def drawStuff(self, background=False): Samp = Sampler() if background: Points = [-2, -1, 4, -1, 4, 1, -2, 1] else: Points = [-1, -1, 1, -1, 1, 1, -1, 1] Indicies = [0, 1, 2, 0, 2, 3] for i in range(len(Points)): Points[i] = Points[i] * self.scale Array = array.array("f", Points) ArrayBuffer = Buffer(Array) IndicieArray = array.array("I", Indicies) IndicieBuffer = Buffer(IndicieArray) TextureBuffer = Buffer(array.array("f", [0, 0, 1, 0, 1, 1, 0, 1])) tmp = array.array("I", [0]) glGenVertexArrays(1, tmp) vao = tmp[0] glBindVertexArray(vao) ArrayBuffer.bind(GL_ARRAY_BUFFER) IndicieBuffer.bind(GL_ELEMENT_ARRAY_BUFFER) glEnableVertexAttribArray(0) glVertexAttribPointer(0, 2, GL_FLOAT, False, 2 * 4, 0) TextureBuffer.bind(GL_ARRAY_BUFFER) glEnableVertexAttribArray(1) glVertexAttribPointer(1, 2, GL_FLOAT, False, 2 * 4, 0) glBindVertexArray(0) self.samp = Samp self.vao = vao
def model_eval(nspins,alpha,op,nsweeps,model_file,state_file=None,opname = 'energy'): wf = Nqs.Nqs(nspins,alpha) wf.load_parameters(model_file) samp = Sampler.Sampler(wf,op,opname=opname) estav = samp.run(nsweeps,state_file) return estav
def update_vector(self, wf, init_state, batch_size, gamma, step, therm=False): self.nvar = wf.N + wf.M + wf.N*wf.M wf.init_theta(init_state) samp = Sampler.Sampler(wf,self.h,mag0=self.m) # if init_state = np.array([]), Sampler.run() will do init_random_state() samp.state = np.copy(init_state) if therm == True: # where change the samp.state samp.thermalize(batch_size) #print(samp.state) results = Parallel(n_jobs=self.parallel_cores)(\ delayed(get_sampler)(samp,self) for i in range(batch_size)) #print(samp.state) #print("parallel_process\n") # three kinds of results with extra dimension "batch_size for" statistic average elocals = np.array([i[0] for i in results]) deriv_vectors = np.array([i[1] for i in results]) states = np.array([i[2] for i in results]) # v = S^(-1)*F; W(p+1) = W(p) - gamma(p)*v; W are all the wf variables # S*v = F; So this is a problem to solve the quation and find v # Thus the cov_operator*v represent S*v, i.e. self.cov_operator ''' cov_operator = LinearOperator((wf.N,wf.N), dtype=complex,\ matvec=lambda v: self.cov_operator(v,deriv_vectors,step)) ''' forces = self.get_forces(elocals, deriv_vectors) vec, info = cg(self.cov_operator(deriv_vectors,step), forces) updates = -gamma * vec self.step_count += batch_size return updates, samp.state, np.mean(elocals) / self.nspins
def setup(): glEnable(GL_BLEND) glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA) glEnable(GL_MULTISAMPLE) samp = Sampler() samp.bind(0) glClearColor(0.2,0.4,0.6,0) prog = Program("vs.txt","fs.txt") prog.use() globs.car = Car()
def __init__(self, fontname, size): if Text.prog == None: Text.prog = Program("TextVertexShader.txt", "TextFragmentShader.txt") self.txt = "temp" self.samp = Sampler() self.font = TTF_OpenFont( os.path.join("assets", fontname).encode(), size) open(os.path.join("assets", fontname)) vbuff = Buffer(array.array("f", [0, 0, 1, 0, 1, 1, 0, 1])) ibuff = Buffer(array.array("I", [0, 1, 2, 0, 2, 3])) tmp = array.array("I", [0]) glGenVertexArrays(1, tmp) self.vao = tmp[0] glBindVertexArray(self.vao) ibuff.bind(GL_ELEMENT_ARRAY_BUFFER) vbuff.bind(GL_ARRAY_BUFFER) glEnableVertexAttribArray(0) glVertexAttribPointer(0, 