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DAMASK_EICMD
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polishing

This commit is contained in:
Martin Diehl 2020-03-13 00:30:49 +01:00
parent 2f16c1cacf
commit 771663c944
2 changed files with 33 additions and 33 deletions
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38
python/damask/mechanics.py
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@ -8,9 +8,9 @@ def Cauchy(P,F):
Parameters Parameters
---------- ----------
F : numpy.array of shape (:,3,3) or (3,3) F : numpy.ndarray of shape (:,3,3) or (3,3)
Deformation gradient. Deformation gradient.
P : numpy.array of shape (:,3,3) or (3,3) P : numpy.ndarray of shape (:,3,3) or (3,3)
1. Piola-Kirchhoff stress. 1. Piola-Kirchhoff stress.
""" """
@ -27,7 +27,7 @@ def deviatoric_part(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the deviatoric part is computed. Tensor of which the deviatoric part is computed.
""" """
@ -44,7 +44,7 @@ def eigenvalues(T_sym):
Parameters Parameters
---------- ----------
T_sym : numpy.array of shape (:,3,3) or (3,3) T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric tensor of which the eigenvalues are computed. Symmetric tensor of which the eigenvalues are computed.
""" """
@ -59,7 +59,7 @@ def eigenvectors(T_sym,RHS=False):
Parameters Parameters
---------- ----------
T_sym : numpy.array of shape (:,3,3) or (3,3) T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric tensor of which the eigenvectors are computed. Symmetric tensor of which the eigenvectors are computed.
RHS: bool, optional RHS: bool, optional
Enforce right-handed coordinate system. Default is False. Enforce right-handed coordinate system. Default is False.
@ -81,7 +81,7 @@ def left_stretch(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the left stretch is computed. Tensor of which the left stretch is computed.
""" """
@ -94,7 +94,7 @@ def maximum_shear(T_sym):
Parameters Parameters
---------- ----------
T_sym : numpy.array of shape (:,3,3) or (3,3) T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric tensor of which the maximum shear is computed. Symmetric tensor of which the maximum shear is computed.
""" """
@ -109,7 +109,7 @@ def Mises_strain(epsilon):
Parameters Parameters
---------- ----------
epsilon : numpy.array of shape (:,3,3) or (3,3) epsilon : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric strain tensor of which the von Mises equivalent is computed. Symmetric strain tensor of which the von Mises equivalent is computed.
""" """
@ -122,7 +122,7 @@ def Mises_stress(sigma):
Parameters Parameters
---------- ----------
sigma : numpy.array of shape (:,3,3) or (3,3) sigma : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric stress tensor of which the von Mises equivalent is computed. Symmetric stress tensor of which the von Mises equivalent is computed.
""" """
@ -135,9 +135,9 @@ def PK2(P,F):
Parameters Parameters
---------- ----------
P : numpy.array of shape (:,3,3) or (3,3) P : numpy.ndarray of shape (:,3,3) or (3,3)
1. Piola-Kirchhoff stress. 1. Piola-Kirchhoff stress.
F : numpy.array of shape (:,3,3) or (3,3) F : numpy.ndarray of shape (:,3,3) or (3,3)
Deformation gradient. Deformation gradient.
""" """
@ -154,7 +154,7 @@ def right_stretch(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the right stretch is computed. Tensor of which the right stretch is computed.
""" """
@ -167,7 +167,7 @@ def rotational_part(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the rotational part is computed. Tensor of which the rotational part is computed.
""" """
@ -180,7 +180,7 @@ def spherical_part(T,tensor=False):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the hydrostatic part is computed. Tensor of which the hydrostatic part is computed.
tensor : bool, optional tensor : bool, optional
Map spherical part onto identity tensor. Default is false Map spherical part onto identity tensor. Default is false
@ -206,7 +206,7 @@ def strain_tensor(F,t,m):
Parameters Parameters
---------- ----------
F : numpy.array of shape (:,3,3) or (3,3) F : numpy.ndarray of shape (:,3,3) or (3,3)
Deformation gradient. Deformation gradient.
t : {‘V’, ‘U’} t : {‘V’, ‘U’}
Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor. Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
@ -241,7 +241,7 @@ def symmetric(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the symmetrized values are computed. Tensor of which the symmetrized values are computed.
""" """
@ -254,7 +254,7 @@ def transpose(T):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the transpose is computed. Tensor of which the transpose is computed.
""" """
@ -268,7 +268,7 @@ def __polar_decomposition(T,requested):
Parameters Parameters
---------- ----------
T : numpy.array of shape (:,3,3) or (3,3) T : numpy.ndarray of shape (:,3,3) or (3,3)
Tensor of which the singular values are computed. Tensor of which the singular values are computed.
requested : iterable of str requested : iterable of str
Requested outputs: ‘R’ for the rotation tensor, Requested outputs: ‘R’ for the rotation tensor,
@ -296,7 +296,7 @@ def __Mises(T_sym,s):
Parameters Parameters
---------- ----------
T_sym : numpy.array of shape (:,3,3) or (3,3) T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
Symmetric tensor of which the von Mises equivalent is computed. Symmetric tensor of which the von Mises equivalent is computed.
s : float s : float
Scaling factor (2/3 for strain, 3/2 for stress). Scaling factor (2/3 for strain, 3/2 for stress).

