def read_scalar(el, ns, set_id, step, newdir, wrt_file):
grain = np.zeros([ns, el**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
if set_id == "cal":
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
grain[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
else:
for filename in os.listdir(nwd):
if filename.endswith('.vtk'):
grain[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
# return to the original directory
os.chdir('..')
np.save('gID_%s%s_s%s' % (ns, set_id, step), grain)
def read_fip(el, ns, set_id, step, newdir, wrt_file):
start = time.time()
fip = np.zeros([ns, el**3])
nwd = os.getcwd() + '\\' + newdir
# nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % step):
fip[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
"""return to the original directory"""
os.chdir('..')
f = h5py.File("fip_%s%s_s%s.hdf5" % (ns, set_id, step), 'a')
f.create_dataset('fip', data=fip)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'fip values read from .vtk file for %s: %s seconds' % (set_id, timeE)
rr.WP(msg, wrt_file)
def read_scalar(el, ns, set_id, step, newdir, wrt_file):
start = time.time()
grain = np.zeros([ns, el**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
# for filename in os.listdir(nwd):
# if filename.endswith('%s.vtk' % step):
# grain[sn, :] = rr.read_vtk_scalar(filename=filename)
# sn += 1
for sn in xrange(ns):
filename = "Ti64_Dream3D_v01_Output_%s.vtk" % str(sn + 1)
grain[sn, :] = rr.read_vtk_scalar(filename=filename)
# return to the original directory
os.chdir('..')
f = h5py.File("data.hdf5", 'a')
dset_name = 'gID_%s%s_s%s' % (ns, set_id, step)
f.create_dataset(dset_name, data=grain)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'The scalar of interest has been read from .vtk file for %s: %s seconds' % (
set_id, timeE)
rr.WP(msg, wrt_file)
def read_fip(ns, set_id, newdir):
start = time.time()
C = const()
fip = np.zeros([ns, C['el']**3])
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
sn = 0
for filename in os.listdir(nwd):
if filename.endswith('%s.vtk' % C['step']):
fip[sn, :] = rr.read_vtk_scalar(filename=filename)
sn += 1
"""return to the original directory"""
os.chdir('..')
f = h5py.File("responses.hdf5", 'a')
f.create_dataset('fip_%s' % set_id, data=(1e9) * fip)
f.close()
end = time.time()
timeE = np.round((end - start), 3)
msg = 'fip values read from .vtk file for %s: %s seconds' % (set_id, timeE)
rr.WP(msg, C['wrt_file'])
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn import linear_model
if __name__ == '__main__':
newdir = 'cal'
pcnt = .0
# nwd = os.getcwd() + '\\' + newdir
nwd = os.getcwd() + '/' + newdir # for unix
os.chdir(nwd)
for filename in os.listdir(nwd):
fip = rr.read_vtk_scalar(filename=filename)
fip = np.sort(fip)
"""return to the original directory"""
os.chdir('..')
"""get the data for the fit"""
x = fip
x = x[np.int64(pcnt * x.size):, None]
y = (np.arange(x.size) + 1) / np.float32(x.size)
"""get the desired fits"""
poly = PolynomialFeatures(degree=20)
X = poly.fit_transform(np.log(x))
clf = linear_model.LinearRegression()
clf.fit(X, y)