diff --git a/contrib/translations/PDE-reduction and translations.ipynb b/contrib/translations/PDE-reduction and translations.ipynb
index 043f61c4a93999533df35269f69685d76d93a3d8..b3771a1e735ac7a0881fd4f9412debf257caa947 100644
--- a/contrib/translations/PDE-reduction and translations.ipynb
+++ b/contrib/translations/PDE-reduction and translations.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -56,20 +56,20 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([ 5.00000000e+01, 2.49673488e+00, 1.47014861e-01,\n",
- " 2.60554926e+00, -9.96815120e-01, 2.40178914e+00,\n",
- " 5.42629997e-02, 3.38317711e-03, 2.93648067e-01,\n",
- " -5.10921327e-03, 3.78790947e-02, -2.80879544e-02,\n",
- " 1.38914975e-01, -2.60301325e-02, 3.05226127e-02])"
+ "array([ 5.00000000e+01, -6.69626794e-01, -2.33234202e+00,\n",
+ " 2.68444605e+00, -8.96436375e-01, 1.81021063e+00,\n",
+ " -3.27003166e-02, -2.03802375e-01, -5.63064621e-03,\n",
+ " -6.23905999e-02, 3.71388362e-02, -1.37580839e-02,\n",
+ " 1.05569439e-01, -1.39756658e-02, 1.91970436e-02])"
]
},
- "execution_count": 55,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -80,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -115,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -135,7 +135,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -144,7 +144,7 @@
"7.3852783309125538e-16"
]
},
- "execution_count": 64,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -155,7 +155,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -166,101 +166,105 @@
" for j in range(n):\n",
" unit_coeffs = np.zeros(n)\n",
" unit_coeffs[j] = 1\n",
- " unit_mexp = type(mexp)(\n",
- " mexp.toy_ctx, mexp.center, mexp.rscale, mexp.order,\n",
- " unit_coeffs, derived_from=mexp)\n",
+ " unit_mexp = mexp.with_coeffs(unit_coeffs)\n",
" \n",
" result[:, j] = t.multipole_expand(unit_mexp, to_center).coeffs\n",
" \n",
" return result\n",
"\n",
- "tmat = build_translation_mat(mexp, np.array([0, 0.5]))"
+ "new_center = np.array([0, 0.5])\n",
+ "tmat = build_translation_mat(mexp, new_center)"
]
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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- " 0. , 0. , 0. , 0. , 0. ,\n",
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- " [ 0. , 1. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0.5 , 0. , 1. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 1. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0.5 , 0. , 0. , 1. ,\n",
- " 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0.125 , 0. , 0.5 , 0. , 0. ,\n",
- " 1. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 0. , 0. ,\n",
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- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 0.5 , 0. ,\n",
- " 0. , 0. , 1. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0.125 , 0. , 0. , 0.5 ,\n",
- " 0. , 0. , 0. , 1. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0.02083333, 0. , 0.125 , 0. , 0. ,\n",
- " 0.5 , 0. , 0. , 0. , 1. ,\n",
- " 0. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0. , 0. , 0. , 0. ,\n",
- " 1. , 0. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 0. , 0. ,\n",
- " 0. , 0.5 , 0. , 0. , 0. ,\n",
- " 0. , 1. , 0. , 0. , 0. ],\n",
- " [ 0. , 0. , 0. , 0.125 , 0. ,\n",
- " 0. , 0. , 0.5 , 0. , 0. ,\n",
- " 0. , 0. , 1. , 0. , 0. ],\n",
- " [ 0. , 0.02083333, 0. , 0. , 0.125 ,\n",
- " 0. , 0. , 0. , 0.5 , 0. ,\n",
- " 0. , 0. , 0. , 1. , 0. ],\n",
- " [ 0.00260417, 0. , 0.02083333, 0. , 0. ,\n",
- " 0.125 , 0. , 0. , 0. , 0.5 ,\n",
