diff --git a/.gitignore b/.gitignore index 0f9df37..e0d6391 100644 --- a/.gitignore +++ b/.gitignore @@ -533,6 +533,7 @@ tests/cases/prof_data_fds*/ tests/cases/geomslice_data_fds*/ tests/cases/cface_data_fds*/ tests/cases/slcf_partial_fds*/ +tests/cases/slcf_masks_fds*/ # Pickle cache files *.pickle diff --git a/examples/pl3d/plot3d.ipynb b/examples/pl3d/plot3d.ipynb index 5fc308a..47c0b7b 100644 --- a/examples/pl3d/plot3d.ipynb +++ b/examples/pl3d/plot3d.ipynb @@ -96,7 +96,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -141,8 +141,8 @@ "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] diff --git a/examples/slcf/slices.ipynb b/examples/slcf/slices.ipynb index 972cd45..57b0675 100644 --- a/examples/slcf/slices.ipynb +++ b/examples/slcf/slices.ipynb @@ -157,11 +157,13 @@ "## Mask obstructions\n", "\n", "FDS also writes values inside obstructions. `Mesh.get_obstruction_mask_slice` returns a mask\n", - "that is `False` there; replacing those values with NaN leaves the wall with the door blank.\n", + "that is `False` there; replacing those values with NaN leaves the wall with the door blank. The short piece at the\n", + "floor of the door is an obstruction without thickness that FDS leaves where the `&HOLE` reaches\n", + "the floor; the mask shows obstructions without thickness as one row, so that thin walls stay visible.\n", "\n", - "> **Known issue:** the mask is currently off by one cell, so the blank wall above the door\n", - "> starts too low and a piece of wall shows up at the floor of the door\n", - "> ([#132](https://github.com/FireDynamics/fdsreader/issues/132))." + "`to_global(masked=True)` does the same for all meshes at once (see below). Its option\n", + "`mask=\"placeholders\"` hides only the values FDS did not compute but filled with the ambient\n", + "temperature, and keeps the gas values FDS writes on the faces of an obstruction." ] }, { @@ -172,7 +174,7 @@ "outputs": [ { "data": { - "image/png": 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JxISMp0JylnBZb27LFLvn+RBTsyf7dyhWenMzZpa/ayPMqbsC4TPGMPSO5fzJM5pHRJmVnHxZNv3QxfF9lhVYU5Y5DMo8MJssEZBFRrlOryOhHpRF6QUTqSdFDqagLFyjfFRM+pOjpxadSzMoS9F7DSQlxlsua4heUGZP0QtAwzQCIXuY/wIP3WPR3jeDMo8iIvUCefKdrOyCY7N50dHfxu7tJgZlRERE5BOsKcschqdEREREAYA1ZURERJR9Hf05zZIDgzIiIiLyCTZfZg6DMiIiIvIJZvTPHAZlRERE5BOsKcscBmVERETkEwzKMoejL4mIiIgCAGvKiIiIyCdYU5Y5DMosZNL3ZzZ9q3SPwa4xBYr2vjUzoodpZPTXzYieaEvUO5aIML/NGKCT/R9y5dHL6J+SopfpPv5yot+mTUpK1JsBIClB7zoFynsDwjSyjYfSxMrLZ9bWKt+0889+OxYKtKBMM6N/CL1vMsKgjIiIiHwCAZZunjJbCoMyE4MyIiIi8gk2X2YOgzIiIiLyCeYpC/KgbNOmTbJu3TqJiIiQJk2aSJ06dTLcJi4uTr788ks5ceKE1K5dWzp27Cg2m027DBEREQWXy5cvS2Kiaz9VxBB58+ZNU9YwDElISJCYmBiP+7NSJsenxLDb7dKqVSt5/PHHZc+ePSo4a9SokTz//PPpbnfo0CEVZH3wwQdy5MgR6dOnj9x9993qpOqUISIiIv80X+ouOp544gkpXry4VKhQwbF07tzZpQy+74cPHy4FCxZUwVr16tXlxx9/1C4TMkEZaq1GjBghv/32m7z55psyY8YMmT9/vowbN0527tzpcbuhQ4dKqVKlZM2aNTJt2jRZtWqVfP3117Jw4UKtMkRERBR8QRncd999cu7cOceC73lnkyZNkvfff1++++47uXTpknTt2lVuu+02OXjwoFaZkArKWrZs6bKuYcOG6u+BAwfcbpOSkiJfffWV9OjRQ1VVQtWqVaV58+aOgMtKGSIiIvJfnzLdxRvx8fEeW8CmTp0q/fr1U3FFdHS0qgRCjdj06dO1yoR0Rv8FCxZIVFSU1KtXz+39aJa8cuWKVKlSxWU9bu/atctyGXfQnnz+/HmXhYiIiAKvpuzzzz+XAgUKSL58+VSfcefv98OHD6uuS82aNXPJgdm0aVPZsGGD5TIhHZThJAwbNkxGjRolJUqUcFvm4sWL6i8uhDNEtuZ9Vsq4g2ZTbGMuZcuWzfRzIiIiCiWZqSk7n6piBJUl7tStW1c1V6LDP4Kx8PBwad26tWrGhJMnT6q/RYsWddkO/dDM+6yUCdmg7I8//lCRbu/evVVg5ok5sgIjK53hQpj3WSnjDjr7YRtziY2NzdRzIiIiIuvKli3rUjmCyhJ3nn76aWncuLEKxsqUKSOffPKJCqSQcSH1gEJnycnJabIwWCkTUikxtm7dKm3atJEHHnhAdfjP6ILlypVLjdZs3769Yz1uV6tWzXIZd9CejCWrpnpB3zcd9mS98obGVDJ2zWMP06xq1plmyd90+y5ERlufCikySm/aJN1pmSRKr7jOaGPd6adsSXqvgcQr1qdxSk7Sm8IpkOi+vnTfS/4USO9TCmIIaHSDmv/Kx8bGSv78+R2rrX4nI4ArWbKk7N+/X91GoAZIieUMgVvp0qUtlwm5mrK//vpLVTnef//98tZbb7kts2TJEnnnnXfU/9Fx/4477pA5c+aoaBZ2794tq1evlnvuucdyGSIiIvI91DJp9yn7LyjLnz+/y2I1KDt27JgcPXpUypUrp26jC1TlypXlhx9+cJRJSkqSn376SfUZs1ompGrK0BaMgAwd69CmO3LkSMd9t99+u6OzP4Ky9evXq7wkMGHCBJVkFqMpb7zxRlVdifwkXbp0cWxvpQwREREFV0b/uLg4lQ5j8ODBUqNGDZWtYdCgQaqVDBU8JuQ8RdyAZs769evL2LFjVbyBblI6ZUKq+dIMtNLjHKABIuFt27bJF198oaod33vvPenUqZNLG7CVMkRERBRcc18WKFBABVMTJ05U/dGLFCkiLVq0kJdeekmNxDQ9/PDDaqDA6NGj5dSpU3LDDTeowQHFihXTKpPVbAbT3LuFkR+4+G26rZPIKM8DBEzsUxYcfVX82aesYLFCWvsuVU6vfEqK9X6CcOLw1ZFIViQnXW3qtyrxiusUJxm5eNZ6ihn2Kcseuu/T7+fW1yrftPPPmkdEmZWcdEk2rOikapec+2r58ztz95P3ST6Nz024kJAkVad9liXHGegCYvQlERERUajL9tGXRERElDPYwvSaI81t6CoGZURERBQUfcpyOgZlRERE5Bvot6s7l6WXc1/mRAzKiIiIyHd5yjQzHTAzwv8xKCMiIqKgyFOW0zEoyya60ybppgmwa07jpLVv7V6ZyQHTtyA8Qm/Yvz+PJ0JzaqOYXHpv18LF/5+zx9cunLuiVf7yhUuWy9rj47X2rZuOxp90Xy+671KbH3tER2hO46W9/2i9B0jRSNNipJq/kChYMSgjIiIin2BH/8xhUEZERES+gdpc3eZI5sRwYFBGREREvuFFSgxsQ1cxKCMiIiKfQL9H3b6P/uwrGWwYlBEREZFvoNaLNWVeY3hKREREFABYU0ZEREQ+wTxlmcOgjIiIiHyCKTEyh0EZERER+QamWNLtuK85LVNOxqCMiIiIfII1ZTkgKIuLi5PY2FipWLGi5MmTJ92yO3fulBQ3UwgVLlxYSpcurf5/9OhROXPmjMv9MTExUrlyZR8fORERETmEeZE8lnNfBkZQtmPHDnn99ddl8eLFcvr0aVm1apW0bNky3W169eolly9fdtxOTk6WXbt2yeDBg+W1115T60aPHi3z58+XsmXLOspVqVJFFi1apH+QdsPS3Hp2Q2/uNUOzvO5cljrzAerPG+e/eTV1J6YNC9ebyzJFc87R8Ejr51E3YWJ4uF753Ln0nmt0qfR/4DhLStR7Deg+13Onclsum3BJb15NQ/P16M95EjXf1tpsYYbfrpNdcw7Rtt02+Xne2TC/zSWs/3nKuTUpBIKyn3/+WRo2bKgCqurVq1vaZuPGjS63v/jiC7n33nulR48eLuvbtm0rCxcu9OnxEhERkWc2m00tOnTL52TZGpT169dP/T18+LDX+/jwww/l5ptvltq1a7ust9vtsm/fPilQoIAUKVIk08dKREREGeDcl6GbPBbB3IoVK6R3795p7kNTJZpC0YRZs2ZNWb16dbYcIxERUah19NddKAcEZbNmzVIDA7p27eqyvnnz5qqW7NChQ3L27Flp0aKFdO7cWQ0m8CQhIUHOnz/vshAREZFmTZk3CylBeyYMw5CPPvpIHnzwwTQjNrEOIzkhOjpapk6dqsp/+eWXHvc3btw41dRpLs6DBIiIiEhj7kvdhYI7KMNITdSGuWu6TC0qKkqKFy+ebk3Z8OHDVWoOc0mvLBEREVGOzFOWHuQcQwqM1DnG0MG/bt26Ur9+fZf1qBFDmozIyEjHuoMHD6qlWrVqHh8HNWpYiIiIyDs2W5hadLehAAjKzp07pzrrnzhxQt3ev3+/FC1aVNVqYYGXX35Z1q9fL9u2bXPZDk2RyHGWWlJSktx0003y1FNPSa1atVS/spEjR0rVqlWlW7duWfjsiIiIQow3zZFsvgycPGUvvPCC+j8CqMmTJ6v/P/HEE2qBMmXKqMSvqZsuUeuFvmPumiqRnwwB2/Tp06VQoULSvXt3efrppyV3busJLImIiEi0k/7qJgHXLZ+TZWtQdscdd6glPaNGjUqz7q677lKLJ2jqfOedd3xyjERERKQzIblmTRmTxwZPn7LshumTrEyhpDvNh+50PzrTJoXStCC601WJXe8Xmc51TUpI0tp3fHyyVvn8+f/fT9KKqEjrH4y6pzEqSm/KnJg8MZbL6uYsMpJC47Xu3fs6cGogdPsNhYVpPFfNKZx06Zz1UPnsJf9gUEZEREQ+7FOmOyE5U2KYGJQRERGRb7D5MlMYlBEREZFPsKN/5jAoIyIiIt/wZtok5ilzYFBGREREvmu+1O0jxtGXATg0h4iIiCiEsaaMiIiIfILTLGUOgzIiIiLyDU6zlCkMyoiIiMg32NE/UxiUERERkW/k8DxliYmJarEqOjpaIiOtz8bCoCyb6E/Yqjn1TJDO9OHvaWRsfpyNJf5SvFb58+f0yhcubH2qIiXC+mvm0iW9KaISE/WmCQvXmAZHpywkJ2gVJx+99+wpKX6dZklHmO5oP83XmM50bsH62esz+G7TzugfPGMOn332WXn77bctl582bZoMGDDAcnkGZUREREQWDRo0SO69994My02ePFl0MSgjIiIi38jhfcqKFSsmlSpVkptvvjnDstddd50ULFhQa/8MyoiIiMg3cvjoyxEjRvilrCl4wlMiIiIKgo7+YZqL90HZU089JTabTYYNG5bmvgkTJki5cuUkJiZG1Wxt2LDBqzJZiUEZERER+Xb0pe7ihSVLlshPP/0kFSpUSHMfOuOPGTNGpk+fLkeOHJFGjRpJu3bt5NixY1plPHn00Udl27Ztbu9bunSpvPLKK149JwZlRERE5NvRl7qLpsOHD0u/fv1k7ty5Ku1EahMnTlT3t2/fXooUKSKTJk2SqKgo+eCDD7TKuLNp0ybZvn276jPmTtu2bWXmzJny77//BldQZhiGfPvtt3LnnXdK9erVZePGjRluM3LkSFXWebn99tvTlFu2bJk60TfccIP06NFD9u3b56dnQURERFklJSVFunXrJoMHD5batWunuf/o0aNy8OBBadmypWNdeHi4tGjRQtatW2e5jCerV6+Wpk2berwfQWLNmjXl999/135u2drRf+jQofLnn3+qoOyrr76Sy5cvZ7jN8ePHpXz58jJ16lTHutRR8vLly9U+x40bp6oj33jjDWnWrJmqaixUqJBfngsREVHIy0Ty2PPnz7usxne7u1qw0aNHq/VPP/20xzjBHCnprHjx4rJnzx7LZdKrpStTpky6ZUqXLi2xsbESVDVlaHNdsWKFdO7cWWu7fPnyudSUVaxYMU1t2oMPPijPPfecNGnSRObNmydXrlyR9957z8fPgIiIiBy0O/n/P4VG2bJlpUCBAo4FFSup/fLLL+q7fNasWaqDv27rXEbbWClTuHBhOXDgQLplcD+aRIMqKHMXAVuBi1KvXj1p1aqVvPzyy3Lx4kXHffg/qgw7dOjgWIc24jZt2siqVat8ctxERETkhs2L/mT/BWWxsbESFxfnWIYPH55m97/++qucPHlS1VQheMKya9cuNYoyIuJq41/JkiXV31OnTrlsi9slSpSwXMYTxB7oy2bWtqW2efNmFaek18SZYzr6IxEbpjlApIyhsIsWLVJNk+ZcVKhWRKRbqlQpl+1wAXCfJwkJCarq1HkhIiKirBl9mT9/fpfFXcUNWsDwHe+8VKtWTXWHSk5OdjQdopuTc0UM+qGhL1jjxo0tl/EE9zds2FBuvPFGNXITQeG5c+dUFykEh7fccov0799fNYXqCrrksWPHjlWd8Uw4Kddee618+umnqkM/TqpZO+YMF9e8YO6gmnTUqFFZNpel7lxterPMhQ7d+foM3TlENSayS0qwPkktpCTpXdXISP8lWIy/4vm94U5ivF75lGS+ggP9/aE9l6X2iLkU/85n6Uc683bawgw/z/cb4AIko/9zzz2natowErJ+/fqquxQqX3r37q1VxpMFCxbIY489Jn369HFZj9o6VBi5a3rNkUGZc0BmtkEjRwmGp4LZhpt6KCpup9e+iwuDGjgTasqwbyIiIgouAwYMUN2ZHnnkEdUkiUwM6MOOGjKdMp6gJu+zzz6TvXv3qiZVNLdi0EDz5s0dTaMhEZSlhmZLtOuiUyDgZFxzzTWyfv16l1QZOGmIhj3xNMqDiIiI/D/60lt///232/XI8u8u079umfRgHkwsvhLwfcpeeuklR3CFAAw1WohIIT4+XkW6aJa87777HNv07dtXZsyY4RjWiiRu//zzj6UqSSIiIgrs5LHZGZNg5KevywZETRk66SPIMvt69ezZU3Lnzq0CLSyA6Q7MxK+RkZFStGhR1akP/0eTJBK0ff/99y6RKqLeQ4cOqWy7qEHD/nFi6tSpk03PlIiIKOczbDa16G4TLM6ePStbtmxxO7VTauj4n9FIzoAKyjBCYfHixWnWI/AyYV4q5BgDDH0dNGiQWjBPFRLBIohLDR3tME0Cpkw4ffq0as5EEEdERERZMCG57jZB5M0331SLFa1btw6eoMxMEJceTx3mMsqmC+awWiIiIgqd0Zf+gmBsypQpXg9OzPEd/YmIiIiyQlhYmFr8hUEZERER+URO71PmbwzKiIiIyDdyePOlvzEoIyIioqDNU5aTMCizMPWI3cIUMYbd8Ou0M7r7p6y/TmGaHToLFcujVb5oIb1fk+cvWn+udkPvvCQlJPt1SikK/PeGlc9FZzbNaZPsgZ9G0yfPUzddaMBPy+RN3rEgylPmbzwTRERE5NM+ZbpLMJk3b57LROaYMWjHjh0uZd566y356quvtPfNoIyIiIjIonXr1jnm24a5c+fKjz/+mGbqp9jYWNHF5ksiIiLyDXb0zxQGZUREROQThi1MLbrb0FUMyoiIiMg3OPoyUxiUERERkU8Y4kVNWRB2b1+yZIkcPnzY0dF/9+7djtvmuurVq2vvl0EZERER+UYI1JSVLFlSVq5cKYcOHXKsu3TpksttKFSokPa+GZQRERERWfTiiy+qxR8YlBEREZEPa8rCcnRNmVV2u1178vLga8glIiKigBQKyWMzcvToUendu7d88MEHootBGREREfk2T5nuEmT27Nkj/fr1k44dO8pzzz0np0+flqSkJBk5cqRUrlxZfv75Z7nxxhu198vmSytzX4al+HwuS+w3R813RpI7f26ts1CqZC6t8vly670GLlyy+W3ew8REvbkvk5Osl7dznlefsflxTkHD0Hs92kRvblh/CubPU51r6s/r74khNrXobhNMTp8+LQ0aNJD8+fOrAGzWrFmyadMmKViwoKxZs0YmTpwoffr0kcjIyOALyq5cuSKffvqpmpIAT+Laa6/NcJtffvlFTXMQEREhTZs2VSfH2eLFi2X9+vVpRksMHDjQ58dPREREoZM8dsGCBVK7dm354YcfJCoqSs6dOyd16tSRmJgYNQdm8eLFvd53tp6Jjz/+WCpVqqSCqAkTJqQZTuqu01yjRo1k2LBhcurUKdm7d6/ccsst8uyzz7qU+/bbb2XZsmUqajWXfPny+fnZEBERUU534MABuf3221VABogx2rRpIw899FCmArJsrymrVauWmtQT+T2szKZus9lkypQp0rBhQ8c6tOd26tRJHn30UbU/U7Vq1VTwRkRERFkkBOa+vHLliuTK5dr9JHfu3Ko5M7OyNSgzO8EhKLMCQZlzQAb16tVTfzEbu3NQhkh29OjRUqBAAdXEWb9+fZ8eOxEREbnyZjRlMI6+XOKU0d9TVv877rhDte4FVZ+yzPrkk08kOjo6TdCFdfHx8aqJc8iQIfLkk0/KpEmTPO4nISFBLabz58/79biJiIhymlDoU1bSTUZ/d1n90e8spIKytWvXqqy648aNk2LFijnWY3gqRkSYHnzwQenQoYPcdttt0rJlS7f7wj5GjRqVJcdNRESUI4XANEsv+jGjf3CFp05+++03FWQ99dRT8swzz7jc5xyQQfv27aVMmTKyevVqj/sbPny4xMXFORY0hxIREZGG/2rKdJZg61PmT0FZU4Z8IO3atVOd+1977TVL26SkpKjmTE/Q3ImFiIiIyJPx48ertBipIU1X+fLlVUXQY489pj3FktqHBLgvv/xStdGaOcY2b94sbdu2VQGZuz5iycnJ8vvvv8vNN9/sWPfFF1/I8ePHpXXr1ll67ERERKEkFJLHVqpUSQ0gdBd/HDlyRJ5++mmVTxVpv4IqKEON1+effy4XLlxQtzFPFHKMId8HFli+fLlKBIugDJ3oEJAhSy4iUueUF/fcc49KIosRmliPCBWjMRHQrVixQl544QUGZURERH4UCh39u3TpohZPMMAQgw937twpNWrU8E9QVqFCBa0dIyVFRpB4zUzuio72JmTFdQ62zNELCLQGDx7sdl9m02N4eLj89NNPKuP/li1b1LZvvPGGpZkC3ElJtovNluLzaWp0y1P2CI+wPjVMrjz/f91aKp9L74MoPExvahidmb+SEvWm/UpKSNIrn5houaxdc8oy8g1bmH9rK3Q/84LsezrLznvAf3fg6Wh39JccpVKlSqpi6a+//vJfUHbw4EGZPn26pbKYHd0KDBfFkp5bb73V8X8ka7OaELZx48ZqISIioqxhSJhadLfJaWJiYiRR48eoV82X6Ljmy6CMiIiIco5QSR6bHnTJQosd+pb5LSjDtAL+KEtEREQULJC9H1NEeuroP3/+fNWEiX7ufgvKnPt5+bIsERER5Qyh0NF/7ty58vbbb6dZjz7t5cqVU8nqx4wZ49W+MzX6Erm/zJGTztBxn4iIiEJLKKTEeOutt9TiD16Fpxjm2axZM1UjVqhQoTQLERERhR7dbP7e1Kxltw0bNsjff//tl317VVPWq1cvKVu2rHzzzTesFSMiIqKQ6eg/Z84cqV69uloCIihD7o3vv/9eChQo4PMDIiIiouAUCs2X/uRVnWHFihXVtEVERERElI01ZRhV8NBDD6ks/Bj2iamNnF1zzTU+OjwiIiIKFqEw+hKmTp0qCxculPQgT9ldd90lfg/K8uXLp3J0tGrVyu39hhHg00AEwFQZNkN3ag3NAyIP59F/JzIpMVmrvO77JFek3vRDEeHW395JCXrHnpSoN81ScpL1/Rt8sWcLv0/fozlNmD05cKaI8qeAnzZJU6g0XxYqVCjDCqi8efNq79eroKx///5yxx13qL9Mf0FERESOaZZsOX+apZ49e8qAAQN8vl+vzsThw4dV4rSbb77ZMQLBeSEiIqLQY9aU6S46jh8/LoMGDZI6deqoLlRoIty4cWOach9++KHUq1dP1WjddtttsmPHDq/KZCWvgrKqVatKbGys74+GiIiIgjwlhm6eMr2gbPDgwVK5cmX57LPPVGquMmXKqO5U+/btc5SZPXu2as1D8PbDDz9IkSJFpGXLlnL69GmtMt6Kj4+Xo0ePZk1Q9uCDD6oFJ2PPnj3yzz//uCxERERE/jB79mzp16+faplDJdHkyZPVnNuYk9I0duxY6du3r3Tr1k2qVasm06dPl6SkJPnggw+0yrjz5JNPyp133un2vj/++EM1a5YqVUq+/PLLrAnKhg4dKlu3bpWOHTuqE1KlShWXhYiIiEJPVjRf2pxq1ux2u8ycOVPy5MkjTZo0UetOnDghu3fvljZt2jjKRUVFqVqwNWvWWC7jCQI4507+cXFx8u6770r9+vVVty7U2E2YMEFVXmVJR//9+/d7sxkRERHlYJnJ6H/+/HmX9dHR0WpxBy11PXr0UPNvY5Tj119/LRUqVFD3mc2GJUqUcNkGtzFFktUyGVm9erXMmDFDpcbAY+NYJ02alKkBAF7VlOHB01uIiIgo9BiGzasFypYtq2YKMhfkQvWkdevWav5JNBfef//9qrO/OR+lmW4oPDzcZZuIiAhVs2a1jCeLFi1SrYTt27dX+1mxYoUaIGDW1GWG5aCsQ4cO4o+y8O+//8rvv/+uIl6rTp48Kdu2bZPLly9nqgwRERH5StjVtBgaixmKxMbGqqZAcxk+fLjHR0FTY9GiRaVGjRoqG0Tx4sUdfcGKFSum/p46dcplG9w277NSxhMMCjCzUHz00UfSrFkz8RXLQRkiQQRPVhaUteLPP/+U7t27q5PaoEED2bRpU4bbJCYmqk555cqVk86dO6sLgc55umWIiIgocOTPn99l8dR06U5kZKT67jdr3EqXLi1r16513I8aLdxu2LCh5TLpZerv06ePDBkyRO3n+eefdxn5mRlazZeIHq0sVv3222+q+m/9+vVaUzytWrVKjfpE3zbkGMHoCeeAzkoZIiIiCq6O/mfPnpWnnnpKDh06pG5j1OX48eNV8+F9993nMkISne83b96sRlQiLjhz5ow89thjWmXcwYDGKVOmqH5p+IuWPjRnzp07V3bt2iUXL14Ub1nu6L9lyxbxNfOJoxrQKnSqw3aITqFr164yevRoFXhh5IPVMkRERBRc0ywVLFhQJY1t166dih1SUlLk+uuvl6VLl0rz5s0d5VCLhZY7rEMNGuKBr776SipWrKhVJj1oQkV8geXAgQNqFOisWbNUyxz6vI0aNUpuvPFG/wRlN9xwg2S3Y8eOqQVNnc5Q1YhI12oZHbawMLX4ek5Fm+Y0FLYwI2DmeAwlKcnW55tMvHK16tyqiHC9D64yec9qlY8rYr3WWldSgt7clyka84LmtLkAs1NgfQ6E+W2uTENvWtiQuabZcf39HZTZbDZV6YIFtWQxMTEuKTJMYWFhaiQkUlOgXznm7PamjFUY5IjKn5EjR6ouXKgEQszht6AsEJhZdpF11xk6+5n3WSnjTkJCglpMqYfmEhERUeBMSJ4rV64My2B0ZUbBlpUyViHQu/XWW9WxJScn628vQQQd+cA5eAJEy+Z9Vsq4g6G3zkNxzaZPIiIi8n9KjGCCQQG//PKLzJs3T6XlMKFvG+bQxLRPSFCrK6hqypBBF9WUqeeTwm2MtLRaxh0MvX322WddasoYmBEREZEz5DHDjEbOmSbQfwwxBkZloskSswI0bdpUsqSm7JNPPlHTE3i6z5cOHjwo27dvV//HNAqYwmDZsmUuNWDIGYJOdVbLuIOht6mH4xIREVFgTbOU3TAYAIMfv//+ezly5IiqLUPftIEDB6qBhuvWrfMqIPO6pgxTG6D9FQHY7bffnuY+5B6zAn28kLICSV4BQ0kxXQJyh2CBV155RaXMQBJY8zaS02LuqUaNGsnUqVPVaAykvDBZKUNERETB26csuyAg6927t2PezAceeEBNtYTWtZ49e2Zq3173KUOytHvuuUfNsu4tzOj++OOPy8svv6xSVWAYKW5jaKvziIbrrrvOcRu1XagyROJZVBfiJCDZm3PNlpUyRERE5FuhUFN29uxZKVmypMu6UqVKSaVKlTK9b6/7lA0bNkxq166tZkHfunWrmmogd+7cWvtAZzgs6XnxxRfTrLvlllvUkh4rZYiIiMh3VJCl2XE/2IIywzBU6i2zBQ+QdPb48eMu69DiV7hw4azr6N+pUyc1m/odd9yhJuJEOysRERGFJrvY1KK7TbB59dVX1eJuvWnatGkyYMCArB19Wb16dRWYoU01dcJWIiIiopxkyJAh8tBDD2VYrnz58tr79ioo69Wrl8ttdKLHaMeXXnpJjXIkIiKi0BMKHf3LlSuXboqtzPAqKMPcTu6y2KLTf2Y6/hMFM8zBpiN/Hr3phEolHdAqf65AHstlo2I8J1Z2JyVJL1O1YQTSdD+UHXSn/NF5yViZCi80p7bKet4kgw225LF79uxRfejLlCmjbiNzP/KjYmYAE1oQkYgerYk6AueVTEREREENPzX1R18Gl6lTp8qiRYsct5Gf7N1333UpM2fOHFm5cmXOzuhPREREgSsUasr8iUEZERER+UQo9CnzJzZfEhEREQUA1pQRERGRT4RK86Vz8lgkjnWXTNYbDMqIiIjIJ9BpX3f8abB19PeUPDb17caNG4suBmVERETkE6FQUzYk0JLHEhEREYViR/9y5cpJ2bJl5ddff5VDhw5JjRo15Prrr/fJvhmUERERkU+EQk2ZYRhqzu+vv/7asW7QoEEyadKkTO+boy+JiIiILEIwtm7dOvnmm2/kyJEj8vHHH8ubb74pu3btksxiTVkG7MkpYrfpTZ9jhV1zSp5Qn7oju+hcp2TNqYeiIvW6t8ZcPKVVvmChS5bLRkRG+3VKqZRk6+X5WiddfM0EjlBovty0aZP07dtXOnTooG737NlTFi9eLFu2bJFq1aplat+sKSMiIiKfsBveLcHkzJkzUqpUKZd1pUuXltOnT2d636wpIyIiIp8IhZoywzDc5iU7evSoyzoEaoULF9baN4MyIiIi8olQ6OjvKU+Zud40bdo0GTBggARVUPbFF1+oDnInTpyQ2rVry5gxY9Jtk61Vq5ZcupS2r0z37t3VtvDCCy/I3LlzXe7HPlesWOGHZ0BERERgGFcXHbrls1uOzVP21Vdfyf333y9vvPGGNGrUSF5//XVp3ry5bN++XYoWLep2G4x2sDt1ev/999+lS5cuansT2nVr1qwp77zzjmNdVFSUn58NERER5XTlypVTiz9ka1A2evRo6dGjh6N6b9asWarz3HvvvScvvvii221Sn4jx48fLNddc4xgFYcqdO7dUqFDBj0dPREREzuxiU4sO3fLZ7d9//5Vz585lWK5YsWJSoECB4Bh9eeHCBdm8ebO0b9/esS4yMlJat24tP/30k6V9XL58WebPny+PPvqohIeHu9y3Zs0aVVuGGjRUNcbFxfn8ORAREVHaPmW6SzAZOXKkVKlSJcNlzpw5wVNTdvjwYfW3ZMmSLutxe+vWrZb28fnnn8vFixflkUcecVlfpEgRVdPWokULldgNfcyWL1+ucotER7vPx5SQkKAW0/nz5714VkRERKErFPqUhYWFqVgCWf3R2uepu1XFihWDJygz+4VFRLgeAmrLrCam/PDDD1VNW+omzVdeeUWdNKhTp46akwpNmQsWLJBevXq53de4ceNk1KhRXj4bIiIiCoWUGBMnTpTGjRvLjBkz5N5775XOnTurFrt27do5Yg9vZVvzpRlZpk62hrZatMNmZM+ePaqJsnfv3mnuS31SkCsEoyB27NjhcX/Dhw9XTZzmEhsbq/FsiIiIKBSSx0ZHR6tBiitXrpSdO3dK9erVVSyCyp+XX35Z5TALuqCsRIkSqoYL80c5++WXX6RBgwaWasmwD0SoGYmPj1cnKb0kbjjJ+fPnd1mIiIiIPEETJVrnDhw4oAKzsWPHqlRfQTn68oknnlDVgMgxhvxj7777ruOJmYYNG6bmk3LOMZacnCyzZ8+Whx9+OE3zZ2JiogwdOlTVfBUvXlz1Oevfv7/KwNu1a1ev5j60h/l+7kvO1ZbzaM9nqvnrMOKKXj/H6CL/7yOZkZSUKO05YXUYwfZTmIi8403H/SDr6O8MmfyRFxUVRYhf+vTpIx07dpSgDMoGDx6sOuLfeOONqqYqJiZG5s2bpwI05+ZMlHGGTvvHjx9XbbipoU9a5cqV1T6vXLmiOuzj/6tWrWKKDCIiIj8KhY7+drtdNV3OnDlT5Vtt2LChDBo0SPUvy5UrV6b2na1BGfp+IZv/hAkTVM4PNEem7g+G+5xHRQISzB48eFDKli2bZp82m03VjGFBfzU0QyJQIyIiIv8KhTxlgwcPlrfeekuNvly0aJGqCILUFUje5CnL9mmWAJGlp+gS6S1SK1iwoFoy4m5bIiIi8o9QqClLSEhQXaWQlguLJ0E59yURERHlDKEwIfmQnDr3JREREVEwKefHuS+zLSUGERER5SyhkKdsz549Lv3HkBEiddL7DRs2yN9//629bwZlRERE5NM+ZbpLMJk6darq4G8aOHCgSunlDPNeYoSmLjZfEhERkU+EwjRL/sSgjIiIiHwCs1rrNkdenQmbgEEZERER+UQopMTwJwZlPsJpk3ImnemBdKcSigzXK2876ZqYMCO5y1ywXNaeksfPU0rxtzAR+caBAwdU4vn169dLVFSUtGjRQmXUTz1n9eLFi+Wdd96RkydPyg033CCjR49OM2rSShl3MJ/2tm3bHFMtOd8213mDHf2JiIgoKDr6X758Wdq3b68Cp0mTJsnzzz+vpjrCOoyCNKEjfpcuXaRTp07y9ttvy9mzZ6VZs2Zq6kWdMp68+uqrUrt2bbXMnz/f5ba5zhusKSMiIiKfsBs2tehuY1WuXLlk+/btEhHx//ClePHiUrduXdmyZYs0aNBArRsxYoQ88sgj8vTTT6vbCJIwlSMmDn/mmWcsl3GHyWOJiIhIQr1Pmc1mcwnIIPWc2Zj3+q+//pJRo0Y51uXOnVtatWolq1atUgGXlTKeXHPNNWrubRyLr7H5koiIiLK9+fL8+fMuC+aYtAI1XpgUHH3CIDY2Vv0tVaqUS7mSJUvK4cOHLZfx5KmnnlLNnf7AoIyIiIh8wvAim78ZlJUtW1YKFCjgWMaNG5fh47344osqSeuCBQskMjJSrTOz62MQgLPo6GhHvzMrZbID+5QRERFRtouNjXUZQYkAKT1jxoyRKVOmyPLly6V+/fqO9UWKFFF///33X5fyuG3eZ6VMdmBQRkRERD5hGDa16G4D+fPnT5PWwpOxY8eqmrRly5ZJ8+bNXe4rX768FCtWTKXMaNeunWP9r7/+qkZbWi2THpSLiYlJt0zjxo2lZs2aooNBGREREQVN8tjx48ergAw1ZMhRlho64Pfp00fee+896dGjh1SsWFHlIsMk4o8++qjlMumZN2+eWtIzbdo0BmVERESUPcx+YrrbWHXs2DEZPny4qlHr27evy30TJ06Uzp07Ozr/o8N+tWrVVP80wzBk7ty5Ur16dUd5K2XSC7gGDBggvpbtNWWnTp1SHfROnDihEq7de++9Eh4e7rH8F198oaoNnWH0BLL5Zma/REREFNg1ZcWKFZOdO3e6va906dKO/6PT/6xZs2Tq1Kkquz4GEaROpWGlTFbL1kffu3evNGnSRGrVqiU33XSTin5nzpypqiQ9BVDff/+9rFu3Tnr27OlYl7pTnjf7Jcpq+WKStMpf2vG33v6r1LNc1m4vobVvu+ZPYd0pqIgoOPk7KIuIiLBUk2UyR3JmtkxWydagbOjQoSq3CAItJH9DVWTVqlVVDVe3bt08bocyzz33nM/3S0RERJSem2++OU1+s6DPU4Y8IBg1gSDJzMZboUIF1WkPE4SmZ9++ffLyyy/L5MmTZePGjT7bLxEREXlPN0eZN33Qslv37t2ldevWOSsoO3jwoMTHx0ulSpVc1uP27t27PW6HERP58uVT/9+xY4caCjtw4MBM7xeZg1NnEyYiIqLAmZA8p8u25kvM9A6pc5KgXffSpUvpTgSKoaumBx54QNq2bSu333673HLLLV7vF8NrnefAIiIiIj12+9VFdxvK5pqyvHnzqr/nzp1zWY/bZk2YO84BGbRp00ZNDrpmzZpM7ReDAeLi4hyLOS8WERERWcOasiANysqVKyd58uSRXbt2uaz/+++/pUaNGlr7Qj8yc+JSb/eL6RzMbMI6WYWJiIjoKgZlQRqUITXF3XffLR9//LEjoNq2bZusXbvWZYqDzz//XHXoN4Mv3O/ss88+k+PHj6smTJ39EhEREQWSbE2JMWHCBGnWrJk0bNhQ6tWrJ0uXLlV9xO666y5HmRUrVqi5qZAcFp38kYE3KSlJ5SA7dOiQ/PDDD2pdq1attPZLREREvoXuYdoZ/XkRAiMoQ56PrVu3qqAJmfeRELZly5YuZe677z5p2rSpoxYMQRjSYGzZskUFYpirChOL6u6XiIiIfAtTFWHR3YYCZJql3Llzq8DLE+fZ203I0o8lM/slIiKi4JuQPCfL9qCMiIiIcgbDi5QY2IauYlBGlA5bmM3y+QnTnFc1X+TVgShWHdn4j1b5qvXcT9rrTu48DbT2bTCxEBG5+2xgTVlwjr4kIiIiov9jTRkRERH5hDdzWQbb3Jf+xKCMiIiIfILNl5nDoIyIiIh8wrAbatHdhq5iUEZEREQ+webLzGFHfyIiIqIAwJoyIiIi8gn2KcscBmVERETkE3a7oRbdbegqBmVERETkE6wpyxwGZUREROQTDMoyh0EZUTZMyaTefGHJWuWPbTilVb5Cmz2WyxYoEq21b1sYxwgRUVp2w1CLDt3yORk/WYmIiIgCAGvKiIiIyCcM+9VFdxu6ikEZERER+YSBf5rNkdiGrmJQRkRERD6BWi87a8qCOyiLjY2VEydOSNWqVSV//vwZlk9JSZHdu3dLRESEVKxYUf11tnfvXjl27JjLujx58kjdunV9fuxERER0FWrJtGvK2NE/MIKy+Ph46datm3zzzTdSvnx5OXjwoEyYMEGefPJJj9uMHTtWpk2bJoULF5YrV65IUlKSvPfee3Lbbbc5ykycOFEWLlwo1atXd6y79tprZfbs2X5/TkRERKGKc18GcVA2atQo2bhxo6rZKlWqlCxevFjuuusuuemmm6Rhw4Zua8gQiO3YsUMFZTBy5Ejp2rWr2kfJkiUdZVu2bKkCMyIiIqJgkK0pMT766CN57LHHVEAGd955p1x33XVqvTvh4eEyZswYR0AG/fr1k8uXL8vmzZvT1MJhHYI1u24DNxEREWkz7IZXC2VzTdnRo0dVP7L69eu7rEct2ZYtWyzv57ffflN/K1Wq5LJ+xYoVcujQITl+/LhER0fL+++/Lx07dvS4n4SEBLWYzp8/r/FsiIiIiBn9g7Sm7MyZM+pvkSJFXNbjtnlfRv7991/V/+y+++6TatWqOda3b99ejhw5Ilu3blUd/h944AHp0qWLqjXzZNy4cVKgQAHHUrZsWa+fGxERUShPSK67UDbXlEVGRjqaGZ2hz1hUVFSG28fFxUmHDh1UP7IZM2a43Id+ac5Nngi4PvjgA1myZIk888wzbvc3fPhwefbZZ11qyhCYGXa7Wihn8Of0QLpV8IboTcuUfF5vWqbEcxcsl40uHa617zDNKaX8eY34/iQKHBx9GaRBGQKesLAwVaPlDLfLlSuX7rYImNq1a6cCrm+//Vby5cuXbnmUQw1c6sdyhiZOLEREROQdZvQP0ubL3LlzS+PGjVXtlenSpUuycuVKadu2rWPdP//849LHzAzI4LvvvlNNjamjdNS2OduzZ49Kt4FBBERERESBKFtTYmAkJQIwNB02atRI5R8rXry49OnTx1Fm/Pjxsn79etm2bZvKSXbrrbfK/v37ZebMmfLXX385ylWpUkVKlCihymDwAEZ11qpVS3X2f/XVV6VevXqqbxkRERH5h90w1KK7DQVAUNaiRQtZtWqVvP322ypfWe3atWXOnDmSN29el2ArMTFR/R+pL2w2m1qHfmLOhg0bphLIoj/aDz/8oAK8KVOmSKFChWTIkCEqSDP7sREREZHvsU9ZkE+z1KRJE7V4MnToUMf/0VS5du3aDPeJvGeoHSMiIqKs481oSo6+DKCgjIiIiHIG5inLHAZlRERE5LvmS930QOxTFhjTLBERERHRVawpIyIiIp9ArZfuaErWlP0fa8qIiIgoqCYkX7t2rfTo0UNuvvlm2b59u9syP/74o5qGsWXLljJw4EA5efKkV2WyEoMyIiIiCpqg7LnnnlOZGWrUqCEbNmyQCxfSTim3YsUKNQ828pUOHjxY5TVt2rSpSq2lUyarsfmSQmZuSn8zML+IhvMJubTKl21bSqt8dJGCEihsGnNl6n5A+/s1w7k1iazD21e34ku3/EsvvaRSZGHGnxdeeMFtmeeff166desmI0aMULebNWum0mV99NFH0r9/f8tlslrwfgMSERFRyNWUFUg1vWJqZ8+elc2bN0vnzp0d6/Lnz6+aKDGVo9Uy2YFBGREREWW78+fPuywJCQle7QfTK0Lp0qVd1uM25sG2WiY7MCgjIiIin06zpLtA2bJlVS2YuaSeTtEqzIEN0dHRLutz5crluM9KmezAPmVERETkE3a7/rRJ2AZiY2NVE6IpdcBkVeHChdXfM2fOuKw/ffq0FClSxHKZ7MCaMiIiIsr2mrL8+fO7LN4GZRUqVFBB12+//eayfuPGjVK3bl3LZbIDgzIiIiIKqjxl6QkLC5OHHnpI3n//fTlx4oRaN3fuXNm3b588/PDDlstkBzZfEhERkU94E2Tpll+0aJFMmDDBMRDgsccek7x586o0FkgoC2PGjJG9e/dKxYoVpUyZMnLs2DEVgNWpU8exHytlshqDMiIiIgoaTZo0kSlTpqRZX65cOZcO+4sXL5bDhw/LqVOnpGrVqpInTx6X8lbKZDUGZUREROQTdtGf+xLb6ChevLharLjmmmvUktkyWYVBGREREQVN82VOxqCMQmoqJJ3pflR5m/Xnak/Wm2bp9GW9kUW16lbQKh9p8ZckJGseu+6Q91B5vYfSlExrv26hVb5p55/9diwUOJxHU+psQ1dl+7frvHnzpGHDhmp4KqY72LZtm0+28Wa/RERE5D3Uetk1F9aUBUhQtnDhQjUktXfv3rJs2TIpWrSomnfq5MmTmdrGm/0SERFR8KfECGbZGpRhOGqvXr3UcNZatWrJ9OnTVe6Qd999N1PbeLNfIiIiopAMyjDZ6J9//ilt27Z1rIuIiJDWrVvL6tWrvd7Gm/0SERFR9mb0p2wMyo4cOaL+lihRwmU9bh89etTrbbzZLyAJXeoZ6omIiEi0Brt4s1A2B2VmZIxaLGe4nZKS4vU23uwXMBu98+z0mK2eiIiIrNPt5G8ulM1BWbFixdTff//912U9sup6SgpnZRtv9gvDhw+XuLg4x4LZ6omIiMg6Nl8GcVCGdBVr1651Wb9mzRq56aabvN7Gm/0CZqNPPUM9ERERWcfRl0E8+nLAgAEyY8YM2bRpk2pafP3119UcVH369HGUGTRokEpnobONlTJEREREgSRbM/o/++yzcuLECWnevLnY7XZVy4UcY9WrV3eUQVOic1OklW2slMmI2TctOfmyz55vTsGM/u4lJ+qdx8uX9AaTXEjQfYB4y0Xj7XrHkpx0Sat8SlKS5bLBnLMolDos6w6G0n3NUOaZ319ZObqR0yxljs0IgLGoycnJcuHCBSlYsKDYbLY0b/ykpCQpUqSI5W10yniCmjV29iciomCHPtL+nnAb39UYJHdb380SGZVPa9ukxAuy9P16qhIm1LsOBcTclxgZWahQIbf3ebpA6W2jU8aT0qVLqxdyvnz5HAEdXnQI1LA+p79w+FxzHl7TnClUrmuoPE9fPlfUuaBiAt9nWcWw69d2YxsKoKAsEGEGAE+/LEJpIACfa87Da5ozhcp1DZXn6avnitqrrMTmy8xhUEZEREQ+4U2G/gDoRRUwsnX0JRERERFdxZoyDchlNmLECPU3p+NzzXl4TXOmULmuofI8g/25IuMBFt1tKIBGXxIREVHwMkdftuu5QSKj8mptm5R4Ub6b3ZCjL1lTRkRERL5iGHa16G5DV7H5koiIiHyCoy8zh0EZERER+Ybd0J+VI4hn8fA1BmVuZgHYvn27Sjxbs2ZNSzMBeLNNIDhz5ozs3btX5WMrVapUumVPnTolu3btSrO+UaNGEh4eLoHuwIEDapaGG2+8UWJiYixtg3Nz7tw5qVGjhuTOnVuCAbqIbt68WSIjI6VOnTrplsW8sL/++mua9dWqVVNTkwW6/fv3y+XLl+Xaa6+VXLlyWdoGU7Zhu3LlykmJEiUkGCQkJKj3Hvrr4Lgz+nz566+/VN8cZ7ieuK6B7tKlS7J79241gwueqxXx8fGyY8cOyZs3r1StWlWCxZEjR9TnaoUKFdSsM+nBZxc+w5zhcxefv5SzMChzgi+oLl26qMSx+CAsXLiwfPXVV+m+0b3ZJhCMHj1aXn31ValUqZIKPh544AE1ibunAGvFihXyyCOPyE033eSy/ttvv1UfhoHqxx9/lNdee01+//13OX36tOzZs0cqV66c7jZnz56Vu+66SwU3JUuWVPOofvDBB9K1a1cJVBi99Prrr8v777+vAg98Aa9fvz7dbZDpu1mzZnL99de7XMOXX35Z2rVrJ4Fq7ty5MnLkSPVjCMEyvrBeeeUVeeqpp9LdbtiwYTJlyhTHa/7RRx+Vt956K2B/ROH6vPjiizJnzhyV3R2vQwRXs2fPlrp163rcrl+/fnLo0CGXoKZNmzbqnAUqvDeHDBkiixcvVkEKAueKFSvKvHnz0g0mly5dKr169VIzt2Af+NxdsmRJQAfca9eulWeeeUYFZAjGEHD37NlT3nnnHY+fv5988omMHTtWvVdNeO1/9913Emjshl0tutvQfzD6kgzj8uXLRunSpY0nnnhCnY7k5GSjY8eORr169Xy6TSBYunSpERERYaxevVrd3r17t1GwYEFj8uTJHreZM2eOUaJECSPYTJ061Vi+fLnxyy+/oH7c2LNnT4bbdO/e3bjuuuuMuLg4dXvatGlGVFSUsX//fiNQ4bU4aNAg9fz69+9vNGzYMMNtzp49q87Jb7/9ZgST8ePHG3v37nXc/vLLL9Xz+Omnnzxus2DBAiM6OtrYuHGjuv3XX38ZefLkMd5//30jUOE54vWLawuJiYnG/fffb5QrV86w2+0et2vSpIkxYsQII5jgenz66adGSkqKuh0fH2+0atXKaNGihcdtjh8/buTNm9d49dVX1W2cpwYNGhidO3c2Atknn3xi7Nixw3F7+/btRkxMjDF9+nSP24wbN86oX7++EcjweYn3Yav7Vhttu2/WWrANto377zM3lDF5rFONz7Fjx9QvU8Avlueff17Vlvzxxx/iq20CwcyZM6Vly5aqlgSqVKki999/v1qfUdMYmmmxoFYwGKD25NZbb1U1mVZcvHhRPvvsMxk4cKBjehPUPOD/+LUaqNB8N2nSpAxrAd3B/HqbNm1STbXBYOjQoarJ0oRaTdRo/vLLLx63wWsbr4MGDRqo29ddd53aLqPXfHbCc8Tr12yaRZP0Y489pmrBcM0ySk/w22+/qVrEYIDrcd999znep8jPhaY5NPF5gvcpoNYJcJ4GDRoky5Ytk5MnT0qg6tatm+oSYUKXF9TspfdcATXDf/75p/z999+SlJQkAT360q65sKbMgUHZf7Zs2aImbXXuW2U21eE+X20TCHBs9evXd1mH4965c2e6wRY+6PBF1rlzZ9VMiyAgp0HflMTERJfzg2C7Xr16AX1NM6Nv377y0EMPSfHixeXBBx9M0x8p0B08eFA1BaUXkHp6zePHUzClakSghWarjPqAvvfee9K7d2+pVauWavIK5B+JzhB0/Pzzz/Luu+/Khx9+mG6TK64pnp9zH1FcUzTlYz+B7MqVK6oZE91CHn/8cdUfGc3pGfUVxPsTTdEI4j7++GMJ5NGXugtdxT5lTp3e0bnUGX6Z5suXT93nq20Cgbvjxm18mKG2xF1/DPTD2bp1q9SuXVvd/uKLL1RfuvLly6u/OYV53dydH9SK5iR4raJ/Fj7o4Z9//pHWrVur2plA/cBPDTUGCChR23DnnXdqv+bxIwSDBfLkySOBbtu2bapfEfrG4dp5gi/5u+++WwVv6Djfo0cPdW6wfSD3/4Rp06apzxl09kdNfqtWrbSvqXlfIMOPCFxH/ABC/7mXXnpJypQp47E8fhTi/Yl+doC+kOjjix8iTZo0kUDCPGWZw5qy/+BDDqN4UsO6qKgon20TCNwdN365gafjRlOCGZDBPffcozqDL1iwQHIS88vO3fkJ5GvqDQQiZkAG+IAfPHiwfP7550Ex7QlGjyLgwJcVBtekd328ec0HEgxM6NChg6qlfuGFF9It2717d8doYVxjDABBbaK7kbaBBoONNm7cKEePHlUDMPCcPdVkBvM1xSAM1JSh9mvDhg1qMBIWT/BZawZkMGDAANUEajbhUs7BoOw/qPE5fvy4y5cRmuvwS9zT0GxvtgkEOO7U/RdwGzV8GMVklZV+EMEG5wbcnZ9Avqa+gmuKL7ZAr2lAQIYRa/hiW7VqlcsXls5rHn3R0qt1CgT79u1TNUZNmzZVIzGt9o80mTXfwfReRVDZv39/FbR4Om5P1xSC6b2KJtiOHTvKN998o7VdoH7+4uvQbjc0l+w+6sDBoOw/bdu2VZ1jf/rpJ8fJMX99N2/e3LEOXwJmM5bVbQINjhsfAOg46nzc6KvgHFziueLLD9AM4gy/UNesWaM66AY7c/CCWVuEL3gMqzehszQ6wuO8BTNcb1xTNJ24u6aAIfYIVFI3CwUS/AhCkyX6HiEgc9eXDO9RPFcTrh06gDv/gMI1DvRritxUCMhQU42BJu5SJqC5z8whiIA6dS2nmTYhkN+r7l6LqAFFXyvkZwM0NeOamj8YcO3wvNHU6fw5hrQhzqkjguG5oibU+T2HgRzONZupt0H6DwwoC8Rrqt3J/7+FrmKfMqdfK2gKefjhh2XcuHGqnwmactDub9Ye4UsN/RzQ7wHVx1a2CUQYWThr1iyVdwtfbgjQ0IHY+UNg+fLl6nkhZxdy6SCPGT4A8OWAgAznACMVhw8fLoEMH25oukEHfkBwhdpNjDg1axAwYsscTQu4lmgCQsd35EhCPjd0EkeTbSDDhzRegwhIkOPKDErQ5wRNQegviNcvalvw/JDTDNcdTWK4xsj5hNcFRiQGau4us88Umm1w/MjdhQUw6MYclYk+j08++aSqtcYXO3JgzZ8/Xz1vvJaRDwtf5uhTF6jwvBCQYeTvE0884ZJ3DkEHarahT58+KrcXuhKgfxLe0+g0jh8XqGlCPzQ8ZyRODlQTJ05U71U00+G1iLyC48ePV+9N83nidY3X76JFi1QfOZS95ZZb5N5771W59fDjCdvgswnXPFDhmO+44w6Vaw6fpXhdYmACcuiZ0KcTzwWfsYAfzJ06dVJ9yxCU4nxhsBW+hwINp1nKnMB95WYDjPZBB0okZ8Sb+o033lAfcCZ8UeELDh/+VrcJRAhG8AE/YcIE9UGAjP5IJ+D86xJl8FzND7dPP/1UjejC88V5wAcLvtjwwRDIUJOCxK+A54MPbEDgfNttt6n/p/61iWAVX4R4ritXrlQf/vhSD/SZCyZPnqwCUMCPAjxHM4Euam/RTIdzgGATnn32WVWzsHDhQpVwFoM5ELRmNBNAdsOxIrUF+h85Q9BspkfAe9QMRgHJV/GaR78dvObR9IUfIYGc5R79qszO36n7keE1jcENgPetORoT6xBYY/Til19+qdajLAKXQDZixAj1OsTrEdcXzY/4fMHAExNGWeKamjVKuLao7cTrHgE6BjEgwMEgh0CGEZdvv/22WvD5ih/3qPHD57AJz79x48aO2/jhjPJIMIvUH0irgebdQBygwo7+mWNDsrJM7oOIiIhCGLryoKn55lu/kYhIvWAxOemSrP/mVjUa1cwPGarYp4yIiIgoALD5koiIiHwiOfGCdsf9lOS0gx9CFYMyIiIiyhT0W8XI7d9/uM+r7bFtVIDnl8sK7FNGREREmYbRpJimzhsIyGKcpswKVQzKiIiIiAIAO/oTERERBQAGZUREREQBgB39icjvMJuAOWMCEpq2aNHC631hGiFzImYkz8SMBEREOQGDMiLKkjkcMdXPXXfdJTfddFOmgjLMx4ps75ioG3O0MigjopyCHf2JyO/++OMPNdcfJkMvWrSoT/aJaZbGjBmjAj4iopyAfcqISBsmMscE5s6OHDmiJsXGVClWXLp0SZXHpMv//POPfP3112piZtPWrVvVXIi4j4goFLD5koi05c6dW7p06SIzZ85UzZJoUsTtYsWKyf33329pH6g1w7Zoyjxz5oyakBkTwD/55JNy7Ngx2bFjh+p/Zk4q37NnT14pIsrRWFNGRNpq1aolEydOlH79+snBgwdl9OjRsn//ftWkqKtKlSqqhmz58uXy5ptvyuuvvy5FihRRTZ7ffPONaqJ88cUXeZWIKMdjUEZEXhkwYIA0bdpUOnbsKK+++qrMmjVL1ZTp6tOnj9hsNvX/Ro0aOdaZsC42NlauXLnCK0VEORqDMiLy2iuvvKKaGdu1ayft27f3ah+FChVy/D86OtrjuoSEBF4pIsrRGJQRkVcMw5DnnntOatSoId99952sX7+eZ5KIKBMYlBGRVyZNmqT6gv3www+qubF79+5qJCUREXmHQRkRaUMnfHS+nz59uhohiQANzYxPPfUUzyYRkZcYlBGRtiVLlsiIESNUhn7IlSuXzJs3T3XGR6Z9TxYtWiQ///yzY4qkrl27Sr58+Rz358+fX61Dyg1T4cKF1bqoqCjHNEvIb4ZcaUREOQkz+hOR3yFtxtChQ9X/b7jhBhk2bJjX+0pKSpIePXqo/2O057Rp03x2nERE2YlBGREREVEAYPMlERERUQBgUEZEREQUABiUEREREQUABmVEREREAYBBGREREVEAYFBGREREFAAYlBEREREFAAZlRERERAGAQRkRERFRAGBQRkRERBQAGJQRERERSfb7H+ORQoIqV0oeAAAAAElFTkSuQmCC", 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", 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" ] diff --git a/examples/smoke3d/smoke3d.ipynb b/examples/smoke3d/smoke3d.ipynb index 4a73e16..de7b0a9 100644 --- a/examples/smoke3d/smoke3d.ipynb +++ b/examples/smoke3d/smoke3d.ipynb @@ -138,8 +138,8 @@ "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] diff --git a/fdsreader/bndf/obstruction.py b/fdsreader/bndf/obstruction.py index 1f9c58e..d82bd31 100644 --- a/fdsreader/bndf/obstruction.py +++ b/fdsreader/bndf/obstruction.py @@ -887,6 +887,10 @@ def vmax( return max(s.vmax(quantity, orientation) for s in self._all_subobstructions) return np.nan + def get_subobstructions(self, mesh: "Mesh") -> List[SubObstruction]: + """All parts of this obstruction in the given mesh; several OBST lines can share an ID.""" + return list(self._subobstructions.get(mesh.id, [])) + def __getitem__(self, key: Union[int, str, "Mesh"]): """Gets either the nth :class:`SubObstruction` or the (first) one with the given mesh-id.""" diff --git a/fdsreader/fds_classes/mesh.py b/fdsreader/fds_classes/mesh.py index 3dbd41d..468e5b3 100644 --- a/fdsreader/fds_classes/mesh.py +++ b/fdsreader/fds_classes/mesh.py @@ -7,7 +7,6 @@ from fdsreader import settings from fdsreader.bndf import Boundary, Patch from fdsreader.utils import Dimension, Extent, Quantity -from fdsreader.utils.extent import extent_index_range class Mesh: @@ -50,16 +49,38 @@ def __init__( self._boundary_data: Dict[int, Boundary] = dict() def get_obstruction_mask( - self, times: Sequence[float], cell_centered=False, region: