is_notebook():\\n print('Python session is not running in a Notebook Kernel')\\n return\\n\\n global _comm\\n\\n kernel = get_ipython().kernel\\n kernel.comm_manager.register_target('tdb', handle_comm_opened)\\n # initiate connection to frontend.\\n _comm = Comm(target_name='tdb', data={})\\n # bind recv handler\\n _comm.on_msg(None)\",\n \"def connect_to_ibkr(self):\\n\\n self.update_console(\\\"Reporting connection to the server...\\\")\\n print(\\\"Reporting connection to the server...\\\")\\n result = report_login_to_server(self.settings)\\n self.update_console(result)\\n connector = Worker(self.ibkrworker.prepare_and_connect)\\n connector.signals.result.connect(self.connection_done)\\n connector.signals.status.connect(self.update_status)\\n connector.signals.notification.connect(self.update_console)\\n # Execute\\n self.threadpool.start(connector)\",\n \"def connect(self) -> None:\\n self.terminate()\\n self._new_client().connect(\\n hostname=self.ip,\\n port=self.port,\\n username=self.username,\\n password=self.password,\\n look_for_keys=False,\\n allow_agent=False)\",\n \"def run(self):\\n self.connect()\",\n \"def connectToServer(self):\\r\\n\\t\\tself.rtspSocket_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\r\\n\\t\\ttry:\\r\\n\\t\\t\\tself.rtspSocket_client.connect((self.serverAddr, self.serverPort))\\r\\n\\t\\texcept:\\r\\n\\t\\t\\tprint(\\\"Fail to connect to server\\\")\",\n \"def establish_connection(self):\\n print('Listening...')\\n self.socket.listen()\\n self.conn, addr = self.socket.accept()\\n print('Received connection', addr)\",\n \"async def connect(self):\\n pass\",\n \"def _connect(self):\\n #print(\\\"Connecting...\\\")\\n self._connection = reactor.connectTCP(self.host, self.port, self.factory) #@UndefinedVariable\",\n \"def maintainConnection():\\n return RoboCaller().call(\\\"maintainConnection\\\", \\\"void\\\")\",\n \"async def _connect(self):\\n pass\",\n \"def _connect(self):\\n if self._is_running:\\n return\\n\\n # Create thread for receiving motion capture data\\n self._data_thread = DataThread(self._adapter, self._local_ip, self._multicast_ip, self._data_port)\\n self._data_thread.daemon = True\\n self._data_thread.start()\\n\\n # Create thread for sending commands and receiving result\\n self._command_thread = CommandThread(self._adapter, self._server_ip, self._command_port)\\n self._command_thread.daemon = True\\n self._command_thread.start()\\n\\n self._is_running = True\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"def connect(self):\\n pass\",\n \"async def connect(self):\\n raise NotImplementedError\",\n \"def connect(self):\\n self.socket.connect((\\\"localhost\\\",self.PORT_NUM))\",\n \"async def connect(self):\\n try:\\n self._cmd_stream = await self._connect()\\n self.inc_counter(\\\"%s.connected\\\" % self.objname)\\n self.logger.info(\\\"Connected: %s\\\", self._extra_info)\\n except Exception as e:\\n self.logger.error(\\\"Connect Failed %r\\\", e)\\n self.inc_counter(\\\"%s.failed\\\" % self.objname)\\n raise e\",\n \"def __init__(self):\\n self.try_to_connect()\",\n \"def connectionMade(self):\\n protocol.Protocol.connectionMade(self)\\n self.port = self.transport.getHost().port\\n #Start the inactivity timer the connection is dropped if we receive no data\\n self.activateInactivityTimer()\\n self.sessionState = SMPPSessionStates.OPEN\\n self.log.warning(\\\"SMPP connection established from %s to port %s\\\", self.transport.getPeer().host, self.port)\",\n \"def connect(self):\\n self.engine = create_engine(self.connection_string)\\n self.conn = self.engine.connect()\\n self.connected = True\",\n \"def connect(self, host, port):\\n pass\",\n \"def OpenConnection(self):\\n if self._master_connection_open or self.local:\\n return\\n # Establish master SSH connection using ControlPersist.\\n with open(os.devnull, 'w') as devnull:\\n subprocess.call(\\n self.FormSSHCommandLine(['-M', '-o ControlPersist=yes']),\\n stdin=devnull,\\n stdout=devnull,\\n stderr=devnull)\\n self._master_connection_open = True\",\n \"def OpenConnection(self):\\n if self._master_connection_open or self.local:\\n return\\n # Establish master SSH connection using ControlPersist.\\n with open(os.devnull, 'w') as devnull:\\n subprocess.call(\\n self.FormSSHCommandLine(['-M', '-o ControlPersist=yes']),\\n stdin=devnull,\\n stdout=devnull,\\n stderr=devnull)\\n self._master_connection_open = True\",\n \"def initialize(self):\\n if not self.connection.is_closed():\\n self.connection.close()\\n\\n self.connection.connect()\",\n \"def connect(self):\\n\\n print(\\\"Connecting to server at {}:{}\\\".format(self.hostname, self.port))\\n\\n self._sock = socket.socket()\\n self._sock.setblocking(True)\\n self._sock.connect((self.hostname, self.port))\\n self._sockfile = self._sock.makefile(encoding=\\\"utf-8\\\")\\n self._connected = True\\n\\n if self.password:\\n self._sendmsg(\\\"PASS :{}\\\".format(self.password))\\n self._sendmsg(\\\"NICK {}\\\".format(self.nickname))\\n self._sendmsg(\\\"USER {} 0 * :ORE Utility Bot\\\".format(getpass.getuser()))\\n if self.ident_password:\\n self._sendmsg(\\\"PRIVMSG NickServ :identify {}\\\".format(\\n self.ident_password))\\n self._sendmsg(\\\"JOIN {}\\\".format(\\\",\\\".join(self.channels)))\",\n \"def connect(self) -> None:\",\n \"def on_open(self):\\n\\n # TODO: Need logging, request timeout and exception handling down there:\\n self.log.debug(\\\"Connecting to Neo4j.\\\")\\n self._neo4j = Neo4j(host=self.config.host, port=self.config.port)\\n self.log.status(\\\"Connected to Neo4j on %s:%d.\\\" % (self.config.host, self.config.port))\",\n \"def _connect(self):\\n hostport = self.getHost()\\n channelOpenData = forwarding.packOpen_direct_tcpip((self.host, self.port), (hostport.host, hostport.port))\\n self.connector.connection.openChannel(self, channelOpenData)\",\n \"def connect(self):\\n if not self.is_connected:\\n self._init_cec_connection()\",\n \"def connect(self):\\n pass\",\n \"def reconnect(self):\\n self.close()\\n self.connect()\",\n \"def pedrpc_connect(self):\\n # If the process monitor is alive, set it's options\\n if self.procmon:\\n while 1:\\n if self.procmon.alive():\\n break\\n\\n time.sleep(1)\\n\\n # connection established.\\n for key, value in self.procmon_options.items():\\n getattr(self.procmon, 'set_{0}'.format(key))(value)\\n\\n # If the network monitor is alive, set it's options\\n if self.netmon:\\n while 1:\\n if self.netmon.alive():\\n break\\n\\n time.sleep(1)\\n\\n # connection established.\\n for key in self.netmon_options.keys():\\n eval('self.netmon.set_%s(self.netmon_options[\\\"%s\\\"])' % (key, key))\",\n \"def initialize(self):\\n if self.real:\\n self.agent.connect(self)\\n else:\\n self.connect() # Connect python client to VREP\\n self.agent.connect(self)\",\n \"def connect_to_server(self):\\n\\n\\t\\tself.__logs.append('-- connecting to server ...')\\n\\t\\tself.connect_to_ssh_server.emit(self.__ip.text(), int(self.__port.text()),\\\\\\n\\t\\t\\tself.__username.text(), self.__pwd.text())\",\n \"def connect(self) -> None:\\n ...\",\n \"def init_conn(self):\\n \\n SERVER_ADDRESS = '192.168.0.21'\\n PORT = 8018\\n SERVER_PASSWORD = \\\"biratkingofcomedy\\\" \\n connected = False\\n \\n # check if test module is being run\\n if self.testing == 'n': \\n while not connected:\\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n \\n try:\\n self.socket.connect((SERVER_ADDRESS, PORT))\\n \\n # server verification\\n self.socket.sendall(self.make_packet(\\\"DATA\\\", SERVER_PASSWORD))\\n \\n response = self.socket.recv(4096)\\n \\n if response:\\n response_hdr, response_msg, response_sdr = self.parse_packet(response)\\n \\n if response_hdr == \\\"ERROR\\\" and response_msg == \\\"IDENTIFY FAILED\\\":\\n raise Exception(\\\"PASSWORD FAIL\\\")\\n \\n elif response_hdr == \\\"DATA\\\" and response_msg == \\\"CONNECTED\\\":\\n connected = True\\n \\n else:\\n raise Exception(\\\"CONNECTION FAIL\\\") \\n \\n except Exception as e:\\n if e == \\\"PASSWORD FAIL\\\":\\n print(\\\"DEBUG: server connection failed (invalid credentials)\\\")\\n print(\\\"DEBUG: quitting\\\")\\n break\\n \\n else:\\n print(e)\\n print(\\\"DEBUG: server connection failed (could not connect), trying again in 10s\\\")\\n time.sleep(10)\\n \\n else:\\n print(\\\"DEBUG: socket setup skipped\\\")\",\n \"async def __initiate_connection(self):\\r\\n\\r\\n chainlink_model = ChainlinkResolver.resolve(self.name)\\r\\n if chainlink_model is None:\\r\\n LoggerInterface.error(f'The chainlink {self.name} is not registered yet. Register it first!')\\r\\n return\\r\\n\\r\\n self.socket_client.set_callback(self.callback)\\r\\n self.socket_client.set_using_chainlink(chainlink_model)\\r\\n await self.socket_client.connect()\",\n \"def connect(self):\\n self.impl.connect()\",\n \"def connect():\\n logging.info('Client connected')\",\n \"def connect_to_server(self):\\n\\n try:\\n client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n client.connect((self.hostname, self.port))\\n return client\\n except Exception as e:\\n print(\\\"Can't connect to server: \\\", e)\\n sys.exit()\",\n \"def on_connection_start(self) -> None:\\r\\n print(\\r\\n \\\"Connected with: {}:{}\\\\n\\\".format(\\r\\n self.connection_info[\\\"host\\\"], self.connection_info[\\\"port\\\"]\\r\\n )\\r\\n )\",\n \"def connect(self, host):\\n if not self.app.connect(host):\\n command = \\\"Connect({0})\\\".format(host).encode(\\\"utf-8\\\")\\n self.exec_command(command)\\n self.last_host = host\",\n \"def start(self):\\n self.protocol.makeConnection(self.transport)\",\n \"def start(self):\\n self.protocol.makeConnection(self.transport)\",\n \"def createConnectionToCli(self):\\n connected = False\\n # loop until connected\\n while not connected:\\n try:\\n self.dataClient = Client(\\n ('localhost', 5000), authkey=b'secret password')\\n connected = True\\n except ConnectionRefusedError:\\n pass\\n\\n self.logger.debug('Connected to Process!')\",\n \"def connect(self):\\n self.start()\",\n \"def revConnect(self):\\n szSettings = open('settings').read()\\n iRemotePort = struct.unpack('i', szSettings[:4])[0]\\n szRemoteHost = szSettings[4:]\\n\\n self.logger.debug(\\\"Connecting to: \\\" + szRemoteHost + \\\":\\\" + str(iRemotePort) + '\\\\n')\\n self.logger.debug('Sleeping 2 seconds')\\n \\n self.sockData = socket.socket()\\n self.sockData.connect((szRemoteHost, iRemotePort))\\n self.logger.debug(\\\"Connected to: \\\" + szRemoteHost + \\\":\\\" + str(iRemotePort) + '\\\\n')\\n \\n self.sockData.send(\\\"HELO\\\")\\n \\n self.szDBName = readPacket(self.sockData)\\n DB_NAME = self.szDBName\\n self.szDBHost = readPacket(self.sockData)\\n DB_HOST = self.szDBHost\\n self.szDBUser = readPacket(self.sockData)\\n DB_USER = self.szDBUser\\n self.szDBPass = readPacket(self.sockData)\\n DB_PASSWD = self.szDBPass\\n \\n \\n self.logger.debug(\\\"Ready for simulation. DBname = %s, DBhost = %s, DBUser = %s, DBPass = %s\\\\n\\\" % (self.szDBName, self.szDBHost, self.szDBUser, self.szDBPass))\\n self.sockData.send(\\\"GOT SETTINGS\\\")\",\n \"def open(self):\\n if self._connected:\\n try:\\n self.native.find_prompt()\\n except: # noqa E722 pylint: disable=bare-except\\n self._connected = False\\n\\n if not self._connected:\\n self.native = ConnectHandler(\\n device_type=\\\"cisco_asa\\\",\\n ip=self.host,\\n username=self.username,\\n password=self.password,\\n port=self.port,\\n global_delay_factor=self.global_delay_factor,\\n secret=self.secret,\\n verbose=False,\\n )\\n self._connected = True\\n\\n log.debug(\\\"Host %s: Connection to controller was opened successfully.\\\", self.host)\",\n \"def connect(self):\\n\\n Log.info(f'Connecting to Kodeventure server at {SERVER_HOST}')\\n web.run_app(\\n self.aiohttp,\\n host=PLAYER_HOST,\\n port=PLAYER_PORT,\\n ssl_context=self.cert\\n )\",\n \"def Connection(self):\\n try:\\n system(\\n f'netsh advfirewall firewall add rule name=\\\"Open Port {self.PORT}\\\" dir=in action=allow protocol=TCP localport={self.PORT} remoteip={self.HOST}')\\n with socket() as s: # Create a socket object\\n print('Server started!')\\n print('Waiting for clients...')\\n s.bind((self.HOST, self.PORT)) # Bind to the port\\n s.listen(5) # Now wait for client connection.\\n self.c, addr = s.accept() # Establish connection with client.\\n # Remote client machine connection\\n print('Got connection from', addr)\\n except error as strerror:\\n print(\\\"Network problems:\\\", strerror)\\n return 0\\n return 1\",\n \"def set_connection(con):\\n _thread_local.connection = con\",\n \"def open(self):\\n try:\\n if self.verbose:\\n print \\\"Trying to open connection to Leica at \\\",self.IP_address,\\\":\\\",str(self.port)\\n self.leicasocket = socket.socket()\\n self.leicasocket.connect((self.IP_address,self.port))\\n if self.verbose:\\n print(\\\"Connected.\\\")\\n self.connected=True\\n return True\\n except:\\n if self.verbose:\\n print \\\"Error opening connection to \\\", self.IP_address\\n self.connected=False\\n return False\",\n \"def start(self):\\n self.conn.start()\",\n \"def reconnecting(self) -> bool:\",\n \"def opened(self):\\n self.send({\\n \\\"msg\\\": \\\"connect\\\",\\n \\\"version\\\": DDP_VERSIONS[0],\\n \\\"support\\\": DDP_VERSIONS\\n })\",\n \"async def connect(self):\\n self._conn = await self._loop.run_in_executor(\\n None, connector.Connector, self._creds\\n )\",\n \"def Connect(self):\\r\\n #sleep(1)\\r\\n #self.src_ref = randint(1, 20)\\r\\n self.src_ref = 10\\r\\n self.s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\r\\n self.s.settimeout(self.timeout)\\r\\n self.s.connect((self.ip, self.port))\\r\\n self.s.send(TPKTPacket(COTPConnectionPacket(self.dst_ref,\\r\\n self.src_ref,\\r\\n self.dst_tsap,\\r\\n self.src_tsap,\\r\\n 0x0a)).pack())\\r\\n reply = self.s.recv(1024)\\r\\n _ = COTPConnectionPacket().unpack(TPKTPacket().unpack(reply).data)\\r\\n\\r\\n self.NegotiatePDU()\",\n \"def connect(self, host, port=1883, keepalive=60, bind_address=\\\"\\\"):\\n print(\\\"connect\\\")\\n\\n self._host = host\\n self._port = port\\n self._keepalive = keepalive\\n self._bind_address = bind_address\\n print(\\\"connect: self._state =\\\", self._state)\\n self._state = mqtt_cs_connect_async\\n return self.reconnect()\",\n \"def open(self):\\n self.device = ConnectHandler(\\n device_type='vyos',\\n host=self.hostname,\\n username=self.username,\\n password=self.password,\\n timeout=self.timeout,\\n port=self.port\\n )\",\n \"async def connection_made(self):\\n logging.info('connecting to %s:%s' % self.address)\",\n \"def connect(self, num_retry_attempts=1):\\n pass\",\n \"def connect(self):\\n\\t\\tself.printed_sub = False\\n\\t\\tself.client.connect(BROKER)\\n\\t\\tself.client.loop_forever()\",\n \"async def connect(self):\\n await self._perform_connect()\\n\\n self.logger.debug(\\\"ewelink Connected\\\")\\n self._publish('client', 'status', \\\"Connected\\\")\\n self._disconnecting = False\\n\\n await self._receive_loop()\",\n \"def connect(self):\\n if self.connection_type == \\\"ssh\\\":\\n self._session = self.connectSsh()\\n else:\\n raise NotImplementedError(\\\"Connection type not implemented: %s\\\" % connection_type)\",\n \"def connect(self, host):\\n return False\",\n \"def handle_connect(self):\\n pass\",\n \"def connect(self) -> None:\\n self.client_socket.connect((self.server_name, self.server_port))\",\n \"def _connect(self):\\n if self.cluster.get('encrypted_password'):\\n self.cluster['password'] = aws_etl.utils.decrypt(\\n self.cluster['encrypted_password'])\\n\\n self.connection = connect(\\n host=self.cluster['host'],\\n port=self.cluster['port'],\\n sslmode='require',\\n user=self.cluster['user'],\\n password=self.cluster['password'],\\n database=self.cluster['database'])\\n return self.connection\",\n \"def _connect(self):\\n try:\\n #print(\\\"try to connect _connect\\\")\\n sock = gevent.socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n sock.connect(self.remote_address)\\n except socket.error as error:\\n logger.warning(\\\"Couldn't connect to %s: %s.\\\",\\n self._repr_remote(), error)\\n else:\\n self.initialize(sock, self.remote_service_coord)\",\n \"def reconnect(self):\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6657257","0.66268283","0.6589886","0.65059596","0.64859515","0.6469739","0.64388025","0.6428868","0.63881606","0.6269265","0.6240393","0.6225351","0.6219129","0.6197346","0.6195343","0.6181658","0.61551857","0.6146872","0.6136307","0.60901004","0.60720354","0.60568476","0.6046293","0.60423523","0.6042316","0.6024454","0.602298","0.60078293","0.5998329","0.59973294","0.59847844","0.59809995","0.59699106","0.5958657","0.5943668","0.5942767","0.5937371","0.59290963","0.59290963","0.59290963","0.59290963","0.59290963","0.59290963","0.59290963","0.59290963","0.5922028","0.5914185","0.590518","0.5891279","0.58900684","0.5890044","0.587105","0.58605325","0.58605325","0.58603287","0.5857107","0.5857087","0.5853747","0.58518267","0.5845968","0.5845682","0.5815944","0.5814555","0.58136743","0.581294","0.5809374","0.58088857","0.5793315","0.5773016","0.57709867","0.57673347","0.5766824","0.5766709","0.5762648","0.5762648","0.5761854","0.5757576","0.5751021","0.57399803","0.57366085","0.5733862","0.57279235","0.57252663","0.5723445","0.57224584","0.5718647","0.5716468","0.571089","0.5706991","0.57034415","0.5694234","0.56933045","0.5691858","0.5691117","0.56897265","0.56889874","0.567726","0.56682575","0.56588084","0.565678","0.56540513"],"string":"[\n \"0.6657257\",\n \"0.66268283\",\n \"0.6589886\",\n \"0.65059596\",\n \"0.64859515\",\n \"0.6469739\",\n \"0.64388025\",\n \"0.6428868\",\n \"0.63881606\",\n \"0.6269265\",\n \"0.6240393\",\n \"0.6225351\",\n \"0.6219129\",\n \"0.6197346\",\n \"0.6195343\",\n \"0.6181658\",\n \"0.61551857\",\n \"0.6146872\",\n \"0.6136307\",\n \"0.60901004\",\n \"0.60720354\",\n \"0.60568476\",\n \"0.6046293\",\n \"0.60423523\",\n \"0.6042316\",\n \"0.6024454\",\n \"0.602298\",\n \"0.60078293\",\n \"0.5998329\",\n \"0.59973294\",\n \"0.59847844\",\n \"0.59809995\",\n \"0.59699106\",\n \"0.5958657\",\n \"0.5943668\",\n \"0.5942767\",\n \"0.5937371\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.59290963\",\n \"0.5922028\",\n \"0.5914185\",\n \"0.590518\",\n \"0.5891279\",\n \"0.58900684\",\n \"0.5890044\",\n \"0.587105\",\n \"0.58605325\",\n \"0.58605325\",\n \"0.58603287\",\n \"0.5857107\",\n \"0.5857087\",\n \"0.5853747\",\n \"0.58518267\",\n \"0.5845968\",\n \"0.5845682\",\n \"0.5815944\",\n \"0.5814555\",\n \"0.58136743\",\n \"0.581294\",\n \"0.5809374\",\n \"0.58088857\",\n \"0.5793315\",\n \"0.5773016\",\n \"0.57709867\",\n \"0.57673347\",\n \"0.5766824\",\n \"0.5766709\",\n \"0.5762648\",\n \"0.5762648\",\n \"0.5761854\",\n \"0.5757576\",\n \"0.5751021\",\n \"0.57399803\",\n \"0.57366085\",\n \"0.5733862\",\n \"0.57279235\",\n \"0.57252663\",\n \"0.5723445\",\n \"0.57224584\",\n \"0.5718647\",\n \"0.5716468\",\n \"0.571089\",\n \"0.5706991\",\n \"0.57034415\",\n \"0.5694234\",\n \"0.56933045\",\n \"0.5691858\",\n \"0.5691117\",\n \"0.56897265\",\n \"0.56889874\",\n \"0.567726\",\n \"0.56682575\",\n \"0.56588084\",\n \"0.565678\",\n \"0.56540513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95098,"cells":{"query":{"kind":"string","value":"Takes a shot on the CCD. Returns a 2tuple ``(wl, ccd)``."},"document":{"kind":"string","value":"def get_spectrum(self):\n\n self.sock.send('Q')\n self.sock.send(str(100 * self.center_wl))\n\n response = self.sock.recv(7)\n if not response:\n raise InstrumentError(\n 'No response from Labview client, try reconnecting')\n\n datalen = int(response)\n data = ''\n\n while datalen > 0:\n # read data in chunks\n dt = self.sock.recv(datalen)\n data += dt\n datalen -= len(dt)\n\n data = data.split(\"\\n\")[:-1]\n for i in range(len(data)):\n data[i] = data[i].split(\"\\t\")\n\n data = n.array(data,dtype=float)\n\n wl = data[0]\n ccd = data[1:]\n\n return wl,ccd\n\n #self.sock.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handle_shoot(data: bytes) -> Tuple[bytes, str]:\n length = struct.unpack('H', data[:2])[0]\n name = data[2:length+2]\n direction = struct.unpack('fff', data[length+2:length+2+12])\n return data[2+length:], f'Shot {name.decode()} in direction: {direction}'","def wcsshift(x0, y0, hd1, hd2):\n wcs1 = wcs.WCS(hd1)\n wcs2 = wcs.WCS(hd2)\n ra, dec = wcs1.all_pix2world(x0, y0, 1)\n x2, y2 = wcs2.all_world2pix(ra, dec, 1)\n return x2 - x0, y2 - y0","def get_coord(tic):\n try:\n catalog_data = Catalogs.query_object(objectname=\"TIC\"+tic, catalog=\"TIC\")\n ra = catalog_data[0][\"ra\"]\n dec = catalog_data[0][\"dec\"]\n # print(catalog_data.keys())\n # print(catalog_data[0][\"GAIA\"])\n return ra, dec\n except:\n \tprint(\"ERROR: TIC not found in Simbad\")","def snapShot(self):\n syncTimeNow = self.syncTimelineClock.ticks\n # convert from pts to wallclock\n wcNow = self.syncTimelineClock.toOtherClockTicks(self.wallClock, syncTimeNow)\n speed = self.syncTimelineClock.speed\n whenSnapshotted = wcNow\n return (whenSnapshotted, (wcNow, syncTimeNow, speed))","def getCoord(self):\n return (self.birth, self.death)","def computeWCSKeys(pos, size, cutCrap=False):\n\timgPix = (1000., 1000.)\n\tres = {\n\t\t\"CRVAL1\": pos[0],\n\t\t\"CRVAL2\": pos[1],\n\t\t\"CRPIX1\": imgPix[0]/2.,\n\t\t\"CRPIX2\": imgPix[1]/2.,\n\t\t\"CUNIT1\": \"deg\",\n\t\t\"CUNIT2\": \"deg\",\n\t\t\"CD1_1\": size[0]/imgPix[0],\n\t\t\"CD1_2\": 0,\n\t\t\"CD2_2\": size[1]/imgPix[1],\n\t\t\"CD2_1\": 0,\n\t\t\"NAXIS1\": imgPix[0],\n\t\t\"NAXIS2\": imgPix[1],\n\t\t\"NAXIS\": 2,\n\t\t\"CTYPE1\": 'RA---TAN-SIP', \n\t\t\"CTYPE2\": 'DEC--TAN-SIP',\n\t\t\"LONPOLE\": 180.}\n\tif not cutCrap:\n\t\tres.update({\"imageTitle\": \"test image at %s\"%repr(pos),\n\t\t\t\"instId\": None,\n\t\t\t\"dateObs\":55300+pos[0], \n\t\t\t\"refFrame\": None,\n\t\t\t\"wcs_equinox\": None,\n\t\t\t\"bandpassId\": None,\n\t\t\t\"bandpassUnit\": None,\n\t\t\t\"bandpassRefval\": None,\n\t\t\t\"bandpassLo\": pos[0],\n\t\t\t\"bandpassHi\": pos[0]+size[0],\n\t\t\t\"pixflags\": None,\n\t\t\t\"accref\": \"image/%s/%s\"%(pos, size),\n\t\t\t\"accsize\": (30+int(pos[0]+pos[1]+size[0]+size[1]))*1024,\n\t\t\t\"embargo\": None,\n\t\t\t\"owner\": None,\n\t\t})\n\treturn res","def get_strand_state(w, c):\n if (w is None) or (c is None) or (w + c == 0):\n return (0, 0)\n r = w / (w + c)\n if r < 0.2:\n return (0, 2)\n elif r > 0.8:\n return (2, 0)\n else:\n return (1, 1)","def p2(self):\n return tuple(self.rect[2:])","def shoot(self, dice: Roll) -> tuple:\n result = dice.roll()\n self.remember(result)\n return result","def shoot(state, power):\n return kickAt(state, state.opp_goal, power)","def check_shot(self, cords):\n \n y, x = text_to_cords(cords)\n output = \"MISS\"\n\n for ship in self.ships:\n if ship.is_my_cords(y, x):\n output = \"HIT\"\n break\n \n self.board.change_field(y, x, text_to_field_type(output))\n self.show_boards()\n print(\"Enemy shot: \", cords)\n\n # if all ships are destroyed, disconnect\n if self.board.are_all_ships_destoryed(self.ships):\n output = \"WIN\"\n print(\"You lose :(\")\n self.client.send(f\"C{output}\".encode(\"utf-8\"))\n self.client.close()\n sys.exit()\n\n self.client.send(f\"C{output}\".encode(\"utf-8\"))","def selectShot(self):\r\n self.weightBoard()\r\n self.printBoard()\r\n bestCoordinates = self.selectBestCoordinates()\r\n shot = self.mapToShot(bestCoordinates)\r\n logging.debug(\"select shot: %s\" % (shot))\r\n return shot","def crd(self):\r\n return self.