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How to use blend_line method in prospector
Best Python code snippet using prospector_python
thar_update.py
Source:thar_update.py 
1import scipy2import pyfits3import numpy as np4import os5import matplotlib.pyplot as plt6from astropy.io import fits7from scipy.signal import find_peaks_cwt8from scipy.optimize import curve_fit9from sklearn.metrics.pairwise import euclidean_distances#pairwise_distances10from astroML.stats import sigmaG11import sys12from astropy import constants as C13from astropy.table import Table, Column14from astropy.io import ascii15import sys16import matplotlib.gridspec as gridspec # GRIDSPEC !17cc = C.c.value*1.e-3 # [km/s]18# Gaussian function19def gauss_function(x, a, x0, sigma, zero):20 return a*np.exp(-(x-x0)**2/(2*sigma**2)) + zero21# Two Gaussian function22def twoGauss_function(x, a, x0, sigma, zero, a2, x02, sigma2):23 return a*np.exp(-(x-x0)**2/(2*sigma**2)) + zero + a2*np.exp(-(x-x02)**2/(2*sigma2**2))24# ===== Master Arc ffor Reference25# cafe2_00126# tpath = '/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/cafextractor/test_data/11_REDUCED/120705/reduced'27# order0 = 6028# norders = 8029# cafe2_00230# tpath = '/Volumes/willyfog/gang5/jlillo/22_RUNS/2018_07_CAHA_2.2_CAFE_CHRONOS/11_REDUCED/181217/reduced'31# order0 = 6032# norders = 8033# cafe2_00334# tpath = '/Volumes/willyfog/gang5/jlillo/22_RUNS/2019_01_CAHA_2.2_CAFE_Recommisioning_Run2/11_REDUCED/190306/reduced'35# order0 = 6336# norders = 8037# cafe2_00438tpath = '/Volumes/willyfog/gang5/jlillo/22_RUNS/2019_01_CAHA_2.2_CAFE_Recommisioning_Run2/11_REDUCED/190425_digest/reduced'39order0 = 6240norders = 7941t = fits.open(tpath+'/MasterArc_0_red.fits') #42# ===== ThAr lines from Lovis+200743lovis_file = '/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/cafextractor/ReferenceFrames/ThAr_ReferenceLines/ThAr_Lovis07.txt'44file = np.genfromtxt(lovis_file,dtype=None)45lovis_lines = file['f0']46lovis_int = file['f2']*1.47lovis_Species = file['f3']48s = 1.e4/lovis_lines49n = 1 + 0.0000834254 + 0.02406147 / (130 - s**2) + 0.00015998 / (38.9 - s**2)50lovis_lines = lovis_lines/n51# ===== ThAr lines from CERES52file2 = np.genfromtxt('/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/cafextractor/ReferenceFrames/ThAr_ReferenceLines/all_ThAr_lines.lis',dtype=None)53ceres_lines = file2['f2']54Xpix = np.arange(2048)*1.55# ===== LOOP for each order (starting at the first order with Lovis+ information)56for oo in np.arange(norders-23)+23:57 wave = t["WAVELENGTH"].data[oo,:]58 flux = t["FLUX"].data[oo,:]59 plt.plot(wave, flux,c='k',zorder=0)60 resolution0 = 2.3 * np.mean(wave[1:-1]-wave[0:-2])61 62 ceres_inorder = np.where((ceres_lines > np.min(wave)) & (ceres_lines < np.max(wave)) )[0]63 for i in range(len(ceres_inorder)): plt.axvline(ceres_lines[ceres_inorder[i]],ls='--',c='red',alpha=0.5,zorder=5)64 lovis_inorder = np.where((lovis_lines > np.min(wave)) & (lovis_lines < np.max(wave)) )[0]65 for i in range(len(lovis_inorder)): 66 plt.axvline(lovis_lines[lovis_inorder[i]],ls=':',c='k',alpha=0.5,zorder=10)67 plt.text(lovis_lines[lovis_inorder[i]], 0.0, i)68 69 ok_line = []70 yes_results = []71 no_results = []72 no_reasons = []73 74 for i,line in enumerate(lovis_lines[lovis_inorder]):75 76 # ===== Subrange77 subspec = np.where((wave > line-3.