diff --git a/examples/visualization/reference/satn_to_movie.ipynb b/examples/visualization/reference/satn_to_movie.ipynb index d21022cbd..d93b6d8d9 100644 --- a/examples/visualization/reference/satn_to_movie.ipynb +++ b/examples/visualization/reference/satn_to_movie.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "a83a0a7b", "metadata": { "execution": { @@ -35,7 +35,7 @@ "" ] }, - "execution_count": 1, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "f4e43eeb", "metadata": { "execution": { @@ -81,58 +81,16 @@ "outputs": [ { "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-04-25T02:53:37.602452\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.5.1, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], + "image/png": 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"text/plain": [ - "
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" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "height": 463, + "width": 463 + } }, "output_type": "display_data" } @@ -163,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "2555e89c", "metadata": { "execution": { @@ -174,10 +132,18 @@ } }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\jeff\\AppData\\Local\\Temp\\ipykernel_20432\\2135606513.py:4: DeprecationWarning: Call to deprecated function (or staticmethod) ibip. (The ibip function will be moved to the ``simulations`` module in a future version)\n", + " out = ps.filters.ibip(im=im, inlets=bd)\n" + ] + }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7f7869fda2b74014b2822c1a9e22708d", + "model_id": "ee7a8a25dc78492f83539c3ebe426900", "version_major": 2, "version_minor": 0 }, @@ -208,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "id": "ec6b9062", "metadata": { "execution": { @@ -221,6 +187,56 @@ "remove_output" ] }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0%| | 0/390 [00:00\n", " \n", " \n", - " 2022-04-25T02:54:01.617722\n", + " 2022-04-25T02:54:25.588046\n", " image/svg+xml\n", " \n", " \n", @@ -272,30 +288,30 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + 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\" id=\"image074451c1cb\" transform=\"matrix(2.47 0 0 -2.47 25.97 254.16625)\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" width=\"100\" height=\"100\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -331,12 +347,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -376,12 +392,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -416,12 +432,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -467,12 +483,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -529,22 +545,22 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -557,12 +573,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -576,12 +592,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -595,12 +611,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -614,12 +630,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -654,7 +670,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -671,1348 +687,37 @@ } ], "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn)\n", - "mov_image_based_ip = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8784e9bf", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-25T05:20:52.199124Z", - "iopub.status.busy": "2022-04-25T05:20:52.198943Z", - "iopub.status.idle": "2022-04-25T05:20:52.207061Z", - "shell.execute_reply": "2022-04-25T05:20:52.206645Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(mov_image_based_ip)" + "mov = ps.visualization.satn_to_movie(im=im, satn=satn, cmap='plasma')\n", + "image_based_ip_cmap = mov.to_jshtml()\n", + "HTML(image_based_ip_cmap)" ] }, { "cell_type": "markdown", - "id": "55888821", + "id": "c037a02c", "metadata": {}, "source": [ - "## `cmap`" + "## `c_under`" ] }, { "cell_type": "markdown", - "id": "20997967", + "id": "d39ded4f", "metadata": {}, "source": [ - "The Colormap used to map invasion sequence values to colors. By default the cmap is 'viridis'." + "Colormap to be assigned to the lowest color bound (under color) in the color map. The voxeled colored by `c_under` are the uninvaded void space. The default under color is grey." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "9eebbbf8", + "execution_count": null, + "id": "2fe8811a", "metadata": { "execution": { - "iopub.execute_input": "2022-04-25T05:20:52.212433Z", - "iopub.status.busy": "2022-04-25T05:20:52.212303Z", - "iopub.status.idle": "2022-04-25T05:21:14.984476Z", - "shell.execute_reply": "2022-04-25T05:21:14.983884Z" + "iopub.execute_input": "2022-04-25T05:21:14.988769Z", + "iopub.status.busy": "2022-04-25T05:21:14.988573Z", + "iopub.status.idle": "2022-04-25T05:21:37.504850Z", + "shell.execute_reply": "2022-04-25T05:21:37.504103Z" } }, "outputs": [ @@ -2034,7 +739,7 @@ " \n", " \n", " \n", - " 2022-04-25T02:54:25.588046\n", + " 2022-04-25T02:54:48.139928\n", " image/svg+xml\n", " \n", " \n", @@ -2066,30 +771,30 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + 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id=\"imagef1dcf921eb\" transform=\"matrix(2.47 0 0 -2.47 25.97 254.16625)\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" width=\"100\" height=\"100\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2125,12 +830,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2170,12 +875,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2210,12 +915,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2261,12 +966,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2323,5918 +1028,22 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, cmap='plasma')\n", - "image_based_ip_cmap = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "847f0026", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_cmap)" - ] - }, - { - "cell_type": "markdown", - "id": "c037a02c", - "metadata": {}, - "source": [ - "## `c_under`" - ] - }, - { - "cell_type": "markdown", - "id": "d39ded4f", - "metadata": {}, - "source": [ - "Colormap to be assigned to the lowest color bound (under color) in the color map. The voxeled colored by `c_under` are the uninvaded void space. The default under color is grey." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "2fe8811a", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-25T05:21:14.988769Z", - "iopub.status.busy": "2022-04-25T05:21:14.988573Z", - "iopub.status.idle": "2022-04-25T05:21:37.504850Z", - "shell.execute_reply": "2022-04-25T05:21:37.504103Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-04-25T02:54:48.139928\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.5.1, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, c_under='green')\n", - "image_based_ip_c_under = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "79a02fce", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_c_under)" - ] - }, - { - "cell_type": "markdown", - "id": "6edfdfc3", - "metadata": {}, - "source": [ - "## `c_over`" - ] - }, - { - "cell_type": "markdown", - "id": "ff9c2933", - "metadata": {}, - "source": [ - "Colormap to be assigned to the highest color bound (over color) in the color map. The voxeled colored by `c_overer` are the solid phase. The default over color is white." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fb8cdf9d", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-25T05:21:37.509070Z", - "iopub.status.busy": "2022-04-25T05:21:37.508915Z", - "iopub.status.idle": "2022-04-25T05:21:59.751378Z", - "shell.execute_reply": "2022-04-25T05:21:59.750706Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-04-25T02:55:10.378412\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.5.1, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n" - ], - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, c_over='yellow')\n", - "image_based_ip_c_over = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "657a859d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_c_over)" - ] - }, - { - "cell_type": "markdown", - "id": "964039c6", - "metadata": {}, - "source": [ - "## `v_under`" - ] - }, - { - "cell_type": "markdown", - "id": "9565edcb", - "metadata": {}, - "source": [ - "This is the lowest bound of `satn` data range that the colormap covers. By default, the `v_under` is 0.001." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "172fb4c7", - "metadata": { - "execution": { - "iopub.execute_input": "2022-04-25T05:21:59.755286Z", - "iopub.status.busy": "2022-04-25T05:21:59.754684Z", - "iopub.status.idle": "2022-04-25T05:22:22.409071Z", - "shell.execute_reply": "2022-04-25T05:22:22.408410Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2022-04-25T02:55:33.719540\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.5.1, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8247,12 +1056,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8266,12 +1075,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8285,12 +1094,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8304,12 +1113,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8344,7 +1153,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8361,1180 +1170,37 @@ } ], "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, v_under=0.2)\n", - "image_based_ip_v_under = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "dc808b38", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_v_under)" + "mov = ps.visualization.satn_to_movie(im=im, satn=satn, c_under='green')\n", + "image_based_ip_c_under = mov.to_jshtml()\n", + "HTML(image_based_ip_c_under)" ] }, { "cell_type": "markdown", - "id": "7fbdec6c", + "id": "6edfdfc3", "metadata": {}, "source": [ - "## `v_over`" + "## `c_over`" ] }, { "cell_type": "markdown", - "id": "96bd8912", + "id": "ff9c2933", "metadata": {}, "source": [ - "This is the highest bound of `satn` data range that the colormap covers. By default, the `v_over` is 1." + "Colormap to be assigned to the highest color bound (over color) in the color map. The voxeled colored by `c_overer` are the solid phase. The default over color is white." ] }, { "cell_type": "code", - "execution_count": 14, - "id": "0dd48cdc", + "execution_count": null, + "id": "fb8cdf9d", "metadata": { "execution": { - "iopub.execute_input": "2022-04-25T05:22:22.413488Z", - "iopub.status.busy": "2022-04-25T05:22:22.413192Z", - "iopub.status.idle": "2022-04-25T05:22:44.785435Z", - "shell.execute_reply": "2022-04-25T05:22:44.784573Z" + "iopub.execute_input": "2022-04-25T05:21:37.509070Z", + "iopub.status.busy": "2022-04-25T05:21:37.508915Z", + "iopub.status.idle": "2022-04-25T05:21:59.751378Z", + "shell.execute_reply": "2022-04-25T05:21:59.750706Z" } }, "outputs": [ @@ -9556,7 +1222,7 @@ " \n", " \n", " \n", - " 2022-04-25T02:55:56.884008\n", + " 2022-04-25T02:55:10.378412\n", " image/svg+xml\n", " \n", " \n", @@ -9588,30 +1254,30 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + "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\" id=\"image0bcc0935cc\" transform=\"matrix(2.47 0 0 -2.47 25.97 254.16625)\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" width=\"100\" height=\"100\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9647,12 +1313,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9692,12 +1358,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9732,12 +1398,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9783,12 +1449,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9845,22 +1511,22 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9873,12 +1539,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9892,12 +1558,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9911,12 +1577,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9930,12 +1596,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9970,7 +1636,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9987,1299 +1653,37 @@ } ], "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, v_over=0.5)\n", - "image_based_ip_v_over = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "3193e40e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_v_over)" + "mov = ps.visualization.satn_to_movie(im=im, satn=satn, c_over='yellow')\n", + "image_based_ip_c_over = mov.to_jshtml()\n", + "HTML(image_based_ip_c_over)" ] }, { "cell_type": "markdown", - "id": "af5571a4", + "id": "964039c6", "metadata": {}, "source": [ - "## `fps`" + "## `v_under`" ] }, { "cell_type": "markdown", - "id": "f50b4a92", + "id": "9565edcb", "metadata": {}, "source": [ - "This is the frames per second that the animation will be saved at. The default value is 10." + "This is the lowest bound of `satn` data range that the colormap covers. By default, the `v_under` is 0.001." ] }, { "cell_type": "code", - "execution_count": 