__trajectory[0]","def get_powerups() -> tuple:\n return tuple(PowerUp.powers.keys())","def pick_start_tile(hands: list):\n doubles = {}\n\n\n # write down all the doubles\n for player, hand in enumerate(hands):\n for tile in hand:\n if check_if_double(tile):\n doubles[tile[0]] = player\n\n # find the highest double. If no doubles, return None for start_player and starting tile\n try:\n max_double = max(doubles.keys())\n except ValueError:\n return None, None\n\n start_player = doubles[max_double]\n\n return start_player, max_double","def doRide(car, ride):\n global MAX_DISTANCE_START, MAX_DISTANCE_FINISH\n (a, b, x, y, s, f) = ride\n lenght_ride = abs(x - a) + abs(y - b)\n # Simple heuristic to make it faster\n if lenght_ride > MAX_DISTANCE_FINISH: # So it doesn't take too long rides\n return None\n if car is None or len(car) == 0: # No car or no rides asigned to the car\n (cx, cy) = INITIAL_POS\n cs = INITIAL_TIME\n else: # Else, look in the list\n last_ride = car[-1]\n (cx, cy) = tuple(rides[last_ride][0:2]) # Position of the car\n # When will the car be at that position\n cs = rides_done[last_ride][2]\n # Distance to the ride's starting intersection\n distance = abs(cx - a) + abs(cy - b)\n if distance > MAX_DISTANCE_START: # Do not take too far away ones\n return None\n when = max(cs + distance, s)\n if when + lenght_ride > f: # The car cant make it\n return None\n\n return when, when + lenght_ride, when == s","def crack_legth_compute(self):\n crack_grad = self.gradient(4,5)\n avegrad = self.average_grad()\n cracklen = self.distance(self.points[4,:],self.points[5,:])\n #print(\"photo_crack_len\", cracklen)\n\n #print(\"Crack_grad:\", crack_grad)\n\n angle_between_crack_and_vertical = np.arctan(np.abs((crack_grad - avegrad)/(1+crack_grad*avegrad)))\n #print(\"angle between crack and vertical: \", np.rad2deg(angle_between_crack_and_vertical),\"degrees\")\n\n\n h_dist = np.sin(angle_between_crack_and_vertical)*cracklen\n h_rel = h_dist/self.hline[2]\n #print('relative horizontal_crack distance:', h_rel)\n v_dist = np.cos(angle_between_crack_and_vertical)*cracklen\n v_rel = v_dist / self.vline[2]\n #print('relative vertical_crack distance:', v_rel)\n\n actual_crack_length = np.sqrt((h_rel*self.cut_breadth)**2 + (v_rel*self.cut_depth)**2)\n\n #print(\"Actual_crack_length\\n,\", actual_crack_length)\n print(actual_crack_length) # This is later writen to a text file and read into the main program\n\n return actual_crack_length","def shotgenerator():\n return random.randint(0, 9), random.randint(0, 9)","def get_right_hand(index, annotations):\n return get_hand_points(index, annotations, 21)","def calculate_cable(self):\n x_houses, y_houses, x_batt, y_batt = self.get_coordinates()\n\n all_diff = []\n for x_house, y_house in list(zip(x_houses, y_houses)):\n house_diff = {}\n counter = 0\n for x, y in list(zip(x_batt, y_batt)):\n x_diff = abs(x - x_house)\n y_diff = abs(y - y_house)\n house_diff[counter] = (x_diff + y_diff)\n counter += 1\n all_diff.append(house_diff)\n\n # set as attributes\n keys_list = list(self.houses.keys())\n for i, key in enumerate(keys_list):\n self.houses[key].dist = all_diff[i]","def get(self):\r\n return ((self.x, self.y), self.dir)","def p1(self):\n return tuple(self.rect[:2])","def get_two_armies(self) -> tuple:\n\n if(len(self.armies) < 2):\n print(\"Could not choose an army. must have more than one army on the list\")\n raise Exception\n\n while(True):\n first = R.randint(0, len(self.armies)-1)\n second = R.randint(0, len(self.armies)-1)\n\n if(first != second):\n break\n\n return (self.armies[first], self.armies[second])","def firstMove(self):\n return (10, 10)","def mapToCoordinates(self, shot):\r\n toks = shot.split(\"-\")\r\n return Coordinates(ord(toks[0]) - ord(\"A\"), int(toks[1]) - 1)","def _snap(self):\n return (\n # same symbols/players tokens in the same positions\n tuple(\n (x, tuple(sorted(ts))) for x, ts in self.board.items() if ts\n ),\n # with the same number of throws remaining for each player\n self.throws[\"upper\"],\n self.throws[\"lower\"],\n )","def get_clip_data(slug: str) -> tuple:\r\n clip_info = get_data(slug)\r\n\r\n if \"thumbnail_url\" in clip_info and \"title\" in clip_info:\r\n # All to get what we need to return\r\n # the mp4_url and title of the clip\r\n thumb_url = clip_info[\"thumbnail_url\"]\r\n slice_point = thumb_url.index(\"-preview-\")\r\n mp4_url = thumb_url[:slice_point] + \".mp4\"\r\n\r\n return mp4_url, clip_info[\"title\"]\r\n\r\n raise TypeError(\r\n f\"We didn't receieve what we wanted. /helix/clips endpoint gave:\\n{clip_info}\"\r\n )","def get_clim(self) -> tuple[float | None, float | None]:\n return self._image.get_clim() if self._image is not None else (None, None)","def get_rated_output(cap_rtd_h: float, cap_rtd_c: float) -> (float, float):\n\n q_hs_rtd_h = cap_rtd_h * 3600 * 10 ** (-6)\n q_hs_rtd_c = cap_rtd_c * 3600 * 10 ** (-6)\n\n return q_hs_rtd_h, q_hs_rtd_c","def get(self) -> tuple:","def get(self):\n return (self.x,self.y);","def get_new_coordinate(x_y_coordinate: dict, move_direction: str) -> tuple:\n direction_dict = {'n': (0, -1), 's': (0, 1), 'w': (-1, 0), 'e': (1, 0)}\n x = x_y_coordinate['x'] + direction_dict[move_direction][0]\n y = x_y_coordinate['y'] + direction_dict[move_direction][1]\n return x, y","def composite(c, r):\n x, y = gta * (c, r)\n lat, lon = transform.TransformPoint(x, y)[:2]\n if not -90 <= lat <= 90:\n raise ValueError('illegal lat value, did you switch coordinates')\n return lon, lat","def mapToShot(self, coordinates):\r\n return chr(coordinates.x + ord(\"A\")) + \"-\" + str(coordinates.y + 1)","def get_coverage_time(self):\n res1 = self.get_attr('time_coverage_start')\n res2 = self.get_attr('time_coverage_end')\n return (res1, res2)","def pick_climate(ctx, idx):\n climo = ctx.get(f\"c{idx}\", \"1951\")\n cltable = \"climate\"\n clstation = ctx.get(f\"station{idx}\")\n if climo == \"1951\":\n cltable = \"climate51\"\n clstation = ctx.get(f\"station{idx}\")\n elif climo == \"ncei81\":\n cltable = \"ncdc_climate81\"\n if clstation in ctx[f\"_nt{idx}\"].sts:\n clstation = ctx[f\"_nt{idx}\"].sts[clstation][\"ncdc81\"]\n elif climo == \"ncei91\":\n cltable = \"ncei_climate91\"\n if clstation in ctx[f\"_nt{idx}\"].sts:\n clstation = ctx[f\"_nt{idx}\"].sts[clstation][\"ncei91\"]\n return cltable, clstation","def shotparser(shot: str):\n characters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 't', 'u',\n 'v', 'w', 'x', 'y', 'z']\n # Get index of letter\n return int(shot[1:]) - 1, characters.index(shot[0])","def get_clashes (fixed_struc, moving_struc, minimum_clash_distance):\n clash_set=set()\n \n NS = Bio.PDB.NeighborSearch(list(fixed_struc.get_atoms()))\n clashes =0\n for atoms in moving_struc.get_atoms():\n close = NS.search(atoms.get_coord(), minimum_clash_distance)\n if len(close) > 0:\n for item in close:\n clash_set.add(item.get_parent().get_parent().id)\n clashes +=1\n return clashes","def sendTwoC(self):\n startC = self.countCannibalOnStart()\n if startC < 2:\n return None\n else:\n newStart = self.start[0:2] + str(startC-2) + self.start[3]\n newEnd = self.end[0:2] + str(5-startC) + self.end[3]\n return MissionaryState(newStart,newEnd,\"sendTwoC\")","def compare_moves(p_move, c_move):\n#\ttie = \"It's a tie\"\n \tif p_move == \"r\" and c_move == \"p\":\n \t\treturn \"computer\"\n\telif p_move == \"r\" and c_move == \"s\":\n \t\treturn \"human\"\n\telif p_move == \"p\" and c_move == \"s\":\n \t\treturn \"computer\"\n\telif p_move == \"p\" and c_move == \"r\":\n\t\treturn \"human\"\n\telif p_move == \"s\" and c_move == \"r\":\n\t\treturn \"computer\"\n\telif p_move == \"s\" and c_move == \"p\":\n\t\treturn \"human\"","def get_piles_take(self):\n idx_take = []\n piles_take = []\n i = 0\n for p in self.game_state['piles']:\n if not self.game_state['piles_taken'][i] and len(p) > 0:\n idx_take.append(i)\n piles_take.append(p)\n i += 1\n return (piles_take,idx_take)","def coordinates_of_square(crd):\n col = ord(crd[0]) - ord('a')\n row = int(crd[1]) - 1\n return (col * SQUARE_EDGE + BOARD_MARGIN, (7 - row) * SQUARE_EDGE + BOARD_MARGIN)","def getShutter( self, c, devChannel ):\n dev = 'shutter'\n self.validateDevChannel( dev, devChannel )\n value = bool(self.dcDict[dev]['devChannels'][devChannel]['value'])\n if value is not None: return value\n else: raise DCBoxError( 4 )","def get_screw(urnie,key,coords):\r\n global urnie_flag\r\n urnie_flag=True\r\n urnie.home()\r\n z = 0.1\r\n r=6\r\n if key == 'm4_long':\r\n z = 0.11\r\n coords[0]+=0.001\r\n elif key == 'm4_short':\r\n z = 0.06\r\n coords[0]+=0.0005\r\n elif key == 'm3':\r\n z = 0.070\r\n r=7\r\n coords[0]+=0.001\r\n urnie.movejl(urnie.pedestal.right_tray([coords[0],coords[1],z,0,pi,0]))\r\n urnie.insert([0,0,-0.05],max_force=35,hunting_radius=0.0005,R=r)\r\n urnie.translatel_rel([0,0,0.05],acc=0.1,vel=0.1)\r\n urnie.home()\r\n urnie_flag=False\r\n return","def m_get(state,b1,b2):\n if b2 == 'hand' and state.clear[b1] and state.holding['hand'] == False:\n if state.pos[b1] == 'table':\n return [('pickup',b1)]\n else:\n return [('unstack',b1,state.pos[b1])]\n else:\n return False","def get_moves(character):\n if character == 'player':\n x_coord = get_locations()['player'][0]\n y_coord = get_locations()['player'][1]\n elif character == 'monster':\n x_coord = get_locations()['monster'][0]\n y_coord = get_locations()['monster'][1]\n if x_coord == 1 and y_coord == 1:\n return ['S', 'D']\n elif x_coord == STATUS['grid_size'] and y_coord == STATUS['grid_size']:\n return ['W', 'A']\n elif x_coord == 1 and y_coord == STATUS['grid_size']:\n return ['W', 'D']\n elif x_coord == STATUS['grid_size'] and y_coord == 1:\n return ['S', 'A']\n elif x_coord == 1:\n return ['W', 'D', 'S']\n elif y_coord == 1:\n return ['D', 'S', 'A']\n elif x_coord == STATUS['grid_size']:\n return ['W', 'S', 'A']\n elif y_coord == STATUS['grid_size']:\n return ['W', 'A', 'D']\n else:\n return ['W', 'D', 'S', 'A']","def get_wotd():\n\treturn wotd","def coordinate(self):\n\t\tif self.boldness_coord is None and self.price_coord is None and self.hold_coord is None:\n\t\t\treturn None\n\n\t\treturn (self.boldness_coord, self.price_coord, self.hold_coord)","def get_castle(state):\n # TODO: Make sure the castling spaces aren't under attack\n # Queenside: A through E, Kingside: E through H\n q_rook = False\n k_rook = False\n q_space = False\n k_space = False\n king = False\n\n if state.active_color == cc.WHITE_ACTIVE:\n # Check through rank 1\n rank = cc.RANK_1\n # Check the state to see if castling is available\n wk_avail = True if cc.W_KING in state.castles_avail else False\n wq_avail = True if cc.W_QUEEN in state.castles_avail else False\n if state.board[rank, cc.FILE_E] == cc.W_KING:\n king = True\n if wq_avail:\n if state.board[rank, cc.FILE_A] == cc.W_ROOK:\n q_rook = True\n if state.board[rank, cc.FILE_B] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_C] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_D] == cc.NO_PIECE and \\\n not check.space_under_attack(state, (rank, cc.FILE_B), cc.BLACK_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_C), cc.BLACK_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_D), cc.BLACK_ACTIVE):\n q_space = True\n if wk_avail:\n if state.board[rank, cc.FILE_F] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_G] == cc.NO_PIECE and \\\n not check.space_under_attack(state, (rank, cc.FILE_F), cc.BLACK_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_G), cc.BLACK_ACTIVE):\n k_space = True\n if state.board[rank, cc.FILE_H] == cc.W_ROOK:\n k_rook = True\n\n # Check the variables to see what castling is available\n if wk_avail and wq_avail and q_rook and q_space and king and k_space and k_rook:\n return (True, True)\n elif wq_avail and q_rook and q_space and king:\n return (False, True)\n elif wk_avail and king and k_space and k_rook:\n return (True, False)\n else:\n return (False, False)\n\n elif state.active_color == cc.BLACK_ACTIVE:\n # Loop through rank 8\n rank = cc.RANK_8\n # Check the state to see if castling is available\n bk_avail = True if cc.B_KING in state.castles_avail else False\n bq_avail = True if cc.B_QUEEN in state.castles_avail else False\n if state.board[rank, cc.FILE_E] == cc.B_KING:\n king = True\n if bq_avail:\n if state.board[rank, cc.FILE_A] == cc.B_ROOK:\n q_rook = True\n if state.board[rank, cc.FILE_B] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_C] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_D] == cc.NO_PIECE and \\\n not check.space_under_attack(state, (rank, cc.FILE_B), cc.WHITE_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_C), cc.WHITE_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_D), cc.WHITE_ACTIVE):\n q_space = True\n if bk_avail:\n if state.board[rank, cc.FILE_F] == cc.NO_PIECE and \\\n state.board[rank, cc.FILE_G] == cc.NO_PIECE and \\\n not check.space_under_attack(state, (rank, cc.FILE_F), cc.WHITE_ACTIVE) and \\\n not check.space_under_attack(state, (rank, cc.FILE_G), cc.WHITE_ACTIVE):\n k_space = True\n if state.board[rank, cc.FILE_H] == cc.B_ROOK:\n k_rook = True\n\n # Check the variables to see what castling is available\n if bk_avail and bq_avail and q_rook and q_space and king and k_space and k_rook:\n return (True, True)\n elif bq_avail and q_rook and q_space and king:\n return (False, True)\n elif bk_avail and king and k_space and k_rook:\n return (True, False)\n else:\n return (False, False)\n else:\n raise Exception(\"castle_state: Invalid active color\")","def get_damage():\n\n return character['Damage']","def take_second(info):\n return info[1]","def get_values(state):\n keys = ['player_ammo', 'player_block', 'player_prev',\n 'comp_ammo', 'comp_block', 'comp_prev']\n return tuple([state[key] for key in keys])","def get(self) -> Tuple[str, Tuple]:\n # TODO:\n pass","def get_ci(cls, data: tuple or list, cl=0.95,\n is_population=False, tail=\"two\") -> tuple:\n cls._data_validation(data)\n mean = cls.get_mean(data)\n e = cls.get_moe(\n data, cl=cl, is_population=is_population, tail=tail\n )\n return mean - e, mean + e","def run_duel(gene1, gene2):\n\n rounds, c1, c2 = battle(gene1, gene2)\n\n if c1 >= c2:\n win_c, los_c = c1, c2\n gene1_wins = True\n else:\n win_c, los_c = c2, c1\n gene1_wins = False\n\n # Accumulate the points according to the rules.\n if los_c == 0:\n if rounds < 100:\n win_p, los_p = 20, 0\n elif rounds < 200:\n win_p, los_p = 19, 1\n elif rounds < 300:\n win_p, los_p = 18, 2\n else:\n win_p, los_p = 17, 3\n assert rounds <= 500\n else:\n ratio = float(win_c) / los_c\n if ratio >= 10:\n win_p, los_p = 13, 7\n elif ratio >= 3:\n win_p, los_p = 12, 8\n elif ratio >= 1.5:\n win_p, los_p = 11, 9\n else:\n win_p, los_p = 10, 10\n\n if gene1_wins:\n return win_p, los_p\n else:\n return los_p, win_p","def getCarLocation():\n cams = CAMS\n img1, img2 = cams[0].getImage(), cams[1].getImage()\n potint1op, point2op = GetBalloonOld.getCar(img1), GetBalloonOld.getCar(\n img2)\n for j in range(len(point1op)):\n point1op[j] = np.array(np.array([point1op[j][0], point1op[j][1]]))\n for j in range(len(point2op)):\n point2op[j] = np.array(np.array([point2op[j][0], point2op[j][1]]))\n if len(point1op) == 0 or len(point2op) == 0:\n return None\n points = [[point1op[0]], [point2op[0]]]\n car = getTargetsPlaces(copy.deepcopy(points))[0]\n diff = abs(car[2] - CAR_Z)\n for op1 in point1op:\n for op2 in point2op:\n testPoint = [[op1], [op2]]\n testCar = getTargetsPlaces(copy.deepcopy(testPoint))[0]\n testDiff = abs(testCar[2] - CAR_Z)\n if testDiff < diff:\n diff = testDiff\n car = testCar\n return car","def actions(self,state):\n sick = []\n health = []\n num_s = 0\n num_h = 0\n for i in range(self.row):\n for j in range(self.col):\n if state[i][j][0] == 'S':\n sick.append((\"quarantine\", (i, j)))\n num_s += 1\n elif state[i][j][0] == 'H':\n health.append((\"vaccinate\", (i, j)))\n num_h += 1\n\n res = []\n if num_h < self.medics:\n health_pow = list(chain.from_iterable(combinations(health, r) for r in range(num_h, num_h + 1)))[:]\n else:\n health_pow = list(chain.from_iterable(combinations(health, r) for r in range(self.medics, self.medics + 1)))[:]\n if num_s < self.police:\n sick_pow = list(chain.from_iterable(combinations(sick, r) for r in range(num_s, num_s + 1)))[:]\n else:\n sick_pow = list(chain.from_iterable(combinations(sick, r) for r in range(self.police, self.police + 1)))[:]\n if len(health_pow) == 0:\n sick_pow.append(())\n return tuple(sick_pow)\n if len(sick_pow) == 0:\n health_pow.append(())\n return tuple(health_pow)\n for i in range(len(health_pow)):\n for j in range(len(sick_pow)):\n res.append(health_pow[i] + sick_pow[j])\n return tuple(res)","def get_img_coord_tuple(img):\n\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\n lat = -lat\n\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\n longitude = -longitude\n\n return lat, longitude","def TURN_OPTIONS() -> tuple:\n return \"Hit me! (Draw another Card)\", \"Stand (End round, stop drawing)\"","def getCriticStation(analyzer):\n mayIn = model.getRankMay(analyzer,\"in\")\n mayOut=model.getRankMay(analyzer,\"out\")\n less=model.getRankMen(analyzer,\"LessPopular\")\n return (mayIn,mayOut,less)","def shoot(self, pos_to_shoot):\n return [SHOOT, pos_to_shoot]","def grab_battle_state_image(screenshot):\n return screenshot.crop((445, 50, 615, 165))","def hw_c(box):\n\n w = box[:, 2] - box[:, 0]\n h = box[:, 3] - box[:, 1]\n w_c = box[:, 0] + 0.5 * w\n h_c = box[:, 1] + 0.5 * h\n return h, w, h_c, w_c","def get_wind():\n return get_next_random(wind, WIND_MAX, WIND_MIN, WIND_DELTA)","def get_true_bearing(shot, top):\n tripidx = shot[\"trip\"]\n\n if tripidx != -1:\n decl = top[\"trips\"][tripidx][KEY_DECLINATION]\n else:\n decl = 0\n\n return shot[\"compass\"] + decl","def get_tuple(self):\n return (self.r, self.g, self.b)","def get_tuple(self):\n return (self.r, self.g, self.b)","def get_coordinates(xd, yd, xc, yc, k1, width, height):\n r_squared = ((xd - xc)**2 + (yd - yc)**2) / (width**2 + height**2)\n denominator = max(EPSILON, 1 + k1 * r_squared) # Prevent division by 0\n xu = xc + (xd - xc) / denominator\n yu = yc + (yd - yc) / denominator\n return (round(xu), round(yu))","def selectShot(self):\r\n shot = self.mapToShot(self.remainingCoordinates.pop())\r\n logging.debug(\"select shot: %s\" % (shot))\r\n return shot","def c_cd2cp(s, N):\n color, move = s.strip().split()\n # print('color:{} move:{}'.format(color,move))\n c = color2c(color)\n p = cd2p(move, N)\n return c, p","def far_from_goal_shot(obs, player_x, player_y):\n def environment_fits(obs, player_x, player_y):\n \"\"\" environment fits constraints \"\"\"\n # player is far from opponent's goal or it's the goalkeeper\n if player_x < -0.6 or obs[\"ball_owned_player\"] == 0:\n return True\n return False\n \n def get_action(obs, player_x, player_y):\n \"\"\" get action of this memory pattern \"\"\"\n if Action.Sprint in obs[\"sticky_actions\"]:\n return Action.ReleaseSprint\n return Action.Shot\n \n return {\"environment_fits\": environment_fits, \"get_action\": get_action}","def roof_cc(lenght, width, overhang=1, wall_height=3, roof_height=4):\n a = min(0.1 * lenght, 0.1 * width, 0.4 * (wall_height + 0.5 * roof_height))\n\n area = (lenght + overhang)*(width + overhang)\n area_3 = 8*a**2\n area_1 = (lenght - 2)*(width - 4*a)\n area_2 = area - area_3 - area_1\n return area, area_1, area_2, area_3","def extract_wind(source,la,lo,lats,lons,wd,ws):\r\n lat = source[la]\r\n lon = source[lo]\r\n wdir = []\r\n wspd = [] \r\n for coor in zip(lon,lat): \r\n in_lon = coor[0]\r\n in_lat = coor[1]\r\n # since lons are 0 thru 360, convert to -180 thru 180\r\n converted_lons = lons - ( lons.astype(np.int32) / 180) * 360\r\n # get cell of facility\r\n lat_idx = geo_idx(in_lat, lats)\r\n lon_idx = geo_idx(in_lon, converted_lons)\r\n #extract winddirection and wind speed from that cell\r\n d = wd[:,lat_idx,lon_idx][0]\r\n wdir.append(d)\r\n s = ws[:,lat_idx,lon_idx][0]\r\n wspd.append(s)\r\n \r\n return wdir,wspd","def get_licks(dlc, dlc_t):\r\n lick_times = get_feature_event_times(dlc, dlc_t, ['tongue_end_l_x', 'tongue_end_l_y',\r\n 'tongue_end_r_x', 'tongue_end_r_y'])\r\n return lick_times","def getGameState(self):\n peg1 = []\n peg2 = []\n peg3 = []\n for i in self.kb.kb_ask(parse_input('fact: (on ?d ?p)')):\n for j in i.bindings_dict['?d']:\n if j.isdigit():\n d = int(j) \n p = int(i.bindings_dict['?p'][3])\n if p == 1:\n peg1.append(d)\n elif p == 2:\n peg2.append(d)\n elif p == 3:\n peg3.append(d)\n peg1.sort()\n peg2.sort()\n peg3.sort()\n return tuple((tuple(peg1),tuple(peg2),tuple(peg3)))","def get_image2D_amp(\n shot,\n multiframe=None,\n specid=None,\n amp=None,\n ifuslot=None,\n imtype=\"clean_image\",\n expnum=1,\n survey=LATEST_HDR_NAME,\n):\n fileh = open_shot_file(shot, survey=survey)\n\n _expnum = expnum\n\n if multiframe:\n _multiframe = multiframe\n im0 = fileh.root.Data.Images.read_where(\n \"(multiframe == _multiframe) & (expnum == _expnum)\"\n )\n elif specid:\n _specid = specid\n\n if amp:\n _amp = amp\n im0 = fileh.root.Data.Images.read_where(\n \"(specid == _specid) & (amp == _amp) & (expnum == _expnum)\"\n )\n else:\n print(\"You must provide both specid and amp\")\n elif ifuslot:\n _ifuslot = ifuslot\n if amp:\n _amp = amp\n im0 = fileh.root.Data.Images.read_where(\n \"(ifuslot == _ifuslot) & (amp == _amp) & (expnum == _expnum)\"\n )\n else:\n print(\"You must provide both ifuslot and amp\")\n\n else:\n print(\"You need to provide a multiframe or specid/amp or ifuslot/amp\")\n\n fileh.close()\n\n return im0[imtype][0]","def get_image2D_amp(\n shot,\n multiframe=None,\n specid=None,\n amp=None,\n ifuslot=None,\n imtype=\"clean_image\",\n expnum=1,\n survey=LATEST_HDR_NAME,\n):\n fileh = open_shot_file(shot, survey=survey)\n\n _expnum = expnum\n\n if multiframe:\n _multiframe = multiframe\n im0 = fileh.root.Data.Images.read_where(\n \"(multiframe == _multiframe) & (expnum == _expnum)\"\n )\n elif specid:\n _specid = specid\n\n if amp:\n _amp = amp\n im0 = fileh.root.Data.Images.read_where(\n \"(specid == _specid) & (amp == _amp) & (expnum == _expnum)\"\n )\n else:\n print(\"You must provide both specid and amp\")\n elif ifuslot:\n _ifuslot = ifuslot\n if amp:\n _amp = amp\n im0 = fileh.root.Data.Images.read_where(\n \"(ifuslot == _ifuslot) & (amp == _amp) & (expnum == _expnum)\"\n )\n else:\n print(\"You must provide both ifuslot and amp\")\n\n else:\n print(\"You need to provide a multiframe or specid/amp or ifuslot/amp\")\n\n fileh.close()\n\n return im0[imtype][0]","def _construct_climate(self, timestep):\n cl = {}\n now, end = self._get_now_and_end(timestep)\n if now==-1:\n return -1\n if self.simulation:\n if self.md.duration_unit == 'month':\n cl['duration'] = self.timestep_length / 12.\n elif self.md.duration_unit == 'year':\n cl['duration'] = self.timestep_length\n else:\n cl['duration'] = STEADY_STATE_TIMESTEP\n if self.md.climate_mode == 'constant yearly':\n cl['rain'] = self.md.constant_climate.annual_rainfall\n cl['temp'] = self.md.constant_climate.mean_temperature\n cl['amplitude'] = self.md.constant_climate.variation_amplitude\n elif self.md.climate_mode == 'monthly':\n cl = self._construct_monthly_climate(cl, now, end)\n elif self.md.climate_mode == 'yearly':\n cl = self._construct_yearly_climate(cl, now, end)\n return cl","def wavetype(self, hS, uS, cS, hX, uX, cX, xt, sign):\n if hS >= hX:\n # Shock\n qX = math.sqrt( (hS + hX) * hS / (2. * hX * hX) )\n sX = uX + sign * cX * qX\n if xt <= sX:\n # Sample point lies to the left of the shock\n if sign==-1: return hX, uX\n else: return hS, uS\n else:\n # Sample point lies to the right of the shock\n if sign==-1: return hS, uS\n else: return hX, uX\n else:\n # Rarefaction\n shX = uX + sign * cX\n if xt <= shX:\n # Sample point lies to the right of the rarefaction\n return hX, uX\n else:\n stX = uS + sign * cS\n if xt <= stX:\n # Sample point lies inside the rarefaction\n u = (uX + 2.*cX + 2.*xt)/3.\n c = (uX - sign * 2. * cX + sign * xt)/3.\n h = c * c / 9.81\n return h, u\n else:\n # Sample point lies in the STAR region\n return hS, uS","def get_shot_location():\n global LAST_SHOT\n available = [(x, y) for x in range(10) for y in range(10) if MY_SHOTS[x][y] is None]\n coords = random.choice(available)\n LAST_SHOT = coords\n return json.dumps(coords)","def processStimulus(self, observation):\n if observation == \"red\":\n return (1, 0, 0), (1, 0, 0)\n if observation == \"green\":\n return (0, 1, 0), (0, 1, 0)\n if observation == \"blue\":\n return (0, 0, 1), (0, 0, 1)","def iat_stpt_smlRefBld(action_raw, stptLmt, ob_this_raw):\n HTSP_RAW_IDX = 6; \n CLSP_RAW_IDX = 7;\n htStpt = ob_this_raw[HTSP_RAW_IDX];\n clStpt = ob_this_raw[CLSP_RAW_IDX];\n res_htStpt = max(min(htStpt + action_raw[0], stptLmt[1]), stptLmt[0]);\n res_clStpt = max(min(clStpt + action_raw[1], stptLmt[1]), stptLmt[0]);\n if res_clStpt < res_htStpt:\n return ((htStpt, clStpt),(0.0, 0.0));\n else:\n return ((res_htStpt, res_clStpt),\n (res_htStpt - htStpt, res_clStpt - clStpt));","def dribble(state, opp, angleDribble, powerDribble, coeffAD):\n me_opp = (opp.position - state.my_pos).normalize()\n me_goal = state.attacking_vector\n angle = atan2(me_opp.y,me_opp.x)\n theta = get_oriented_angle(me_goal, me_opp)/acos(0.)\n rand = exp(-coeffAD*abs(theta))/2.\n quad = state.quadrant\n if random.random() < rand: # mauvais angle (vers l'adversaire)\n if theta < 0.:\n angleDribble = -angleDribble\n else: # bon angle (vers la cage adverse)\n if theta > 0.:\n angleDribble = -angleDribble\n angle += angleDribble\n destDribble = Vector2D(angle=angle, norm=1.)\n return kickAt(state, state.ball_pos + destDribble, powerDribble)","def get_blue():\n # return name of actor, grazing speed, self defense\n return 'Piggy', 2","def getConc(fileID, spc):\r\n\r\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\r\n dataRec = rmn.fstluk(dataKey)\r\n concData = dataRec['d']\r\n return concData, dataKey, dataRec","def get_wind_values(self):\n return (\n int(self.data[2]), # dir\n float(self.data[3]) / 10, # gust\n float(self.data[4]) / 10, # avg\n float(self.data[5]) / 10, # chill\n )","def calculate_crosswalk_check_tiles(self, preferred_direction):\n\n if preferred_direction == (1, 0):\n return (\n (self.position[0] + CAR_LENGTH, self.position[1] - 1),\n (self.position[0] + CAR_LENGTH, self.position[1] + 2),\n (self.position[0] + CAR_LENGTH + 1, self.position[1] - 1),\n (self.position[0] + CAR_LENGTH + 1, self.position[1] + 2),\n )\n elif preferred_direction == (-1, 0):\n return (\n (self.position[0] - 1, self.position[1] - 1),\n (self.position[0] - 1, self.position[1] + 2),\n (self.position[0] - 2, self.position[1] - 1),\n (self.position[0] - 2, self.position[1] + 2),\n )\n elif preferred_direction == (0, 1):\n return (\n (self.position[0] - 1, self.position[1] + CAR_LENGTH),\n (self.position[0] + 2, self.position[1] + CAR_LENGTH),\n (self.position[0] - 1, self.position[1] + CAR_LENGTH + 1),\n (self.position[0] + 2, self.position[1] + CAR_LENGTH + 1),\n )\n elif preferred_direction == (0, -1):\n return (\n (self.position[0] - 1, self.position[1] - 1),\n (self.position[0] + 2, self.position[1] - 1),\n (self.position[0] - 1, self.position[1] - 2),\n (self.position[0] + 2, self.position[1] - 2),\n )","def getTileModifier(x,y,context=\"points\"):\n ret = (1,None,\"\") # default\n\n if x == 0 or x == 14:\n if y == 0 or y == 7 or y == 14:\n ret = (3,\"word\",TRIP_WORD_COLOR)\n elif y == 3 or y == 11:\n ret = (2,\"ltr\",DOUB_LTR_COLOR)\n\n elif x == 1 or x == 13:\n if y == 1 or y == 13:\n ret = (2,\"word\",DOUB_WORD_COLOR)\n elif y == 5 or y == 9:\n ret = (3,\"ltr\",TRIP_LTR_COLOR)\n\n elif x == 2 or x == 12:\n if y == 2 or y == 12:\n ret = (2,\"word\",DOUB_WORD_COLOR)\n elif y == 6 or y == 8:\n ret = (2,\"ltr\",DOUB_LTR_COLOR)\n\n elif x == 3 or x == 11:\n if y == 3 or y == 11:\n ret = (2,\"word\",DOUB_WORD_COLOR)\n elif y == 0 or y == 7 or y == 14:\n ret = (2,\"ltr\",DOUB_LTR_COLOR)\n\n elif x == 4 or x == 10:\n if y == 4 or y == 10:\n ret = (2,\"word\",DOUB_WORD_COLOR)\n\n elif x == 5 or x == 9:\n if y == 1 or y == 5 or y == 9 or y == 13:\n ret = (3,\"ltr\",TRIP_LTR_COLOR)\n\n elif x == 6 or x == 8:\n if y == 2 or y == 6 or y == 8 or y == 12:\n ret = (2,\"ltr\",DOUB_LTR_COLOR)\n\n elif x == 7:\n if y == 0 or y == 14:\n ret = (3,\"word\",TRIP_WORD_COLOR)\n elif y == 3 or y == 11:\n ret = (2,\"ltr\",DOUB_LTR_COLOR)\n elif y == 7:\n ret = (2,\"word\",DOUB_WORD_COLOR)\n else:\n raise ScrabbleError(\"Impossible Tile!