*resolution0) & (wave < line+3.*resolution0))[0]78 x0, y0, xpix0 = wave[subspec], flux[subspec], Xpix[subspec]79 x, y, xpix = x0[~np.isnan(y0)], y0[~np.isnan(y0)], xpix0[~np.isnan(y0)]80 81 resolution = 2.3 * np.mean(x[1:-1]-x[0:-2])82 if len(y) == 0: continue83 84 # ===== Look for the closest line and decide if fitting 1 or 2 Gaussians85 twoGauss = None86 diff = line-lovis_lines[lovis_inorder]87 sorting = np.argsort(np.abs(diff))88 intensity = lovis_int[lovis_inorder]89 int_ratio = intensity[sorting[1]]/intensity[sorting[0]]90 blend_line = lovis_lines[lovis_inorder[sorting[1]]]91 if (np.abs(diff[sorting[1]]) > 3.0*resolution): 92 twoGauss = False93 elif (np.abs(diff[sorting[1]]) > 1.5*resolution):94 if (int_ratio > 0.1) : twoGauss = True95 if (int_ratio < 0.1) : twoGauss = False96 elif ( (np.abs(diff[sorting[1]]) < 1.5*resolution) & (int_ratio < 0.1) ):97 twoGauss = False98 # ===== Try fitting 1-Gaussian99 if twoGauss == False:100 try:101 popt, pcov = curve_fit(gauss_function, x, y, 102 p0 = [np.max(y), line, resolution, 0.0],103 bounds=([0.0,line-resolution,0.0,-np.inf],104 [np.inf,line+resolution,3*resolution,np.inf]))105 perr = np.sqrt(np.diag(pcov))106 except:107 popt = np.zeros(4)108 109 ymodel = gauss_function(x,popt[0],popt[1],popt[2],popt[3])110 try:111 xpix0 = np.interp(line,x,xpix)112 poptX, pcovX = curve_fit(gauss_function, xpix, y, 113 p0 = [np.max(y), xpix0, 2.3, 0.0],114 bounds=([0.0,xpix0-2.3,0.0,-np.inf],115 [np.inf,xpix0+2.3,3*2.3,np.inf]))116 perrX = np.sqrt(np.diag(pcovX))117 except:118 poptX = np.zeros(4)119 120 # ===== Try fitting 2-Gaussians121 if twoGauss == True:122 try:123 popt, pcov = curve_fit(twoGauss_function, x, y, 124 p0 = [np.max(y), line, resolution, 0.0, np.max(y)*int_ratio, blend_line, resolution],125 bounds=([0.0,line-resolution,0.0,-np.inf,0.0,blend_line-resolution,0.0],126 [np.inf,line+resolution,3*resolution,np.inf,np.inf,blend_line+resolution,3*resolution]))127 perr = np.sqrt(np.diag(pcov))128 except:129 popt = np.zeros(7)130 ymodel = twoGauss_function(x,popt[0],popt[1],popt[2],popt[3],popt[4],popt[5],popt[6]) 131 132 try:133 xpix0 = np.interp(line,x,xpix)134 xpix0_blend = np.interp(blend_line,x,xpix)135 poptX, pcovX = curve_fit(twoGauss_function, xpix, y, 136 p0 = [np.max(y), xpix0, 2.3, 0.0, np.max(y)*int_ratio, xpix0_blend, 2.3],137 bounds=([0.0,xpix0-2.3,0.0,-np.inf,0.0,xpix0_blend-2.3,0.0],138 [np.inf,xpix0+2.3,3*2.3,np.inf,np.inf,xpix0_blend+2.3,3*2.3]))139 perrX = np.sqrt(np.diag(pcovX))140 except:141 poptX = np.zeros(7)142 143 if twoGauss == None:144 popt = np.zeros(4)145 ymodel = x*0.0146 poptX = np.zeros(4)147 #| Region around the maximum to check fitting: +/- 2 pix ~ 1xFWHM148 elem_check = np.where( (x > line-1.*resolution) & (x < line+1.