diff --git a/demo/intersection_over_target_area_explained.ipynb b/demo/intersection_over_target_area_explained.ipynb index a89d01b..be7ac55 100644 --- a/demo/intersection_over_target_area_explained.ipynb +++ b/demo/intersection_over_target_area_explained.ipynb @@ -6,7 +6,7 @@ "id": "f785e68e-9440-4721-a1fe-c701ca1faca6", "metadata": {}, "source": [ - "# `intersection_over_target_area` explained" + "# `intersection_over_target_obj_area` explained" ] }, { @@ -15,7 +15,7 @@ "id": "80a48c40-471b-48d8-a299-f64d9e3f0e0d", "metadata": {}, "source": [ - "This notebook illustrates how the `intersection_over_target_area` variable works.\n", + "This notebook illustrates how the `intersection_over_target_obj_area` variable works.\n", "\n", "We create two label images: one with a square and another with a triangle. We then slide the triangle over the square, like shown below.\n", "\n", @@ -91,6 +91,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "dde0a80c-5621-4689-bdf9-264fadc7ca30", "metadata": {}, @@ -102,9 +103,9 @@ "But if the square is much bigger, the jaccard index is still low. It does not portraits how much of the triangle is 'inside' the square.\n", "Therefore, we need a different metric for that.\n", "\n", - "We could use the intersection divided by the area of the object of interest, in this case, the triangle:\n", + "We could use the intersection divided by the area of the target object of interest, in this case, the triangle:\n", "\n", - "$ Io\\_Object\\_Area = \\frac{\\Delta \\cap \\square }{\\Delta } $\n", + "$ Io[Target\\_Object\\_Area] = \\frac{\\Delta \\cap \\square }{\\Delta } $\n", "\n", "This tells us, in this example, how much of the triangle area is yellow.\n", "\n", @@ -119,7 +120,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -140,7 +141,8 @@ " Io_object_area = intersection_area / triangle_area\n", " \n", " ax[i].imshow(image, vmax=3)\n", - " ax[i].set_title('IoU: ' + str(np.around(IoU,2)) + '\\nIo_Object_Area: ' + str(np.around(Io_object_area,2)))" + " ax[i].set_title('IoU: ' + str(np.around(IoU,2)) + '\\nIo_[Target_Object_Area]: ' + str(np.around(Io_object_area,2)))\n", + "plt.tight_layout()" ] }, { @@ -149,11 +151,11 @@ "id": "d24f0247-6e80-4aab-a92d-f914bb1fc7cf", "metadata": {}, "source": [ - "In the `link_two_label_images` function, used by `Regionprops Measure Things Inside Things` plugin module, the `intersection_over_target_area` parameter is actually a threshold for this 'Io_Object_Area' metric.\n", + "In the `link_two_label_images` function, used by `Regionprops Measure Relationship to Other Channels` plugin module, the `intersection_over_target_obj_area` parameter is actually a threshold for this 'Io[Target_Object_Area]' metric.\n", "\n", - "This means that, for example, if we choose `intersection_over_target_area = 0.1`, then if an object in a first channel whose 10% of its area or more overalaps with an object in second channel, this first object is considered to 'belong' to the second. On the other hand, if we choose `intersection_over_target_area = 1`, then an object in the first channel only belongs to the second if it is fully 'inside' the second.\n", + "This means that, for example, if we choose `intersection_over_target_obj_area = 0.1`, then if an object in a channel whose 10% of its area or more overalaps with an object in the reference channel, that object is considered to 'belong' to the one in the reference channel. On the other hand, if we choose `intersection_over_target_obj_area = 1`, then an object in a channel only belongs to the reference if it is fully 'inside' the reference object.\n", "\n", - "Below we define the function `highest_overlap`, which is the core used to define these relationships and display the same images as before with different `intersection_over_target_area` values." + "Below we define the function `highest_overlap`, which is the core used to define these relationships and display the same images as before with different `intersection_over_target_obj_area` values." ] }, { @@ -209,7 +211,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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+ff369bh586b+zOaVydIxR48ejZiYGBw4cABbt27FpEmTYG9vr7+9f//+mD17Ni5evGh0eHllKm1diwvFkm8OmHsYNHDncZ0zZw7WrFmDmJgYffvq1asN+rm4uKBbt244duwYmjVrBkdHR4vna44ePXogPj4eR48eNThL/4oVK6DRaNCtWzeD/hs2bMDcuXP1h8jn5ORg69at6Ny5s8FjZSmNRmO0rj///DNSUlIQEhJS7vaWPIfvday4uLhyn7/Ana8OlGfgwIFYsGAB0tLS9GPn5ORgw4YNePzxx+HgUPpLSQcHBwwYMABff/01zp07pz9iQkSQmJiI+vXrl/q3g4iI/otFOxGRQurXrw9nZ2esWrUKjRo1gpubG4KCghAUFISPPvoIffr0Qe/evTFq1CgEBwfj+vXrSE1NxdGjR7Fu3boy952VlYVu3bph+PDhaNiwIdzd3XHo0CEkJibqP/lzc3PDokWLEB0djevXr2Pw4MHw8/PD1atXcfz4cVy9elX/6d/8+fPRqVMntG3bFq+//joeeOABXL58GVu2bMFHH30Ed3d3NG3aFADw8ccfw93dHU5OTggPDzd5aHvPnj3Ru3dvvPbaa8jOzkbHjh3x888/Y/r06WjRogWeeeaZSl5ty8ccNmwYJk2ahGHDhiEvL8/ou/AdO3bE2LFjMXr0aBw+fBhdunSBq6sr0tPTceDAAURGRhoUwhUVGRkJ4M7PaUVHR0Or1SIiIgIdOnSAl5cXxo0bh+nTp0Or1WLVqlU4fvy42ft+9NFH0bFjR0yePBnZ2dlo1aoVUlJSsGLFCgCGn4wuXLgQnTp1QufOnRETE4OwsDDk5OTgt99+w9atW/XfNy8r1+WZOHEiVqxYgX79+mHmzJkIDQ3Ftm3bsHjxYsTExKBBgwYG/e3t7dGzZ09MmjQJRUVFeO+995CdnY0ZM2aYvQam9O/fH2+//TamT5+OqKgonDp1CjNnzkR4eHi5P3tWzNzn8L2OFRYWZvIrBRURGxuLL774Qr/+Op0Os2fPRm5urtHP9j3wwAMA7nx/v9jbb7+Nb7/9Fo8++iji4uLg4eGBTz/9FMePHzf6qTsiIiqFNc+CR0R0Pyrt7PFNmjQx6lvyDOoiIl999ZU0bNhQtFqt0dm6jx8/LkOHDhU/Pz/RarUSEBAg3bt3l6VLlxqNf+jQIYP95ubmyrhx46RZs2bi4eEhzs7OEhERIdOnT5ebN28a9N2/f7/069dPateuLVqtVoKDg6Vfv36ybt06g34nT56UIUOGiLe3tzg6OkrdunVl1KhRBj/PtmDBAgkPDxd7e3uDM4ibuu+3bt2S1157TUJDQ0Wr1UpgYKDExMTIn3/+adAvNDRU+vXrZ7SeUVFR5Z4RuyRzxyw2fPhwASAdO3YsdZ/Lli2Ttm3biqurqzg7O0v9+vVl5MiRcvjwYYO5msqEuaZOnSpBQUFiZ2dncIb+5ORkad++vbi4uIivr68899xzcvToUaOzt0dHR4urq6vJfV+/fl1Gjx4ttWrVEhcXF+nZs6ccPHhQAMjChQsN+p49e1bGjBkjwcHBotVqxdfXVzp06CCzZs0y6FdWrstz/vx5GT58uHh7e4tWq5WIiAiZO3euwdnWi8/a/t5778mMGTOkTp064ujoKC1atJAdO3YY7K/4OXLu3Dmz55CXlyexsbESHBwsTk5O0rJlS9m0aZNRjovnMXfuXJP7Mec5bO5Y1eW3336TJ554Qjw8PMTFxUV69OghR44cMeoXGhpqcn4nTpyQfv36ibu7uzg5OUm7du1k69at1TBzIqL7g0bk/75URERERFSKL7/8Ek8//TR++OEHdOjQwdrTuScLFy7EhAkTkJOTc0/fcyciIqoOPDyeiIiIDHz11Ve4ePEiIiMjYWdnh4MHD2Lu3Lno0qWLTRfsWVlZSElJQUJCApo2bcqCnYiIbAKLdiIiui+V991fOzs75c5cXVRUpP9t69KUdeKvyuLu7o7Vq1dj1qxZuHnzJgIDAzFq1CjMmjWr0sYQkXJ/N93e3r5Sz7B/7NgxDBw4EM2aNcNnn31mtXkQERFZgofHExHRfam8Iis6OhoJCQnVMxkzxcXFlXvCtLNnz1baScasKSkpyejM7yUtX77c6GR/lS0hIQGjR48us8++ffvQtWvXKp0HERFRaVi0ExHRfenw4cNl3u7j46Nc8Xvp0iX974+XpryfV7MVOTk5OHXqVJl9SvulgcqUmZmJs2fPltknIiLCrJ9HIyIiqgos2omIiIiIiIgUpdaX+YiIiIiIiIhIj0U7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJsomhPTk5GXFwcbty4YdF2YWFhGDVqVJXMqSIqez5du3ZF06ZNK21/xfvs2rVrpe7zfsM8msY8WgfzaBrzaB3Mo2nMo3Uwj6Yxj9bBPJrGPJrHwdoTMEdycjJmzJiBUaNGoVatWmZvt3HjRnh4eFTdxKhGYh5JJcwjqYR5JJUwj6QS5pHuhU0U7Za6desWnJ2d0aJFC2tPhYh5JKUwj6QS5pFUwjySSphHupvyh8fHxcXh1VdfBQCEh4dDo9FAo9EgKSkJwJ1DNPr3748NGzagRYsWcHJywowZM/S33X34Rm5uLiZPnozmzZvD09MTtWvXRvv27bF582ajcTUaDcaPH48vvvgCjRo1gouLCx566CF88803Rn03b96MZs2aQafToV69eli4cCHi4uKg0WjKvX/Z2dmIjY1FeHg4HB0dERwcjAkTJuDmzZtmr9H333+Pdu3awdnZGcHBwZg2bRoKCwsN+ty+fRuzZs1Cw4YNodPp4Ovri9GjR+Pq1avl7v/69et48cUXERwcDEdHR9SrVw9vvvkm8vLyDPpVxZqtW7cObdu2haenJ1xcXFCvXj2MGTPG7LWpbMxj+ZjH6sM8lo95rD7MY/mYx+rDPJaPeaw+zGP5mMdyiOLS0tLk5ZdfFgCyYcMGSUlJkZSUFMnKyhIRkdDQUAkMDJR69erJsmXLZN++ffLvf/9bf1t0dLR+Xzdu3JBRo0bJF198IXv37pXExESJjY0VOzs7+fzzzw3GBSBhYWHSpk0bWbt2rWzfvl26du0qDg4O8vvvv+v7ffvtt2JnZyddu3aVjRs3yrp166Rt27YSFhYmJZe35Hxu3rwpzZs3Fx8fH5k/f77s3r1bFi5cKJ6entK9e3cpKioqc22ioqLE29tbgoKC5IMPPpAdO3bIK6+8IgDkpZde0vcrLCyURx99VFxdXWXGjBmya9cu+fTTTyU4OFgaN24sf//9t8E+o6Ki9Ndv3bolzZo1E1dXV3n//fdl586dMm3aNHFwcJC+fftW6ZolJyeLRqORp556SrZv3y579+6V5cuXyzPPPFPmulQl5rF0zGP1Yx5LxzxWP+axdMxj9WMeS8c8Vj/msXTMo3mUL9pFRObOnSsA5OzZs0a3hYaGir29vZw6dcrkbXeHqqSCggLJz8+XZ599Vlq0aGFwGwDx9/eX7OxsfVtGRobY2dlJfHy8vu3hhx+WkJAQycvL07fl5OSIt7d3uSGPj48XOzs7OXTokEG/r7/+WgDI9u3bS527yJ1AApDNmzcbtD///PNiZ2cn58+fFxGRr776SgDI+vXrDfodOnRIAMjixYsN9nl3yJcuXSoAZO3atQbbvvfeewJAdu7cqW+r7DV7//33BYDcuHGjzHWobsyjacyjdTCPpjGP1sE8msY8WgfzaBrzaB3Mo2nMo3mUPzzeHM2aNUODBg3M6rtu3Tp07NgRbm5ucHBwgFarxWeffYbU1FSjvt26dYO7u7v+ur+/P/z8/HD+/HkAwM2bN3H48GE88cQTcHR01Pdzc3PDY489Vu5cvvnmGzRt2hTNmzdHQUGB/tK7d2+DQ2bK4u7ujscff9ygbfjw4SgqKsJ3332nH6dWrVp47LHHDMZp3rw5AgICyhxn7969cHV1xeDBgw3aiw/T2bNnj0F7Za7Zww8/DAAYOnQo1q5di4sXL5a7HipgHplHlTCPzKNKmEfmUSXMI/OoEuaReSzLfVG0BwYGmtVvw4YNGDp0KIKDg7Fy5UqkpKTg0KFDGDNmDHJzc436e3t7G7XpdDrcunULAPDnn39CRODv72/Uz1RbSZcvX8bPP/8MrVZrcHF3d4eI4Nq1a+Xuw9Q4AQEBAIDMzEz9ODdu3ICjo6PRWBkZGWWOk5mZiYCAAKPvZvj5+cHBwUE/RrHKXLMuXbpg06ZNKCgowMiRI1GnTh00bdoUX331VanzVQHzaIh5tC7m0RDzaF3MoyHm0bqYR0PMo3Uxj4aYR0P3xdnjzTlBAgCsXLkS4eHhWLNmjcE2JU9AYC4vLy9oNBpcvnzZ6LaMjIxyt/fx8YGzszOWLVtW6u3lKWvs4sD5+PjA29sbiYmJJvdx9ztJJXl7e+PHH3+EiBis2ZUrV1BQUGDWHO9m6ZoNGDAAAwYMQF5eHg4ePIj4+HgMHz4cYWFhaN++vUVjVxfm0fTYzKN1MI+mx2YerYN5ND0282gdzKPpsZlH62AeTY/NPN5hE5+063Q6ANC/u1FRGo0Gjo6OBg9WRkaGybMtmsPV1RWtW7fGpk2bcPv2bX37X3/9ZfIMgyX1798fv//+O7y9vdG6dWujS1hYWLn7yMnJwZYtWwzavvzyS9jZ2aFLly76cTIzM1FYWGhynIiIiFL336NHD/z111/YtGmTQfuKFSv0t1uiomum0+kQFRWF9957DwBw7Ngxi8atTMxj6ZjH6sc8lo55rH7MY+mYx+rHPJaOeax+zGPpmMfy2cQn7ZGRkQCAhQsXIjo6GlqtFhEREWW+o2JK8U8pvPjiixg8eDDS0tLw9ttvIzAwEKdPn67Q3GbOnIl+/fqhd+/e+Oc//4nCwkLMnTsXbm5uuH79epnbTpgwAevXr0eXLl0wceJENGvWDEVFRbhw4QJ27tyJyZMno23btmXuw9vbGzExMbhw4QIaNGiA7du345NPPkFMTAzq1q0LAHjqqaewatUq9O3bF//85z/Rpk0baLVa/PHHH9i3bx8GDBiAgQMHmtz/yJEj8eGHHyI6Ohrnzp1DZGQkDhw4gHfffRd9+/bFI488UmVr9tZbb+GPP/5Ajx49UKdOHdy4cQMLFy6EVqtFVFSUxeNWFuaxdMxj9WMeS8c8Vj/msXTMY/VjHkvHPFY/5rF0zKMZ7uk0dtVo6tSpEhQUJHZ2dgJA9u3bJyJ3zmDYr18/k9uYOtvi7NmzJSwsTHQ6nTRq1Eg++eQTmT59utGZEVHiZwbK2ufGjRslMjJSHB0dpW7dujJ79mx55ZVXxMvLq9xt//rrL/nXv/4lERER4ujoKJ6enhIZGSkTJ06UjIyMMtckKipKmjRpIklJSdK6dWvR6XQSGBgob7zxhuTn5xv0zc/Pl/fff18eeughcXJyEjc3N2nYsKG88MILcvr0aYN93n22RRGRzMxMGTdunAQGBoqDg4OEhobK1KlTJTc3t0rX7JtvvpE+ffpIcHCwODo6ip+fn/Tt21e+//77MtelOjCPxphH62EejTGP1sM8GmMerYd5NMY8Wg/zaIx5NI/m/yZHlSg/Px/NmzdHcHAwdu7cae3p2ASuWdXh2lqOa1Z1uLaW45pVHa6t5bhmVYdrazmuWdXh2lquKtfMJg6PV92zzz6Lnj17IjAwEBkZGVi6dClSU1OxcOFCa09NWVyzqsO1tRzXrOpwbS3HNas6XFvLcc2qDtfWclyzqsO1tVx1rhmL9kqQk5OD2NhYXL16FVqtFi1btsT27dsr9P2ImoJrVnW4tpbjmlUdrq3luGZVh2trOa5Z1eHaWo5rVnW4tparzjXj4fFEREREREREirKJn3wjIiIiIiIiqolYtBMREREREREpikV7FUpOTkZcXBxu3LhRqfuNi4uDRqPBtWvXKnW/dH9jHkklzCNVhqrKEVFFMI+kGmby/sGivQolJydjxowZfKKQEphHUgnzSJWBOSKVMI+kGmby/sGinYiIiMjG/f3339aeApEe80iqsfVMsmivInFxcXj11VcBAOHh4dBoNNBoNEhKSgIAFBUVYc6cOWjYsCF0Oh38/PwwcuRI/PHHH2aPkZaWhkGDBsHDwwOenp4YMWIErl69atAnLy8PkydPRkBAAFxcXNClSxccOXIEYWFhGDVqVGXdXVIc80gqYR6pMlRljq5evYqxY8ciJCQEOp0Ovr6+6NixI3bv3q3vIyKYM2cOQkND4eTkhJYtW+Lbb79F165d0bVrV32/hIQEaDQanDt3zmCMpKQkg/kCwK5duzBgwADUqVMHTk5OeOCBB/DCCy8Yfd2j+GsgR48exeDBg+Hl5YX69evr57V48WI0b94czs7O8PLywuDBg3HmzBkLVpcsxTwyj6phJu+vTPJ32qvIc889h+vXr2PRokXYsGEDAgMDAQCNGzcGAMTExODjjz/G+PHj0b9/f5w7dw7Tpk1DUlISjh49Ch8fn3LHGDhwIIYOHYpx48bhl19+wbRp03Dy5En8+OOP0Gq1AIDRo0djzZo1mDJlCrp3746TJ09i4MCByM7Orro7T8phHkklzCNVhqrM0TPPPIOjR4/inXfeQYMGDXDjxg0cPXoUmZmZ+j4zZszAjBkz8Oyzz2Lw4MFIS0vD888/j8LCQkRERFToPv3+++9o3749nnvuOXh6euLcuXOYP38+OnXqhBMnTuizW2zQoEF46qmnMG7cONy8eRMA8MILLyAhIQGvvPIK3nvvPVy/fh0zZ85Ehw4dcPz4cfj7+1doblQ25pF5VA0zeZ9lUqjKzJ07VwDI2bNnDdpTU1MFgLz44osG7T/++KMAkDfeeKPM/U6fPl0AyMSJEw3aV61aJQBk5cqVIiLyyy+/CAB57bXXDPp99dVXAkCio6MrdsfIJjGPpBLmkSpDVeXIzc1NJkyYUOrtf/75pzg5OcnAgQMN2n/44QcBIFFRUfq25cuXm5zjvn37BIDs27fP5BhFRUWSn58v58+fFwCyefNm/W3FOX/rrbcMtklJSREAMm/ePIP2tLQ0cXZ2lilTppRxr+leMY/Mo2qYyfsnkzw83gr27dsHAEaHX7Zp0waNGjXCnj17zNrP008/bXB96NChcHBw0O9///79+va7DR48GA4OPMiC7mAeSSXMI1WGe81RmzZtkJCQgFmzZuHgwYPIz883uD0lJQW5ublGOevQoQNCQ0MrPO8rV65g3LhxCAkJgYODA7RarX5/qampRv2ffPJJg+vffPMNNBoNRowYgYKCAv0lICAADz30kMFhplR9mEfmUTXMpO1lkkW7FRQfOlJ8mMrdgoKCDA4tKUtAQIDBdQcHB3h7e+u3L/5vycM8ivsRAcwjqYV5pMpwrzlas2YNoqOj8emnn6J9+/aoXbs2Ro4ciYyMDIP9l8xZaW3mKCoqQq9evbBhwwZMmTIFe/bswb///W8cPHgQAHDr1i2jbUrev8uXL0NE4O/vD61Wa3A5ePAgfwrRSphH5lE1zKTtZZIfJ1hB8QvC9PR01KlTx+C2S5cumfV9TQDIyMhAcHCw/npBQQEyMzP1+y/+7+XLl032IwKYR1IL80iV4V5z5OPjgwULFmDBggW4cOECtmzZgtdffx1XrlxBYmKifv/FL1DvlpGRgbCwMP11JycnAHdOfHi3ki8O/9//+384fvw4EhISEB0drW//7bffSp2nRqMxmrdGo8H3338PnU5n1N9UG1U95pF5VA0zaXuZ5CftVaj4gS/5zk/37t0BACtXrjRoP3ToEFJTU9GjRw+z9r9q1SqD62vXrkVBQYH+jIxdunQBcOfdsLt9/fXXKCgoMO9O0H2DeSSVMI9UGao6RwBQt25djB8/Hj179sTRo0cBAO3atYOTk5NRzpKTk3H+/HmDtuIXpz///LNB+5YtWwyuF7+4LPmi8aOPPjJ7rv3794eI4OLFi2jdurXRJTIy0ux9keWYR0PMo/Uxk4ZsOZP8pL0KFT/wCxcuRHR0NLRaLSIiIhAREYGxY8di0aJFsLOzQ58+ffRnbAwJCcHEiRPN2v+GDRvg4OCAnj176s+O/NBDD+m/o9mkSRMMGzYM8+bNg729Pbp3745ffvkF8+bNg6enJ+zs+J5NTcI8kkqYR6oMVZGjrKwsdOvWDcOHD0fDhg3h7u6OQ4cOITExEYMGDQIAeHl5ITY2FrNmzcJzzz2HIUOGIC0tDXFxcUaHfj788MOIiIhAbGwsCgoK4OXlhY0bN+LAgQMG/Ro2bIj69evj9ddfh4igdu3a2Lp1K3bt2mX2enTs2BFjx47F6NGjcfjwYXTp0gWurq5IT0/HgQMHEBkZiZiYGLP3R5ZhHg0xj9bHTBqy6Uxa7RR4NcTUqVMlKChI7OzsDM6AWFhYKO+99540aNBAtFqt+Pj4yIgRIyQtLa3cfRafEfHIkSPy2GOPiZubm7i7u8uwYcPk8uXLBn1zc3Nl0qRJ4ufnJ05OTtKuXTtJSUkRT09Po7Mr0/2PeSSVMI9UGSo7R7m5uTJu3Dhp1qyZeHh4iLOzs0RERMj06dPl5s2b+n5FRUUSHx8vISEh4ujoKM2aNZOtW7dKVFSUwZmRRUT+85//SK9evcTDw0N8fX3l5Zdflm3bthmdGfnkyZPSs2dPcXd3Fy8vLxkyZIhcuHBBAMj06dP1/YpzfvXqVZP3YdmyZdK2bVtxdXUVZ2dnqV+/vowcOVIOHz5s0dqS5ZhHY8yjdTGTxmwxkxoRkep+o4CsKzk5GR07dsSqVaswfPhwa0+HajjmkVTCPNK9Kv4KhspnIaaag3kk1TCTFcPD4+9zu3btQkpKClq1agVnZ2ccP34cs2fPxoMPPqg/hIWoujCPpBLmkYiIiGwBi/b7nIeHB3bu3IkFCxYgJycHPj4+6NOnD+Lj4/VnaySqLswjqYR5JCIiIlvAw+OJiIiIiIiIFMXT4xIREREREREpikU7ERERERERkaJYtBMREREREREpyiZORFdUVIRLly7B3d0dGo3G2tMhKxER5OTkICgoCHZ21nu/iXmkYipkknmkYswjqUSFPALMJN3BPJJKKpJHmyjaL126hJCQEGtPgxSRlpaGOnXqWG185pFKsmYmmUcqiXkklfDfbFIJ80gqsSSPNlG0u7u7AwA6oS8coLXybMhaCpCPA9iuz4O1MI9UTIVMMo9UjHkklaiQR4CZpDuYR1JJRfJYoaJ98eLFmDt3LtLT09GkSRMsWLAAnTt3LrX//v37MWnSJPzyyy8ICgrClClTMG7cOLPHKz58xAFaOGgY8Brr/36csOThRMwjWY2JTDKPZDXMI6mE/2aTSphHUkkpeSyLxV/qWLNmDSZMmIA333wTx44dQ+fOndGnTx9cuHDBZP+zZ8+ib