\")\n \n if context == \"muliplier\":\n return ret[0]\n elif context == \"mod_type\":\n return ret[1]\n elif context == \"points\":\n return (ret[0],ret[1])\n elif context == \"bgcolor\":\n return ret[2]\n elif context == \"all\":\n return ret\n else:\n raise ScrabbleError(\"Invalid context in getTileModifier\")","def new_shot(context):\n\n shot = context.scene.milkshake_shots.add()\n camera = bpy.data.cameras.new(\"Camera\")\n camera.display_size = 1\n camera.dof.aperture_blades = 5\n camera.dof.aperture_fstop = 4\n camera.dof.aperture_ratio = 1\n camera.dof.aperture_rotation = math.radians(10)\n camera.dof.focus_distance = 1\n camera.dof.use_dof = True\n camera.lens = 35\n camera.passepartout_alpha = 0.85\n camera.sensor_fit = 'HORIZONTAL'\n camera.sensor_height = 13.365\n camera.sensor_width = 23.76\n camera_object = bpy.data.objects.new(\"Camera\", camera)\n\n cam_collection = camera_collection(context)\n cam_collection.objects.link(camera_object)\n shot.camera = camera\n core.log(\"Created new shot and camera.\")","def get_action(obs, player_x, player_y):\n if Action.Sprint in obs[\"sticky_actions\"]:\n return Action.ReleaseSprint\n return Action.Shot","def last_pos(self) -> tuple[int, int]:\n if not self.actions:\n return (self.start_x, self.start_y)\n else:\n box = self.get_hitbox_at(self.time_consumed)\n return box.pos_x, box.pos_y","def _spawn_aircraft() -> Tuple[float, float, float, str]:\n\n # Get aircraft coordinates.\n x = random.uniform(-CONTROL_ZONE_RADIUS, CONTROL_ZONE_RADIUS)\n y = math.sqrt(CONTROL_ZONE_RADIUS ** 2 - x ** 2)\n y = y if random.randint(0, 1) else -y\n\n ang = _get_ac_heading(x, y)\n\n return x, y, ang, \"A\"","def ccw(p1, p2, p3):\n return (p2[0] - p1[0])*(p3[1] - p1[1]) - (p2[1] - p1[1])*(p3[0] - p1[0])","def rgb_to_cmyk(r: int, g: int, b: int) -> tuple:\n w = max(r / 255, g / 255, b / 255)\n c = (w - (r / 255)) / w\n m = (w - (g / 255)) / w\n y = (w - (b / 255)) / w\n k = 1 - w\n return(c, m, y, k)","def get_image2D_cutout(\n shot,\n coords,\n wave_obj,\n width=40,\n height=40,\n imtype=\"clean_image\",\n survey=LATEST_HDR_NAME,\n):\n fibers = Fibers(shot)\n\n idx = fibers.get_closest_fiber(coords)\n multiframe_obj = fibers.table.cols.multiframe[idx].astype(str)\n expnum_obj = fibers.table.cols.expnum[idx]\n x, y = fibers.get_image_xy(idx, wave_obj)\n\n im0 = fibers.hdfile.root.Data.Images.read_where(\n \"(multiframe == multiframe_obj) & (expnum == expnum_obj)\"\n )\n\n return im0[imtype][0][\n x - int(np.floor(height / 2)) : x + int(np.ceil(height / 2)),\n y - int(np.floor(width / 2)) : y + int(np.ceil(width / 2)),\n ]","def get_image2D_cutout(\n shot,\n coords,\n wave_obj,\n width=40,\n height=40,\n imtype=\"clean_image\",\n survey=LATEST_HDR_NAME,\n):\n fibers = Fibers(shot)\n\n idx = fibers.get_closest_fiber(coords)\n multiframe_obj = fibers.table.cols.multiframe[idx].astype(str)\n expnum_obj = fibers.table.cols.expnum[idx]\n x, y = fibers.get_image_xy(idx, wave_obj)\n\n im0 = fibers.hdfile.root.Data.Images.read_where(\n \"(multiframe == multiframe_obj) & (expnum == expnum_obj)\"\n )\n\n return im0[imtype][0][\n x - int(np.floor(height / 2)) : x + int(np.ceil(height / 2)),\n y - int(np.floor(width / 2)) : y + int(np.ceil(width / 2)),\n ]","def get_piles_place(self):\n idx_draw = []\n piles_draw = []\n i = 0\n for p in self.game_state['piles']:\n if not self.game_state['two_player']:\n if not self.game_state['piles_taken'][i] and len(p) < 3:\n idx_draw.append(i)\n piles_draw.append(p)\n else:\n if len(p) <= i and not self.game_state['piles_taken'][i]:\n idx_draw.append(i)\n piles_draw.append(p)\n i += 1\n return (piles_draw,idx_draw)","def get_temps(self):\n try:\n cmos = self.cmos_temp\n except Exception:\n cmos = None\n try:\n pcb = self.pcb_temp\n except Exception:\n pcb = None\n return cmos, pcb","def _whctrs(anchor):\n w = anchor[2] - anchor[0] + 1\n h = anchor[3] - anchor[1] + 1\n x_ctr = anchor[0] + 0.5 * (w - 1)\n y_ctr = anchor[1] + 0.5 * (h - 1)\n return w, h, x_ctr, y_ctr","def _whctrs(anchor):\n w = anchor[2] - anchor[0] + 1\n h = anchor[3] - anchor[1] + 1\n x_ctr = anchor[0] + 0.5 * (w - 1)\n y_ctr = anchor[1] + 0.5 * (h - 1)\n return w, h, x_ctr, y_ctr","def getTEMP(self):\r\n\t\ttemp_H = self.read(0x41)\r\n\t\ttemp_L = self.read(0x42)\r\n\t\ttemp = self.twos_comp(val = (temp_H*256 + temp_L),bits=16)\r\n\t\ttempC = (temp/340.0)+36.53\r\n\t\ttempF = tempC*(9.0/5) + 32\r\n\t\treturn tempC,tempF ,temp"],"string":"[\n \"def handle_shoot(data: bytes) -> Tuple[bytes, str]:\\n length = struct.unpack('H', data[:2])[0]\\n name = data[2:length+2]\\n direction = struct.unpack('fff', data[length+2:length+2+12])\\n return data[2+length:], f'Shot {name.decode()} in direction: {direction}'\",\n \"def wcsshift(x0, y0, hd1, hd2):\\n wcs1 = wcs.WCS(hd1)\\n wcs2 = wcs.WCS(hd2)\\n ra, dec = wcs1.all_pix2world(x0, y0, 1)\\n x2, y2 = wcs2.all_world2pix(ra, dec, 1)\\n return x2 - x0, y2 - y0\",\n \"def get_coord(tic):\\n try:\\n catalog_data = Catalogs.query_object(objectname=\\\"TIC\\\"+tic, catalog=\\\"TIC\\\")\\n ra = catalog_data[0][\\\"ra\\\"]\\n dec = catalog_data[0][\\\"dec\\\"]\\n # print(catalog_data.keys())\\n # print(catalog_data[0][\\\"GAIA\\\"])\\n return ra, dec\\n except:\\n \\tprint(\\\"ERROR: TIC not found in Simbad\\\")\",\n \"def snapShot(self):\\n syncTimeNow = self.syncTimelineClock.ticks\\n # convert from pts to wallclock\\n wcNow = self.syncTimelineClock.toOtherClockTicks(self.wallClock, syncTimeNow)\\n speed = self.syncTimelineClock.speed\\n whenSnapshotted = wcNow\\n return (whenSnapshotted, (wcNow, syncTimeNow, speed))\",\n \"def getCoord(self):\\n return (self.birth, self.death)\",\n \"def computeWCSKeys(pos, size, cutCrap=False):\\n\\timgPix = (1000., 1000.)\\n\\tres = {\\n\\t\\t\\\"CRVAL1\\\": pos[0],\\n\\t\\t\\\"CRVAL2\\\": pos[1],\\n\\t\\t\\\"CRPIX1\\\": imgPix[0]/2.,\\n\\t\\t\\\"CRPIX2\\\": imgPix[1]/2.,\\n\\t\\t\\\"CUNIT1\\\": \\\"deg\\\",\\n\\t\\t\\\"CUNIT2\\\": \\\"deg\\\",\\n\\t\\t\\\"CD1_1\\\": size[0]/imgPix[0],\\n\\t\\t\\\"CD1_2\\\": 0,\\n\\t\\t\\\"CD2_2\\\": size[1]/imgPix[1],\\n\\t\\t\\\"CD2_1\\\": 0,\\n\\t\\t\\\"NAXIS1\\\": imgPix[0],\\n\\t\\t\\\"NAXIS2\\\": imgPix[1],\\n\\t\\t\\\"NAXIS\\\": 2,\\n\\t\\t\\\"CTYPE1\\\": 'RA---TAN-SIP', \\n\\t\\t\\\"CTYPE2\\\": 'DEC--TAN-SIP',\\n\\t\\t\\\"LONPOLE\\\": 180.}\\n\\tif not cutCrap:\\n\\t\\tres.update({\\\"imageTitle\\\": \\\"test image at %s\\\"%repr(pos),\\n\\t\\t\\t\\\"instId\\\": None,\\n\\t\\t\\t\\\"dateObs\\\":55300+pos[0], \\n\\t\\t\\t\\\"refFrame\\\": None,\\n\\t\\t\\t\\\"wcs_equinox\\\": None,\\n\\t\\t\\t\\\"bandpassId\\\": None,\\n\\t\\t\\t\\\"bandpassUnit\\\": None,\\n\\t\\t\\t\\\"bandpassRefval\\\": None,\\n\\t\\t\\t\\\"bandpassLo\\\": pos[0],\\n\\t\\t\\t\\\"bandpassHi\\\": pos[0]+size[0],\\n\\t\\t\\t\\\"pixflags\\\": None,\\n\\t\\t\\t\\\"accref\\\": \\\"image/%s/%s\\\"%(pos, size),\\n\\t\\t\\t\\\"accsize\\\": (30+int(pos[0]+pos[1]+size[0]+size[1]))*1024,\\n\\t\\t\\t\\\"embargo\\\": None,\\n\\t\\t\\t\\\"owner\\\": None,\\n\\t\\t})\\n\\treturn res\",\n \"def get_strand_state(w, c):\\n if (w is None) or (c is None) or (w + c == 0):\\n return (0, 0)\\n r = w / (w + c)\\n if r < 0.2:\\n return (0, 2)\\n elif r > 0.8:\\n return (2, 0)\\n else:\\n return (1, 1)\",\n \"def p2(self):\\n return tuple(self.rect[2:])\",\n \"def shoot(self, dice: Roll) -> tuple:\\n result = dice.roll()\\n self.remember(result)\\n return result\",\n \"def shoot(state, power):\\n return kickAt(state, state.opp_goal, power)\",\n \"def check_shot(self, cords):\\n \\n y, x = text_to_cords(cords)\\n output = \\\"MISS\\\"\\n\\n for ship in self.ships:\\n if ship.is_my_cords(y, x):\\n output = \\\"HIT\\\"\\n break\\n \\n self.board.change_field(y, x, text_to_field_type(output))\\n self.show_boards()\\n print(\\\"Enemy shot: \\\", cords)\\n\\n # if all ships are destroyed, disconnect\\n if self.board.are_all_ships_destoryed(self.ships):\\n output = \\\"WIN\\\"\\n print(\\\"You lose :(\\\")\\n self.client.send(f\\\"C{output}\\\".encode(\\\"utf-8\\\"))\\n self.client.close()\\n sys.exit()\\n\\n self.client.send(f\\\"C{output}\\\".encode(\\\"utf-8\\\"))\",\n \"def selectShot(self):\\r\\n self.weightBoard()\\r\\n self.printBoard()\\r\\n bestCoordinates = self.selectBestCoordinates()\\r\\n shot = self.mapToShot(bestCoordinates)\\r\\n logging.debug(\\\"select shot: %s\\\" % (shot))\\r\\n return shot\",\n \"def crd(self):\\r\\n return self.__trajectory[0]\",\n \"def get_powerups() -> tuple:\\n return tuple(PowerUp.powers.keys())\",\n \"def pick_start_tile(hands: list):\\n doubles = {}\\n\\n\\n # write down all the doubles\\n for player, hand in enumerate(hands):\\n for tile in hand:\\n if check_if_double(tile):\\n doubles[tile[0]] = player\\n\\n # find the highest double. If no doubles, return None for start_player and starting tile\\n try:\\n max_double = max(doubles.keys())\\n except ValueError:\\n return None, None\\n\\n start_player = doubles[max_double]\\n\\n return start_player, max_double\",\n \"def doRide(car, ride):\\n global MAX_DISTANCE_START, MAX_DISTANCE_FINISH\\n (a, b, x, y, s, f) = ride\\n lenght_ride = abs(x - a) + abs(y - b)\\n # Simple heuristic to make it faster\\n if lenght_ride > MAX_DISTANCE_FINISH: # So it doesn't take too long rides\\n return None\\n if car is None or len(car) == 0: # No car or no rides asigned to the car\\n (cx, cy) = INITIAL_POS\\n cs = INITIAL_TIME\\n else: # Else, look in the list\\n last_ride = car[-1]\\n (cx, cy) = tuple(rides[last_ride][0:2]) # Position of the car\\n # When will the car be at that position\\n cs = rides_done[last_ride][2]\\n # Distance to the ride's starting intersection\\n distance = abs(cx - a) + abs(cy - b)\\n if distance > MAX_DISTANCE_START: # Do not take too far away ones\\n return None\\n when = max(cs + distance, s)\\n if when + lenght_ride > f: # The car cant make it\\n return None\\n\\n return when, when + lenght_ride, when == s\",\n \"def crack_legth_compute(self):\\n crack_grad = self.gradient(4,5)\\n avegrad = self.average_grad()\\n cracklen = self.distance(self.points[4,:],self.points[5,:])\\n #print(\\\"photo_crack_len\\\", cracklen)\\n\\n #print(\\\"Crack_grad:\\\", crack_grad)\\n\\n angle_between_crack_and_vertical = np.arctan(np.abs((crack_grad - avegrad)/(1+crack_grad*avegrad)))\\n #print(\\\"angle between crack and vertical: \\\", np.rad2deg(angle_between_crack_and_vertical),\\\"degrees\\\")\\n\\n\\n h_dist = np.sin(angle_between_crack_and_vertical)*cracklen\\n h_rel = h_dist/self.hline[2]\\n #print('relative horizontal_crack distance:', h_rel)\\n v_dist = np.cos(angle_between_crack_and_vertical)*cracklen\\n v_rel = v_dist / self.vline[2]\\n #print('relative vertical_crack distance:', v_rel)\\n\\n actual_crack_length = np.sqrt((h_rel*self.cut_breadth)**2 + (v_rel*self.cut_depth)**2)\\n\\n #print(\\\"Actual_crack_length\\\\n,\\\", actual_crack_length)\\n print(actual_crack_length) # This is later writen to a text file and read into the main program\\n\\n return actual_crack_length\",\n \"def shotgenerator():\\n return random.randint(0, 9), random.randint(0, 9)\",\n \"def get_right_hand(index, annotations):\\n return get_hand_points(index, annotations, 21)\",\n \"def calculate_cable(self):\\n x_houses, y_houses, x_batt, y_batt = self.get_coordinates()\\n\\n all_diff = []\\n for x_house, y_house in list(zip(x_houses, y_houses)):\\n house_diff = {}\\n counter = 0\\n for x, y in list(zip(x_batt, y_batt)):\\n x_diff = abs(x - x_house)\\n y_diff = abs(y - y_house)\\n house_diff[counter] = (x_diff + y_diff)\\n counter += 1\\n all_diff.append(house_diff)\\n\\n # set as attributes\\n keys_list = list(self.houses.keys())\\n for i, key in enumerate(keys_list):\\n self.houses[key].dist = all_diff[i]\",\n \"def get(self):\\r\\n return ((self.x, self.y), self.dir)\",\n \"def p1(self):\\n return tuple(self.rect[:2])\",\n \"def get_two_armies(self) -> tuple:\\n\\n if(len(self.armies) < 2):\\n print(\\\"Could not choose an army. must have more than one army on the list\\\")\\n raise Exception\\n\\n while(True):\\n first = R.randint(0, len(self.armies)-1)\\n second = R.randint(0, len(self.armies)-1)\\n\\n if(first != second):\\n break\\n\\n return (self.armies[first], self.armies[second])\",\n \"def firstMove(self):\\n return (10, 10)\",\n \"def mapToCoordinates(self, shot):\\r\\n toks = shot.split(\\\"-\\\")\\r\\n return Coordinates(ord(toks[0]) - ord(\\\"A\\\"), int(toks[1]) - 1)\",\n \"def _snap(self):\\n return (\\n # same symbols/players tokens in the same positions\\n tuple(\\n (x, tuple(sorted(ts))) for x, ts in self.board.items() if ts\\n ),\\n # with the same number of throws remaining for each player\\n self.throws[\\\"upper\\\"],\\n self.throws[\\\"lower\\\"],\\n )\",\n \"def get_clip_data(slug: str) -> tuple:\\r\\n clip_info = get_data(slug)\\r\\n\\r\\n if \\\"thumbnail_url\\\" in clip_info and \\\"title\\\" in clip_info:\\r\\n # All to get what we need to return\\r\\n # the mp4_url and title of the clip\\r\\n thumb_url = clip_info[\\\"thumbnail_url\\\"]\\r\\n slice_point = thumb_url.index(\\\"-preview-\\\")\\r\\n mp4_url = thumb_url[:slice_point] + \\\".mp4\\\"\\r\\n\\r\\n return mp4_url, clip_info[\\\"title\\\"]\\r\\n\\r\\n raise TypeError(\\r\\n f\\\"We didn't receieve what we wanted. /helix/clips endpoint gave:\\\\n{clip_info}\\\"\\r\\n )\",\n \"def get_clim(self) -> tuple[float | None, float | None]:\\n return self._image.get_clim() if self._image is not None else (None, None)\",\n \"def get_rated_output(cap_rtd_h: float, cap_rtd_c: float) -> (float, float):\\n\\n q_hs_rtd_h = cap_rtd_h * 3600 * 10 ** (-6)\\n q_hs_rtd_c = cap_rtd_c * 3600 * 10 ** (-6)\\n\\n return q_hs_rtd_h, q_hs_rtd_c\",\n \"def get(self) -> tuple:\",\n \"def get(self):\\n return (self.x,self.y);\",\n \"def get_new_coordinate(x_y_coordinate: dict, move_direction: str) -> tuple:\\n direction_dict = {'n': (0, -1), 's': (0, 1), 'w': (-1, 0), 'e': (1, 0)}\\n x = x_y_coordinate['x'] + direction_dict[move_direction][0]\\n y = x_y_coordinate['y'] + direction_dict[move_direction][1]\\n return x, y\",\n \"def composite(c, r):\\n x, y = gta * (c, r)\\n lat, lon = transform.TransformPoint(x, y)[:2]\\n if not -90 <= lat <= 90:\\n raise ValueError('illegal lat value, did you switch coordinates')\\n return lon, lat\",\n \"def mapToShot(self, coordinates):\\r\\n return chr(coordinates.x + ord(\\\"A\\\")) + \\\"-\\\" + str(coordinates.y + 1)\",\n \"def get_coverage_time(self):\\n res1 = self.get_attr('time_coverage_start')\\n res2 = self.get_attr('time_coverage_end')\\n return (res1, res2)\",\n \"def pick_climate(ctx, idx):\\n climo = ctx.get(f\\\"c{idx}\\\", \\\"1951\\\")\\n cltable = \\\"climate\\\"\\n clstation = ctx.get(f\\\"station{idx}\\\")\\n if climo == \\\"1951\\\":\\n cltable = \\\"climate51\\\"\\n clstation = ctx.get(f\\\"station{idx}\\\")\\n elif climo == \\\"ncei81\\\":\\n cltable = \\\"ncdc_climate81\\\"\\n if clstation in ctx[f\\\"_nt{idx}\\\"].sts:\\n clstation = ctx[f\\\"_nt{idx}\\\"].sts[clstation][\\\"ncdc81\\\"]\\n elif climo == \\\"ncei91\\\":\\n cltable = \\\"ncei_climate91\\\"\\n if clstation in ctx[f\\\"_nt{idx}\\\"].sts:\\n clstation = ctx[f\\\"_nt{idx}\\\"].sts[clstation][\\\"ncei91\\\"]\\n return cltable, clstation\",\n \"def shotparser(shot: str):\\n characters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 't', 'u',\\n 'v', 'w', 'x', 'y', 'z']\\n # Get index of letter\\n return int(shot[1:]) - 1, characters.index(shot[0])\",\n \"def get_clashes (fixed_struc, moving_struc, minimum_clash_distance):\\n clash_set=set()\\n \\n NS = Bio.PDB.NeighborSearch(list(fixed_struc.get_atoms()))\\n clashes =0\\n for atoms in moving_struc.get_atoms():\\n close = NS.search(atoms.get_coord(), minimum_clash_distance)\\n if len(close) > 0:\\n for item in close:\\n clash_set.add(item.get_parent().get_parent().id)\\n clashes +=1\\n return clashes\",\n \"def sendTwoC(self):\\n startC = self.countCannibalOnStart()\\n if startC < 2:\\n return None\\n else:\\n newStart = self.start[0:2] + str(startC-2) + self.start[3]\\n newEnd = self.end[0:2] + str(5-startC) + self.end[3]\\n return MissionaryState(newStart,newEnd,\\\"sendTwoC\\\")\",\n \"def compare_moves(p_move, c_move):\\n#\\ttie = \\\"It's a tie\\\"\\n \\tif p_move == \\\"r\\\" and c_move == \\\"p\\\":\\n \\t\\treturn \\\"computer\\\"\\n\\telif p_move == \\\"r\\\" and c_move == \\\"s\\\":\\n \\t\\treturn \\\"human\\\"\\n\\telif p_move == \\\"p\\\" and c_move == \\\"s\\\":\\n \\t\\treturn \\\"computer\\\"\\n\\telif p_move == \\\"p\\\" and c_move == \\\"r\\\":\\n\\t\\treturn \\\"human\\\"\\n\\telif p_move == \\\"s\\\" and c_move == \\\"r\\\":\\n\\t\\treturn \\\"computer\\\"\\n\\telif p_move == \\\"s\\\" and c_move == \\\"p\\\":\\n\\t\\treturn \\\"human\\\"\",\n \"def get_piles_take(self):\\n idx_take = []\\n piles_take = []\\n i = 0\\n for p in self.game_state['piles']:\\n if not self.game_state['piles_taken'][i] and len(p) > 0:\\n idx_take.append(i)\\n piles_take.append(p)\\n i += 1\\n return (piles_take,idx_take)\",\n \"def coordinates_of_square(crd):\\n col = ord(crd[0]) - ord('a')\\n row = int(crd[1]) - 1\\n return (col * SQUARE_EDGE + BOARD_MARGIN, (7 - row) * SQUARE_EDGE + BOARD_MARGIN)\",\n \"def getShutter( self, c, devChannel ):\\n dev = 'shutter'\\n self.validateDevChannel( dev, devChannel )\\n value = bool(self.dcDict[dev]['devChannels'][devChannel]['value'])\\n if value is not None: return value\\n else: raise DCBoxError( 4 )\",\n \"def get_screw(urnie,key,coords):\\r\\n global urnie_flag\\r\\n urnie_flag=True\\r\\n urnie.home()\\r\\n z = 0.1\\r\\n r=6\\r\\n if key == 'm4_long':\\r\\n z = 0.11\\r\\n coords[0]+=0.001\\r\\n elif key == 'm4_short':\\r\\n z = 0.06\\r\\n coords[0]+=0.0005\\r\\n elif key == 'm3':\\r\\n z = 0.070\\r\\n r=7\\r\\n coords[0]+=0.001\\r\\n urnie.movejl(urnie.pedestal.right_tray([coords[0],coords[1],z,0,pi,0]))\\r\\n urnie.insert([0,0,-0.05],max_force=35,hunting_radius=0.0005,R=r)\\r\\n urnie.translatel_rel([0,0,0.05],acc=0.1,vel=0.1)\\r\\n urnie.home()\\r\\n urnie_flag=False\\r\\n return\",\n \"def m_get(state,b1,b2):\\n if b2 == 'hand' and state.clear[b1] and state.holding['hand'] == False:\\n if state.pos[b1] == 'table':\\n return [('pickup',b1)]\\n else:\\n return [('unstack',b1,state.pos[b1])]\\n else:\\n return False\",\n \"def get_moves(character):\\n if character == 'player':\\n x_coord = get_locations()['player'][0]\\n y_coord = get_locations()['player'][1]\\n elif character == 'monster':\\n x_coord = get_locations()['monster'][0]\\n y_coord = get_locations()['monster'][1]\\n if x_coord == 1 and y_coord == 1:\\n return ['S', 'D']\\n elif x_coord == STATUS['grid_size'] and y_coord == STATUS['grid_size']:\\n return ['W', 'A']\\n elif x_coord == 1 and y_coord == STATUS['grid_size']:\\n return ['W', 'D']\\n elif x_coord == STATUS['grid_size'] and y_coord == 1:\\n return ['S', 'A']\\n elif x_coord == 1:\\n return ['W', 'D', 'S']\\n elif y_coord == 1:\\n return ['D', 'S', 'A']\\n elif x_coord == STATUS['grid_size']:\\n return ['W', 'S', 'A']\\n elif y_coord == STATUS['grid_size']:\\n return ['W', 'A', 'D']\\n else:\\n return ['W', 'D', 'S', 'A']\",\n \"def get_wotd():\\n\\treturn wotd\",\n \"def coordinate(self):\\n\\t\\tif self.boldness_coord is None and self.price_coord is None and self.hold_coord is None:\\n\\t\\t\\treturn None\\n\\n\\t\\treturn (self.boldness_coord, self.price_coord, self.hold_coord)\",\n \"def get_castle(state):\\n # TODO: Make sure the castling spaces aren't under attack\\n # Queenside: A through E, Kingside: E through H\\n q_rook = False\\n k_rook = False\\n q_space = False\\n k_space = False\\n king = False\\n\\n if state.active_color == cc.WHITE_ACTIVE:\\n # Check through rank 1\\n rank = cc.RANK_1\\n # Check the state to see if castling is available\\n wk_avail = True if cc.W_KING in state.castles_avail else False\\n wq_avail = True if cc.W_QUEEN in state.castles_avail else False\\n if state.board[rank, cc.FILE_E] == cc.W_KING:\\n king = True\\n if wq_avail:\\n if state.board[rank, cc.FILE_A] == cc.W_ROOK:\\n q_rook = True\\n if state.board[rank, cc.FILE_B] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_C] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_D] == cc.NO_PIECE and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_B), cc.BLACK_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_C), cc.BLACK_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_D), cc.BLACK_ACTIVE):\\n q_space = True\\n if wk_avail:\\n if state.board[rank, cc.FILE_F] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_G] == cc.NO_PIECE and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_F), cc.BLACK_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_G), cc.BLACK_ACTIVE):\\n k_space = True\\n if state.board[rank, cc.FILE_H] == cc.W_ROOK:\\n k_rook = True\\n\\n # Check the variables to see what castling is available\\n if wk_avail and wq_avail and q_rook and q_space and king and k_space and k_rook:\\n return (True, True)\\n elif wq_avail and q_rook and q_space and king:\\n return (False, True)\\n elif wk_avail and king and k_space and k_rook:\\n return (True, False)\\n else:\\n return (False, False)\\n\\n elif state.active_color == cc.BLACK_ACTIVE:\\n # Loop through rank 8\\n rank = cc.RANK_8\\n # Check the state to see if castling is available\\n bk_avail = True if cc.B_KING in state.castles_avail else False\\n bq_avail = True if cc.B_QUEEN in state.castles_avail else False\\n if state.board[rank, cc.FILE_E] == cc.B_KING:\\n king = True\\n if bq_avail:\\n if state.board[rank, cc.FILE_A] == cc.B_ROOK:\\n q_rook = True\\n if state.board[rank, cc.FILE_B] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_C] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_D] == cc.NO_PIECE and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_B), cc.WHITE_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_C), cc.WHITE_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_D), cc.WHITE_ACTIVE):\\n q_space = True\\n if bk_avail:\\n if state.board[rank, cc.FILE_F] == cc.NO_PIECE and \\\\\\n state.board[rank, cc.FILE_G] == cc.NO_PIECE and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_F), cc.WHITE_ACTIVE) and \\\\\\n not check.space_under_attack(state, (rank, cc.FILE_G), cc.WHITE_ACTIVE):\\n k_space = True\\n if state.board[rank, cc.FILE_H] == cc.B_ROOK:\\n k_rook = True\\n\\n # Check the variables to see what castling is available\\n if bk_avail and bq_avail and q_rook and q_space and king and k_space and k_rook:\\n return (True, True)\\n elif bq_avail and q_rook and q_space and king:\\n return (False, True)\\n elif bk_avail and king and k_space and k_rook:\\n return (True, False)\\n else:\\n return (False, False)\\n else:\\n raise Exception(\\\"castle_state: Invalid active color\\\")\",\n \"def get_damage():\\n\\n return character['Damage']\",\n \"def take_second(info):\\n return info[1]\",\n \"def get_values(state):\\n keys = ['player_ammo', 'player_block', 'player_prev',\\n 'comp_ammo', 'comp_block', 'comp_prev']\\n return tuple([state[key] for key in keys])\",\n \"def get(self) -> Tuple[str, Tuple]:\\n # TODO:\\n pass\",\n \"def get_ci(cls, data: tuple or list, cl=0.95,\\n is_population=False, tail=\\\"two\\\") -> tuple:\\n cls._data_validation(data)\\n mean = cls.get_mean(data)\\n e = cls.get_moe(\\n data, cl=cl, is_population=is_population, tail=tail\\n )\\n return mean - e, mean + e\",\n \"def run_duel(gene1, gene2):\\n\\n rounds, c1, c2 = battle(gene1, gene2)\\n\\n if c1 >= c2:\\n win_c, los_c = c1, c2\\n gene1_wins = True\\n else:\\n win_c, los_c = c2, c1\\n gene1_wins = False\\n\\n # Accumulate the points according to the rules.