How to use get_rc method in yandex-tank

Best Python code snippet using yandex-tank

process_raw.py

Source:process_raw.py

...44	sun=re.findall("[-+]?\d*\.\d+|\d+", rows[0][0])45	#sys.stderr.write("SUN = %s\n"%(repr(sun),))46	azimuth=sun[0]47	elevation=sun[1]48	def get_rc(row,col):49		return float(rows[row][col])50	# Global results51	num_res=int(get_rc(1,0)) # number of global results52	num_rec=int(get_rc(1,1))53	num_hst=get_rc(1,2)54	num_rays=get_rc(1,3)55	potential=get_rc(2,0) #W56	potential_err=get_rc(2,1)57	absorbed=get_rc(3,0)58	absorbed_err=get_rc(3,1)    59	Fcos=get_rc(4,0)60	Fcos_err=get_rc(4,1)  61	shadow_loss=get_rc(5,0)62	shadow_err=get_rc(5,1)    63	missing_loss=get_rc(6,0)64	missing_err=get_rc(6,1) 65	material_loss=get_rc(7,0)66	material_err=get_rc(7,1)    67	atmospheric_loss=get_rc(8,0)68	atmospheric_err=get_rc(8,1)  69	# Target (receiver)70	# 0 receiver name71	# 1 - 2 id and area72	# 3 - 24 (total 22) front73	# 25- 46 (total 22) back 74	rec_area=0. # m2  75	rec_front_income=0.76	rec_front_income_err=0.77	#rec_no_material_loss=get_rc(num_res+2,5)78	#rec_no_material_loss_err=get_rc(num_res+2,6)79	#rec_no_atmo_loss=get_rc(num_res+2,7)80	#rec_no_atmo_loss_err=get_rc(num_res+2,8)81	#rec_material_loss=get_rc(num_res+2,9)82	#rec_material_loss_err=get_rc(num_res+2,10)83	rec_front_absorbed=0.84	rec_front_absorbed_err=0.85	rec_front_eff=0.86	rec_front_eff_err=0.87	rec_back_income=0.88	rec_back_income_err=0.89	rec_back_absorbed=0.90	rec_back_absorbed_err=0.91	rec_back_eff=0.92	rec_back_eff_err=0.93	rec_id={}94	for i in range(num_rec-1): # -1 the virtual target95		rec_id[i]=get_rc(num_res+2+i,1) # the id number of the receiver96		rec_area+=get_rc(num_res+2+i,2) # m2  97	98		rec_front_income+=get_rc(num_res+2+i,3)99		rec_front_income_err+=get_rc(num_res+2+i,4)100		#rec_no_material_loss=get_rc(num_res+2,5)101		#rec_no_material_loss_err=get_rc(num_res+2,6)102		#rec_no_atmo_loss=get_rc(num_res+2,7)103		#rec_no_atmo_loss_err=get_rc(num_res+2,8)104		#rec_material_loss=get_rc(num_res+2,9)105		#rec_material_loss_err=get_rc(num_res+2,10)106		rec_front_absorbed+=get_rc(num_res+2+i,13)107		rec_front_absorbed_err+=get_rc(num_res+2+i,14)108		rec_front_eff+=get_rc(num_res+2+i,23)109		rec_front_eff_err+=get_rc(num_res+2+i,24)110		rec_back_income+=get_rc(num_res+2+i,25)111		rec_back_income_err+=get_rc(num_res+2+i,26)112		rec_back_absorbed+=get_rc(num_res+2+i,35)113		rec_back_absorbed_err+=get_rc(num_res+2+i,36)114		rec_back_eff+=get_rc(num_res+2+i,-2)115		rec_back_eff_err+=get_rc(num_res+2+i,-1)116	#Virtual target117	vir_area=get_rc(num_res+2+num_rec-1,2)118	vir_income=get_rc(num_res+2+num_rec-1,3)119	vir_income_err=get_rc(num_res+2+num_rec-1,4)120	121	raw_res=np.array([122		['name','value', 'error']123		,['sun_azimuth', azimuth,'']124		,['sun_elevation', elevation, '']125		,['num hst', num_hst,'']126		,['num rays',num_rays, '']127		,['potential flux', potential, potential_err]128		,['absorbed flux', absorbed, absorbed_err]129		,['Cosine factor', Fcos, Fcos_err]130		,['shadow loss', shadow_loss, shadow_err]131		,['Mising loss', missing_loss, missing_err]132		,['materials loss', material_loss, material_err]133		,['atomospheric loss', atmospheric_loss, atmospheric_err]134		,['','','']135		,['Target', '','']136		,['area', rec_area, '']137		,['front income flux', rec_front_income, rec_front_income_err]138		,['back income flux', rec_back_income, rec_back_income_err]139		,['front absorbed flux', rec_front_absorbed, rec_front_absorbed_err]140		,['back absorbed flux', rec_back_absorbed, rec_back_absorbed_err]141		,['front efficiency', rec_front_eff, rec_front_eff_err]142		,['back efficiency', rec_back_eff, rec_back_eff_err]143		,['','','']144		,['Virtual plane','','']145		,['area', vir_area, '']146		,['income flux', vir_income,vir_income_err]147	])148	#sys.stderr.write(repr(raw_res))149	#sys.stderr.write("SHAPE = %s" % (repr(raw_res.shape)))150	Qtotal=ufloat(potential, 0)151	Fcos=ufloat(Fcos,Fcos_err) 152	Qcos=Qtotal*(1.