2, GL_FLOAT, False, 2 * 4, 0) glBindVertexArray(0) self.tex = DataTexture2DArray(1, 1, 1, array.array("B", [0, 0, 0, 0])) self.textQuadSize = vec2(0, 0) self.pos = vec2(0, 0) self.dirty = False surf1p = TTF_RenderUTF8_Blended(self.font, self.txt.encode(), SDL_Color(255, 255, 255, 255)) surf2p = SDL_ConvertSurfaceFormat(surf1p, SDL_PIXELFORMAT_ABGR8888, 0) w = surf2p.contents.w h = surf2p.contents.h pitch = surf2p.contents.pitch if pitch != w * 4: print("Uh Oh!", pitch, w) pix = surf2p.contents.pixels B = string_at(pix, pitch * h) self.tex.setData(w, h, 1, B) SDL_FreeSurface(surf2p) SDL_FreeSurface(surf1p) self.textQuadSize = vec2(w, h) self.dirty = False Program.setUniform("textPosInPixels", self.pos) Program.setUniform("textQuadSizeInPixels", self.textQuadSize) Program.updateUniforms()
raw_input("Press enter to exit...") # ================================================= # PHASE I: scan for transition configurations # ================================================= with env: # instantitate multi-modal sampler query = [np.array([-5, 0, 20.3]), np.array(final_task[0])] n = float(robots.instance_number - 1) pr = (float(RADIUS * 2) * (n - 2)) / 100 pair0 = (UNLOCK, LOCK0) pair1 = (UNLOCK, LOCKN) sampler01 = Sampler(mode=3, is_trans=True, pair=pair0, pair_range=pr) sampler02 = Sampler(mode=4, is_trans=True, pair=pair1, pair_range=pr) trans_samplers = [sampler01] #trans_samplers = [sampler01, sampler02] # multiModalPlanning init_node, goal_node, _ = effMultiModalPlanner(query, robots, trans_samplers, CLIFF, pr) # heuristic search frontier = [(0, init_node)] parent = {init_node: (0, None)} while True: cost, node = hq.heappop(frontier) if node == goal_node:
keys = ['event_info', 'jet1_PFCands', 'jet1_extraInfo', 'jet2_PFCands', 'jet2_extraInfo', 'jet_kinematics', 'truth_label'] base_dir = "/eos/user/t/tloesche/CASE_data/CASE_pancakes/" out_dir = "/eos/cms/store/group/phys_b2g/CASE/h5_files/2017/BB_norepeats_v3_2500/" qcd1_name = base_dir + "QCD/qcd_1000to1500_merge.h5" qcd2_name = base_dir + "QCD/qcd_1500to2000_merge.h5" qcd3_name = base_dir + "QCD/qcd_2000toInf_merge.h5" sig1_name = base_dir + "signal/grav_merge.h5" sig2_name = base_dir + "signal/wprime_0.h5" sig3_name = base_dir + "signal/wkk_0.h5" sig4_name = base_dir + "signal/bstar_merge.h5" qcd1 = Sampler( qcd1_name, pbTofb * 1088., lumi, holdout_frac = bkg_holdout_frac) qcd2 = Sampler( qcd2_name, pbTofb * 99.11, lumi, holdout_frac = bkg_holdout_frac) qcd3 = Sampler( qcd3_name, pbTofb * 20.23, lumi, holdout_frac = bkg_holdout_frac) sig1 = Sampler(sig1_name, n_sig, 1., isSignal = True, holdout_frac = sig_holdout_frac) sig2 = Sampler(sig2_name, n_sig, 1., isSignal = True, holdout_frac = sig_holdout_frac) sig3 = Sampler(sig3_name, n_sig, 1., isSignal = True, holdout_frac = sig_holdout_frac) sig4 = Sampler(sig4_name, n_sig, 1., isSignal = True, holdout_frac = sig_holdout_frac) ws = [qcd1, qcd2, qcd3, sig1, sig2, sig3, sig4] BB = BlackBox(ws, keys, nBatches = options.nBatch) os.system("mkdir %s" % out_dir) f_out_name = out_dir + 'BB' BB.writeOut(f_out_name) #h_name = out_dir + "BB_testset" #BB.writeHoldOut(h_name + ".h5")