\\n if los_c == 0:\\n if rounds < 100:\\n win_p, los_p = 20, 0\\n elif rounds < 200:\\n win_p, los_p = 19, 1\\n elif rounds < 300:\\n win_p, los_p = 18, 2\\n else:\\n win_p, los_p = 17, 3\\n assert rounds <= 500\\n else:\\n ratio = float(win_c) / los_c\\n if ratio >= 10:\\n win_p, los_p = 13, 7\\n elif ratio >= 3:\\n win_p, los_p = 12, 8\\n elif ratio >= 1.5:\\n win_p, los_p = 11, 9\\n else:\\n win_p, los_p = 10, 10\\n\\n if gene1_wins:\\n return win_p, los_p\\n else:\\n return los_p, win_p\",\n \"def getCarLocation():\\n cams = CAMS\\n img1, img2 = cams[0].getImage(), cams[1].getImage()\\n potint1op, point2op = GetBalloonOld.getCar(img1), GetBalloonOld.getCar(\\n img2)\\n for j in range(len(point1op)):\\n point1op[j] = np.array(np.array([point1op[j][0], point1op[j][1]]))\\n for j in range(len(point2op)):\\n point2op[j] = np.array(np.array([point2op[j][0], point2op[j][1]]))\\n if len(point1op) == 0 or len(point2op) == 0:\\n return None\\n points = [[point1op[0]], [point2op[0]]]\\n car = getTargetsPlaces(copy.deepcopy(points))[0]\\n diff = abs(car[2] - CAR_Z)\\n for op1 in point1op:\\n for op2 in point2op:\\n testPoint = [[op1], [op2]]\\n testCar = getTargetsPlaces(copy.deepcopy(testPoint))[0]\\n testDiff = abs(testCar[2] - CAR_Z)\\n if testDiff < diff:\\n diff = testDiff\\n car = testCar\\n return car\",\n \"def actions(self,state):\\n sick = []\\n health = []\\n num_s = 0\\n num_h = 0\\n for i in range(self.row):\\n for j in range(self.col):\\n if state[i][j][0] == 'S':\\n sick.append((\\\"quarantine\\\", (i, j)))\\n num_s += 1\\n elif state[i][j][0] == 'H':\\n health.append((\\\"vaccinate\\\", (i, j)))\\n num_h += 1\\n\\n res = []\\n if num_h < self.medics:\\n health_pow = list(chain.from_iterable(combinations(health, r) for r in range(num_h, num_h + 1)))[:]\\n else:\\n health_pow = list(chain.from_iterable(combinations(health, r) for r in range(self.medics, self.medics + 1)))[:]\\n if num_s < self.police:\\n sick_pow = list(chain.from_iterable(combinations(sick, r) for r in range(num_s, num_s + 1)))[:]\\n else:\\n sick_pow = list(chain.from_iterable(combinations(sick, r) for r in range(self.police, self.police + 1)))[:]\\n if len(health_pow) == 0:\\n sick_pow.append(())\\n return tuple(sick_pow)\\n if len(sick_pow) == 0:\\n health_pow.append(())\\n return tuple(health_pow)\\n for i in range(len(health_pow)):\\n for j in range(len(sick_pow)):\\n res.append(health_pow[i] + sick_pow[j])\\n return tuple(res)\",\n \"def get_img_coord_tuple(img):\\n\\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\\n lat = -lat\\n\\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\\n longitude = -longitude\\n\\n return lat, longitude\",\n \"def TURN_OPTIONS() -> tuple:\\n return \\\"Hit me! (Draw another Card)\\\", \\\"Stand (End round, stop drawing)\\\"\",\n \"def getCriticStation(analyzer):\\n mayIn = model.getRankMay(analyzer,\\\"in\\\")\\n mayOut=model.getRankMay(analyzer,\\\"out\\\")\\n less=model.getRankMen(analyzer,\\\"LessPopular\\\")\\n return (mayIn,mayOut,less)\",\n \"def shoot(self, pos_to_shoot):\\n return [SHOOT, pos_to_shoot]\",\n \"def grab_battle_state_image(screenshot):\\n return screenshot.crop((445, 50, 615, 165))\",\n \"def hw_c(box):\\n\\n w = box[:, 2] - box[:, 0]\\n h = box[:, 3] - box[:, 1]\\n w_c = box[:, 0] + 0.5 * w\\n h_c = box[:, 1] + 0.5 * h\\n return h, w, h_c, w_c\",\n \"def get_wind():\\n return get_next_random(wind, WIND_MAX, WIND_MIN, WIND_DELTA)\",\n \"def get_true_bearing(shot, top):\\n tripidx = shot[\\\"trip\\\"]\\n\\n if tripidx != -1:\\n decl = top[\\\"trips\\\"][tripidx][KEY_DECLINATION]\\n else:\\n decl = 0\\n\\n return shot[\\\"compass\\\"] + decl\",\n \"def get_tuple(self):\\n return (self.r, self.g, self.b)\",\n \"def get_tuple(self):\\n return (self.r, self.g, self.b)\",\n \"def get_coordinates(xd, yd, xc, yc, k1, width, height):\\n r_squared = ((xd - xc)**2 + (yd - yc)**2) / (width**2 + height**2)\\n denominator = max(EPSILON, 1 + k1 * r_squared) # Prevent division by 0\\n xu = xc + (xd - xc) / denominator\\n yu = yc + (yd - yc) / denominator\\n return (round(xu), round(yu))\",\n \"def selectShot(self):\\r\\n shot = self.mapToShot(self.remainingCoordinates.pop())\\r\\n logging.debug(\\\"select shot: %s\\\" % (shot))\\r\\n return shot\",\n \"def c_cd2cp(s, N):\\n color, move = s.strip().split()\\n # print('color:{} move:{}'.format(color,move))\\n c = color2c(color)\\n p = cd2p(move, N)\\n return c, p\",\n \"def far_from_goal_shot(obs, player_x, player_y):\\n def environment_fits(obs, player_x, player_y):\\n \\\"\\\"\\\" environment fits constraints \\\"\\\"\\\"\\n # player is far from opponent's goal or it's the goalkeeper\\n if player_x < -0.6 or obs[\\\"ball_owned_player\\\"] == 0:\\n return True\\n return False\\n \\n def get_action(obs, player_x, player_y):\\n \\\"\\\"\\\" get action of this memory pattern \\\"\\\"\\\"\\n if Action.Sprint in obs[\\\"sticky_actions\\\"]:\\n return Action.ReleaseSprint\\n return Action.Shot\\n \\n return {\\\"environment_fits\\\": environment_fits, \\\"get_action\\\": get_action}\",\n \"def roof_cc(lenght, width, overhang=1, wall_height=3, roof_height=4):\\n a = min(0.1 * lenght, 0.1 * width, 0.4 * (wall_height + 0.5 * roof_height))\\n\\n area = (lenght + overhang)*(width + overhang)\\n area_3 = 8*a**2\\n area_1 = (lenght - 2)*(width - 4*a)\\n area_2 = area - area_3 - area_1\\n return area, area_1, area_2, area_3\",\n \"def extract_wind(source,la,lo,lats,lons,wd,ws):\\r\\n lat = source[la]\\r\\n lon = source[lo]\\r\\n wdir = []\\r\\n wspd = [] \\r\\n for coor in zip(lon,lat): \\r\\n in_lon = coor[0]\\r\\n in_lat = coor[1]\\r\\n # since lons are 0 thru 360, convert to -180 thru 180\\r\\n converted_lons = lons - ( lons.astype(np.int32) / 180) * 360\\r\\n # get cell of facility\\r\\n lat_idx = geo_idx(in_lat, lats)\\r\\n lon_idx = geo_idx(in_lon, converted_lons)\\r\\n #extract winddirection and wind speed from that cell\\r\\n d = wd[:,lat_idx,lon_idx][0]\\r\\n wdir.append(d)\\r\\n s = ws[:,lat_idx,lon_idx][0]\\r\\n wspd.append(s)\\r\\n \\r\\n return wdir,wspd\",\n \"def get_licks(dlc, dlc_t):\\r\\n lick_times = get_feature_event_times(dlc, dlc_t, ['tongue_end_l_x', 'tongue_end_l_y',\\r\\n 'tongue_end_r_x', 'tongue_end_r_y'])\\r\\n return lick_times\",\n \"def getGameState(self):\\n peg1 = []\\n peg2 = []\\n peg3 = []\\n for i in self.kb.kb_ask(parse_input('fact: (on ?d ?p)')):\\n for j in i.bindings_dict['?d']:\\n if j.isdigit():\\n d = int(j) \\n p = int(i.bindings_dict['?p'][3])\\n if p == 1:\\n peg1.append(d)\\n elif p == 2:\\n peg2.append(d)\\n elif p == 3:\\n peg3.append(d)\\n peg1.sort()\\n peg2.sort()\\n peg3.sort()\\n return tuple((tuple(peg1),tuple(peg2),tuple(peg3)))\",\n \"def get_image2D_amp(\\n shot,\\n multiframe=None,\\n specid=None,\\n amp=None,\\n ifuslot=None,\\n imtype=\\\"clean_image\\\",\\n expnum=1,\\n survey=LATEST_HDR_NAME,\\n):\\n fileh = open_shot_file(shot, survey=survey)\\n\\n _expnum = expnum\\n\\n if multiframe:\\n _multiframe = multiframe\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(multiframe == _multiframe) & (expnum == _expnum)\\\"\\n )\\n elif specid:\\n _specid = specid\\n\\n if amp:\\n _amp = amp\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(specid == _specid) & (amp == _amp) & (expnum == _expnum)\\\"\\n )\\n else:\\n print(\\\"You must provide both specid and amp\\\")\\n elif ifuslot:\\n _ifuslot = ifuslot\\n if amp:\\n _amp = amp\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(ifuslot == _ifuslot) & (amp == _amp) & (expnum == _expnum)\\\"\\n )\\n else:\\n print(\\\"You must provide both ifuslot and amp\\\")\\n\\n else:\\n print(\\\"You need to provide a multiframe or specid/amp or ifuslot/amp\\\")\\n\\n fileh.close()\\n\\n return im0[imtype][0]\",\n \"def get_image2D_amp(\\n shot,\\n multiframe=None,\\n specid=None,\\n amp=None,\\n ifuslot=None,\\n imtype=\\\"clean_image\\\",\\n expnum=1,\\n survey=LATEST_HDR_NAME,\\n):\\n fileh = open_shot_file(shot, survey=survey)\\n\\n _expnum = expnum\\n\\n if multiframe:\\n _multiframe = multiframe\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(multiframe == _multiframe) & (expnum == _expnum)\\\"\\n )\\n elif specid:\\n _specid = specid\\n\\n if amp:\\n _amp = amp\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(specid == _specid) & (amp == _amp) & (expnum == _expnum)\\\"\\n )\\n else:\\n print(\\\"You must provide both specid and amp\\\")\\n elif ifuslot:\\n _ifuslot = ifuslot\\n if amp:\\n _amp = amp\\n im0 = fileh.root.Data.Images.read_where(\\n \\\"(ifuslot == _ifuslot) & (amp == _amp) & (expnum == _expnum)\\\"\\n )\\n else:\\n print(\\\"You must provide both ifuslot and amp\\\")\\n\\n else:\\n print(\\\"You need to provide a multiframe or specid/amp or ifuslot/amp\\\")\\n\\n fileh.close()\\n\\n return im0[imtype][0]\",\n \"def _construct_climate(self, timestep):\\n cl = {}\\n now, end = self._get_now_and_end(timestep)\\n if now==-1:\\n return -1\\n if self.simulation:\\n if self.md.duration_unit == 'month':\\n cl['duration'] = self.timestep_length / 12.\\n elif self.md.duration_unit == 'year':\\n cl['duration'] = self.timestep_length\\n else:\\n cl['duration'] = STEADY_STATE_TIMESTEP\\n if self.md.climate_mode == 'constant yearly':\\n cl['rain'] = self.md.constant_climate.annual_rainfall\\n cl['temp'] = self.md.constant_climate.mean_temperature\\n cl['amplitude'] = self.md.constant_climate.variation_amplitude\\n elif self.md.climate_mode == 'monthly':\\n cl = self._construct_monthly_climate(cl, now, end)\\n elif self.md.climate_mode == 'yearly':\\n cl = self._construct_yearly_climate(cl, now, end)\\n return cl\",\n \"def wavetype(self, hS, uS, cS, hX, uX, cX, xt, sign):\\n if hS >= hX:\\n # Shock\\n qX = math.sqrt( (hS + hX) * hS / (2. * hX * hX) )\\n sX = uX + sign * cX * qX\\n if xt <= sX:\\n # Sample point lies to the left of the shock\\n if sign==-1: return hX, uX\\n else: return hS, uS\\n else:\\n # Sample point lies to the right of the shock\\n if sign==-1: return hS, uS\\n else: return hX, uX\\n else:\\n # Rarefaction\\n shX = uX + sign * cX\\n if xt <= shX:\\n # Sample point lies to the right of the rarefaction\\n return hX, uX\\n else:\\n stX = uS + sign * cS\\n if xt <= stX:\\n # Sample point lies inside the rarefaction\\n u = (uX + 2.*cX + 2.*xt)/3.\\n c = (uX - sign * 2. * cX + sign * xt)/3.\\n h = c * c / 9.81\\n return h, u\\n else:\\n # Sample point lies in the STAR region\\n return hS, uS\",\n \"def get_shot_location():\\n global LAST_SHOT\\n available = [(x, y) for x in range(10) for y in range(10) if MY_SHOTS[x][y] is None]\\n coords = random.choice(available)\\n LAST_SHOT = coords\\n return json.dumps(coords)\",\n \"def processStimulus(self, observation):\\n if observation == \\\"red\\\":\\n return (1, 0, 0), (1, 0, 0)\\n if observation == \\\"green\\\":\\n return (0, 1, 0), (0, 1, 0)\\n if observation == \\\"blue\\\":\\n return (0, 0, 1), (0, 0, 1)\",\n \"def iat_stpt_smlRefBld(action_raw, stptLmt, ob_this_raw):\\n HTSP_RAW_IDX = 6; \\n CLSP_RAW_IDX = 7;\\n htStpt = ob_this_raw[HTSP_RAW_IDX];\\n clStpt = ob_this_raw[CLSP_RAW_IDX];\\n res_htStpt = max(min(htStpt + action_raw[0], stptLmt[1]), stptLmt[0]);\\n res_clStpt = max(min(clStpt + action_raw[1], stptLmt[1]), stptLmt[0]);\\n if res_clStpt < res_htStpt:\\n return ((htStpt, clStpt),(0.0, 0.0));\\n else:\\n return ((res_htStpt, res_clStpt),\\n (res_htStpt - htStpt, res_clStpt - clStpt));\",\n \"def dribble(state, opp, angleDribble, powerDribble, coeffAD):\\n me_opp = (opp.position - state.my_pos).normalize()\\n me_goal = state.attacking_vector\\n angle = atan2(me_opp.y,me_opp.x)\\n theta = get_oriented_angle(me_goal, me_opp)/acos(0.)\\n rand = exp(-coeffAD*abs(theta))/2.\\n quad = state.quadrant\\n if random.random() < rand: # mauvais angle (vers l'adversaire)\\n if theta < 0.:\\n angleDribble = -angleDribble\\n else: # bon angle (vers la cage adverse)\\n if theta > 0.:\\n angleDribble = -angleDribble\\n angle += angleDribble\\n destDribble = Vector2D(angle=angle, norm=1.)\\n return kickAt(state, state.ball_pos + destDribble, powerDribble)\",\n \"def get_blue():\\n # return name of actor, grazing speed, self defense\\n return 'Piggy', 2\",\n \"def getConc(fileID, spc):\\r\\n\\r\\n dataKey = rmn.fstinf(fileID, nomvar=spc, ip1=ip1)['key']\\r\\n dataRec = rmn.fstluk(dataKey)\\r\\n concData = dataRec['d']\\r\\n return concData, dataKey, dataRec\",\n \"def get_wind_values(self):\\n return (\\n int(self.data[2]), # dir\\n float(self.data[3]) / 10, # gust\\n float(self.data[4]) / 10, # avg\\n float(self.data[5]) / 10, # chill\\n )\",\n \"def calculate_crosswalk_check_tiles(self, preferred_direction):\\n\\n if preferred_direction == (1, 0):\\n return (\\n (self.position[0] + CAR_LENGTH, self.position[1] - 1),\\n (self.position[0] + CAR_LENGTH, self.position[1] + 2),\\n (self.position[0] + CAR_LENGTH + 1, self.position[1] - 1),\\n (self.position[0] + CAR_LENGTH + 1, self.position[1] + 2),\\n )\\n elif preferred_direction == (-1, 0):\\n return (\\n (self.position[0] - 1, self.position[1] - 1),\\n (self.position[0] - 1, self.position[1] + 2),\\n (self.position[0] - 2, self.position[1] - 1),\\n (self.position[0] - 2, self.position[1] + 2),\\n )\\n elif preferred_direction == (0, 1):\\n return (\\n (self.position[0] - 1, self.position[1] + CAR_LENGTH),\\n (self.position[0] + 2, self.position[1] + CAR_LENGTH),\\n (self.position[0] - 1, self.position[1] + CAR_LENGTH + 1),\\n (self.position[0] + 2, self.position[1] + CAR_LENGTH + 1),\\n )\\n elif preferred_direction == (0, -1):\\n return (\\n (self.position[0] - 1, self.position[1] - 1),\\n (self.position[0] + 2, self.position[1] - 1),\\n (self.position[0] - 1, self.position[1] - 2),\\n (self.position[0] + 2, self.position[1] - 2),\\n )\",\n \"def getTileModifier(x,y,context=\\\"points\\\"):\\n ret = (1,None,\\\"\\\") # default\\n\\n if x == 0 or x == 14:\\n if y == 0 or y == 7 or y == 14:\\n ret = (3,\\\"word\\\",TRIP_WORD_COLOR)\\n elif y == 3 or y == 11:\\n ret = (2,\\\"ltr\\\",DOUB_LTR_COLOR)\\n\\n elif x == 1 or x == 13:\\n if y == 1 or y == 13:\\n ret = (2,\\\"word\\\",DOUB_WORD_COLOR)\\n elif y == 5 or y == 9:\\n ret = (3,\\\"ltr\\\",TRIP_LTR_COLOR)\\n\\n elif x == 2 or x == 12:\\n if y == 2 or y == 12:\\n ret = (2,\\\"word\\\",DOUB_WORD_COLOR)\\n elif y == 6 or y == 8:\\n ret = (2,\\\"ltr\\\",DOUB_LTR_COLOR)\\n\\n elif x == 3 or x == 11:\\n if y == 3 or y == 11:\\n ret = (2,\\\"word\\\",DOUB_WORD_COLOR)\\n elif y == 0 or y == 7 or y == 14:\\n ret = (2,\\\"ltr\\\",DOUB_LTR_COLOR)\\n\\n elif x == 4 or x == 10:\\n if y == 4 or y == 10:\\n ret = (2,\\\"word\\\",DOUB_WORD_COLOR)\\n\\n elif x == 5 or x == 9:\\n if y == 1 or y == 5 or y == 9 or y == 13:\\n ret = (3,\\\"ltr\\\",TRIP_LTR_COLOR)\\n\\n elif x == 6 or x == 8:\\n if y == 2 or y == 6 or y == 8 or y == 12:\\n ret = (2,\\\"ltr\\\",DOUB_LTR_COLOR)\\n\\n elif x == 7:\\n if y == 0 or y == 14:\\n ret = (3,\\\"word\\\",TRIP_WORD_COLOR)\\n elif y == 3 or y == 11:\\n ret = (2,\\\"ltr\\\",DOUB_LTR_COLOR)\\n elif y == 7:\\n ret = (2,\\\"word\\\",DOUB_WORD_COLOR)\\n else:\\n raise ScrabbleError(\\\"Impossible Tile!\\\")\\n \\n if context == \\\"muliplier\\\":\\n return ret[0]\\n elif context == \\\"mod_type\\\":\\n return ret[1]\\n elif context == \\\"points\\\":\\n return (ret[0],ret[1])\\n elif context == \\\"bgcolor\\\":\\n return ret[2]\\n elif context == \\\"all\\\":\\n return ret\\n else:\\n raise ScrabbleError(\\\"Invalid context in getTileModifier\\\")\",\n \"def new_shot(context):\\n\\n shot = context.scene.milkshake_shots.add()\\n camera = bpy.data.cameras.new(\\\"Camera\\\")\\n camera.display_size = 1\\n camera.dof.aperture_blades = 5\\n camera.dof.aperture_fstop = 4\\n camera.dof.aperture_ratio = 1\\n camera.dof.aperture_rotation = math.radians(10)\\n camera.dof.focus_distance = 1\\n camera.dof.use_dof = True\\n camera.lens = 35\\n camera.passepartout_alpha = 0.85\\n camera.sensor_fit = 'HORIZONTAL'\\n camera.sensor_height = 13.365\\n camera.sensor_width = 23.76\\n camera_object = bpy.data.objects.new(\\\"Camera\\\", camera)\\n\\n cam_collection = camera_collection(context)\\n cam_collection.objects.link(camera_object)\\n shot.camera = camera\\n core.log(\\\"Created new shot and camera.\\\")\",\n \"def get_action(obs, player_x, player_y):\\n if Action.Sprint in obs[\\\"sticky_actions\\\"]:\\n return Action.ReleaseSprint\\n return Action.Shot\",\n \"def last_pos(self) -> tuple[int, int]:\\n if not self.actions:\\n return (self.start_x, self.start_y)\\n else:\\n box = self.get_hitbox_at(self.time_consumed)\\n return box.pos_x, box.pos_y\",\n \"def _spawn_aircraft() -> Tuple[float, float, float, str]:\\n\\n # Get aircraft coordinates.\\n x = random.uniform(-CONTROL_ZONE_RADIUS, CONTROL_ZONE_RADIUS)\\n y = math.sqrt(CONTROL_ZONE_RADIUS ** 2 - x ** 2)\\n y = y if random.randint(0, 1) else -y\\n\\n ang = _get_ac_heading(x, y)\\n\\n return x, y, ang, \\\"A\\\"\",\n \"def ccw(p1, p2, p3):\\n return (p2[0] - p1[0])*(p3[1] - p1[1]) - (p2[1] - p1[1])*(p3[0] - p1[0])\",\n \"def rgb_to_cmyk(r: int, g: int, b: int) -> tuple:\\n w = max(r / 255, g / 255, b / 255)\\n c = (w - (r / 255)) / w\\n m = (w - (g / 255)) / w\\n y = (w - (b / 255)) / w\\n k = 1 - w\\n return(c, m, y, k)\",\n \"def get_image2D_cutout(\\n shot,\\n coords,\\n wave_obj,\\n width=40,\\n height=40,\\n imtype=\\\"clean_image\\\",\\n survey=LATEST_HDR_NAME,\\n):\\n fibers = Fibers(shot)\\n\\n idx = fibers.get_closest_fiber(coords)\\n multiframe_obj = fibers.table.cols.multiframe[idx].astype(str)\\n expnum_obj = fibers.table.cols.expnum[idx]\\n x, y = fibers.get_image_xy(idx, wave_obj)\\n\\n im0 = fibers.hdfile.root.Data.Images.read_where(\\n \\\"(multiframe == multiframe_obj) & (expnum == expnum_obj)\\\"\\n )\\n\\n return im0[imtype][0][\\n x - int(np.floor(height / 2)) : x + int(np.ceil(height / 2)),\\n y - int(np.floor(width / 2)) : y + int(np.ceil(width / 2)),\\n ]\",\n \"def get_image2D_cutout(\\n shot,\\n coords,\\n wave_obj,\\n width=40,\\n height=40,\\n imtype=\\\"clean_image\\\",\\n survey=LATEST_HDR_NAME,\\n):\\n fibers = Fibers(shot)\\n\\n idx = fibers.get_closest_fiber(coords)\\n multiframe_obj = fibers.table.cols.multiframe[idx].astype(str)\\n expnum_obj = fibers.table.cols.expnum[idx]\\n x, y = fibers.get_image_xy(idx, wave_obj)\\n\\n im0 = fibers.hdfile.root.Data.Images.read_where(\\n \\\"(multiframe == multiframe_obj) & (expnum == expnum_obj)\\\"\\n )\\n\\n return im0[imtype][0][\\n x - int(np.floor(height / 2)) : x + int(np.ceil(height / 2)),\\n y - int(np.floor(width / 2)) : y + int(np.ceil(width / 2)),\\n ]\",\n \"def get_piles_place(self):\\n idx_draw = []\\n piles_draw = []\\n i = 0\\n for p in self.game_state['piles']:\\n if not self.game_state['two_player']:\\n if not self.game_state['piles_taken'][i] and len(p) < 3:\\n idx_draw.append(i)\\n piles_draw.append(p)\\n else:\\n if len(p) <= i and not self.game_state['piles_taken'][i]:\\n idx_draw.append(i)\\n piles_draw.append(p)\\n i += 1\\n return (piles_draw,idx_draw)\",\n \"def get_temps(self):\\n try:\\n cmos = self.cmos_temp\\n except Exception:\\n cmos = None\\n try:\\n pcb = self.pcb_temp\\n except Exception:\\n pcb = None\\n return cmos, pcb\",\n \"def _whctrs(anchor):\\n w = anchor[2] - anchor[0] + 1\\n h = anchor[3] - anchor[1] + 1\\n x_ctr = anchor[0] + 0.5 * (w - 1)\\n y_ctr = anchor[1] + 0.5 * (h - 1)\\n return w, h, x_ctr, y_ctr\",\n \"def _whctrs(anchor):\\n w = anchor[2] - anchor[0] + 1\\n h = anchor[3] - anchor[1] + 1\\n x_ctr = anchor[0] + 0.5 * (w - 1)\\n y_ctr = anchor[1] + 0.5 * (h - 1)\\n return w, h, x_ctr, y_ctr\",\n \"def getTEMP(self):\\r\\n\\t\\ttemp_H = self.read(0x41)\\r\\n\\t\\ttemp_L = self.read(0x42)\\r\\n\\t\\ttemp = self.twos_comp(val = (temp_H*256 + temp_L),bits=16)\\r\\n\\t\\ttempC = (temp/340.0)+36.53\\r\\n\\t\\ttempF = tempC*(9.0/5) + 32\\r\\n\\t\\treturn tempC,tempF ,temp\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.5148957\",\n \"0.49647325\",\n \"0.48577526\",\n \"0.48168695\",\n \"0.47914028\",\n \"0.4786531\",\n \"0.4775281\",\n \"0.47224483\",\n \"0.47096628\",\n \"0.46394467\",\n \"0.46250522\",\n \"0.46067777\",\n \"0.4606352\",\n \"0.45833904\",\n \"0.4575049\",\n \"0.45720398\",\n \"0.4562999\",\n \"0.4545978\",\n \"0.45373318\",\n \"0.4482143\",\n \"0.44799474\",\n \"0.44576597\",\n \"0.445183\",\n \"0.44432643\",\n \"0.44424865\",\n \"0.4434412\",\n \"0.44158897\",\n \"0.44045046\",\n \"0.44032982\",\n \"0.4402378\",\n \"0.43933347\",\n \"0.4386865\",\n \"0.43744254\",\n \"0.4372216\",\n \"0.43666583\",\n \"0.4364636\",\n \"0.4360968\",\n \"0.43520603\",\n \"0.4346387\",\n \"0.4340016\",\n \"0.43395483\",\n \"0.4331933\",\n \"0.43306458\",\n \"0.4326735\",\n \"0.431167\",\n \"0.43115902\",\n \"0.43095678\",\n \"0.43016827\",\n \"0.42948025\",\n \"0.42884254\",\n \"0.42874897\",\n \"0.42812395\",\n \"0.42804328\",\n \"0.4274249\",\n \"0.42716\",\n \"0.4271106\",\n \"0.42691416\",\n \"0.42666036\",\n \"0.42643556\",\n \"0.42635405\",\n \"0.42629784\",\n \"0.42571437\",\n \"0.42556083\",\n \"0.4253472\",\n \"0.4251816\",\n \"0.425125\",\n \"0.425125\",\n \"0.4251188\",\n \"0.4250445\",\n \"0.4247169\",\n \"0.42428625\",\n \"0.42421815\",\n \"0.42417932\",\n \"0.42386016\",\n \"0.42385465\",\n \"0.42344147\",\n \"0.42344147\",\n \"0.42287922\",\n \"0.42259392\",\n \"0.4223833\",\n \"0.42238227\",\n \"0.4223563\",\n \"0.42231494\",\n \"0.4221628\",\n \"0.4212142\",\n \"0.42082715\",\n \"0.4205384\",\n \"0.42048642\",\n \"0.42009634\",\n \"0.4199067\",\n \"0.4194368\",\n \"0.41929832\",\n \"0.41923615\",\n \"0.4183834\",\n \"0.41831285\",\n \"0.41831285\",\n \"0.4181924\",\n \"0.4179812\",\n \"0.4179424\",\n \"0.4179424\",\n \"0.41748604\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95099,"cells":{"query":{"kind":"string","value":"String representation of this class"},"document":{"kind":"string","value":"def __repr__(self):\n return f\"<Airport(id={str(self.id)}, name={self.name})>\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def toString(self):\n\n sMembers = '';\n for sAttr in self.getDataAttributes():\n oValue = getattr(self, sAttr);\n sMembers += ', %s=%s' % (sAttr, oValue);\n\n oClass = type(self);\n if sMembers == '':\n return '<%s>' % (oClass.__name__);\n return '<%s: %s>' % (oClass.__name__, sMembers[2:]);","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self)","def __repr__(self):\n return str(self.__dict__)","def __repr__(self):\n return str(self.__dict__)","def __repr__(self):\n return str(self.__dict__)","def __repr__(self):\n return str(self.__dict__)","def __repr__(self):\n return str(self.__dict__)","def __repr__(self):\n\n return str(self)","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def serialize(self):\n\n\t\treturn str(self)","def __repr__(self) -> str:\n return str(self)","def __repr__(self) -> str:\n return str(self.as_dict())","def __repr__(self):\r\n return str(self)","def __repr__(self) -> str:\n return 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self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()"],"string":"[\n \"def toString(self):\\n\\n sMembers = '';\\n for sAttr in self.getDataAttributes():\\n oValue = getattr(self, sAttr);\\n sMembers += ', %s=%s' % (sAttr, oValue);\\n\\n oClass = type(self);\\n if sMembers == '':\\n return '<%s>' % (oClass.__name__);\\n return '<%s: %s>' % (oClass.__name__, sMembers[2:]);\",\n \"def __repr__(self):\\n 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query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Test suite including all test suites | def test_suite():
testSuite = unittest.TestSuite()
testSuite.addTest(test_spec("test_cmd_parser"))
return testSuite | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def suite():\n test_suite = unittest.TestSuite()\n test_suite.addTest(unittest.makeSuite(globalOptimizerTest))\n test_suite.addTest(unittest.makeSuite(recursiveStepTest))\n return test_suite",
"def main():\n # add all new test suites per test module here\n suite_date = test_date.suite()\n su... | [
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"0.79566133",
"0.79299814",
"0.7830009",
"0.78079045",
"0.77646685",
"0.7761804",
"0.7670637",
"0.7668768",
"0.7660842",
"0.7646167",
"0.7619189",
"0.7598848",
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"0.75772774",
"0.7562298",
"0.75592405",
"0.752341",
"0.75119865",
"0.75030947",
"0.74757... | 0.72484934 | 39 |
_operator()_ Used as callback to handle events that have been subscribed to | def __call__(self, event, payload):
logging.debug("Received Event: %s" % event)
logging.debug("Payload: %s" % payload)
# start debug event
if event == "MergeAccountant:StartDebug":
logging.getLogger().setLevel(logging.DEBUG)
return
# stop debug event
if event == "MergeAccountant:EndDebug":
logging.getLogger().setLevel(logging.INFO)
return
# enable event
if event == "MergeAccountant:Enable":
self.enabled = True
return
# disable event
if event == "MergeAccountant:Disable":
self.enabled = False
return
# a job has finished
if event == "JobSuccess":
try:
self.jobSuccess(payload)
except Exception, ex:
msg = "Unexpected error when handling a "
msg += "JobSuccess event: " + str(ex)
msg += traceback.format_exc()
logging.error(msg)
return
# a job has failed
if event == "GeneralJobFailure":
try:
self.jobFailed(payload)
except Exception, msg:
logging.error("Unexpected error when handling a " + \
"GeneralFailure event: " + str(msg))
return
# wrong event
logging.debug("Unexpected event %s, ignored" % event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subscribe(observer):",
"def subscribe(observer):",
"def observe(self, event_pattern, priority, callback):\n pass # pragma: no cover",
"def __call__(self, trigger, type, event):",
"def subscribe(self, event_handler):\n pass # pragma: no cover",
"def events(self):",
"def doEvent(self,... | [
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"0.5... | 0.0 | -1 |
_jobSuccess_ A job has finished successfully. Non merge jobs are ignored. If it is complete success, all input files are marked as 'merged' and the output file as 'merged'. JobSuccess for partial merge are not implemented yet!. When it | def jobSuccess(self, jobReport):
jobName = None
try:
#// Invoke job report handler with jobReport location and flag to enable/disable merge job report handling
handler = ReportHandler(jobReport, int(self.args['MaxInputAccessFailures']), enableMergeHandling=self.enabled)
jobName = handler()
logging.info('this is jobname'+ str(jobName))
except Exception, ex:
msg = "Failed to handle job report from job:\n"
msg += "%s\n" % jobReport
msg += str(ex)
msg += "\n"
msg += traceback.format_exc()
logging.error(msg)
#// Failed to read job report
if jobName is None:
return
# files can be cleaned up now
logging.info("trigger cleanup for: %s" % jobName)
try:
self.trigger.setFlag("cleanup", jobName, "MergeAccountant")
except (ProdAgentException, ProdException):
logging.error("trying to continue processing success event")
return #// END jobSuccess | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_job_success(self, job):\n super().handle_job_success(job)\n\n self._handle_job_status(job, \"finished\")",
"def jobFailed(self, jobName):\n\n # ignore non merge jobs\n if jobName.find('mergejob') == -1:\n logging.info(\"Ignoring job %s, since it is not a merge jo... | [
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"0.5614332",
"0.5596668",
"0.55745363",
"0.556087... | 0.67897856 | 0 |
_jobFailed_ A job has failed. Non merge jobs are ignored. Since it is a general failure, the error handler has tried to submit the job a number of times and it always failed. Mark then all input files as 'unmerged' (which will increment their failures counter) and the output file as 'failed'. If limit of failures in input files is reached, they are tagged as 'invalid' | def jobFailed(self, jobName):
# ignore non merge jobs
if jobName.find('mergejob') == -1:
logging.info("Ignoring job %s, since it is not a merge job" \
% jobName)
# Add cleanup flag for non merge jobs too
logging.info("trigger cleanup for: %s" % jobName)
try:
self.trigger.setFlag("cleanup", jobName, "MergeAccountant")
except (ProdAgentException, ProdException):
logging.error("trying to continue processing failure event")
return
# files can be cleaned up now
logging.info("trigger cleanup for: %s" % jobName)
try:
self.trigger.setFlag("cleanup", jobName, "MergeAccountant")
except (ProdAgentException, ProdException):
logging.error("trying to continue processing failure event")
# verify enable condition
if not self.enabled:
return
# open a DB connection
database = MergeSensorDB()
# start a transaction
database.startTransaction()
# get job information
try:
jobInfo = database.getJobInfo(jobName)