-Fcos)153	Qshade=ufloat(shadow_loss,shadow_err)154	Qfield_abs=(Qtotal-Qcos-Qshade)*(1.-float(rho_mirror))155	Qattn=ufloat(atmospheric_loss, atmospheric_err)156	Qabs=ufloat(absorbed, absorbed_err)157	Qspil=ufloat(vir_income,vir_income_err)-Qabs158	Qrefl=ufloat(rec_front_income,rec_front_income_err)+ufloat(rec_back_income,rec_back_income_err)-Qabs159	Qblock=Qtotal-Qcos-Qshade-Qfield_abs-Qspil-Qabs-Qrefl-Qattn160	organised=np.array([161		['Name', 'Value', '+/-Error']162		,['Qall (kW)', Qtotal.n/1000., Qtotal.s/1000.]163		,['Qcos (kW)', Qcos.n/1000.,Qcos.s/1000.]164		,['Qshad (kW)', Qshade.n/1000., Qshade.s/1000.]165		,['Qfield_abs (kW)', Qfield_abs.n/1000., Qfield_abs.s/1000.]166		,['Qblcok (kW)', Qblock.n/1000.,  Qblock.s/1000.]167		,['Qattn (kW)',Qattn.n/1000., Qattn.s/1000.]168		,['Qspil (kW)', Qspil.n/1000., Qspil.s/1000.]169		,['Qrefl (kW)', Qrefl.n/1000.,Qrefl.s/1000.]170		,['Qabs (kW)', Qabs.n/1000., Qabs.s/1000.]171		,['rays', num_rays,'-']172	])173	efficiency_total=Qabs/Qtotal174	# per heliostat results, and175	# per receiver per heliostat results176	num_hst=int(num_hst)    177	heliostats=np.zeros((num_hst,28))178	for i in range(num_hst):179		l1=2+num_res+num_rec+i # the line number of the per heliostat result180		per_hst=rows[l1]181		hst_idx=re.findall("[-+]?\d*\.\d+|\d+", per_hst[0] ) 182		hst_area=per_hst[2]183		hst_sample=per_hst[3]184		hst_cos=per_hst[4]185		hst_shad=per_hst[6]186		heliostats[i,0]=hst_idx[0]187		heliostats[i,1]=hst_area188		heliostats[i,2]=hst_sample189		heliostats[i,3]=hst_cos190		heliostats[i,4]=hst_shad191		# per heliostat per receiver192		for j in range(num_rec-1):193			l2=2+num_res+num_rec+num_hst*(j+1)+i  194			per_hst=rows[l2]   195			hst_in=float(per_hst[2])+float(per_hst[22]) # front+back196			hst_in_mat=float(per_hst[8])+float(per_hst[28])197			hst_in_atm=float(per_hst[10])+float(per_hst[30])198			hst_abs=float(per_hst[12])+float(per_hst[32])199			hst_abs_mat=float(per_hst[18])+float(per_hst[38])200			hst_abs_atm=float(per_hst[20])+float(per_hst[40])201			  202			heliostats[i,5]+=hst_in203			heliostats[i,6]+=hst_in_mat204			heliostats[i,7]+=hst_in_atm205			heliostats[i,8]+=hst_abs206			heliostats[i,9]+=hst_abs_mat207			heliostats[i,10]+=hst_abs_atm208		# per heliostat per virtual target209		l3=2+num_res+num_rec+(num_rec)*num_hst+i  210		per_hst=rows[l3] 211		hst_in=float(per_hst[2])+float(per_hst[22]) # front+back212		hst_in_mat=float(per_hst[8])+float(per_hst[28])213		hst_in_atm=float(per_hst[10])+float(per_hst[30])214		hst_abs=float(per_hst[12])+float(per_hst[32])215		hst_abs_mat=float(per_hst[18])+float(per_hst[38])216		hst_abs_atm=float(per_hst[20])+float(per_hst[40])217		  218		heliostats[i,11]=hst_in219		heliostats[i,12]=hst_in_mat220		heliostats[i,13]=hst_in_atm221		heliostats[i,14]=hst_abs222		heliostats[i,15]=hst_abs_mat223		heliostats[i,16]=hst_abs_atm224		hst_tot=float(hst_area)*1000.225		hst_cos=hst_tot*(1.-float(hst_cos))226		hst_shad=float(hst_shad)227		hst_abs=(hst_tot-hst_cos-hst_shad)*(1.