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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]...
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