9++6Ny5M44dO4Y33ngDr7zyCtavX2/p0ERGmEdSCfNIKmEeSTXMJKmEeSRbohERsWSDtm3bomXLlliyZIm+rVGjRnjiiScQHx9v1P+1117Dli1bkJqaqm8bN24cjh8/jpSUFLPGzM7OhqenJ7piAN+VqsEKJB9J2IysrCx4eHgAYB7Jukpmknkka2IeSSX8N5tUwjySSkzlsTwWfdJ++/ZtHDlyBL169TJo79WrF5KTk01uk5KSYtS/d+/eOHz4MPLz801uk5eXh+zsbIMLUUnMI6mEeSSVMI+kGmaSVMI8kq2xqGi/du0aCgsL4e/vb9Du7++PjIwMk9tkZGSY7F9QUIBr166Z3CY+Ph6enp76C8+ySKYwj6QS5pFUwjySaphJUgnzSLamQj9UWPJL8yJS5hfpTfU31V5s6tSpyMrK0l/S0tIqMk2qIZhHUgnzSCphHkk1zCSphHkkW2HR2eN9fHxgb29v9A7UlStXjN55KhYQEGCyv4ODA7y9vU1uo9PpoNPpLJka1UDMI6mEeSSVMI+kGmaSVMI8kq2x6JN2R0dHtGrVCrt27TJo37VrFzp06GBym/bt2xv137lzJ1q3bg2tlidgoIpjHkklzCOphHkk1TCTpBLmkWyNxYfHT5o0CZ9++imWLVuG1NRUTJw4ERcuXND/RuHUqVMxcuRIff9x48bh/PnzmDRpElJTU7Fs2TJ89tlniI2Nrbx7QTUW80gqYR5JJcwjqYaZJJUwj2RLLDo8HgD+8Y9/IDMzEzNnzkR6ejqaNm2K7du3IzQ0FACQnp5u8PuG4eHh2L59OyZOnIgPP/wQQUFB+OCDD/Dkk09W3r2gGot5JJUwj6QS5pFUw0ySSphHsiUW/067NfA3De+4Nra9tadgVYW3c3Fi+ZsW/aZhVWAe76jpeQTUyCTzeAfzyDyqhHlUI48AM1mspmeSeVQL82h5Hit09ngiIiIiIiIiqnos2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgU5WDtCagma/sDFm/j2fe3KpgJUcUww2TrKpJhbKr0aRBV2JG4JRXarlVcTCXPhOjeVCTLzDGp5H7JMD9pJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlKURUV7fHw8Hn74Ybi7u8PPzw9PPPEETp06VeY2SUlJ0Gg0Rpdff/31niZOxDySSphHUg0zSSphHkklzCPZGouK9v379+Oll17CwYMHsWvXLhQUFKBXr164efNmudueOnUK6enp+suDDz5Y4UkTAcwjqYV5JNUwk6QS5pFUwjySrXGwpHNiYqLB9eXLl8PPzw9HjhxBly5dytzWz88PtWrVsniCRKVhHkklzCOphpkklTCPpBLmkWzNPX2nPSsrCwBQu3btcvu2aNECgYGB6NGjB/bt21dm37y8PGRnZxtciMrDPJJKmEdSTVVkknmkiuLfSFIJ80iqq3DRLiKYNGkSOnXqhKZNm5baLzAwEB9//DHWr1+PDRs2ICIiAj169MB3331X6jbx8fHw9PTUX0JCQio6TaohmEdSCfNIqqmqTDKPVBH8G0kqYR7JFmhERCqy4UsvvYRt27bhwIEDqFOnjkXbPvbYY9BoNNiyZYvJ2/Py8pCXl6e/np2djZCQEHTFADhotBWZrtmytj9g8TaefX+rgpkYuza2fbWMo6rC27k4sfxNZGVlwcPDw+C2+zWPFVFdGa7peQRKzyTzeG8qkuH8Tb5VMBPbYo2/kTUhjxWx49JPFdquVVxM5U7Eivhvtloq+m/2kbglFm+jYo6ZR7VU52tIFTNcVh5LU6FP2l9++WVs2bIF+/btszjcANCuXTucPn261Nt1Oh08PDwMLkSlYR5JJcwjqaYqM8k8kqX4N5JUwjySrbDoRHQigpdffhkbN25EUlISwsPDKzTosWPHEBgYWKFtiYoxj6QS5pFUw0ySSphHUgnzSLbGoqL9pZdewpdffonNmzfD3d0dGRkZAABPT084OzsDAKZOnYqLFy9ixYoVAIAFCxYgLCwMTZo0we3bt7Fy5UqsX78e69evr+S7QjUN80gqYR5JNcwkqYR5JJUwj2RrLCralyy5852Arl27GrQvX74co0aNAgCkp6fjwoUL+ttu376N2NhYXLx4Ec7OzmjSpAm2bduGvn373tvMqcZjHkklzCOphpkklTCPpBLmkWyNxYfHlychIcHg+pQpUzBlyhSLJkVkDuaRVMI8kmqYSVIJ80gqYR7J1tzT77QTERERERERUdVh0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKIt+8s3WZG1/QNlxPPv+VgUzofuNyhnGpkqfBt2HqivDR+KWVGi7VnExlTwTut9ULMM/VWisiuSYGSZzVPRvZHWNxRyTOaorxypmmJ+0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRohysPQFLZH9dD/auOmtPo1JkbX/A8o02Vfo0qBpV6DFX2JG4JRXarlVcTCXPhKrL/ZZhoGI5ZoZtFzN8BzNs2yqW458qexqVijmuWSr6GlJlltyn7JwieC23bP/8pJ2IiIiIiIhIURYV7XFxcdBoNAaXgICAMrfZv38/WrVqBScnJ9SrVw9Lly69pwkTFWMeSSXMI6mGmSSVMI+kEuaRbI3Fh8c3adIEu3fv1l+3t7cvte/Zs2fRt29fPP/881i5ciV++OEHvPjii/D19cWTTz5ZsRkT3YV5JJUwj6QaZpJUwjySSphHsiUWF+0ODg7lvhNVbOnSpahbty4WLFgAAGjUqBEOHz6M999/nwGnSsE8kkqYR1INM0kqYR5JJcwj2RKLv9N++vRpBAUFITw8HE899RTOnDlTat+UlBT06tXLoK137944fPgw8vPzLZ8tUQnMI6mEeSTVMJOkEuaRVMI8ki2xqGhv27YtVqxYgR07duCTTz5BRkYGOnTogMzMTJP9MzIy4O/vb9Dm7++PgoICXLt2rdRx8vLykJ2dbXAhKol5JJUwj6Sa6sgk80jm4t9IUgnzSLbGoqK9T58+ePLJJxEZGYlHHnkE27ZtAwB8/vnnpW6j0WgMrouIyfa7xcfHw9PTU38JCQmxZJpUQzCPpBLmkVRTHZlkHslc/BtJKmEeydbc00++ubq6IjIyEqdPnzZ5e0BAADIyMgzarly5AgcHB3h7e5e636lTpyIrK0t/SUtLu5dpUg3BPJJKmEdSTVVkknmkiuLfSFIJ80iqs/hEdHfLy8tDamoqOnfubPL29u3bY+vWrQZtO3fuROvWraHVakvdr06ng06nu5epUQ3EPJJKmEdSTVVkknmkiuLfSFIJ80iqs+iT9tjYWOzfvx9nz57Fjz/+iMGDByM7OxvR0dEA7rybNHLkSH3/cePG4fz585g0aRJSU1OxbNkyfPbZZ4iNja3ce0E1EvNIKmEeSTXMJKmEeSSVMI9kayz6pP2PP/7AsGHDcO3aNfj6+qJdu3Y4ePAgQkNDAQDp6em4cOGCvn94eDi2b9+OiRMn4sMPP0RQUBA++OAD/jQCVQrmkVTCPJJqmElSCfNIKmEeydZYVLSvXr26zNsTEhKM2qKionD06FGLJkVkDuaRVMI8kmqYSVIJ80gqYR7J1tzTd9qrm8fgM3DQlP69kfvfb9aegFUViG3/DqZn3/vr8euN5hXazgcplTsRK7L1TFpK5QxXNI8VoWqGa1oeK6K6MlydeayI6sgw81h1KpJj1TNZEZbkmHlUS++g5taeglXdyeMZi7a5p7PHExEREREREVHVYdFOREREREREpCgW7URERERERESKYtFOREREREREpCgW7URERERERESKYtFOREREREREpCgW7URERERERESKYtFOREREREREpCgW7URERERERESKYtFOREREREREpCgW7URERERERESKcrD2BMwhIgCAAuQDYuXJkNUUIB/Af/NgLcwjFVMhk8wjFWMeSSUq5PHu8ZnJmo15JJVUJI82UbTn5OQAAA5gu5VnQirIycmBp6enVccHmEf6L2tmknmkkphHUgn/zSaVMI+kEkvyqBFrv+VkhqKiIly6dAnu7u7QaDT69uzsbISEhCAtLQ0eHh5WnKF11ZR1EBHk5OQgKCgIdnbW+2ZHaXkEas5jUZaatAYqZJJ5LFtNWgPmUX01aQ1UyCPA15BlqUlrwDyqryatQUXyaBOftNvZ2aFOnTql3u7h4XHfP7jmqAnrYM13R4uVl0egZjwW5akpa2DtTDKP5qkpa8A82oaasgbWziPA15DmqClrwDzahpqyBpbmkSeiIyIiIiIiIlIUi3YiIiIiIiIiRdl00a7T6TB9+nTodDprT8WquA7q4GPBNVAJHwuugUr4WHANVMLHgmugEj4WXIPy2MSJ6IiIiIiIiIhqIpv+pJ2IiIiIiIjofsainYiIiIiIiEhRLNqJiIiIiIiIFMWinYiIiIiIiEhRNl20L168GOHh4XByckKrVq3w/fffW3tK1SYuLg4ajcbgEhAQYO1p1WjMI/OokpqcR4CZVA3zyDyqhHlkHlVTkzPJPJrHZov2NWvWYMKECXjzzTdx7NgxdO7cGX369MGFCxesPbVq06RJE6Snp+svJ06csPaUaizmkXlUCfN4BzOpBubxDuZRDczjHcyjOphJ5tEcNlu0z58/H88++yyee+45NGrUCAsWLEBISAiWLFli7alVGwcHBwQEBOgvvr6+1p5SjcU8Mo8qYR7vYCbVwDzewTyqgXm8g3lUBzPJPJrDJov227dv48iRI+jVq5dBe69evZCcnGylWVW/06dPIygoCOHh4Xjqqadw5swZa0+pRmIe72Ae1cA8/hczaX3M438xj9bHPP4X86gGZvIO5rF8Nlm0X7t2DYWFhfD39zdo9/f3R0ZGhpVmVb3atm2LFStWYMeOHfjkk0+QkZGBDh06IDMz09pTq3GYR+ZRJczjHcykGpjHO5hHNTCPdzCP6mAmmUdzOVh7AvdCo9EYXBcRo7b7VZ8+ffT/HxkZifbt26N+/fr4/PPPMWnSJCvOrOZiHu9gHtVQk/MIMJOqYR6ZR5Uwj8yjampyJplH89jkJ+0+Pj6wt7c3egfqypUrRu9U1RSurq6IjIzE6dOnrT2VGod5NMY8Wg/zaBozaR3Mo2nMo3Uwj6Yxj9bDTBpjHk2zyaLd0dERrVq1wq5duwzad+3ahQ4dOlhpVtaVl5eH1NRUBAYGWnsqNQ7zaIx5tB7m0TRm0jqYR9OYR+tgHk1jHq2HmTTGPJZCbNTq1atFq9XKZ599JidPnpQJEyaIq6urnDt3ztpTqxaTJ0+WpKQkOXPmjBw8eFD69+8v7u7uNeb+q4Z5ZB5VUtPzKMJMqoR5ZB5Vwjwyj6qp6ZlkHs1js99p/8c//oHMzEzMnDkT6enpaNq0KbZv347Q0FBrT61a/PHHHxg2bBiuXbsGX19ftGvXDgcPHqwx9181zCPzqJKankeAmVQJ88g8qoR5ZB5VU9MzyTyaRyMiYu1JEBEREREREZExm/xOOxEREREREVFNwKKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFEs2omIiIiIiIgUxaKdiIiIiIiISFH/Hxn9Ud5KRi0IAAAAAElFTkSuQmCC", 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", 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", 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", 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"text/plain": [ "
" ] @@ -270,15 +272,15 @@ ], "source": [ "for i in range(6):\n", - " intersection_over_target_area = i/5\n", + " intersection_over_target_obj_area = i/5\n", " \n", " fig, ax = plt.subplots(1,6, figsize = [10,2])\n", - " title = 'intersection_over_target_area = ' + str(intersection_over_target_area)\n", + " title = 'intersection_over_target_obj_area = ' + str(intersection_over_target_obj_area)\n", " plt.suptitle(title)\n", " for i, probe in enumerate(probe_list):\n", " ax[i].imshow(ref_labels + probe, vmax=3)\n", " value = highest_overlap(regionmask = probe, intensity_image = ref_labels,\n", - " overlap_threshold=intersection_over_target_area)\n", + " overlap_threshold=intersection_over_target_obj_area)\n", " ax[i].set_title('triangle belongs\\nto ' + label_dict[value])\n", " plt.tight_layout()" ] @@ -297,9 +299,9 @@ "id": "9932b1ef-6695-447a-8358-177363d5576b", "metadata": {}, "source": [ - "If we have two 'square' labels, the behavior is the same: the triangle will belong to the one which encompasses more than `intersection_over_target_area` of the triangle area.\n", + "If we have two 'square' labels, the behavior is the same: the triangle will belong to the one which encompasses more than `intersection_over_target_obj_area` of the triangle area.\n", "\n", - "Notice that, if none of the squares can fit this criterion (like in some cases when `intersection_over_target_area` > 0.8), then the triangle is considered to belong to the background." + "Notice that, if none of the squares can fit this criterion (like in some cases when `intersection_over_target_obj_area` > 0.8), then the triangle is considered to belong to the background." ] }, { @@ -337,7 +339,7 @@ "outputs": [ { "data": { - "image/png": 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rPj5eL774ogYOHKgBAwZo9OjRSkhI0LFjx7R792598cUXWrFiRYX7PnHihK655hqNHDlSrVq1UlhYmLZv365169Y5X70PDQ3Vc889p7S0NB07dkzDhg1TdHS0jhw5ol27dunIkSNauHChJOmZZ57R1VdfrS5duujhhx/Wr371Kx06dEjvv/++XnzxRYWFhTlfDfzrX/+qsLAwBQYGKjk52e1b26+99loNGDBADz30kE6ePKkePXo4r+TeoUMH3XnnnRf5bHt+zBEjRmjy5MkaMWKE8vPzy3wWvkePHrr77rs1ZswY7dixQ7169VJISIiys7P16aefKjU11WUiXF2pqamSpAULFigtLU1+fn5KSUlR9+7d1ahRI40bN04zZsyQn5+f3njjDe3atavK+77uuuvUo0cPTZkyRSdPnlSnTp2UmZmp1157TZJcrnC/YMECXX311erZs6fGjx+vpKQknTp1Sv/617/0wQcfOK8ZUFHXlRkyZIhWrVqle++9V8OGDVNWVpb+/Oc/Ky4uTnv27KnSY6rqOC/0WFV9TFUxduxYPf/887r11ls1d+5cRUdH64UXXtD3339f5isa+/Xrp61bt7p8jdvUqVP1+uuva/DgwZo1a5YCAgI0d+5c5eXlVfnr9gAAF4GXL4QHAHCjvKvHt2nTpsy6pa+gbowxb731lmnVqpXx8/Mrc7XuXbt2meHDh5vo6Gjj5+dnYmNjTd++fc2iRYvKHH/79u0u+83LyzPjxo0z7dq1M+Hh4SYoKMikpKSYGTNmmNOnT7usu3XrVjN48GDTuHFj4+fnZxISEszgwYPNihUrXNb79ttvza233moiIyONv7+/ad68uRk9erTJy8tzrjN//nyTnJxsGjRo4HIFcXeP/cyZM+ahhx4yiYmJxs/Pz8TFxZnx48eb//znPy7rJSYmmsGDB5c5n7179za9e/cus7wiVT1miZEjRxpJpkePHuXuc/HixaZLly4mJCTEBAUFmcsuu8yMGjXK7Nixw2Ws7pqoqunTp5v4+Hjj4+PjcoX+jIwM061bNxMcHGyaNGlifve735kvvviizNXb09LSTEhIiNt9Hzt2zIwZM8Y0bNjQBAcHm2uvvdZs27bNSDILFixwWXffvn1m7NixJiEhwfj5+ZkmTZqY7t27m9mzZ7usV1HXlZk7d65JSkoyAQEBpnXr1uall15yXqn9fJLMfffd53YfVR1nVY9VG3JycsyoUaNM48aNTWBgoOnatavZuHFjmfVKvomgtH/961/mxhtvNOHh4SY4ONj069fP7Ny5szaGDgD4Pw5jjPHKswUAAKBeefPNN3X77bfrH//4h7p37+7t4QAAUCcwaQcAABfdW2+9pQMHDig1NVU+Pj7atm2b5s2bpw4dOji/Eg4AAFSOz7QDAFCO8z/f646Pj4/L57NtUFxcrOLi4grXqY0LiIWFhentt9/W7Nmzdfr0acXFxWn06NGaPXv2RTuGMUZFRUUVrtOgQQO+TxwAUKfxSjsAAOWobLKXlpampUuX1s5gqig9Pb3Srwzbt2+fkpKSamdANWjLli265pprKlxnyZIlZS72BwBAXcKkHQCAcuzYsaPC+6Oioqyb/B48eFAHDx6scJ3Kvrasrjh16pS+//77Ctcp75sGAACoK5i0AwAAAABgKbs+iAcAAAAAAJyYtAMAAAAAYCkm7QAAAAAAWIpJOwAAAAAAlmLSDgAAAACApZi0AwAAAABgKSbtAAAAAABYikk7AAAAAACWYtIOAAAAAIClmLQDAAAAAGCpOjFpz8jIUHp6uo4fP+7RdklJSRo9enSNjKk6LvZ4+vTpo7Zt2160/ZXss0+fPhd1n5caenSPHr2DHt2jR++gR/fo0Tvo0T169A56dI8eq8bX2wOoioyMDM2cOVOjR49Ww4YNq7zd6tWrFR4eXnMDQ71Ej7AJPcIm9Aib0CNsQo+4EHVi0u6pM2fOKCgoSB06dPD2UAB6hFXoETahR9iEHmETesT5rH97fHp6uh588EFJUnJyshwOhxwOh7Zs2SLp3Fs0hgwZolWrVqlDhw4KDAzUzJkznfed//aNvLw8TZkyRe3bt1dERIQaN26sbt26ac2aNWWO63A4NGHCBL3++utq3bq1goODdcUVV+jDDz8ss+6aNWvUrl07BQQEqEWLFlqwYIHS09PlcDgqfXwnT57U1KlTlZycLH9/fyUkJGjixIk6ffp0lc/RJ598oq5duyooKEgJCQl67LHHVFRU5LLO2bNnNXv2bLVq1UoBAQFq0qSJxowZoyNHjlS6/2PHjunee+9VQkKC/P391aJFCz366KPKz893Wa8mztmKFSvUpUsXRUREKDg4WC1atNDYsWOrfG4uNnqsHD3WHnqsHD3WHnqsHD3WHnqsHD3WHnqsHD1WwlguKyvL3H///UaSWbVqlcnMzDSZmZnmxIkTxhhjEhMTTVxcnGnRooVZvHix2bx5s/n888+d96WlpTn3dfz4cTN69Gjz+uuvm48//tisW7fOTJ061fj4+JhXX33V5biSTFJSkuncubN55513zNq1a02fPn2Mr6+v+fe//+1c73/+53+Mj4+P6dOnj1m9erVZsWKF6dKli0lKSjKlT2/p8Zw+fdq0b9/eREVFmWeeecZ89NFHZsGCBSYiIsL07dvXFBcXV3huevfubSIjI018fLx59tlnzfr1680DDzxgJJn77rvPuV5RUZG57rrrTEhIiJk5c6bZuHGjefnll01CQoK5/PLLzS+//OKyz969ezt/PnPmjGnXrp0JCQkxTz31lNmwYYN57LHHjK+vrxk0aFCNnrOMjAzjcDjMbbfdZtauXWs+/vhjs2TJEnPnnXdWeF5qEj2Wjx5rHz2Wjx5rHz2Wjx5rHz2Wjx5rHz2Wjx6rxvpJuzHGzJs3z0gy+/btK3NfYmKiadCggfn+++/d3nd+VKUVFhaagoICc9ddd5kOHTq43CfJxMTEmJMnTzqX5eTkGB8fHzNnzhznsquuuso0a9bM5OfnO5edOnXKREZGVhr5nDlzjI+Pj9m+fbvLeu+++66RZNauXVvu2I05F6Qks2bNGpflv//9742Pj4/58ccfjTHGvPXWW0aSWblypct627dvN5LMCy+84LLP8yNftGiRkWTeeecdl22feOIJI8ls2LDBuexin7OnnnrKSDLHjx+v8DzUNnp0jx69gx7do0fvoEf36NE76NE9evQOenSPHqvG+rfHV0W7du3UsmXLKq27YsUK9ejRQ6GhofL19ZWfn59eeeUV7d69u8y611xzjcLCwpw/x8TEKDo6Wj/++KMk6fTp09qxY4duvPFG+fv7O9cLDQ3V9ddfX+lYPvzwQ7Vt21bt27dXYWGh8zZgwACXt8xUJCwsTDfccIPLspEjR6q4uFh///vfncdp2LChrr/+epfjtG/fXrGxsRUe5+OPP1ZISIiGDRvmsrzkbTqbNm1yWX4xz9lVV10lSRo+fLjeeecdHThwoNLzYQN6pEeb0CM92oQe6dEm9EiPNqFHeqzIJTFpj4uLq9J6q1at0vDhw5WQkKBly5YpMzNT27dv19ixY5WXl1dm/cjIyDLLAgICdObMGUnSf/7zHxljFBMTU2Y9d8tKO3TokL766iv5+fm53MLCwmSM0dGjRyvdh7vjxMbGSpJyc3Odxzl+/Lj8/f3LHCsnJ6fC4+Tm5io2NrbMZzOio6Pl6+vrPEaJi3nOevXqpffee0+FhYUaNWqUmjZtqrZt2+qtt94qd7w2oEdX9Ohd9OiKHr2LHl3Ro3fRoyt69C56dEWPri6Jq8dX5QIJkrRs2TIlJydr+fLlLtuUvgBBVTVq1EgOh0OHDh0qc19OTk6l20dFRSkoKEiLFy8u9/7KVHTskuCioqIUGRmpdevWud3H+c8klRYZGanPPvtMxhiXc3b48GEVFhZWaYzn8/ScDR06VEOHDlV+fr62bdumOXPmaOTIkUpKSlK3bt08OnZtoUf3x6ZH76BH98emR++gR/fHpkfvoEf3x6ZH76BH98emx3PqxCvtAQEBkuR8dqO6HA6H/P39XX5ZOTk5bq+2WBUhISG68sor9d577+ns2bPO5T///LPbKwyWNmTIEP373/9WZGSkrrzyyjK3pKSkSvdx6tQpvf/++y7L3nzzTfn4+KhXr17O4+Tm5qqoqMjtcVJSUsrdf79+/fTzzz/rvffec1n+2muvOe/3RHXPWUBAgHr37q0nnnhCkvTll196dNyLiR7LR4+1jx7LR4+1jx7LR4+1jx7LR4+1jx7LR4+VqxOvtKempkqSFixYoLS0NPn5+SklJaXCZ1TcKfkqhXvvvVfDhg1TVlaW/vznPysuLk579uyp1thmzZqlwYMHa8CAAfrDH/6goqIizZs3T6GhoTp27FiF206cOFErV65Ur169NGnSJLVr107FxcXav3+/NmzYoClTpqhLly4V7iMyMlLjx4/X/v371bJlS61du1YvvfSSxo8fr+bNm0uSbrvtNr3xxhsaNGiQ/vCHP6hz587y8/PTTz/9pM2bN2vo0KG66aab3O5/1KhRev7555WWlqYffvhBqamp+vTTT/X4449r0KBB+s1vflNj5+xPf/qTfvrpJ/Xr109NmzbV8ePHtWDBAvn5+al3794eH/diocfy0WPto8fy0WPto8fy0WPto8fy0WPto8fy0WMVXNBl7GrR9OnTTXx8vPHx8TGSzObNm40x565gOHjwYLfbuLva4ty5c01SUpIJCAgwrVu3Ni+99JKZMWNGmSsjqtTXDFS0z9WrV5vU1FTj7+9vmjdvbubOnWseeOAB06hRo0q3/fnnn80f//hHk5KSYvz9/U1ERIRJTU01kyZNMjk5ORWek969e5s2bdqYLVu2mCuvvNIEBASYuLg488gjj5iCggKXdQsKCsxTTz1lrrjiChMYGGhCQ0NNq1atzD333GP27Nnjss/zr7ZojDG5ublm3LhxJi4uzvj6+prExEQzffp0k5eXV6Pn7MMPPzQDBw40CQkJxt/f30RHR5tBgwaZTz75pMLzUhvosSx69B56LIsevYcey6JH76HHsujRe+ixLHqsGsf/DQ4XUUFBgdq3b6+EhARt2LDB28OpEzhnNYdz6znOWc3h3HqOc1ZzOLee45zVHM6t5zhnNYdz67maPGd14u3xtrvrrrt07bXXKi4uTjk5OVq0aJF2796tBQsWeHto1uKc1RzOrec4ZzWHc+s5zlnN4dx6jnNWczi3nuOc1RzOredq85wxab8ITp06palTp+rIkSPy8/NTx44dtXbt2mp9PqK+4JzVHM6t5zhnNYdz6znOWc3h3HqOc1ZzOLee45zVHM6t52rznPH2eAAAAAAALFUnvvINAAAAAID6iEk7AAAAAACWYtJegzIyMpSenq7jx49f1P2mp6fL4XDo6NGjF3W/uLTRI2xCj7AJPcIm9Aib0KMdmLTXoIyMDM2cOfOiRw5UBz3CJvQIm9AjbEKPsAk92oFJOwAAAAAAlmLSXkPS09P14IMPSpKSk5PlcDjkcDi0ZcsWSVJxcbGefPJJtWrVSgEBAYqOjtaoUaP0008/VfkYWVlZuvnmmxUeHq6IiAjdcccdOnLkiMs6+fn5mjJlimJjYxUcHKxevXpp586dSkpK0ujRoy/Ww4Xl6BE2oUfYhB5hE3qETejRIgY1Iisry9x///1Gklm1apXJzMw0mZmZ5sSJE8YYY+6++24jyUyYMMGsW7fOLFq0yDRp0sQ0a9bMHDlypMJ9z5gxw0gyiYmJ5sEHHzTr1683zzzzjAkJCTEdOnQwZ8+