-rho_mirror)228		hst_atm=float(heliostats[i,10])229		hst_rec_abs=float(heliostats[i,8])230		hst_spil=float(heliostats[i,11])-hst_rec_abs231		hst_rec_refl=float(heliostats[i,5])-float(heliostats[i,8])232		hst_block=hst_tot-hst_cos-hst_shad-hst_abs-hst_atm-hst_spil-hst_rec_abs-hst_rec_refl233		heliostats[i,19]=hst_tot234		heliostats[i,20]=hst_cos235		heliostats[i,21]=hst_shad236		heliostats[i,22]=hst_abs237		heliostats[i,23]=hst_block238		heliostats[i,24]=hst_atm239		heliostats[i,25]=hst_spil240		heliostats[i,26]=hst_rec_refl241		heliostats[i,27]=hst_rec_abs242	idx=heliostats[:, 0].argsort()243	heliostats=heliostats[idx]244	performance_hst=heliostats[:, 19:]245	heliostats_title=np.array(['hst_idx', 'area', 'sample', 'cos', 'shade', 'incoming', 'in-mat-loss','in-atm-loss', 'absorbed', 'abs-mat-loss', 'abs-atm-loss', 'vir_incoming', 'vir_in-mat-loss','vir_in-atm-loss', 'vir_absorbed', 'vir_abs-mat-loss', 'vir_abs-atm-loss', '', '', 'total', 'cos', 'shad', 'hst_abs', 'block', 'atm', 'spil', 'rec_refl', 'rec_abs' ]) 246	heliostats_details=np.vstack((heliostats_title, heliostats))247	if verbose:248		np.savetxt(savedir+'/result-formatted.csv', organised, fmt='%s', delimiter=',')249		np.savetxt(savedir+'/heliostats-raw.csv', heliostats_details, fmt='%s', delimiter=',')250		np.savetxt(savedir+'/result-raw.csv', raw_res, fmt='%s', delimiter=',')251	else:252		os.system('rm -rf %s'%savedir)253	return efficiency_total, performance_hst254def process_raw_results_multi_aperture(rawfile, savedir,rho_mirror,dni,verbose=False):255	"""Process the raw Solstice `simul` output into readable CSV files for multi-aperture central receiver systems256	``Arguments``257	  * rawfile (str): the directory of the `simul` file that generated by Solstice258	  * savedir (str): the directory for saving the organised results259	  * rho_mirror (float): mirror reflectivity (needed for reporting energy sums)260	  * dni (float): the direct normal irradiance (W/m2), required to obtain performance of individual heliostat261	  * verbose (bool), write results to disk or not262	``Returns``263	  * efficiency_total (float): the total optical efficiency264	  * performance_hst (numpy array): the breakdown of losses of each individual heliostat265	  * The simulation results are created and written in the `savedir`266		267	"""268	# FIXME this approach seems fundamentally a bit messy... we are carefully269	# load the text output from 'solppraw' and then assuming that we can270	# correctly find the right bits of data in the right place. Wouldn't it271	# be easier and more robust to modify solppraw to create output that can272	# be directly loaded, along with data labels, eg a YAML file? Or to273	# create 'result-raw.csv' directly?274	rows = []275	index = 0276	with open(rawfile) as f:277		for r in f.readlines():278			if index<20:279				pass #sys.stderr.write("Line %d: %s"%(index,r))280			if r[0] == "#":281				#sys.stderr.write("^------Comment\n")282				#comment line283				rows.append([r])284			else:285				rows.append(r.split())			286			index+=1287	# sun direction288	#sys.stderr.write("SUN DIRECTION?\n")289	#sys.stderr.write(rows[0][0]+"\n")290	sun=re.findall("[-+]?\d*\.\d+|\d+", rows[0][0])291	#sys.stderr.write("SUN = %s\n"%(repr(sun),))292	azimuth=sun[0]293	elevation=sun[1]294	def get_rc(row,col):295		return float(rows[row][col])296	# Global results297	num_res=int(get_rc(1,0)) # number of global results298	num_rec=int(get_rc(1,1))299	num_hst=get_rc(1,2)300	num_rays=get_rc(1,3)301	potential=get_rc(2,0) #W302	potential_err=get_rc(2,1)303	absorbed=get_rc(3,0)304	absorbed_err=get_rc(3,1)    305	Fcos=get_rc(4,0)306	Fcos_err=get_rc(4,1)  307	shadow_loss=get_rc(5,0)308	shadow_err=get_rc(5,1)    309	missing_loss=get_rc(6,0)310	missing_err=get_rc(6,1) 311	material_loss=get_rc(7,0)312	material_err=get_rc(7,1)    313	atmospheric_loss=get_rc(8,0)314	atmospheric_err=get_rc(8,1)  315	# Target (receiver)316	# 0 receiver name317	# 1 - 2 id and area318	# 3 - 24 (total 22) front319	# 25- 46 (total 