28
python/damask/table.py
View File

@ -5,22 +5,22 @@ import numpy as np
from . import version from . import version
class Table(): class Table:
"""Store spreadsheet-like data.""" """Store spreadsheet-like data."""
def __init__(self,data,shapes,comments=None): def __init__(self,data,shapes,comments=None):
""" """
New spreadsheet. New spreadsheet.
Parameters Parameters
---------- ----------
data : numpy.ndarray or pandas.DataFrame data : numpy.ndarray or pandas.DataFrame
Data. Column labels from a pandas.DataFrame will be replaced. Data. Column labels from a pandas.DataFrame will be replaced.
shapes : dict with str:tuple pairs shapes : dict with str:tuple pairs
Shapes of the columns. Example 'F':(3,3) for a deformation gradient. Shapes of the columns. Example 'F':(3,3) for a deformation gradient.
comments : iterable of str, optional comments : iterable of str, optional
Additional, human-readable information. Additional, human-readable information.
""" """
self.comments = [] if comments is None else [c for c in comments] self.comments = [] if comments is None else [c for c in comments]
self.data = pd.DataFrame(data=data) self.data = pd.DataFrame(data=data)
@ -35,8 +35,8 @@ class Table():
size = int(np.prod(shape)) size = int(np.prod(shape))
labels += ['{}{}'.format('' if size == 1 else '{}_'.format(i+1),label) for i in range(size)] labels += ['{}{}'.format('' if size == 1 else '{}_'.format(i+1),label) for i in range(size)]
self.data.columns = labels self.data.columns = labels
def __label_condensed(self): def __label_condensed(self):
"""Label data condensed, e.g. 1_v 2_v 3_v ==> v v v.""" """Label data condensed, e.g. 1_v 2_v 3_v ==> v v v."""
labels = [] labels = []
@ -51,7 +51,7 @@ class Table():
' '+str(shape) if np.prod(shape,dtype=int) > 1 else '', ' '+str(shape) if np.prod(shape,dtype=int) > 1 else '',
info)) info))
@staticmethod @staticmethod
def from_ASCII(fname): def from_ASCII(fname):
""" """
@ -88,7 +88,7 @@ class Table():
comments.append(line.lstrip('#').strip()) comments.append(line.lstrip('#').strip())
line = f.readline().strip() line = f.readline().strip()
labels = line.split() labels = line.split()
shapes = {} shapes = {}
for label in labels: for label in labels:
tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label) tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label)
@ -101,7 +101,7 @@ class Table():
shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),) shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
else: else:
shapes[label] = (1,) shapes[label] = (1,)
data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+') data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+')
return Table(data,shapes,comments) return Table(data,shapes,comments)
@ -133,7 +133,7 @@ class Table():
except TypeError: except TypeError:
f = fname f = fname
f.seek(0) f.seek(0)
content = f.readlines() content = f.readlines()
comments = ['table.py:from_ang v {}'.format(version)] comments = ['table.py:from_ang v {}'.format(version)]
@ -142,7 +142,7 @@ class Table():
comments.append(line.strip()) comments.append(line.strip())
else: else:
break break
data = np.loadtxt(content) data = np.loadtxt(content)
for c in range(data.shape[1]-10): for c in range(data.shape[1]-10):
shapes['n/a_{}'.format(c+1)] = (1,) shapes['n/a_{}'.format(c+1)] = (1,)
@ -168,7 +168,7 @@ class Table():
if re.match(r'[0-9]*?_',label): if re.match(r'[0-9]*?_',label):
idx,key = label.split('_',1) idx,key = label.split('_',1)
data = self.data[key].to_numpy()[:,int(idx)-1].reshape((-1,1)) data = self.data[key].to_numpy()[:,int(idx)-1].reshape((-1,1))
else: else:
data = self.data[label].to_numpy().reshape((-1,)+self.shapes[label]) data = self.data[label].to_numpy().reshape((-1,)+self.shapes[label])
return data.astype(type(data.flatten()[0])) return data.astype(type(data.flatten()[0]))
@ -194,7 +194,7 @@ class Table():
idx,key = label.split('_',1) idx,key = label.split('_',1)
iloc = self.data.columns.get_loc(key).tolist().index(True) + int(idx) -1 iloc = self.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
self.data.iloc[:,iloc] = data self.data.iloc[:,iloc] = data
else: else:
self.data[label] = data.reshape(self.data[label].shape) self.data[label] = data.reshape(self.data[label].shape)