本文整理汇总了Python中numpy.int_函数 的典型用法代码示例。如果您正苦于以下问题：Python int_函数的具体用法？Python int_怎么用？Python int_使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了int_函数 的20个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: _find_nearest_node_ndarray
def _find_nearest_node_ndarray(rmg, coords, mode='raise'):
column_indices = np.int_(
np.around((coords[0] - rmg.node_x[0]) / rmg.node_spacing))
row_indices = np.int_(
np.around((coords[1] - rmg.node_y[0]) / rmg.node_spacing))
return rmg.grid_coords_to_node_id(row_indices, column_indices, mode=mode)开发者ID:brigidlynch， 项目名称:landlab， 代码行数:7， 代码来源:raster_funcs.py
示例2: Init
def Init(self):
#boundary and domain condition
self.lat = io.read_PETSc_vec(self.config["-Metos3DBoundaryConditionInputDirectory"][0] + self.config["-Metos3DLatitudeFileFormat"][0])
dz = io.read_PETSc_vec(self.config["-Metos3DDomainConditionInputDirectory"][0] + self.config["-Metos3DLayerHeightFileFormat"][0])
z = io.read_PETSc_vec(self.config["-Metos3DDomainConditionInputDirectory"][0] + self.config["-Metos3DLayerDepthFileFormat"][0])
self.lsm = io.read_PETSc_mat(self.config["-Metos3DProfileInputDirectory"][0] + self.config["-Metos3DProfileMaskFile"][0])
self.fice = np.zeros((self.profiles,np.int_(self.config["-Metos3DIceCoverCount"][0])),dtype=np.float_)
for i in range(np.int_(self.config["-Metos3DIceCoverCount"][0])):
self.fice[:,i] = io.read_PETSc_vec(self.config["-Metos3DBoundaryConditionInputDirectory"][0] + (self.config["-Metos3DIceCoverFileFormat"][0] % i))
self.bc = np.zeros(2,dtype=np.float_)
self.dc = np.zeros((self.ny,2),dtype=np.float_)
self.dc[:,0] = z
self.dc[:,1] = dz
self.u = np.array(self.config["-Metos3DParameterValue"],dtype=np.float_)
self.dt = np.float_(self.config["-Metos3DTimeStep"][0])
self.nspinup = np.int_(self.config["-Metos3DSpinupCount"][0])
self.ntimestep = np.int_(self.config["-Metos3DTimeStepCount"][0])
self.matrixCount = np.int_(self.config["-Metos3DMatrixCount"][0])
self.U_PODN = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'N/'+ self.config["-Metos3DMatrixPODFileFormat"][0])
self.U_PODDOP = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'DOP/'+ self.config["-Metos3DMatrixPODFileFormat"][0])
self.U_DEIMN = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'N/'+ self.config["-Metos3DMatrixDEIMFileFormat"][0])
self.U_DEIMDOP = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'DOP/'+ self.config["-Metos3DMatrixDEIMFileFormat"][0])
self.DEIM_IndicesN = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'N/'+ self.config["-Metos3DDEIMIndicesFileFormat"][0])
self.DEIM_IndicesDOP = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'DOP/'+ self.config["-Metos3DDEIMIndicesFileFormat"][0])
self.AN = np.ndarray(shape=(self.matrixCount,self.U_PODN.shape[1],self.U_PODN.shape[1]), dtype=np.float_, order='C')
self.ADOP = np.ndarray(shape=(self.matrixCount,self.U_PODDOP.shape[1],self.U_PODDOP.shape[1]), dtype=np.float_, order='C')
for i in range(0,self.matrixCount):
self.AN[i] = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'N/'+ self.config["-Metos3DMatrixReducedFileFormat"][0] % i)
self.ADOP[i] = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'DOP/'+ self.config["-Metos3DMatrixReducedFileFormat"][0] % i)
self.PN = np.ndarray(shape=(self.matrixCount,self.U_PODN.shape[1],self.U_DEIMN.shape[1]), dtype=np.float_, order='C')
self.PDOP = np.ndarray(shape=(self.matrixCount,self.U_PODDOP.shape[1],self.U_DEIMDOP.shape[1]), dtype=np.float_, order='C')
for i in range(0,self.matrixCount):
self.PN[i] = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'N/'+ self.config["-Metos3DMatrixReducedDEINFileFormat"][0] % i)
self.PDOP[i] = np.load(self.config["-Metos3DMatrixInputDirectory"][0] +'DOP/'+ self.config["-Metos3DMatrixReducedDEINFileFormat"][0] % i)
#precomputin the interplaton indices for a year
[self.interpolation_a,self.interpolation_b,self.interpolation_j,self.interpolation_k] = util.linearinterpolation(2880,12,0.0003472222222222)
self.yN = np.ones(self.ny,dtype=np.float_) * np.float_(self.config["-Metos3DTracerInitValue"])[0]
self.yDOP = np.ones(self.ny,dtype=np.float_) * np.float_(self.config["-Metos3DTracerInitValue"])[1]