Tuple[slice, slice, slice] | None = None + self, + times: Sequence[float], + cell_centered=False, + region: Tuple[slice, slice, slice] | None = None, + mode: Literal["obstructions", "placeholders"] = "obstructions", ) -> np.ndarray: - """Marks all cells which are blocked by an obstruction. + """Marks all cells (or nodes) which are blocked by an obstruction. + + With ``mode="obstructions"`` (the default) the mask shows where the obstructions are: the cells inside an + obstruction, or the nodes inside it and on its faces. An obstruction without thickness (a thin wall) blocks + the nodes on it but no cells, as no cell lies inside it. For cell-centred data this is what Smokeview blanks; + for nodes Smokeview blanks the areas between them next to solid cells, so a gas cell between two + obstructions one cell apart has all its nodes masked here but is shown in Smokeview. + + With ``mode="placeholders"`` it marks only the values FDS fills with a placeholder (the ambient value) + instead of a value of the gas: the cells inside an obstruction, and the nodes where all cells of this mesh + around them are, as FDS averages a node from the gas cells around it (CORNER_VALUE in dump.f90). So the + nodes on the faces of an obstruction keep their gas values, except where obstructions touch each other or + an obstruction ends at the edge of the mesh. For cell-centred data both modes are the same. + + Both masks are derived from &OBST input only; solids defined with &GEOM are not included, and FDS writes + face quantities such as velocities differently. :param times: All timesteps of the simulation. :param region: Index ranges along x, y and z, as slices without a step, to which the mask is limited. The whole mesh by default. - :returns: A 4-dimensional array with time as first and x,y,z as last dimensions. The array - depends on time as obstructions may be hidden as specific points in time. + :param mode: ``"obstructions"`` or ``"placeholders"``, see above. + :returns: A 4-dimensional array with time as first and x,y,z as last dimensions, False where blocked. The + array depends on time as obstructions may be hidden as specific points in time. """ + if mode not in ("obstructions", "placeholders"): + raise ValueError(f"mode must be 'obstructions' or 'placeholders', not {mode!r}") shape = self.dimension.shape(cell_centered=cell_centered) if region is None: region = (slice(None),) * 3 @@ -68,43 +89,73 @@ def get_obstruction_mask( # Resolve open ends and negative indices like numpy indexing does region = tuple(slice(*r.indices(n)[:2]) for r, n in zip(region, shape)) region = tuple(slice(r.start, max(r.start, r.stop)) for r in region) - mask = np.ones((len(times),) + tuple(r.stop - r.start for r in region), dtype=bool) - c = 1 if cell_centered else 0 + times = np.asarray(times, dtype=float) + + if mode == "obstructions": + mask = np.ones((len(times),) + tuple(r.stop - r.start for r in region), dtype=bool) + for visible, blocked in self._obstruction_parts(times, region, nodes=not cell_centered): + mask[(visible,) + blocked] = False + return mask + # Placeholders: the cells needed are those of the region, or for nodes the cells on both sides of each node + n_cells = self.dimension.shape(cell_centered=True) + offset = 0 if cell_centered else 1 + window = tuple(slice(r.start - offset, r.stop) for r in region) + inside = tuple(slice(max(w.start, 0), min(w.stop, n)) for w, n in zip(window, n_cells)) + solid = np.zeros((len(times),) + tuple(max(c.stop - c.start, 0) for c in inside), dtype=bool) + + for visible, cells in self._obstruction_parts(times, inside, nodes=False): + solid[(visible,) + cells] = True + + # Cells outside the mesh are left out of a node's neighbourhood, i.e. count as solid + padding = [(0, 0)] + [(c.start - w.start, w.stop - c.stop) for c, w in zip(inside, window)] + solid = np.pad(solid, padding, constant_values=True) + + if cell_centered: + return ~solid + # A node is blocked if all (up to 8) cells around it are solid + nodes = np.ones((len(times),) + tuple(r.stop - r.start for r in region), dtype=bool) + for dx in (0, 1): + for dy in (0, 1): + for dz in (0, 1): + nodes &= solid[:, dx : dx + nodes.shape[1], dy : dy + nodes.shape[2], dz : dz + nodes.shape[3]] + return ~nodes + + def _obstruction_parts(self, times: np.ndarray, window: Tuple[slice, slice, slice], nodes: bool): + """The parts of all obstructions in this mesh that lie in the window (index ranges along x, y, z): for each, + the indices of the times it is visible at and its cells (low..high-1 between the nodes of its faces) or + nodes (low..high), relative to the window.""" for obst in self.obstructions: - subobst = obst[self] - blocked = list() - for dim, r in zip(("x", "y", "z"), region): - low, high = subobst.bound_indices[dim] - high = max(high + c, low + 1) - blocked.append(slice(max(low, r.start) - r.start, min(high, r.stop) - r.start)) - if any(b.stop <= b.start for b in blocked): - continue # the obstruction lies outside of the region - t_idx = 0 - for t in subobst.get_visible_times(times): - while not np.isclose(t, times[t_idx]): - t_idx += 1 - mask[(t_idx,) + tuple(blocked)] = False - return mask - - def get_obstruction_mask_slice(self, subslice): - """Marks all cells of a single subslice which are blocked by an obstruction. + for subobst in obst.get_subobstructions(self): + ranges = list() + for dim, w in zip(("x", "y", "z"), window): + low, high = subobst.bound_indices[dim] + high = high + 1 if nodes else high + ranges.append(slice(max(low, w.start) - w.start, max(min(high, w.stop) - w.start, 0))) + if any(r.stop <= r.start for r in ranges): + continue # none in the window, or no cells (an obstruction without thickness) + yield self._time_indices(times, subobst.get_visible_times(times)), tuple(ranges) + + @staticmethod + def _time_indices(times: np.ndarray, selected: Sequence[float]) -> np.ndarray: + """Indices of the given times (a subset of times) in the sorted array times.""" + selected = np.asarray(selected, dtype=float) + if len(times) < 2 or selected.size == 0: + return np.zeros(selected.size, dtype=int) + idx = np.clip(np.searchsorted(times, selected), 1, len(times) - 1) + return idx - (np.abs(times[idx - 1] - selected) <= np.abs(times[idx] - selected)) + + def get_obstruction_mask_slice(self, subslice, mode: Literal["obstructions", "placeholders"] = "obstructions"): + """Marks the cells, or nodes, of a single subslice which are blocked by an obstruction, by the rules of + :meth:`get_obstruction_mask` for the given mode. :returns: A 4-dimensional array with time as first and x,y,z as last dimensions, matching the shape of the subslice's data. The array depends on time as obstructions may be hidden at specific points in time. For a 2D slice the axis of its orientation has length 1. """ - orientation = subslice.orientation - cell_centered = subslice.cell_centered - region = list() - for axis, dim in enumerate(("x", "y", "z"), start=1): - if axis == orientation: - value = subslice.extent[orientation][0] - slc_index = self.coordinate_to_index((value,), dimension=(orientation,), cell_centered=cell_centered)[0] - region.append(slice(slc_index, slc_index + 1)) - else: # a slice defined with XB may cover only part of the mesh - region.append(extent_index_range(self.coordinates[dim], subslice.extent[dim], cell_centered)) - return self.get_obstruction_mask(subslice.times, cell_centered=cell_centered, region=tuple(region)) + ranges = subslice.index_ranges() + region = tuple(ranges[dim] for dim in ("x", "y", "z")) + return self.get_obstruction_mask(subslice.times, cell_centered=subslice.cell_centered, region=region, mode=mode) def coordinate_to_index( self, diff --git a/fdsreader/pl3d/pl3d.py b/fdsreader/pl3d/pl3d.py index 3275c03..ba98919 100644 --- a/fdsreader/pl3d/pl3d.py +++ b/fdsreader/pl3d/pl3d.py @@ -111,13 +111,20 @@ def clear_cache(self): subplot.clear_cache() def to_global( - self, masked: bool = False, fill: float = 0, return_coordinates: bool = False + self, + masked: bool = False, + fill: float = 0, + return_coordinates: bool = False, + mask: Literal["obstructions", "placeholders"] = "obstructions", ) -> Union[np.ndarray, Tuple[np.ndarray, Dict[Literal["x", "y", "z"], np.ndarray]]]: """Creates a global numpy ndarray from all subplots. :param masked: Whether to apply the obstruction mask to the data or not. :param fill: The fill value to use for masked entries. Only used when masked=True. :param return_coordinates: If true, return the matching coordinate for each value on the generated grid. + :param mask: Which obstruction mask to apply with ``masked=True``: ``"obstructions"`` (default) hides + everything inside an obstruction and on its faces, ``"placeholders"`` only the values FDS did not + compute but filled with the ambient value; see :meth:`Mesh.get_obstruction_mask`. """ if len(self._subplots) == 0: if return_coordinates: @@ -168,7 +175,7 @@ def to_global( for subplot in self._subplots.values(): subplot_data = self._data_on_global_times(subplot) if masked: - mask = subplot.mesh.get_obstruction_mask(self.times) + mesh_mask = subplot.mesh.get_obstruction_mask(self.times, mode=mask) start_idx = { dim: int(round((subplot.mesh.coordinates[dim][0] - coord_min[dim]) / step_sizes_min[dim])) @@ -191,12 +198,12 @@ def to_global( end_idx[dim] += 