22) back 320	num_apertures=num_rec-1 # -1 the virtual target321	rec_id={}322	rec_area=[] # m2  323	rec_front_income=[]324	rec_front_income_err=[]325	rec_front_absorbed=[]326	rec_front_absorbed_err=[]327	rec_front_eff=[]328	rec_front_eff_err=[]329	rec_back_income=[]330	rec_back_income_err=[]331	rec_back_absorbed=[]332	rec_back_absorbed_err=[]333	rec_back_eff=[]334	rec_back_eff_err=[]335	for i in range(num_apertures): 336		rec_id[i]=get_rc(num_res+2+i,1) # the id number of the receiver337		rec_area.append(get_rc(num_res+2+i,2)) # m2  338	339		rec_front_income.append(get_rc(num_res+2+i,3))340		rec_front_income_err.append(get_rc(num_res+2+i,4))341		rec_front_absorbed.append(get_rc(num_res+2+i,13))342		rec_front_absorbed_err.append(get_rc(num_res+2+i,14))343		rec_front_eff.append(get_rc(num_res+2+i,23))344		rec_front_eff_err.append(get_rc(num_res+2+i,24))345		rec_back_income.append(get_rc(num_res+2+i,25))346		rec_back_income_err.append(get_rc(num_res+2+i,26))347		rec_back_absorbed.append(get_rc(num_res+2+i,35))348		rec_back_absorbed_err.append(get_rc(num_res+2+i,36))349		rec_back_eff.append(get_rc(num_res+2+i,-2))350		rec_back_eff_err.append(get_rc(num_res+2+i,-1))351	#Virtual target352	vir_area=get_rc(num_res+2+num_rec-1,2)353	vir_income=get_rc(num_res+2+num_rec-1,3)354	vir_income_err=get_rc(num_res+2+num_rec-1,4)355	356	raw_res=np.array([357		['name','value', 'error']358		,['sun_azimuth', azimuth,'']359		,['sun_elevation', elevation, '']360		,['num hst', num_hst,'']361		,['num rays',num_rays, '']362		,['potential flux', potential, potential_err]363		,['absorbed flux', absorbed, absorbed_err]364		,['Cosine factor', Fcos, Fcos_err]365		,['shadow loss', shadow_loss, shadow_err]366		,['Mising loss', missing_loss, missing_err]367		,['materials loss', material_loss, material_err]368		,['atomospheric loss', atmospheric_loss, atmospheric_err]369		,['','','']370		,['','','']371		,['Virtual plane','','']372		,['area', vir_area, '']373		,['income flux', vir_income,vir_income_err]])374	for i in range(num_apertures):375		aperture_i = np.array([376		 ['','','']377		,['Aperture', i,'']378		,['area', rec_area[i], '']379		,['front income flux', rec_front_income[i], rec_front_income_err[i]]380		,['back income flux', rec_back_income[i], rec_back_income_err[i]]381		,['front absorbed flux', rec_front_absorbed[i], rec_front_absorbed_err[i]]382		,['back absorbed flux', rec_back_absorbed[i], rec_back_absorbed_err[i]]383		,['front efficiency', rec_front_eff[i], rec_front_eff_err[i]]384		,['back efficiency', rec_back_eff[i], rec_back_eff_err[i]]])385		raw_res=np.vstack((raw_res, aperture_i))386	#sys.stderr.write(repr(raw_res))387	#sys.stderr.write("SHAPE = %s" % (repr(raw_res.shape)))388	Qtotal=ufloat(potential, 0)389	Fcos=ufloat(Fcos,Fcos_err) 390	Qcos=Qtotal*(1.-Fcos)391	Qshade=ufloat(shadow_loss,shadow_err)392	Qfield_abs=(Qtotal-Qcos-Qshade)*(1.-float(rho_mirror))393	Qattn=ufloat(atmospheric_loss, atmospheric_err)394	Qabs=ufloat(absorbed, absorbed_err)395	Qspil=ufloat(vir_income,vir_income_err)-Qabs396	Qrefl=ufloat(sum(rec_front_income),sum(rec_front_income_err))+ufloat(sum(rec_back_income),sum(rec_back_income_err))-Qabs397	Qblock=Qtotal-Qcos-Qshade-Qfield_abs-Qspil-Qabs-Qrefl-Qattn398	organised=np.array([399		['Name', 'Value', '+/-Error']400		,['Qall (kW)', Qtotal.n/1000., Qtotal.s/1000.]401		,['Qcos (kW)', Qcos.n/1000.,Qcos.s/1000.]402		,['Qshad (kW)', Qshade.n/1000., Qshade.s/1000.]403		,['Qfield_abs (kW)', Qfield_abs.n/1000., Qfield_abs.s/1000.]404		,['Qblcok (kW)', Qblock.n/1000.,  Qblock.s/1000.]405		,['Qattn (kW)',Qattn.n/1000., Qattn.s/1000.]406		,['Qspil (kW)', Qspil.n/1000., Qspil.s/1000.]407		,['Qrefl (kW)', Qrefl.n/1000.,Qrefl.s/1000.]408		,['Qabs (kW)', Qabs.n/1000., Qabs.s/1000.]409		,['rays', num_rays,'-']410	])411	efficiency_total=Qabs/Qtotal412	# per heliostat results, and413	# per receiver per heliostat results414	num_hst=int(num_hst)    415	heliostats=np.zeros((num_hst,28))416	for i in range(num_hst):417		l1=2+num_res+num_rec+i # the line number of the per heliostat result418		per_hst=rows[l1]419		hst_idx=re.findall("[-+]?