self.y_redN = np.dot(self.U_PODN.T,self.yN)
self.y_redDOP = np.dot(self.U_PODDOP.T,self.yDOP)
self.qN = np.zeros(self.DEIM_IndicesN.shape[0],dtype=np.float_)
self.qDOP = np.zeros(self.DEIM_IndicesDOP.shape[0],dtype=np.float_)
self.J,self.PJ = util.generateIndicesForNonlinearFunction(self.lsm,self.profiles,self.ny)
self.out_pathN = self.config["-Metos3DTracerOutputDirectory"][0] +self.config["-Metos3DSpinupMonitorFileFormatPrefix"][0] + self.config["-Metos3DSpinupMonitorFileFormatPrefix"][1] +self.config["-Metos3DTracerOutputFile"][0]
self.out_pathDOP = self.config["-Metos3DTracerOutputDirectory"][0] +self.config["-Metos3DSpinupMonitorFileFormatPrefix"][0] + self.config["-Metos3DSpinupMonitorFileFormatPrefix"][1] +self.config["-Metos3DTracerOutputFile"][1]
self.monitor_path = self.config["-Metos3DTracerMointorDirectory"][0] +self.config["-Metos3DSpinupMonitorFileFormatPrefix"][0] + self.config["-Metos3DSpinupMonitorFileFormatPrefix"][1] +self.config["-Metos3DTracerOutputFile"][0]开发者ID:neeljp， 项目名称:pod_deim， 代码行数:60， 代码来源:ROM_ndop.py
示例3: voigtking
def voigtking(v,a):
oneonsqrtpi=0.56418958354775630
h0 = np.array([ 1.0e0, 0.9975031223974601240368798e0, 0.9900498337491680535739060e0, 0.9777512371933363639286036e0, 0.9607894391523232094392107e0, 0.9394130628134757861197108e0, 0.9139311852712281867473535e0, 0.8847059049434835594929548e0, 0.8521437889662113384563470e0, 0.8166864825981108401538061e0, 0.7788007830714048682451703e0, 0.7389684882589442416058206e0, 0.6976763260710310572091293e0, 0.6554062543268405127576690e0, 0.6126263941844160689885800e0, 0.5697828247309230097666297e0, 0.5272924240430485572436946e0, 0.4855368951540794399916001e0, 0.4448580662229411344814454e0, 0.4055545050633205516443034e0, 0.3678794411714423215955238e0, 0.3320399453446606420249195e0, 0.2981972794298873779316010e0, 0.2664682978135241116965901e0, 0.2369277586821217567233665e0, 0.2096113871510978225241101e0, 0.1845195239929892676298138e0, 0.1616211924653392539324509e0, 0.1408584209210449961479715e0, 0.1221506695399900084151679e0, 0.1053992245618643367832177e0, 0.9049144166369591062935159e-1, 0.7730474044329974599046566e-1, 0.6571027322750286139200605e-1, 0.5557621261148306865356766e-1, 0.4677062238395898365276137e-1, 0.3916389509898707373977109e-1, 0.3263075599289603180381419e-1, 0.2705184686635041108596167e-1, 0.2231491477696640649487920e-1, 0.1831563888873418029371802e-1, 0.1495813470057748930092482e-1, 0.1215517832991493721502629e-1, 0.9828194835379685936011149e-2, 0.7907054051593440493635646e-2, 0.6329715427485746576865117e-2, 0.5041760259690979102410257e-2, 0.3995845830084632413030896e-2, 0.3151111598444440557819106e-2, 0.2472563035874193226953048e-2, 0.1930454136227709242213512e-2, 0.1499685289329846120368399e-2, 0.1159229173904591150012118e-2, 0.8915937199952195568639939e-3, 0.6823280527563766163014506e-3, 0.5195746821548384817648154e-3, 0.3936690406550782109805393e-3, 0.2967857677932108344855019e-3, 0.2226298569188890101840659e-3, 0.1661698666072774484528398e-3, 0.1234098040866795494976367e-3, 0.9119595636226606575873788e-4, 0.6705482430281108867614262e-4, 0.4905835745620769579106241e-4, 0.3571284964163521691234528e-4, 0.2586810022265412127035909e-4, 0.1864374233151683041526522e-4, 0.1336996212084380475632834e-4, 0.9540162873079234841590110e-5, 0.6773449997703748098370991e-5, 0.4785117392129009089609771e-5, 0.3363595724825637829225185e-5, 0.2352575200009772922652510e-5, 0.1637237807196195233271403e-5, 0.1133727138747965652009438e-5, 0.7811489408304490795473004e-6, 0.5355347802793106157479094e-6, 0.3653171341207511214363159e-6, 0.2479596018045029629499234e-6, 0.1674635703137489046698250e-6, 0.1125351747192591145137752e-6, 0.7524623257644829651017174e-7, 0.5006218020767042215644986e-7, 0.3314082270898834287088712e-7, 0.2182957795125479209083827e-7, 0.1430724191856768833467676e-7, 0.9330287574504991120387842e-8, 0.6054282282484886644264747e-8, 0.3908938434264861859681131e-8, 0.2511212833271291589987176e-8, 0.1605228055185611608653934e-8, 0.1020982947159334870301705e-8, 0.6461431773106108989429857e-9, 0.4068811450655793356678124e-9, 0.2549381880391968872012880e-9, 