*resolution) )[0] 149 ground_noise = np.nanmedian(flux[400:-400])/10.150 snr = popt[0]/ground_noise151 resid = np.nanstd(y[elem_check]-ymodel[elem_check])/ground_noise152 chi2 = np.nansum((y[elem_check]-ymodel[elem_check])**2/(9.*np.abs(y[elem_check]))) / (1.*len(x[elem_check]))153 154 if i == 20000:155 plt.close()156 plt.errorbar(x,y,yerr=3.*np.sqrt(y))157 #plt.plot(x,y)158 plt.plot(x[elem_check],y[elem_check])159 plt.plot(x,ymodel,c='red')160 print twoGauss, int_ratio, np.abs(diff[sorting[1]])/resolution, resolution161 plt.show()162 plt.close()163 164 #diff = np.sort(np.abs(line-lovis_lines[lovis_inorder]))165 166 if ((np.abs(popt[1]-line)*1.e3 < 10.) & (chi2 < 10.) & (snr > 5) ): 167 168 if (np.abs(diff[1]) > 2.0*resolution): 169 ok = 'yes'170 elif (np.abs(diff[1]) > 1.5*resolution):171 ok = 'yes' if ((int_ratio < 0.05) | (int_ratio > 0.3)) else ' no'172 elif (np.abs(diff[1]) < 1.5*resolution):173 ok = 'yes' if (int_ratio < 0.05) else ' no'174 else:175 ok = ' no' 176 else:177 ok = ' no'178 179 if ok == 'yes': ok_line.append(line)180 #print i, ok, np.abs(popt[1]-line)/resolution, chi2, snr, np.abs(diff[sorting[1]])/resolution,int_ratio, intensity[sorting[0]], intensity[sorting[1]], twoGauss181 if ok == 'yes':182 #print i, ok, round(poptX[1],2), line, intensity[sorting[0]], twoGauss183 yes_results.append([i, ok, round(poptX[1],2), line, intensity[sorting[0]], lovis_Species[lovis_inorder[sorting[0]]], twoGauss])184 else:185 no_results.append([i, ok, round(poptX[1],2), line, intensity[sorting[0]], lovis_Species[lovis_inorder[sorting[0]]], twoGauss])186 no_reasons.append([i, ok, np.abs(popt[1]-line)*1.e3, chi2, snr, np.abs(diff[1])/resolution, int_ratio, intensity[sorting[0]], intensity[sorting[1]], twoGauss])187 188 ok_line = np.array(ok_line)189 print oo,len(ok_line), len(ceres_inorder)190 for i in ok_line: plt.axvline(i,ls=':',c='blue',zorder=15)191 192 if 1:193 fyes = open('auxiliar/yes_cafeX_order_'+str(order0+oo).zfill(3)+'.dat','w')194 fno = open('auxiliar/no_cafeX_order_'+str(order0+oo).zfill(3)+'.dat','w')195 f = open('auxiliar/cafeX_order_'+str(order0+oo).zfill(3)+'.dat','w')196 fyes.write("%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))197 f.write( "%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))198 fno.write( "%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))199 for hh in yes_results:200 fyes.write("%5i %10.1f %20.8f %15.1f %10s \n" % (hh[0],hh[2],hh[3],hh[4],hh[5]))201 f.write( "%5i %10.1f %20.8f %15.1f %10s \n" % (hh[0],hh[2],hh[3],hh[4],hh[5]))202 for hh in no_results:203 fno.write("%5i %10.1f %20.8f %15.1f %10s \n" % (hh[0],hh[2],hh[3],hh[4],hh[5]))204 fyes.close()205 fno.close()206 f.close()207 208 fnoreasons = open('auxiliar/noreasons_cafeX_order_'+str(order0+oo).zfill(3)+'.dat','w')209 fnoreasons.write("%5s %5s %10s %10s %10s %10s %10s %10s %10s \n" % ("ID","ok","Diff(mA)","chi2","SNR","Diff/R","I1/I0","I0","I1"))210 for hh in no_reasons: 211 fnoreasons.write("%5s %5s %10.1f %10.1f %10.1f %10.1f %10.3f %10.1f %10.1f \n" % (hh[0],hh[1],hh[2],hh[3],hh[4],hh[5],hh[6],hh[7],hh[8]))212 fnoreasons.close()213 214 plt.ylim(-500,3000)215 #plt.show()216 #sys.exit()...