eda47YsQI4+PjYx5++GGzYcMGM3/+fNOsWTMTERFh0tLSavIUwCL0CJvQI2xCj7AJPcIm9GgPJu01aN68eUaS2bdvn8vy3bt3G0nm3nvvdVn+2WefGUnmkUceqXC/JZFPmjTJZfkbb7xhJJlly5YZY4z55ptvjCTz0EMPuaz31ltvGUn1JnKcQ4+wCT3CJvQIm9AjbEKPduDt8V6wefNmSSrzdo7OnTurdevW2rRpU5X2c/vtt7v8PHz4cPn6+jr3v3XrVufy8w0bNky+vr7VGTouQfQIm9AjbEKPsAk9wib0WLuYtHtBbm6uJCkuLq7MffHx8c77KxMbG+vys6+vryIjI53bl/wzJibG7XqARI+wCz3CJvQIm9AjbEKPtYtJuxeUBJadnV3mvoMHDyoqKqpK+8nJyXH5ubCwULm5uc79l/zz0KFDbtcDJHqEXegRNqFH2IQeYRN6rF1M2mtQQECAJOnMmTMuy/v27StJWrZsmcvy7du3a/fu3erXr1+V9v/GG2+4/PzOO++osLBQffr0kST16tVLkrR8+XKX9d59910VFhZW7UHgkkGPsAk9wib0CJvQI2xCj3aoPx8E8ILU1FRJ0oIFC5SWliY/Pz+lpKQoJSVFd999t5577jn5+Pho4MCB+uGHH/TYY4+pWbNmmjRpUpX2v2rVKvn6+uraa6/VN998o8cee0xXXHGF8zMfbdq00YgRI/T000+rQYMG6tu3r7755hs9/fTTioiIkI8Pz9nUJ/QIm9AjbEKPsAk9wib0aAlvXwnvUjd9+nQTHx9vfHx8jCSzefNmY4wxRUVF5oknnjAtW7Y0fn5+Jioqytxxxx0mKyur0n2WXG1x586d5vrrrzehoaEmLCzMjBgxwhw6dMhl3by8PDN58mQTHR1tAgMDTdeuXU1mZqaJiIgoc7VGXProETahR9iEHmETeoRN6NH7HMYY452nC+AtGRkZ6tGjh9544w2NHDnS28NBPUePsAk9wib0CJvQI2xS33pk0n6J27hxozIzM9WpUycFBQVp165dmjt3riIiIvTVV18pMDDQ20NEPUKPsAk9wib0CJvQI2xCj3ym/ZIXHh6uDRs2aP78+Tp16pSioqI0cOBAzZkzp14EDrvQI2xCj7AJPcIm9Aib0COvtAMAAAAAYK16crk9AAAAAADqHibtAAAAAABYikk7AAAAAACWqhMXoisuLtbBgwcVFhYmh8Ph7eHAS4wxOnXqlOLj4+Xj473nm+gRJWxokh5Rgh5hExt6lGgS59AjbFKdHuvEpP3gwYNq1qyZt4cBS2RlZalp06ZeOz49ojRvNkmPKI0eYRP+nQ2b0CNs4kmPdWLSHhYWJkm6WoPkKz8vjwbeUqgCfaq1zh68hR5RwoYm6REl6BE2saFHiSZxDj3CJtXpsVqT9hdeeEHz5s1Tdna22rRpo/nz56tnz57lrr9161ZNnjxZ33zzjeLj4zVt2jSNGzeuyscrefuIr/zk6yDweuv/vpyw9NuJ6BFe46ZJeoTX0CNswr+zYRN6hE3K6bEiHn+oY/ny5Zo4caIeffRRffnll+rZs6cGDhyo/fv3u11/3759GjRokHr27Kkvv/xSjzzyiB544AGtXLnS00MDZdAjbEKPsAk9wjY0CZvQI+oShzHGeLJBly5d1LFjRy1cuNC5rHXr1rrxxhs1Z86cMus/9NBDev/997V7927nsnHjxmnXrl3KzMys0jFPnjypiIgI9dFQnpWqxwpNgbZojU6cOKHw8HBJ9AjvKt0kPcKb6BE24d/ZsAk9wibueqyMR6+0nz17Vjt37lT//v1dlvfv318ZGRlut8nMzCyz/oABA7Rjxw4VFBS43SY/P18nT550uQGl0SNsQo+wCT3CNjQJm9Aj6hqPJu1Hjx5VUVGRYmJiXJbHxMQoJyfH7TY5OTlu1y8sLNTRo0fdbjNnzhxFREQ4b1xlEe7QI2xCj7AJPcI2NAmb0CPqmmp9UWHpD80bYyr8IL279d0tLzF9+nSdOHHCecvKyqrOMFFP0CNsQo+wCT3CNjQJm9Aj6gqPrh4fFRWlBg0alHkG6vDhw2WeeSoRGxvrdn1fX19FRka63SYgIEABAQGeDA31ED3CJvQIm9AjbEOTsAk9oq7x6JV2f39/derUSRs3bnRZvnHjRnXv3t3tNt26dSuz/oYNG3TllVfKz48LMKD66BE2oUfYhB5hG5qETegRdY3Hb4+fPHmyXn75ZS1evFi7d+/WpEmTtH//fud3FE6fPl2jRo1yrj9u3Dj9+OOPmjx5snbv3q3FixfrlVde0dSpUy/eo0C9RY+wCT3CJvQI29AkbEKPqEs8enu8JP32t79Vbm6uZs2apezsbLVt21Zr165VYmKiJCk7O9vl+w2Tk5O1du1aTZo0Sc8//7zi4+P17LPP6pZbbrl4jwL1Fj3CJvQIm9AjbEOTsAk9oi7x+HvavaHkOw07vjtJDULq7+dCCt5r4u0heFXR2Tx9veRRj77TsCbQ4zn1vUfJjibp8Rx6pEeb0KMdPUo0WaK+N0mPdqFHz3us1tXjAQAAAABAzWPSDgAAAACApZi0AwAAAABgKSbtAAAAAABYikk7AAAAAACWYtIOAAAAAIClmLQDAAAAAGApJu0AAAAAAFiKSTsAAAAAAJZi0g4AAAAAgKWYtAMAAAAAYCkm7QAAAAAAWMrX2wOwzbb273q8Tdf/HVYDIwGqh4ZR11Wn4U7vja+BkQDVszN9YbW265ROx7BLdVqmY9jkUmmYV9oBAAAAALAUk3YAAAAAACzFpB0AAAAAAEsxaQcAAAAAwFJM2gEAAAAAsBSTdgAAAAAALMWkHQAAAAAASzFpBwAAAADAUkzaAQAAAACwlEeT9jlz5uiqq65SWFiYoqOjdeONN+r777+vcJstW7bI4XCUuX333XcXNHCAHmETeoRtaBI2oUfYhB5R13g0ad+6davuu+8+bdu2TRs3blRhYaH69++v06dPV7rt999/r+zsbOft17/+dbUHDUj0CLvQI2xDk7AJPcIm9Ii6xteTldetW+fy85IlSxQdHa2dO3eqV69eFW4bHR2thg0bejxAoDz0CJvQI2xDk7AJPcIm9Ii65oI+037ixAlJUuPGjStdt0OHDoqLi1O/fv20efPmCtfNz8/XyZMnXW5AZegRNqFH2KYmmqRHVBd/I2ETeoTtqj1pN8Zo8uTJuvrqq9W2bdty14uLi9Nf//pXrVy5UqtWrVJKSor69eunv//97+VuM2fOHEVERDhvzZo1q+4wUU/QI2xCj7BNTTVJj6gO/kbCJvSIusCjt8efb8KECfrqq6/06aefVrheSkqKUlJSnD9369ZNWVlZeuqpp8p9+8n06dM1efJk588nT54kclSIHmETeoRtaqpJekR18DcSNqFH1AXVeqX9/vvv1/vvv6/NmzeradOmHm/ftWtX7dmzp9z7AwICFB4e7nIDykOPsAk9wjY12SQ9wlP8jYRN6BF1hUevtBtjdP/992v16tXasmWLkpOTq3XQL7/8UnFxcdXaFihBj7AJPcI2NAmb0CNsQo+oazyatN9333168803tWbNGoWFhSknJ0eSFBERoaCgIEnn3gZy4MABvfbaa5Kk+fPnKykpSW3atNHZs2e1bNkyrVy5UitXrrzIDwX1DT3CJvQI29AkbEKPsAk9oq7xaNK+cOFCSVKfPn1cli9ZskSjR4+WJGVnZ2v//v3O+86ePaupU6fqwIEDCgoKUps2bfS3v/1NgwYNurCRo96jR9iEHmEbmoRN6BE2oUfUNR6/Pb4yS5cudfl52rRpmjZtmkeDAqqCHmETeoRtaBI2oUfYhB5R11zQ97QDAAAAAICaw6QdAAAAAABLMWkHAAAAAMBSTNoBAAAAALAUk3YAAAAAACzFpB0AAAAAAEt59JVvdc229u9ae5yu/zusBkaCS43NDXd6b3wNjASXmtpqeGf6wmpt1ymdjlGx2mpYql7HNIyqqO7fyNo6Fh2jKmqrYxsb5pV2AAAAAAAsxaQdAAAAAABLMWkHAAAAAMBSTNoBAAAAALAUk3YAAAAAACzFpB0AAAAAAEsxaQcAAAAAwFJM2gEAAAAAsBSTdgAAAAAALMWkHQAAAAAASzFpBwAAAADAUkzaAQAAAACwlK+3B+CJTe3WKDzs0nieYVv7dz3eptN742tgJKgt1fmd22xn+sJqbdcpnY7rqkutYal6HdNw3UXD59Bw3UbH59Bx3VXd/4a0mSeP6eSpYjVa4tn+L40ZMAAAAAAAlyCPJu3p6elyOBwut9jY2Aq32bp1qzp16qTAwEC1aNFCixYtuqABAyXoETahR9iGJmETeoRN6BF1jcdvj2/Tpo0++ugj588NGjQod919+/Zp0KBB+v3vf69ly5bpH//4h+699141adJEt9xyS/VGDJyHHmETeoRtaBI2oUfYhB5Rl3g8aff19a30magSixYtUvPmzTV//nxJUuvWrbVjxw499dRTBI6Lgh5hE3qEbWgSNqFH2IQeUZd4/Jn2PXv2KD4+XsnJybrtttu0d+/ectfNzMxU//79XZYNGDBAO3bsUEFBgeejBUqhR9iEHmEbmoRN6BE2oUfUJR5N2rt06aLXXntN69ev10svvaScnBx1795dubm5btfPyclRTEyMy7KYmBgVFhbq6NGj5R4nPz9fJ0+edLkBpdEjbEKPsE1tNEmPqCr+RsIm9Ii6xqNJ+8CBA3XLLbcoNTVVv/nNb/S3v/1NkvTqq6+Wu43D4XD52Rjjdvn55syZo4iICOetWbNmngwT9QQ9wib0CNvURpP0iKribyRsQo+oay7oK99CQkKUmpqqPXv2uL0/NjZWOTk5LssOHz4sX19fRUZGlrvf6dOn68SJE85bVlbWhQwT9QQ9wib0CNvURJP0iOribyRsQo+wnccXojtffn6+du/erZ49e7q9v1u3bvrggw9clm3YsEFXXnml/Pz8yt1vQECAAgICLmRoqIfoETahR9imJpqkR1QXfyNhE3qE7Tx6pX3q1KnaunWr9u3bp88++0zDhg3TyZMnlZaWJuncs0mjRo1yrj9u3Dj9+OOPmjx5snbv3q3FixfrlVde0dSpUy/uo0C9RI+wCT3CNjQJm9AjbEKPqGs8eqX9p59+0ogRI3T06FE1adJEXbt21bZt25SYmChJys7O1v79+53rJycna+3atZo0aZKef/55xcfH69lnn+WrEXBR0CNsQo+wDU3CJvQIm9Aj6hqPJu1vv/12hfcvXbq0zLLevXvriy++8GhQQFXQI2xCj7ANTcIm9Aib0CPqmgv6THttu6llqnwd5X9u5FIXpUxvD8GrCo1d34MZPmyvRz0OUPuaG0wdcil1bFOTnvZYHTR8jq0N17ceq4OGz6mNhm3qUbK3yeqg43M86Zge7VLfGz7X416Ptrmgq8cDAAAAAICaw6QdAAAAAABLMWkHAAAAAMBSTNoBAAAAALAUk3YAAAAAACzFpB0AAAAAAEsxaQcAAAAAwFJM2gEAAAAAsBSTdgAAAAAALMWkHQAAAAAASzFpBwAAAADAUr7eHkBVGGMkSYUqkIyXBwOvKVSBpP/24C30iBI2NEmPKEGPsIkNPZ5/fJqs3+gRNqlOj3Vi0n7q1ClJ0qda6+WRwAanTp1SRESEV48v0SP+y5tN0iNKo0fYhH9nwyb0CJt40qPDePsppyooLi7WwYMHFRYWJofD4Vx+8uRJNWvWTFlZWQoPD/fiCL2rvpwHY4xOnTql+Ph4+fh475Md5fUo1Z/fRUXq0zmwoUl6rFh9Ogf0aL/6dA5s6FHivyErUp/OAT3arz6dg+r0WCdeaffx8VHTpk3LvT88PPyS/+VWRX04D958drREZT1K9eN3UZn6cg683SQ9Vk19OQf0WDfUl3Pg7R4l/huyKurLOaDHuqG+nANPe+RCdAAAAAAAWIpJOwAAAAAAlqrTk/aAgADNmDFDAQEB3h6KV3Ee7MHvgnNgE34XnAOb8LvgHNiE3wXnwCb8LjgHlakTF6IDAAAAAKA+qtOvtAMAAAAAcClj0g4AAAAAgKWYtAMAAAAAYCkm7QAAAAAAWKpOT9pfeOEFJScnKzAwUJ06ddInn3zi7SHVmvT0dDkcDpdbbGyst4dVr9EjPdqkPvco0aRt6JEebUKP9Gib+twkPVZNnZ20L1++XBMnTtSjjz6qL7/8Uj179tTAgQO1f/9+bw+t1rRp00bZ2dnO29dff+3tIdVb9EiPNqHHc2jSDvR4Dj3agR7PoUd70CQ9VkWdnbQ/88wzuuuuu/S73/1OrVu31vz589WsWTMtXLjQ20OrNb6+voqNjXXemjRp4u0h1Vv0SI82ocdzaNIO9HgOPdqBHs+hR3vQJD1WRZ2ctJ89e1Y7d+5U//79XZb3799fGRkZXhpV7duzZ4/i4+OVnJys2267TXv37vX2kOolejyHHu1Aj/9Fk95Hj/9Fj95Hj/9Fj3agyXPosXJ1ctJ+9OhRFRUVKSYmxmV5TEyMcnJyvDSq2tWlSxe99tprWr9+vV566SXl5OSoe/fuys3N9fbQ6h16pEeb0OM5NGkHejyHHu1Aj+fQoz1okh6rytfbA7gQDofD5WdjTJlll6qBAwc6/3dqaqq6deumyy67TK+++qomT57sxZHVX/R4Dj3aoT73KNGkbeiRHm1Cj/Rom/rcJD1WTZ18pT0qKkoNGjQo8wzU4cOHyzxTVV+EhIQoNTVVe/bs8fZQ6h16LIsevYce3aNJ76BH9+jRO+jRPXr0Hposix7dq5OTdn9/f3Xq1EkbN250Wb5x40Z1797dS6Pyrvz8fO3evVtxcXHeHkq9Q49l0aP30KN7NOkd9OgePXoHPbpHj95Dk2XRYzlMHfX2228bPz8/88orr5hvv/3WTJw40YSEhJgffvjB20OrFVOmTDFbtmwxe/fuNdu2bTNDhgwxYWFh9ebx24Ye6dEm9b1HY2jSJvRIjzahR3q0TX1vkh6rps5+pv23v/2tcnNzNWvWLGVnZ6tt27Zau3atEhMTvT20WvHTTz9pxIgROnr0qJo0aaKuXbtq27Zt9ebx24Ye6dEm9b1HiSZtQo/0aBN6pEfb1Pcm6bFqHMYY4+1BAAAAAACAsurkZ9oBAAAAAKgPmLQDAAAAAGApJu0AAAAAAFiKSTsAAAAAAJZi0g4AAAAAgKWYtAMAAAAAYCkm7QAAAAAAWIpJOwAAAAAAlmLSDgAAAACApZi0AwAAAABgKSbtAAAAAABYikk7AAAAAACW+v/vVp4SR9k7dAAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", + "image/png": 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", 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vX1/hvq9fv44ePXpg6NChaNasGVxdXZGWloYdO3boP713cXHBwoULERcXh5ycHAwaNAg+Pj64cuUKjh07hitXrmDx4sUAgA8++ACPPPIIIiIi8Ne//hV/+tOfcOnSJWzduhUff/wxXF1d9Z8GfvLJJ3B1dYWDgwNCQkJMntr+2GOPISYmBq+//jpyc3MRGRmJ48ePY/r06Wjbti2ef/75+7za5o85ZMgQTJw4EUOGDEFBQYHRd+EjIyPx0ksvYfjw4Th8+DC6desGZ2dnZGZm4rvvvkNYWJhBIVxdYWFhAIAFCxYgLi4OWq0WoaGh6NKlC+rXr49Ro0Zh+vTp0Gq1+Pzzz3Hs2LEq77t3796IjIzEpEmTkJubi/bt2yM1NRUrVqwAAIOfEFuwYAEeeeQRdO3aFaNHj0ZwcDDy8vLwyy+/YNu2bfprBlSU68rExsZi48aNGDNmDAYNGoSMjAy888478Pf3x+nTp6t0TFWd572OVdVjqooRI0Zg0aJFePrppzF79mz4+Pjgo48+wqlTp4x+ojE6Ohr79+9HUVERgDtvCowZMwYffPABhg0bhmeeeQa2trbYvHkzVq9erf85PyIiqgWWvQ4eERGZUt7V41u2bGnUt+wV1EVEvvjiC2nWrJlotVqjq3UfO3ZMBg8eLD4+PqLVasXPz0969uwpSUlJRuOnpaUZ7PfWrVsyatQoCQ8PFzc3N3F0dJTQ0FCZPn265OfnG/Tdv3+/9OvXTzw8PESr1UpgYKD069dP1q9fb9Dv5MmT8vTTT4unp6fY29tLo0aNJD4+3uDn2ebPny8hISFia2trcAVxU8d+8+ZNef311yUoKEi0Wq34+/vL6NGj5ffffzfoFxQUJP369TNaz6ioKImKijJqr0hVxyw1dOhQASCRkZHl7nPJkiUSEREhzs7O4ujoKE2aNJFhw4bJ4cOHDeZqKhNVNXXqVAkICBAbGxuDK/QfOHBAOnfuLE5OTuLt7S0vvPCC/PDDD0ZXb4+LixNnZ2eT+87JyZHhw4dLvXr1xMnJSR577DE5ePCgAJAFCxYY9D1z5oyMGDFCAgMDRavVire3t3Tp0kVmzZpl0K+iXFdm9uzZEhwcLDqdTpo3by6ffvqp/krtdwMgY8eONbmPqs6zqmPVhqysLBk2bJh4eHiIg4ODdOrUyehnB0X++0sEdysuLpZPP/1UOnToIPXq1RM3Nzdp27at/P3vf5fbt2/X1iEQEdV5GhERi7xbQERERHXK6tWr8ec//xnff/89unTpYunpEBERWQUW7URERHTfffHFF7hw4QLCwsJgY2ODgwcPYu7cuWjbtq3+J+GIiIiocvxOOxERUTlKv99bHhsbG4PvZ6ugpKQEJSUlFfaxs6v5l39XV1esWbMGs2bNQn5+Pvz9/REfH49Zs2bdtzFEBMXFxRX2sbW1ve9X2CciIqpN/KSdiIioHJUVe3FxcVi2bFntTKaKEhISKv3JsDNnziA4OLh2JlSDUlJS0KNHjwr7LF261Ohif0RERNaERTsREVE5Dh8+XOHjXl5eyhW/Fy9exMWLFyvsU9nPllmLvLw8nDp1qsI+5f3SABERkbVg0U5ERERERESkKLW+iEdEREREREREeizaiYiIiIiIiBTFop2IiIiIiIhIUSzaiYiIiIiIiBTFop2IiIiIiIhIUSzaiYiIiIiIiBTFop2IiIiIiIhIUSzaiYiIiIiIiBTFop2IiIiIiIhIUSzaiYiIiIiIiBRlFUX7gQMHkJCQgGvXrpm1XXBwMOLj42tkTtVxv+fTvXt3tGrV6r7tr3Sf3bt3v6/7fNAwj6Yxj5bBPJrGPFoG82ga82gZzKNpzKNlMI+mMY9VY2fpCVTFgQMHMGPGDMTHx6NevXpV3m7Tpk1wc3OruYlRncQ8kkqYR1IJ80gqYR5JJcwj3QurKNrNdfPmTTg6OqJt27aWngoR80hKYR5JJcwjqYR5JJUwj3Q35U+PT0hIwGuvvQYACAkJgUajgUajQUpKCoA7p2jExsZi48aNaNu2LRwcHDBjxgz9Y3efvnHr1i1MmjQJbdq0gbu7Ozw8PNC5c2ds2bLFaFyNRoNx48Zh5cqVaN68OZycnNC6dWt89dVXRn23bNmC8PBw6HQ6NG7cGAsWLEBCQgI0Gk2lx5ebm4vJkycjJCQE9vb2CAwMxPjx45Gfn1/lNfr222/RqVMnODo6IjAwENOmTUNxcbFBn9u3b2PWrFlo1qwZdDodvL29MXz4cFy5cqXS/efk5GDMmDEIDAyEvb09GjdujDfffBMFBQUG/WpizdavX4+IiAi4u7vDyckJjRs3xogRI6q8Nvcb81g55rH2MI+VYx5rD/NYOeax9jCPlWMeaw/zWDnmsRKiuIyMDHnllVcEgGzcuFFSU1MlNTVVrl+/LiIiQUFB4u/vL40bN5YlS5bIvn375F//+pf+sbi4OP2+rl27JvHx8bJy5UrZu3ev7NixQyZPniw2NjayfPlyg3EBSHBwsHTs2FHWrVsn27dvl+7du4udnZ385z//0ff75z//KTY2NtK9e3fZtGmTrF+/XiIiIiQ4OFjKLm/Z+eTn50ubNm3Ey8tLPvjgA9mzZ48sWLBA3N3dpWfPnlJSUlLh2kRFRYmnp6cEBATIhx9+KDt37pRXX31VAMjYsWP1/YqLi6V3797i7OwsM2bMkN27d8s//vEPCQwMlBYtWsgff/xhsM+oqCj9/Zs3b0p4eLg4OzvLvHnzZNeuXTJt2jSxs7OTvn371uiaHThwQDQajTz77LOyfft22bt3ryxdulSef/75CtelJjGP5WMeax/zWD7msfYxj+VjHmsf81g+5rH2MY/lYx6rRvmiXURk7ty5AkDOnDlj9FhQUJDY2trKqVOnTD52d6jKKioqksLCQhk5cqS0bdvW4DEA4uvrK7m5ufq2rKwssbGxkcTERH3bww8/LA0bNpSCggJ9W15ennh6elYa8sTERLGxsZG0tDSDfl9++aUAkO3bt5c7d5E7gQQgW7ZsMWh/8cUXxcbGRs6dOyciIl988YUAkA0bNhj0S0tLEwDy0UcfGezz7pAnJSUJAFm3bp3Btu+9954AkF27dunb7veazZs3TwDItWvXKlyH2sY8msY8WgbzaBrzaBnMo2nMo2Uwj6Yxj5bBPJrGPFaN8qfHV0V4eDgeeuihKvVdv349IiMj4eLiAjs7O2i1Wnz22WdIT0836tujRw+4urrq7/v6+sLHxwfnzp0DAOTn5+Pw4cMYMGAA7O3t9f1cXFzQv3//Sufy1VdfoVWrVmjTpg2Kior0t5iYGINTZiri6uqKxx9/3KBt6NChKCkpwTfffKMfp169eujfv7/BOG3atIGfn1+F4+zduxfOzs4YNGiQQXvpaTrJyckG7fdzzR5++GEAwODBg7Fu3TpcuHCh0vVQAfPIPKqEeWQeVcI8Mo8qYR6ZR5Uwj8xjRR6Iot3f379K/TZu3IjBgwcjMDAQq1atQmpqKtLS0jBixAjcunXLqL+np6dRm06nw82bNwEAv//+O0QEvr6+Rv1MtZV16dIlHD9+HFqt1uDm6uoKEcHVq1cr3Yepcfz8/AAA2dnZ+nGuXbsGe3t7o7GysrIqHCc7Oxt+fn5G383w8fGBnZ2dfoxS93PNunXrhs2bN6OoqAjDhg1DgwYN0KpVK3zxxRflzlcFzKMh5tGymEdDzKNlMY+GmEfLYh4NMY+WxTwaYh4NPRBXj6/KBRIAYNWqVQgJCcHatWsNtil7AYKqql+/PjQaDS5dumT0WFZWVqXbe3l5wdHREUuWLCn38cpUNHZp4Ly8vODp6YkdO3aY3Mfd7ySV5enpiUOHDkFEDNbs8uXLKCoqqtIc72bumj3xxBN44oknUFBQgIMHDyIxMRFDhw5FcHAwOnfubNbYtYV5ND0282gZzKPpsZlHy2AeTY/NPFoG82h6bObRMphH02Mzj3dYxSftOp0OAPTvblSXRqOBvb29wZOVlZVl8mqLVeHs7IwOHTpg8+bNuH37tr79xo0bJq8wWFZsbCz+85//wNPTEx06dDC6BQcHV7qPvLw8bN261aBt9erVsLGxQbdu3fTjZGdno7i42OQ4oaGh5e4/OjoaN27cwObNmw3aV6xYoX/cHNVdM51Oh6ioKLz33nsAgKNHj5o17v3EPJaPeax9zGP5mMfaxzyWj3msfcxj+ZjH2sc8lo95rJxVfNIeFhYGAFiwYAHi4uKg1WoRGhpa4TsqppT+lMKYMWMwaNAgZGRk4J133oG/vz9Onz5drbnNnDkT/fr1Q0xMDP7yl7+guLgYc+fOhYuLC3Jycircdvz48diwYQO6deuGCRMmIDw8HCUlJTh//jx27dqFSZMmISIiosJ9eHp6YvTo0Th//jweeughbN++HZ9++ilGjx6NRo0aAQCeffZZfP755+jbty/+8pe/oGPHjtBqtfjtt9+wb98+PPHEExg4cKDJ/Q8bNgyLFi1CXFwczp49i7CwMHz33Xd499130bdvXzz66KM1tmZvv/02fvvtN0RHR6NBgwa4du0aFixYAK1Wi6ioKLPHvV+Yx/Ixj7WPeSwf81j7mMfyMY+1j3ksH/NY+5jH8jGPVXBPl7GrRVOnTpWAgACxsbERALJv3z4RuXMFw379+pncxtTVFmfPni3BwcGi0+mkefPm8umnn8r06dONroyIMj8zUNE+N23aJGFhYWJvby+NGjWS2bNny6uvvir169evdNsbN27IW2+9JaGhoWJvby/u7u4SFhYmEyZMkKysrArXJCoqSlq2bCkpKSnSoUMH0el04u/vL2+88YYUFhYa9C0sLJR58+ZJ69atxcHBQVxcXKRZs2by8ssvy+nTpw32effVFkVEsrOzZdSoUeLv7y92dnYSFBQkU6dOlVu3btXomn311VfSp08fCQwMFHt7e/Hx8ZG+ffvKt99+W+G61Abm0RjzaDnMozHm0XKYR2PMo+Uwj8aYR8thHo0xj1Wj+f/J0X1UWFiINm3aIDAwELt27bL0dKwC16zmcG3NxzWrOVxb83HNag7X1nxcs5rDtTUf16zmcG3NV5NrZhWnx6tu5MiReOyxx+Dv74+srCwkJSUhPT0dCxYssPTUlMU1qzlcW/NxzWoO19Z8XLOaw7U1H9es5nBtzcc1qzlcW/PV5pqxaL8P8vLyMHnyZFy5cgVarRbt2rXD9u3bq/X9iLqCa1ZzuLbm45rVHK6t+bhmNYdraz6uWc3h2pqPa1ZzuLbmq8014+nxRERERERERIqyip98IyIiIiIiIqqLWLQTERERERERKcpqivYDBw4gISEB165ds/RUiJhHontUU39DCQkJ0Gg0uHr16n3dLz3YmMe6SYXnvXv37ujevft9HZ+sE/NIFbGqon3GjBkskkgJzCPRveHfEKmEeayb+LyTSphHqojVFO0qEhHcvHnT0tN4IBQWFqKoqMjS07BqzOP9wzwSERGppbi4GAUFBZaeBhGA2s+jVRTtCQkJeO211wAAISEh0Gg00Gg0SElJAQCUlJRgzpw5aNasGXQ6HXx8fDBs2DD89ttvVdr/li1bEB4eDp1Oh8aNG2PBggX6U0nuptFoMG7cOCQlJaF58+bQ6XRYvnw5AOD06dMYOnQofHx8oNPp0Lx5cyxatMhorNzcXEyePBkhISGwt7dHYGAgxo8fj/z8fJNjrVy5Es2bN4eTkxNat26Nr776qtLjKSkpwaxZsxAaGgpHR0fUq1cP4eHhRr8Z+PXXX6NNmzbQ6XQICQnBvHnzjI777Nmz0Gg0WLZsmdE4Go0GCQkJ+vu//PILhg8fjqZNm8LJyQmBgYHo378/fvrpJ4PtUlJSoNFosHLlSkyaNAmBgYHQ6XT45ZdfAAB79uxBdHQ03Nzc4OTkhMjISCQnJ1d63LWFeWQeVcqjNarpvyEAyMjIwJNPPgk3Nze4u7vjueeew5UrVwz6FBQUYNKkSfDz84OTkxO6deuGI0eOIDg4GPHx8ffrcElxzGPdpMrzXlbpa1LpPEqV9/p3+PBhPP744/Dw8ICDgwPatm2LdevWVWl+ixcvRuvWreHi4gJXV1c0a9YMb