0.1589391009451636652873474e-9, 0.9859505575991508240729766e-10, 0.6085665105518337082108266e-10, 0.3737571327944262032923964e-10, 0.2284017657993705413027994e-10, 0.1388794386496402059466176e-10, 0.8402431396484308187150245e-11, 0.5058252742843793235026422e-11, 0.3029874246723653849216172e-11, 0.1805831437513215621913785e-11, 0.1070923238250807645586450e-11, 0.6319285885175366663984108e-12, 0.3710275783094727281418983e-12, 0.2167568882618961942307398e-12, 0.1259993054847742150188394e-12, 0.7287724095819692419343177e-13, 0.4194152536192217185131208e-13, 0.2401734781620959445230543e-13, 0.1368467228126496785536523e-13, 0.7758402075696070467242451e-14, 0.4376618502870849893821267e-14, 0.2456595368792144453705261e-14, 0.1372009419645128473380053e-14, 0.7624459905389739760616425e-15, 0.4215893238174252040735029e-15, 0.2319522830243569388312264e-15, 0.1269802641377875575018264e-15, 0.6916753975541448863883054e-16, 0.3748840457745443581785685e-16, 0.2021715848695342027119482e-16, 0.1084855264042937802512215e-16, 0.5792312885394857923477507e-17, 0.3077235638152508657901574e-17, 0.1626664621453244338034305e-17, 0.8555862896902856300749061e-18, 0.4477732441718301199042103e-18, 0.2331744656246116743545942e-18, 0.1208182019899973571654094e-18, 0.6228913128535643653088166e-19, 0.3195366717748344275120932e-19, 0.1631013922670185678641901e-19, 0.8283677007682876110228791e-20, 0.4186173006145967657832773e-20, 0.2104939978339734445589080e-20, 0.1053151347744013743766989e-20, 0.5242885663363463937171805e-21, 0.2597039249246848208769072e-21, 0.1280015319051641983953037e-21, 0.6277407889747195099574399e-22, 0.3063190864577440373821128e-22, 0.1487292181651270619154227e-22, 0.7185335635902193010046941e-23, 0.3454031957013868448981675e-23, 0.1652091782314268593068387e-23, 0.7862678502984538622254116e-24, 0.3723363121750510429289070e-24, 0.1754400713566556605465117e-24, 0.8225280651606668501925640e-25, 0.3837082905344536379879530e-25, 0.1781066634757091357021587e-25, 0.8225980595143903024275237e-26, 0.3780277844776084635218009e-26, 0.1728575244037268289032505e-26, 0.7864685935766448441713277e-27, 0.3560434556451067378310069e-27, 0.1603810890548637852976087e-27, 0.7188393394953158727447087e-28, 0.3205819323394999444158648e-28, 0.1422573701362478490703169e-28, 0.6281148147605989215436687e-29, 0.2759509067522042024589005e-29, 0.1206293927781149203841840e-29, 0.5246902396795390138796640e-30, 0.2270812922026396509517690e-30, 0.9778860615814667663870901e-31, 0.4190093194494397377123780e-31, 0.1786436718517518413888050e-31, 0.7578445267618382646037748e-32, 0.3198903416725805416294188e-32, 0.1343540197758737662452134e-32, 0.5614728092387934579799402e-33, 0.2334722783487267408869808e-33, 0.9659851300583384710233199e-34, 0.3976803097901655265751816e-34, 0.1629019426220514693169818e-34, 0.6639677199580734400702255e-35, 0.2692751000456178970430831e-35, 0.1086610640745980532852592e-35, 0.4362950029268711046345153e-36, 0.1743070896645292498913954e-36, 0.6929124938815710000577778e-37, 0.2740755284722598699701951e-37, 0.1078675105373929991550997e-37, 0.4224152406206200437573993e-38, 0.1645951484063258284098658e-38, 0.6381503448060790393554118e-39, 0.2461826907787885454919214e-39, 0.9449754976491185028813549e-40, 0.3609209642415355020302235e-40, 0.1371614910949353618952282e-40, 0.5186576811908572940413120e-41, 0.1951452380295377748121319e-41, 0.7305730197111493885868359e-42, 0.2721434140093713884466599e-42, 0.1008696596314342558322441e-42, 0.3720075976020835962959696e-43, 0.1365122395620087240477630e-43 ], dtype=np.float64)
