polishing
This commit is contained in:
Martin Diehl
parent
2f16c1cacf
commit
771663c944
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@ -8,9 +8,9 @@ def Cauchy(P,F):
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Parameters
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----------
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F : numpy.array of shape (:,3,3) or (3,3)
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F : numpy.ndarray of shape (:,3,3) or (3,3)
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Deformation gradient.
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P : numpy.array of shape (:,3,3) or (3,3)
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P : numpy.ndarray of shape (:,3,3) or (3,3)
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1. Piola-Kirchhoff stress.
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"""
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@ -27,7 +27,7 @@ def deviatoric_part(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the deviatoric part is computed.
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"""
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@ -44,7 +44,7 @@ def eigenvalues(T_sym):
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Parameters
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----------
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T_sym : numpy.array of shape (:,3,3) or (3,3)
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the eigenvalues are computed.
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"""
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@ -59,7 +59,7 @@ def eigenvectors(T_sym,RHS=False):
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Parameters
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----------
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T_sym : numpy.array of shape (:,3,3) or (3,3)
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the eigenvectors are computed.
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RHS: bool, optional
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Enforce right-handed coordinate system. Default is False.
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@ -81,7 +81,7 @@ def left_stretch(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the left stretch is computed.
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"""
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@ -94,7 +94,7 @@ def maximum_shear(T_sym):
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Parameters
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----------
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T_sym : numpy.array of shape (:,3,3) or (3,3)
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the maximum shear is computed.
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"""
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@ -109,7 +109,7 @@ def Mises_strain(epsilon):
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Parameters
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----------
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epsilon : numpy.array of shape (:,3,3) or (3,3)
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epsilon : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric strain tensor of which the von Mises equivalent is computed.
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"""
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@ -122,7 +122,7 @@ def Mises_stress(sigma):
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Parameters
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----------
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sigma : numpy.array of shape (:,3,3) or (3,3)
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sigma : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric stress tensor of which the von Mises equivalent is computed.
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"""
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@ -135,9 +135,9 @@ def PK2(P,F):
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Parameters
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----------
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P : numpy.array of shape (:,3,3) or (3,3)
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P : numpy.ndarray of shape (:,3,3) or (3,3)
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1. Piola-Kirchhoff stress.
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F : numpy.array of shape (:,3,3) or (3,3)
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F : numpy.ndarray of shape (:,3,3) or (3,3)
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Deformation gradient.
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"""
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@ -154,7 +154,7 @@ def right_stretch(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the right stretch is computed.
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"""
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@ -167,7 +167,7 @@ def rotational_part(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the rotational part is computed.
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"""
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@ -180,7 +180,7 @@ def spherical_part(T,tensor=False):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the hydrostatic part is computed.
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tensor : bool, optional
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Map spherical part onto identity tensor. Default is false
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@ -206,7 +206,7 @@ def strain_tensor(F,t,m):
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Parameters
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----------
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F : numpy.array of shape (:,3,3) or (3,3)
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F : numpy.ndarray of shape (:,3,3) or (3,3)
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Deformation gradient.
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t : {‘V’, ‘U’}
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Type of the polar decomposition, ‘V’ for left stretch tensor and ‘U’ for right stretch tensor.
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@ -241,7 +241,7 @@ def symmetric(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the symmetrized values are computed.
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"""
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@ -254,7 +254,7 @@ def transpose(T):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the transpose is computed.
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"""
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@ -268,7 +268,7 @@ def __polar_decomposition(T,requested):
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Parameters
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----------
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T : numpy.array of shape (:,3,3) or (3,3)
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T : numpy.ndarray of shape (:,3,3) or (3,3)
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Tensor of which the singular values are computed.
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requested : iterable of str
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Requested outputs: ‘R’ for the rotation tensor,
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@ -296,7 +296,7 @@ def __Mises(T_sym,s):
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Parameters
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----------
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T_sym : numpy.array of shape (:,3,3) or (3,3)
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T_sym : numpy.ndarray of shape (:,3,3) or (3,3)
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Symmetric tensor of which the von Mises equivalent is computed.
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s : float
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Scaling factor (2/3 for strain, 3/2 for stress).
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@ -5,22 +5,22 @@ import numpy as np
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from . import version
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class Table():
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class Table:
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"""Store spreadsheet-like data."""
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def __init__(self,data,shapes,comments=None):
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"""
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New spreadsheet.
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Parameters
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----------
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data : numpy.ndarray or pandas.DataFrame
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Data. Column labels from a pandas.DataFrame will be replaced.
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shapes : dict with str:tuple pairs
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Shapes of the columns. Example 'F':(3,3) for a deformation gradient.
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Shapes of the columns. Example 'F':(3,3) for a deformation gradient.
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comments : iterable of str, optional
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Additional, human-readable information.
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"""
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self.comments = [] if comments is None else [c for c in comments]
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self.data = pd.DataFrame(data=data)
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@ -35,8 +35,8 @@ class Table():
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size = int(np.prod(shape))
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labels += ['{}{}'.format('' if size == 1 else '{}_'.format(i+1),label) for i in range(size)]
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self.data.columns = labels
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def __label_condensed(self):
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"""Label data condensed, e.g. 1_v 2_v 3_v ==> v v v."""
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labels = []
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@ -51,7 +51,7 @@ class Table():
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' '+str(shape) if np.prod(shape,dtype=int) > 1 else '',
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info))
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@staticmethod
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def from_ASCII(fname):
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"""
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@ -88,7 +88,7 @@ class Table():
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comments.append(line.lstrip('#').strip())
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line = f.readline().strip()
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labels = line.split()
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shapes = {}
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for label in labels:
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tensor_column = re.search(r'[0-9,x]*?:[0-9]*?_',label)
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@ -101,7 +101,7 @@ class Table():
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shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
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else:
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shapes[label] = (1,)
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data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+')
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return Table(data,shapes,comments)
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@ -133,7 +133,7 @@ class Table():
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except TypeError:
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f = fname
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f.seek(0)
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content = f.readlines()
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comments = ['table.py:from_ang v {}'.format(version)]
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@ -142,7 +142,7 @@ class Table():
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comments.append(line.strip())
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else:
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break
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data = np.loadtxt(content)
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for c in range(data.shape[1]-10):
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shapes['n/a_{}'.format(c+1)] = (1,)
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@ -168,7 +168,7 @@ class Table():
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if re.match(r'[0-9]*?_',label):
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idx,key = label.split('_',1)
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data = self.data[key].to_numpy()[:,int(idx)-1].reshape((-1,1))
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else:
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else:
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data = self.data[label].to_numpy().reshape((-1,)+self.shapes[label])
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return data.astype(type(data.flatten()[0]))
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@ -194,7 +194,7 @@ class Table():
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idx,key = label.split('_',1)
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iloc = self.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
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self.data.iloc[:,iloc] = data
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else:
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else:
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self.data[label] = data.reshape(self.data[label].shape)
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