- " 0. , 0. , 0. , 0. , 1. ]])"
+ "<matplotlib.image.AxesImage at 0x7fb83bb70f28>"
]
},
- "execution_count": 62,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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y/FKhDL9UqP8H8zJzDMuV3ZMAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7fb83be18dd8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
],
"source": [
- "tmat"
+ "plt.imshow(tmat)"
]
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f981a252748>"
+ "(15, 9)"
]
},
- "execution_count": 63,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
- },
+ }
+ ],
+ "source": [
+ "nullsp.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "if 1:\n",
+ " reduction_mat = nullsp.T\n",
+ " expansion_mat = nullsp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_coeffs(expn, coeffs, **kwargs):\n",
+ " x = [cid[0] for cid in expn.get_coefficient_identifiers()]\n",
+ " y = [cid[1] for cid in expn.get_coefficient_identifiers()]\n",
+ " plt.scatter(x, y, c=coeffs, **kwargs)\n",
+ " plt.colorbar()\n",
+ "\n",
+ " for cid, coeff in zip(expn.get_coefficient_identifiers(), coeffs):\n",
+ " plt.text(cid[0], cid[1]+0.2, \"%.1f\" % coeff)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
{
"data": {
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jxo38+te/BiArKyui8XdHy3POyck5qnVYVVUVtDvqggsuYO3atbz33nuMHDmS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\nuVULyvkAAAAASUVORK5CYII=\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x7f981a4e8780>"
+ "<matplotlib.figure.Figure at 0x7fb83bb10b38>"
]
},
"metadata": {},
@@ -268,7 +272,88 @@
}
],
"source": [
- "plt.imshow(tmat)"
+ "proj_mexp = mexp.with_coeffs(expansion_mat @ reduction_mat @ mexp.coeffs)\n",
+ "\n",
+ "proj_resid = proj_mexp.coeffs - mexp.coeffs\n",
+ "\n",
+ "plot_coeffs(mpole_expn, np.log10(1e-15+np.abs(proj_resid)), vmin=-15, vmax=2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.24344978758e-14\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(t.l_inf(proj_mexp - mexp, 1.2, center=[3,0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.06581410364e-14\n"
+ ]
+ }
+ ],
+ "source": [
+ "trans_unproj = t.multipole_expand(mexp, new_center)\n",
+ "trans_proj = t.multipole_expand(proj_mexp, new_center)\n",
+ "\n",
+ "print(t.l_inf(trans_unproj - trans_proj, 1.2, center=[3,0]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0.00000000e+00 1.11022302e-16 0.00000000e+00 -2.24732834e+00\n",
+ " 2.22044605e-16 -2.24732834e+00 1.91654814e-02 -9.90567683e-01\n",
+ " 1.91654814e-02 -9.90567683e-01 -5.35500227e-02 2.34496156e-02\n",
+ " -3.03526052e-01 2.34496156e-02 -2.49976029e-01]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(trans_proj.coeffs - trans_unproj.coeffs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "9.4262983793905305e-15"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "la.norm(reduction_mat @ (trans_proj.coeffs - trans_unproj.coeffs))"
]
},
{
diff --git a/sumpy/toys.py b/sumpy/toys.py
index bf153fd060164e90ed1c764f9493bc4f1fd9fe36..5177d869e99c62ebd5035037ae00a532411d00ff 100644
--- a/sumpy/toys.py
+++ b/sumpy/toys.py
@@ -445,6 +445,11 @@ class ExpansionPotentialSource(PotentialSource):
self.expn_style = expn_style
self.text_kwargs = text_kwargs
+ def with_coeffs(self, coeffs):
+ return type(self)(self.toy_ctx, self.center, self.rscale, self.order,
+ coeffs, self.derived_from, radius=self.radius,
+ expn_style=self.expn_style, text_kwargs=self.text_kwargs)
+
class MultipoleExpansion(ExpansionPotentialSource):
def eval(self, targets):