\d*\.\d+|\d+", per_hst[0] ) 420		hst_area=per_hst[2]421		hst_sample=per_hst[3]422		hst_cos=per_hst[4]423		hst_shad=per_hst[6]424		heliostats[i,0]=hst_idx[0]425		heliostats[i,1]=hst_area426		heliostats[i,2]=hst_sample427		heliostats[i,3]=hst_cos428		heliostats[i,4]=hst_shad429		# per heliostat per receiver430		for j in range(num_rec-1):431			l2=2+num_res+num_rec+num_hst*(j+1)+i  432			per_hst=rows[l2]   433			hst_in=float(per_hst[2])+float(per_hst[22]) # front+back434			hst_in_mat=float(per_hst[8])+float(per_hst[28])435			hst_in_atm=float(per_hst[10])+float(per_hst[30])436			hst_abs=float(per_hst[12])+float(per_hst[32])437			hst_abs_mat=float(per_hst[18])+float(per_hst[38])438			hst_abs_atm=float(per_hst[20])+float(per_hst[40])439			  440			heliostats[i,5]+=hst_in441			heliostats[i,6]+=hst_in_mat442			heliostats[i,7]+=hst_in_atm443			heliostats[i,8]+=hst_abs444			heliostats[i,9]+=hst_abs_mat445			heliostats[i,10]+=hst_abs_atm446		# per heliostat per virtual target447		l3=2+num_res+num_rec+(num_rec)*num_hst+i  448		per_hst=rows[l3] 449		hst_in=float(per_hst[2])+float(per_hst[22]) # front+back450		hst_in_mat=float(per_hst[8])+float(per_hst[28])451		hst_in_atm=float(per_hst[10])+float(per_hst[30])452		hst_abs=float(per_hst[12])+float(per_hst[32])453		hst_abs_mat=float(per_hst[18])+float(per_hst[38])454		hst_abs_atm=float(per_hst[20])+float(per_hst[40])455		  456		heliostats[i,11]=hst_in457		heliostats[i,12]=hst_in_mat458		heliostats[i,13]=hst_in_atm459		heliostats[i,14]=hst_abs460		heliostats[i,15]=hst_abs_mat461		heliostats[i,16]=hst_abs_atm462		hst_tot=float(hst_area)*1000.463		hst_cos=hst_tot*(1.-float(hst_cos))464		hst_shad=float(hst_shad)465		hst_abs=(hst_tot-hst_cos-hst_shad)*(1.-rho_mirror)466		hst_atm=float(heliostats[i,10])467		hst_rec_abs=float(heliostats[i,8])468		hst_spil=float(heliostats[i,11])-hst_rec_abs469		hst_rec_refl=float(heliostats[i,5])-float(heliostats[i,8])470		hst_block=hst_tot-hst_cos-hst_shad-hst_abs-hst_atm-hst_spil-hst_rec_abs-hst_rec_refl471		heliostats[i,19]=hst_tot472		heliostats[i,20]=hst_cos473		heliostats[i,21]=hst_shad474		heliostats[i,22]=hst_abs475		heliostats[i,23]=hst_block476		heliostats[i,24]=hst_atm477		heliostats[i,25]=hst_spil478		heliostats[i,26]=hst_rec_refl479		heliostats[i,27]=hst_rec_abs480	idx=heliostats[:, 0].argsort()481	heliostats=heliostats[idx]482	performance_hst=heliostats[:, 19:]483	heliostats_title=np.array(['hst_idx', 'area', 'sample', 'cos', 'shade', 'incoming', 'in-mat-loss','in-atm-loss', 'absorbed', 'abs-mat-loss', 'abs-atm-loss', 'vir_incoming', 'vir_in-mat-loss','vir_in-atm-loss', 'vir_absorbed', 'vir_abs-mat-loss', 'vir_abs-atm-loss', '', '', 'total', 'cos', 'shad', 'hst_abs', 'block', 'atm', 'spil', 'rec_refl', 'rec_abs' ]) 484	heliostats_details=np.vstack((heliostats_title, heliostats))485	if verbose:486		np.savetxt(savedir+'/result-formatted.csv', organised, fmt='%s', delimiter=',')487		np.savetxt(savedir+'/heliostats-raw.csv', heliostats_details, fmt='%s', delimiter=',')488		np.savetxt(savedir+'/result-raw.csv', raw_res, fmt='%s', delimiter=',')489	else:490		os.system('rm -rf %s'%savedir)491	return efficiency_total, performance_hst492def get_breakdown(casedir):493	"""Postprocess the .csv output