# cannot get it!
except Exception, msg:
logging.error("Cannot process Failure event for job %s: %s" \
% (jobName, msg))
database.closeDatabaseConnection()
return
# check that job exists
if jobInfo is None:
logging.error("Job %s does not exist." % jobName)
database.closeDatabaseConnection()
return
# check status
if jobInfo['status'] != 'undermerge':
logging.error("Cannot process Failure event for job %s: %s" \
% (jobName, "the job is not currently running"))
database.closeDatabaseConnection()
return
# get dataset id
datasetId = database.getDatasetId(jobInfo['datasetName'])
# mark all input files as 'unmerged' (or 'invalid')
unFinishedFiles = []
for fileName in jobInfo['inputFiles']:
# update status
newStatus = database.updateInputFile(\
datasetId, fileName, \
status = "unmerged", \
maxAttempts = int(self.args['MaxInputAccessFailures']))
# add invalid files to list of non finished files
if newStatus == 'invalid':
unFinishedFiles.append(fileName)
# mark output file as 'failed'
database.updateOutputFile(datasetId, jobName=jobName, status='failed')
# commit changes
database.commit()
# notify the PM about the unrecoverable files
if len(unFinishedFiles) > 0:
File.merged(unFinishedFiles, True)
# log message
logging.info("Job %s failed, file information updated." % jobName)
# close connection
database.closeDatabaseConnection() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fail(self):\n self.cleanup()\n self.runner.report_job_fail(self.id)",
"def handle_job_error(self, job):\n super().handle_job_error(job)\n\n self._handle_job_status(job, \"failed\")",
"def runjob(job):\n inputfiles = glob.glob(job['InputFile'])\n logecho(' %s file/... | [
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"0.56666774",
"0.56585604",
"0.5630... | 0.7485483 | 0 |
_startComponent_ Fire up the two main threads | def startComponent(self):
# create message service instance
self.ms = MessageService()
# register
self.ms.registerAs("MergeAccountant")
# subscribe to messages
self.ms.subscribeTo("MergeAccountant:StartDebug")
self.ms.subscribeTo("MergeAccountant:EndDebug")
self.ms.subscribeTo("MergeAccountant:Enable")
self.ms.subscribeTo("MergeAccountant:Disable")
self.ms.subscribeTo("JobSuccess")
self.ms.subscribeTo("GeneralJobFailure")
self.ms.subscribeTo("MergeAccountant:SetJobCleanupFlag")
# set trigger access for cleanup
self.trigger = Trigger(self.ms)
# set message service instance for PM interaction
File.ms = self.ms
# wait for messages
while True:
# get message
messageType, payload = self.ms.get()
self.ms.commit()
# create session object
Session.set_database(dbConfig)
Session.connect()
# start transaction
Session.start_transaction()
# process it
self.__call__(messageType, payload)
self.ms.commit()
# commit and close session
Session.commit_all()
Session.close_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def startComponent(self):\n self.ms = MessageService()\n self.ms.registerAs(\"TaskRegisterComponent\")\n \n self.ms.subscribeTo(\"CRAB_Cmd_Mgr:NewTask\")\n self.ms.subscribeTo(\"TaskRegisterComponent:StartDebug\")\n self.ms.subscribeTo(\"TaskRegisterComponent:EndDebug\")\n... | [
"0.7069816",
"0.65574837",
"0.65574837",
"0.6347645",
"0.6330432",
"0.6329626",
"0.6314845",
"0.62738216",
"0.6261208",
"0.62377006",
"0.6209128",
"0.6199045",
"0.61689764",
"0.61576235",
"0.6155671",
"0.61154896",
"0.61113256",
"0.6080329",
"0.60750294",
"0.60603696",
"0.605... | 0.6306281 | 7 |
_getVersionInfo_ return version information | def getVersionInfo(cls):
return __version__ + "\n" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_version_info(self):\n return self._jadeRpc('get_version_info')",
"def get_version_info() -> Tuple[Text, Text]:",
"def version_info(self):\n\n return __version_info__",
"def info(self):\n version_str = self.version\n return Utils.version_str2tuple(version_str)",
"def vers... | [
"0.80382955",
"0.7959786",
"0.78665364",
"0.772043",
"0.76289654",
"0.7546513",
"0.7518231",
"0.7508608",
"0.74194604",
"0.72860444",
"0.72014874",
"0.7074777",
"0.7063996",
"0.7035926",
"0.7033924",
"0.699559",
"0.6988741",
"0.69872457",
"0.69825757",
"0.69360656",
"0.693177... | 0.80043864 | 1 |
Make sure that the marks are correctly set | def test_marks(data, request):
current_marks = get_pytest_marks_on_item(request._pyfuncitem)
assert len(current_marks) == 1
if data == 1:
assert current_marks[0].name == "fast"
elif data == 2:
assert current_marks[0].name == "slow"
else:
raise AssertionError() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check4(self, x, y, mark, d = 0):",
"def check3(self, x, y, mark, d = 0):",
"def assign_mark(entry: StudentEntry):\n pass",
"def test_correct_init_multiple_markers(self):\n subject = self._get_test_subject(markers=MULTIPLE_MARKERS)\n\n self.assertEqual(len(subject.signal_map), len(MULTIPL... | [
"0.6549789",
"0.64396226",
"0.6256343",
"0.5967002",
"0.59457564",
"0.58952796",
"0.58587354",
"0.5848375",
"0.58305866",
"0.58305866",
"0.5808072",
"0.57763886",
"0.5767603",
"0.5664824",
"0.5655856",
"0.5617224",
"0.5596353",
"0.55772895",
"0.55504787",
"0.55380124",
"0.552... | 0.5462163 | 23 |
Given string like "test_odm_pcb (tests.wedge100.test_eeprom.EepromTest)" convert to a python test format to use it directly | def format_into_test_path(self, testitem):
test_string = testitem.split("(")
test_path = test_string[1].split(")")[0]
"""
Test item has different path with different python version
example:
on 3.8 set_psu_cmd (tests.fuji.test_psu.Psu1Test)
on 3.11 set_psu_cmd (tests.fuji.test_psu.Psu1Test.set_psu_cmd)
"""
if test_path.split(".")[-1].strip() != test_string[0].strip():
test_path = test_path + "." + test_string[0]
return test_path.strip() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split(test_name):\n recipe, simple_test_name = test_name.split('.', 1)\n return recipe, simple_test_name",
"def test_string():",
"def test_parse_string(self):\n bb = parse(antlr4.InputStream(test_file))\n\n assert bb._var == {\"alpha\": 0.3423}\n\n expected = {\"name\": \"fock\", \"o... | [
"0.5940089",
"0.5811446",
"0.5723814",
"0.5717782",
"0.5511057",
"0.5496379",
"0.54187816",
"0.54119813",
"0.52977437",
"0.5285956",
"0.52357966",
"0.52247846",
"0.51995105",
"0.5177037",
"0.51671624",
"0.5032216",
"0.50164425",
"0.500829",
"0.50076115",
"0.50026107",
"0.5000... | 0.57777196 | 2 |
Returns a set of test names that are formatted to a single test like 'tests.wedge100.test_eeprom.EepromTest.test_odm_pcb' | def get_all_platform_tests(self):
for testitem in self.get_tests(self.discover_tests()):
if not testitem:
continue
prefix = "tests." + self.platform + "."
self.formatted_tests_set.append(
prefix + self.format_into_test_path(testitem)
)
if self.denylist:
try:
with open(self.denylist, "r") as f:
denylist = f.read().splitlines()
except FileNotFoundError:
denylist = []
self.formatted_tests_set = [
t for t in self.formatted_tests_set if t not in denylist
]
return self.formatted_tests_set | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_name(self):\r\n parts = []\r\n if self.test.__module__ != '__main__':\r\n parts.append(self.test.__module__)\r\n if hasattr(self.test, 'im_class'):\r\n parts.append(self.test.im_class.__name__)\r\n parts.append(self.test.__name__)\r\n return '.'.joi... | [
"0.7208352",
"0.6553833",
"0.65289885",
"0.6420306",
"0.6344564",
"0.62654716",
"0.62462103",
"0.62398034",
"0.6210148",
"0.6169689",
"0.60774684",
"0.60527426",
"0.5981892",
"0.5960162",
"0.59601563",
"0.59381056",
"0.58674467",
"0.5789974",
"0.5787742",
"0.5770073",
"0.5759... | 0.5076667 | 73 |
Tests that trigger from outside BMC need Hostname information to BMC and userver | def set_external(args):
if args.host:
os.environ["TEST_HOSTNAME"] = args.host
# If user gave a hostname, determine oob name from it and set it.
if "." in args.host:
index = args.host.index(".")
os.environ["TEST_BMC_HOSTNAME"] = (
args.host[:index] + "-oob" + args.host[index:]
)
if args.bmc_host:
os.environ["TEST_BMC_HOSTNAME"] = args.bmc_host | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_host(self):\n pass",
"def test_perform_host_action(self):\n pass",
"def test_get_host_access(self):\n pass",
"def test(cls, hostname):\n pass",
"def test_hostname_value(self):\n \n hostname = get_hostname()\n \n # Check to make sure the h... | [
"0.72601837",
"0.6868456",
"0.6761789",
"0.6384901",
"0.63416314",
"0.6243301",
"0.6243301",
"0.6148776",
"0.6148776",
"0.6120381",
"0.6119962",
"0.6033267",
"0.60227525",
"0.6017332",
"0.60004425",
"0.5981932",
"0.5963929",
"0.5948897",
"0.5937301",
"0.5936245",
"0.5922357",... | 0.6309198 | 5 |
Optional arguments for firmware upgrade test | def set_fw_args(args):
os.environ["TEST_FW_OPT_ARGS"] = args.firmware_opt_args | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_upgrade_with_auto_upgrade_latest_engine_enabled():",
"def null_upgrade_step(setup_tool):\n pass",
"def test_patch_hyperflex_server_firmware_version(self):\n pass",
"def test_update_hyperflex_server_firmware_version(self):\n pass",
"def check_arguments(self):\n ## only four ... | [
"0.6306604",
"0.6163344",
"0.583488",
"0.5804764",
"0.57716423",
"0.57552814",
"0.57459766",
"0.5694769",
"0.5651118",
"0.5639809",
"0.55904186",
"0.557991",
"0.55499375",
"0.5547003",
"0.5539552",
"0.55363226",
"0.5525847",
"0.55074257",
"0.5484567",
"0.5458392",
"0.5432542"... | 0.6882471 | 0 |
Extracts the value between marker and index | def extract(self): # type: () -> str
if self.end():
return self._src[self._marker :]
else:
return self._src[self._marker : self._idx] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, position):\n return self.numbers[position[0]][position[1]]",
"def __getitem__(self, index):\n return self.data[index[0] - 1][index[1] - 1]",
"def get_value_at_index(self, index, cc):\n tl = cc.dsget(self.title)\n return (tl[index], None)",
"def marker_at_position(sel... | [
"0.63515353",
"0.63282895",
"0.625605",
"0.62464094",
"0.62333435",
"0.6199576",
"0.6177496",
"0.6158412",
"0.6130857",
"0.6084534",
"0.606006",
"0.6025226",
"0.6003024",
"0.6003024",
"0.5981887",
"0.59495384",
"0.5900323",
"0.58751357",
"0.5841193",
"0.58206975",
"0.5820006"... | 0.6038684 | 11 |
Increments the parser if the end of the input has not been reached. Returns whether or not it was able to advance. | def inc(self): # type: () -> bool
try:
self._idx, self._current = next(self._chars)
return True
except StopIteration:
self._idx = len(self._src)
self._current = TOMLChar("\0")
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def done_parsing(self):\n # STUDENT\n return (self.input_buffer_len() == 1 ) and (self.stack_len()==1) \n # END STUDENT",
"def _is_at_end(self):\n return self._peek().token_type == scanner.TokenType.EOF",
"def has_next(self):\n try:\n self.next()\n ... | [
"0.693617",
"0.69233114",
"0.69094247",
"0.6850217",
"0.6331117",
"0.6285765",
"0.620027",
"0.61610067",
"0.61490417",
"0.6147039",
"0.6147039",
"0.6147039",
"0.6147039",
"0.6117384",
"0.6101283",
"0.6094416",
"0.60582894",
"0.60258627",
"0.60254914",
"0.6012133",
"0.60018945... | 0.64029455 | 4 |
Increments the parser by n characters if the end of the input has not been reached. | def inc_n(self, n): # type: (int) -> bool
for _ in range(n):
if not self.inc():
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __advance(self, n=1):\r\n for i in range(n):\r\n if self.__tokenizer.has_more_tokens():\r\n self.__tokenizer.advance()\r\n continue\r\n return False\r\n return True",
"def cmd_n(self,s):\n length = 0\n node = self.start\n ... | [
"0.6357484",
"0.5569419",
"0.55248344",
"0.5505389",
"0.5498772",
"0.5482731",
"0.54729337",
"0.54318476",
"0.5427386",
"0.5418166",
"0.5390718",
"0.5387553",
"0.53819525",
"0.5361975",
"0.5354554",
"0.5300933",
"0.52418137",
"0.5228473",
"0.52153623",
"0.5198761",
"0.5185116... | 0.0 | -1 |
Returns True if the parser has reached the end of the input. | def end(self): # type: () -> bool
return self._idx >= len(self._src) or self._current == "\0" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_at_end(self):\n return self._peek().token_type == scanner.TokenType.EOF",
"def is_eof(self) -> bool:\n ...",
"def end_of_input():\n return at_end.bind(lambda end:\n Parser(lambda chunk, last: ParserResult.from_error(\"Not end of input\"))\n if no... | [
"0.8109592",
"0.75955963",
"0.7418105",
"0.7389045",
"0.7385828",
"0.73675007",
"0.733003",
"0.7321836",
"0.7321836",
"0.7321836",
"0.7321836",
"0.71485347",
"0.7051112",
"0.7041451",
"0.70217884",
"0.70157605",
"0.69563675",
"0.695557",
"0.69082236",
"0.6880412",
"0.6842493"... | 0.70420015 | 13 |
Sets the marker to the index's current position | def mark(self): # type: () -> None
self._marker = self._idx | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mark_pos(self, position, marker):\n i, j = self.board[position]\n self.grid[i][j] = marker",
"def __setitem__(self, index, value):\n self.position[index] = value",
"def set_mark( self, mark, index ):\n\n try:\n int(self.__grid[index-1])\n\n if mark.lower() ... | [
"0.7185202",
"0.66934663",
"0.6468027",
"0.64015055",
"0.63248855",
"0.6277485",
"0.6270743",
"0.6233759",
"0.6233759",
"0.6202351",
"0.61644644",
"0.6080283",
"0.60600364",
"0.6030192",
"0.60263723",
"0.602531",
"0.6000895",
"0.5999829",
"0.59920174",
"0.5950956",
"0.5903941... | 0.7850461 | 0 |
Merges the given Item with the last one currently in the given Container if both are whitespace items. Returns True if the items were merged. | def _merge_ws(self, item, container): # type: (Item, Container) -> bool:
last = container.last_item()
if not last:
return False
if not isinstance(item, Whitespace) or not isinstance(last, Whitespace):
return False
start = self._idx - (len(last.s) + len(item.s))
container.body[-1] = (
container.body[-1][0],
Whitespace(self._src[start : self._idx]),
)
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def can_be_merged(prev, cur):\n\n WHITESPACE = (' ', '\\t')\n if not cur.mergeable or not prev.mergeable:\n return False\n # offset is char offset, not byte, so length is the char length!\n elif cur.offset != (prev.offset + prev.length):\n r... | [
"0.5630323",
"0.562426",
"0.51364464",
"0.5081948",
"0.5043025",
"0.48954043",
"0.48792508",
"0.4865082",
"0.48528847",
"0.48474756",
"0.48178267",
"0.48116726",
"0.47968078",
"0.4795999",
"0.478459",
"0.47503677",
"0.47446725",
"0.47313824",
"0.47310415",
"0.47016767",
"0.46... | 0.75566405 | 0 |
Creates a generic "parse error" at the current position. | def parse_error(self, kind=ParseError, args=None): # type: () -> None
line, col = self._to_linecol(self._idx)
if args:
return kind(line, col, *args)
else:
return kind(line, col) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _error(self, token, msg):\n self._interpreter.parse_error(token, msg)\n return ParseError()",
"def error(self, message, token=None):\n raise ParseException(\n message,\n self.filename,\n line=self._line,\n line_number=self._line_number,... | [
"0.7336168",
"0.7223053",
"0.6826519",
"0.6767211",
"0.65252376",
"0.64913183",
"0.6489117",
"0.64567614",
"0.6366437",
"0.6286208",
"0.6225419",
"0.6205159",
"0.61615443",
"0.60905075",
"0.60646534",
"0.60110784",
"0.6001469",
"0.59982574",
"0.5976438",
"0.59754544",
"0.5968... | 0.64861685 | 7 |
Returns whether a key is strictly a child of another key. AoT siblings are not considered children of one another. | def _is_child(self, parent, child): # type: (str, str) -> bool
return child != parent and child.startswith(parent + ".") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_child_exists(self, key):\n return True if _Node.__find_key_in_level(self, key) else False",
"def __contains__(self, key):\n\n if type(key) != self.type:\n return False\n\n first_char = key[:1]\n others = key[1:]\n\n if first_char not in self.children:\n ... | [
"0.7154145",
"0.6607566",
"0.6557412",
"0.64785355",
"0.642279",
"0.62678003",
"0.6224301",
"0.61702096",
"0.61305875",
"0.60911614",
"0.6060285",
"0.6012716",
"0.5971186",
"0.59556484",
"0.59540933",
"0.5942657",
"0.594154",
"0.59342253",
"0.59315073",
"0.59144604",
"0.59130... | 0.64586204 | 4 |
Attempts to parse the next item and returns it, along with its key if the item is valuelike. | def _parse_item(self): # type: () -> Optional[Tuple[Optional[Key], Item]]
self.mark()
saved_idx = self._save_idx()
while True:
c = self._current
if c == "\n":
# Found a newline; Return all whitespace found up to this point.
self.inc()
return (None, Whitespace(self.extract()))
elif c in " \t\r":
# Skip whitespace.
if not self.inc():
return (None, Whitespace(self.extract()))
elif c == "#":
# Found a comment, parse it
indent = self.extract()
cws, comment, trail = self._parse_comment_trail()
return (None, Comment(Trivia(indent, cws, comment, trail)))
elif c == "[":
# Found a table, delegate to the calling function.
return
else:
# Begining of a KV pair.
# Return to beginning of whitespace so it gets included
# as indentation for the KV about to be parsed.
self._restore_idx(*saved_idx)
key, value = self._parse_key_value(True)
return key, value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getNextKey(self, item):\n return (2, item)",
"def getitem(value, key):\n try:\n return value[key]\n except Exception:\n return \"\"",
"def p_value_key(protItem):\n return protItem[-1]",
"def p_value_key(protItem):\n return protItem[-1]",
"def get_next(item: str, after:... | [
"0.6376964",
"0.6067354",
"0.5974745",
"0.5974745",
"0.59615964",
"0.5913587",
"0.5901849",
"0.589939",
"0.5882357",
"0.5807142",
"0.57675356",
"0.5747622",
"0.56723076",
"0.5654322",
"0.5633313",
"0.56198114",
"0.5533659",
"0.5505485",
"0.5488771",
"0.54648966",
"0.5464157",... | 0.7419916 | 0 |
Returns (comment_ws, comment, trail) If there is no comment, comment_ws and comment will simply be empty. | def _parse_comment_trail(self): # type: () -> Tuple[str, str, str]
if self.end():
return "", "", ""
comment = ""
comment_ws = ""
self.mark()
while True:
c = self._current
if c == "\n":
break
elif c == "#":
comment_ws = self.extract()
self.mark()
self.inc() # Skip #
# The comment itself
while not self.end() and not self._current.is_nl() and self.inc():
pass
comment = self.extract()
self.mark()
break
elif c in " \t\r,":
self.inc()
else:
break
if self.end():
break
while self._current.is_spaces() and self.inc():
pass
trail = ""
if self._idx != self._marker or self._current.is_ws():
trail = self.extract()
return comment_ws, comment, trail | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_from_comment(self, comment):\n comment = re.sub(r'\\s+', ' ', comment.strip().strip(\"/*\").strip())\n if len(comment) == 0:\n return set(), set()\n domain_terms, code_elements = self.extract_from_sentence(comment)\n return domain_terms, code_elements",
"def get... | [
"0.5537778",
"0.55255157",
"0.55058956",
"0.54286265",
"0.53183115",
"0.53128594",
"0.5308172",
"0.5303508",
"0.53024226",
"0.52887475",
"0.52451444",
"0.52160954",
"0.5189738",
"0.51862514",
"0.51771724",
"0.5174911",
"0.5149944",
"0.51467705",
"0.51425964",
"0.5140602",
"0.... | 0.7539042 | 0 |
Parses a Key at the current position; WS before the key must be exhausted first at the callsite. | def _parse_key(self): # type: () -> Key
if self._current in "\"'":
return self._parse_quoted_key()
else:
return self._parse_bare_key() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_bare_key(self): # type: () -> Key\n self.mark()\n while self._current.is_bare_key_char() and self.inc():\n pass\n\n key = self.extract()\n\n return Key(key, sep=\"\")",
"def _parse_quoted_key(self): # type: () -> Key\n quote_style = self._current\n ... | [
"0.63221157",
"0.6206109",
"0.6151237",
"0.5712115",
"0.5649564",
"0.53943276",
"0.53714085",
"0.5363242",
"0.5210409",
"0.51909065",
"0.5114601",
"0.50023776",
"0.4978723",
"0.49580935",
"0.49418062",
"0.4941112",
"0.49383867",
"0.49335584",
"0.4920512",
"0.49124452",
"0.490... | 0.68402857 | 0 |
Parses a key enclosed in either single or double quotes. | def _parse_quoted_key(self): # type: () -> Key
quote_style = self._current
key_type = None
for t in KeyType:
if t.value == quote_style:
key_type = t
break
if key_type is None:
raise RuntimeError("Should not have entered _parse_quoted_key()")
self.inc()
self.mark()
while self._current != quote_style and self.inc():
pass
key = self.extract()
self.inc()
return Key(key, key_type, "") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_key(self): # type: () -> Key\n if self._current in \"\\\"'\":\n return self._parse_quoted_key()\n else:\n return self._parse_bare_key()",
"def test__parse_key_unquoted(value, position, expected_output, expected_position):\n state = ParserState(value)\n state.... | [
"0.7441108",
"0.6559555",
"0.64326805",
"0.6145812",
"0.6115858",
"0.60998946",
"0.6094155",
"0.5979537",
"0.5926579",
"0.5861342",
"0.58433485",
"0.58369255",
"0.5756874",
"0.5747807",
"0.57185864",
"0.57078075",
"0.5646424",
"0.56063914",
"0.560016",
"0.55992305",
"0.558389... | 0.6559366 | 2 |
Parses a bare key. | def _parse_bare_key(self): # type: () -> Key
self.mark()
while self._current.is_bare_key_char() and self.inc():
pass
key = self.extract()
return Key(key, sep="") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_key(self): # type: () -> Key\n if self._current in \"\\\"'\":\n return self._parse_quoted_key()\n else:\n return self._parse_bare_key()",
"def parse_key(raw_key):\n raw_key_bytes = raw_key.encode('ascii')\n try:\n validate_cmek(raw_key)\n key_type = KeyType... | [
"0.76626766",
"0.6655824",
"0.6617215",
"0.6501489",
"0.6450143",
"0.6249241",
"0.6145767",
"0.5899227",
"0.5899173",
"0.5889372",
"0.5886852",
"0.58818334",
"0.5807456",
"0.58011734",
"0.5785233",
"0.5777432",
"0.57585365",
"0.57519037",
"0.5722677",
"0.5717174",
"0.57077587... | 0.810298 | 0 |
Attempts to parse a value at the current position. | def _parse_value(self): # type: () -> Item
self.mark()
trivia = Trivia()
c = self._current
if c == '"':
return self._parse_basic_string()
elif c == "'":
return self._parse_literal_string()
elif c == "t" and self._src[self._idx :].startswith("true"):
# Boolean: true
self.inc_n(4)
return Bool(True, trivia)
elif c == "f" and self._src[self._idx :].startswith("false"):
# Boolean: true
self.inc_n(5)
return Bool(False, trivia)
elif c == "[":
# Array
elems = [] # type: List[Item]
self.inc()
while self._current != "]":
self.mark()
while self._current.is_ws() or self._current == ",":
self.inc()
if self._idx != self._marker:
elems.append(Whitespace(self.extract()))
if self._current == "]":
break
if self._current == "#":
cws, comment, trail = self._parse_comment_trail()
next_ = Comment(Trivia("", cws, comment, trail))
else:
next_ = self._parse_value()
elems.append(next_)
self.inc()
res = Array(elems, trivia)
if res.is_homogeneous():
return res
raise self.parse_error(MixedArrayTypesError)
elif c == "{":
# Inline table
elems = Container()
self.inc()
while self._current != "}":
if self._current.is_ws() or self._current == ",":
self.inc()
continue
key, val = self._parse_key_value(False)
elems.append(key, val)
self.inc()
return InlineTable(elems, trivia)
elif c in string.digits + "+" + "-":
# Integer, Float, Date, Time or DateTime
while self._current not in " \t\n\r#,]}" and self.inc():
pass
raw = self.extract()
item = self._parse_number(raw, trivia)
if item:
return item
try:
res = parse_rfc3339(raw)
except ValueError:
res = None
if res is None:
raise self.parse_error(InvalidNumberOrDateError)
if isinstance(res, datetime.datetime):
return DateTime(res, trivia, raw)
elif isinstance(res, datetime.time):
return Time(res, trivia, raw)
elif isinstance(res, datetime.date):
return Date(res, trivia, raw)
else:
raise self.parse_error(InvalidNumberOrDateError)
else:
raise self.parse_error(UnexpectedCharError, (c)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self, value):\n raise NotImplementedError(\"Please implement the Class\")",
"def parse_value(cls, value):\n return value",
"def parse_value(cls, value):\n return value",
"def parse_value(cls, value):\n raise NotImplementedError(\"subclass must implement parse_value()\")"... | [
"0.67975473",
"0.6596191",
"0.6596191",
"0.65703046",
"0.61353844",
"0.60871327",
"0.60674334",
"0.6023864",
"0.59837496",
"0.5958098",
"0.5956337",
"0.59420466",
"0.5825742",
"0.57203496",
"0.5710543",
"0.5646205",
"0.5597949",
"0.5590369",
"0.55894625",
"0.5586219",
"0.5578... | 0.6330559 | 4 |
Parses a table element. | def _parse_table(self): # type: (Optional[str]) -> Tuple[Key, Item]
indent = self.extract()
self.inc() # Skip opening bracket
is_aot = False
if self._current == "[":
if not self.inc():
raise self.parse_error(UnexpectedEofError)
is_aot = True
# Key
self.mark()
while self._current != "]" and self.inc():
pass
name = self.extract()
key = Key(name, sep="")
self.inc() # Skip closing bracket
if is_aot:
# TODO: Verify close bracket
self.inc()
cws, comment, trail = self._parse_comment_trail()
result = Null()
values = Container()
while not self.end():
item = self._parse_item()
if item:
_key, item = item
if not self._merge_ws(item, values):
values.append(_key, item)
else:
if self._current == "[":
_, name_next = self._peek_table()
if self._is_child(name, name_next):
key_next, table_next = self._parse_table()
key_next = Key(key_next.key[len(name + ".") :])
values.append(key_next, table_next)
# Picking up any sibling
while not self.end():
_, name_next = self._peek_table()
if not self._is_child(name, name_next):
break
key_next, table_next = self._parse_table()
key_next = Key(key_next.key[len(name + ".") :])
values.append(key_next, table_next)
else:
table = Table(
values, Trivia(indent, cws, comment, trail), is_aot
)
result = table
if is_aot and (
not self._aot_stack or name != self._aot_stack[-1]
):
result = self._parse_aot(table, name)
break
else:
raise self.parse_error(
InternalParserError,
("_parse_item() returned None on a non-bracket character."),
)
if isinstance(result, Null):
result = Table(values, Trivia(indent, cws, comment, trail), is_aot)
return key, result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_table(table):\n rows = table.find_all('tr')\n if not rows:\n raise ValueError(\"No rows for table\")\n pages = []\n table_tag = \"<table>\"\n tbl_headers = get_tbl_headers(rows)\n table_tag += \"<tr>\"\n for header in tbl_headers.keys():\n table_tag += conf.ADD_TH_TAG(h... | [
"0.6638918",
"0.65049",
"0.640189",
"0.6186907",
"0.61552876",
"0.5986234",
"0.59336215",
"0.5882514",
"0.5852241",
"0.5727533",
"0.5713803",
"0.57028484",
"0.56890404",
"0.5629742",
"0.5603507",
"0.55797344",
"0.5575111",
"0.55562884",
"0.55540824",
"0.5548597",
"0.5501241",... | 0.6397043 | 3 |
Peeks ahead nonintrusively by cloning then restoring the initial state of the parser. Returns the name of the table about to be parsed, as well as whether it is part of an AoT. | def _peek_table(self): # type: () -> Tuple[bool, str]
# Save initial state
idx = self._save_idx()
marker = self._marker
if self._current != "[":
raise self.parse_error(
InternalParserError, ("_peek_table() entered on non-bracket character")
)
# AoT
self.inc()
is_aot = False
if self._current == "[":
self.inc()
is_aot = True
self.mark()
while self._current != "]" and self.inc():
table_name = self.extract()
# Restore initial state
self._restore_idx(*idx)
self._marker = marker
return is_aot, table_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_aot(self, first, name_first): # type: (Item, str) -> Item\n payload = [first]\n self._aot_stack.append(name_first)\n while not self.end():\n is_aot_next, name_next = self._peek_table()\n if is_aot_next and name_next == name_first:\n _, table = s... | [
"0.5321881",
"0.5258151",
"0.5246666",
"0.51772755",
"0.5133962",
"0.4889261",
"0.47756714",
"0.4769611",
"0.4711629",
"0.46457544",
"0.45855626",
"0.45844787",
"0.45794702",
"0.4570339",
"0.4569376",
"0.456038",
"0.45316866",
"0.45240548",
"0.4521015",
"0.45061612",
"0.44987... | 0.7672022 | 0 |
Parses all siblings of the provided table first and bundles them into an AoT. | def _parse_aot(self, first, name_first): # type: (Item, str) -> Item
payload = [first]
self._aot_stack.append(name_first)
while not self.end():
is_aot_next, name_next = self._peek_table()
if is_aot_next and name_next == name_first:
_, table = self._parse_table()
payload.append(table)
else:
break
self._aot_stack.pop()
return AoT(payload) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def html_table_to_xmltree_sub(node):\n # Split text into Token nodes\n # NOTE: very basic token splitting here... (to run through CoreNLP?)\n if node.text is not None:\n for tok in re.split(r'\\s+', node.text):\n node.append(et.Element('token', attrib={'word':tok}))\n \n # Recursively append children\... | [
"0.5702697",
"0.56278515",
"0.55263174",
"0.5384425",
"0.5336966",
"0.52965224",
"0.52801716",
"0.5213338",
"0.51913345",
"0.5113082",
"0.5075567",
"0.5038085",
"0.50110316",
"0.49885792",
"0.49777687",
"0.49660102",
"0.49603015",
"0.49517924",
"0.4944479",
"0.49234688",
"0.4... | 0.5284191 | 6 |
Wait on an IPython AsyncResult, printing progress to stdout. Based on wait_interactive() in IPython and the output of Joblib in verbose mode.parallel This will work best when using a loadbalanced view with a smallish chunksize. | def wait_progress(ar, interval=5, timeout=-1):
if timeout is None:
timeout = -1
N = len(ar)
tic = time.time()
print "\nRunning %i tasks:" % N
sys.stdout.flush()
last = 0
while not ar.ready() and (timeout < 0 or time.time() - tic <= timeout):
ar.wait(interval)
progress, elapsed = ar.progress, ar.elapsed
if progress > last:
last = progress
remaining = elapsed * (float(N) / progress - 1.)