thar_update_ceres.py
Source:thar_update_ceres.py
1import scipy2import pyfits3import numpy as np4import os5import matplotlib.pyplot as plt6from astropy.io import fits7from scipy.signal import find_peaks_cwt8from scipy.optimize import curve_fit9from sklearn.metrics.pairwise import euclidean_distances#pairwise_distances10from astroML.stats import sigmaG11import sys12from astropy import constants as C13from astropy.table import Table, Column14from astropy.io import ascii15import sys16import matplotlib.gridspec as gridspec # GRIDSPEC !17cc = C.c.value*1.e-3 # [km/s]18# Gaussian function19def gauss_function(x, a, x0, sigma, zero):20 return a*np.exp(-(x-x0)**2/(2*sigma**2)) + zero21# Two Gaussian function22def twoGauss_function(x, a, x0, sigma, zero, a2, x02, sigma2):23 return a*np.exp(-(x-x0)**2/(2*sigma**2)) + zero + a2*np.exp(-(x-x02)**2/(2*sigma2**2))24# ===== Master Arc ffor Reference25# tpath = '/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/test_data/12_REDUCED/180725_digest/reduced'26# cafe2_00327# tpath = '/Volumes/willyfog/gang5/jlillo/22_RUNS/2019_01_CAHA_2.2_CAFE_Recommisioning_Run2/11_REDUCED/190306/reduced'28# order0 = 6329# norders = 8130# cafe2_00431tpath = '/Volumes/willyfog/gang5/jlillo/22_RUNS/2019_01_CAHA_2.2_CAFE_Recommisioning_Run2/11_REDUCED/190425_digest/reduced'32order0 = 6233norders = 7934t = fits.open(tpath+'/MasterArc_0_red.fits') #35# ===== ThAr lines from Lovis+200736lovis_file = '/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/cafextractor/ReferenceFrames/ThAr_ReferenceLines/ThAr_Lovis07.txt'37file = np.genfromtxt(lovis_file,dtype=None)38lovis_lines = file['f0']39lovis_int = file['f2']*1.40lovis_Species = file['f3']41s = 1.e4/lovis_lines42n = 1 + 0.0000834254 + 0.02406147 / (130 - s**2) + 0.00015998 / (38.9 - s**2)43lovis_lines = lovis_lines/n44# ===== ThAr lines from CERES45file2 = np.genfromtxt('/Users/lillo_box/00_Instrumentation/CAFE/CAFExtractor/cafextractor/ReferenceFrames/ThAr_ReferenceLines/all_ThAr_lines.lis',dtype=None)46ceres_lines = file2['f2']47ceres_pix = file2['f1']48ceres_Species = file2['f3']49ceres_int = ceres_pix*0.0+1.050Xpix = np.arange(2048)*1.51# ===== LOOP for each order (starting at the first order with CERES information)52for oo in np.arange(norders):53 wave = t["WAVELENGTH"].data[oo,:]54 flux = t["FLUX"].data[oo,:]55 plt.plot(wave, flux,c='k',zorder=0)56 resolution0 = 2.3 * np.mean(wave[1:-1]-wave[0:-2])57 58 ceres_inorder = np.where((ceres_lines > np.min(wave)) & (ceres_lines < np.max(wave)) )[0]59 for i in range(len(ceres_inorder)): plt.axvline(ceres_lines[ceres_inorder[i]],ls='--',c='red',alpha=0.5,zorder=5)60 