files (heliostats-raw.csv, before trimming), to obtain the breakdown of total energy losses of the designed field (after trimming) for central receiver systems494	``Argument``495		* casedir (str): the directory of the case that contains the folder of sunpos_1, sunpos_2, ..., and all the other case-related details496	    * verbose (bool), write results to disk or not497	``Outputs``498		* output file: OELT_Solstice_breakdown.motab, it contains the annual lookup tables of each breakdown of energy  499		* output files: result-formatted-designed.csv file in each sunpos folder, each of them is a list of the breakdown of energy at this sun position500	501	"""502	table=np.loadtxt(casedir+'/table_view.csv', dtype=str, delimiter=',')503	idx=np.loadtxt(casedir+'/selected_hst.csv', dtype=int, delimiter=',') #index of the selected heliostats504	cosn=table505	shad=table506	hsta=table507	blck=table508	attn=table509	spil=table510	refl=table511	absr=table512	breakdown=np.array([absr, cosn, shad, hsta, blck, attn, spil, refl])513	title_breakdown=['eta_rcv_absorption','eta_cosine', 'eta_shading', 'eta_helios_absorption', 'eta_blocking', 'eta_attenuation', 'eta_spillage', 'eta_rcv_reflection']514	tot=len(title_breakdown)515	for a in range(len(table[3:])):516		for b in range(len(table[0,3:])):517			val=re.findall(r'\d+',table[a+3,b+3])518			if len(val)==0:519				for i in range(tot):520					breakdown[i][a+3,b+3]=0521			else:522				c=val[0]523				resfile=casedir+'/sunpos_%s/result-formatted-designed.csv'%c524				if os.path.exists(resfile):525					res=np.loadtxt(resfile, dtype=str, delimiter=',')526					eta_cos=res[2,2].astype(float)527					eta_shad=res[3,2].astype(float)528					eta_hst=res[4,2].astype(float)529					eta_block=res[5,2].astype(float)530					eta_attn=res[6,2].astype(float)531					eta_spil=res[7,2].astype(float)532					eta_refl=res[8,2].astype(float)533					eta_abs=res[9,2].astype(float)534				else:535					raw=np.loadtxt(casedir+'/sunpos_%s/heliostats-raw.csv'%c, delimiter=',', skiprows=1)536					data=raw[:, -9:]537					res_selected=data[idx]538					Qtot=np.sum(res_selected[:,0])539					Qcos=np.sum(res_selected[:,1])540					Qshad=np.sum(res_selected[:,2])541					Qhst=np.sum(res_selected[:,3])542					Qblock=np.sum(res_selected[:,4])543					Qattn=np.sum(res_selected[:,5])544					Qspil=np.sum(res_selected[:,6])545					Qrefl=np.sum(res_selected[:,7])546					Qabs=np.sum(res_selected[:,8])547					eta_cos=Qcos/Qtot548					eta_shad=Qshad/Qtot549					eta_hst=Qhst/Qtot550					eta_block=Qblock/Qtot551					eta_attn=Qattn/Qtot552					eta_spil=Qspil/Qtot553					eta_refl=Qrefl/Qtot554					eta_abs=Qabs/Qtot	555					res=np.array([556					 ['Name', 'Value (kW)', 'eta Ratio']557				    ,['Qall', Qtot,   1]558					,['Qcos', Qcos,   eta_cos]559					,['Qshad', Qshad, eta_shad]560					,['Qfield_abs', Qhst, eta_hst]561					,['Qblcok', Qblock, eta_block]562					,['Qattn',Qattn,  eta_attn]563					,['Qspil ', Qspil,eta_spil]564					,['Qrefl', Qrefl, eta_refl]565					,['Qabs ', Qabs,  eta_abs]566					,['After trimming', 'postprocessed results','-']567					])568					np.savetxt(casedir+'/sunpos_%s/result-formatted-designed.csv'%c, res, fmt='%s', delimiter=',')569				eta_all=[eta_abs, eta_cos, eta_shad, eta_hst, eta_block, eta_attn, eta_spil, eta_refl]570				for i in range(tot):571					if eta_all[i]<1e-8:572						breakdown[i][a+3,b+3]=0.573					else:574						breakdown[i][a+3,b+3]=eta_all[i]575	output_motab(table=breakdown, savedir=casedir+'/OELT_Solstice_breakdown.motab', title=title_breakdown)576	577	# at design point578	raw=np.loadtxt(casedir+'/des_point/heliostats-raw.csv', delimiter=',', skiprows=1)579	data=raw[:, -9:]580	