1 subplot_data = resample_mesh_axis(subplot_data, axis + 1, n_repeat, keep_last) if masked: - mask = resample_mesh_axis(mask, axis + 1, n_repeat, keep_last) + mesh_mask = resample_mesh_axis(mesh_mask, axis + 1, n_repeat, keep_last) # If the slice should be masked, we set all cells at which an obstruction is in the # simulation space to the fill value set by the user if masked: - subplot_data = np.where(mask, subplot_data, fill) + subplot_data = np.where(mesh_mask, subplot_data, fill) grid[:, start_idx["x"] : end_idx["x"], start_idx["y"] : end_idx["y"], start_idx["z"] : end_idx["z"]] = ( subplot_data.reshape( diff --git a/fdsreader/simulation.py b/fdsreader/simulation.py index c6e4060..2014d06 100644 --- a/fdsreader/simulation.py +++ b/fdsreader/simulation.py @@ -120,7 +120,7 @@ class Simulation: # Bump when the pickled structure of a Simulation or its data classes changes, so caches # written by an older layout are parsed again instead of being loaded with missing attributes. - _CACHE_FORMAT = 3 + _CACHE_FORMAT = 4 _loading = False def __new__(cls, path: str): @@ -711,6 +711,7 @@ def _load_slice(self, smv_file: TextIO, line: str): { "dimension": dimension, "extent": extent, + "bound_indices": tuple(bound_indices), "mesh": mesh, "filename": filename, "quantity": quantity, diff --git a/fdsreader/slcf/slice.py b/fdsreader/slcf/slice.py index 1bef95d..420ac34 100644 --- a/fdsreader/slcf/slice.py +++ b/fdsreader/slcf/slice.py @@ -37,11 +37,21 @@ class SubSlice: _offset = 3 * fdtype.new((("c", 30),)).itemsize + fdtype.new((("i", 6),)).itemsize - def __init__(self, parent_slc, filename: str, dimension: Dimension, extent: Extent, mesh: Mesh): + def __init__( + self, + parent_slc, + filename: str, + dimension: Dimension, + extent: Extent, + mesh: Mesh, + bound_indices: Tuple[int, int, int, int, int, int] | None = None, + ): self._parent_slice = parent_slc self.mesh = mesh self.dimension = dimension self.extent = extent + # Node indices i1, i2, j1, j2, k1, k2 of the slice in its mesh, as in the .smv file + self.bound_indices = bound_indices self.filename = filename @@ -55,6 +65,7 @@ def get_coordinates(self, ignore_cell_centered: bool = False) -> Dict[Literal["x :param ignore_cell_centered: Whether to shift the coordinates when the slice is cell_centered or not. """ cell_centered = self.cell_centered and not ignore_cell_centered + ranges = self.index_ranges(cell_centered) coords: Dict[Literal["x", "y", "z"], np.ndarray] = {} for dim in ("x", "y", "z"): co = self.mesh.coordinates[dim] @@ -63,10 +74,34 @@ def get_coordinates(self, ignore_cell_centered: bool = False) -> Dict[Literal["x # mesh with only a single cell along this axis leaves just one coordinate afterwards. if cell_centered: co = co[:-1] + np.diff(co) / 2 - coords[dim] = co[extent_index_range(co, self.extent[dim])].copy() + coords[dim] = co[ranges[dim]].copy() return coords + def index_ranges(self, cell_centered: bool | None = None) -> Dict[Literal["x", "y", "z"], slice]: + """Indices of the nodes, or of the cells, of the mesh that this subslice covers along each axis. + + FDS writes a cell-centred slice ``SLCC ... i1 i2`` for the cells between nodes i1 and i2, and a + cell-centred plane ``SLCC ... k k`` for the cell below node k. + + :param cell_centered: Whether to give cells or nodes; by default as the slice is written. + """ + if cell_centered is None: + cell_centered = self.cell_centered + ranges = dict() + for axis, dim in enumerate(("x", "y", "z")): + if self.bound_indices is None: # not read from an .smv file + ranges[dim] = extent_index_range(self.mesh.coordinates[dim], self.extent[dim], cell_centered) + continue + low, high = self.bound_indices[2 * axis], self.bound_indices[2 * axis + 1] + if not cell_centered: + ranges[dim] = slice(low, high + 1) + elif high > low: + ranges[dim] = slice(low, high) + else: + ranges[dim] = slice(max(low - 1, 0), max(low, 1)) + return ranges + @property def shape(self) -> Tuple[int, int]: """2D-shape of the slice.""" @@ -213,7 +248,12 @@ def __init__(self, root_path: str, slice_id: str, cell_centered: bool, multimesh if mesh_data["mesh"].id not in self._subslices: self.quantity = Quantity(mesh_data["quantity"], mesh_data["short_name"], mesh_data["unit"]) self._subslices[mesh_data["mesh"].id] = SubSlice( - self, mesh_data["filename"], mesh_data["dimension"], mesh_data["extent"], mesh_data["mesh"] + self, + mesh_data["filename"], + mesh_data["dimension"], + mesh_data["extent"], + mesh_data["mesh"], + mesh_data.get("bound_indices"), ) if "-VELOCITY" in mesh_data["quantity"]: @@ -359,14 +399,8 @@ def get_coordinates(self, ignore_cell_centered: bool = False) -> Dict[Literal["x coords = {"x": list(), "y": list(), "z": list()} for dim in ("x", "y", "z"): if orientation == dim: - coord = self.extent[dim][0] - if self.cell_centered and not ignore_cell_centered: - mesh = next(iter(self._subslices.values())).mesh - mesh_co = mesh.coordinates[dim] - idx = int(np.argmin(np.abs(mesh_co - coord))) - if idx > 0: - coord = (float(mesh_co[idx - 1]) + float(mesh_co[idx])) / 2 - coords[dim] = np.array([coord]) + first = next(iter(self._subslices.values())) + coords[dim] = first.get_coordinates(ignore_cell_centered)[dim][:1] continue for slc in self._subslices.values(): co = slc.get_coordinates(ignore_cell_centered)[dim] @@ -417,6 +451,14 @@ def get_2d_slice_from_3d(self, slice_direction: Literal["x", "y", "z", 1, 2, 3], # Check if the new slice cuts through the subslice cut_index = mesh.coordinate_to_index((value,), (slice_direction,), cell_centered=self.cell_centered) cut_value = mesh.get_nearest_coordinate((value,), (slice_direction,), cell_centered=self.cell_centered)[0] + if self.cell_centered: + # A value on a node lies between two cells: take the cell above it, as FDS does for a plane + # written with e.g. PBZ (and the last cell at the upper end of the mesh) + co = mesh.coordinates[slice_direction] + node = int(np.argmin(np.abs(co - value))) + if np.isclose(co[node], value, rtol=0, atol=1e-6 * max(1.0, abs(value))): + cut_index = (min(node, len(co) - 2),) + cut_value = (co[cut_index[0]] + co[cut_index[0] + 1]) / 2 if ( mesh.coordinates[slice_direction][0] <= value <= mesh.coordinates[slice_direction][-1] and subslc.extent[slice_dim][0] <= cut_value <= subslc.extent[slice_dim][1] @@ -428,7 +470,9 @@ def get_2d_slice_from_3d(self, slice_direction: Literal["x", "y", "z", 1, 2, 3], indices.extend((slice(shape[0]), slice(shape[1]))) else: indices.extend((slice(shape[0]), slice(shape[1]), slice(shape[2]))) - indices[slice_dim] = slice(cut_index[0], cut_index[0] + 1, 1) + # The data of a slice defined with XB starts at its own first node or cell, not the mesh's + local_index = cut_index[0] - subslc.index_ranges()[slice_direction].start + indices[slice_dim] = slice(local_index, local_index + 1, 1) subslc._data = np.squeeze(subslc.data[tuple(indices)], axis=slice_dim) for direction in subslc.vector_filenames.keys(): @@ -436,10 +480,19 @@ def get_2d_slice_from_3d(self, slice_direction: Literal["x", "y", "z", 1, 2, 3], subslc.vector_data[direction][tuple(indices)], axis=slice_dim ) - # Change 3D extent to 2D one + # Change 3D extent to 2D one, and the node indices as FDS writes them for a plane (for a + # cell-centred plane the node above the cell, see SubSlice.index_ranges) new_extent = subslc.extent.as_list(reduced=False) new_extent[(slice_dim - 1) * 2 : slice_dim * 2] = (cut_value, cut_value) subslc.extent = Extent(*new_extent) + if subslc.bound_indices is not None: + plane = cut_index[0] + (1 if self.cell_centered else 0) + bounds = list(subslc.bound_indices) + bounds[(slice_dim - 1) * 2 : slice_dim * 2] = (plane, plane) + subslc.bound_indices = tuple(bounds) + sizes = [subslc.dimension.x, subslc.dimension.y, subslc.dimension.z] + sizes[slice_dim - 1] = 1 + subslc.dimension = Dimension(*sizes) else: to_remove.append(mesh_id) @@ -504,7 +557,11 @@ def sort_subslices_cartesian(self): return slices_cart def to_global( - self, masked: bool = False, fill: float = 0, return_coordinates: bool = False + self, + masked: bool = False, + fill: float = 0, + return_coordinates: bool = False, + mask: Literal["obstructions", "placeholders"] = "obstructions", ) -> Union[ np.ndarray, Tuple[np.ndarray, np.ndarray], @@ -529,6 +586,9 @@ def to_global( :param masked: Whether to apply the obstruction mask to the slice or not. :param fill: The fill value to use for masked slice entries. Only used when masked=True. :param return_coordinates: If true, return the matching coordinate for each value on the generated grid. + :param mask: Which obstruction mask to apply with ``masked=True``: ``"obstructions"`` (default) hides + everything inside an obstruction and on its faces, ``"placeholders"`` only the values FDS did not + compute but filled with the ambient value; see :meth:`Mesh.get_obstruction_mask`. """ if len(self._subslices) == 0: if return_coordinates: @@ -600,7 +660,7 @@ def place(mesh: str, slc: SubSlice): slc_data = ( slc.data.copy() if slc.orientation == 0 else np.expand_dims(slc.data.copy(), axis=slc.orientation) ) - mask = slc.mesh.get_obstruction_mask_slice(slc) if masked else None + slc_mask = slc.mesh.get_obstruction_mask_slice(slc, mode=mask) if masked else None slc_nodes = nodes[mesh] start_idx = dict() @@ -624,7 +684,7 @@ def