16, - "id": "bd979a75", + "execution_count": null, + "id": "172fb4c7", "metadata": { "execution": { - "iopub.execute_input": "2022-04-25T05:22:44.790174Z", - "iopub.status.busy": "2022-04-25T05:22:44.789980Z", - "iopub.status.idle": "2022-04-25T05:23:07.035094Z", - "shell.execute_reply": "2022-04-25T05:23:07.033866Z" + "iopub.execute_input": "2022-04-25T05:21:59.755286Z", + "iopub.status.busy": "2022-04-25T05:21:59.754684Z", + "iopub.status.idle": "2022-04-25T05:22:22.409071Z", + "shell.execute_reply": "2022-04-25T05:22:22.408410Z" } }, "outputs": [ @@ -11301,7 +1705,7 @@ " \n", " \n", " \n", - " 2022-04-25T02:56:19.970467\n", + " 2022-04-25T02:55:33.719540\n", " image/svg+xml\n", " \n", " \n", @@ -11333,30 +1737,30 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + "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\" id=\"image35fc1635ec\" transform=\"matrix(2.47 0 0 -2.47 25.97 254.16625)\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" width=\"100\" height=\"100\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11392,12 +1796,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11437,12 +1841,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11477,12 +1881,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11528,12 +1932,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11590,22 +1994,22 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11618,12 +2022,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11637,12 +2041,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11656,12 +2060,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11675,12 +2079,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11715,7 +2119,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11732,1436 +2136,37 @@ } ], "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, fps=5)\n", - "image_based_ip_fps = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "5c768766", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_fps)" + "mov = ps.visualization.satn_to_movie(im=im, satn=satn, v_under=0.2)\n", + "image_based_ip_v_under = mov.to_jshtml()\n", + "HTML(image_based_ip_v_under)" ] }, { "cell_type": "markdown", - "id": "9979b71d", + "id": "7fbdec6c", "metadata": {}, "source": [ - "## `repeat`" + "## `v_over`" ] }, { "cell_type": "markdown", - "id": "ec2a67ab", + "id": "96bd8912", "metadata": {}, "source": [ - "This variable indicates whether the animation [repeats](https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.animation.ArtistAnimation.html#matplotlib.animation.ArtistAnimation) when the sequence of frames is completed. By default `repeat`=True." + "This is the highest bound of `satn` data range that the colormap covers. By default, the `v_over` is 1." ] }, { "cell_type": "code", - "execution_count": 18, - "id": "1d873dfc", + "execution_count": null, + "id": "0dd48cdc", "metadata": { "execution": { - "iopub.execute_input": "2022-04-25T05:23:07.038893Z", - "iopub.status.busy": "2022-04-25T05:23:07.038355Z", - "iopub.status.idle": "2022-04-25T05:23:29.661031Z", - "shell.execute_reply": "2022-04-25T05:23:29.660532Z" + "iopub.execute_input": "2022-04-25T05:22:22.413488Z", + "iopub.status.busy": "2022-04-25T05:22:22.413192Z", + "iopub.status.idle": "2022-04-25T05:22:44.785435Z", + "shell.execute_reply": "2022-04-25T05:22:44.784573Z" } }, "outputs": [ @@ -13183,7 +2188,7 @@ " \n", " \n", " \n", - " 2022-04-25T02:56:43.848957\n", + " 2022-04-25T02:55:56.884008\n", " image/svg+xml\n", " \n", " \n", @@ -13215,30 +2220,30 @@ "z\n", "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", + " \n", " \n", + 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id=\"image4a7a9c272e\" transform=\"matrix(2.47 0 0 -2.47 25.97 254.16625)\" style=\"image-rendering:crisp-edges;image-rendering:pixelated\" width=\"100\" height=\"100\"/>\n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13274,12 +2279,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13319,12 +2324,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13359,12 +2364,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13410,12 +2415,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13472,22 +2477,22 @@ " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13500,12 +2505,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13519,12 +2524,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13538,12 +2543,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13557,12 +2562,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13597,7 +2602,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13614,1313 +2619,9 @@ } ], "source": [ - "mov = ps.visualization.satn_to_movie(im=im, satn=satn, repeat=False)\n", - "image_based_ip_repeat = mov.to_html5_video()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "9de6aff2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "HTML(image_based_ip_repeat)" + "mov = ps.visualization.satn_to_movie(im=im, satn=satn, v_over=0.5)\n", + "image_based_ip_v_over = mov.to_jshtml()\n", + "HTML(image_based_ip_v_over)" ] } ], diff --git a/src/porespy/metrics/_meshtools.py b/src/porespy/metrics/_meshtools.py index 2bbebbae0..c4b40f09a 100644 --- a/src/porespy/metrics/_meshtools.py +++ b/src/porespy/metrics/_meshtools.py @@ -58,9 +58,6 @@ def region_volumes(regions, mode='marching_cubes', voxel_size=(1, 1, 1)): slices = spim.find_objects(regions) vols = np.zeros([len(slices), ]) msg = "Computing region volumes".ljust(60) - voxel_size = np.array(voxel_size, dtype=float, ndmin=1) - if np.size(voxel_size) == 1: - voxel_size = np.array([voxel_size for i in range(regions.ndim)]).flatten() for i, s in enumerate(tqdm(slices, desc=msg, **settings.tqdm)): region = regions[s] == (i + 1) if mode == 'marching_cubes': @@ -70,7 +67,7 @@ def region_volumes(regions, mode='marching_cubes', voxel_size=(1, 1, 1)): return vols -def mesh_volume(region, voxel_size=(1, 1, 1)): +def mesh_volume(region, voxel_size=(1., 1., 1.)): r""" Compute the volume of a single region by meshing it @@ -103,9 +100,6 @@ def mesh_volume(region, voxel_size=(1, 1, 1)): msg = 'The trimesh package can be installed with pip install trimesh' raise ModuleNotFoundError(msg) - voxel_size = np.array(voxel_size, dtype=float, ndmin=1) - if np.size(voxel_size) == 1: - voxel_size = np.array([voxel_size for i in range(region.ndim)]).flatten() mc = mesh_region(region > 0, voxel_size=voxel_size) m = Trimesh(vertices=mc.verts, faces=mc.faces, vertex_normals=mc.norm) if m.is_watertight: @@ -170,7 +164,7 @@ def region_surface_areas(regions, voxel_size=(1, 1, 1), strel=None): mask_im = sub_im == i mesh = mesh_region(region=mask_im, strel=strel, - voxel_size=np.array(voxel_size, ndmin=1)) + voxel_size=voxel_size) sa[reg] = mesh_surface_area(mesh) result = sa return result @@ -280,9 +274,6 @@ def region_interface_areas(regions, areas, voxel_size=1, strel=None): cn = [] # Start extracting area from im msg = "Computing interfacial area between regions".ljust(60) - voxel_size = np.array(voxel_size, dtype=float, ndmin=1) - if np.size(voxel_size) == 1: - voxel_size = np.array([voxel_size for i in range(im.ndim)]).flatten() for i in tqdm(Ps, desc=msg, **settings.tqdm): reg = i - 1 if slices[reg] is not None: diff --git a/src/porespy/tools/_funcs.py b/src/porespy/tools/_funcs.py index d1f3a69e1..5b3cd1a5a 100644 --- a/src/porespy/tools/_funcs.py +++ b/src/porespy/tools/_funcs.py @@ -1084,9 +1084,11 @@ def mesh_region(region: bool, strel=None, voxel_size=(1.0, 1.0, 1.0)): padded_mask = np.reshape(im, (1,) + im.shape) padded_mask = np.pad(padded_mask, pad_width=pad_width, mode='constant') + # It seems like skimage has changed marching cubes to only accept a list of + # spacing values with length 3, so we are checking this here. voxel_size = np.array(voxel_size, dtype=float, ndmin=1) - if np.size(voxel_size) == 1: - voxel_size = np.array([voxel_size for i in range(im.ndim)]).flatten() + if np.size(voxel_size) < 3: + voxel_size = np.array([voxel_size for i in range(3)]).flatten() verts, faces, norm, val = marching_cubes(padded_mask, spacing=voxel_size) result = Results() result.verts = verts - pad_width diff --git a/test/unit/test_metrics.py b/test/unit/test_metrics.py index bce53f91e..0a6a83b76 100644 --- a/test/unit/test_metrics.py +++ b/test/unit/test_metrics.py @@ -185,6 +185,9 @@ def test_region_volumes_for_sphere(self): assert_allclose(vol_march, 4102.28678846) assert_allclose(vol_vox, 4169.) + def test_region_interface_areas(self): + pass + def test_phase_fraction(self): im = np.reshape(np.random.randint(0, 10, 1000), [10, 10, 10]) labels = np.unique(im, return_counts=True)[1]