res_selected=data[idx]581	Qtot=np.sum(res_selected[:,0])582	Qcos=np.sum(res_selected[:,1])583	Qshad=np.sum(res_selected[:,2])584	Qhst=np.sum(res_selected[:,3])585	Qblock=np.sum(res_selected[:,4])586	Qattn=np.sum(res_selected[:,5])587	Qspil=np.sum(res_selected[:,6])588	Qrefl=np.sum(res_selected[:,7])589	Qabs=np.sum(res_selected[:,8])590	eta_cos=Qcos/Qtot591	eta_shad=Qshad/Qtot592	eta_hst=Qhst/Qtot593	eta_block=Qblock/Qtot594	eta_attn=Qattn/Qtot595	eta_spil=Qspil/Qtot596	eta_refl=Qrefl/Qtot597	eta_abs=Qabs/Qtot	598	res=np.array([599	 ['Name', 'Value (kW)', 'eta Ratio']600    ,['Qall', Qtot,   1]601	,['Qcos', Qcos,   eta_cos]602	,['Qshad', Qshad, eta_shad]603	,['Qfield_abs', Qhst, eta_hst]604	,['Qblcok', Qblock, eta_block]605	,['Qattn',Qattn,  eta_attn]606	,['Qspil ', Qspil,eta_spil]607	,['Qrefl', Qrefl, eta_refl]608	,['Qabs ', Qabs,  eta_abs]609	,['After trimming', 'postprocessed results','-']610	])611	np.savetxt(casedir+'/des_point/result-formatted-designed.csv', res, fmt='%s', delimiter=',')	612def process_raw_results_dish(rawfile, savedir,rho_mirror,dni,verbose=False):613	"""Process the raw Solstice `simul` output into readable CSV files for dish systems614	``Arguments``615	  * rawfile (str): the directory of the `simul` file that generated by Solstice616	  * savedir (str): the directory for saving the organised results617	  * rho_mirror (float): mirror reflectivity (needed for reporting energy sums)618	  * dni (float): the direct normal irradiance (W/m2), required to obtain performance of individual heliostat619	``Returns``620	  * efficiency_total (float): the total optical efficiency621	  * The simulation results are created and written in the `savedir`622		623	"""624	# FIXME this approach seems fundamentally a bit messy... we are carefully625	# load the text output from 'solppraw' and then assuming that we can626	# correctly find the right bits of data in the right place. Wouldn't it627	# be easier and more robust to modify solppraw to create output that can628	# be directly loaded, along with data labels, eg a YAML file? Or to629	# create 'result-raw.csv' directly?630	rows = []631	index = 0632	with open(rawfile) as f:633		for r in f.readlines():634			if index<20:635				pass #sys.stderr.write("Line %d: %s"%(index,r))636			if r[0] == "#":637				#sys.stderr.write("^------Comment\n")638				#comment line639				rows.append([r])640			else:641				rows.append(r.split())			642			index+=1643	results=np.array([])644	# sun direction645	#sys.stderr.write("SUN DIRECTION?\n")646	#sys.stderr.write(rows[0][0]+"\n")647	sun=re.findall("[-+]?\d*\.\d+|\d+", rows[0][0])648	#sys.stderr.write("SUN = %s\n"%(repr(sun),))649	azimuth=sun[0]650	elevation=sun[1]651	def get_rc(row,col):652		return float(rows[row][col])653	# Global results654	num_res=int(get_rc(1,0)) # number of global results655	num_rec=int(get_rc(1,1))656	num_hst=get_rc(1,2)657	num_rays=get_rc(1,3)658	potential=get_rc(2,0) #W659	potential_err=get_rc(2,1)660	absorbed=get_rc(3,0)661	absorbed_err=get_rc(3,1)    662	Fcos=get_rc(4,0)663	Fcos_err=get_rc(4,1)  664	shadow_loss=get_rc(5,0)665	shadow_err=get_rc(5,1)    666	missing_loss=get_rc(6,0)667	missing_err=get_rc(6,1) 668	material_loss=get_rc(7,0)669	material_err=get_rc(7,1)    670	atmospheric_loss=get_rc(8,0)671	atmospheric_err=get_rc(8,1)  672	# Target (receiver)673	# 0 receiver name674	# 1 - 2 id and area675	# 3 - 24 (total 22) front676	# 25- 46 (total 22) back 677	rec_area=get_rc(num_res+2,2) # m2  678	679	rec_front_income=get_rc(num_res+2,3)680	rec_front_income_err=get_rc(num_res+2,4)681	rec_front_absorbed=get_rc(num_res+2,5)682	rec_front_absorbed_err=get_rc(num_res+2,6)683	rec_front_eff=get_rc(num_res+2,23)684	