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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