place(mesh: str, slc: SubSlice): if n_repeat > 1: slc_data = np.repeat(slc_data, n_repeat, axis=axis + 1) if masked: - mask = np.repeat(mask, n_repeat, axis=axis + 1) + slc_mask = np.repeat(slc_mask, n_repeat, axis=axis + 1) continue end_idx[dim] += 1 @@ -632,23 +692,23 @@ def place(mesh: str, slc: SubSlice): inner_axes.append(axis) slc_data = resample_mesh_axis(slc_data, axis + 1, n_repeat, keep_last=True) if masked: - mask = resample_mesh_axis(mask, axis + 1, n_repeat, keep_last=True) + slc_mask = resample_mesh_axis(slc_mask, axis + 1, n_repeat, keep_last=True) region = tuple(slice(start_idx[dim], end_idx[dim]) for dim in ("x", "y", "z")) shape = (self.n_t,) + tuple(end_idx[dim] - start_idx[dim] for dim in ("x", "y", "z")) # If the slice should be masked, we set all cells at which an obstruction is in the # simulation space to the fill value set by the user if masked: - mask = mask.reshape(shape) - slc_data = np.where(mask, slc_data.reshape(shape), fill) - return slc_data.reshape(shape), mask, region, inner_axes + slc_mask = slc_mask.reshape(shape) + slc_data = np.where(slc_mask, slc_data.reshape(shape), fill) + return slc_data.reshape(shape), slc_mask, region, inner_axes # Grid nodes some subslice has written, and the node rows of upper faces that lie inside the # simulation space, which were left to a neighbouring mesh (node-based slices only) written = None if self.cell_centered else np.zeros(grid.shape[1:], dtype=bool) inner_faces = list() for mesh, slc in subslices.items(): - slc_data, mask, region, inner_axes = place(mesh, slc) + slc_data, slc_mask, region, inner_axes = place(mesh, slc) # The node row on an upper face inside the simulation space coincides with the first row of the # neighbouring mesh, which supplies it, so it is left out here. A copy of it is kept for the case @@ -656,14 +716,14 @@ def place(mesh: str, slc: SubSlice): for axis in inner_axes: last = region[axis].stop - 1 face_region = tuple(slice(last, last + 1) if a == axis else r for a, r in enumerate(region)) - face_mask = None if mask is None else np.take(mask, [-1], axis=axis + 1) + face_mask = None if slc_mask is None else np.take(slc_mask, [-1], axis=axis + 1) inner_faces.append((np.take(slc_data, [-1], axis=axis + 1), face_mask, face_region)) region = tuple(slice(r.start, last) if a == axis else r for a, r in enumerate(region)) without_face = (slice(None),) * (axis + 1) + (slice(None, -1),) slc_data = slc_data[without_face] - if mask is not None: - mask = mask[without_face] + if slc_mask is not None: + slc_mask = slc_mask[without_face] grid[(slice(None),) + region] = slc_data if written is not None: @@ -688,8 +748,18 @@ def place(mesh: str, slc: SubSlice): if return_coordinates: coordinates = dict() + first = next(iter(subslices.values())) for dim_index, dim in enumerate(("x", "y", "z")): - coordinates[dim] = np.linspace(coord_min[dim], coord_max[dim], grid.shape[dim_index + 1]) + if self.cell_centered and dim_index == self.orientation - 1: + # The cell layer of the plane, see SubSlice.index_ranges + coordinates[dim] = first.get_coordinates()[dim][:1] + elif self.cell_centered: + half = step_sizes_min[dim] / 2 + coordinates[dim] = np.linspace( + coord_min[dim] + half, coord_max[dim] - half, grid.shape[dim_index + 1] + ) + else: + coordinates[dim] = np.linspace(coord_min[dim], coord_max[dim], grid.shape[dim_index + 1]) # Remove dimension corresponding to orientation for 2D slices if self.orientation != 0: diff --git a/fdsreader/smoke3d/smoke3d.py b/fdsreader/smoke3d/smoke3d.py index 4ee021a..a6ac4d0 100644 --- a/fdsreader/smoke3d/smoke3d.py +++ b/fdsreader/smoke3d/smoke3d.py @@ -392,6 +392,7 @@ def to_global( temp_min: float | None = None, temp_max: float | None = None, kind: Literal["gas", "hrrpuv", "temperature"] | None = None, + mask: Literal["obstructions", "placeholders"] = "obstructions", ) -> Union[np.ndarray, tuple]: """Creates a global numpy ndarray from all subsmokes. @@ -406,6 +407,9 @@ def to_global( :param temp_min: Overrides ``TEMP_MIN_SMV``, see :meth:`SubSmoke3D.to_physical`. :param temp_max: Overrides ``TEMP_MAX_SMV``, see :meth:`SubSmoke3D.to_physical`. :param kind: Overrides :attr:`kind`, see :meth:`SubSmoke3D.to_physical`. + :param mask: Which obstruction mask to apply with ``masked=True``: ``"obstructions"`` (default) hides + everything inside an obstruction and on its faces, ``"placeholders"`` only the values FDS did not + compute but filled with the ambient value; see :meth:`Mesh.get_obstruction_mask`. :returns: The global array, followed by the saturation mask if ``return_saturated`` and the coordinates if ``return_coordinates``. """ @@ -470,7 +474,7 @@ def to_global( else: subsmoke_data = subsmoke._codes().copy() if masked: - mask = subsmoke.mesh.get_obstruction_mask(self.times) + mesh_mask = subsmoke.mesh.get_obstruction_mask(self.times, mode=mask) start_idx = { dim: int(round((subsmoke.mesh.coordinates[dim][0] - coord_min[dim]) / step_sizes_min[dim])) @@ -495,14 +499,14 @@ def to_global( if return_saturated: subsmoke_saturated = resample_mesh_axis(subsmoke_saturated, axis + 1, n_repeat, keep_last) if masked: - mask = resample_mesh_axis(mask, axis + 1, n_repeat, keep_last) + mesh_mask = resample_mesh_axis(mesh_mask, axis + 1, n_repeat, keep_last) # If the slice should be masked, we set all cells at which an obstruction is in the # simulation space to the fill value set by the user if masked: - subsmoke_data = np.where(mask, subsmoke_data, fill) + subsmoke_data = np.where(mesh_mask, subsmoke_data, fill) if return_saturated: - subsmoke_saturated = subsmoke_saturated & mask + subsmoke_saturated = subsmoke_saturated & mesh_mask region = ( slice(None), @@ -542,6 +546,7 @@ def to_physical( temp_min: float | None = None, temp_max: float | None = None, kind: Literal["gas", "hrrpuv", "temperature"] | None = None, + mask: Literal["obstructions", "placeholders"] = "obstructions", ): """Physical values of all meshes on one grid; the same as ``to_global(physical=True)``. See :meth:`SubSmoke3D.to_physical` for the conversion and its limits.""" @@ -555,6 +560,7 @@ def to_physical( temp_min=temp_min, temp_max=temp_max, kind=kind, + mask=mask, ) def __getitem__(self, mesh: Mesh): diff --git a/tests/cases/fds_inputs/slcf_masks.fds b/tests/cases/fds_inputs/slcf_masks.fds new file mode 100644 index 0000000..4365b62 --- /dev/null +++ b/tests/cases/fds_inputs/slcf_masks.fds @@ -0,0 +1,45 @@ +&HEAD CHID='masks', TITLE='Obstruction masks for fdsreader (#132)' / +&MESH ID='M1', IJK=10,10,10, XB=0.0,1.0,0.0,1.0,0.0,1.0 / +&MESH ID='M2', IJK=10,10,10, XB=1.0,2.0,0.0,1.0,0.0,1.0 / +&MESH ID='M3', IJK=5,10,10, XB=0.0,0.5,1.0,2.0,0.0,1.0 / +&TIME T_END=10.0 / +&DUMP DT_SLCF=5.0, SMOKE3D=F / +&REAC FUEL='PROPANE' / +&SURF ID='FIRE', HRRPUA=1000.0 / +&VENT XB=0.1,1.9,0.1,0.9,0.0,0.0, SURF_ID='FIRE' / +&VENT XB=0.1,0.4,1.1,1.9,0.0,0.0, SURF_ID='FIRE' / +&VENT MB='XMIN', SURF_ID='OPEN' / &VENT MB='XMAX', SURF_ID='OPEN' / +&VENT MB='ZMAX', SURF_ID='OPEN' / + +Inside one mesh, across the mesh boundary, and up to the outside of the domain +&OBST ID='Box', XB=0.3,0.7,0.3,0.6,0.2,0.6 / +&OBST ID='Across', XB=0.8,1.3,0.7,0.9,0.2,0.6 / +&OBST ID='Span', XB=1.7,2.0,0.3,0.6,0.2,0.6 / +Two obstructions sharing a face +&OBST ID='Left', XB=1.2,1.4,0.1,0.3,0.6,0.9 / +&OBST ID='Right', XB=1.4,1.6,0.1,0.3,0.6,0.9 / +Ends at the mesh boundary x = 1 with gas beyond in M2 +&OBST ID='Ends', XB=0.8,1.0,0.1,0.2,0.6,0.9 / +Two obstructions with the same ID in one mesh +&OBST ID='Twin', XB=0.1,0.2,0.7,0.9,0.6,0.8 / +&OBST ID='Twin', XB=0.4,0.5,0.7,0.9,0.6,0.8 / +Hole in an obstruction +&OBST ID='Holey', XB=1.6,1.9,0.6,0.9,0.6,0.9 / +&HOLE XB=1.7,1.8,0.6,0.9,0.7,0.8 / +At y = 1 where M3 adjoins M1 only for x < 0.5: inside the domain and at its outside +&OBST ID='Under', XB=0.2,0.4,0.8,1.0,0.6,0.8 / +&OBST ID='Outside', XB=0.6,0.8,0.8,1.0,0.6,0.8 / + +3D slices +&SLCF XB=0.0,2.0,0.0,2.0,0.0,1.0, QUANTITY='TEMPERATURE', ID='vol' / +&SLCF XB=0.0,2.0,0.0,2.0,0.0,1.0, QUANTITY='TEMPERATURE', CELL_CENTERED=T, ID='vol_cc' / +2D slices through the obstructions and on their faces +&SLCF PBZ=0.4, QUANTITY='TEMPERATURE', ID='z04' / +&SLCF PBZ=0.4, QUANTITY='TEMPERATURE', CELL_CENTERED=T, ID='z04_cc' / +&SLCF PBZ=0.45, QUANTITY='TEMPERATURE', CELL_CENTERED=T, ID='z045_cc' / +&SLCF PBZ=0.2, QUANTITY='TEMPERATURE', ID='z02' / +&SLCF PBZ=0.2, QUANTITY='TEMPERATURE', CELL_CENTERED=T, ID='z02_cc' / +&SLCF PBZ=0.7, QUANTITY='TEMPERATURE', ID='z07' / +&SLCF PBX=0.5, QUANTITY='TEMPERATURE', CELL_CENTERED=T, ID='x05_cc' / +&SLCF PBY=0.8, QUANTITY='TEMPERATURE', ID='y08' / +&TAIL / diff --git a/tests/cases/slcf_masks_fds611.tgz b/tests/cases/slcf_masks_fds611.tgz new file mode 100644 index 0000000..262fbd5 --- /dev/null +++ b/tests/cases/slcf_masks_fds611.tgz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:13e9f6a34b6d6bc09d230902fb1b4a42da64b22546c4c00fe5f3c39030b32719 +size 193980 diff --git a/tests/fds_cases.py b/tests/fds_cases.py new file mode 100644 index 0000000..9e9c67d --- /dev/null +++ b/tests/fds_cases.py @@ -0,0 +1,35 @@ +"""Shared helpers for tests on stored FDS cases that also run against fresh FDS output. + +The FDS compatibility and nightly workflows run tests/cases/fds_inputs/.fds through a new FDS build and point +FDS_FRESH_OUTPUT_DIR at the output, so