h1 = np.array([ -1.128379167095512573896159e0, -1.122746665023313894112994e0, -1.105961434222613497822717e0, -1.078356949458362356972974e0, -1.040477963566390226869037e0, -0.9930644092865188274925694e0, -0.9370297574325730524254160e0, -0.8734346738611667009559691e0, -0.8034569860177944012914767e0, -0.7283590897795191457635390e0, -0.6494539941944691013512214e0, -0.5680712138345335512208471e0, -0.4855236771153186839197872e0, -0.4030767281964792012404736e0, -0.3219201665209207840831093e0, -0.2431441002236951675148354e0, -0.1677191974661332963609891e0, -0.9648171389061105293546881e-1, -0.3012346558870770535102483e-1, 0.3081328457047809980986685e-1, 0.8593624458727488433391777e-1, 0.1349991935349749351748713e0, 0.1778942744880748462232135e0, 0.2146410885736963723412265e0, 0.2453732617833523433216744e0, 0.2703231847626659615037426e0, 0.2898056218155761132507312e0, 0.3042008523837261147222841e0, 0.3139379509747736418513567e0, 0.3194787353320834397089635e0, 0.3213028233267945998845488e0, 0.3198941423604233541674753e0, 0.3157291364070343763776039e0, 0.3092668200208504802085382e0, 0.3009407397271468294117335e0, 0.2911528243392948676821857e0, 0.2802690390913659378360681e0, 0.2686167052981096351368975e0, 0.2564833079412283848897372e0, 0.2441165877658165024921633e0, 0.2317257011687522312257119e0, 0.2194832289213470945135105e0, 0.2075278218310246553881156e0, 0.1959672858880207128215797e0, 0.1848819293094190730287360e0, 0.1743280173110208640535652e0, 0.1643412057011470302647273e0, 0.1549398500207542791790132e0, 0.1461281117364874603340094e0, 0.1378988059908943461128856e0, 0.1302359559637753421977543e0, 0.1231170365911391556632533e0, 0.1165149050377156668055896e0, 0.1103994269264874144398788e0, 0.1047388160423518894772002e0, 0.9950071130235648759030670e-1, 0.9465301854781620910441970e-1, 0.9016454652735125189272609e-1, 0.8600546667768981700419079e-1, 0.8214762533231104047151097e-1, 0.7856473513008974607178765e-1, 0.7523246995193424459351750e-1, 0.7212848493340500348466924e-1, 0.6923238018945846374255513e-1, 0.6652562400245432725286132e-1, 0.6399144848312167544450556e-1, 0.6161472819590847810012464e-1, 0.5938184999317344054777048e-1, 0.5728058034957269600588669e-1, 0.5529993483145627029203620e-1, 0.5343005296426139233134751e-1, 0.5166208065197234887486323e-1, 0.4998806142885727821214551e-1, 0.4840083715410895783485349e-1, 0.4689395826338997495993764e-1, 0.4546160333748704598916335e-1, 0.4409850750954268216573793e-1, 0.4279989908392569899980027e-1, 0.4156144366035708515282858e-1, 0.4037919502845779134315796e-1, 0.3924955210570969222557380e-1, 0.3816922122416471946490538e-1, 0.3713518311895684989765586e-1, 0.3614466402785612590311943e-1, 0.3519511037069617482332004e-1, 0.3428416653694949866994660e-1, 0.3340965536664229903158673e-1, 0.3256956096272257612903376e-1, 0.3176201352112533673779090e-1, 0.3098527590780517228496903e-1, 0.3023773174995156695256252e-1, 0.2951787484170619418302355e-1, 0.2882429969333463230632146e-1, 0.2815569307740452259166926e-1, 0.2751082644654734935368337e-1, 0.2688854911528297388431485e-1, 0.2628778211358937241904422e-1, 0.2570751263279204975253415e-1, 0.2514678899527364475073049e-1, 0.2460471608876676259183765e-1, 0.2408045121385331090696902e-1, 0.2357320029997478838776359e-1, 0.2308221445094914570064896e-1, 0.2260678678585010840991674e-1, 0.2214624954526743636682309e-1, 0.2169997143654264861646818e-1, 0.2126735519465680897241377e-1, 0.2084783533811200664569883e-1, 0.2044087610146017752978434e-1, 0.2004596952814515567227767e-1, 0.1966263370908071277476715e-1, 0.1929041115392591487587378e-1, 0.1892886728337045173071115e-1, 0.1857758903193275942486415e-1, 0.1823618355182474294515453e-1, 0.1790427700936730343669473e-1, 0.1758151346626646308038721e-1, 0.1726755383879409857500321e-1, 0.1696207492857163038741910e-1, 0.1666476851923932358834102e-1, 0.1637534053381661837450139e-1, 0.1609351024802744708797459e-1, 0.1581900955528515170398058e-1, 0.1555158227940989996039230e-1, 0.1529098353149220739767610e-1, 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h3 = np.array([ -0.7522527780636750492641059e0, -0.7447490315497708463240858e0, -0.7224619689626252165385118e0, -0.6860552061846493969863268e0, -0.6366054955061156295204758e0, -0.5755603365344096850483262e0, -0.5046815829547811446478382e0, -0.4259777864640005624125117e0, -0.3416285184773921405216660e0, -0.2539042236274465364534081e0, -0.1650852727968867264939651e0, -0.7738379667939842709258988e-1, 0.7128394424195324853014844e-2, 0.8658293927736663174097951e-1, 0.1593668102410841966827594e0, 0.2241613263920280449352809e0, 0.2799673824845877680517527e0, 0.3261167006652041288605015e0, 0.3622695948610319801705815e0, 0.3884003473857446343896496e0, 0.4047718038942624860766923e0, 0.4119011753186058824533937e0, 