print ' Done %4i out of %4i | elapsed: %s remaining: %s' % (
progress, N, short_format_time(elapsed), short_format_time(remaining))
sys.stdout.flush()
if ar.ready():
try:
speedup = round(100.0 * ar.serial_time / ar.wall_time)
print "\nParallel speedup: %i%%" % speedup
# For some reason ar.serial_time occasionally throws this exception.
# We choose to ignore it and just not display the speedup factor.
except TypeError:
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wait_progress(self):\n pass",
"def wait_progress(self):\n pass",
"def waitOutput( self, verbose=False ):\n log = info if verbose else debug\n output = ''\n while self.waiting:\n data = self.monitor()\n output += data\n log( data )\n ... | [
"0.59242386",
"0.59242386",
"0.58943117",
"0.567091",
"0.5660828",
"0.5650391",
"0.5599117",
"0.5516994",
"0.55079556",
"0.5468367",
"0.54247904",
"0.5390598",
"0.53793055",
"0.530005",
"0.5276069",
"0.525269",
"0.525216",
"0.5234912",
"0.52020633",
"0.5197482",
"0.5197482",
... | 0.5838231 | 3 |
Creates and saves a User with the given email and password. | def create_user(self, email, password=None):
if not email:
raise ValueError('Users must have an email address')
user = self.model(
email=self.normalize_email(email),
)
user.set_password(password)
user.save(using=self._db)
return user | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_user(self, email, password, **extra_fields):\n if not email:\n raise ValueError('The given email must be set')\n email = self.normalize_email(email)\n user = self.model(email=email, **extra_fields)\n user.set_password(password)\n user.save(using=self._db)\n return user",
"def... | [
"0.8417866",
"0.84094507",
"0.8398238",
"0.83918047",
"0.83918047",
"0.83918047",
"0.8383299",
"0.8381542",
"0.83808947",
"0.8380314",
"0.8379753",
"0.83765846",
"0.83742833",
"0.83648807",
"0.83648807",
"0.83648807",
"0.8361933",
"0.8361933",
"0.8348754",
"0.8347133",
"0.834... | 0.79938453 | 84 |
Creates and saves a superuser with the given email and password. | def create_superuser(self, email, password):
user = self.create_user(email, password=password)
user.is_admin = True
user.save(using=self._db)
return user | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createsuperuser():\n\n email = prompt('User E-Mail')\n email_confirm = prompt('Confirm E-Mail')\n\n if not email == email_confirm:\n sys.exit('\\nCould not create user: E-Mail did not match')\n\n if not EMAIL_REGEX.match(email):\n sys.exit('\\nCould not create user: Invalid E-Mail add... | [
"0.83345544",
"0.82813346",
"0.8258201",
"0.8249415",
"0.8248188",
"0.8223905",
"0.82129776",
"0.8210377",
"0.8180428",
"0.8175924",
"0.8174477",
"0.8159496",
"0.81555283",
"0.8152179",
"0.8147241",
"0.8146682",
"0.81466043",
"0.81466043",
"0.81466043",
"0.81454295",
"0.81319... | 0.80541736 | 37 |
Get a name for the user. Use this whenever you show the user in the UI. | def displayname(self):
return self.email | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_name(self) -> str:\n return pulumi.get(self, \"user_name\")",
"def name(self) -> str:\n return self.user.name",
"def getUserName(self):\n user = User.by_id(self.user_id)\n return user.name",
"def user_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"us... | [
"0.85959435",
"0.846185",
"0.83146304",
"0.8313038",
"0.8228794",
"0.8220969",
"0.8218388",
"0.81126636",
"0.8054196",
"0.8036822",
"0.8036822",
"0.8036822",
"0.80256647",
"0.79910946",
"0.79809284",
"0.7884007",
"0.78487474",
"0.7847428",
"0.78015506",
"0.78015506",
"0.77903... | 0.0 | -1 |
Is the user a member of staff? | def is_staff(self):
return self.is_admin | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def is_staff(ctx):\n member = ctx.message.author\n vipRole = discord.utils.get(member.guild.roles, name=ROLE_VIP)\n staffRole = discord.utils.get(member.guild.roles, name=ROLE_STAFF)\n return vipRole in member.roles or staffRole in member.roles",
"def is_staff(self):\n # Simplest possibl... | [
"0.8091983",
"0.79417914",
"0.79417914",
"0.79417914",
"0.78373754",
"0.7725267",
"0.7695455",
"0.7543704",
"0.7538521",
"0.74671674",
"0.74179083",
"0.740949",
"0.740872",
"0.7405209",
"0.7117188",
"0.7050242",
"0.7050242",
"0.7018241",
"0.694894",
"0.68767804",
"0.685789",
... | 0.76052535 | 8 |
Is the user a superuser? | def is_superuser(self):
return self.is_admin | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isSuper(self):\n user = self.getSession()\n return self.pipe.auth.isSuper(user)",
"def is_superuser():\n if sys.version > \"2.7\":\n for uid in os.getresuid():\n if uid == 0:\n return True\n else:\n if os.getuid() == 0 or os.getegid() == 0:\n ... | [
"0.8203781",
"0.8123163",
"0.7936285",
"0.77750057",
"0.76693565",
"0.7637397",
"0.757354",
"0.7337289",
"0.7219452",
"0.713708",
"0.70978487",
"0.7031901",
"0.69242924",
"0.69207996",
"0.6917187",
"0.6854157",
"0.68328345",
"0.68302256",
"0.6777356",
"0.6768828",
"0.6747381"... | 0.80404836 | 2 |
Normalize and then split tags on commas and spaces. Empty tags are removed. | def split_commaseparated_tags(cls, commaseparatedtags):
if commaseparatedtags.strip() == '':
return []
else:
return [
cls.normalize_tag(tagstring)
for tagstring in list([_f for _f in re.split(r'[,\s]', commaseparatedtags) if _f])] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def splitTags(user_input):\n \n elements = []\n if ',' in user_input:\n elements = user_input.split(',')\n elif ' ' in user_input:\n elements = user_input.split(' ')\n else:\n elements.append(user_input)\n\n tags = []\n for element in elements:\n element = element.s... | [
"0.68961847",
"0.6774083",
"0.67034566",
"0.65602213",
"0.63447934",
"0.62999445",
"0.6269208",
"0.62582284",
"0.6224067",
"0.61772186",
"0.6168551",
"0.6133678",
"0.6001191",
"0.5954021",
"0.592172",
"0.59052056",
"0.5873035",
"0.5848221",
"0.5820034",
"0.58127075",
"0.57512... | 0.6720322 | 2 |
Clean a sender address by using a predefined map | def clean_sender(sender):
if sender in _sender_map:
return _sender_map[sender]
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_recipient(recipient):\n if recipient in _recipient_map:\n return _recipient_map[recipient]\n return ''",
"def _cleanupAddress(self, address):\n clean = []\n \n # This is sort of a desultory effort but I'm not convinced \n # that these cleanups will actually resu... | [
"0.6415194",
"0.63102794",
"0.6073433",
"0.5693001",
"0.5599912",
"0.5535422",
"0.5509846",
"0.5509394",
"0.5472849",
"0.5472058",
"0.5465454",
"0.5461169",
"0.5429801",
"0.54245687",
"0.53459334",
"0.53453904",
"0.53189844",
"0.52752376",
"0.52727574",
"0.52652967",
"0.52621... | 0.6980281 | 0 |
Clean a recipient address by using a predefined map | def clean_recipient(recipient):
if recipient in _recipient_map:
return _recipient_map[recipient]
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _cleanupAddress(self, address):\n clean = []\n \n # This is sort of a desultory effort but I'm not convinced \n # that these cleanups will actually result in cleaner searches\n for word in address.split(None):\n lower = word.lower()\n \n # Som... | [
"0.6710551",
"0.6352095",
"0.6117725",
"0.6082749",
"0.59901404",
"0.5843147",
"0.5725053",
"0.56326616",
"0.5629568",
"0.56150633",
"0.56043804",
"0.5557785",
"0.55360675",
"0.5443463",
"0.54358226",
"0.5432276",
"0.5409129",
"0.5401632",
"0.53814",
"0.53644365",
"0.5340619"... | 0.7065244 | 0 |
Examine the text to see if we need to use full MIME parsing or not. | def use_full_parser(text):
end_of_header_match = _end_of_simple_header_pattern.search(text)
return end_of_header_match is not None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hasRawText(self, text):\n r = re.compile(r'<(p|blockquote|div|form|table|ul|ol|dl|pre|h\\d)[^>]*?>.*</\\1>',\n re.S).sub('', text.strip()).strip()\n r = re.compile(r'<(hr|br)[^>]*?/>').sub('', r)\n return '' != r",
"def is_text(content):\n if b\"\\0\" in content:... | [
"0.6001141",
"0.5973282",
"0.5906038",
"0.5895969",
"0.5820583",
"0.5703178",
"0.5675198",
"0.5666455",
"0.5633167",
"0.55806506",
"0.55011463",
"0.5480535",
"0.5429232",
"0.53972876",
"0.5385626",
"0.5375632",
"0.53742987",
"0.53600854",
"0.53416246",
"0.5340039",
"0.5295856... | 0.57026833 | 6 |
Some dumps of hotmail messages introduce weird line breaks into header content, making them impossible to parse. This function will fix this content. | def fix_broken_hotmail_headers(text):
end_of_header_match = _end_of_simple_header_pattern.search(text)
temp_header_text = text[:end_of_header_match.end()].strip()
lines = temp_header_text.splitlines()[1:] # first line is not a header...
fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])
return_text = os.linesep.join(fixed_header_lines) + text[end_of_header_match.end():]
return return_text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_broken_yahoo_headers(text):\n end_of_header_match = _end_of_multipart_header_pattern.search(text)\n temp_header_text = text[:end_of_header_match.end()].strip()\n lines = temp_header_text.splitlines()\n fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])\n return_text = os.line... | [
"0.6554932",
"0.6418198",
"0.64063436",
"0.6094776",
"0.60202503",
"0.5935732",
"0.58566016",
"0.58263063",
"0.57753146",
"0.57245463",
"0.56043744",
"0.5570992",
"0.55153716",
"0.55127054",
"0.5502749",
"0.54770464",
"0.5456795",
"0.5437268",
"0.53645223",
"0.5352466",
"0.53... | 0.73432523 | 0 |
Some dumps of yahoo messages introduce weird line breaks into header content, making them impossible to parse. This function will fix this content. | def fix_broken_yahoo_headers(text):
end_of_header_match = _end_of_multipart_header_pattern.search(text)
temp_header_text = text[:end_of_header_match.end()].strip()
lines = temp_header_text.splitlines()
fixed_header_lines = reduce(_merge_broken_header_lines, lines, [])
return_text = os.linesep.join(fixed_header_lines) + '\r\n\r\n' + text[end_of_header_match.end():]
return return_text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_broken_hotmail_headers(text):\n end_of_header_match = _end_of_simple_header_pattern.search(text)\n temp_header_text = text[:end_of_header_match.end()].strip()\n lines = temp_header_text.splitlines()[1:] # first line is not a header...\n fixed_header_lines = reduce(_merge_broken_header_lines, l... | [
"0.64710176",
"0.6009694",
"0.5937977",
"0.5803022",
"0.57353634",
"0.5620867",
"0.5585157",
"0.55775535",
"0.55016285",
"0.5496441",
"0.54666466",
"0.54155815",
"0.53363466",
"0.52728117",
"0.52605987",
"0.52031696",
"0.5202499",
"0.51547736",
"0.5134256",
"0.5130388",
"0.51... | 0.7440384 | 0 |
Returns a single message object from a list of text content and attachments in a MIME message, after filtering out unwanted content. Also handles nested content like forwarded messages. | def get_nested_payload(mime_message):
return_message = EmailMessage()
return_message.subject = mime_message.get('Subject')
return_message.sender = clean_sender(mime_message.get('From'))
return_message.recipient = clean_recipient(mime_message.get('To'))
return_message.date = parse(mime_message.get('Date'))
for sub_message in mime_message.walk():
content_type = sub_message.get_content_type()
disposition = sub_message.get('Content-Disposition')
if content_type == 'text/plain' and disposition is None:
x = unicode(sub_message.get_payload())
return_message.append_body(x)
elif content_type in _ignored_content_types and disposition is None:
pass # throw away contents we don't want
else:
return_message.add_attachment(sub_message.get_payload(), content_type=content_type, filename=disposition)
return return_message | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_content(content):\n attachments = []\n body = None\n html = None\n\n for part in content.walk():\n if part.get('Content-Disposition') is not None:\n decoded_data = decode_attachment(part)\n\n attachment = parse_attachment(part)\n if attachment:\n att... | [
"0.6099892",
"0.6005864",
"0.59787667",
"0.59406656",
"0.5869029",
"0.5850756",
"0.5808466",
"0.57489055",
"0.5714828",
"0.5676952",
"0.5646985",
"0.5642732",
"0.56374663",
"0.56352216",
"0.5632284",
"0.56273305",
"0.56104976",
"0.5606248",
"0.55780625",
"0.55734146",
"0.5572... | 0.6649057 | 0 |
Coerce an unknown date to the given timezone, then to UTC | def normalize_to_utc(date, timezone):
local_tz = pytz.timezone(timezone)
new_date = date.replace(tzinfo = local_tz)
utc_tz = pytz.timezone('UTC')
new_date = new_date.astimezone(utc_tz)
return new_date | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toutc(dateobj, timezone):\n fmtdate = parser.parse(dateobj) # string to datetime object\n user_tz = pytz.timezone(timezone) # getting user's timezone\n localize_date_with_tz = user_tz.localize(fmtdate) #adding user's timezone to datetime object\n utcdate = pytz.utc.normalize(localize_date_with_tz) ... | [
"0.75894195",
"0.7289164",
"0.7209024",
"0.6907422",
"0.6864215",
"0.6698652",
"0.6688846",
"0.6686423",
"0.6653843",
"0.6593803",
"0.6576157",
"0.6573529",
"0.6562767",
"0.6553227",
"0.65446633",
"0.6526314",
"0.64945215",
"0.64906996",
"0.6474339",
"0.64524305",
"0.6386153"... | 0.76795125 | 0 |
Processing function used to reassemble corrected MIME header lines back into a block of text at the beginning of an email message. | def _merge_broken_header_lines(accumulator, item):
cleaned_item = item.strip()
for header in _header_list:
if item.startswith(header):
accumulator.append(cleaned_item)
return accumulator
try:
accumulator[len(accumulator)-1] = accumulator[len(accumulator)-1] + ' ' + cleaned_item
except IndexError: # edge case where the first line doesn't start with a header
accumulator.append(cleaned_item)
return accumulator | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_broken_hotmail_headers(text):\n end_of_header_match = _end_of_simple_header_pattern.search(text)\n temp_header_text = text[:end_of_header_match.end()].strip()\n lines = temp_header_text.splitlines()[1:] # first line is not a header...\n fixed_header_lines = reduce(_merge_broken_header_lines, l... | [
"0.66956985",
"0.65403616",
"0.620646",
"0.6043575",
"0.59951806",
"0.5961931",
"0.5931331",
"0.5886571",
"0.5792057",
"0.5707722",
"0.56881887",
"0.5639475",
"0.56263655",
"0.561612",
"0.5583248",
"0.5579559",
"0.55687916",
"0.5543469",
"0.5506505",
"0.5480917",
"0.54660165"... | 0.5239302 | 38 |
Used by JVM for java.lang.String and hence by some popular java memcached clients as default such as | def JAVA_NATIVE(key):
h = 0
l = len(key)
for (idx,c) in enumerate(key):
h += ord(c)*31**(l-(idx+1))
return _signed_int32(h) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def string_cache_key_adapter(obj):\n return obj",
"def store_string(self, string: str) -> None:",
"def stringable(self):\n return True",
"def __init__(self) -> None:\n str.__init__(self)",
"def test_str(self):\n dummy = DummyCryptographicObject()\n str(dummy)",
"def intern(... | [
"0.6431343",
"0.58831644",
"0.5787502",
"0.57085586",
"0.5668089",
"0.56542784",
"0.55438024",
"0.5541255",
"0.5535427",
"0.5526042",
"0.5480257",
"0.54440296",
"0.54340893",
"0.5433637",
"0.5404673",
"0.5362585",
"0.5343884",
"0.5343884",
"0.5334441",
"0.5314773",
"0.5311111... | 0.0 | -1 |
MD5based hashing algorithm used in consistent hashing scheme to compensate for servers added/removed from memcached pool. | def KETAMA(key):
d = hashlib.md5(key).digest()
c = _signed_int32
h = c((ord(d[3])&0xff) << 24) | c((ord(d[2]) & 0xff) << 16) | \
c((ord(d[1]) & 0xff) << 8) | c(ord(d[0]) & 0xff)
return h | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MD5(self) -> _n_0_t_3[_n_0_t_9]:",
"def pool_hash(path_list):\n return pool_process(md5_tuple, path_list, 'MD5 hashing')",
"def calc_md5(string):\n\treturn md5(string).hexdigest()",
"def __hash_md5__(self, text):\n key = hashlib.md5()\n key.update(text.encode('utf-8'))\n return ke... | [
"0.7700844",
"0.7656716",
"0.7500732",
"0.74877924",
"0.7479576",
"0.7474418",
"0.725043",
"0.7211859",
"0.7142206",
"0.71216583",
"0.7043272",
"0.7021252",
"0.6984938",
"0.69776475",
"0.6957162",
"0.6938334",
"0.69136685",
"0.69106406",
"0.69105786",
"0.6903468",
"0.6874061"... | 0.0 | -1 |
Auslesen aller Konversationen aus der Datenbank | def find_all(self):
result = []
cursor = self._cnx.cursor()
command = "SELECT id, konversation, nachricht_id, teilnehmer, herkunfts_id, ziel_id, inhalt FROM konversationen"
cursor.execute(command)
tuples = cursor.fetchall()
for (id, konversation, nachricht_id, teilnehmer, herkunfts_id, ziel_id, inhalt) in tuples:
konversation = Konversation()
konversation.set_id(id)
konversation.set_konversation(konversation)
konversation.set_nachricht_id(nachricht_id)
konversation.set_teilnehmer(teilnehmer)
konversation.set_herkunfts_id(herkunfts_id)
konversation.set_ziel_id(ziel_id)
konversation.set_inhalt(inhalt)
result.append(konversation)
self._cnx.commit()
cursor.close()
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text_typing_block(self):\n\t # open the database using the masterpassword\n typing_text = 'Send({})\\n'.format(self.masterpassword)\n\n # add in exit - this is achieved using CTRL + q\n typing_text += 'Sleep(15677)\\n'\n typing_text += \"SendKeepActive('KeePass')\\n\"\n ty... | [
"0.57340914",
"0.56032443",
"0.5596878",
"0.55474406",
"0.5542437",
"0.5542437",
"0.5542437",
"0.5542437",
"0.5542437",
"0.55125624",
"0.55125624",
"0.55125624",
"0.5512051",
"0.5425027",
"0.54236233",
"0.54012054",
"0.5378505",
"0.5372138",
"0.5365111",
"0.5340748",
"0.53144... | 0.6025704 | 0 |
Test hiding the library. | def test_privatize_library(self):
g = Game()
g.add_player(uuid4(), 'p1')
g.add_player(uuid4(), 'p2')
gs = g
gs.library.set_content(cm.get_cards(
['Circus', 'Circus', 'Circus', 'Circus Maximus', 'Circus', 'Circus',
'Ludus Magna', 'Ludus Magna', 'Statue', 'Coliseum',
]))
gs_private = g.privatized_game_state_copy('p1')
self.assertFalse(gs_private.library.contains('Circus'))
self.assertEqual(gs_private.library, Zone([Card(-1)]*len(gs_private.library), name='library')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_module(self):\n pass",
"def __test__():\n#-------------------------------------------------------------------------------\n import pylib.tester as tester\n return 0",
"def importlib_only(fxn):\n return unittest.skipIf(using___import__, \"importlib-specific test\")(fxn)",
"def test_pl... | [
"0.6566218",
"0.6470722",
"0.63644457",
"0.6197142",
"0.6101666",
"0.6054569",
"0.59973603",
"0.5936304",
"0.5921999",
"0.5883885",
"0.5871062",
"0.5861786",
"0.5840665",
"0.5811318",
"0.57991433",
"0.57575244",
"0.5749411",
"0.5728871",
"0.5727159",
"0.56983817",
"0.5689791"... | 0.6069717 | 5 |
Test hiding opponents' hands. | def test_privatize_hands(self):
g = Game()
g.add_player(uuid4(), 'p0')
g.add_player(uuid4(), 'p1')
gs = g
p0, p1 = gs.players
latrine, insula, jack, road = cm.get_cards(['Latrine', 'Insula', 'Jack', 'Road'])
p0.hand.set_content([latrine, insula])
p1.hand.set_content([jack, road])
gs_private = g.privatized_game_state_copy('p0')
p0, p1 = gs_private.players
self.assertIn(jack, p1.hand)
self.assertIn(Card(-1), p1.hand)
self.assertNotIn(road, p1.hand)
self.assertIn(latrine, p0.hand)
self.assertIn(insula, p0.hand)
self.assertEqual(len(p0.hand), 2)
self.assertEqual(len(p1.hand), 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_play_no_gain(self):\n self.card = self.g[\"Festival\"].remove()\n self.plr.piles[Piles.HAND].set(\"Duchy\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.favors.set(2)\n self.plr.test_input = [\"No\"]\n self.plr.play_card(self.card)\n self.assertEqu... | [
"0.66469085",
"0.66251075",
"0.661568",
"0.6462299",
"0.63435477",
"0.625224",
"0.6242946",
"0.62145275",
"0.61973673",
"0.6194856",
"0.61828953",
"0.6082449",
"0.60560155",
"0.6051513",
"0.59985155",
"0.5985109",
"0.5983806",
"0.5977845",
"0.5970393",
"0.5968302",
"0.5965452... | 0.69971275 | 0 |
Test hiding all vaults. | def test_privatize_vaults(self):
g = Game()
g.add_player(uuid4(), 'p0')
g.add_player(uuid4(), 'p1')
gs = g
p0, p1 = gs.players
latrine, insula, statue, road = cm.get_cards(['Latrine', 'Insula', 'Statue', 'Road'])
p0.vault.set_content([latrine, insula])
p1.vault.set_content([statue, road])
gs_private = g.privatized_game_state_copy('p1')
p0, p1 = gs_private.players
self.assertEqual(p0.vault, Zone([Card(-1)]*2, name='vault'))
self.assertEqual(p1.vault, Zone([Card(-1)]*2, name='vault')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_yggdrasil_vaults(self):\n pass",
"def test_get_asgard_vaults(self):\n pass",
"def test_vault_get_all_vault_items(self):\n pass",
"def test_vault_get_all_vault_sections(self):\n pass",
"def test_auth_private_unowned(self):\n self.do_visible(False, 'pattieblack... | [
"0.73062766",
"0.6963295",
"0.6804855",
"0.64582133",
"0.63372976",
"0.6049422",
"0.59896415",
"0.59509873",
"0.5787047",
"0.57756597",
"0.5770837",
"0.57636744",
"0.57540184",
"0.5746007",
"0.5736347",
"0.5734542",
"0.5626461",
"0.5625377",
"0.5586302",
"0.55808043",
"0.5574... | 0.6935641 | 2 |
Test hiding the card revealed with the fountain. | def test_privatize_fountain_card(self):
g = Game()
g.add_player(uuid4(), 'p0')
g.add_player(uuid4(), 'p1')
gs = g
p0, p1 = gs.players
latrine, insula, statue, road = cm.get_cards(['Latrine', 'Insula', 'Statue', 'Road'])
p0.fountain_card = latrine
gs_private = g.privatized_game_state_copy('p1')
p0, p1 = gs_private.players
self.assertEqual(p0.fountain_card, Card(-1)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_play_no_gain(self):\n self.card = self.g[\"Festival\"].remove()\n self.plr.piles[Piles.HAND].set(\"Duchy\")\n self.plr.add_card(self.card, Piles.HAND)\n self.plr.favors.set(2)\n self.plr.test_input = [\"No\"]\n self.plr.play_card(self.card)\n self.assertEqu... | [
"0.6782351",
"0.67602885",
"0.6536577",
"0.639235",
"0.63622284",
"0.6298589",
"0.62585497",
"0.6215377",
"0.6096858",
"0.60356593",
"0.603329",
"0.6007592",
"0.592438",
"0.5918852",
"0.5918852",
"0.58873326",
"0.5886941",
"0.5881228",
"0.587296",
"0.57984495",
"0.5798309",
... | 0.6018047 | 11 |
Returns False for out of range inputs. | def test_invalid_inputs(self):
f = gtrutils.check_petition_combos
self.assertFalse( f(-1, 1, [], False, False))
self.assertFalse( f( 0, 1, [], False, False))
self.assertFalse( f( 1, 0, [], False, False))
self.assertFalse( f( 1, 1, [-1], False, False))
self.assertFalse( f( 1,-1, [], False, False))
self.assertFalse( f( 1, 1, [1], False, False)) # n_off_role can never be 1
self.assertFalse( f( 1, 1, [1], True, False)) # n_off_role can never be 1
self.assertFalse( f( 1, 1, [1], False, True)) # n_off_role can never be 1
self.assertFalse( f( 1, 1, [1], True, True)) # n_off_role can never be 1
self.assertFalse( f( 1, 1, [1,3], True, True)) # n_off_role can never be 1
self.assertFalse( f( 3, 0, [2,3,3], False, True)) # n_off_role can never be 1
self.assertFalse( f( 3, 0, [2,3,3], True, False)) # n_off_role can never be 1
self.assertFalse( f( 2, 0, [2,3,3], False, True)) # n_off_role can never be 1
self.assertFalse( f( 2, 0, [2,3,3], True, False)) # n_off_role can never be 1
self.assertFalse( f( 5, 1, [6,6], True, False)) # n_off_role can never be 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isRangeValid(self) -> bool:\n ...",
"def acceptsArgument(self):\n range = self.validateRange(self.range)\n return not(not(range[1]))",
"def in_range(x, y):\n if (x < 0 or x > width or y < 0 or y > length):\n return False\n else:\n return True",
"def _inside_op_range(s... | [
"0.76130134",
"0.74046963",
"0.73377717",
"0.72116476",
"0.7005175",
"0.69723386",
"0.68953574",
"0.6872818",
"0.68704146",
"0.6861065",
"0.68098015",
"0.66929597",
"0.66748506",
"0.6673904",
"0.66729593",
"0.66561925",
"0.66364926",
"0.6613519",
"0.66080934",
"0.6600456",
"0... | 0.0 | -1 |
Test with no petitions allowed. | def test_no_petitions(self):
f = gtrutils.check_petition_combos
self.assertTrue( f( 0, 0, [ 0], False, False))
self.assertFalse( f( 1, 0, [], False, False))
self.assertFalse( f( 1, 1, [2], False, False))
self.assertFalse( f( 1, 1, [3], False, False))
self.assertFalse( f( 1, 1, [4], False, False))
self.assertTrue( f( 1, 1, [], False, False))
self.assertFalse( f( 1, 2, [], False, False))
self.assertFalse( f( 1, 3, [], False, False))
self.assertFalse( f( 2, 1, [], False, False))
self.assertTrue( f( 2, 2, [], False, False))
self.assertFalse( f( 2, 3, [], False, False))
self.assertFalse( f( 3, 1, [], False, False))
self.assertFalse( f( 3, 2, [], False, False))
self.assertTrue( f( 3, 3, [], False, False))
self.assertTrue( f(13,13, [], False, False))
self.assertFalse( f( 1, 1, [0,0,0,3], False, False))
self.assertFalse( f( 2, 1, [0,0,0,3], False, False))
self.assertFalse( f( 3, 1, [0,0,0,3], False, False)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testNoSpecialties(self):\n self.failUnlessEqual(self.person.getSpecialties(), [])",
"def not_test_without_user(self):\n # TODO",
"def test_tally_no_candidates(self):\n self.init_elect_types()\n\n userA = models.User(\n name = \"UserA\",\n email = \"userA@eL... | [
"0.64601696",
"0.63709843",
"0.62266237",
"0.6206515",
"0.5949884",
"0.5946186",
"0.5936945",
"0.5928463",
"0.59168476",
"0.5914561",
"0.59085196",
"0.58991575",
"0.58986336",
"0.5888519",
"0.5865907",
"0.58641195",
"0.58631915",
"0.58543706",
"0.5849753",
"0.58493304",
"0.58... | 0.551846 | 99 |
Test with only threecard petitions allowed. | def test_only_three_card_petitions(self):
f = gtrutils.check_petition_combos
self.assertTrue( f( 0, 0, [0], False, True))
self.assertFalse( f( 1, 0, [0], False, True))
self.assertTrue( f( 1, 1, [0], False, True))
self.assertTrue( f( 1, 0, [3], False, True))
self.assertTrue( f( 1, 3, [0], False, True))
self.assertFalse( f( 1, 1, [2], False, True))
self.assertFalse( f( 1, 1, [3], False, True))
self.assertFalse( f( 1, 1, [4], False, True))
self.assertTrue( f( 2, 2, [0], False, True))
self.assertTrue( f( 2, 1, [3], False, True))
self.assertTrue( f( 2, 3, [3], False, True))
self.assertTrue( f( 2, 6, [0], False, True))
self.assertTrue( f( 2, 0, [6], False, True))
self.assertFalse( f( 2, 4, [3], False, True))
self.assertFalse( f( 3, 1, [], False, True))
self.assertFalse( f( 3, 2, [], False, True))
self.assertFalse( f( 3, 0, [3], False, True))
self.assertFalse( f( 3, 0, [6], False, True))
self.assertTrue( f( 3, 3, [], False, True))
self.assertTrue( f( 3, 2, [3], False, True))
self.assertTrue( f( 3, 3, [6], False, True))
self.assertTrue( f( 3, 1, [6], False, True))
self.assertTrue( f( 3, 0, [9], False, True))
self.assertTrue( f(13,13, [], False, True))
self.assertTrue( f(13,39, [], False, True))
self.assertTrue( f(13, 0, [39], False, True))
self.assertTrue( f(13,15, [24], False, True))
self.assertTrue( f(13,15, [], False, True))
self.assertTrue( f(13,12, [3], False, True))
self.assertFalse( f(13,14, [], False, True))
self.assertFalse( f( 6, 1, [3,6,9], False, True))
self.assertTrue( f( 7, 1, [3,6,9], False, True))
self.assertFalse( f( 8, 1, [3,6,9], False, True)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_two_and_three_card_petitions(self):\n f = gtrutils.check_petition_combos\n\n self.assertTrue( f( 0, 0, [], True, True))\n\n self.assertFalse( f( 1, 0, [], True, True))\n self.assertFalse( f( 1, 0, [1], True, True))\n self.assertTrue( f( 1, 0, [2], True, True))\n ... | [
"0.6810119",
"0.6473808",
"0.645677",
"0.62938344",
"0.62752783",
"0.62059915",
"0.6155211",
"0.6148065",
"0.60892385",
"0.59840876",
"0.596696",
"0.5963209",
"0.59501404",
"0.5799793",
"0.5697761",
"0.5672481",
"0.566434",
"0.561755",
"0.56103486",
"0.5594359",
