int is 32 bit on Windows (cause trouble for hybrid_IA)
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@ -754,8 +754,8 @@ class Grid:
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# materials: 1
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"""
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offset_ = np.array(offset,int) if offset is not None else np.zeros(3,int)
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cells_ = np.array(cells,int) if cells is not None else self.cells
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offset_ = np.array(offset,np.int64) if offset is not None else np.zeros(3,np.int64)
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cells_ = np.array(cells,np.int64) if cells is not None else self.cells
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canvas = np.full(cells_,np.nanmax(self.material) + 1 if fill is None else fill,self.material.dtype)
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@ -797,7 +797,7 @@ class Grid:
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>>> import numpy as np
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>>> import damask
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>>> g = damask.Grid(np.zeros([32]*3,int), np.ones(3)*1e-4)
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>>> g = damask.Grid(np.zeros([32]*3,int),np.ones(3)*1e-4)
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>>> g.mirror('xy',True)
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cells : 64 x 64 x 32
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size : 0.0002 x 0.0002 x 0.0001 m³
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@ -159,8 +159,8 @@ def coordinates0_point(cells: _IntSequence,
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"""
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size_ = _np.array(size,float)
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start = origin + size_/_np.array(cells,int)*.5
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end = origin + size_ - size_/_np.array(cells,int)*.5
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start = origin + size_/_np.array(cells,_np.int64)*.5
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end = origin + size_ - size_/_np.array(cells,_np.int64)*.5
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return _np.stack(_np.meshgrid(_np.linspace(start[0],end[0],cells[0]),
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_np.linspace(start[1],end[1],cells[1]),
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@ -290,7 +290,7 @@ def cellsSizeOrigin_coordinates0_point(coordinates0: _np.ndarray,
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coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
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mincorner = _np.array(list(map(min,coords)))
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maxcorner = _np.array(list(map(max,coords)))
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cells = _np.array(list(map(len,coords)),int)
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cells = _np.array(list(map(len,coords)),_np.int64)
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size = cells/_np.maximum(cells-1,1) * (maxcorner-mincorner)
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delta = size/cells
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origin = mincorner - delta*.5
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@ -455,7 +455,7 @@ def cellsSizeOrigin_coordinates0_node(coordinates0: _np.ndarray,
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coords = [_np.unique(coordinates0[:,i]) for i in range(3)]
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mincorner = _np.array(list(map(min,coords)))
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maxcorner = _np.array(list(map(max,coords)))
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cells = _np.array(list(map(len,coords)),int) - 1
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cells = _np.array(list(map(len,coords)),_np.int64) - 1
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size = maxcorner-mincorner
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origin = mincorner
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@ -431,7 +431,7 @@ def hybrid_IA(dist: np.ndarray,
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scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
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while (not np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):
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repeats = np.rint(scale*dist).astype(int)
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repeats = np.rint(scale*dist).astype(np.int64)
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N_inv_samples = np.sum(repeats)
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scale_,scale,inc_factor = (scale,scale+inc_factor*0.5*(scale - scale_), inc_factor*2.0) \
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if N_inv_samples < N_opt_samples else \
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