0.4105192820995319949018743e0, 0.4015255845130582620257648e0, 0.3859413195031716183649201e0, 0.3648629230000597762360636e0, 0.3394176769351978836202936e0, 0.3107232057693364099667621e0, 0.2798520840662402744643034e0, 0.2478024303401173430156194e0, 0.2154749773684402246897790e0, 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-0.4885732890847829717111949e-3, -0.4714984476509869340551945e-3, -0.4551758640732029088500233e-3, -0.4395654037105695480727411e-3, -0.4246294549008608587718018e-3, -0.4103327457346023732872108e-3, -0.3966421759777806984761265e-3, -0.3835266627330082944382909e-3, -0.3709569985755748701109446e-3, -0.3589057210304776810509891e-3, -0.3473469923714317173865229e-3, -0.3362564888248703524021643e-3, -0.3256112983526542094353014e-3, -0.3153898262679901745636708e-3, -0.3055717080111181576022737e-3, -0.2961377284756872027881530e-3, -0.2870697473343167903391904e-3, -0.2783506298634098374691914e-3, -0.2699641828135376810549337e-3, -0.2618950949132550960609061e-3, -0.2541288816315519571599965e-3, -0.2466518338577700959600751e-3, -0.2394509701881161861915701e-3, -0.2325139925352426415436720e-3, -0.2258292448020649292499711e-3, -0.2193856743833149971866920e-3, -0.2131727962785441468085678e-3, -0.2071806596186039850962257e-3, -0.2013998164242456949121338e-3, -0.1958212924305587453675523e-3, -0.1904365598246742268269672e-3, -0.1852375117566223449189077e-3, -0.1802164384945805472907308e-3, -0.1753660051060874920343825e-3, -0.1706792305562262391906103e-3, -0.1661494681223850440721571e-3, -0.1617703870330642541013594e-3, -0.1575359552453832883093564e-3, -0.1534404232825155716084045e-3, -0.1494783090582982775062280e-3, -0.1456443836217787948749789e-3, -0.1419336577595168918308957e-3, -0.1383413693981019940302776e-3, -0.1348629717536061671270311e-3, -0.1314941221786090162018978e-3, -0.1282306716610312631043834e-3, -0.1250686549323268134999138e-3, -0.1220042811456336331711188e-3, -0.1190339250872943430156140e-3, -0.1161541188877486230913414e-3, -0.1133615442001899065679247e-3, -0.1106530248175853517419676e-3, -0.1080255197006960803598953e-3, -0.1054761163916181391183883e-3, -0.1030020247891063146774180e-3, -0.1006005712635544029343839e-3, -0.9826919309099737327798045e-4, -0.9600543318688272806740460e-4, -0.9380693512163903486436983e-4, -0.9167143840125715403094134e-4, -0.8959677399720145006388879e-4, -0.8758086011099098595144745e-4, -0.8562169815974051700802759e-4, -0.8371736896983366064768422e-4, -0.8186602916672109476829247e-4, -0.8006590774959976520266573e-4, -0.7831530284043921555152064e-4, -0.7661257859748228262605498e-4, -0.7495616228396319961002592e-4, -0.7334454148335998246272097e-4, -0.7177626145303295708079228e-4, -0.7024992260860025649833230e-4, -0.6876417813186671874001603e-4, -0.6731773169555726054493046e-4, -0.6590933529851172806481185e-4, -0.6453778720537748581672358e-4, -0.6320192998519050404738797e-4, -0.6190064864356719221383246e-4, -0.6063286884353932444622322e-4, -0.5939755521035460581086281e-4, -0.5819370971583712468698264e-4 ], dtype=np.float64)
# Voigt function is symmetric, so -v = v
if len(np.argwhere(v<0.0)) != 0: v[np.argwhere(v<0.0)] *= -1.0
# if a is exactly zero go to 3 for exact expression
if (a == 0.0):
return np.exp(-(v*v))
# Scale up v for ease with lookup tables
v0 = v*10.0
n=np.array(v0,dtype=np.int_)
voigt_prof = np.zeros(np.size(v))
nl=np.argwhere(n<100)
nh=np.argwhere(n>=100)
if len(nh) != 0:
r=1.0/v[nh]**2
voigt_prof[nh] = a*r*oneonsqrtpi*(1.0 + r*(1.5 + r*(3.75 + r*(13.125 + 59.0625*r))) - a*a*r*(1.0 + r*(5.0 +26.25*r)))
if len(nl) != 0:
v0[nl] = 2.0*v[nl]*10.0
p=np.int_(v0[nl])
p1=p+1
p2=p+2
x=0.5*np.int_(v0[nl])
y=x+0.5
z=x+1.0
v1 = v0[nl] * 0.5
voigt_prof[nl] = 2.0*((v1-y)*(v1-z)*(h0[p]+a*(h1[p]+a*(h2[p]+a*h3[p]))) - (v1-x)*(v1-z)*2.0*(h0[p1] + a*(h1[p1]+a*(h2[p1]+a*h3[p1]))) + (v1-x)*(v1-y)*(h0[p2] + a*(h1[p2]+a*(h2[p2]+a*h3[p2]))))
del nl, nh
return voigt_prof开发者ID:jbwhit， 项目名称:project-alis， 代码行数:33， 代码来源:alfunc_lineemission.py
示例4: add_pbc_jncol
def add_pbc_jncol(data,rand):