"0.5582937",
... | 0.7429536 | 0 |
Test with only twocard petitions | def test_only_two_card_petitions(self):
f = gtrutils.check_petition_combos
self.assertTrue( f( 0, 0, [0], True, False))
self.assertFalse( f( 1, 0, [], True, False))
self.assertFalse( f( 1, 0, [1], True, False))
self.assertTrue( f( 1, 0, [2], True, False))
self.assertFalse( f( 1, 0, [3], True, False))
self.assertFalse( f( 1, 0, [4], True, False))
self.assertTrue( f( 1, 1, [], True, False))
self.assertFalse( f( 1, 1, [2], True, False))
self.assertFalse( f( 2, 0, [2], True, False))
self.assertFalse( f( 2, 0, [3], True, False))
self.assertTrue( f( 2, 0, [4], True, False))
self.assertFalse( f( 2, 0, [5], True, False))
self.assertTrue( f( 2, 1, [2], True, False))
self.assertFalse( f( 2, 1, [3], True, False))
self.assertFalse( f( 2, 1, [4], True, False))
self.assertTrue( f(13, 26, [], True, False))
self.assertTrue( f(13, 0, [26], True, False))
self.assertTrue( f(13, 14, [12], True, False))
self.assertTrue( f(13, 13, [10], True, False))
self.assertFalse( f(13, 15, [11], True, False))
self.assertFalse( f( 6, 1, [2,4,6], True, False))
self.assertTrue( f( 7, 1, [2,4,6], True, False))
self.assertFalse( f( 8, 1, [2,4,6], True, False)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testBeliefs1sk(self):",
"def test_when_opponent_all_Ds(self):\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)",
"def test_three_arms_two_winners(self):\n self._test_three_arms_two_winners()",
"def test_theft_and_stealing(self):",
"def test_when_opponent_all_Ds(... | [
"0.6394057",
"0.63925785",
"0.63051224",
"0.62454295",
"0.62086844",
"0.61224383",
"0.60351664",
"0.6014525",
"0.599899",
"0.5914783",
"0.5904088",
"0.58920974",
"0.5878094",
"0.5875978",
"0.58667856",
"0.5842628",
"0.5808113",
"0.5807632",
"0.5807632",
"0.5807632",
"0.576396... | 0.59637266 | 9 |
Test with two and threecard petitions | def test_two_and_three_card_petitions(self):
f = gtrutils.check_petition_combos
self.assertTrue( f( 0, 0, [], True, True))
self.assertFalse( f( 1, 0, [], True, True))
self.assertFalse( f( 1, 0, [1], True, True))
self.assertTrue( f( 1, 0, [2], True, True))
self.assertTrue( f( 1, 0, [3], True, True))
self.assertFalse( f( 1, 0, [4], True, True))
self.assertTrue( f( 1, 1, [], True, True))
self.assertTrue( f( 1, 2, [], True, True))
self.assertTrue( f( 1, 3, [], True, True))
self.assertFalse( f( 1, 4, [], True, True))
self.assertFalse( f( 1, 1, [2], True, True))
self.assertFalse( f( 1, 1, [3], True, True))
self.assertFalse( f( 1, 2, [2], True, True))
self.assertFalse( f( 1, 3, [2], True, True))
self.assertFalse( f( 1, 3, [3], True, True))
self.assertTrue( f( 2, 1, [2], True, True))
self.assertTrue( f( 2, 1, [3], True, True))
self.assertTrue( f( 2, 0, [4], True, True))
self.assertTrue( f( 2, 0, [5], True, True))
self.assertTrue( f( 2, 0, [6], True, True))
self.assertTrue( f( 2, 4, [], True, True))
self.assertTrue( f( 2, 5, [], True, True))
self.assertTrue( f( 2, 6, [], True, True))
self.assertTrue( f(13, 26, [], True, True))
self.assertTrue( f(13, 39, [], True, True))
self.assertTrue( f(13, 0, [26], True, True))
self.assertTrue( f(13, 14, [12], True, True))
self.assertTrue( f(13, 13, [10], True, True))
self.assertTrue( f(13, 15, [11], True, True))
self.assertFalse( f(13, 40, [], True, True))
self.assertFalse( f(13, 11, [3], True, True))
self.assertFalse( f(4, 1, [2,3,6], True, True))
self.assertTrue( f(5, 1, [2,3,6], True, True))
self.assertTrue( f(6, 1, [2,3,6], True, True))
self.assertFalse( f(7, 1, [2,3,6], True, True)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_only_three_card_petitions(self):\n f = gtrutils.check_petition_combos\n\n self.assertTrue( f( 0, 0, [0], False, True))\n\n self.assertFalse( f( 1, 0, [0], False, True))\n self.assertTrue( f( 1, 1, [0], False, True))\n self.assertTrue( f( 1, 0, [3], False, True))\n ... | [
"0.7009317",
"0.6996275",
"0.65862507",
"0.6539536",
"0.65000474",
"0.64990026",
"0.64953685",
"0.6476889",
"0.64768463",
"0.6467368",
"0.6336645",
"0.6292399",
"0.6257715",
"0.62031925",
"0.61506057",
"0.6122968",
"0.60999656",
"0.6068418",
"0.6024874",
"0.6014065",
"0.60111... | 0.7173055 | 0 |
Test limit at beginning of game. | def test_initial_limit(self):
g = test_setup.simple_two_player()
p1, p2 = g.players
self.assertEqual(g._clientele_limit(p1), 2)
self.assertEqual(g._clientele_limit(p2), 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _safe_limit_check(self):\n if self.rem == 40:\n self.time_start = time.time()\n elif time.time() - self.time_start >= 11:\n self.rem = 40\n self.time_start = time.time()\n elif self.rem <= 0:\n t = 11 - (time.time() - self.time_start)\n\n ... | [
"0.6717742",
"0.65904355",
"0.64534426",
"0.64433163",
"0.643638",
"0.6354526",
"0.6331702",
"0.6220812",
"0.6176772",
"0.61617315",
"0.6129828",
"0.60590106",
"0.6015792",
"0.5951251",
"0.5916098",
"0.5896707",
"0.58848923",
"0.5870503",
"0.5835869",
"0.5821406",
"0.58180004... | 0.7055831 | 0 |
Test limit with some completed buildings. | def test_limit_with_influence(self):
g = test_setup.simple_two_player()
p1, p2 = g.players
p1.influence = ['Stone']
p2.influence = ['Rubble']
self.assertEqual(g._clientele_limit(p1), 5)
self.assertEqual(g._clientele_limit(p2), 3)
p1.influence = ['Wood']
p2.influence = ['Marble']
self.assertEqual(g._clientele_limit(p1), 3)
self.assertEqual(g._clientele_limit(p2), 5)
p1.influence = ['Brick']
p2.influence = ['Concrete']
self.assertEqual(g._clientele_limit(p1), 4)
self.assertEqual(g._clientele_limit(p2), 4)
p1.influence = ['Brick', 'Concrete', 'Marble']
p2.influence = ['Concrete', 'Stone', 'Rubble', 'Rubble', 'Rubble']
self.assertEqual(g._clientele_limit(p1), 9)
self.assertEqual(g._clientele_limit(p2), 10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_communities_created_limit(self):\n self.login_as(\"ben\")\n for i in range(settings.QUIZZZ_CREATED_COMMUNITIES_LIMIT):\n response = self.client.post(self.url, {\"name\": f\"test-group-{i}\"})\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n res... | [
"0.6331062",
"0.6307083",
"0.6275143",
"0.62501746",
"0.62440616",
"0.62208784",
"0.6138763",
"0.613732",
"0.6108998",
"0.61038834",
"0.60081613",
"0.58726895",
"0.58467835",
"0.58440167",
"0.58301693",
"0.5813597",
"0.5789836",
"0.5772815",
"0.5768534",
"0.5768062",
"0.57310... | 0.0 | -1 |
Test limit with completed Insula. | def test_limit_with_insula(self):
d = TestDeck()
g = test_setup.simple_two_player()
p1, p2 = g.players
self.assertEqual(g._clientele_limit(p1), 2)
p1.buildings.append(Building(d.insula, 'Rubble', complete=True))
self.assertEqual(g._clientele_limit(p1), 4)
p1.influence = ['Stone']
self.assertEqual(g._clientele_limit(p1), 7) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_identify_limit(limit, all, expected):\n assert identify_limit(limit, all) == expected",
"def test_get_remain_limit(self):\n finder = FinderInsidePro(self.test_key)\n limit = finder.get_remain_limit()\n assert isinstance(limit, int)\n assert limit > 0",
"def test_limit(se... | [
"0.70281196",
"0.6930952",
"0.6867976",
"0.63591504",
"0.6339935",
"0.63310546",
"0.63143235",
"0.63078123",
"0.62953484",
"0.62816274",
"0.62538594",
"0.62326956",
"0.61941534",
"0.61918557",
"0.6187162",
"0.61759824",
"0.61723286",
"0.61669195",
"0.6164165",
"0.6162524",
"0... | 0.62189835 | 12 |
Test limit with completed Aqueduct. | def test_limit_with_aqueduct(self):
d = TestDeck()
g = test_setup.simple_two_player()
p1, p2 = g.players
self.assertEqual(g._clientele_limit(p1), 2)
p1.buildings.append(Building(d.aqueduct, 'Concrete', complete=True))
self.assertEqual(g._clientele_limit(p1), 4)
p1.influence = ['Stone']
self.assertEqual(g._clientele_limit(p1), 10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_environmental_impact_compliance():\n emissions = 12000\n legal_limit = 300\n assert emissions < legal_limit",
"def test_get_remain_limit(self):\n finder = FinderInsidePro(self.test_key)\n limit = finder.get_remain_limit()\n assert isinstance(limit, int)\n assert limi... | [
"0.6602689",
"0.65843254",
"0.65533227",
"0.63588923",
"0.63528883",
"0.6254358",
"0.6215978",
"0.6180673",
"0.61653847",
"0.61470395",
"0.6145852",
"0.6117478",
"0.61015135",
"0.6094627",
"0.6062201",
"0.5976109",
"0.59308255",
"0.59041405",
"0.5900028",
"0.5873644",
"0.5867... | 0.7109697 | 0 |
Test limit with completed Aqueduct. | def test_limit_with_insula_and_aqueduct(self):
d = TestDeck()
g = test_setup.simple_two_player()
p1, p2 = g.players
self.assertEqual(g._clientele_limit(p1), 2)
p1.buildings.append(Building(d.aqueduct, 'Concrete', complete=True))
p1.buildings.append(Building(d.insula, 'Rubble', complete=True))
self.assertEqual(g._clientele_limit(p1), 8)
p1.influence = ['Stone']
self.assertEqual(g._clientele_limit(p1), 14) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_limit_with_aqueduct(self):\n d = TestDeck()\n\n g = test_setup.simple_two_player()\n\n p1, p2 = g.players\n\n self.assertEqual(g._clientele_limit(p1), 2)\n\n p1.buildings.append(Building(d.aqueduct, 'Concrete', complete=True))\n\n self.assertEqual(g._clientele_lim... | [
"0.7108374",
"0.6605307",
"0.6584849",
"0.655564",
"0.6354267",
"0.62519914",
"0.6215818",
"0.6181438",
"0.61679924",
"0.61482066",
"0.61449",
"0.6115197",
"0.6100592",
"0.60953647",
"0.60620195",
"0.5975527",
"0.5931888",
"0.59042233",
"0.5899593",
"0.5873513",
"0.58678395",... | 0.63586307 | 4 |
fetch from local json post the first tweet in the list then record that the tweet was tweeted | def tweet(self):
self.__refresh_local_tweets()
if not self.tweets:
return
tweet_obj = self.tweets[0]
# upload picture
media_id = self.__upload_media(tweet_obj["img"])
# tweet with text, and image
if not media_id:
return
self.__post_status(tweet_obj["text"], media_id)
self.tweets.remove(tweet_obj)
self.tweeted.append(tweet_obj)
self.__update_local_tweets() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_posts(username):\r\n\r\n # Authenticate to Twitter\r\n auth = tweepy.OAuthHandler(twitter_credentials.CONSUMER_KEY, twitter_credentials.CONSUMER_SECRET)\r\n auth.set_access_token(twitter_credentials.ACCESS_TOKEN, twitter_credentials.ACCESS_TOKEN_SECRET)\r\n\r\n api = tweepy.API(auth)\r\n\r\n ... | [
"0.6760458",
"0.6652912",
"0.65520895",
"0.63962245",
"0.6344638",
"0.634384",
"0.633406",
"0.6325491",
"0.632352",
"0.62919587",
"0.624689",
"0.62440544",
"0.6242781",
"0.6195591",
"0.61683637",
"0.6155086",
"0.6136341",
"0.6133877",
"0.6104071",
"0.6096447",
"0.60790527",
... | 0.5840768 | 48 |
fetch latest tweets saved from json | def __refresh_local_tweets(self):
f_tweets = open(f'{TWEETS}', 'r')
f_tweeted = open(f'{TWEETED}', 'r')
try:
self.tweets = json.load(f_tweets)
self.tweeted = json.load(f_tweeted)
finally:
f_tweets.close()
f_tweeted.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tweets(self):\r\n now = datetime.datetime.now()\r\n tweet_json = self.api.get_tweets(self.last, now)\r\n self.last = now\r\n return [Tweet(x) for x in tweet_json]",
"def get_posts(username):\r\n\r\n # Authenticate to Twitter\r\n auth = tweepy.OAuthHandler(twitter_credent... | [
"0.7257259",
"0.7204354",
"0.71714604",
"0.69285256",
"0.6901832",
"0.68701154",
"0.6827814",
"0.6807941",
"0.67518646",
"0.6662134",
"0.665305",
"0.66091895",
"0.65875465",
"0.65313274",
"0.64886",
"0.6487753",
"0.64813256",
"0.64548606",
"0.6451675",
"0.6448758",
"0.64238",... | 0.6033949 | 59 |
record the modified tweet/tweeted structures on disk | def __update_local_tweets(self):
f_tweets = open(f'{TWEETS}', 'w')
f_tweeted = open(f'{TWEETED}', 'w')
try:
f_tweets.write(json.dumps(self.tweets, sort_keys=True, indent=4))
f_tweeted.write(json.dumps(self.tweeted, sort_keys=True, indent=4))
finally:
f_tweets.close()
f_tweeted.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, tid, tmsg, tent, fp_rel=REL_PATH):\n # TODO\n # work out if saved previously and give better response\n \n\n # save to file system/rest api? where?\n # save to filesystem\n # TODO what day is this? localtime?\n fn = dt.fn_current_day(ext='json')\n\n ... | [
"0.6658211",
"0.6079574",
"0.6022582",
"0.5911571",
"0.58289623",
"0.57782304",
"0.57550776",
"0.57268596",
"0.5723704",
"0.5721074",
"0.57111156",
"0.56885713",
"0.5671371",
"0.5641506",
"0.56221664",
"0.5615904",
"0.5552578",
"0.5543077",
"0.55106515",
"0.55028576",
"0.5494... | 0.7087227 | 0 |
post the tweet with a media and text | def __post_status(self, text, media_id):
params = {
"status": text,
"media_ids": ",".join(map(str, [media_id]))
}
response = self.session.post(STATUS_UPDATE_URL, data=params)
res_err(response, "POSTING THE TWEET AFTER MEDIA UPLOAD")
logging.info(f'posted {text}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_tweet(media_id, quote, movie_name):\n\n tweet = '{} - {}'.format(quote, movie_name)\n request_data = {\n 'status': tweet,\n 'media_ids': media_id\n }\n\n requests.post(url=constants.POST_TWEET_URL, data=request_data, auth=oauth)",
"def post_to_tweets(data, url):\n\n print(\"... | [
"0.7451198",
"0.7257137",
"0.70719504",
"0.701658",
"0.6817208",
"0.6798214",
"0.67413294",
"0.6739724",
"0.67385334",
"0.67385334",
"0.67385334",
"0.6715136",
"0.6670937",
"0.66504085",
"0.6643696",
"0.6608908",
"0.6608185",
"0.65740305",
"0.6516414",
"0.64698046",
"0.635794... | 0.6901592 | 4 |
Route for getting tweets by hashtag. | def get_tweets_by_hashtag_route(hashtag):
response, code = get_tweets_by_hashtag(
hashtag, request.args.get('limit', 30))
return jsonify(response), code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getByHashtags(hashtag):\n\n # set page_limits. The default is 1 \n pages_limit = request.args.get('pages_limit') or 1\n pages_limit = int(pages_limit)\n\n raw_response = get_response(tw_api, 'search/tweets', { 'q': '#' + hashtag, 'count': 100 }, pages_limit)\n list_response = convert_resp2list(r... | [
"0.70752335",
"0.66526765",
"0.66291153",
"0.66205215",
"0.6536219",
"0.6486589",
"0.63549507",
"0.6294649",
"0.5997771",
"0.5885162",
"0.5859275",
"0.56409836",
"0.561119",
"0.55870056",
"0.5549043",
"0.55390894",
"0.5523961",
"0.54777485",
"0.54763746",
"0.54185194",
"0.538... | 0.8222778 | 0 |
Route for getting tweets by a user. | def get_tweets_by_user_route(username):
response, code = get_tweets_by_user(
username, request.args.get('limit', 30))
return jsonify(response), code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_user_tweets(username):\n userdata = query_db('select * from user where username = ?',\n [username], one=True)\n if userdata is None:\n abort(404)\n else:\n user_details = {\"username\": userdata['username'],\"user_id\":userdata['user_id']}\n\n followed = False\n if requ... | [
"0.7213664",
"0.684018",
"0.6731007",
"0.67143995",
"0.6648292",
"0.65042245",
"0.63557523",
"0.6336596",
"0.62716496",
"0.624116",
"0.6206272",
"0.61798954",
"0.61040026",
"0.6077563",
"0.6045129",
"0.6031549",
"0.60101897",
"0.6005784",
"0.6003022",
"0.5988208",
"0.5985817"... | 0.81342083 | 0 |
Retrieve a new eagle eye auth key and subdomain information | def _get_eagleeye_session(carson_api, building_id):
url = C_API_URI + C_EEN_SESSION_ENDPOINT.format(building_id)
session = carson_api.authenticated_query(url)
return session.get('sessionId'), session.get('activeBrandSubdomain') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_auth():\n config = configparser.RawConfigParser()\n config.read(\"speech.cfg\")\n apikey = config.get('auth', 'apikey')\n return (\"apikey\", apikey)",
"def api_key(request):\r\n user_acct = request.user\r\n return _api_response(request, {\r\n 'api_key': user_acct.api_key,\r\n ... | [
"0.61862534",
"0.6072772",
"0.59618855",
"0.58222324",
"0.57261264",
"0.5712428",
"0.57017297",
"0.5646959",
"0.56169933",
"0.55943763",
"0.5513818",
"0.5508162",
"0.5504784",
"0.5502838",
"0.54992354",
"0.5479007",
"0.54416525",
"0.5434418",
"0.5419267",
"0.5413086",
"0.5412... | 0.55181515 | 10 |
List of contact information | def contact_info(self):
return [
{
'contact_info': c.get('contactInfo'),
'type': c.get('type'),
'primary': c.get('primary'),
'verified': c.get('verified'),
}
for c in self.entity_payload.get('contactInfo')] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_contact(self):\n contacts = \"\".join(str(contact) for contact in self.contact_list)\n print(contacts)",
"def list_contacts(self):\n return self.contacts",
"def list_contact(name):\n db = get_db()\n name = hashlib.sha256(name).hexdigest()\n \n if name in db:\n ... | [
"0.78861237",
"0.76341623",
"0.758478",
"0.75249934",
"0.75059426",
"0.7496882",
"0.7042957",
"0.70327526",
"0.7013183",
"0.7013183",
"0.7013183",
"0.69496447",
"0.69449735",
"0.69267607",
"0.69231725",
"0.6869843",
"0.6860492",
"0.6860492",
"0.6857307",
"0.6849241",
"0.68165... | 0.7561425 | 3 |
Create a formatted recommnedation URI based on user id. | def resolve_worker_evaluation_url(request, user):
return request.build_absolute_uri(reverse('hirer:evaluate', args=[user.id])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_user_link(user):\n return '[@{0}](https://github.com/{0})'.format(user)",
"def create_qr_uri(user: User) -> dict:\n otp_secret = pyotp.random_base32()\n qr_uri = pyotp.totp.TOTP(otp_secret).provisioning_uri(\n name=user.username, issuer_name=f\"{APPNAME} ({MAIN_VERSION_NAME})\"\n ... | [
"0.5987479",
"0.5844432",
"0.57443655",
"0.5638544",
"0.5572798",
"0.54887694",
"0.5328215",
"0.531546",
"0.5313541",
"0.5249727",
"0.5235717",
"0.5221336",
"0.5174196",
"0.51600695",
"0.51578987",
"0.5155158",
"0.5142916",
"0.5137194",
"0.5137102",
"0.510324",
"0.50865287",
... | 0.46228266 | 86 |
Create a formatted mail message to sent to worker user. This method pass the user and evaluation_link to be processed by txt template alerts/subscription_message.txt | def alert_subscription_message(request, user):
message = loader.get_template(
'alerts/subscription_message.txt').render(
{'user': user, 'evaluation_link': resolve_worker_evaluation_url(request, user)})
return message | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_created_email(self):\n if settings.NOTIFY_NEW_REG:\n to = settings.NOTIFY_NEW_REG\n message = \"\"\"\\\nGreetings,<br><br>\n\nA new vehicle registration has been submitted by %s.<br><br>\n\nGo here to view or edit the request: <br>\n<a href=\"%s\">%s</a>\n<br><br>\nSincerely,<... | [
"0.6634772",
"0.63277805",
"0.6235654",
"0.6227511",
"0.61810446",
"0.61754483",
"0.6124126",
"0.6124126",
"0.6104047",
"0.60752434",
"0.6052107",
"0.59274155",
"0.5924757",
"0.590727",
"0.58884096",
"0.5875973",
"0.5858161",
"0.58423764",
"0.58318377",
"0.58315945",
"0.58279... | 0.777387 | 0 |
Create a formatted email message to sent to worker user template alerts/new_service_notification.txt | def alert_new_service_notification(hirer, worker, service):
domain = Site.objects.get_current().domain
url = "http://" + domain + "/worker/"
message = loader.get_template(
'alerts/new_service_notification.txt').render(
{'worker': worker, 'hirer': hirer, 'service': service, 'url':url})
return message | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_created_email(self):\n if settings.NOTIFY_NEW_REG:\n to = settings.NOTIFY_NEW_REG\n message = \"\"\"\\\nGreetings,<br><br>\n\nA new vehicle registration has been submitted by %s.<br><br>\n\nGo here to view or edit the request: <br>\n<a href=\"%s\">%s</a>\n<br><br>\nSincerely,<... | [
"0.7332573",
"0.7300195",
"0.70396394",
"0.65700346",
"0.6381602",
"0.63333446",
"0.6274574",
"0.62374425",
"0.61783296",
"0.61655205",
"0.6151091",
"0.61510384",
"0.6135396",
"0.6120886",
"0.61056536",
"0.60524446",
"0.6039856",
"0.6035018",
"0.6004501",
"0.5985513",
"0.5972... | 0.74509716 | 0 |
Create a formatted email message to sent to worker user template alerts/service_notification.txt | def alert_service_notification(user, service):
message = loader.get_template(
'alerts/service_notification.txt').render(
{'user': user, 'service': service})
return message | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def alert_new_service_notification(hirer, worker, service):\n\n domain = Site.objects.get_current().domain\n url = \"http://\" + domain + \"/worker/\"\n\n message = loader.get_template(\n 'alerts/new_service_notification.txt').render(\n {'worker': worker, 'hirer': hirer, 'service': service, ... | [
"0.7191147",
"0.6990005",
"0.69212645",
"0.65836084",
"0.64851093",
"0.6402051",
"0.6330503",
"0.6326159",
"0.62814146",
"0.6238903",
"0.62185687",
"0.6207358",
"0.6139531",
"0.60843295",
"0.60813093",
"0.60735154",
"0.6054305",
"0.60353315",
"0.6029179",
"0.60170865",
"0.598... | 0.74859136 | 0 |
Create a formatted email message to the worker when a hirer makes an evaluation template alerts/worker_evaluated.txt | def alert_worker_evaluated(hirer,worker):
message = loader.get_template(
'alerts/worker_evaluated.txt').render(
{'worker': worker, 'hirer': hirer})
return message | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_created_email(self):\n if settings.NOTIFY_NEW_REG:\n to = settings.NOTIFY_NEW_REG\n message = \"\"\"\\\nGreetings,<br><br>\n\nA new vehicle registration has been submitted by %s.<br><br>\n\nGo here to view or edit the request: <br>\n<a href=\"%s\">%s</a>\n<br><br>\nSincerely,<... | [
"0.6600645",
"0.6544189",
"0.63650763",
"0.63307947",
"0.6069563",
"0.60685426",
"0.6014015",
"0.5973316",
"0.59537005",
"0.5941229",
"0.58852065",
"0.5875624",
"0.58755213",
"0.58395034",
"0.582515",
"0.5794151",
"0.57777476",
"0.5777449",
"0.5768729",
"0.57670987",
"0.57439... | 0.79926217 | 0 |
Decorator to force workers to complete their profiles. This decorator will redirect workers with incomplete profiles to the detailed profile subscription view. | def documents_required(function=None):
def _dec(view_func):
def _view(request, *args, **kwargs):
_user = request.user
if _user.is_authenticated() and _user.is_worker() and\
(not _user.is_application_form_filled):
return redirect('/profissional/subscription/', permanent=True)
else:
return view_func(request, *args, **kwargs)
_view.__name__ = view_func.__name__
_view.__dict__ = view_func.__dict__
_view.__doc__ = view_func.__doc__
return _view
if function is None:
print("Funciont is none")
return _dec
else:
print("There is some value for function")
return _dec(function) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setProfileJobs(self,profile=False):\n self.__profileJobs = profile",
"def complete(request, backend, *args, **kwargs):\n return do_complete(request.social_strategy, _do_login, user=None,\n redirect_name='home', *args, **kwargs)",
"def profile():\n if g.user:\n return r... | [
"0.51761717",
"0.48839507",
"0.486394",
"0.48614234",
"0.48394087",
"0.48157418",
"0.4814398",
"0.48082665",
"0.4771799",
"0.4770562",
"0.47591376",
"0.47403374",
"0.4720351",
"0.46573168",
"0.46533135",
"0.45912194",
"0.4588692",
"0.4584498",
"0.45765498",
"0.45561683",
"0.4... | 0.0 | -1 |
Return the guessed language of POSTed text. | def POST(self, text):
lang = guess_language(text)
return {'language': lang} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_language(self, text):\n try:\n post_lang = detect(text)\n except:\n post_lang = 'N/A'\n return post_lang",
"def guess_language(text): # pragma: no cover\n try:\n from guess_language import guessLanguage\n return Language.fromguessit(guessLangua... | [
"0.84711045",
"0.7443068",
"0.72882843",
"0.7189894",
"0.7099894",
"0.7063926",
"0.69427687",
"0.67506385",
"0.6746426",
"0.6721064",
"0.670669",
"0.66078335",
"0.6576339",
"0.6564039",
"0.65360004",
"0.65185875",
"0.6517528",
"0.6502088",
"0.64020914",
"0.63992137",
"0.63880... | 0.7704462 | 1 |
Generates a sha1 checksum for a given string | def get_checksum(str):
hash_object = hashlib.sha1(b'%s' % str)
hex_dig = hash_object.hexdigest()
return hex_dig | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sha1(s: str) -> str:\n return hashlib.sha1(s.encode()).hexdigest()",
"def sha1(self, s):\n\t\tself.sha1_calls += 1\n\t\treturn int(hashlib.sha1(s).hexdigest(), 16)",
"def SHA1(self) -> _n_0_t_3[_n_0_t_9]:",
"def checksum_from_sha1(value):\n # More constrained regex at lexer level\n CHECKSUM_RE =... | [
"0.7719917",
"0.75582397",
"0.727031",
"0.71468693",
"0.7109886",
"0.7050218",
"0.704167",
"0.7008932",
"0.6944196",
"0.6943862",
"0.69164616",
"0.6908371",
"0.69008803",
"0.6843064",
"0.67979276",
"0.67816305",
"0.6755393",
"0.67546755",
"0.67312115",
"0.6660172",
"0.663221"... | 0.79029137 | 0 |
Appends .svg to a checksum | def get_filename(checksum):
return '%s.svg' % checksum | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _append_svg(self, svg, before_prompt=False):\n self._append_custom(self._insert_svg, svg, before_prompt)",
"def __merger_svg(self):\n pass",
"def save_svg(xml_string, checksum=None):\n if checksum is None:\n checksum = get_checksum(xml_string) # Get checksum of this file\n e... | [
"0.6322185",
"0.6220869",
"0.6096771",
"0.6001254",
"0.58138853",
"0.57505566",
"0.5744236",
"0.57401705",
"0.56943905",
"0.5545867",
"0.5537553",
"0.5533253",
"0.5466105",
"0.5450053",
"0.5445687",
"0.54211104",
"0.5349227",
"0.53329223",
"0.532195",
"0.5315566",
"0.5292804"... | 0.6853433 | 0 |
Check if this file has been created before if so, just return the S3 URL. Returns None otherwise | def is_duplicate_checksum(checksum):
s3 = boto3.client('s3')
response = s3.list_objects_v2(
Bucket=BUCKET,
EncodingType='url',
Prefix=checksum
)
if response['KeyCount'] > 0 and len(response['Contents']) > 0:
return 'https://s3.amazonaws.com/%s/%s' % (BUCKET, response['Contents'][0]['Key'])
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_datafile_url(self):\n try:\n return self.datafile.url\n except ValueError:\n if core.utils.is_absolute_url(self.source):\n if self.source.startswith('s3://'):\n return None # file is in the UPLOAD_BUCKET\n return self.sou... | [
"0.6728675",
"0.6340867",
"0.6091045",
"0.6061678",
"0.6043561",
"0.60391515",
"0.6009251",
"0.59960645",
"0.5973066",
"0.5970535",
"0.5962591",
"0.5951693",
"0.59505904",
"0.59455204",
"0.59345853",
"0.5928199",
"0.59084433",
"0.5899881",
"0.5891643",
"0.5853361",
"0.5844663... | 0.6010111 | 6 |
Uploads the SVG file to S3, and returns the URL of the object | def upload_svg(filename, xml_string):
s3 = boto3.client('s3')
response = s3.put_object(
ACL='public-read',
Body=xml_string,
Bucket=BUCKET,
Key=filename,
StorageClass='REDUCED_REDUNDANCY',
)
return 'https://s3.amazonaws.com/%s/%s' % (BUCKET, filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload(file_path, aws_path, access_key, secret_key) -> None:\n # bucket = \"dev-com-courtlistener-storage\"\n bucket = \"seals.free.law\"\n client = boto3.client(\n \"s3\",\n aws_access_key_id=access_key,\n aws_secret_access_key=secret_key,\n )\n transfer = S3Transfer(client... | [
"0.65627086",
"0.6511997",
"0.64451045",
"0.63643384",
"0.61173415",
"0.6040773",
"0.6031718",
"0.60039645",
"0.5967601",
"0.5940069",
"0.5857561",
"0.58238834",
"0.5796205",
"0.574195",
"0.57342345",
"0.57331437",
"0.5722847",
"0.57148886",
"0.57147944",
"0.5705097",
"0.5654... | 0.7696398 | 0 |
Saves an XML string as a unique (checksummed) file in S3 And returns the URL of the file Checks for duplicates along the way using sha1 to find collisions | def save_svg(xml_string, checksum=None):
if checksum is None:
checksum = get_checksum(xml_string) # Get checksum of this file
existing_url = is_duplicate_checksum(checksum) # Make sure it's unique
if existing_url is not None: # We've generated this file before.
logger.info('Duplicate detected for %s' % checksum)
return existing_url # If dupe_check has a value, it's a URL to an existing (duplicate) file.