7zxhkGfgwcPIjIyEg4ODggICMDUqVPx6aefQqPR4OzZs/p+ZV+DS5XN75UrVzBmzBi0aNECLi4u8PHxQc+ePfHtt9+aPN45c+Zg1qxZCAkJgU6nw759++75uFXEPDKPlbGK32l/4YUXkJOTg4ULF2Ljxo3w9/cHALRo0QIAMHr0aHzyyScYN24cYmNjcfbsWUybNg0pKSn44Ycf4OXlVe6+d+zYgSeffBLdunXD2rVrUVRUhHnz5uHSpUsm+2/evBnffvst3n77bfj5+cHHxwcnT55Ely5d0KhRI7z//vvw8/PDzp078eqrr+Lq1auYPn06AOCPP/5AVFQUfvvtN7zxxhsIDw/HiRMn8Pbbb+Onn37Cnj17DAqUr7/+GmlpaZg5cyZcXFwwZ84cDBw4EKdOnULjxo3LPaY5c+YgISEBb731Frp164bCwkL8/PPPBqfbJCcn44knnkDnzp2xZs0aFBcXY86cOeUed1VcvHgRnp6emD17Nry9vZGTk4Ply5cjIiICR48eRWhoqEH/qVOnonPnzkhKSoKNjQ18fHywatUqDBs2DE888QSWL18OrVaLjz/+GDExMdi5cyeio6OrPb/7hXlkHlXKozWqyb+hUgMHDsTgwYMxatQonDhxAtOmTcPJkydx6NAhaLVaAMDw4cOxdu1aTJkyBT179sTJkycxcOBA5Obm1tzBk3KYx7pJlef9Xuzbtw+9e/dGREQEkpKS4O7ujjVr1uCZZ57BH3/8UeGbPWvWrMGYMWPwyiuvYN68ebCxscEvv/yCkydP6vucPHkS0dHRCA4OxrJly+Dk5ISPPvoIq1evrvacc3JyAADTp0+Hn58fbty4gU2bNqF79+5ITk42+j71hx9+iIceegjz5s2Dm5sbmjZtek/HrSrmkXmslFiJuXPnCgA5c+aMQXt6eroAkDFjxhi0Hzp0SADIG2+8UeF+H374YWnYsKEUFBTo2/Ly8sTT01PKLg8AcXd3l5ycHIP2mJgYadCggVy/ft2gfdy4ceLg4KDvn5iYKDY2NpKWlmbQ78svvxQAsn37doOxfH19JTc3V9+WlZUlNjY2kpiYWOExxcbGSps2bSrsExERIQEBAXLz5k19W25urnh4eBgc95kzZwSALF261GgfAGT69OnljlFUVCS3b9+Wpk2byoQJE/Tt+/btEwDSrVs3g/75+fni4eEh/fv3N2gvLi6W1q1bS8eOHSs8ptrEPDKPKuXRGtXU39D06dMFgMFzLCLy+eefCwBZtWqViIicOHFCAMjrr79u0O+LL74QABIXF1e9AyOrxDzWTZZ+3kVEoqKiJCoqSn+/9DVp3759Btuaev1r1qyZtG3bVgoLCw36xsbGir+/vxQXF5c7x3Hjxkm9evUqPI5nnnlGHB0dJSsrS99WVFQkzZo1M1q38l6Dg4KCKsxvUVGRFBYWSnR0tAwcOFDfXnq8TZo0kdu3bxtscy/HrTLmkXmsiFWcHl+R0tMSyr6L0bFjRzRv3rzC01jz8/Nx+PBhDBgwAPb29vp2FxcX9O/f3+Q2PXv2RP369fX3b926heTkZAwcOBBOTk4oKirS3/r27Ytbt27h4MGDAICvvvoKrVq1Qps2bQz6xcTEmDz1pEePHnB1ddXf9/X1hY+PD86dO1fhmnTs2BHHjh3DmDFjsHPnTqN36fPz85GWloYnn3wSDg4O+nZXV9dyj7sqioqK8O6776JFixawt7eHnZ0d7O3tcfr0aaSnpxv1f+qppwzuHzhwADk5OYiLizNYn5KSEvTu3RtpaWlGp22rhnk0xjySOe7lb+huf/7znw3uDx48GHZ2dvr979+/X99+t0GDBsHOzipOQqNawDzWTbX1vN+LX375BT///LN+jLKv95mZmTh16lS523fs2BHXrl3DkCFDsGXLFpNXFt+3bx+io6Ph6+urb7O1tcUzzzxzT3NPSkpCu3bt4ODgADs7O2i1WiQnJ5t8bX788ccNPgW+1+O2RszjHXU9j1ZftGdnZwOA/jSSuwUEBOgfN+X333+HiBg8+aVMtZkaJzs7G0VFRVi4cCG0Wq3BrW/fvgCgD96lS5dw/Phxo36urq4QEaOAenp6Go2v0+kqvdjY1KlTMW/ePBw8eBB9+vSBp6cnoqOjcfjwYf1xl5SUwM/Pz2hbU21VNXHiREybNg0DBgzAtm3bcOjQIaSlpaF169Ym51x2LUtPhR40aJDRGr333nsQEf1pLKpiHo0xj2SOe/kbulvZ7NjZ2cHT01O/fen/lv3bKu1HBDCPdVVtPe/3ovQ1avLkyUavUWPGjAGACn/i6/nnn8eSJUtw7tw5PPXUU/Dx8UFERAR2796t75OdnX3fX5s/+OADjB49GhEREdiwYQMOHjyItLQ09O7d26zX5uoetzViHu+o63m0+rdvS1/MMjMz0aBBA4PHLl68WOF3POrXrw+NRmPye7NZWVkmtyl7MbD69evD1tYWzz//PMaOHWtym5CQEACAl5cXHB0dsWTJEpP9qvJ9lKqws7PDxIkTMXHiRFy7dg179uzBG2+8gZiYGGRkZOiP29Qxlm0r/eSz7NURTf2Bl37/99133zVov3r1KurVq2fUv+xalh7/woUL0alTJ5PHVl7xqgrm0RjzSOa4l7+hu2VlZSEwMFB/v6ioCNnZ2fr9l/7vpUuXTPYjApjHuqq2nndTynudK/sf/qVzmDp1Kp588kmT+yp77Zayhg8fjuHDhyM/Px/ffPMNpk+fjtjYWPzv//4vgoKC4OnpWaXXZuDOm/imrqRdNr+rVq1C9+7dsXjxYoP2vLw8k3Ms77X5Xo7b2jCPzCNgRUW7TqcDAKN3PXr27AngzqI//PDD+va0tDSkp6fjzTffLHefzs7O6NChAzZv3ox58+bpT0m+ceNGla6KDQBOTk7o0aMHjh49ivDwcIPTmsuKjY3Fu+++C09PT33hVNPq1auHQYMG4cKFCxg/fjzOnj2LFi1aoGPHjti4cSPmzp2r/4PMy8vDtm3bDLb39fWFg4MDjh8/btC+ZcsWo7E0Go3+eSr19ddf48KFC/jTn/5U6VwjIyNRr149nDx5EuPGjTP3UGsV81g9zCOVqom/obt9/vnnaN++vf7+unXrUFRUpL+oTLdu3QAAa9euRbt27fT9vvzyS/7cXx3EPNZNln7eTQkODgYAHD9+HDExMfr2rVu3GvQLDQ1F06ZNcezYMaM3p83l7OyMPn364Pbt2xgwYABOnDiBoKAg9OjRA1u3bsWlS5f0b1IXFxdj7dq1Judd9rV57969uHHjhkGbqdfm48ePIzU1FQ0bNqx0rvfzuFXDPN7BPJpmNUV7WFgYAGDBggWIi4uDVqtFaGgoQkND8dJLL2HhwoWwsbFBnz599FdUbNiwISZMmFDhfmfOnIl+/fohJiYGf/nLX1BcXIy5c+fCxcWlyqe+LliwAI888gi6du2K0aNHIzg4GHl5efjll1+wbds27N27FwAwfvx4bNiwAd26dcOECRMQHh6OkpISnD9/Hrt27cKkSZMQERFxbwsFoH///mjVqhU6dOgAb29vnDt3DvPnz0dQUBCaNm0KAHjnnXfQu3dvPPbYY5g0aRKKi4vx3nvvwdnZ2eC4NRoNnnvuOSxZsgRNmjRB69at8a9//cvklRpjY2OxbNkyNGvWDOHh4Thy5Ajmzp1r9K5geVxcXLBw4ULExcUhJycHgwYNgo+PD65cuYJjx47hypUrRu+EWQrzWHXMI5lSU39DpTZu3Ag7Ozs89thj+qvktm7dWv+d4ZYtW2LIkCF4//33YWtri549e+LEiRN4//334e7uDhsbq//2GJmBeaybLP28m+Ln54dHH30UiYmJqF+/PoKCgpCcnIyNGzca9f3444/Rp08fxMTEID4+HoGBgcjJyUF6ejp++OEHrF+/vtxxXnzxRTg6OiIyMhL+/v7IyspCYmIi3N3d9YXhW2+9ha1bt6Jnz554++234eTkhEWLFpm8nsvzzz+PadOm4e2330ZUVBROnjyJv//973B3dzfoFxsbi3feeQfTp09HVFQUTp06hZkzZyIkJKTKb1Ddy3GrjHlkHit0Xy5nV0umTp0qAQEBYmNjY3Alw+LiYnnvvffkoYceEq1WK15eXvLcc89JRkZGlfa7adMmCQsLE3t7e2nUqJHMnj1bXn31Valfv75BPwAyduxYk/s4c+aMjBgxQgIDA0Wr1Yq3t7d06dJFZs2aZdDvxo0b8tZbb0loaKjY29uLu7u7hIWFyYQJEwyuhljeWJVd9VBE5P3335cuXbqIl5eX/phGjhwpZ8+eNei3detWCQ8PNzju0itM3u369evywgsviK+vrzg7O0v//v3l7NmzRldm/P3332XkyJHi4+MjTk5O8sgjj8i3335b7pUo169fb3L++/fvl379+omHh4dotVoJDAyUfv36ldvfUphH5pHuTU38DZVm5siRI9K/f39xcXERV1dXGTJkiFy6dMmg761bt2TixIni4+MjDg4O0qlTJ0lNTRV3d3ejq+zSg495rJss/byXfU0SEcnMzJRBgwaJh4eHuLu7y3PPPSeHDx82+espx44dk8GDB4uPj49otVrx8/OTnj17SlJSUoVzXL58ufTo0UN8fX3F3t5eAgICZPDgwXL8+HGDft9//7106tRJdDqd+Pn5yWuvvSaffPKJ0dW6CwoKZMqUKdKwYUNxdHSUqKgo+fHHH43+O6GgoEAmT54sgYGB4uDgIO3atZPNmzdLXFycBAUF6fuVXq177ty5Judf3eNWHfPIPJZHIyJyf8r/B0dhYSHatGmDwMBA7Nq1y9LTqVUJCQmYMWMGGAt1MI/MI9WeAwcOIDIyEp9//jmGDh1q6elQHcc8koqWLVuG4cOH48yZM/rTp4kspa7k0WpOj69JI0eOxGOPPaY/HSMpKQnp6elYsGCBpadGdRDzSFQ7du/ejdTUVLRv3x6Ojo44duwYZs+ejaZNm5Z7QRmimsI8EhFReVi0484FryZPnowrV65Aq9WiXbt22L59Ox599FFLT43qIOaRqHa4ublh165dmD9/PvLy8uDl5YU+ffogMTFRf0FEotrCPBIRUXl4ejwRERERERGRong5UiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFWcWF6EpKSnDx4kW4urpCo9FYejpkISKCvLw8BAQEwMbGcu83MY9USoVMMo9UinkklaiQR4CZpDuYR1JJdfJoFUX7xYsX0bBhQ0tPgxSRkZGBBg0aWGx85pHKsmQmmUcqi3kklfA1m1TCPJJKzMmjVRTtrq6uAIBH0Bd20Fp4NmQpRSjEd9iuz4OlMI9USoVMMo9UinkklaiQR4CZpDuYR1JJdfJYraL9o48+wty5c5GZmYmWLVti/vz56Nq1a7n99+/fj4kTJ+LEiRMICAjAlClTMGrUqCqPV3r6iB20sNMw4HXW//84YdnTiZhHshgTmWQeyWKYR1IJX7NJJcwjqaScPFbE7C91rF27FuPHj8ebb76Jo0ePomvXrujTpw/Onz9vsv+ZM2fQt29fdO3aFUePHsUbb7yBV199FRs2bDB3aCIjzCOphHkklTCPpBpmklTCPJI10YiImLNBREQE2rVrh8WLF+vbmjdvjgEDBiAxMdGo/+uvv46tW7ciPT1d3zZq1CgcO3YMqampVRozNzcX7u7u6I4n+K5UHVYkhUjBFly/fh1ubm4AmEeyrLKZZB7JkphHUglfs0klzCOpxFQeK2PWJ+23b9/GkSNH0KtXL4P2Xr164cCBAya3SU1NNeofExODw4cPo7Cw0OQ2BQUFyM3NNbgRlcU8kkqYR1IJ80iqYSZJJcwjWRuzivarV6+iuLgYvr6+Bu2+vr7IysoyuU1WVpbJ/kVFRbh69arJbRITE+Hu7q6/8SqLZArzSCphHkklzCOphpkklTCPZG2q9UOFZb80LyIVfpHeVH9T7aWmTp2K69ev628ZGRnVmSbVEcwjqYR5JJUwj6QaZpJUwjyStTDr6vFeXl6wtbU1egfq8uXLRu88lfLz8zPZ387ODp6enia30el00Ol05kyN6iDmkVTCPJJKmEdSDTNJKmEeydqY9Um7vb092rdvj927dxu07969G126dDG5TefOnY3679q1Cx06dIBWywswUPUxj6QS5pFUwjySaphJUgnzSNbG7NPjJ06ciH/84x9YsmQJ0tPTMWHCBJw/f17/G4VTp07FsGHD9P1HjRqFc+fOYeLEiUhPT8eSJUvw2WefYfLkyffvKKjOYh5JJcwjqYR5JNUwk6QS5pGsiVmnxwPAM888g+zsbMycOROZmZlo1aoVtm/fjqCgIABAZmamwe8bhoSEYPv27ZgwYQIWLVqEgIAAfPjhh3jqqafu31FQncU8kkqYR1IJ80iqYSZJJcwjWROzf6fdEkp/07DdlxNg61x3vxdSuNnb0lOwqOLbt/DT0jfN+k3DmsA83lHX8wiokUnm8Q7mkXlUCfOoRh4BZrJUXc8k86gW5tH8PFbr6vFEREREREREVPNYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpys7SE1DNwTZfmr1Npx8H1cBMiKqHGSZrV50Mt988ugZmQlQ9RxIWV2u79gnMMamlOllmjkklD0qG+Uk7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaLMKtoTExPx8MMPw9XVFT4+PhgwYABOnTpV4TYpKSnQaDRGt59//vmeJk7EPJJKmEdSDTNJKmEeSSXMI1kbs4r2/fv3Y+zYsTh48CB2796NoqIi9OrVC/n5+ZVue+rUKWRmZupvTZs2rfakiQDmkdTCPJJqmElSCfNIKmEeydrYmdN5x44dBveXLl0KHx8fHDlyBN26datwWx8fH9SrV8/sCRKVh3kklTCPpBpmklTCPJJKmEeyNvf0nfbr168DADw8PCrt27ZtW/j7+yM6Ohr79u2rsG9BQQFyc3MNbkSVYR5JJcwjqaYmMsk8UnXx30hSCfNIqqt20S4imDhxIh555BG0atWq3H7+/v745JNPsGHDBmzcuBGhoaGIjo7GN998U+42iYmJcHd3198aNmxY3WlSHcE8kkqYR1JNTWWSeaTq4L+RpBLmkayBWafH323cuHE4fvw4vvvuuwr7hYaGIjQ0VH+/c+fOyMjIwLx588o9/WTq1KmYOHGi/n5ubi5DThViHkklzCOppqYyyTxSdfDfSFIJ80jWoFqftL/yyivYunUr9u3bhwYNGpi9fadOnXD69OlyH9fpdHBzczO4EZWHeSSVMI+kmprMJPNI5uK/kaQS5pGshVmftIsIXnnlFWzatAkpKSkICQmp1qBHjx6Fv79/tbYlKsU8kkqYR1INM0kqYR5JJcwjWRuzivaxY8di9erV2LJlC1xdXZGVlQUAcHd3h6OjI4A7p4FcuHABK1asAADMnz8fwcHBaNmyJW7fvo1Vq1Zhw4YN2LBhw30+FKprmEdSCfNIqmEmSSXMI6mEeSRrY1bRvnjxYgBA9+7dDdqXLl2K+Ph4AEBmZibOnz+vf+z27duYPHkyLly4AEdHR7Rs2RJff/01+vbte28zpzqPeSSVMI+kGmaSVMI8kkqYR7I2Zp8eX5lly5YZ3J8yZQqmTJli1qSIqoJ5JJUwj6QaZpJUwjySSphHsjb39DvtRERERERERFRzWLQTERERERERKYpFOxEREREREZGiWLQTERERERERKYpFOxEREREREZGiWLQTERERERERKcqsn3yzNgfbfKnsOJ1+HFQDM6EHjcoZbr95dA3MhB40tZXhIwmLq7Vd+wTmmCpWWxkGqpdjZpiqorr/RtbWWMwxVUVt5VjFDPOTdiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFsWgnIiIiIiIiUhSLdiIiIiIiIiJFsWgnIiIiIiIiUpSdpSdgjuTwLXBzfTDeZzjY5kuzt2m/eXQNzIRqS3Wec5UdSVhcre3aJzDH1upByzBQvRwzw9aLGb6DGbZuzPEdzLH1qu5/Q6rMnGPKzStB/aXm7f/BqICJiIiIiIiIHkBmFe0JCQnQaDQGNz8/vwq32b9/P9q3bw8HBwc0btwYSUlJ9zRholLMI6mEeSTVMJOkEuaRVMI8krUx+/T4li1bYs+ePfr7tra25fY9c+YM+vbtixdffBGrVq3C999/jzFjxsDb2xtPPfVU9WZMdBfmkVTCPJJqmElSCfNIKmEeyZqYXbTb2dlV+k5UqaSkJDRq1Ajz588HADRv3hyHDx/GvHnzGHC6L5hHUgnzSKphJkklzCOphHkka2L2d9pPnz6NgIAAhISE4Nlnn8Wvv/5abt/U1FT06tXLoC0mJgaHDx9GYWGh+bMlKoN5JJUwj6QaZpJUwjySSphHsiZmFe0RERFYsWIFdu7ciU8//RRZWVno0qULsrOzTfbPysqCr6+vQZuvry+Kiopw9erVcscpKChAbm6uwY2oLOaRVMI8kmpqI5PMI1UV/40klTCPZG3MKtr79OmDp556CmFhYXj00Ufx9ddfAwCWL19e7jYajcbgvoiYbL9bYmIi3N3d9beGDRuaM02qI5hHUgnzSKqpjUwyj1RV/DeSVMI8krW5p598c3Z2RlhYGE6fPm3ycT8/P2RlZRm0Xb58GXZ2dvD09Cx3v1OnTsX169f1t4yMjHuZJtURzCOphHkk1dREJplHqi7+G0kqYR5JdWZfiO5uBQUFSE9PR9euXU0+3rlzZ2zbts2gbdeuXejQoQO0Wm25+9XpdNDpdPcyNaqDmEdSCfNIqqmJTDKPVF38N5JUwjyS6sz6pH3y5MnYv38/zpw5g0OHDmHQoEHIzc1FXFwcgDvvJg0bNkzff9SoUTh37hwmTpyI9PR0LFmyBJ999hkmT558f4+C6iTmkVTCPJJqmElSCfNIKmEeydqY9Un7b7/9hiFDhuDq1avw9vZGp06dcPDgQQQFBQEAMjMzcf78eX3/kJAQbN++HRMmTMCiRYsQEBCADz/8kD+NQPcF80gqYR5JNcwkqYR5JJUwj2RtzCra16xZU+Hjy5YtM2qLiorCDz/8YNakiKqCeSSVMI+kGmaSVMI8kkqYR7I29/Sd9to28KEw2GnK/97Ig84LqZaegkUViVq/g+k26Fez8hiDNjU3GSvyIOVYpUyam8fqYIbvUDXDdS2P1cEM31EbGVYpj4C6mawO5vgOc3LMPKqlrmf4Th5/NWube7p6PBERERERERHVHBbtRERERERERIpi0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKBbtRERERERERIpi0U5ERERERESkKDtLT6AqRAQAUIRCQCw8GbKYIhQC+G8eLIV5pFIqZJJ5pFLMI6lEhTzePT4zWbcxj6SS6uTRKor2vLw8AMB32G7hmZAK8vLy4O7ubtHxAeaR/suSmWQeqSzmkVTC12xSCfNIKjEnjxqx9FtOVVBSUoKLFy/C1dUVGo1G356bm4uGDRsiIyMDbm5uFpyhZdWVdRAR5OXlISAgADY2lvtmR3l5BOrOc1GRurQGKmSSeaxYXVoD5lF9dWkNVMgjwP+GrEhdWgPmUX11aQ2qk0er+KTdxsYGDRo0KPdxNze3B/7JrYq6sA6WfHe0VGV5BOrGc1GZurIGls4k81g1dWUNmEfrUFfWwNJ5BPjfkFVRV9aAebQOdWUNzM0jL0RHREREREREpCgW7URERERERESKsuqiXafTYfr06dDpdJaeikVxHdTB54JroBI+F1wDlfC54BqohM8F10AlfC64BpWxigvREREREREREdVFVv1JOxEREREREdGDjEU7ERERERERkaJYtBMREREREREpikU7ERERERERkaKsumj/6KOPEBISAgcHB7Rv3x7ffvutpadUaxISEqDRaAxufn5+lp5WncY8Mo8qqct5BJhJ1TCPzKNKmEfmUTV1OZPMY9VYbdG+du1ajB8/Hm+++SaOHj2Krl27ok+fPjh//rylp1ZrWrZsiczMTP3tp59+svSU6izmkXlUCfN4BzOpBubxDuZRDczjHcyjOphJ5rEqrLZo/+CDDzBy5Ei88MILaN68OebPn4+GDRti8eLFlp5arbGzs4Ofn5/+5u3tbekp1VnMI/OoEubxDmZSDczjHcyjGpjHO5hHdTCTzGNVWGXRfvv2bRw5cgS9evUyaO/VqxcOHDhgoVnVvtOnTyMgIAAhISF49tln8euvv1p6SnUS83gH86gG5vG/mEnLYx7/i3m0PObxv5hHNTCTdzCPlbPKov3q1asoLi6Gr6+vQbuvry+ysrIsNKvaFRERgRUrVmDnzp349NNPkZWVhS5duiA7O9vSU6tzmEfmUSXM4x3MpBqYxzuYRzUwj3cwj+pgJpnHqrKz9ATuhUajMbgvIkZtD6o+ffro/39YWBg6d+6MJk2aYPny5Zg4caIFZ1Z3MY93MI9qqMt5BJhJ1TCPzKNKmEfmUTV1OZPMY9VY5SftXl5esLW1NXoH6vLly0bvVNUVzs7OCAsLw+nTpy09lTqHeTTGPFoO82gaM2kZzKNpzKNlMI+mMY+Ww0waYx5Ns8qi3d7eHu3bt8fu3bsN2nfv3o0uXbpYaFaWVVBQgPT0dPj7+1t6KnUO82iMebQc5tE0ZtIymEfTmEfLYB5NYx4th5k0xjyWQ6zUmjVrRKvVymeffSYnT56U8ePHi7Ozs5w9e9bSU6sVkyZNkpSUFPn111/l4MGDEhsbK66urnXm+FXDPDKPKqnreRRhJlXCPDKPKmEemUfV1PVMMo9VY7XfaX/mmWeQnZ2NmTNnIjMzE61atcL27dsRFBRk6anVit9++w1DhgzB1atX4e3tjU6dOuHgwYN15vhVwzwyjyqp63kEmEmVMI/Mo0qYR+ZRNXU9k8xj1WhERCw9CSIiIiIiIiIyZpXfaSciIiIiIiKqC1i0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESnq/wCinTnUY0e0MAAAAABJRU5ErkJggg==", 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", 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dxo4dW8Wrbf6Yo0aNwsyZMzFq1Cjk5eUZfRa+W7duePbZZzFhwgQkJSWhZ8+ecHR0RGpqKr7++msEBQUZFMKVFRQUBABYunQpIiMjodVqERgYiK5du6JBgwZ4/vnnERMTA61Wiw0bNuD06dMV3veAAQPQrVs3zJo1C1lZWejQoQMSEhLw6aefAjD8KrKlS5eie/fu6NGjByZPngx/f39kZ2fj/Pnz2L17t/6aAWXlujwRERGIj4/HlClTMGLECKSkpODNN9+Et7c3zp07V6Fjqug873esih5TRW3duhUA9F8XmJSUBCcnJwCGH2Hp27cvjh8/bvA1blFRUVi3bh0GDx6MefPmQafTYcGCBcjNza3w1+0REVEVsPCF8IiIyITSrh7funVro74lr6AuIrJx40Zp2bKlaLVao6t1nz59WkaOHCkeHh6i1WrFy8tL+vTpIytXrjQaPzEx0WC/ubm58vzzz0twcLDUq1dP7O3tJTAwUGJiYiQnJ8eg7/Hjx2Xw4MHSsGFD0Wq14uvrK4MHD5YtW7YY9Dtz5ow88cQT4urqKra2ttKkSRMZP3685Obm6vssWbJEAgICxNra2uAK2KaO/fbt2/Lyyy+Ln5+faLVa8fb2lsmTJ8uff/5p0M/Pz08GDx5stJ5hYWESFhZm1F6Wio5ZbPTo0QJAunXrVuo+V61aJaGhoeLo6Cj29vbSrFkzGTdunCQlJRnM1VQmKio6Olp8fHzEysrK4Ar9J06ckC5duoiDg4O4u7vLM888I99//73R1ccjIyPF0dHR5L4zMzNlwoQJUr9+fXFwcJB+/frJyZMnBYAsXbrUoO/Fixdl4sSJ4uvrK1qtVtzd3aVr164yf/58g35l5bo8CxYsEH9/f9HpdNKqVSv56KOP9FdqvxcAmTp1qsl9VHSeFR2rJqCMK9Dfq/ibCEo6f/68DB06VOrVqycODg7St29fOXXqVE1Nn4iIREQjIlKzLxMQERFRXfTZZ5/h6aefxjfffIOuXbtaejpERES1Aot2IiIiqnIbN27E77//jqCgIFhZWeHkyZNYtGgR2rVrp/9KOCIiIiofP9NORERUins/32uKlZWVweezVVBUVISioqIy+9TEBcScnZ3x+eefY/78+cjJyYG3tzfGjx+P+fPnV9kYIoLCwsIy+1hbW/P7xImIqFbjO+1ERESlKK/Yi4yMxJo1a2pmMhUUGxtb7leGXbx4Ef7+/jUzoWp07Ngx9O7du8w+q1evNrrYHxERUW3Cop2IiKgUSUlJZT7u5uamXPF79epVXL16tcw+5X1tWW2RnZ2NX375pcw+pX3TABERUW3Bop2IiIiIiIhIUWp9EI+IiIiIiIiI9Fi0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmKRTsRERERERGRoli0ExERERERESmqVhTtJ06cQGxsLG7cuGHWdv7+/hg/fny1zKkyqno+vXr1Qps2bapsf8X77NWrV5Xu80HDPJrGPFoG82ga82gZzKNpzKNlMI+mMY+WwTyaxjxWjI2lJ1ARJ06cwNy5czF+/HjUr1+/wttt374d9erVq76JUZ3EPJJKmEdSCfNIKmEeSSXMI92PWlG0m+v27duwt7dHu3btLD0VIuaRlMI8kkqYR1IJ80gqYR7pXsqfHh8bG4uXXnoJABAQEACNRgONRoNjx44BuHuKRkREBOLj49GuXTvY2dlh7ty5+sfuPX0jNzcXs2bNQkhICFxcXNCwYUN06dIFO3fuNBpXo9Fg2rRpWLduHVq1agUHBwe0bdsWe/bsMeq7c+dOBAcHQ6fToWnTpli6dCliY2Oh0WjKPb6srCxERUUhICAAtra28PX1xfTp05GTk1PhNfrqq6/QuXNn2Nvbw9fXF3PmzEFhYaFBnzt37mD+/Plo2bIldDod3N3dMWHCBKSnp5e7/8zMTEyZMgW+vr6wtbVF06ZN8dprryEvL8+gX3Ws2ZYtWxAaGgoXFxc4ODigadOmmDhxYoXXpqoxj+VjHmsO81g+5rHmMI/lYx5rDvNYPuax5jCP5WMeyyGKS0lJkRdeeEEASHx8vCQkJEhCQoLcvHlTRET8/PzE29tbmjZtKqtWrZKjR4/Kd999p38sMjJSv68bN27I+PHjZd26dXLkyBHZt2+fREVFiZWVlaxdu9ZgXADi7+8vnTp1ks2bN8vevXulV69eYmNjI7/++qu+37/+9S+xsrKSXr16yfbt22XLli0SGhoq/v7+UnJ5S84nJydHQkJCxM3NTd599105dOiQLF26VFxcXKRPnz5SVFRU5tqEhYWJq6ur+Pj4yHvvvSf79++XF198UQDI1KlT9f0KCwtlwIAB4ujoKHPnzpWDBw/Kxx9/LL6+vvLQQw/JX3/9ZbDPsLAw/f3bt29LcHCwODo6yuLFi+XAgQMyZ84csbGxkUGDBlXrmp04cUI0Go089dRTsnfvXjly5IisXr1axo4dW+a6VCfmsXTMY81jHkvHPNY85rF0zGPNYx5LxzzWPOaxdMxjxShftIuILFq0SADIxYsXjR7z8/MTa2tr+eWXX0w+dm+oSiooKJD8/HyZNGmStGvXzuAxAOLp6SlZWVn6trS0NLGyspK4uDh928MPPyyNGzeWvLw8fVt2dra4urqWG/K4uDixsrKSxMREg35bt24VALJ3795S5y5yN5AAZOfOnQbtf//738XKykouX74sIiIbN24UALJt2zaDfomJiQJA3n//fYN93hvylStXCgDZvHmzwbYLFy4UAHLgwAF9W1Wv2eLFiwWA3Lhxo8x1qGnMo2nMo2Uwj6Yxj5bBPJrGPFoG82ga82gZzKNpzGPFKH96fEUEBwejRYsWFeq7ZcsWdOvWDU5OTrCxsYFWq8Unn3yC5ORko769e/eGs7Oz/r6npyc8PDxw+fJlAEBOTg6SkpIwdOhQ2Nra6vs5OTlhyJAh5c5lz549aNOmDUJCQlBQUKC/hYeHG5wyUxZnZ2c8+uijBm2jR49GUVERvvzyS/049evXx5AhQwzGCQkJgZeXV5njHDlyBI6OjhgxYoRBe/FpOocPHzZor8o1e/jhhwEAI0eOxObNm/H777+Xux4qYB6ZR5Uwj8yjSphH5lElzCPzqBLmkXksywNRtHt7e1eoX3x8PEaOHAlfX1+sX78eCQkJSExMxMSJE5Gbm2vU39XV1ahNp9Ph9u3bAIA///wTIgJPT0+jfqbaSrp27Rp+/PFHaLVag5uzszNEBNevXy93H6bG8fLyAgBkZGTox7lx4wZsbW2NxkpLSytznIyMDHh5eRl9NsPDwwM2Njb6MYpV5Zr17NkTO3bsQEFBAcaNG4dGjRqhTZs22LhxY6nzVQHzaIh5tCzm0RDzaFnMoyHm0bKYR0PMo2Uxj4aYR0MPxNXjK3KBBABYv349AgICsGnTJoNtSl6AoKIaNGgAjUaDa9euGT2WlpZW7vZubm6wt7fHqlWrSn28PGWNXRw4Nzc3uLq6Yt++fSb3ce8rSSW5urri22+/hYgYrNkff/yBgoKCCs3xXuau2WOPPYbHHnsMeXl5OHnyJOLi4jB69Gj4+/ujS5cuZo1dU5hH02Mzj5bBPJoem3m0DObR9NjMo2Uwj6bHZh4tg3k0PTbzeFeteKddp9MBgP7VjcrSaDSwtbU1eLLS0tJMXm2xIhwdHdGxY0fs2LEDd+7c0bffunXL5BUGS4qIiMCvv/4KV1dXdOzY0ejm7+9f7j6ys7Oxa9cug7bPPvsMVlZW6Nmzp36cjIwMFBYWmhwnMDCw1P337dsXt27dwo4dOwzaP/30U/3j5qjsmul0OoSFhWHhwoUAgB9++MGscasS81g65rHmMY+lYx5rHvNYOuax5jGPpWMeax7zWDrmsXy14p32oKAgAMDSpUsRGRkJrVaLwMDAMl9RMaX4qxSmTJmCESNGICUlBW+++Sa8vb1x7ty5Ss1t3rx5GDx4MMLDw/GPf/wDhYWFWLRoEZycnJCZmVnmttOnT8e2bdvQs2dPzJgxA8HBwSgqKsKVK1dw4MABzJo1C6GhoWXuw9XVFZMnT8aVK1fQokUL7N27Fx999BEmT56MJk2aAACeeuopbNiwAYMGDcI//vEPdOrUCVqtFr/99huOHj2Kxx57DMOGDTO5/3HjxmH58uWIjIzEpUuXEBQUhK+//hpvvfUWBg0ahEceeaTa1uyNN97Ab7/9hr59+6JRo0a4ceMGli5dCq1Wi7CwMLPHrSrMY+mYx5rHPJaOeax5zGPpmMeaxzyWjnmsecxj6ZjHCrivy9jVoOjoaPHx8RErKysBIEePHhWRu1cwHDx4sMltTF1tccGCBeLv7y86nU5atWolH330kcTExBhdGRElvmagrH1u375dgoKCxNbWVpo0aSILFiyQF198URo0aFDutrdu3ZLXX39dAgMDxdbWVlxcXCQoKEhmzJghaWlpZa5JWFiYtG7dWo4dOyYdO3YUnU4n3t7e8uqrr0p+fr5B3/z8fFm8eLG0bdtW7OzsxMnJSVq2bCnPPfecnDt3zmCf915tUUQkIyNDnn/+efH29hYbGxvx8/OT6Ohoyc3NrdY127NnjwwcOFB8fX3F1tZWPDw8ZNCgQfLVV1+VuS41gXk0xjxaDvNojHm0HObRGPNoOcyjMebRcphHY8xjxWj+f3JUhfLz8xESEgJfX18cOHDA0tOpFbhm1Ydraz6uWfXh2pqPa1Z9uLbm45pVH66t+bhm1Ydra77qXLNacXq86iZNmoR+/frB29sbaWlpWLlyJZKTk7F06VJLT01ZXLPqw7U1H9es+nBtzcc1qz5cW/NxzaoP19Z8XLPqw7U1X02uGYv2KpCdnY2oqCikp6dDq9Wiffv22Lt3b6U+H1FXcM2qD9fWfFyz6sO1NR/XrPpwbc3HNas+XFvzcc2qD9fWfDW5Zjw9noiIiIiIiEhRteIr34iIiIiIiIjqIhbtRERERERERIqqNUX7iRMnEBsbixs3blh6KkS1UnX9DMXGxkKj0eD69etVul96sDGPpBLmkVTCPNZNKjzvvXr1Qq9evap0fKoataponzt3Lot2okrizxCphHkklTCPpBLmsW7i805lqTVFu4pEBLdv37b0NB4I+fn5KCgosPQ0iIiIiIiIylRYWIi8vLwaG69WFO2xsbF46aWXAAABAQHQaDTQaDQ4duwYAKCoqAhvv/02WrZsCZ1OBw8PD4wbNw6//fZbhfa/c+dOBAcHQ6fToWnTpli6dKn+VJJ7aTQaTJs2DStXrkSrVq2g0+mwdu1aAMC5c+cwevRoeHh4QKfToVWrVli+fLnRWFlZWYiKikJAQABsbW3h6+uL6dOnIycnx+RY69atQ6tWreDg4IC2bdtiz5495R5PUVER5s+fj8DAQNjb26N+/foIDg42+s7AL774AiEhIdDpdAgICMDixYuNjvvSpUvQaDRYs2aN0TgajQaxsbH6++fPn8eECRPQvHlzODg4wNfXF0OGDMFPP/1ksN2xY8eg0Wiwbt06zJo1C76+vtDpdDh//jwA4NChQ+jbty/q1asHBwcHdOvWDYcPHy73uKl01f0zBAApKSkYPnw46tWrBxcXF4wZMwbp6ekGffLy8jBr1ix4eXnBwcEBPXv2xKlTp+Dv74/x48dX1eGS4phHUgnzSCphHusmVZ73kor/Zi+eR7HS6oOkpCQ8+uijaNiwIezs7NCuXTts3ry5QvNbsWIF2rZtCycnJzg7O6Nly5Z49dVXDfqcPHkS3bp1g52dHXx8fBAdHY2PPvoIGo0Gly5d0vcrWaMUK5nf9PR0TJkyBQ899BCcnJzg4eGBPn364KuvvjJ5vG+//Tbmz5+PgIAA6HQ6HD169L6Pu8KkFkhJSZEXXnhBAEh8fLwkJCRIQkKC3Lx5U0REnn32WQEg06ZNk3379snKlSvF3d1dGjduLOnp6WXu+1//+pdYWVlJr169ZPv27bJlyxYJDQ0Vf39/Kbk8AMTX11eCg4Pls88+kyNHjsj//u//ys8//ywuLi4SFBQkn376qRw4cEBmzZolVlZWEhsbq98+JydHQkJCxM3NTd599105dOiQLF26VFxcXKRPnz5SVFRkMJa/v7906tRJNm/eLHv37pVevXqJjY2N/Prrr2UeU1xcnFhbW0tMTIwcPnxY9u3bJ0uWLDGYy6FDh8Ta2lq6d+8u8fHxsmXLFnn44YelSZMmBsd98eJFASCrV682GgeAxMTE6O8fP35cZs2aJVu3bpXjx4/L9u3bZejQoWJvby9nz57V9zt69Kh+LUeMGCG7du2SPXv2SEZGhqxbt040Go0MHTpU4uPjZffu3RIRESHW1tZy6NChMo+bSledP0MxMTECQPz8/OSll16S/fv3y7vvviuOjo7Srl07uXPnjr7vqFGjxMrKSl555RU5cOCALFmyRBo3biwuLi4SGRlZnUtACmEeSSXMI6mEeaybVHnew8LCJCwsTH+/+G/2o0ePGuzTVH1w5MgRsbW1lR49esimTZtk3759Mn78+FLriHtt3LhRAMgLL7wgBw4ckEOHDsnKlSvlxRdf1Pf5+eefxcHBQR566CHZuHGj7Ny5U8LDw/W1y8WLF/V9S9Yoxfz8/Azye/bsWZk8ebJ8/vnncuzYMdmzZ49MmjRJrKysDI65+Hh9fX2ld+/esnXrVjlw4IBcvHjxvo7bHLWiaBcRWbRokdETIiKSnJwsAGTKlCkG7d9++60AkFdffbXM/T788MPSuHFjycvL07dlZ2eLq6uryaLdxcVFMjMzDdrDw8OlUaNG+h+sYtOmTRM7Ozt9/7i4OLGyspLExESDflu3bhUAsnfvXoOxPD09JSsrS9+WlpYmVlZWEhcXV+YxRURESEhISJl9QkNDxcfHR27fvq1vy8rKkoYNG1a6aC+poKBA7ty5I82bN5cZM2bo24t/AfTs2dOgf05OjjRs2FCGDBli0F5YWCht27aVTp06lXlMVLbq+hkq/sfg3udYRGTDhg0CQNavXy8id3/ZApCXX37ZoF/xL2r+EVC3MI+kEuaRVMI81k2Wft5F7q9ob9mypbRr107y8/MN+kZERIi3t7cUFhaWOsdp06ZJ/fr1yzyOJ598Uuzt7SUtLU3fVlBQIC1btqx00V5SQUGB5OfnS9++fWXYsGH69uLjbdasmcGLHCL3d9zmqBWnx5el+LSEkqfqdOrUCa1atSrztOqcnBwkJSVh6NChsLW11bc7OTlhyJAhJrfp06cPGjRooL+fm5uLw4cPY9iwYXBwcEBBQYH+NmjQIOTm5uLkyZMAgD179qBNmzYICQkx6BceHm7y1JPevXvD2dlZf9/T0xMeHh64fPlymWvSqVMnnD59GlOmTMH+/fuRlZVldNyJiYkYPnw47Ozs9O3Ozs6lHndFFBQU4K233sJDDz0EW1tb2NjYwNbWFufOnUNycrJR/8cff9zg/okTJ5CZmYnIyEiD9SkqKsKAAQOQmJho9DECun/38zN0r6efftrg/siRI2FjY6Pf//Hjx/Xt9xoxYgRsbGwqM3V6ADGPpBLmkVTCPNZNNfW834/z58/j7Nmz+jFK1kOpqan45ZdfSt2+U6dOuHHjBkaNGoWdO3eavNL90aNH0bdvX3h6eurbrK2t8eSTT97X3FeuXIn27dvDzs4ONjY20Gq1OHz4sMna5dFHH4VWq9Xfv9/jNketL9ozMjIAAN7e3kaP+fj46B835c8//4SIGDz5xUy1mRonIyMDBQUFWLZsGbRarcFt0KBBAKAP3rVr1/Djjz8a9XN2doaIGAXU1dXVaHydTlfuxe+io6OxePFinDx5EgMHDoSrqyv69u2LpKQk/XEXFRXBy8vLaFtTbRU1c+ZMzJkzB0OHDsXu3bvx7bffIjExEW3btjU555Jree3aNQB3/1EouUYLFy6EiCAzM7PS8yPT7udn6F4ls2NjYwNXV1f99sX/LfmzVdyPCGAeSS3MI6mEeaybaup5vx/Ff8NHRUUZ/Q0/ZcoUACjzK+fGjh2LVatW4fLly3j88cfh4eGB0NBQHDx4UN8nIyOjymuXd999F5MnT0ZoaCi2bduGkydPIjExEQMGDDCrdqnscZuj1r9cVvzLIzU1FY0aNTJ47OrVq3Bzcyt12wYNGkCj0egX/F5paWkmtyl5cboGDRrA2toaY8eOxdSpU01uExAQAABwc3ODvb09Vq1aZbJfWXM1h42NDWbOnImZM2fixo0bOHToEF599VWEh4cjJSVFf9ymjrFkW/E78SWvjmjqB3z9+vUYN24c3nrrLYP269evo379+kb9S65l8fEvW7YMnTt3Nnlspb2YQpV3Pz9D90pLS4Ovr6/+fkFBATIyMvT7L/7vtWvXTPYjAphHUgvzSCphHuummnreTSmtDihZiBbPITo6GsOHDze5r8DAwDLnN2HCBEyYMAE5OTn48ssvERMTg4iICPznP/+Bn58fXF1dK1S7AHff5DR1ZfeS+V2/fj169eqFFStWGLRnZ2ebnGNptcv9HHdF1Zp32nU6HQAYverRp08fAHcX/V6JiYlITk5G3759S92no6MjOnbsiB07duDOnTv69lu3blXoKu0A4ODggN69e+OHH35AcHAwOnbsaHQr/mGIiIjAr7/+CldXV5P9/P39KzSmOerXr48RI0Zg6tSpyMzMxKVLl+Do6IhOnTohPj4eubm5+r7Z2dnYvXu3wfaenp6ws7PDjz/+aNC+c+dOo7E0Go3+eSr2xRdf4Pfff6/QXLt164b69evjzJkzJtenY8eOBh9jIPNUx8/QvTZs2GBwf/PmzSgoKECvXr0AAD179gQAbNq0yaDf1q1b+XV/dRDzSCphHkklzGPdZOnn3ZTi2qRkHbBr1y6D+4GBgWjevDlOnz5d6t/w937ktyyOjo4YOHAgXnvtNdy5cwc///wzgLsfGz58+LDBm62FhYVGOS2ed8k5HzlyBLdu3TJoM1W7/Pjjj0hISKjQXKvyuMtTa95pDwoKAgAsXboUkZGR0Gq1CAwMRGBgIJ599lksW7YMVlZWGDhwIC5duoQ5c+agcePGmDFjRpn7nTdvHgYPHozw8HD84x//QGFhIRYtWgQnJ6cKn4q9dOlSdO/eHT169MDkyZPh7++P7OxsnD9/Hrt378aRI0cAANOnT8e2bdvQs2dPzJgxA8HBwSgqKsKVK1dw4MABzJo1C6Ghofe3UACGDBmCNm3aoGPHjnB3d8fly5exZMkS+Pn5oXnz5gCAN998EwMGDEC/fv0wa9YsFBYWYuHChXB0dDQ4bo1GgzFjxmDVqlVo1qwZ2rZti++++w6fffaZ0bgRERFYs2YNWrZsieDgYJw6dQqLFi0yelWwNE5OTli2bBkiIyORmZmJESNGwMPDA+np6Th9+jTS09ONXgmjiquun6Fi8fHxsLGxQb9+/fDzzz9jzpw5aNu2rf4zca1bt8aoUaPwzjvvwNraGn369MHPP/+Md955By4uLrCyqjWvIVIVYB5JJcwjqYR5rJss/byb4uXlhUceeQRxcXFo0KAB/Pz8cPjwYcTHxxv1/eCDDzBw4ECEh4dj/Pjx8PX1RWZmJpKTk/H9999jy5YtpY7z97//Hfb29ujWrRu8vb2RlpaGuLg4uLi44OGHHwYAvP7669i1axf69OmDN954Aw4ODli+fLnJ612NHTsWc+bMwRtvvIGwsDCcOXMG//M//wMXFxeDfhEREXjzzTcRExODsLAw/PLLL5g3bx4CAgIq/ALV/Ry3WarkcnY1JDo6Wnx8fMTKysrgSoaFhYWycOFCadGihWi1WnFzc5MxY8ZISkpKhfa7fft2CQoKEltbW2nSpIksWLBAXnzxRWnQoIFBPwAydepUk/u4ePGiTJw4UXx9fUWr1Yq7u7t07dpV5s+fb9Dv1q1b8vrrr0tgYKDY2trqvypuxowZBldDLG2s8q56KCLyzjvvSNeuXcXNzU1/TJMmTZJLly4Z9Nu1a5cEBwcbHHfxFSbvdfPmTXnmmWfE09NTHB0dZciQIXLp0iWjKzP++eefMmnSJPHw8BAHBwfp3r27fPXVV6VeiXLLli0m53/8+HEZPHiwNGzYULRarfj6+srgwYNL7U8VVx0/Q8WZOXXqlAwZMkScnJzE2dlZRo0aJdeuXTPom5ubKzNnzhQPDw+xs7OTzp07S0JCgri4uBhd1ZQefMwjqYR5JJUwj3WTpZ/3kn+zi4ikpqbKiBEjpGHDhuLi4iJjxoyRpKQkk98udfr0aRk5cqR4eHiIVqsVLy8v6dOnj6xcubLMOa5du1Z69+4tnp6eYmtrKz4+PjJy5Ej58ccfDfp988030rlzZ9HpdOLl5SUvvfSSfPjhh0ZXj8/Ly5PZs2dL48aNxd7eXsLCwuTf//63UR2Vl5cnUVFR4uvrK3Z2dtK+fXvZsWOHREZGip+fn75f8dXjFy1aZHL+lT1uc2hERKqm/H9w5OfnIyQkBL6+vjhw4IClp1OjYmNjMXfuXDAWVFNOnDiBbt26YcOGDRg9erSlp0N1HPNIKmEeSSXMI6lozZo1mDBhAi5evFgtHzVWRa05Pb46TZo0Cf369dOfjrFy5UokJydj6dKllp4a0QPl4MGDSEhIQIcOHWBvb4/Tp09jwYIFaN68eakX8CCqLswjqYR5JJUwj0RqYdGOuxdgi4qKQnp6OrRaLdq3b4+9e/fikUcesfTUiB4o9erVw4EDB7BkyRJkZ2fDzc0NAwcORFxcnP4KpUQ1hXkklTCPpBLmkUgtPD2eiIiIiIiISFG8/CMRERERERGRoli0ExERERERESmKRTsRERERERGRomrFheiKiopw9epVODs7Q6PRWHo6ZCEiguzsbPj4+MDKynKvNzGPVEyFTDKPVIx5JJWokEeAmaS7mEdSSWXyWCuK9qtXr6Jx48aWngYpIiUlBY0aNbLY+MwjlWTJTDKPVBLzSCrhv9mkEuaRVGJOHmtF0e7s7AwA6I5BsIHWwrMhSylAPr7GXn0eLIV5pGIqZJJ5pGLMI6lEhTwCzCTdxTySSiqTx0oV7e+//z4WLVqE1NRUtG7dGkuWLEGPHj1K7X/8+HHMnDkTP//8M3x8fDB79mw8//zzFR6v+PQRG2hho2HA66z//3LCkqcTMY9kMSYyyTySxTCPpBL+m00qYR5JJaXksSxmf6hj06ZNmD59Ol577TX88MMP6NGjBwYOHIgrV66Y7H/x4kUMGjQIPXr0wA8//IBXX30VL774IrZt22bu0ERGmEdSCfNIKmEeSTXMJKmEeaTaRCMiYs4GoaGhaN++PVasWKFva9WqFYYOHYq4uDij/i+//DJ27dqF5ORkfdvzzz+P06dPIyEhoUJjZmVlwcXFBb3wGF+VqsMKJB/HsBM3b95EvXr1ADCPZFklM8k8kiUxj6QS/ptNKmEeSSWm8lges95pv3PnDk6dOoX+/fsbtPfv3x8nTpwwuU1CQoJR//DwcCQlJSE/P9/kNnl5ecjKyjK4EZXEPJJKmEdSCfNIqmEmSSXMI9U2ZhXt169fR2FhITw9PQ3aPT09kZaWZnKbtLQ0k/0LCgpw/fp1k9vExcXBxcVFf+NVFskU5pFUwjySSphHUg0zSSphHqm2qdQXFZb80LyIlPlBelP9TbUXi46Oxs2bN/W3lJSUykyT6gjmkVTCPJJKmEdSDTNJKmEeqbYw6+rxbm5usLa2NnoF6o8//jB65amYl5eXyf42NjZwdXU1uY1Op4NOpzNnalQHMY+kEuaRVMI8kmqYSVIJ80i1jVnvtNva2qJDhw44ePCgQfvBgwfRtWtXk9t06dLFqP+BAwfQsWNHaLW8AANVHvNIKmEeSSXMI6mGmSSVMI9U25h9evzMmTPx8ccfY9WqVUhOTsaMGTNw5coV/XcURkdHY9y4cfr+zz//PC5fvoyZM2ciOTkZq1atwieffIKoqKiqOwqqs5hHUgnzSCphHkk1zCSphHmk2sSs0+MB4Mknn0RGRgbmzZuH1NRUtGnTBnv37oWfnx8AIDU11eD7DQMCArB3717MmDEDy5cvh4+PD9577z08/vjjVXcUVGcxj6QS5pFUwjySaphJUgnzSLWJ2d/TbgnF32nYfusMWDvW3c+F5O9wt/QULKrwTi5+Wv2aWd9pWB2Yx7vqeh4BNTLJPN7FPDKPKmEe1cgjwEwWq+uZZB7Vwjyan8dKXT2eiIiIiIiIiKofi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRdlYegKqORmy1extOv97RDXMhKhymGGq7SqT4Q47JlfDTIgq51Tsikpt1yGWOSa1VCbLzDGp5EHJMN9pJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlKUWUV7XFwcHn74YTg7O8PDwwNDhw7FL7/8UuY2x44dg0ajMbqdPXv2viZOxDySSphHUg0zSSphHkklzCPVNmYV7cePH8fUqVNx8uRJHDx4EAUFBejfvz9ycnLK3faXX35Bamqq/ta8efNKT5oIYB5JLcwjqYaZJJUwj6QS5pFqGxtzOu/bt8/g/urVq+Hh4YFTp06hZ8+eZW7r4eGB+vXrmz1BotIwj6QS5pFUw0ySSphHUgnzSLXNfX2m/ebNmwCAhg0bltu3Xbt28Pb2Rt++fXH06NEy++bl5SErK8vgRlQe5pFUwjySaqojk8wjVRZ/R5JKmEdSXaWLdhHBzJkz0b17d7Rp06bUft7e3vjwww+xbds2xMfHIzAwEH379sWXX35Z6jZxcXFwcXHR3xo3blzZaVIdwTySSphHUk11ZZJ5pMrg70hSCfNItYFZp8ffa9q0afjxxx/x9ddfl9kvMDAQgYGB+vtdunRBSkoKFi9eXOrpJ9HR0Zg5c6b+flZWFkNOZWIeSSXMI6mmujLJPFJl8HckqYR5pNqgUu+0v/DCC9i1axeOHj2KRo0amb19586dce7cuVIf1+l0qFevnsGNqDTMI6mEeSTVVGcmmUcyF39HkkqYR6otzHqnXUTwwgsvYPv27Th27BgCAgIqNegPP/wAb2/vSm1LVIx5JJUwj6QaZpJUwjySSphHqm3MKtqnTp2Kzz77DDt37oSzszPS0tIAAC4uLrC3twdw9zSQ33//HZ9++ikAYMmSJfD390fr1q1x584drF+/Htu2bcO2bduq+FCormEeSSXMI6mGmSSVMI+kEuaRahuzivYVK1YAAHr16mXQvnr1aowfPx4AkJqaiitXrugfu3PnDqKiovD777/D3t4erVu3xhdffIFBgwbd38ypzmMeSSXMI6mGmSSVMI+kEuaRahuzT48vz5o1awzuz549G7NnzzZrUkQVwTySSphHUg0zSSphHkklzCPVNvf1Pe1EREREREREVH1YtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpyqyvfKttToZsVXaczv8eUQ0zoQeNyhnusGNyNcyEHjQ1leFTsSsqtV2HWOaYylZTGQYql2NmmCqisr8ja2os5pgqoqZyrGKG+U47ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpikU7ERERERERkaJYtBMREREREREpysbSEzDH4eCdqOf8YLzOcDJkq9nbdNgxuRpmQjWlMs+5yk7FrqjUdh1imePa6kHLMFC5HDPDtRczfBczXLsxx3cxx7VXZf+GVJk5x5SVXYQGq83b/4NRARMRERERERE9gMwq2mNjY6HRaAxuXl5eZW5z/PhxdOjQAXZ2dmjatClWrlx5XxMmKsY8kkqYR1INM0kqYR5JJcwj1TZmnx7funVrHDp0SH/f2tq61L4XL17EoEGD8Pe//x3r16/HN998gylTpsDd3R2PP/545WZMdA/mkVTCPJJqmElSCfNIKmEeqTYxu2i3sbEp95WoYitXrkSTJk2wZMkSAECrVq2QlJSExYsXM+BUJZhHUgnzSKphJkklzCOphHmk2sTsz7SfO3cOPj4+CAgIwFNPPYULFy6U2jchIQH9+/c3aAsPD0dSUhLy8/PNny1RCcwjqYR5JNUwk6QS5pFUwjxSbWJW0R4aGopPP/0U+/fvx0cffYS0tDR07doVGRkZJvunpaXB09PToM3T0xMFBQW4fv16qePk5eUhKyvL4EZUEvNIKmEeSTU1kUnmkSqKvyNJJcwj1TZmFe0DBw7E448/jqCgIDzyyCP44osvAABr164tdRuNRmNwX0RMtt8rLi4OLi4u+lvjxo3NmSbVEcwjqYR5JNXURCaZR6oo/o4klTCPVNvc11e+OTo6IigoCOfOnTP5uJeXF9LS0gza/vjjD9jY2MDV1bXU/UZHR+PmzZv6W0pKyv1Mk+oI5pFUwjySaqojk8wjVRZ/R5JKmEdSndkXortXXl4ekpOT0aNHD5OPd+nSBbt37zZoO3DgADp27AitVlvqfnU6HXQ63f1Mjeog5pFUwjySaqojk8wjVRZ/R5JKmEdSnVnvtEdFReH48eO4ePEivv32W4wYMQJZWVmIjIwEcPfVpHHjxun7P//887h8+TJmzpyJ5ORkrFq1Cp988gmioqKq9iioTmIeSSXMI6mGmSSVMI+kEuaRahuz3mn/7bffMGrUKFy/fh3u7u7o3LkzTp48CT8/PwBAamoqrly5ou8fEBCAvXv3YsaMGVi+fDl8fHzw3nvv8asRqEowj6QS5pFUw0ySSphHUgnzSLWNWUX7559/Xubja9asMWoLCwvD999/b9akiCqCeSSVMI+kGmaSVMI8kkqYR6pt7usz7TVtWIsg2GhK/9zIg84NCZaegkUViFrfg1lvxAWz8hiOkOqbTC3yIOVYpUyam8fKYIbvUjXDdS2PlcEM31UTGVYpj4C6mawM5vguc3LMPKqlrmf4bh4vmLXNfV09noiIiIiIiIiqD4t2IiIiIiIiIkWxaCciIiIiIiJSFIt2IiIiIiIiIkWxaCciIiIiIiJSFIt2IiIiIiIiIkWxaCciIiIiIiJSFIt2IiIiIiIiIkWxaCciIiIiIiJSFIt2IiIiIiIiIkWxaCciIiIiIiJSlI2lJ1ARIgIAKEA+IBaeDFlMAfIB/DcPlsI8UjEVMsk8UjHmkVSiQh7vHZ+ZrNuYR1JJZfJYK4r27OxsAMDX2GvhmZAKsrOz4eLiYtHxAeaR/suSmWQeqSTmkVTCf7NJJcwjqcScPGrE0i85VUBRURGuXr0KZ2dnaDQafXtWVhYaN26MlJQU1KtXz4IztKy6sg4iguzsbPj4+MDKynKf7Cgtj0DdeS7KUpfWQIVMMo9lq0trwDyqry6tgQp5BPg3ZFnq0howj+qrS2tQmTzWinfarays0KhRo1Ifr1ev3gP/5FZEXVgHS746Wqy8PAJ147koT11ZA0tnknmsmLqyBsxj7VBX1sDSeQT4N2RF1JU1YB5rh7qyBubmkReiIyIiIiIiIlIUi3YiIiIiIiIiRdXqol2n0yEmJgY6nc7SU7EoroM6+FxwDVTC54JroBI+F1wDlfC54BqohM8F16A8teJCdERERERERER1Ua1+p52IiIiIiIjoQcainYiIiIiIiEhRLNqJiIiIiIiIFMWinYiIiIiIiEhRtbpof//99xEQEAA7Ozt06NABX331laWnVGNiY2Oh0WgMbl5eXpaeVp3GPDKPKqnLeQSYSdUwj8yjSphH5lE1dTmTzGPF1NqifdOmTZg+fTpee+01/PDDD+jRowcGDhyIK1euWHpqNaZ169ZITU3V33766SdLT6nOYh6ZR5Uwj3cxk2pgHu9iHtXAPN7FPKqDmWQeK6LWFu3vvvsuJk2ahGeeeQatWrXCkiVL0LhxY6xYscLSU6sxNjY28PLy0t/c3d0tPaU6i3lkHlXCPN7FTKqBebyLeVQD83gX86gOZpJ5rIhaWbTfuXMHp06dQv/+/Q3a+/fvjxMnTlhoVjXv3Llz8PHxQUBAAJ566ilcuHDB0lOqk5jHu5hHNTCP/8VMWh7z+F/Mo+Uxj//FPKqBmbyLeSxfrSzar1+/jsLCQnh6ehq0e3p6Ii0tzUKzqlmhoaH49NNPsX//fnz00UdIS0tD165dkZGRYemp1TnMI/OoEubxLmZSDczjXcyjGpjHu5hHdTCTzGNF2Vh6AvdDo9EY3BcRo7YH1cCBA/X/HxQUhC5duqBZs2ZYu3YtZs6cacGZ1V3M413Moxrqch4BZlI1zCPzqBLmkXlUTV3OJPNYMbXynXY3NzdYW1sbvQL1xx9/GL1SVVc4OjoiKCgI586ds/RU6hzm0RjzaDnMo2nMpGUwj6Yxj5bBPJrGPFoOM2mMeTStVhbttra26NChAw4ePGjQfvDgQXTt2tVCs7KsvLw8JCcnw9vb29JTqXOYR2PMo+Uwj6Yxk5bBPJrGPFoG82ga82g5zKQx5rEUUkt9/vnnotVq5ZNPPpEzZ87I9OnTxdHRUS5dumTpqdWIWbNmybFjx+TChQty8uRJiYiIEGdn5zpz/KphHplHldT1PIowkyphHplHlTCPzKNq6nommceKqbWfaX/yySeRkZGBefPmITU1FW3atMHevXvh5+dn6anViN9++w2jRo3C9evX4e7ujs6dO+PkyZN15vhVwzwyjyqp63kEmEmVMI/Mo0qYR+ZRNXU9k8xjxWhERCw9CSIiIiIiIiIyVis/005ERERERERUF7BoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlIUi3YiIiIiIiIiRbFoJyIiIiIiIlLU/wGRmu1MiceD2AAAAABJRU5ErkJggg==", 