223031012
/
DAMASK_EICMD
mirror of https://github.com/achalhp/DAMASK_EICMD.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge remote-tracking branch 'origin/development' into keyword-view

This commit is contained in:
Martin Diehl 2021-12-19 22:58:38 +01:00
parent 2fc25c6fa9 eecfed7cda
commit 25ab62402a
5 changed files with 70 additions and 55 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
PRIVATE

@ -1 +1 @@
Subproject commit 2ad27552c43316735b6ef425737fe3c8a5231598
Subproject commit 96c32ba4237a51eaad92cd139e1a716ee5b32493

2
python/damask/VERSION
View File

@ -1 +1 @@
v3.0.0-alpha5-283-gdacd08f39
v3.0.0-alpha5-297-g5ecfba1e5

28
python/damask/_colormap.py
View File

@ -6,6 +6,10 @@ from pathlib import Path
from typing import Sequence, Union, TextIO
import numpy as np
try:
from numpy.typing import ArrayLike
except ImportError:
ArrayLike = Union[np.ndarray,Sequence[float]] # type: ignore
import scipy.interpolate as interp
import matplotlib as mpl
if os.name == 'posix' and 'DISPLAY' not in os.environ:
@ -78,8 +82,8 @@ class Colormap(mpl.colors.ListedColormap):
@staticmethod
def from_range(low: Sequence[float],
high: Sequence[float],
def from_range(low: ArrayLike,
high: ArrayLike,
name: str = 'DAMASK colormap',
N: int = 256,
model: str = 'rgb') -> 'Colormap':
@ -129,7 +133,7 @@ class Colormap(mpl.colors.ListedColormap):
if model.lower() not in toMsh:
raise ValueError(f'Invalid color model: {model}.')
low_high = np.vstack((low,high))
low_high = np.vstack((low,high)).astype(float)
out_of_bounds = np.bool_(False)
if model.lower() == 'rgb':
@ -142,7 +146,7 @@ class Colormap(mpl.colors.ListedColormap):
out_of_bounds = np.any(low_high[:,0]<0)
if out_of_bounds:
raise ValueError(f'{model.upper()} colors {low} | {high} are out of bounds.')
raise ValueError(f'{model.upper()} colors {low_high[0]} | {low_high[1]} are out of bounds.')
low_,high_ = map(toMsh[model.lower()],low_high)
msh = map(functools.partial(Colormap._interpolate_msh,low=low_,high=high_),np.linspace(0,1,N))
@ -225,7 +229,7 @@ class Colormap(mpl.colors.ListedColormap):
def shade(self,
field: np.ndarray,
bounds: Sequence[float] = None,
bounds: ArrayLike = None,
gap: float = None) -> Image:
"""
Generate PIL image of 2D field using colormap.
@ -235,7 +239,7 @@ class Colormap(mpl.colors.ListedColormap):
field : numpy.array, shape (:,:)
Data to be shaded.
bounds : sequence of float, len (2), optional
Value range (low,high) spanned by colormap.
Value range (left,right) spanned by colormap.
gap : field.dtype, optional
Transparent value. NaN will always be rendered transparent.
@ -248,17 +252,17 @@ class Colormap(mpl.colors.ListedColormap):
mask = np.logical_not(np.isnan(field) if gap is None else \
np.logical_or (np.isnan(field), field == gap)) # mask NaN (and gap if present)
lo,hi = (field[mask].min(),field[mask].max()) if bounds is None else \
(min(bounds[:2]),max(bounds[:2]))
l,r = (field[mask].min(),field[mask].max()) if bounds is None else \
np.array(bounds,float)[:2]
delta,avg = hi-lo,0.5*(hi+lo)
delta,avg = r-l,0.5*abs(r+l)
if delta * 1e8 <= avg: # delta is similar to numerical noise
hi,lo = hi+0.5*avg,lo-0.5*avg # extend range to have actual data centered within
if abs(delta) * 1e8 <= avg: # delta is similar to numerical noise
l,r = l-0.5*avg*np.sign(delta),r+0.5*avg*np.sign(delta), # extend range to have actual data centered within
return Image.fromarray(
(np.dstack((
self.colors[(np.round(np.clip((field-lo)/(hi-lo),0.0,1.0)*(self.N-1))).astype(np.uint16),:3],
self.colors[(np.round(np.clip((field-l)/delta,0.0,1.0)*(self.N-1))).astype(np.uint16),:3],
mask.astype(float)
)
)*255

