more descriptive names
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07e9778798
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@ -6,40 +6,40 @@ import numpy as np
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class Table():
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"""Store spreadsheet-like data."""
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def __init__(self,array,headings,comments=None):
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def __init__(self,data,shapes,comments=None):
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"""
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New spreadsheet data.
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New spreadsheet.
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Parameters
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----------
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array : numpy.ndarray
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data : numpy.ndarray
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Data.
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headings : dict
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Column headings. Labels as keys and shape as tuple. Example 'F':(3,3) for a deformation gradient.
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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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comments : iterable of str, optional
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Additional, human-readable information
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Additional, human-readable information.
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"""
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self.data = pd.DataFrame(data=array)
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self.data = pd.DataFrame(data=data)
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d = {}
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labels = {}
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i = 0
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for label in headings:
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for components in range(np.prod(headings[label])):
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d[i] = label
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for label in shapes.keys():
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for components in range(np.prod(shapes[label])):
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labels[i] = label
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i+=1
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if i != self.data.shape[1]:
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raise IndexError('Mismatch between array shape and headings')
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raise IndexError('Shape mismatch between shapes and data')
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self.data.rename(columns=d,inplace=True)
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self.data.rename(columns=labels,inplace=True)
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if comments is None:
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self.comments = []
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else:
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self.comments = [c for c in comments]
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self.headings = headings
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self.shapes = shapes
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@staticmethod
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def from_ASCII(fname):
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@ -47,8 +47,8 @@ class Table():
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Create table from ASCII file.
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The first line needs to indicate the number of subsequent header lines as 'n header'.
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Vector data labels are indicated by '1_v, 2_v, ..., n_v'.
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Tensor data labels are indicated by '3x3:1_T, 3x3:2_T, ..., 3x3:9_T'.
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Vector data column labels are indicated by '1_v, 2_v, ..., n_v'.
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Tensor data column labels are indicated by '3x3:1_T, 3x3:2_T, ..., 3x3:9_T'.
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Parameters
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----------
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@ -69,20 +69,20 @@ class Table():
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comments = [f.readline()[:-1] for i in range(header-1)]
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labels = f.readline().split()
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headings = {}
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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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if tensor_column:
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my_shape = tensor_column.group().split(':',1)[0].split('x')
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headings[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
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shapes[label.split('_',1)[1]] = tuple([int(d) for d in my_shape])
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else:
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vector_column = re.match(r'[0-9]*?_',label)
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if vector_column:
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headings[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
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shapes[label.split('_',1)[1]] = (int(label.split('_',1)[0]),)
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else:
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headings[label]=(1,)
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shapes[label]=(1,)
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return Table(np.loadtxt(f),headings,comments)
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return Table(np.loadtxt(f),shapes,comments)
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def get_array(self,label):
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"""
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@ -98,7 +98,7 @@ class Table():
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idx,key = label.split('_',1)
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return self.data[key].to_numpy()[:,int(idx)-1]
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else:
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return self.data[label].to_numpy().reshape((-1,)+self.headings[label])
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return self.data[label].to_numpy().reshape((-1,)+self.shapes[label])
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def set_array(self,label,array,info):
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"""
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@ -129,7 +129,7 @@ class Table():
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def get_labels(self):
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"""Return the labels of all columns."""
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return [label for label in self.headings]
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return list(self.shapes.keys())
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def add_array(self,label,array,info):
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"""
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@ -150,7 +150,7 @@ class Table():
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else:
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self.comments.append('{} {}: {}'.format(label,array.shape[1:],info))
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self.headings[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
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self.shapes[label] = array.shape[1:] if len(array.shape) > 1 else (1,)
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size = np.prod(array.shape[1:],dtype=int)
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new_data = pd.DataFrame(data=array.reshape(-1,size),
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columns=[label for l in range(size)])
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@ -167,15 +167,15 @@ class Table():
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"""
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labels = []
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for l in self.headings:
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if(self.headings[l] == (1,)):
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for l in self.shapes:
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if(self.shapes[l] == (1,)):
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labels.append('{}'.format(l))
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elif(len(self.headings[l]) == 1):
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elif(len(self.shapes[l]) == 1):
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labels+=['{}_{}'.format(i+1,l)\
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for i in range(self.headings[l][0])]
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for i in range(self.shapes[l][0])]
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else:
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labels+=['{}:{}_{}'.format('x'.join([str(d) for d in self.headings[l]]),i+1,l)\
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for i in range(np.prod(self.headings[l],dtype=int))]
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labels+=['{}:{}_{}'.format('x'.join([str(d) for d in self.shapes[l]]),i+1,l)\
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for i in range(np.prod(self.shapes[l],dtype=int))]
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header = ['{} header'.format(len(self.comments)+1)]\
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+ self.comments\
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