tests using these helpers check that build too. +""" + +import os +from pathlib import Path + +from fdsreader import Simulation, settings + +CASES_DIR = Path(__file__).parent / "cases" + + +def case_dirs(stored: str, fresh: str) -> list[Path]: + """The stored case, and the fresh output of tests/cases/fds_inputs/.fds if the workflow provides it.""" + dirs = [CASES_DIR / stored] + fresh_dir = Path(os.environ.get("FDS_FRESH_OUTPUT_DIR") or "/nonexistent") / fresh + if fresh_dir.is_dir(): + dirs.append(fresh_dir) + return dirs + + +def case_id(case_dir: Path) -> str: + return "fresh" if not case_dir.is_relative_to(CASES_DIR) else case_dir.name.rsplit("_", 1)[-1] + + +def load(case_dir: Path) -> Simulation: + """The simulation, parsed without the pickle cache.""" + original = settings.ENABLE_CACHING + settings.ENABLE_CACHING = False + try: + return Simulation(str(case_dir)) + finally: + settings.ENABLE_CACHING = original diff --git a/tests/test_obstruction_masks.py b/tests/test_obstruction_masks.py new file mode 100644 index 0000000..db66377 --- /dev/null +++ b/tests/test_obstruction_masks.py @@ -0,0 +1,263 @@ +"""Obstruction masks and the planes of cell-centred slices (#132), on slcf_masks_fds611 (FDS 6.11.1; input in +tests/cases/fds_inputs/slcf_masks.fds). + +Three meshes: M1 (x 0..1) and M2 (x 1..2) with 0.1 m cells, y 0..1, and M3 (x 0..0.5, y 1..2), which adjoins M1 +only partly. A fire covers the floor. Obstructions: inside a mesh ('Box'), across the mesh boundary ('Across'), up +to the outside of the domain ('Span'), two sharing a face ('Left', 'Right'), one with a hole ('Holey'), and two +ending at y = 1, where M3 adjoins ('Under') or the domain ends ('Outside'). + +FDS writes a placeholder, the ambient value (20 C), into the cells inside an obstruction, and into a node if all +cells of its mesh around it are solid (CORNER_VALUE in dump.f90; Smokeview blanks nodes the same way). After 10 s of +fire no gas cell or node is at exactly 20 C, so the mask must block exactly the placeholder values. +""" + +import numpy as np +import pytest +from fds_cases import CASES_DIR, case_dirs, case_id, load + +SLICE_IDS = ["vol", "vol_cc", "z04", "z04_cc", "z045_cc", "z02", "z02_cc", "z07", "x05_cc", "y08"] +PLACEHOLDER = 20.0 + + +@pytest.fixture(scope="module", params=case_dirs("slcf_masks_fds611", "slcf_masks"), ids=case_id) +def sim(request): + return load(request.param) + + +def _mesh(sim, mesh_id): + return next(mesh for mesh in sim.meshes if mesh.id == mesh_id) + + +def _shared_frames(a, b): + """Index pairs of the frames two slices have in common.""" + pairs = [(i, int(np.argmin(np.abs(b.times - t)))) for i, t in enumerate(a.times)] + pairs = [(i, j) for i, j in pairs if np.isclose(a.times[i], b.times[j], atol=1e-3)] + assert len(pairs) > 1 + return pairs + + +@pytest.mark.parametrize("slice_id", SLICE_IDS) +def test_placeholder_mask_blocks_exactly_the_placeholder_values(sim, slice_id): + for sub in sim.slices.get_by_id(slice_id).subslices: + data = sub.data[-1] + if sub.orientation: + data = np.expand_dims(data, sub.orientation - 1) + blocked = ~sub.mesh.get_obstruction_mask_slice(sub, mode="placeholders")[-1] + np.testing.assert_array_equal(blocked, data == PLACEHOLDER, err_msg=f"{slice_id} in {sub.mesh.id}") + + +def test_cells_and_nodes_of_an_obstruction(sim): + # 'Box' spans the nodes 3..7, 3..6, 2..6 of M1: its cells are 3..6, 3..5, 2..5, its inner nodes 4..6, 4..5, 3..5 + times = sim.slices.get_by_id("vol").times + m1 = _mesh(sim, "M1") + for mode in ("obstructions", "placeholders"): + cells = ~m1.get_obstruction_mask(times, cell_centered=True, mode=mode)[-1] + assert cells[3:7, 3:6, 2:6].all() and not cells[7, 4, 4] and not cells[2, 4, 4] + nodes = ~m1.get_obstruction_mask(times, mode="placeholders")[-1] + assert nodes[4:7, 4:6, 3:6].all() + assert not nodes[3, 4, 4] and not nodes[7, 4, 4] # the faces of 'Box' at x = 0.3 and 0.7 hold gas values + nodes = ~m1.get_obstruction_mask(times)[-1] + assert nodes[3:8, 3:7, 2:7].all() and not nodes[2, 4, 4] and not nodes[8, 4, 4] # with its faces + + +def test_placeholder_nodes_where_obstructions_touch_or_meet_the_mesh_edge(sim): + times = sim.slices.get_by_id("vol").times + m1, m2, m3 = (_mesh(sim, mesh_id) for mesh_id in ("M1", "M2", "M3")) + nodes = {mesh.id: ~mesh.get_obstruction_mask(times, mode="placeholders")[-1] for mesh in (m1, m2, m3)} + assert nodes["M2"][4, 2, 7] # the face shared by 'Left' (x 2..4) and 'Right' (x 4..6) + assert nodes["M1"][10, 8, 4] and nodes["M2"][0, 8, 4] # 'Across' on both sides of the mesh boundary + assert nodes["M2"][10, 4, 4] # 'Span' at the outside of the domain + assert nodes["M1"][3, 10, 7] # 'Under' ends at the edge of M1, although M3 has gas beyond + assert not nodes["M3"][3, 0, 7] # ... which M3 writes on its own edge + assert nodes["M1"][7, 10, 7] # 'Outside' ends at y = 1, where the domain ends + + +def _inside_obstructions(sub, cell_centered): + """Expected obstruction mask of a subslice from the coordinates of the obstructions in the .smv file: the cells + whose centre lies inside an obstruction, or the nodes inside or on it.""" + coords = sub.get_coordinates() + grids = np.meshgrid(*(coords[dim] for dim in "xyz"), indexing="ij") + spacing = {dim: np.diff(sub.mesh.coordinates[dim]).min() for dim in "xyz"} + blocked = np.zeros(grids[0].shape, dtype=bool) + for obst in sub.mesh.obstructions: + for subobst in obst.get_subobstructions(sub.mesh): + inside = np.ones(blocked.shape, dtype=bool) + for grid, dim in zip(grids, "xyz"): + low, high = subobst.extent[dim] + tol = 1e-4 * spacing[dim] + inside &= (grid > low - tol) & (grid < high + tol) + blocked |= inside + return blocked + + +@pytest.mark.parametrize("slice_id", SLICE_IDS) +def test_obstruction_mask_covers_the_obstructions_and_their_faces(sim, slice_id): + for sub in sim.slices.get_by_id(slice_id).subslices: + blocked = ~sub.mesh.get_obstruction_mask_slice(sub)[-1] + expected = _inside_obstructions(sub, sub.cell_centered) + np.testing.assert_array_equal(blocked, expected, err_msg=f"{slice_id} in {sub.mesh.id}") + + +@pytest.mark.parametrize("slice_id", SLICE_IDS) +def test_obstruction_mask_contains_every_placeholder(sim, slice_id): + # Everything FDS did not compute lies inside an obstruction or on its faces + for sub in sim.slices.get_by_id(slice_id).subslices: + obstructions = ~sub.mesh.get_obstruction_mask_slice(sub)[-1] + placeholders = ~sub.mesh.get_obstruction_mask_slice(sub, mode="placeholders")[-1] + assert not (placeholders & ~obstructions).any() + + +# Smokeview 6.11.2 on this case, read off by hand (1 = blanked) for the areas of 0.1 m between the nodes, x 0..2 left to +# right, the other axis from 1.0 (top) down to 0.0: the slices PBZ=0.7 seen from above, PBY=0.8 seen from the front and +# PBZ=0.4 with CELL_CENTERED=T seen from above, at t = 10 s with the obstacles hidden +SMOKEVIEW = { + "z07": """ + 00110011000000000000 + 01111011000000001110 + 01001000000000001110 + 00000000000000001110 + 00000000000000000000 + 00000000000000000000 + 00000000000000000000 + 00000000000011110000 + 00000000110011110000 + 00000000000000000000 + """, + "y08": """ + 00000000000000000000 + 00000000000000001110 + 01111011000000001010 + 01111011000000001110 + 00000000111110000000 + 00000000111110000000 + 00000000111110000000 + 00000000111110000000 + 00000000000000000000 + 00000000000000000000 + """, + # cell-centred PBZ=0.4 (the cells z 0.4..0.5) seen from above, per cell + "z04_cc": """ + 00000000000000000000 + 00000000111110000000 + 00000000111110000000 + 00000000000000000000 + 00011110000000000111 + 00011110000000000111 + 00011110000000000111 + 00000000000000000000 + 00000000000000000000 + 00000000000000000000 + """, +} + + +@pytest.mark.parametrize("slice_id, axis, node", [("z07", 2, 7), ("y08", 1, 8)]) +def test_obstruction_mask_matches_smokeview(sim, slice_id, axis, node): + # Smokeview blanks an area of the plane if a cell next to it, on either side, is solid + smokeview = np.array([[int(c) for c in row] for row in SMOKEVIEW[slice_id].split()])[::-1].T + times = sim.slices.get_by_id(slice_id).times + blanked = np.zeros((20, 10), dtype=int) + for mesh, offset in ((_mesh(sim, "M1"), 0), (_mesh(sim, "M2"), 10)): + cells = ~mesh.get_obstruction_mask(times, cell_centered=True)[-1] + sides = np.take(cells, node - 1, axis=axis) | np.take(cells, node, axis=axis) + blanked[offset : offset + 10] = sides + np.testing.assert_array_equal(blanked, smokeview) + + +def test_masked_cell_centred_slice_matches_smokeview(sim): + # Through the whole path: the cell layer of the plane, its mask and to_global + smokeview = np.array([[int(c) for c in row] for row in SMOKEVIEW["z04_cc"].split()])[::-1].T + values, coords = sim.slices.get_by_id("z04_cc").to_global(masked=True, fill=np.nan, return_coordinates=True) + centres = np.arange(0.05, 1.0, 0.1) + ix = np.searchsorted(coords["x"], np.arange(0.05, 2.0, 0.1) - 1e-6) + iy = np.searchsorted(coords["y"], centres - 1e-6) + np.testing.assert_array_equal(np.isnan(values[-1][np.ix_(ix, iy)]), smokeview.astype(bool)) + + +def test_unknown_mask_mode_is_rejected(sim): + with pytest.raises(ValueError, match="mode"): + sim.meshes[0].get_obstruction_mask([0.0], mode="solid") + + +@pytest.mark.parametrize( + "slice_id, axis, layer, centre", + [("z04_cc", "z", 