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", 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" ] @@ -398,15 +400,15 @@ ], "source": [ "for i in range(6):\n", - " intersection_over_target_area = i/5\n", + " intersection_over_target_obj_area = i/5\n", " \n", " fig, ax = plt.subplots(1,6, figsize = [10,2])\n", - " title = 'intersection_over_target_area = ' + str(intersection_over_target_area)\n", + " title = 'intersection_over_target_obj_area = ' + str(intersection_over_target_obj_area)\n", " plt.suptitle(title)\n", " for i, probe in enumerate(probe_list):\n", " ax[i].imshow(ref_labels2 + probe, vmax=3)\n", " value = highest_overlap(regionmask = probe, intensity_image = ref_labels2,\n", - " overlap_threshold=intersection_over_target_area)\n", + " overlap_threshold=intersection_over_target_obj_area)\n", " ax[i].set_title('triangle belongs\\nto ' + label_dict[value])\n", " plt.tight_layout()" ] diff --git a/demo/measure_relationship_to_other_channels_functions.ipynb b/demo/measure_relationship_to_other_channels_functions.ipynb new file mode 100644 index 0000000..e492a1a --- /dev/null +++ b/demo/measure_relationship_to_other_channels_functions.ipynb @@ -0,0 +1,1471 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "46649d66-03c2-4f41-ad06-a5b5c5159b57", + "metadata": {}, + "source": [ + "# Measure Relationship to Other Channels by Code" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c2ad93cc-d06e-4bd5-bfda-1ae52e07249b", + "metadata": {}, + "source": [ + "This notebook shows how to measure features of segmentated objects arising from multichannel images. It shows the similar steps done via the \"Regionprops Measure Relationship to Other Channels\" plugin module, but here these are applied by calling different functions. \n", + "\n", + "It shows how to do that in the following ways (the first 2 are a wrapper around the regular single channel \"Regionprops\" plugin module):\n", + "\n", + "1. **measure_labels**: Get features from on one segmented image (it needs one label image);\n", + " \n", + "2. **measure_labels_with_intensity**: Get features (including intensity) from one segmented image and one intensity image (it needs one label image and one intensity image);\n", + "\n", + "3. **link_two_label_images**: Get which objects from one segmented image \"belong\" or \"are inside\" another reference image, returning a relationship table;\n", + " \n", + "4. **measure_labels_in_labels**: Get features from two segmented images, relating objects from one image to another image (it needs two label images, one being a reference), returning a (list of) relationship table(s) with features;\n", + " \n", + "5. **measure_labels_in_labels_with_intensity**: Get features (including intensity features) from two segmented images and two intensity images (it needs two label images and two intensity images), returning a (list of) relationship table(s) with features;\n", + "\n", + "6. **measure_summary_statistics**: Transforms a (list of) relationship table(s) to return a single summary statistics table instead of individual direct relationships.\n", + "\n", + "We start by loading the same cropped images from the [BBBC007 dataset](https://bbbc.broadinstitute.org/BBBC007): two label images and two intensity images." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b2aa6b54-0cb3-4550-b9c4-37796d455c28", + "metadata": {}, + "outputs": [], + "source": [ + "from skimage import io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import napari\n", + "from napari.utils import nbscreenshot\n", + "from napari_skimage_regionprops import measure_labels, measure_labels_with_intensity\n", + "from napari_skimage_regionprops import measure_labels_in_labels, measure_labels_in_labels_with_intensity\n", + "from napari_skimage_regionprops import link_two_label_images, make_summary_table\n", + "from napari_skimage_regionprops import regionprops_table" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "60af03d0-e028-4b52-9ead-d0001e3d29fc", + "metadata": {}, + "outputs": [], + "source": [ + "image_channel1 = io.imread('../data/actin.tif')\n", + "image_channel2 = io.imread('../data/dna.tif')\n", + "cell_labels = io.imread('../data/cell_labels.tif')\n", + "dna_labels = io.imread('../data/dna_labels.tif')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3cdab0f3-1e65-479a-b7c8-df460265d443", + "metadata": {}, + "outputs": [], + "source": [ + "viewer = napari.Viewer()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4f84587-009a-4f7b-86da-c9649091e717", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/html": [ + "\"\"" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viewer.add_image(image_channel1, name = 'cell', colormap='green')\n", + "viewer.add_image(image_channel2, name = 'dna', colormap='magenta', blending='additive')\n", + "cell_labels_layer = viewer.add_labels(cell_labels)\n", + "cell_labels_layer.contour = 1\n", + "dna_labels_layer = viewer.add_labels(dna_labels)\n", + "nbscreenshot(viewer, canvas_only=True)" + ] + }, + { + "cell_type": "markdown", + "id": "8ed80a5b-67fd-40fd-8ccd-8c46682dd5c1", + "metadata": {}, + "source": [ + "## 1. Measure Lables" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "872e57d6-c438-42cf-9a3d-e65454206e10", + "metadata": {}, + "source": [ + "To measure cell size features from one label image, we can use the function `measure_labels` providing the labeled image as an argument. By doing that, we get a table with size and shape properties." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5143dd10-6885-48c0-9ed0-8a93146d85fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelareabbox_areaequivalent_diameterconvex_area
0144264823.722813461
12910124034.038919972
231407189242.3255011452
34911132034.057616941
451185168038.8431311231
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" + ], + "text/plain": [ + " label area bbox_area equivalent_diameter convex_area\n", + "0 1 442 648 23.722813 461\n", + "1 2 910 1240 34.038919 972\n", + "2 3 1407 1892 42.325501 1452\n", + "3 4 911 1320 34.057616 941\n", + "4 5 1185 1680 38.843131 1231\n", + "5 6 1803 2400 47.912951 1919" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements = measure_labels(label_image_reference=cell_labels)\n", + "measurements" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0e994fab", + "metadata": {}, + "source": [ + "Extra properties can be provided to get other features like `shape`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cca15459", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelareabbox_areaequivalent_diameterconvex_areaextentlocal_centroid-0local_centroid-1solidityferet_diameter_maxmajor_axis_lengthminor_axis_lengthorientationeccentricityaspect_ratioroundnesscircularity
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12910124034.0389199720.73387115.03406621.5483520.93621440.60788138.48026730.5813971.2191380.6069641.2582900.7824840.820115
231407189242.32550114520.74365821.18550121.7839370.96900849.04079947.78993937.7074900.7390530.6143601.2673860.7843890.863983
34911132034.0576169410.69015215.39626818.6706920.96811942.15447841.24815928.7129211.1860240.7179431.4365710.6817410.856081
451185168038.84313112310.70535722.18227820.6616030.96263245.48626241.26868737.257503-0.8967980.4300541.1076610.8859050.833792
561803240047.91295119190.75125023.46977324.1530780.93955253.85164852.24872444.3924370.8934560.5273681.1769740.8409200.770883
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" + ], + "text/plain": [ + " label area bbox_area equivalent_diameter convex_area extent \\\n", + "0 1 442 648 23.722813 461 0.682099 \n", + "1 2 910 1240 34.038919 972 0.733871 \n", + "2 3 1407 1892 42.325501 1452 0.743658 \n", + "3 4 911 1320 34.057616 941 0.690152 \n", + "4 5 1185 1680 38.843131 1231 0.705357 \n", + "5 6 1803 2400 47.912951 1919 0.751250 \n", + "\n", + " local_centroid-0 local_centroid-1 solidity feret_diameter_max \\\n", + "0 12.248869 11.158371 0.958785 28.792360 \n", + "1 15.034066 21.548352 0.936214 40.607881 \n", + "2 21.185501 21.783937 0.969008 49.040799 \n", + "3 15.396268 18.670692 0.968119 42.154478 \n", + "4 22.182278 20.661603 0.962632 45.486262 \n", + "5 23.469773 24.153078 0.939552 53.851648 \n", + "\n", + " major_axis_length minor_axis_length orientation eccentricity \\\n", + "0 26.534396 21.946451 1.114467 0.562063 \n", + "1 38.480267 30.581397 1.219138 0.606964 \n", + "2 47.789939 37.707490 0.739053 0.614360 \n", + "3 41.248159 28.712921 1.186024 0.717943 \n", + "4 41.268687 37.257503 -0.896798 0.430054 \n", + "5 52.248724 44.392437 0.893456 0.527368 \n", + "\n", + " aspect_ratio roundness circularity \n", + "0 1.209052 0.799308 0.884261 \n", + "1 1.258290 0.782484 0.820115 \n", + "2 1.267386 0.784389 0.863983 \n", + "3 1.436571 0.681741 0.856081 \n", + "4 1.107661 0.885905 0.833792 \n", + "5 1.176974 0.840920 0.770883 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements = measure_labels(label_image_reference=cell_labels, shape=True)\n", + "measurements" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "79a08c47-8a7a-4e62-9d72-d3cf38cd9c9d", + "metadata": {}, + "source": [ + "## 2. Measure cell intensity features\n", + "\n", + "To also include intensity features, we use the `measure_labels_with_intensity` function, which needs a label image and an intensity single-channel image." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b0631c10-2a51-4514-b299-b57a74b2ba75", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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labelareabbox_areaequivalent_diameterconvex_areamax_intensitymean_intensitymin_intensitystandard_deviation_intensity
0144264823.72281346164.023.9886889.010.084142
12910124034.03891997296.042.54285716.014.954136
231407189242.3255011452128.072.48045524.016.704404
34911132034.057616941105.045.80351314.016.341103
451185168038.8431311231135.051.27848120.019.006819
561803240047.9129511919136.056.30116516.024.383326
\n", + "
" + ], + "text/plain": [ + " label area bbox_area equivalent_diameter convex_area max_intensity \\\n", + "0 1 442 648 23.722813 461 64.0 \n", + "1 2 910 1240 34.038919 972 96.0 \n", + "2 3 1407 1892 42.325501 1452 128.0 \n", + "3 4 911 1320 34.057616 941 105.0 \n", + "4 5 1185 1680 38.843131 1231 135.0 \n", + "5 6 1803 2400 47.912951 1919 136.0 \n", + "\n", + " mean_intensity min_intensity standard_deviation_intensity \n", + "0 23.988688 9.0 10.084142 \n", + "1 42.542857 16.0 14.954136 \n", + "2 72.480455 24.0 16.704404 \n", + "3 45.803513 14.0 16.341103 \n", + "4 51.278481 20.0 19.006819 \n", + "5 56.301165 16.0 24.383326 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements_with_intensity = measure_labels_with_intensity(\n", + " label_image_reference=cell_labels,\n", + " intensity_image_reference=image_channel1\n", + ")\n", + "measurements_with_intensity" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "d0e803bc-8ccc-4144-b603-3d387240559c", + "metadata": {}, + "source": [ + "## 3. Link each cell to objects in another channel" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a282dc12", + "metadata": {}, + "source": [ + "To get the relationship of each labeled object in one label image (reference) to other labeled objects in a second (target) label image, we can use the `link_two_label_images` function." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4e52d594-4768-4a30-8f9f-f0462162bc65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " label_reference label_target\n", + "0 0 8\n", + "1 1 7\n", + "2 2 6\n", + "3 3 5\n", + "4 4 4\n", + "5 5 3\n", + "6 6 1\n", + "7 6 2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels_relationship = link_two_label_images(\n", + " label_image_reference=cell_labels,\n", + " label_image_other_channel=dna_labels,\n", + ")\n", + "labels_relationship" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a89c68c9", + "metadata": {}, + "source": [ + "This tells us that the object with label `8` from the DNA labels (`label_target` column) 'belongs' to the background of the cell labels (label `0` from `label_reference` column).\n", + "\n", + "Also, that the object `7` from the DNA labels 'belongs' to the cell with label `1`.\n", + "\n", + "Finally, labels `1` and `2` from the DNA labels are considered 'inside' cell label `6`. That's why label `6` appears twice.\n", + "\n", + "To understand in more detail how an object is considered to belong to another, please refer to the [´intersection_over_target_area_explained´ notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/tree/master/demo/intersection_over_target_area_explained.ipynb)." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "bef36a5b-b5f1-431c-99aa-4d11ee98fe54", + "metadata": {}, + "source": [ + "## 4. Link each cell with its features to objects in another channel, also with their features\n", + "\n", + "To get not only the label relationships shown above, but also to get the corresponding properties in a same table, we can use the `measure_labels_in_labels` function. We can optionally provide `suffixes` to better identify the sources, otherwise '_reference' and increasing numbers will be used.\n", + "\n", + "*Notice that the return output of this function is a list of tables (each relationship of a channel to the reference yields a new table with possibly different shapes).*\n", + "*In this example, we get the single element of the list `[0]` since we have a single extra channel besides the reference.*" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "dacc2b95-9b45-4d62-9e89-7dea1a9781a0", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:86: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", + " output_table = pd.merge(table_reference_labels_properties,\n" + ] + }, + { + "data": { + "text/html": [ + "