'''If the input is a periodic box and los is along z axis then jacknife region is simply equal area region in the x-y space which can be done in using this function and not needed to be supplied with data file make sure that njn is a perfect square'''
#adding jacknife regions
if(args.njn>0 and args.los==1):
POS_min,POS_max, blen=getminmax(data,rand=rand)
NJNx=np.int(np.sqrt(args.njn))
NJNy=np.int(args.njn/NJNx)
for ii in (0,2):
if(ii==0): mat=data
else: mat=rand
#get the x and y indx as integers
indx=np.int_(NJNx*(mat[:,0]-POS_min[0])/blen[0])
indy=np.int_(NJNy*(mat[:,1]-POS_min[1])/blen[1])
#apply modulo operation on x an y index
indx=np.mod(indx,NJNx)
indy=np.mod(indy,NJNy)
#convert index to integers
#indx.astype(np.int64); indy.astype(np.int64);
jnreg=NJNy*indx+indy
mat=np.column_stack([mat,jnreg])
if(ii==0): data=mat
else: rand=mat
return data,rand
else:
print('not appropriate input to add jacknife internally')
sys.exit()
return 0开发者ID:desihub， 项目名称:LSS， 代码行数:32， 代码来源:Runme_Correlation.py
示例5: initialize
def initialize(video_capture,rot_angle, pt1, pt2, ppl_width):
#read image
ret, image = video_capture.read()
(hh, ww) = image.shape[:2]
#rotate
M = None;
if (rot_angle != 0):
center = (ww / 2, hh / 2)
M = cv2.getRotationMatrix2D(center, rot_angle, 1.0)
image = imutils.resize(image, width=min(400, image.shape[1]))
##mask after resize
resize_ratio = image.shape[1] / float(ww)
#max_min_ppl_size
ppl_size=[50,100]
ppl_size[0] = np.ceil(ppl_width * resize_ratio * 1.4)
ppl_size[1] = np.ceil(ppl_width * resize_ratio * 0.8)
#print max_ppl_size
ROI_1 = np.int_(np.dot(pt1,resize_ratio))
ROI_2 = np.int_(np.dot(pt2,resize_ratio))
return [ww, hh, M, ppl_size, ROI_1, ROI_2]开发者ID:ChuaKhangHui， 项目名称:Raspi， 代码行数:28， 代码来源:pb_combine.py
示例6: DepositDataToGrid3D
def DepositDataToGrid3D(data, coords, N, hsml, gridres, rmax, griddata):
norm = 2.5464790894703255 #8/np.pi for 3D
grid_dx = 2*rmax/(gridres-1)
zSqr = coords[:,2]*coords[:,2]
hsml_plane = np.sqrt(hsml[:]*hsml[:] - zSqr)
shift_coords = coords[:,:2] + rmax
gxmin = np.int_((shift_coords[:,0] - hsml_plane[:])/grid_dx + 0.5)
gxmax = np.int_((shift_coords[:,0] + hsml_plane[:])/grid_dx)
gymin = np.int_((shift_coords[:,1] - hsml_plane[:])/grid_dx + 0.5)
gymax = np.int_((shift_coords[:,1] + hsml_plane[:])/grid_dx)
for i in xrange(N):
h = hsml[i]
mh3 = data[i,:]/h**3
z2 = zSqr[i]
if gxmin[i] < 0:
gxmin[i] = 0
if gxmax[i] > gridres - 1:
gxmax[i] = gridres - 1
if gymin[i] < 0:
gymin[i] = 0
if gymax[i] > gridres - 1:
gymax[i] = gridres - 1
for gx in xrange(gxmin[i], gxmax[i]+1):
for gy in xrange(gymin[i], gymax[i]+1):
q = np.sqrt((shift_coords[i,0] - gx*grid_dx)**2 + (shift_coords[i,1] - gy*grid_dx)**2 + z2)/h
if q <= 0.5:
griddata[gy, gx,:] += (1 - 6*q**2 + 6*q**3) * mh3
elif q <= 1.0:
griddata[gy, gx,:] += (2*(1-q)**3) * mh3
griddata[:] = norm*griddata[:]开发者ID:omgspace， 项目名称:GHOST， 代码行数:34， 代码来源:GridDeposit.py
示例7: get_many_patches
def get_many_patches(image, patch_shape, centers,
flat=True, step=1, force_pure_python=False):
"""Return the patches at given centers"""
patch_shape = tuple(patch_shape)
centers = np.reshape(np.asarray(centers, dtype=np.int_), (-1, len(patch_shape)))
ndims = len(patch_shape)
if ndims in [2,3] and "_get_many_patches" in globals() and not force_pure_python:
# 3d version (efficient Cython implementation)
patches = _get_many_patches(ndims, image, patch_shape, centers, step)
else:
# Extract patches (pure Python version)
grid_slices = tuple(slice(-(i//2), i-i//2, step) for i in patch_shape)
grid = np.reshape(np.mgrid[grid_slices], (len(patch_shape), -1))
points = tuple(np.int_(centers.T[:,:,np.newaxis]) + np.int_(grid[:,np.newaxis,:]))
patches = image[points]
# Compute the final patch shape taking into acount the step
final_shape = tuple((sh - 1)/step + 1 for sh in patch_shape)
channels = image.shape[len(patch_shape):]
if not flat:
patches = np.reshape(patches, (-1,) + tuple(final_shape) + channels)
else:
patches = np.reshape(patches, (len(patches), np.prod(final_shape + channels)))
return patches开发者ID:cjaques， 项目名称:ilastik， 代码行数:27， 代码来源:patch_utils.py
示例8: azimToBeam
def azimToBeam(self, azim):
''' Get azimuth of given beam. Return a negative beam number (offset by
one instead of zero) if the azimuth corresponds to the back lobe.