65
src/math.f90
View File

@ -961,39 +961,36 @@ pure function math_3333toVoigt66(m3333)
end function math_3333toVoigt66
!--------------------------------------------------------------------------------------------------
!> @brief draw a random sample from Gauss variable
!> @brief Draw a sample from a normal distribution.
!> @details https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform
!> https://masuday.github.io/fortran_tutorial/random.html
!--------------------------------------------------------------------------------------------------
real(pReal) function math_sampleGaussVar(mu, sigma, width)
impure elemental subroutine math_normal(x,mu,sigma)
real(pReal), intent(in) :: mu, & !< mean
sigma !< standard deviation
real(pReal), intent(in), optional :: width !< cut off as multiples of standard deviation
real(pReal), intent(out) :: x
real(pReal), intent(in), optional :: mu, sigma
real(pReal), dimension(2) :: rnd ! random numbers
real(pReal) :: scatter, & ! normalized scatter around mean
width_
real(pReal) :: sigma_, mu_
real(pReal), dimension(2) :: rnd
if (abs(sigma) < tol_math_check) then
math_sampleGaussVar = mu
if (present(mu)) then
mu_ = mu
else
if (present(width)) then
width_ = width
else
width_ = 3.0_pReal ! use +-3*sigma as default scatter
endif
mu_ = 0.0_pReal
end if
if (present(sigma)) then
sigma_ = sigma
else
sigma_ = 1.0_pReal
end if
do
call random_number(rnd)
scatter = width_ * (2.0_pReal * rnd(1) - 1.0_pReal)
if (rnd(2) <= exp(-0.5_pReal * scatter**2)) exit ! test if scattered value is drawn
enddo
x = mu_ + sigma_ * sqrt(-2.0_pReal*log(1.0_pReal-rnd(1)))*cos(2.0_pReal*PI*(1.0_pReal - rnd(2)))
math_sampleGaussVar = scatter * sigma
endif
end function math_sampleGaussVar
end subroutine math_normal
!--------------------------------------------------------------------------------------------------
@ -1434,6 +1431,26 @@ subroutine selfTest
if (dNeq0(math_LeviCivita(ijk(1),ijk(2),ijk(3)))) &
error stop 'math_LeviCivita'
normal_distribution: block
real(pReal), dimension(500000) :: r
real(pReal) :: mu, sigma
call random_number(mu)
call random_number(sigma)
sigma = 1.0_pReal + sigma*5.0_pReal
mu = (mu-0.5_pReal)*10_pReal
call math_normal(r,mu,sigma)
if (abs(mu -sum(r)/real(size(r),pReal))>5.0e-2_pReal) &
error stop 'math_normal(mu)'
mu = sum(r)/real(size(r),pReal)
if (abs(sigma**2 -1.0_pReal/real(size(r)-1,pReal) * sum((r-mu)**2))/sigma > 5.0e-2_pReal) &
error stop 'math_normal(sigma)'
end block normal_distribution
end subroutine selfTest
end module math

18
src/phase_mechanical_plastic_nonlocal.f90
View File

@ -1592,21 +1592,15 @@ subroutine stateInit(ini,phase,Nentries)
stt%rhoSglMobile(s,e) = densityBinning
end do
else ! homogeneous distribution with noise
do e = 1, Nentries
do f = 1,size(ini%N_sl,1)
from = 1 + sum(ini%N_sl(1:f-1))
upto = sum(ini%N_sl(1:f))
do s = from,upto
noise = [math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u), &
math_sampleGaussVar(0.0_pReal, ini%sigma_rho_u)]
stt%rho_sgl_mob_edg_pos(s,e) = ini%rho_u_ed_pos_0(f) + noise(1)
stt%rho_sgl_mob_edg_neg(s,e) = ini%rho_u_ed_neg_0(f) + noise(1)
stt%rho_sgl_mob_scr_pos(s,e) = ini%rho_u_sc_pos_0(f) + noise(2)
stt%rho_sgl_mob_scr_neg(s,e) = ini%rho_u_sc_neg_0(f) + noise(2)
end do
stt%rho_dip_edg(from:upto,e) = ini%rho_d_ed_0(f)
stt%rho_dip_scr(from:upto,e) = ini%rho_d_sc_0(f)
end do
call math_normal(stt%rho_sgl_mob_edg_pos(from:upto,:),ini%rho_u_ed_pos_0(f),ini%sigma_rho_u)
call math_normal(stt%rho_sgl_mob_edg_neg(from:upto,:),ini%rho_u_ed_neg_0(f),ini%sigma_rho_u)
call math_normal(stt%rho_sgl_mob_scr_pos(from:upto,:),ini%rho_u_sc_pos_0(f),ini%sigma_rho_u)
call math_normal(stt%rho_sgl_mob_scr_neg(from:upto,:),ini%rho_u_sc_neg_0(f),ini%sigma_rho_u)
stt%rho_dip_edg(from:upto,:) = ini%rho_d_ed_0(f)
stt%rho_dip_scr(from:upto,:) = ini%rho_d_sc_0(f)
end do
end if