#!python3.6 #coding:utf-8 import time import Player import Sampler import BaseWaveMaker import MusicTheory.EqualTemperament import MusicTheory.Scale import MusicTheory.tempo import WaveFile if __name__ == "__main__": wm = BaseWaveMaker.BaseWaveMaker() sampler = Sampler.Sampler() et = MusicTheory.EqualTemperament.EqualTemperament() scale = MusicTheory.Scale.Scale() timebase = MusicTheory.tempo.TimeBase() timebase.BPM = 120 timebase.Metre = (4, 4) nv = MusicTheory.tempo.NoteValue(timebase) p = Player.Player() p.Open() print('---------- メジャー・スケール ----------') for key in [ 'C', 'C+', 'D', 'D+', 'E', 'F', 'F+', 'G', 'G+', 'A', 'A+', 'B' ]: print(key, 'メジャー・スケール') scale.Major(key=key) # print(','.join([MusicTheory.Key.Key.ValueToName(k) for k in scale.Scales])) # print(','.join([str(f0) for f0 in scale.Frequencies]))
from Sampler import * np.random.seed(123) pbTofb = 1000. qcd1 = Sampler("../H5_maker/test_files/QCD_HT1000to1500_test.h5", pbTofb * 1088., 1.) qcd2 = Sampler("../H5_maker/test_files/QCD_HT1500to2000_test.h5", pbTofb * 99.11, 1.) qcd3 = Sampler("../H5_maker/test_files/QCD_HT2000toInf_test.h5", pbTofb * 20.23, 1.) sig = Sampler( "../H5_maker/test_files/WprimeToWZToWhadZhad_narrow_M-3500_TuneCP5_13TeV-madgraph_test.h5", 30000., 1.) ws = [qcd1, qcd2, qcd3, sig] #keys= ['event_info', 'jet_kinematics', 'truth_label', 'jet1_extraInfo', 'jet1_PFCands'] keys = [] BB = BlackBox(ws, keys) #print(BB['truth_label'].shape) #print(BB['jet_kinematics'].shape) BB.writeOut('BB_test_Wprime.h5')
from Sampler import * np.random.seed(123) pbTofb = 1000. bkg_holdout_frac = 0.05 sig_holdout_frac = 0.2 nBatches = 2 lumi = 1. qcd1 = Sampler("../H5_maker/test_files/QCD_HT1000to1500_test.h5", pbTofb * 1088., lumi, holdout_frac=bkg_holdout_frac) qcd2 = Sampler("../H5_maker/test_files/QCD_HT1500to2000_test.h5", pbTofb * 99.11, lumi, holdout_frac=bkg_holdout_frac) qcd3 = Sampler("../H5_maker/test_files/QCD_HT2000toInf_test.h5", pbTofb * 20.23, lumi, holdout_frac=bkg_holdout_frac) sig = Sampler( "../H5_maker/test_files/WprimeToWZToWhadZhad_narrow_M-3500_TuneCP5_13TeV-madgraph_test.h5", 20000., lumi, isSignal=True, holdout_frac=sig_holdout_frac) ws = [qcd1, qcd2, qcd3, sig] #ws = [qcd1] keys = [
np.random.seed(123) pbTofb = 1000. lumi = 41.5 n_sig = 100000. n_batches = 40 keys = [] #everything base_dir = "/eos/cms/store/group/phys_b2g/CASE/h5_files/2017/" qcd1_name = base_dir + "QCD_HT1000to15000_merge.h5" qcd2_name = base_dir + "QCD_HT1500to2000_merge.h5" qcd3_name = base_dir + "QCD_HT2000toInf_merge.h5" sig1_name = base_dir + "BulkGravToZZToZhadZhad_narrow_M-2500.h5" sig2_name = base_dir + "WprimeToWZToWhadZhad_narrow_M-3500.h5" sig3_name = base_dir + "WkkToWRadionToWWW_M2500-R0-08.h5" for i in range(n_batches): print("starting batch %i" % i) qcd1 = Sampler(qcd1_name, pbTofb * 1088., lumi / n_batches) qcd2 = Sampler(qcd2_name, pbTofb * 99.11, lumi / n_batches) qcd3 = Sampler(qcd3_name, pbTofb * 20.23, lumi / n_batches) sig1 = Sampler(sig1_name, n_sig / n_batches, 1., isSignal=True) sig2 = Sampler(sig2_name, n_sig / n_batches, 1., isSignal=True) sig3 = Sampler(sig3_name, n_sig / n_batches, 1., isSignal=True) ws = [qcd1, qcd2, qcd3, sig1, sig2, sig3] BB = BlackBox(ws, keys) BB.writeOut(base_dir + 'BB_batch%i.h5' % i)