Return np.nan if the azimuth is not covered by any beam.
**Args**:
* **azim** (float): beam azimuth [deg. East]
**Returns**:
* **beam** (int): beam number
'''
import numpy as np
# Assume the azimuth comes from the front lobe
phi = np.radians(azim - self.boresite)
delta = np.degrees(np.arctan2(np.sin(phi), np.cos(phi)))
beam = np.round(delta / self.bmsep + (self.maxbeam - 1) / 2.)
if beam < 0.0 or beam > self.maxbeam:
# This azimuth lies outside the front lobe
phi = np.radians(self.boresite - azim - 180.0)
delta = np.degrees(np.arctan2(np.sin(phi), np.cos(phi)))
beam = np.round(delta / self.bmsep + (self.maxbeam - 1) / 2.)
# Seperate back lobe azimuths from azimuths outside of either
# field-of-view
if beam >= 0 and beam < self.maxbeam:
beam = -np.int_(beam + 1)
else:
beam = np.nan
else:
beam = np.int_(beam)
return beam开发者ID:doby123， 项目名称:davitpy， 代码行数:33， 代码来源:radStruct.py
示例9: EField
def EField(X,Q,gamma,kern,steps):
N=X.shape[0];
X[:,2]=X[:,2]*gamma
X=X/steps
X_min=np.min(X,axis=0)
X_mid=np.dot(Q,X)/np.sum(Q);
X_off=np.floor(X_min-X_mid)+X_mid;
X=X-X_off
nx,ny,nz=np.int_(3+np.floor(np.max(X,axis=0)))
nzny=nz*ny
Xi=np.int_(np.floor(X)+1)
inds=np.int_(Xi[:,0]*nzny+Xi[:,1]*nz+Xi[:,2]) # 3d -> 1d
q=np.bincount(inds,Q,nzny*nx)
print len(q), nx*ny*nz
q=q.reshape(nx,ny,nz)
#t0=time.time()
print q.shape, steps
p,kern=Phi(q,kern,steps)
#t1=time.time(); print t1-t0
Ex=np.zeros(p.shape);Ey=np.zeros(p.shape);Ez=np.zeros(p.shape);
Ex[:nx-1,:,:]=(p[:nx-1,:,:]-p[1:nx,:,:])/steps[0]
Ey[:,:ny-1,:]=(p[:,:ny-1,:]-p[:,1:ny,:])/steps[1]
Ez[:,:,:nz-1]=(p[:,:,:nz-1]-p[:,:,1:nz])/steps[2]
Exyz=np.zeros((N,3))
Exyz[:,0]=ndimage.map_coordinates(Ex,np.c_[X[:,0],X[:,1]+0.5,X[:,2]+0.5].T,order=1)*gamma
Exyz[:,1]=ndimage.map_coordinates(Ey,np.c_[X[:,0]+0.5,X[:,1],X[:,2]+0.5].T,order=1)*gamma
Exyz[:,2]=ndimage.map_coordinates(Ez,np.c_[X[:,0]+0.5,X[:,1]+0.5,X[:,2]].T,order=1)
#t1=time.time(); print t1-t0
return Exyz开发者ID:iagapov， 项目名称:desy， 代码行数:30， 代码来源:space_charge_ver05.py
示例10: totalPower
def totalPower(latitude, timeTuple):
global shell_normal
global shell_faceO
global shell_vertO
matrixImport()
month = timeTuple[1]
day = timeTuple[2]
hour = timeTuple[3]
heading = 85 # Moving SSE
shell_heading = heading
shell_azimuths = 180/math.pi*numpy.arctan2(-shell_normal[:,1] ,shell_normal[:,0]) + heading
shell_tilts = 90 - 180/math.pi*numpy.arcsin(shell_normal[:,2])
a = shell_vertO[numpy.int_(shell_faceO[:,0]),:]
b = shell_vertO[numpy.int_(shell_faceO[:,1]),:]
c = shell_vertO[numpy.int_(shell_faceO[:,2]),:]
v1 = b - a
v2 = c - a
temp = numpy.cross(v1,v2)**2
temp = numpy.sum(temp, 1)
shell_Area = 0.5*temp**0.5
#shell_area = numpy.sum(shell_Area)
shell_flux = incident_radiation(month, day, hour, shell_tilts, shell_azimuths, latitude)
shell_power = numpy.dot(shell_flux,shell_Area)
#shell_fluxavg = shell_power/shell_area
#return shell_fluxavg
return shell_power开发者ID:kagelump， 项目名称:calsol， 代码行数:26， 代码来源:Predictor.py
示例11: _find_nearest_node_ndarray
def _find_nearest_node_ndarray(rmg, coords, mode='raise'):
"""Find the node nearest to a point.