lovis_inorder = np.where((lovis_lines > np.min(wave)) & (lovis_lines < np.max(wave)) )[0]61 for i in range(len(lovis_inorder)): 62 plt.axvline(lovis_lines[lovis_inorder[i]],ls=':',c='k',alpha=0.5,zorder=10)63 plt.text(lovis_lines[lovis_inorder[i]], 0.0, i)64 65 ok_line = []66 yes_results = []67 no_results = []68 no_reasons = []69 70 for i,line in enumerate(ceres_lines[ceres_inorder]):71 72 # ===== Subrange73 subspec = np.where((wave > line-3.*resolution0) & (wave < line+3.*resolution0))[0]74 x0, y0, xpix0 = wave[subspec], flux[subspec], Xpix[subspec]75 x, y, xpix = x0[~np.isnan(y0)], y0[~np.isnan(y0)], xpix0[~np.isnan(y0)]76 77 resolution = 2.3 * np.mean(x[1:-1]-x[0:-2])78 if len(y) == 0: continue79 80 # ===== Look for the closest line and decide if fitting 1 or 2 Gaussians81 twoGauss = None82 diff = line-ceres_lines[ceres_inorder]83 sorting = np.argsort(np.abs(diff))84 intensity = ceres_int[ceres_inorder]85 int_ratio = intensity[sorting[1]]/intensity[sorting[0]]86 blend_line = ceres_lines[ceres_inorder[sorting[1]]]87 if (np.abs(diff[sorting[1]]) > 3.0*resolution): 88 twoGauss = False89 elif (np.abs(diff[sorting[1]]) > 1.5*resolution):90 if (int_ratio > 0.1) : twoGauss = True91 if (int_ratio < 0.1) : twoGauss = False92 elif ( (np.abs(diff[sorting[1]]) < 1.5*resolution) & (int_ratio < 0.1) ):93 twoGauss = False94 # ===== Try fitting 1-Gaussian95 if twoGauss == False:96 try:97 popt, pcov = curve_fit(gauss_function, x, y, 98 p0 = [np.max(y), line, resolution, 0.0],99 bounds=([0.0,line-resolution,0.0,-np.inf],100 [np.inf,line+resolution,3*resolution,np.inf]))101 perr = np.sqrt(np.diag(pcov))102 except:103 popt = np.zeros(4)104 105 ymodel = gauss_function(x,popt[0],popt[1],popt[2],popt[3])106 try:107 xpix0 = np.interp(line,x,xpix)108 poptX, pcovX = curve_fit(gauss_function, xpix, y, 109 p0 = [np.max(y), xpix0, 2.3, 0.0],110 bounds=([0.0,xpix0-2.3,0.0,-np.inf],111 [np.inf,xpix0+2.3,3*2.3,np.inf]))112 perrX = np.sqrt(np.diag(pcovX))113 except:114 poptX = np.zeros(4)115 116 # ===== Try fitting 2-Gaussians117 if twoGauss == True:118 try:119 popt, pcov = curve_fit(twoGauss_function, x, y, 120 p0 = [np.max(y), line, resolution, 0.0, np.max(y)*int_ratio, blend_line, resolution],121 bounds=([0.0,line-resolution,0.0,-np.inf,0.0,blend_line-resolution,0.0],122 [np.inf,line+resolution,3*resolution,np.inf,np.inf,blend_line+resolution,3*resolution]))123 perr = np.sqrt(np.diag(pcov))124 except:125 popt = np.zeros(7)126 ymodel = twoGauss_function(x,popt[0],popt[1],popt[2],popt[3],popt[4],popt[5],popt[6]) 127 128 try:129 xpix0 = np.interp(line,x,xpix)130 