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)