4, 0.45), ("z045_cc", "z", 4, 0.45), ("z02_cc", "z", 2, 0.25), ("x05_cc", "x", 5, 0.55)], +) +def test_cell_centred_plane_is_the_cell_below_the_node_in_the_smv_file(sim, slice_id, axis, layer, centre): + # FDS writes a cell-centred plane SLCC ... k k for the cell below node k; its data equals that layer of the + # cell-centred 3D slice + plane = sim.slices.get_by_id(slice_id) + volume = sim.slices.get_by_id("vol_cc") + sub = next(s for s in plane.subslices if s.mesh.id == "M1") + ref = next(s for s in volume.subslices if s.mesh.id == "M1") + axis_index = "xyz".index(axis) + for i, j in _shared_frames(plane, volume): + np.testing.assert_array_equal(sub.data[i], np.take(ref.data[j], layer, axis=axis_index)) + assert sub.get_coordinates()[axis] == pytest.approx([centre]) + assert plane.get_coordinates()[axis] == pytest.approx([centre]) + + +def test_to_global_coordinates_of_cell_centred_slices_are_the_cell_centres(sim): + _, coords = sim.slices.get_by_id("z04_cc").to_global(return_coordinates=True) + np.testing.assert_allclose(coords["x"], np.arange(0.05, 2.0, 0.1), atol=1e-6) + np.testing.assert_allclose(coords["y"], np.arange(0.05, 2.0, 0.1), atol=1e-6) + assert coords["z"] == pytest.approx([0.45]) + _, coords = sim.slices.get_by_id("vol_cc").to_global(return_coordinates=True) + np.testing.assert_allclose(coords["z"], np.arange(0.05, 1.0, 0.1), atol=1e-6) + _, coords = sim.slices.get_by_id("z04").to_global(return_coordinates=True) + np.testing.assert_allclose(coords["x"], np.arange(0.0, 2.05, 0.1), atol=1e-6) + + +@pytest.mark.parametrize("slice_id", ["z04", "z04_cc", "vol", "vol_cc"]) +def test_to_global_with_placeholder_mask_hides_exactly_the_placeholder_values(sim, slice_id): + slc = sim.slices.get_by_id(slice_id) + values = slc.to_global(masked=True, fill=np.nan, mask="placeholders")[-1] + raw = slc.to_global()[-1] + covered = ~np.isnan(raw) # the global grid also spans x > 0.5, y > 1, where there is no mesh + np.testing.assert_array_equal(np.isnan(values)[covered], (raw == PLACEHOLDER)[covered]) + + +@pytest.mark.parametrize("volume_id, plane_id, value", [("vol", "z04", 0.4), ("vol_cc", "z04_cc", 0.45)]) +@pytest.mark.parametrize("masked", [False, True]) +def test_2d_cut_of_a_3d_slice_equals_the_2d_slice(sim, volume_id, plane_id, value, masked): + volume, plane = sim.slices.get_by_id(volume_id), sim.slices.get_by_id(plane_id) + cut, cut_coords = volume.get_2d_slice_from_3d("z", value).to_global( + masked=masked, fill=np.nan, return_coordinates=True + ) + reference, coords = plane.to_global(masked=masked, fill=np.nan, return_coordinates=True) + for i, j in _shared_frames(plane, volume): + np.testing.assert_array_equal(cut[j], reference[i]) + assert cut_coords["z"] == pytest.approx(coords["z"]) + + +def test_2d_cut_of_a_partial_3d_slice(): + # The data of a slice defined with XB starts at its own first node, not at the mesh's + sim = load(CASES_DIR / "slcf_partial_fds611") + volume, plane = sim.slices.get_by_id("vol_part"), sim.slices.get_by_id("z_part") + cut = volume.get_2d_slice_from_3d("z", 0.4).to_global(masked=True, fill=np.nan) + reference = plane.to_global(masked=True, fill=np.nan) + for i, j in _shared_frames(plane, volume): + np.testing.assert_array_equal(cut[j], reference[i]) + + +def test_2d_cut_of_a_cell_centred_slice_on_a_node_takes_the_cell_above(sim): + # Like FDS for &SLCF PBZ=0.4, CELL_CENTERED=T: the cell from 0.4 to 0.5 + volume, plane = sim.slices.get_by_id("vol_cc"), sim.slices.get_by_id("z04_cc") + cut_slice = volume.get_2d_slice_from_3d("z", 0.4) + cut, coords = cut_slice.to_global(return_coordinates=True) + reference = plane.to_global() + for i, j in _shared_frames(plane, volume): + np.testing.assert_array_equal(cut[j], reference[i]) + assert coords["z"] == pytest.approx([0.45]) + for sub in cut_slice.subslices: + assert sub.dimension.z == 1 + assert sub.data.shape[1:] == sub.dimension.shape(cell_centered=True)[:2] + + +def test_time_indices_of_visible_times(): + from fdsreader.fds_classes.mesh import Mesh + + times = np.array([0.0, 1.0, 2.0, 3.0]) + assert list(Mesh._time_indices(times, [0.0, 2.0, 3.0])) == [0, 2, 3] + assert list(Mesh._time_indices(times, [])) == [] + assert list(Mesh._time_indices(np.array([5.0]), [5.0])) == [0] diff --git a/tests/test_slice_partial_xb.py b/tests/test_slice_partial_xb.py index 881744e..86675ba 100644 --- a/tests/test_slice_partial_xb.py +++ b/tests/test_slice_partial_xb.py @@ -9,33 +9,16 @@ matching section of the full one, and the raw data of each mesh. """ -import os -from pathlib import Path - import numpy as np import pytest +from fds_cases import case_dirs, case_id, load -from fdsreader import Simulation, settings - -CASES_DIR = Path(__file__).parent / "cases" PARTIAL_IDS = ["z_part", "z_part_cc", "vol_part", "vol_part_cc", "z_part_m1"] -# The FDS compatibility workflow runs tests/cases/fds_inputs/slcf_partial.fds through the newest FDS release -# and points FDS_FRESH_OUTPUT_DIR at the output, so the same checks run against that release too. -_CASES = [CASES_DIR / "slcf_partial_fds611"] -_FRESH = Path(os.environ.get("FDS_FRESH_OUTPUT_DIR") or "/nonexistent") / "slcf_partial" -if _FRESH.is_dir(): - _CASES.append(_FRESH) - -@pytest.fixture(scope="module", params=_CASES, ids=lambda case: "fresh" if case == _FRESH else "fds611") +@pytest.fixture(scope="module", params=case_dirs("slcf_partial_fds611", "slcf_partial"), ids=case_id) def sim(request): - original = settings.ENABLE_CACHING - settings.ENABLE_CACHING = False - try: - yield Simulation(str(request.param)) - finally: - settings.ENABLE_CACHING = original + return load(request.param) def _full_slice(sim, partial): @@ -144,7 +127,11 @@ def test_obstruction_mask_of_a_region_equals_the_cropped_mask_of_the_mesh(sim): assert region[0].start > 0 mask = mesh.get_obstruction_mask(subslice.times, region=region) np.testing.assert_array_equal(mask, full[(slice(None),) + region]) - assert (~mask).any() and mask.any() + # The cells inside each obstruction are blocked (a thin one may have no nodes inside it) + full_cells = mesh.get_obstruction_mask(subslice.times, cell_centered=True) + cells = mesh.get_obstruction_mask(subslice.times, cell_centered=True, region=region) + np.testing.assert_array_equal(cells, full_cells[(slice(None),) + region]) + assert (~cells).any() and cells.any() meshes_with_obstruction += 1 assert meshes_with_obstruction == 3 # M0, M1 and M3 diff --git a/tests/test_slice_to_global.py b/tests/test_slice_to_global.py index 4c86cda..1c698e3 100644 --- a/tests/test_slice_to_global.py +++ b/tests/test_slice_to_global.py @@ -179,7 +179,7 @@ def test_to_global_fills_every_node_of_a_3d_slice_with_a_step_in_height(): class FakeObstruction: - """Obstruction visible at all times, covering the given node index ranges of one mesh.""" + """Obstruction visible at all times, between the given node indices (its faces) of one mesh.""" def __init__(self, mesh, x, y, z): self._sub = {mesh: self} @@ -188,19 +188,23 @@ def __init__(self, mesh, x, y, z): def __getitem__(self, mesh): return self._sub[mesh] + def get_subobstructions(self, mesh): + return [self._sub[mesh]] if mesh in self._sub else [] + def get_visible_times(self, times): return times def _add_obstruction(slc, mesh_id, x, y): - """Puts an obstruction on a node of the given mesh at the slice height z = 1 (node index 1).""" + """Puts an obstruction over the nodes x = (first, last), y = (first, last) of the given mesh at the slice height + z = 1 (node index 1); the obstruction mask covers the nodes inside an obstruction and on its faces.""" mesh = slc._subslices[mesh_id].mesh - mesh.obstructions.append(FakeObstruction(mesh, x, y, (1, 2))) + mesh.obstructions.append(FakeObstruction(mesh, x, y, (1, 1))) def test_to_global_masks_a_recovered_upper_face_node(): slc = BoxSlice([box + (0.25,) for box in L_SHAPE]) - _add_obstruction(slc, "M1", x=(4, 5), y=(1, 2)) # B's right face at x = 1, y = 1.25: no neighbour supplies it + _add_obstruction(slc, "M1", x=(4, 4), y=(1, 1)) # B's right face at x = 1, y = 1.25: no neighbour supplies it grid = np.asarray(slc.to_global(masked=True, fill=np.nan))[0] (X, Y), inside = _union_grid(L_SHAPE, 0.25) @@ -214,7 +218,7 @@ def test_to_global_masks_a_recovered_node_if_any_supplying_mesh_masks_it(order): # Corner x = 1, y = 1 lies on upper faces of A, B and C, so pass 2 supplies it; only B has the obstruction boxes = [L_SHAPE[i] for i in order] slc = BoxSlice([box + (0.25,) for box in boxes]) - _add_obstruction(slc, f"M{order.index(1)}", x=(4, 5), y=(0, 1)) + _add_obstruction(slc, f"M{order.index(1)}", x=(4, 4), y=(0, 0)) grid = np.asarray(slc.to_global(masked=True, fill=np.nan))[0] (X, Y), inside = _union_grid(L_SHAPE, 0.25) @@ -266,7 +270,7 @@ def test_to_global_masks_a_recovered_face_node_of_a_coarse_mesh(): # C has twice the cell size; an obstruction on its coarse node x = 1.5, y = 1 (top face, no neighbour above) # covers the fine nodes x = 1.5 and x = 1.75 once C's values are repeated onto the global grid slc = BoxSlice([L_SHAPE[0] + (0.25,), L_SHAPE[1] + (0.25,), L_SHAPE[2] + (0.5,)]) - _add_obstruction(slc, "M2", x=(1, 2), y=(2, 3)) + _add_obstruction(slc, "M2", x=(1, 1), y=(2, 2)) grid = np.asarray(slc.to_global(masked=True, fill=np.nan))[0] (X, Y), inside = _union_grid(L_SHAPE, 0.25)