xpix0_blend = np.interp(blend_line,x,xpix)131 poptX, pcovX = curve_fit(twoGauss_function, xpix, y, 132 p0 = [np.max(y), xpix0, 2.3, 0.0, np.max(y)*int_ratio, xpix0_blend, 2.3],133 bounds=([0.0,xpix0-2.3,0.0,-np.inf,0.0,xpix0_blend-2.3,0.0],134 [np.inf,xpix0+2.3,3*2.3,np.inf,np.inf,xpix0_blend+2.3,3*2.3]))135 perrX = np.sqrt(np.diag(pcovX))136 except:137 poptX = np.zeros(7)138 139 if twoGauss == None:140 popt = np.zeros(4)141 ymodel = x*0.0142 poptX = np.zeros(4)143 #| Region around the maximum to check fitting: +/- 2 pix ~ 1xFWHM144 elem_check = np.where( (x > line-1.*resolution) & (x < line+1.*resolution) )[0] 145 ground_noise = np.nanmedian(flux[400:-400])/10.146 snr = popt[0]/ground_noise147 resid = np.nanstd(y[elem_check]-ymodel[elem_check])/ground_noise148 chi2 = np.nansum((y[elem_check]-ymodel[elem_check])**2/(9.*np.abs(y[elem_check]))) / (1.*len(x[elem_check]))149 150 if i == 20000:151 plt.close()152 plt.errorbar(x,y,yerr=3.*np.sqrt(y))153 #plt.plot(x,y)154 plt.plot(x[elem_check],y[elem_check])155 plt.plot(x,ymodel,c='red')156 print twoGauss, int_ratio, np.abs(diff[sorting[1]])/resolution, resolution157 plt.show()158 plt.close()159 160 #diff = np.sort(np.abs(line-ceres_lines[ceres_inorder]))161 162 if ((np.abs(popt[1]-line)*1.e3 < 10.) & (chi2 < 10.) & (snr > 5) ): 163 164 if (np.abs(diff[1]) > 2.0*resolution): 165 ok = 'yes'166 elif (np.abs(diff[1]) > 1.5*resolution):167 ok = 'yes' if ((int_ratio < 0.05) | (int_ratio > 0.3)) else ' no'168 elif (np.abs(diff[1]) < 1.5*resolution):169 ok = 'yes' if (int_ratio < 0.05) else ' no'170 else:171 ok = ' no' 172 else:173 ok = ' no'174 175 if ok == 'yes': ok_line.append(line)176 #print i, ok, np.abs(popt[1]-line)/resolution, chi2, snr, np.abs(diff[sorting[1]])/resolution,int_ratio, intensity[sorting[0]], intensity[sorting[1]], twoGauss177 if ok == 'yes':178 #print i, ok, round(poptX[1],2), line, intensity[sorting[0]], twoGauss179 yes_results.append([i, ok, round(poptX[1],2), line, intensity[sorting[0]], ceres_Species[ceres_inorder[sorting[0]]], twoGauss])180 else:181 no_results.append([i, ok, round(poptX[1],2), line, intensity[sorting[0]], ceres_Species[ceres_inorder[sorting[0]]], twoGauss])182 no_reasons.append([i, ok, np.abs(popt[1]-line)*1.e3, chi2, snr, np.abs(diff[1])/resolution, int_ratio, intensity[sorting[0]], intensity[sorting[1]], twoGauss])183 184 ok_line = np.array(ok_line)185 print oo,len(ok_line), len(ceres_inorder)186 for i in ok_line: plt.axvline(i,ls=':',c='blue',zorder=15)187 188 if 1:189 fyes = open('auxiliar/yes_ceres_order_'+str(order0+oo).zfill(3)+'.dat','w')190 fno = open('auxiliar/no_ceres_order_'+str(order0+oo).zfill(3)+'.dat','w')191 f = open('auxiliar/ceres_order_'+str(order0+oo).zfill(3)+'.dat','w')192 fyes.write("%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))193 f.write( "%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))194 fno.write( "%5s %10s %20s %15s %10s \n" % ("# ID","Xpix","Wavelength","Intens","Line"))195 for hh in yes_results:196 fyes.write("%5i %10.1f %20.8f %15.1f %10s \n" % (hh[0],hh[2],hh[3],hh[4],hh[5]))197 f.write( "%5i %10.1f %20.8f %15.1f %10s \n" % (1,hh[2],hh[3],hh[4],hh[5]))198 for hh in no_results:199 fno.write("%5i %10.1f %20.8f %15.1f %10s \n" % (hh[0],hh[2],hh[3],hh[4],hh[5]))200 fyes.close()201 fno.close()202 f.close()203 204 fnoreasons = open('auxiliar/noreasons_ceres_order_'+str(order0+oo).zfill(3)+'.dat','w')205 fnoreasons.write("%5s %5s %10s %10s %10s %10s %10s %10s %10s \n" % ("ID","ok","Diff(mA)","chi2","SNR","Diff/R","I1/I0","I0","I1"))206 for hh in no_reasons: 207 fnoreasons.write("%5s %5s %10.1f %10.1f %10.1f %10.1f %10.3f %10.1f %10.1f \n" % (hh[0],hh[1],hh[2],hh[3],hh[4],hh[5],hh[6],hh[7],hh[8]))208 fnoreasons.close()209 210 plt.ylim(-500,3000)211 #plt.show()212 #sys.exit()...
curve.py
Source:curve.py
...77 x = (1 - t) * x1 + t * x278 y = (1 - t) * y1 + t * y279 draw_point((x,y))80 draw_point(p2)81def blend_line(p1,p2,p3,p4):82 x1, y1 = p183 x2, y2 = p284 x3, y3 = p385 x4, y4 = p486 draw_big_point(p1)87 draw_big_point(p2)88 draw_big_point(p3)89 draw_big_point(p4)90 for i in range(0,100,2):91 t= i/10092 lx = (1 - t)* x2 + t*x393 ly = (1 - t)*y2 + t*y394 rx = (1 - t) * x4 + t * x195 ry = (1 - t) * y4 + t * y196 draw_point((lx, ly))97 draw_point((rx, ry))98 x = (1 - t) * lx + t * rx99 y = (1 - t) * ly + t * ry100 draw_point((x,y))101def draw_curve_3_points(p1, p2, p3):102 draw_big_point(p1)103 draw_big_point(p2)104 draw_big_point(p3)105 x1, y1 = p1; x2, y2 = p2; x3, y3 = p3106 for i in range(0, 100, 2):107 t = i / 100108 x = (2 * t ** 2 - 3 * t + 1) * x1 + (-4 * t ** 2 + 4 * t) * x2 + (2 * t ** 2 - t) * x3109 y = (2 * t ** 2 - 3 * t + 1) * y1 + (-4 * t ** 2 + 4 * t) * y2 + (2 * t ** 2 - t) * y3110 draw_point((x, y))111 draw_point(p3)112prepare_turtle_canvas()113p1 = x1, y1 = random.randint(100, 300), random.randint(100, 300)114p2 = x2, y2 = random.randint(-300, -200), random.randint(100, 300)115p3 = x3, y3 = random.randint(-300, -200), random.randint(-300, -100)116p4 = x4, y4 = random.randint(200, 300), random.randint(-300, -100)117#draw_curve_3_points(p1, p2, p3)118#blend_line(p1, p2, p3, p4)119draw_curve_4_points(p1, p2, p3, p4)...
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