rec_front_eff_err=get_rc(num_res+2,24)685	rec_back_income=get_rc(num_res+2,25)686	rec_back_income_err=get_rc(num_res+2,26)687	rec_back_absorbed=get_rc(num_res+2,35)688	rec_back_absorbed_err=get_rc(num_res+2,36)689	rec_back_eff=get_rc(num_res+2,-2)690	rec_back_eff_err=get_rc(num_res+2,-1)691		692	raw_res=np.array([693		['name','value', 'error']694		,['sun_azimuth', azimuth,'']695		,['sun_elevation', elevation, '']696		,['num hst', num_hst,'']697		,['num rays',num_rays, '']698		,['potential flux', potential, potential_err]699		,['absorbed flux', absorbed, absorbed_err]700		,['Cosine factor', Fcos, Fcos_err]701		,['shadow loss', shadow_loss, shadow_err]702		,['Mising loss', missing_loss, missing_err]703		,['materials loss', material_loss, material_err]704		,['atomospheric loss', atmospheric_loss, atmospheric_err]...

xcat_nodes

Source:xcat_nodes

1#!/usr/bin/python2import sqlite33import os, signal4import sys5import time6from datetime import datetime7state_db_file="/etc/xcat/nodelist.sqlite" #<node:0>,<status:2>,<statustime:3>8mode_db_file="/etc/xcat/chain.sqlite" # <node:hostname:0>,<currstat:mode:1>9alias_db_file="/etc/xcat/hosts.sqlite" # <node:hostname:0>,<hostnames:alias:2>10mgt_db_file="/etc/xcat/nodehm.sqlite" 11id_db_file="/etc/xcat/ppc.sqlite" 12group=False13if len(sys.argv) == 2:14   group=sys.argv[1]15#ID16conn = sqlite3.connect(id_db_file)17c = conn.cursor()18if group:19    sqr="select  node,id from ppc where node like '" + (group + '%') + "' order by cast(id as interger)"20else:21    sqr="select  node,id from ppc order by cast(id as interger)"22get_rc=c.execute(sqr).fetchall()23conn.close()24print("%8s %15s %15s %10s %5s %13s" % ("Nodes","Hostname","Alias","Mode","H.C.","OS State"))25for ii in get_rc:26   #ID27   nodename="n%05d" % int(ii[1])28   # Alias29   conn = sqlite3.connect(alias_db_file)30   c = conn.cursor()31   sqr="select hostnames from hosts where node='%s'" % (ii[0])32   get_rc=c.execute(sqr).fetchone()33   conn.close()34   if get_rc is None or get_rc[0] is None or get_rc[0] == "":35      alias_str="-"36   else:37      alias_str=get_rc[0]38   #MGT39   conn = sqlite3.connect(mgt_db_file)40   c = conn.cursor()41   sqr="select mgt from nodehm where node='%s'" % (ii[0])42   get_rc=c.execute(sqr).fetchone()43   conn.close()44   if get_rc is None:45      mgt_str="-"46   else:47      mgt_str=get_rc[0]48   # Node state49   conn = sqlite3.connect(state_db_file)50   c = conn.cursor()51   sqr="select status,statustime from nodelist where node='%s'" % (ii[0])52   get_rc=c.execute(sqr).fetchone()53   conn.close()54   if get_rc[0] is None or get_rc[0] == "":55      state="-"56   else:57      if get_rc[1] is None:58         node_time=059      else:60         node_time=int(time.mktime(time.strptime(get_rc[1],"%m-%d-%Y %H:%M:%S")))61      now_time=int(datetime.now().strftime("%s"))62      if get_rc[0] == "maint":63         if (now_time - node_time) > (400 * 2):64             state="maint"65         else:66             state="*maint"67      else:68         if (now_time - node_time) > (400 * 2):69             state="Unknown"70         else:71             state=get_rc[0]72      73   # Mode74   conn = sqlite3.connect(mode_db_file)75   c = conn.cursor()76   sqr="select currstate from chain where node='%s'" % (ii[0])77   get_rc=c.execute(sqr).fetchone()78   conn.close()79   if get_rc is None or get_rc[0] == "" or nodename == ii[0]:80      mode_str="-"81   else:82      mode_str=get_rc[0].split(" ")[0]83   # hostname84   if nodename == ii[0]:85      hostname="-"86   else:87      hostname=ii[0]...

Automation Testing Tutorials

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