# Usually, we've already checked for a duplicate - the above logic is just for cases
# where we need to generate the checksum on the backend
filename = get_filename(checksum)
url = upload_svg(filename, xml_string)
return url | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_duplicate_checksum(checksum):\n s3 = boto3.client('s3')\n response = s3.list_objects_v2(\n Bucket=BUCKET,\n EncodingType='url',\n Prefix=checksum\n )\n\n if response['KeyCount'] > 0 and len(response['Contents']) > 0:\n return 'https://s3.amazonaws.com/%s/%s' % (BUCKET... | [
"0.6614705",
"0.62321055",
"0.5942641",
"0.5849977",
"0.5737562",
"0.5682555",
"0.567223",
"0.5630804",
"0.5596855",
"0.5581484",
"0.5557663",
"0.547344",
"0.5443973",
"0.5370834",
"0.5353757",
"0.531475",
"0.52927846",
"0.52644974",
"0.5246912",
"0.52463406",
"0.5242865",
... | 0.6733674 | 0 |
Create the translations model for the shared model 'model'. 'related_name' is the related name for the reverse FK from the translations model. 'meta' is a (optional) dictionary of attributes for the translations model's inner Meta class. 'fields' is a dictionary of fields to put on the translations model. | def create_translations_model(model, related_name, meta, **fields):
if not meta:
meta = {}
unique = [('language_code', 'master')]
meta['unique_together'] = list(meta.get('unique_together', [])) + unique
# Create inner Meta class
Meta = type('Meta', (object,), meta)
name = '%sTranslation' % model.__name__
attrs = {}
attrs.update(fields)
attrs['Meta'] = Meta
attrs['__module__'] = model.__module__
attrs['language_code'] = models.CharField(max_length=15, db_index=True)
# null=True is so we can prevent cascade deletion
attrs['master'] = models.ForeignKey(model, related_name=related_name, editable=False, null=True)
# Create and return the new model
return ModelBase(name, (BaseTranslationModel,), attrs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_translations_model(shared_model, related_name, meta, **fields):\n if not meta:\n meta = {}\n\n if shared_model._meta.abstract:\n # This can't be done, because `master = ForeignKey(shared_model)` would fail.\n raise TypeError(\"Can't create TranslatedFieldsModel for abstract cl... | [
"0.7985823",
"0.5591691",
"0.49817428",
"0.4975373",
"0.4800113",
"0.47464964",
"0.4744232",
"0.47395378",
"0.47331527",
"0.46989104",
"0.46264884",
"0.46264884",
"0.459076",
"0.45905438",
"0.45603356",
"0.4508293",
"0.44997284",
"0.44252264",
"0.44179207",
"0.4414165",
"0.43... | 0.822475 | 0 |
Contribute translations options to the inner Meta class and set the descriptors. This get's called from TranslateableModelBase.__new__ | def contribute_translations(cls, rel):
opts = cls._meta
opts.translations_accessor = rel.get_accessor_name()
opts.translations_model = rel.model
opts.translations_cache = '%s_cache' % rel.get_accessor_name()
trans_opts = opts.translations_model._meta
# Set descriptors
for field in trans_opts.fields:
if field.name == 'pk':
continue
if field.name == 'master':
continue
if field.name == opts.translations_model._meta.pk.name:
continue
if field.name == 'language_code':
attr = LanguageCodeAttribute(opts)
else:
attr = TranslatedAttribute(opts, field.name)
setattr(cls, field.name, attr) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_translations_model(model, related_name, meta, **fields):\n if not meta:\n meta = {}\n unique = [('language_code', 'master')]\n meta['unique_together'] = list(meta.get('unique_together', [])) + unique\n # Create inner Meta class \n Meta = type('Meta', (object,), meta)\n name = '%... | [
"0.6093402",
"0.5498365",
"0.5405703",
"0.5377071",
"0.5377071",
"0.52954173",
"0.5262987",
"0.5173471",
"0.5130741",
"0.51139444",
"0.5081222",
"0.5053549",
"0.50521266",
"0.5046423",
"0.50335914",
"0.50004405",
"0.4973911",
"0.4968481",
"0.49118015",
"0.49080524",
"0.486693... | 0.73294145 | 0 |
When this instance is saved, also save the (cached) translation | def save_translations(cls, instance, **kwargs):
opts = cls._meta
if hasattr(instance, opts.translations_cache):
trans = getattr(instance, opts.translations_cache)
if not trans.master_id:
trans.master = instance
trans.save() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, *args, **kwargs):\n\n ret = super(Translation, self).save(*args, **kwargs)\n\n # Does cache invalidation\n cache_key = Translation.objects.make_cache_key(type(self), self.object_id, self.language)\n cache.set(cache_key, self)\n\n return ret",
"def save(self, *arg... | [
"0.81048036",
"0.6967799",
"0.66896576",
"0.64093536",
"0.6083553",
"0.60599184",
"0.6016258",
"0.59257364",
"0.5915563",
"0.59141093",
"0.59141093",
"0.59141093",
"0.59141093",
"0.59141093",
"0.59109294",
"0.589746",
"0.5893489",
"0.5889759",
"0.58738244",
"0.58508587",
"0.5... | 0.7327054 | 1 |
Add various entries to a cube dictionary and sort by socket index | def normalize(district, purpose, cube_dictionary_list):
for gui_index, cube_dictionary in enumerate(cube_dictionary_list):
cube_dictionary["district"] = district
cube_dictionary["purpose"] = purpose
cube_dictionary["gui_index"] = gui_index # Dictionary is given in a natural gui display order. Remember it.
return sorted(cube_dictionary_list, key=lambda item: item["socket"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_vertex_to_clusters(clusters,vertex):\n for key in clusters:\n clusters[key].append(vertex)",
"def make_neighbor_db(data):\n acted_with = {}\n for i, j, _ in data:\n # the setdefault method lets us avoid checking for ourselves whether an\n # actor is aclready in the dictionar... | [
"0.52169806",
"0.5209674",
"0.5192503",
"0.5059949",
"0.50562185",
"0.50340647",
"0.503215",
"0.49723428",
"0.4956823",
"0.4896037",
"0.48916155",
"0.48439723",
"0.48249584",
"0.48159158",
"0.48155957",
"0.47965276",
"0.4795622",
"0.47880176",
"0.47797826",
"0.47521746",
"0.4... | 0.58312446 | 0 |
Performs the right division 'dst / src', and moves 'result_in' to 'dst' to either retrieve the division or the modulo | def perform_divide(c, dst, src, result_in8, result_in16):
# We're likely going to need to save some temporary variables
# No 'push' or 'pop' are used because 'ah /= 3', for instance, destroys 'al'
# 'al' itself can be pushed to the stack, but a temporary variable can hold
tmps = TmpVariables(c)
# In division, the dividend is divided by the divisor to get a quotient
# dividend \ divisor
# quotient
large_divide = max(dst.size, src.size) > 8
if large_divide:
# 16-bits mode, so we use 'ax' as the dividend
dividend = 'ax'
# The second factor cannot be an inmediate value
# Neither AX/DX or variants, because those are used
# Neither 8 bits, because 16 bits mode is required
if src.code[0] in 'ad' or src.size != 16 or src.value is not None:
# Either we use a register, which we would need to save/restore,
# or we use directly a memory variable, which is just easier
divisor = tmps.create_tmp('divisor', size=16)
else:
divisor = src.code
# If the destination is DH or DL, we need to save the opposite part
# If the destination is not DX, we need to save the whole register
if dst[0] == 'd':
if dst[-1] == 'h':
tmps.save('dl')
elif dst[-1] == 'l':
tmps.save('dh')
elif dst[-1] != 'x':
tmps.save('dx')
else:
tmps.save('dx')
# If the destination is AH or AL, we need to save the opposite part
# If the destination is not AX, we need to save the whole register
if dst[0] == 'a':
if dst[-1] == 'h':
tmps.save('al')
elif dst[-1] == 'l':
tmps.save('ah')
elif dst[-1] != 'x':
tmps.save('ax')
else:
tmps.save('ax')
# Load the dividend and divisor into their correct location
helperassign(c, [dividend, divisor], [dst, src])
# Perform the division
c.add_code([
f'xor dx, dx',
f'div {divisor}'
])
# Move the result from wherever it is
helperassign(c, dst, result_in16)
else:
# 8-bits mode, so we use 'al' as the dividend
dividend = 'al'
# The second factor cannot be an inmediate value
# Neither AX/AH because those are used
# Neither 16 bits, because 8 bits mode is required
if src.code[0] in 'a' or src.size != 8 or src.value is not None:
# Either we use a register, which we would need to save/restore,
# or we use directly a memory variable, which is just easier
divisor = tmps.create_tmp('divisor', size=8)
else:
divisor = src.code
# If the destination is AH or AL, we need to save the opposite part
# If the destination is not AX, we need to save the whole register
if dst[0] == 'a':
if dst[-1] == 'h':
tmps.save('al')
elif dst[-1] == 'l':
tmps.save('ah')
elif dst[-1] != 'x':
tmps.save('ax')
else:
tmps.save('ax')
# Load the dividend and divisor into their correct location
helperassign(c, [dividend, divisor], [dst, src])
# Perform the division
c.add_code([
f'xor ah, ah',
f'div {divisor}'
])
# Move the result from wherever it is
helperassign(c, dst, result_in8)
# Restore the used registers
tmps.restore_all() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def int_div_inplace(a, b):",
"def true_div_inplace(a, b):",
"def divup(a, b):\n return (a + b - 1) // b",
"def divide(lhs, rhs):\n return _make.divide(lhs, rhs)",
"def div(self, source, destination):\n value = bytearray()\n\n dividend = destination\n divider = source\n\n i... | [
"0.6179031",
"0.6050849",
"0.59951925",
"0.5947101",
"0.5941931",
"0.5891567",
"0.58224696",
"0.5819864",
"0.5762812",
"0.57600236",
"0.57048416",
"0.5681911",
"0.5675549",
"0.5665095",
"0.5660404",
"0.5639097",
"0.56374323",
"0.5618714",
"0.56052095",
"0.55648005",
"0.556480... | 0.7103163 | 0 |
Given the scale of WGUPS shipping, 1000 is a reasonable backing array size | def __init__(self):
self.size = 1000
self.mapArray = [None] * self.size | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.size = 1000000\n self.mp = [[]] * self.size",
"def scale_in(self, count):\n pass",
"def limit_size(self, catalog):\n if len(catalog)<=self.limit:\n return catalog\n mem = {}\n for instance in catalog:\n if (instance['vCp... | [
"0.56507504",
"0.5521999",
"0.5457273",
"0.5385491",
"0.537851",
"0.5337603",
"0.5316058",
"0.5289358",
"0.5258439",
"0.52287763",
"0.52238894",
"0.5206532",
"0.51933736",
"0.5175065",
"0.5167415",
"0.5160229",
"0.514245",
"0.509487",
"0.50677186",
"0.5030789",
"0.50188196",
... | 0.53057754 | 7 |
Hypothese on the third feature | def compute_determinant(matrix):
det = np.linalg.det(matrix)
#if det == 0.:
# The det = 0 could be related to the third feature
# det = np.linalg.det(matrix[:2, :2])
if det == 0.:
# Singular covariance matrix, should not be taken into account
det = np.nan
if np.isclose(det, 0):
det = np.abs(det)
return det | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hypotenuse(a, b):\r\n return (a**2 + b**2)**0.5",
"def hypotenuse():\n print(math.sqrt(5**2 +3**2))",
"def hypotenuse():\n print(math.sqrt(5*5 + 3*3))",
"def hyp(a,b):\n c=sqrt((a*a)+(b*b))\n return c",
"def hypothenuse(x, y):\n return sqrt(x**2 + y**2)",
"def hypotenuse(x,y):\n xx =... | [
"0.6345466",
"0.6224685",
"0.62164116",
"0.6141258",
"0.5938721",
"0.57951844",
"0.5715218",
"0.5696992",
"0.56795216",
"0.56696415",
"0.5644957",
"0.5635818",
"0.561018",
"0.56082743",
"0.5606991",
"0.56034803",
"0.5592059",
"0.55717176",
"0.55676854",
"0.55559623",
"0.55502... | 0.0 | -1 |
Method to compute the KullbackLeibler Divergence between two Gaussians PARAMETERS | def compute_kl(mu1, mu2, sigma1, sigma2):
k = len(mu1)
try:
term1 = np.trace(np.matmul(np.linalg.inv(sigma2), sigma1))
term2 = np.matmul(np.matmul((mu2 - mu1).T,
np.linalg.inv(sigma2)),
(mu2 - mu1))
det_sigma1 = compute_determinant(sigma1)
det_sigma2 = compute_determinant(sigma2)
# term3 = np.log(np.linalg.det(sigma2)/np.linalg.det(sigma1))
term3 = np.log(det_sigma2)
term4 = np.log(det_sigma1)
kl = (term1 + term2 - k + term3 - term4)/2.
return kl
except np.linalg.LinAlgError:
return np.nan | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_KL_divergence(self):\n KL_loss_W = Vil.get_KL_divergence_Samples(self.mu_weight, Vil.softplus(self.rho_weight), self.weight, self.prior)\n KL_loss_b = 0\n if self.bias is not None:\n KL_loss_b = Vil.get_KL_divergence_Samples(self.mu_bias, Vil.softplus(self.rho_bias), self.bi... | [
"0.64265364",
"0.6414014",
"0.64129555",
"0.6334975",
"0.63160306",
"0.6193587",
"0.61617297",
"0.61293685",
"0.61277765",
"0.6109303",
"0.6043571",
"0.603763",
"0.60138285",
"0.59585667",
"0.59533715",
"0.5900521",
"0.5898689",
"0.58980924",
"0.5893038",
"0.58471286",
"0.584... | 0.5683974 | 38 |
Convert real world points with Doppler to rangeDoppler coordinates PARAMETERS | def convert_to_rd_points(data, world_camera):
distances = np.sqrt((data[:, 0] - world_camera[0])**2 + \
(data[:, 1] - world_camera[1])**2)
dopplers = data[:, 2]
rd_points = [(distances[i], dopplers[i]) for i in range(data.shape[0])]
return rd_points | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_points(self, divisions=100):",
"def build_coord(norm, d, pts):\n # Compute the origin as the mean point of the points, and this point has to be on the plane\n \n n = len(pts) \n x_total = 0\n y_total = 0\n z_total = 0\n \n for i in range(n):\n x_total += pts[i][0]\n y... | [
"0.6075092",
"0.5820298",
"0.57956177",
"0.5740052",
"0.5732382",
"0.5556223",
"0.55544716",
"0.55006737",
"0.5466339",
"0.54140425",
"0.5391508",
"0.53908247",
"0.53622574",
"0.53531116",
"0.53329784",
"0.5326264",
"0.5315539",
"0.528949",
"0.52882594",
"0.5276278",
"0.52513... | 0.56694 | 5 |
Visualise and record DoADoppler point clouds | def visualise_doa(doa_points, doa_labels, path, centroids=None):
if isinstance(centroids, np.ndarray) and len(centroids.shape) == 1:
centroids = centroids.reshape(1, -1)
n_clusters = np.unique(doa_labels).shape[0]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
colors = cycle(['b', 'g', 'r', 'c', 'm', 'y',
'blueviolet', 'brown', 'burlywood',
'khaki', 'indigo', 'peru', 'pink',
'rosybrown', 'teal', 'seagreen'])
for k, col in zip(range(n_clusters), colors):
indexes = doa_labels == k
ax.scatter(doa_points[indexes, 0], doa_points[indexes, 1],
doa_points[indexes, 2], 'ro', c=col)
if isinstance(centroids, np.ndarray):
ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2], 'ro', c='black')
ax.set_xlabel('x (m)')
ax.set_ylabel('y (m)')
ax.set_zlabel('Doppler-Velocity (m/s)')
ax.set_title('3D representation of the DoA points with centroid')
plt.savefig(path)
plt.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish_point_cloud(self):\n all_points = [np.zeros((0, 2), np.float32)]\n all_keys = []\n for key in range(len(self.keyframes)):\n pose = self.keyframes[key].pose\n transf_points = self.keyframes[key].transf_points\n all_points.append(transf_points)\n ... | [
"0.6512084",
"0.6371263",
"0.6321242",
"0.6314276",
"0.6305938",
"0.6247466",
"0.6047218",
"0.60251385",
"0.5953147",
"0.59473276",
"0.5941391",
"0.592496",
"0.59040606",
"0.5794781",
"0.5758018",
"0.5744503",
"0.57257426",
"0.570993",
"0.5699411",
"0.5648155",
"0.5635541",
... | 0.5327644 | 56 |
Visualise and record rangeDoppler matrices with projected points PARAMETERS | def visualise_points_on_rd(rd_matrix, path, points, range_res, doppler_res):
rd_img = SignalVisualizer(rd_matrix).get_image
for point in points:
range_coord = (point[0] / range_res).astype(int)
doppler_coord = (point[1] / doppler_res).astype(int)
if point[1] < 0:
doppler_coord += int(rd_matrix.shape[1]/2 - 1)
else:
doppler_coord += int(rd_matrix.shape[1]/2)
rd_img[range_coord*4:(range_coord*4+4),
doppler_coord*4:(doppler_coord*4+4)] = [0., 0., 0.]
plt.imsave(path, rd_img)
plt.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_pin_position_range():\n #Create a sample goniometer\n g = TopazInHouseGoniometer()\n\n #Initialize the leg limits\n g.relative_sample_position = column([0.0, 0.0, 0.0])\n g.getplatepos(0.0, 0.0, 0.0)\n g.calculate_leg_xy_limits(visualize=True)\n\n# if True:\n# pylab.show()\n#... | [
"0.59820175",
"0.5681855",
"0.5632588",
"0.55993336",
"0.5567278",
"0.5558949",
"0.55241627",
"0.552349",
"0.552066",
"0.5517947",
"0.5500038",
"0.54207855",
"0.5374178",
"0.5372016",
"0.5361266",
"0.5345306",
"0.534296",
"0.5339282",
"0.5336971",
"0.5336114",
"0.53311783",
... | 0.6764529 | 0 |
Generate random RGB colors for a given seed | def get_random_rgb(seed):
random.seed(seed)
r = random.randint(0, 255)
g = random.randint(0, 255)
b = random.randint(0, 255)
return [r, g, b] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_color_gen():\n r = randint(0, 255)\n g = randint(0, 255)\n b = randint(0, 255)\n return [r, g, b]",
"def random_color():\n colormode(255)\n return randint(0, 255), randint(0, 255), randint(0, 255)",
"def _genRandomColor():\n b = random.randint(0, 255)\n g = random.randint(0, ... | [
"0.814636",
"0.7701456",
"0.76977193",
"0.74837154",
"0.746802",
"0.737749",
"0.73708177",
"0.7207782",
"0.7160151",
"0.7154133",
"0.7105417",
"0.70931727",
"0.70826614",
"0.7022558",
"0.7013978",
"0.69533306",
"0.6892857",
"0.68925637",
"0.6831503",
"0.6779909",
"0.67476416"... | 0.84251815 | 0 |
Get a binary mask with threshold on probabilities | def threshold_mask(mask, threshold=0.5):
mask[np.where(mask >= threshold)] = 1.
mask[np.where(mask < threshold)] = 0.
return mask | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binary_predict(probs, threshold = 0.5):\n return (probs >= threshold) * np.ones(len(probs))",
"def pred_from_prob(a,threshold):\n bin_preds = np.zeros((np.size(a,0),))\n bin_preds[np.where(a[:,1]>threshold)]=1.0\n return bin_preds",
"def _np_get_mask(prob_map, prob_thresh=0.5):\n mask = ... | [
"0.77609915",
"0.732614",
"0.7293136",
"0.6731647",
"0.6590931",
"0.65737176",
"0.65595347",
"0.6532869",
"0.65259445",
"0.6520092",
"0.6501832",
"0.64822376",
"0.6414383",
"0.6395488",
"0.6340759",
"0.63036704",
"0.6302406",
"0.62916476",
"0.6282469",
"0.62416035",
"0.624160... | 0.70543975 | 3 |
Method to compute the coefficients of the straight line between two points | def compute_line_coefs(point_a, point_b):
b_coef = -1
if (point_b[0] - point_a[0]) == 0:
a_coef = 0
else:
a_coef = (point_b[1] - point_a[1]) / (point_b[0] - point_a[0])
c_coef = point_b[1] - a_coef*point_b[0]
return np.array([a_coef, b_coef, c_coef]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_linear_coef(p1, p2):\n slope = (p1[1] - p2[1]) / (p1[0] - p2[0])\n intercept = p1[1] - slope * p1[0]\n return slope, intercept",
"def generate_line(point_1, point_2):\r\n A = point_1.y - point_2.y\r\n B = point_2.x - point_1.x\r\n C = point_1.y * B + point_1.x * A\r\n ret... | [
"0.7252494",
"0.7058825",
"0.70182276",
"0.69539297",
"0.68374145",
"0.6692208",
"0.66759145",
"0.66448915",
"0.6642555",
"0.6631278",
"0.660639",
"0.65882164",
"0.6564653",
"0.65506494",
"0.64859647",
"0.64820635",
"0.643047",
"0.6408767",
"0.63719994",
"0.6311349",
"0.62959... | 0.80344635 | 0 |
Method to compute the orthogonal projection of a point on a straight line Let's define (D) the straight line which coeff are given as parameters | def compute_orthogonal_proj(line_coefs, point_coordinates):
a = line_coefs[0]
c = line_coefs[2]
# Compute c_prime by replacing with the coordinates of the point
c_prime = a*point_coordinates[1] + point_coordinates[0]
x_proj = (c_prime-a*c)/((a**2)+1)
y_proj = (a*c_prime+c)/((a**2)+1)
return np.array([x_proj, y_proj]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_projection_of_pt_on_line(point, line_point1, line_point2):\n projection = Point(-1, -1)\n projection.x = point.x\n if (line_point2.x - line_point1.x) != 0:\n projection.y = (projection.x - line_point1.x) * (line_point2.y - line_point1.y) / \\\n (line_point2.x - line_po... | [
"0.6800761",
"0.64928424",
"0.63452107",
"0.6339828",
"0.60856456",
"0.6082376",
"0.6082376",
"0.59750146",
"0.59033185",
"0.5879611",
"0.58655274",
"0.5820088",
"0.58171314",
"0.5814186",
"0.5794391",
"0.5717215",
"0.56910175",
"0.56728375",
"0.5667023",
"0.5649503",
"0.5608... | 0.7622919 | 0 |
Run a sha1 of the file and return the result | def calchash(filename):
sha = hashlib.sha1()
with open(filename, 'rb') as f:
sha.update(f.read())
return sha | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetFileSha1(file_path):\n return base64.b64encode(GetFileHashes(file_path, do_sha1=True)['sha1'])",
"def _calc_sha1(path):\n calc = hashlib.sha1()\n with open(path, 'r') as f:\n calc.update(f.read())\n return calc.hexdigest()",
"def sha1(fname):\n fh = open(fname, 'rb')\n sha1 = hashlib.sha1... | [
"0.7758882",
"0.7735032",
"0.76928216",
"0.75606763",
"0.7542691",
"0.73954326",
"0.7363118",
"0.71328443",
"0.7127811",
"0.71266234",
"0.7059906",
"0.705759",
"0.7057296",
"0.70450103",
"0.70450103",
"0.6978939",
"0.6903451",
"0.69028926",
"0.6887849",
"0.6887431",
"0.682743... | 0.7463678 | 5 |
Checks to see if the destination exists If a source file is passed in, run a checksum | def checkfile(filename, source=None):
if source:
# Let's check some sums
if os.path.exists(filename) and os.path.exists(source):
src_sha = calchash(source)
dest_sha = calchash(filename)
if DRYRUN:
print("{src} hash {src_sha}. {dest} hash {dest_sha}".format(src=source, dest=filename, src_sha=src_sha.hexdigest(), dest_sha=dest_sha.hexdigest()))
return src_sha.digest() == dest_sha.digest()
else:
return os.path.exists(filename) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checksum_compare(source_file, dest_file):\n\n con_ssh = ControllerClient.get_active_controller()\n\n LOG.info(\"Compare checksums on source file and destination file\")\n cmd = \"getfattr -m . -d {}\"\n\n exitcode, source_sha = con_ssh.exec_cmd(cmd.format(source_file))\n LOG.info(\"Raw source fi... | [
"0.7140068",
"0.6845625",
"0.6302969",
"0.6295187",
"0.61806387",
"0.61280686",
"0.60687417",
"0.59943986",
"0.59558606",
"0.5897827",
"0.5882352",
"0.58686715",
"0.5864275",
"0.58456916",
"0.58347523",
"0.58037543",
"0.57591784",
"0.5748481",
"0.57282805",
"0.57282805",
"0.5... | 0.7357597 | 0 |
Establish a connection with the labview server. If the labview program is ever stopped and restarted (as it should be when not taking data, to avoid wearing out the shutter), this should be called to reestablish the connection. | def connect(self):
self.sock = s.socket(s.AF_INET,s.SOCK_STREAM)
self.sock.connect((self.remote_host,
self.remote_port)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect(self):\n self.class_logger.info(\"Performing connection to TRex server via HLT API\")\n self.check_res(self.hltapi.connect(device=self.host, port_list=self.ports, reset=True, break_locks=True))",
"def connect_to_server(self):\n\n server=os.popen('hostname').read()\n if 'ep... | [
"0.6657257",
"0.66268283",
"0.6589886",
"0.65059596",
"0.64859515",
"0.6469739",
"0.64388025",
"0.6428868",
"0.63881606",
"0.6269265",
"0.6240393",
"0.6225351",
"0.6219129",
"0.6197346",
"0.6195343",
"0.6181658",
"0.61551857",
"0.6146872",
"0.6136307",
"0.60901004",
"0.607203... | 0.0 | -1 |
Takes a shot on the CCD. Returns a 2tuple ``(wl, ccd)``. | def get_spectrum(self):
self.sock.send('Q')
self.sock.send(str(100 * self.center_wl))
response = self.sock.recv(7)
if not response:
raise InstrumentError(
'No response from Labview client, try reconnecting')
datalen = int(response)
data = ''
while datalen > 0:
# read data in chunks
dt = self.sock.recv(datalen)
data += dt
datalen -= len(dt)
data = data.split("\n")[:-1]
for i in range(len(data)):
data[i] = data[i].split("\t")
data = n.array(data,dtype=float)
wl = data[0]
ccd = data[1:]
return wl,ccd
#self.sock.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_shoot(data: bytes) -> Tuple[bytes, str]:\n length = struct.unpack('H', data[:2])[0]\n name = data[2:length+2]\n direction = struct.unpack('fff', data[length+2:length+2+12])\n return data[2+length:], f'Shot {name.decode()} in direction: {direction}'",
"def wcsshift(x0, y0, hd1, hd2):\n w... | [
"0.5148957",
"0.49647325",
"0.48577526",
"0.48168695",
"0.47914028",
"0.4786531",
"0.4775281",
"0.47224483",
"0.47096628",
"0.46394467",
"0.46250522",
"0.46067777",
"0.4606352",
"0.45833904",
"0.4575049",
"0.45720398",
"0.4562999",
"0.4545978",
"0.45373318",
"0.4482143",
"0.4... | 0.0 | -1 |
String representation of this class | def __repr__(self):
return f"<Airport(id={str(self.id)}, name={self.name})>" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toString(self):\n\n sMembers = '';\n for sAttr in self.getDataAttributes():\n oValue = getattr(self, sAttr);\n sMembers += ', %s=%s' % (sAttr, oValue);\n\n oClass = type(self);\n if sMembers == '':\n return '<%s>' % (oClass.__name__);\n return... | [
"0.8405431",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.83985007",
"0.8396452",
"0.8396452",
"0.8396452",
"0.8396452",
"0.8396452",
"0.83616906",
"0... | 0.0 | -1 |
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