Parameters
----------
rmg : RasterModelGrid
A RasterModelGrid.
coords : tuple of float
Coordinates of test points as *x*, then *y*.
mode : {'raise', 'wrap', 'clip'}, optional
What to do with out-of-bounds indices (as with
numpy.ravel_multi_index).
Returns
-------
ndarray
Nodes that are closest to the points.
Examples
--------
>>> from landlab.grid.raster_funcs import _find_nearest_node_ndarray
>>> from landlab import RasterModelGrid
>>> import numpy as np
>>> grid = RasterModelGrid((4, 5))
>>> _find_nearest_node_ndarray(grid, (.25, 1.25))
5
>>> _find_nearest_node_ndarray(grid, (.75, 2.25))
11
"""
column_indices = np.int_(
np.around((coords[0] - rmg.node_x[0]) / rmg.node_spacing))
row_indices = np.int_(
np.around((coords[1] - rmg.node_y[0]) / rmg.node_spacing))
return rmg.grid_coords_to_node_id(row_indices, column_indices, mode=mode)开发者ID:jennyknuth， 项目名称:landlab， 代码行数:35， 代码来源:raster_funcs.py
示例12: getSuperPixelColorHistogram
def getSuperPixelColorHistogram(superpixels, image):
colors = []
#newIm = image
numSuperpixels = np.max(superpixels)+1
for i in xrange(0,numSuperpixels):
temp = np.zeros((1,64),dtype = float)
indices = np.where(superpixels==i)
color = image[indices]
for j in xrange(0,color.shape[0]):
r = np.int_(color[j][0]/0.25)
g = np.int_(color[j][1]/0.25)
b = np.int_(color[j][2]/0.25)
if r ==4:
r = 3
if g == 4:
g = 3
if b == 4:
b = 3
x = 16*r+4*g+b*1
temp[0][x] = temp[0][x]+1
#min_max_scaler = preprocessing.MinMaxScaler()
#t = min_max_scaler.fit_transform(temp[0])
#print t
colors.append(temp[0])
#showPlots(newIm, numSuperpixels, superpixels)
return np.array(colors)开发者ID:HastyJ， 项目名称:Road-Estimation， 代码行数:26， 代码来源:superpixel.py
示例13: main
def main():
# объект dtype=float32
f = np.float32(1.0)
print('Объект: {}\nТип данных: {}'.format(f, type(f)))
# объект np.ndarray, полученный из python списка, с автоматическим определеникм dtype
ar = np.array([1, 2, 3])
print('Массив: {}\nТип данных массива (dtype): {}\nТип данных элемента массива: {}'.format(ar, type(ar), type(ar[0])))
# объект np.ndarray, полученный из python списка
ar_int32 = np.array([1, 2, 3], dtype=np.int32)
print('Массив: {}\nТип данных массива (dtype): {}\nТип данных элемента массива: {}'.format(ar_int32, type(ar_int32), type(ar_int32[0])))
# объект np.ndarray, полученный при помощи конструктора типа dtype
i_int = np.int_(10)
print('Объект: {}\nТип данных: {}'.format(i_int, type(i_int)))
ar_int = np.int_([10, 20, 30])
print('Массив: {}\nТип данных масива (dtype: {}\nТип данных элемента массива: {}'.format(ar_int, type(ar_int), type(ar_int[0])))
ar_bool = np.bool_([0, 1, 0, 0, 1, 1, 1])
print('Тип данных массива: {}'.format(ar_bool.dtype))
ar_int64 = np.array(range(100), dtype=np.int_)
print('Тип данных массива: {}'.format(ar_int64.dtype))
ar_float = np.array([1.03, 1, -5.9, 4.6], dtype=np.float16)
print('Массив ndarray: {}'.format(ar_float))
print('Тип данных массива: {}'.format(type(ar_float)))
ar_scalar = ar_float[3]
print('Значение скаляра массива: {}'.format(ar_scalar))
print('Тип данных скаляра массива: {}'.format(ar_scalar.dtype))开发者ID:arheo， 项目名称:python_core， 代码行数:33， 代码来源:data_type.py
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