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label_cellsarea_cellsbbox_area_cellsequivalent_diameter_cellsconvex_area_cellslabel_dnaarea_dnabbox_area_dnaequivalent_diameter_dnaconvex_area_dna
70NaNNaNNaNNaN866889.16699670
01442.0648.023.722813461.0713015612.865502135
12910.01240.034.038919972.0628335718.982276293
231407.01892.042.3255011452.0521043716.351768271
34911.01320.034.057616941.0427534218.712052284
451185.01680.038.8431311231.0332946220.466944345
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661803.02400.047.9129511919.0228365.97082129
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" + ], + "text/plain": [ + " label_cells area_cells bbox_area_cells equivalent_diameter_cells \\\n", + "7 0 NaN NaN NaN \n", + "0 1 442.0 648.0 23.722813 \n", + "1 2 910.0 1240.0 34.038919 \n", + "2 3 1407.0 1892.0 42.325501 \n", + "3 4 911.0 1320.0 34.057616 \n", + "4 5 1185.0 1680.0 38.843131 \n", + "5 6 1803.0 2400.0 47.912951 \n", + "6 6 1803.0 2400.0 47.912951 \n", + "\n", + " convex_area_cells label_dna area_dna bbox_area_dna \\\n", + "7 NaN 8 66 88 \n", + "0 461.0 7 130 156 \n", + "1 972.0 6 283 357 \n", + "2 1452.0 5 210 437 \n", + "3 941.0 4 275 342 \n", + "4 1231.0 3 329 462 \n", + "5 1919.0 1 413 551 \n", + "6 1919.0 2 28 36 \n", + "\n", + " equivalent_diameter_dna convex_area_dna \n", + "7 9.166996 70 \n", + "0 12.865502 135 \n", + "1 18.982276 293 \n", + "2 16.351768 271 \n", + "3 18.712052 284 \n", + "4 20.466944 345 \n", + "5 22.931374 437 \n", + "6 5.970821 29 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements_linked = measure_labels_in_labels(label_image_reference=cell_labels,\n", + " label_image_other_channel=dna_labels,\n", + " suffixes=['_cells', '_dna'])\n", + "measurements_linked[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a61f5f53", + "metadata": {}, + "source": [ + "*Currently, background feature calculation is not implemented, so NaN are returned for it.*\n", + "\n", + "Similarly to the ouput of `link_two_label_images`, this table provides direct relationships, so some labels may show up duplicated. To have unique labels in the first column, we should later apply the `make_summary_table` function explained in the last section of this notebook." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "6fff90c6-c411-4b8c-96ea-4a0d99d81cd4", + "metadata": {}, + "source": [ + "## 5. Link each cell with its features (including intensity features) to objects in another channel, also with their features\n", + "\n", + "To also include intensity features, we use the function `measure_labels_in_labels_with_intensity`, which needs the corresponding intensity images as extra parameters.\n", + "\n", + "*Similarly, this funtion also returns a list of tables.*" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "6c60bf3b-2e13-4b3f-8ad5-93c240b39baf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:86: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", + " output_table = pd.merge(table_reference_labels_properties,\n" + ] + }, + { + "data": { + "text/html": [ + "
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label_cellsarea_cellsbbox_area_cellsequivalent_diameter_cellsconvex_area_cellsmax_intensity_cellsmean_intensity_cellsmin_intensity_cellsstandard_deviation_intensity_cellslabel_dnaarea_dnabbox_area_dnaequivalent_diameter_dnaconvex_area_dnamax_intensity_dnamean_intensity_dnamin_intensity_dnastandard_deviation_intensity_dna
70NaNNaNNaNNaNNaNNaNNaNNaN866889.1669967094.046.03030322.017.076450
01442.0648.023.722813461.064.023.9886889.010.084142713015612.865502135183.0104.34615419.040.762674
12910.01240.034.038919972.096.042.54285716.014.954136628335718.982276293182.099.56890528.034.877849
231407.01892.042.3255011452.0128.072.48045524.016.704404521043716.351768271247.0127.58571424.055.615558
34911.01320.034.057616941.0105.045.80351314.016.341103427534218.712052284173.096.65818223.037.846574
451185.01680.038.8431311231.0135.051.27848120.019.006819332946220.466944345161.079.70820710.032.925170
561803.02400.047.9129511919.0136.056.30116516.024.383326141355122.931374437132.058.98789314.024.761279
661803.02400.047.9129511919.0136.056.30116516.024.383326228365.9708212956.035.60714313.011.564537
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" + ], + "text/plain": [ + " label_cells area_cells bbox_area_cells equivalent_diameter_cells \\\n", + "7 0 NaN NaN NaN \n", + "0 1 442.0 648.0 23.722813 \n", + "1 2 910.0 1240.0 34.038919 \n", + "2 3 1407.0 1892.0 42.325501 \n", + "3 4 911.0 1320.0 34.057616 \n", + "4 5 1185.0 1680.0 38.843131 \n", + "5 6 1803.0 2400.0 47.912951 \n", + "6 6 1803.0 2400.0 47.912951 \n", + "\n", + " convex_area_cells max_intensity_cells mean_intensity_cells \\\n", + "7 NaN NaN NaN \n", + "0 461.0 64.0 23.988688 \n", + "1 972.0 96.0 42.542857 \n", + "2 1452.0 128.0 72.480455 \n", + "3 941.0 105.0 45.803513 \n", + "4 1231.0 135.0 51.278481 \n", + "5 1919.0 136.0 56.301165 \n", + "6 1919.0 136.0 56.301165 \n", + "\n", + " min_intensity_cells standard_deviation_intensity_cells label_dna \\\n", + "7 NaN NaN 8 \n", + "0 9.0 10.084142 7 \n", + "1 16.0 14.954136 6 \n", + "2 24.0 16.704404 5 \n", + "3 14.0 16.341103 4 \n", + "4 20.0 19.006819 3 \n", + "5 16.0 24.383326 1 \n", + "6 16.0 24.383326 2 \n", + "\n", + " area_dna bbox_area_dna equivalent_diameter_dna convex_area_dna \\\n", + "7 66 88 9.166996 70 \n", + "0 130 156 12.865502 135 \n", + "1 283 357 18.982276 293 \n", + "2 210 437 16.351768 271 \n", + "3 275 342 18.712052 284 \n", + "4 329 462 20.466944 345 \n", + "5 413 551 22.931374 437 \n", + "6 28 36 5.970821 29 \n", + "\n", + " max_intensity_dna mean_intensity_dna min_intensity_dna \\\n", + "7 94.0 46.030303 22.0 \n", + "0 183.0 104.346154 19.0 \n", + "1 182.0 99.568905 28.0 \n", + "2 247.0 127.585714 24.0 \n", + "3 173.0 96.658182 23.0 \n", + "4 161.0 79.708207 10.0 \n", + "5 132.0 58.987893 14.0 \n", + "6 56.0 35.607143 13.0 \n", + "\n", + " standard_deviation_intensity_dna \n", + "7 17.076450 \n", + "0 40.762674 \n", + "1 34.877849 \n", + "2 55.615558 \n", + "3 37.846574 \n", + "4 32.925170 \n", + "5 24.761279 \n", + "6 11.564537 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements_with_intensity_linked = measure_labels_in_labels_with_intensity(label_image_reference=cell_labels,\n", + " intensity_image_reference=image_channel1,\n", + " label_image_other_channel=dna_labels,\n", + " intensity_image_other_channel=image_channel2,\n", + " suffixes=['_cells', '_dna'])\n", + "measurements_with_intensity_linked[0]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0ffd12fa-4b94-48c8-bdaa-cdebd07f1ae3", + "metadata": {}, + "source": [ + "## 6. Get individual cell summary statistics for each feature\n", + "\n", + "To get summary statistics instead of individual direct relationships, we can use the `make_linked_summary` function. We must provide as input the list of direct relationships tables and a list of desired statistics. \n", + "\n", + "Below, we provide 'count' and 'average'. So, for each cell label, we get the number of objects 'inside' it and the average of `size` features, like average area of the DNA objects inside each cell." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8e968d8b-6816-4084-bfc3-ab7f3fb58d89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " label_cells counts_dna area_dna average bbox_area_dna average \\\n", + "0 0 1 66.0 88.0 \n", + "1 1 1 130.0 156.0 \n", + "2 2 1 283.0 357.0 \n", + "3 3 1 210.0 437.0 \n", + "4 4 1 275.0 342.0 \n", + "5 5 1 329.0 462.0 \n", + "6 6 2 220.5 293.5 \n", + "\n", + " equivalent_diameter_dna average convex_area_dna average \n", + "0 9.166996 70.0 \n", + "1 12.865502 135.0 \n", + "2 18.982276 293.0 \n", + "3 16.351768 271.0 \n", + "4 18.712052 284.0 \n", + "5 20.466944 345.0 \n", + "6 14.451098 233.0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "measurements_linked_summary = make_summary_table(measurements_linked,\n", + " statistics_list=['count', 'average'])\n", + "measurements_linked_summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91c84b11", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "napari-skimage-regionprops-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "89194614874d20ddf8746d6fd6c19366af8b6d367ee602507dbc652a381a6ce7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/demo/measure_relationship_to_other_channels_plugin.ipynb b/demo/measure_relationship_to_other_channels_plugin.ipynb new file mode 100644 index 0000000..e7959c3 --- /dev/null +++ b/demo/measure_relationship_to_other_channels_plugin.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "46649d66-03c2-4f41-ad06-a5b5c5159b57", + "metadata": {}, + "source": [ + "# Measure Relationship to Other Channels via Plugin\n", + "This notebook shows an example of how to use the \"Regionprops Measure Relationship to Other Channels\" module from napari-skimage-regionprops.\n", + "\n", + "The goal is to measure properties from two labeled images produced from a multi-channel image and generate a table that, not only displays these properties, but also shows how properties from objects in a \"target\" channel relate to objects in a \"reference\" channel.\n", + "\n", + "Thus, in this notebook we:\n", + " - count how many objects from a DNA channel belong or are 'inside' an actin channel (cell bodies)\n", + " - get the average area of DNA which are 'inside' each cell\n", + " - get the average of intensity features of DNA which are 'inside' each cell\n", + "\n", + "*This results in new features for the reference labels, some of which may display an odd looking name at first, like `mean_intensity average`. That's because `mean_intensity` is an intensity feature and `mean_intensity average` represents the average of this feature inside a cell/object.*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b2aa6b54-0cb3-4550-b9c4-37796d455c28", + "metadata": {}, + "outputs": [], + "source": [ + "from skimage import io\n", + "import numpy as np\n", + "import pandas as pd\n", + "import napari\n", + "from napari.utils import nbscreenshot\n", + "from napari_skimage_regionprops import regionprops_measure_relationship_to_other_channels" + ] + }, + { + "cell_type": "markdown", + "id": "6232e6bf", + "metadata": {}, + "source": [ + "## Open images in napari" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "c3aec745", + "metadata": {}, + "source": [ + "In this small example, we open two cropped images from the [BBBC007 dataset](https://bbbc.broadinstitute.org/BBBC007), and two label images generated from the intensity images.\n", + "\n", + "Thus, we have two intensity images (a multichannel image) and two labels images. The cell labels are shown as contours and the DNA labels are shown as filled regions for better differentiation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "60af03d0-e028-4b52-9ead-d0001e3d29fc", + "metadata": {}, + "outputs": [], + "source": [ + "image_channel1 = io.imread('../data/actin.tif')\n", + "image_channel2 = io.imread('../data/dna.tif')\n", + "cell_labels = io.imread('../data/cell_labels.tif')\n", + "dna_labels = io.imread('../data/dna_labels.tif')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3cdab0f3-1e65-479a-b7c8-df460265d443", + "metadata": {}, + "outputs": [], + "source": [ + "viewer = napari.Viewer()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4f84587-009a-4f7b-86da-c9649091e717", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/html": [ + "\"\"" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "viewer.add_image(image_channel1, name = 'cell', colormap='green')\n", + "viewer.add_image(image_channel2, name = 'dna', colormap='magenta', blending='additive')\n", + "cell_labels_layer = viewer.add_labels(cell_labels)\n", + "cell_labels_layer.contour = 1\n", + "dna_labels_layer = viewer.add_labels(dna_labels)\n", + "nbscreenshot(viewer)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "85cc716c", + "metadata": {}, + "source": [ + "## Open the plugin\n", + "We open the plugin by the Tools menu (Tools -> Measurement tables -> Object Features/Properties (scikit-image, nsr))." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4d2d9ee6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/html": [ + "\"\"" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbscreenshot(viewer)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "b07b3a6c-1337-41fa-b5db-b2fc5708c003", + "metadata": {}, + "source": [ + "## Count number of objects 'inside' objects in another channel\n", + "\n", + "If we want to count how many labels from one labels layer can be considered 'inside' another labels layer, we have to:\n", + " 1. ensure the `relate to other channel(s)` checkbox is enabled;\n", + " 2. click on the `+` button in front of `Label Image(s) from other Channel(s)`;\n", + " 3. assign the second labels layer to it (in this case, the 'dna_labels');\n", + " 4. click on `configure summary statistics`, (extra options show up, notice that `average` is enabled by default), uncheck `average` and check `count`.\n", + "\n", + "It should look like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87b01f8b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/html": [ + "\"\"" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:86: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", + " output_table = pd.merge(table_reference_labels_properties,\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Napari status bar display of label properties disabled because https://github.com/napari/napari/issues/5417 and https://github.com/napari/napari/issues/4342\n" + ] + } + ], + "source": [ + "nbscreenshot(viewer)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "dbc6f513", + "metadata": {}, + "source": [ + "Then hit the 'Run' button.\n", + "\n", + "This returns a small table in a new tab indicating how many labels (`counts`) from the second labels layer are 'inside' the reference labels layer, i.e., how many DNA objects are inside each cell.\n", + "\n", + "The row where label is `0` means background." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d008795a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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of objects 'inside' objects in another channel" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "ff54a8a8", + "metadata": {}, + "source": [ + "Let's say we want to do more than count objects. We are also interested in knowing the average area of the objects that are 'inside' objects in another channel. To do that, we should:\n", + "\n", + " 1. click on `configure summary statistics`, check `average`;\n", + " 2. hit the Run button.\n", + "\n", + "We now get a larger table, with all the average values (related to `size`) of the objects 'inside'." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "41e9f6a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Resulting geometry: 3840x2004+0+56 (frame: 3872x2092-16-16) margins: 16, 72, 16, 16 minimum size: 938x684 MINMAXINFO maxSize=0,0 maxpos=0,0 mintrack=2846,2140 maxtrack=0,0)\n" + ] + } + ], + "source": [ + "nbscreenshot(viewer)" + ] + }, + { + "cell_type": "markdown", + "id": "90e70fc3", + "metadata": {}, + "source": [ + "\n", + "If we check other features, like `perimeter`, `shape`, `position` and `moments`, we will also get average values for each property involved in these features.\n", + "\n", + "If we check other statistics, like `std`, `minimum`, etc; we will get each of these statistics for every property selected. \n", + "\n", + "*Keep in mind that the table may become **very** wide if you select everything!*" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "39640685-eb60-4353-9a5b-965adc576396", + "metadata": {}, + "source": [ + "## Include intensity features\n", + "\n", + "We can also measure intensity features if we enable the `intensity` checkbox. Of course, we need intensity images to get these features from. Therefore, one (if `relate to other channel(s)` if disabled) or two (if `relate to other channel(s)` is enabled) extra inputs will be added to the interface. \n", + "\n", + "We should select an `Intensity Image Reference` (related to the reference labels) and, if `relate to other channel(s)` is checked, another `Intensity Image(s) from other Channel(s)` (related to the other labels layer)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2013c75d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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EJv2jtDTbHOQ6Xykhv5xDue2wVps1IR5NCf82XUspazEpyNmS62yGS2TqfpOCyU3Xbu1aW9RQh0xzuGvZ9VH2Xv22aruZP5nh/C0t7Z/hsyZPlwxIOk/2722r613T63fSNaj8hrh5yKYMc5Hl2p9U+/4xdvzgeHJ3DiHzfD/nCnpcbfj53fC5PCgGNF4wo9lpnTg7Kfybdo3mc4OB3a/F5qrpddssjYuHRISmXwBAKzXJH2TISIy9vfmuvE00ydQk5xFKJk+JsWsy5GjS5nHqx/xgPDa5/vcrh8eo8ojNFS08/v6Nv6dZXX0op/ek5krS5zByz5FULpuVoaFgcu4mKVxb8URMqYjkRm9ZapYkSw4maZ5LLo9/HPhi7Nvf3H8H7IjKecNrx1lSEqXRvX/X9cSJk/HrN9+KK/6v4XHGGYXNUnz55Zex8zdvxYkTJ128gBY7QwnqPjSnzYopaT84q2LV/Fnxo/WHU54fHtdNz+M2bzueiSnTZnVw+DfSdLbL1Gk21bZYMi1dHbI5HC//eFZMmZau225tXR/fkfL0iBExtmAHOypGjUgZz0u51L92bFOW7WyH+bg2bp/ct8laSfdlMPWLetbwb9KxPJN8R+UR342ZA532ZNaZO3uecYYuzkAhHY6q/a14e90/igAAAAAAQPd1MFbNGhNlTQJZe+LJ6+6Pxv9rvDak2lKJYbKKe7MH2DY8uybq/5/28u/dGEMzvnJrLB45Jk1gdFPcNqtxG7Vh0YSxjC9v/P8HMgZON8VtI8dE2cjEGhTSpvi31Y13OS2/akKG1w2Om7/XGCIsdLfLXFWtnhNlI1PnbVPcdu/WlBeuiRtGpoYcU9dSXbgq0caFUZZ2LhPWxYIxsbhhI7XBuUxrrH5dpw+Kphl3oolLGsJwVavnZA2w7Vr2XMNxlc6YHZMSfnbJgP4NY9n8anMBv8Tul5lDnruWXR9lI8fEDatzuEPu7tdic/1JUDogLmn3VVN7fmYK6+Y2X7WhwsTjTa1zu6/VHCWGyOtr0XQt1V5nFidcZLJf8/JUcX+UpdvvxoXJNR0zLnmfQ26Mf60Pmlfcnz1wvXFFrGq4YN8QN2epV8W96T5rWnVlivfaIKRZce+YKEsT4N2wYE7jsaa5BjV+bmQJjW9cGGUJa7FJ7Qv0OVfI42qbz+8UCcHuqi2vNbtGJl01OumcT7tGdy+Pa0aOibLEOmVU2zW9vgYD2/+CCQB0NSn5g+r1d2fMqGx+JCVHkFVd9qNJpiY1j1Ab3k2y/8X4QcY8Tq19a+5IyqKUXTGqxSUoLenbMObt23LIwpTfkhSErlw2K0MOoypWPfFKVGfZ1OZHZsWUaZnCvxG1uZuEepVcHv84sDX/EfFEszmkzY8k5nL6xshRufzm6rbYlvCe0oFOrU8//XP87r/fKfh2f/ff78Sf//xnBQZapdsHgCuXZf+i0fiF4+l4OeWTvGTEFTEoh31k+1LV7lI721VXR1Ueb9+35o4sX1YSt/tK/Cht8Dfly8ZLqV+QCvjlYeBF0S/lS2leIZ6KJ3II2LbO2GuuTuk0uDMeT7tWRsU/JQWFcw1up345a/5L3b41d9R+SUz8k8tdZQCgk0u8K1yLvicdPKyIAAAAAACQIHuYZ1O8lvD7BwMuHtzCvQyO8WPqw5Bb47XmOgLu3hcH6v+eMbh4MFbNyhKySgw/tirMsyluK2hgrdGuVysa/3+O8ivThxqHjIuxDamoNfFvGztgkVStiX/JFADc+HpCWHJrLM5Yq32xvwDzsfdAQmhxwKAMayyiavXivLuk1msMluUS3E08rsSw6OC4uLGdZcFDiruWXZ9DZ8098d6BdGNrD82cn3nOV2LQOuvNCIpmrSYGuiMq7s3eRTgxNJlLkDPX8/aGLIH6pOtPynU28SYJzd90IHGdZa5Xc2H6XCV21s6pU2y+Zcs6zsSQaOo1KK8LWcbat9XnXMuPqy0+v7PP64H39hR2Uncvj2sWNH/zjF3vVRXg+w4AQK1Boy5vzB9UvxKPFCjvULksW/YjOY/Qr3/LWqMk/Y7lRRdFy771lkbpRfV/zy0fMvaK4Y0PMjRrK6xDcaghJNM3+vVvh39eqW6sbUlJvzzf0z5j7AyqDx+JfQcKlyHad6Aqqg8fUVig1Xp278M/HIcO5vraqlj18s6YOi/hw7/k8vjHgS/GvqxfGvIJarbDF77UYEsbqXw5x8Do/vfjUERCCLbuy8P+AgwiNexcbFLuJFP7xfmJ2Jz2tSOiLPGLxfp16V+XweZf7YxbRwxP+VJXFZBOTU3n7QL85Zc1JhBosca7whVK3Y1N9qstAAAAAAC0maQQay6BrdoAXnlpRENwsYABytqQT91vTZbfEDcP2dTisGir7H4tNldNr+usWBtq3JASrkoM4uXSFbFjZZurupBgHr9/POnhLQ2dePNaYzkFdzNJDG7mEtxNPK66AN7uiIhBMbDFa75/zJwzIZ5c0Ppuz3sPHIwo758ytmKQUJ+c5mtT/NvqG6K8ritt+VUTIjZuKpq12nQ9DoqG/HdOocnlsaJiet16r+3A+uTuPR00N4mBz4Oxf28+66w+OLknqDNxSVTeM9rndzMSO6bnu+ZKZ8yOScuyh+xzvGBGVYyO0qgPQVvHAEDL/3vjH0c0/p5jzpmNjvgvs+kPxmOT2yK70i/61YdEcmryMipGNWQ2DsfLLxW6sd+oWLh2blLWo62NvX1lkxxKS9QGsmvnqDbULV8SEbHn93vjvK/0jv/x9a+3ajt/+MPHsef3exW0o863kqvjsbVXZ3nBznh82hN55bJo38+I6vV3t3lTz86kpxLkoWJHVM4bnnCxKGBYtZ0kfkjXXtRyCTG3pdq7m5QlJHUL9uXhYHVUx/CEEPDwGFUesbmiGGZiVCxMDJNHZL2bTNJdZ+JwbN9W1bpa1N0xR1df0vniiy+jZ8/O2SD+9OkvTCDQcZp89wAAAAAAALJJDNm02CUDGrN0pdPjue3T22HkWUJ8G1+PintGR23Os3/MXLklZkbUdsxso26/mcb45M+2xsy6cFj5926MoRsT958cxGt5qLWzGBw3v/BsXSC6FWusqiJebWnQNSm4OToWbd8Si9ppLby65WDMnFEfTL8rKrffFRERFfeOKUjn1KKSVOfcOiQnBfcHDIqhEcUbiE9aj7l1qU0KNLZH+LCuU2vT0y0xnJ1wfSwSSeugrsPsro7afwZD5z0fz83o3y7XjVaH1Zs7rg75/M5hvK9WRNWM6XVjS7hWV9wfZQW4eQIAQOskhF/zaobXerXdYrOHtQoVTC2ogRdFYz/c3DoGN7/Na+Oph65ux98VLY2ZS++LqX45tV3U1NTEm2+9HSPL/q/o3atXi7Zx9NixePN3b0dNjeZmQGEIAOelDcOq7aVJMKVvTH3olqjqsDsXVEXV+xFt8u2nSXfhiLJ5D8bMg3fEqv0dOw1jb0+968TOePyRbRm/sJVelPi4BV88U2tRUhKlIQCc5utaRPTo9lX44osvOm0A+NSpk5YxtMt/3KoBAAAAAABAepvitlmD4qWV05NzU6nhpvYIBCeGkUvLY/yQ5bGrPgyZ2Hmx4rmO6VLcXtJ2yTwYq2Zdn3Tc7Resa6WkgGtudi27PhYPaNr1uPyeLVF5T+PjLhcIzjEg22kd2Ffknbs7s8J3iG+dCfHo9rsitXF51eo5cc2yhED3kBubfv50a4Pj4nwvmLuXxzX3Dmj6uZFw84TaC6ZAMABAgwyB2Mpls5IbpJXfEutSm6jlva/EQG/7Sxtyrn4lfjQ/sRtzAQO7aWt2OF7+cXImpu06LndPp06djp2/fitGXnF5nNUzv9jdqdO17z116rRCAgUjANzd7P9VbK++OuXLxPC4de3KuLXLtTDfFtt2zI2ypC9YfWPqQytjapovPe2m/JYmX/oql2Wre+KdehLni0I7ffp09Ox5li+sp7+Ic87pnHU4cfyEhQztcr08pQgAAAAAAEDxKZYwzu7lcc3I5THp4aaBywZ1geC2DVxuitcq7ory8oiI/jF2/OB4cndtUGzSnMaAWMUvu3KAaUI8mhjiau0aKVTgslAB8DzGs2HBmNjQTDiw/J4tUfm99u5WTfe0NRaPXBgbimlIew9EVYyuOz/6x8BLomgCwJMeTgz/FmHtOsXnd24dwSMiYuPCKNvYTOf48ruicvsNTW4mAQDQfgrUzbbVSmPmLQnh3yZh2Db0/vv57ae6unWt/1JyIE0CzgU3KhYmhn93PBNTMjado9COHjsav33rv2P4Ny+LHj1yazJXU1MTb/3u7Th67KgCtoltsWTatuzXo/rwfXtei8jbvjV3xJQ16pCPM5QgH3Xdajv5Max6eWeGn9UGS9cl/FlY3rmPdvNLr0R12p/UBoETj/Wp6e1x78OUL2F1X8SyfvHr4LvUdCeffvpHRYiIEyc6b7Dv86N/MYHQDo75D1MAAAAAAKAYDRgUQ4toOBsWjImykfV/5sSqNL9lWn7PkpjUlmP45daGv5eOGVdXnwkxruH3QbbGaxu77pIYOu+GxtBcIQJmhVpjpQPikpa+d/e+ONDS8exeHteMTFiX925NM7bp8a/zBneNBdCaOrvmtfXk1HbYLSa7X4vNCdfp8qsmFMe4htwYsxOu2V0u/Numa3lPvHegpWtuTzx5XeLn+P3R9Ff8+sfM+27sxOchANC59Y3SgUUwjPIpjc3p2iNwt//9OFT/94suikH5vLekJFqeGCmNmVMbcyBtH/6NGDT9u1FW/0D4t0N89Ic/xO/3vpfz63//7nvx4UcfKxxQcALA3VHFE/Gj9YdzemnZvPqA7IMxc2AnPNb9L8YPlu3M7fvc5PvaPPg89va5jV/CIiJiZzzui1jR+PCDw3HypA6yx4+fipqazjfuL7+sic8+F0qEtnbixIk4dtS5BgAAAAAAFIm9Bxq7txR12C8hSDRrTULHmdExbmIb7nbjisbgcWl5jB8SEROvbAjFVq1e0fWCZAkuGdC/4e9VB/Z17BpLDO4WS/hx48KGcNvihF9cbgyLd0ItqPPQixN+BbxQXZ6L6JpXkPOgIPbF/obB13XYLbLr9KtbDjY+LL8hbi6G8/SSAY0hhaoDsdfndwfZFLelu3lC/WcrAEC7OBSHGrqj9Y1+/Tt+RIP69218kG9H3vaQGBhuVWi6X/RraHN8OA4dbPuhl5Y01ra6+pDl30He23cgDh850uzrDh85Eu/tP6BgQJsQAO6m9q25I6ZMeyYqc35Hbcfc9umSW2AVT8SUaXfHy9W5v6Vs3spYd/uowo6j/Ja4dUTyU5XLnojNeW9oZzw+bVZMadWfluy3m/xn0YF93T4EXFNTE8dPnOx04/7886NR8+WXFjG0oRMnTsSnf/xEITLpXxIlqgAAAAAAAO0rKew3OmZ3hs6lu5fHioSw5YCL23LMiYG2/jF2/OCYdNXouscHY/Ore6yhvNZYa4K7yeHHmXMmtH47BQzNbXh2TV5hvMZQ6cHYX1SJxOQ6jx3f3Pk1If55RuNvzVf8clMnWo853EAgqXtsR5/zid1YI8q/V3ydU3e9WpFwg4aWdHedEI++oCNsMX1+7z3Q+BlYsNB54s01ctluQoi7Y0P4AEDnVxVV7zc+KrtiVDesQUIIOqeOvsmh6ZGjSi2jOonh7UMHqxQki9+9/U78+bPPMv78z3/+LH739i6FAtqMAHBeSqP0oq50PNtiSX0gNI8uuW3VHbetv+yuml93rD9+JXLKAo+YW8DA86hYOG948lM7noklFc6qYnPi5InY+/td8ckfPorTp09FRE23rMNf/tL5AsB//vPnFjC0gZqaiFOnTsWnn/4pPvn4D/FFFw7aV1UfLvAWD0fVfmsIAAAAAABaJtdOkZvi31Y3tl0pnfFsPJpLR90hN8ZLbRDSmvTwlhz2PzguHtD46MB7OQbyWhhETQy0lc54NhY1tP+tiFd3d58VVTpgUJafJodAs63Flgd398STP0voGll+V7yUU+BtQjy6fUlMSthOY4gyh3WQ61rPq8to4hquiveKah0l17l0xqKsXVyHzrshGn8Vamu8trHYV3LyNa/8nsS1keaaNGd6wrymnvPt35E3OWg+PZ57OJfzaXDc/MLz7dONd/fy+JeE+kbp9Hgu18+KiUuicvtdUd62F7Ks4fyk+Y7OtZbb6vN713uNQYbmb7iR61obFAMbCt38TRASu4zn/JkPAJDB5l8l5D5GfDdmDszyrWX6d6OsPQd30UWR+b+8S2Pm1OEF2EliCDqXjr5V8V87Gn83tGTylBib5dVjr7k6h0YwzXRfLp8SU7Nt5GB1Y54lpxBzRElJvyw/HRX/NLlv/v950dBhuH06GndmX3zxZez8zVtx4mTTjMWJkydj55tvxRdffKFQQJvpqQT56Bf9Uj6Iu8ydLiqeiCkVyV8CFq6dm/YLX9nUa2NQxYvRae9Ft//F+MG0F5O/TC69L+2XrJLJU2LsmtZ3yx17e2otd8bjj2zLcbzvx6GIhC+SdV9U9zsj29KRI9Vx5Eh1tz3+nmf1jPIxo6PnmWd2ivGe/uKL+P3v98TpU6ctXqDF9h08HBEJ/whS9w8ruX7nSbwbGgAAAAAA0Fp1Ice634Isv2dJTNq4MDbU/3jikqi86vUoW7Apdi17LipmNIauyu/ZEpXfWxM3XLc8mvTemLgkKu+p64BbtaZNRl5+z5aovCciKu6PsgVNu4lOevjZmNnw2535BA5rw3rlpRH1nSlfTTjGofOej3+NxXHNspRw0e7XYnPV9IR91qr4WZr6dDEbfrk1FpXXzXf5XVH5cDSZk0kPb2kMRWdYi0/+bGvMvCdhOy8Marq+EtdWJhtXxKrvjW6Yi9IZz0bljK2xeGTC2q435MZ4aWV9mG9rvJbwo70HDkaU94/6DrdP7m4mUFY6PZ7bPj0iDsaqWdfHk7vT7Cth7FVbXsu+NoaMi7GN7Sxjb7FNfFKd+8fMlVti7Oo5KefG4Lj5hWeTzouq1SuazkMRSr7mjY5F27fEuHvHxG1J15IJ8WhKGLXpOZ/7dbZgdi+PFRXTG8+58ruicvsN6ddl0jEcjFXtVt/FsWpMwtqoP38yXNOHzns+npvRvw3X8+tRcc/ohPleEpF6zcjl+lP0a7mNPr/3HoiqGB2lEVE6ZlwMXbanmc++2mvGzIioanLdqL92JJxbzd5MY3CMH1OsHdMBgE6pYkdUzhtel03oG1MfejDix3fEqv2J/zBxS6ybN7xdhrNv2xtRPbkuNFtydTy2NOJH85OzJoOmPxiPTS7c71fWNnvpG/UdfVftr8p9jDE8bl17S8S05IxI82PcFtt2zI2yEbWPyuatjIUxK6UhXOYMTpKkfMjwuPX2UbE5IVsy9vaVMepXs2LJr3bGrSPq5nHE3Fh3e8SUlAzK2NtXxq0jWlLFUTGq4X2a3eTi+PHj8evfvhVll/9DnNGjthfnlzU18evfvhXHjx9XIKBNCQDnY+BFkXzfjJ2xrct2cN0WS6ZtS/8lJM8wTPGr7Q68Km0QuABh2/JbmnypqlzWmlBxbl9UoTVOnzodB/YfiEsuubhTjHf/vgPCv0Dxqa4On9YAAAAAANBySeHNupDbosQXVLxe95dNcdusQQlhyUgIPHag8ruicvtdWV+SX+BwT7y65WDMrA+apTnGqtXp35cUYI2IztHptABSAre5zEn67SyMxVclBIVbvL72xJPX3R8Dk4KZadZ2M2o7Wtaug9rOxrl2lGwMtmW2NVYsy769oePLG8+1A/uKMEi+J568e02MTbgm1Iats52Ma+JflnWWzpyb4rZ7r0wKfDbceCCTivtTAsL5XmcLeG1fMCcuTgpf57Iu23n9XDcnIiUg3uLrRwHm+99W3xDlDSHj/K8ZRb2W2/rze/e+aMi513VQzvWa1ex1I3K4mUbiDROKrmM6ANA5bYsly0YkBHz7xtSHVsbUjhrO/hfjhR1XN+YlSq6Ox9Ze3aa7TGz2UtsZt6rZMT6y/vKEgO/wuHXtyrg1z/1ufumVuG5EY4fgsnkrY928ls1hYpg4RsyNdWvnJr2i8lcRUbEuXp46vDHfkuZ1LVY+ojEj5Hddc/anP34a7+zaE5d+Y2hERPz3rt3xpz9+qjBAmztDCXI39pqrIykb2i4fdHUB1A79grizyMbUVqpi1ROvRHXKsfZr1Q0aR8XC1Lvn7Hgm5U4vuXzBS36mZMQVMcgpSRs7sP9gnDxxsujHeeLkyag6cNCEAa13sLrJ94B8vvOUlqTc/e3997vQDVMAAAAAAOiOhs57Piq3b4nK7VuSuxuWTo/n6p6vfOHGGNpWA9i4MBbn+v+v714e14y8Pzr6Pu578/j/LtN3Fsxu17LFsaolv6yy8fXk2lS8XphOp0NujJfq10JigKsu0Fe7fp6Pm4d01IzUBkGbK1nFvWPihtXZ527Dgjk51H5rLL53azOv2RS3jZzTsnlMN5/lV8akrOdGbfgtNxm6ESdJ7GYZUfHLTcV5Adu9PK6ZtSa33+2quD/KrutkHbE3LoyyZtda47UmYxfffK6zhTwvrxvTAfvNf4zNXRfS1roN1lJO1/6K+6Ms1zVfbOdqm35+b4rXGjY+OsZNzD7v7+V+wYyKJp2303yXSrxhQqE+ewEAKp6IH60/3MyLdsbj056JynYYzuZHmt9P9fq7Y0qTXEpLj39H4/5GjIixObxl35o74vEdzQ3ylfjRj1PzJAn2vxiP5FT3u+Pl6tbXLH2+panKZbNyWA/Jxl7RmHGp3vErv+uah4OHqqPq0KGoOngoDh2qVhCgXQgA52rgtXFdShfXNvmg2/9+HEp6orbbay4GTf9ucqfegn1B3NEuX/yKwv5fxfYCfgaPvT2le3LsjMcf2Zb3dqqqU76QlVwd/1TutKRtffHFF7H3veL/Ov/eu+/FF198YcKANvgekPv3sIhRMSr1u2L1ITUFAAAAAIBW2rBgTIaQ28FY9WxqoG1T3DZyTJRlDVcejFWzxkTZyDFtFNK6vnbb2cJMFfdH2cgxeYd/a2ULpWXr3JoYgEpXuy5s9/K4ZmSGsGHdXNy2MffaZ1yPs8ZEWbPh2ZRtjcwegqy4t26tNtlucqBt9rzBWfZVf15kCzPmMf6kbpZF3km6bu4zBmWr1sQNI8dkDscWu40Ls89rjtea/K6zBb6+j8wesq1aPafuHLg+nuyAzqkN1/SswdqtsbjuHLumzbpIZ7v+1O1/QWe+rrft5/eGXzbWrfx72W9cUr8us815/bps/rOjk9wwAQDolPatuSOmZAiaVi6bFVOmPRGb220022LJtAwh1OpX4kfTZsUP1lQVdH+NDdaGx3XTc/s9z82PzIopaUPRh+PlH8+KKfNfbDYfVFv39OHd/OpeW7O0oeTqV+Lf6/+bf/+L8YNMr9vxTEyZNivP5nQRyb/veji2b9P/N1/v7Pp9vLP79wpRdKpi1fxZMWVabuczdCY9Bn/jmzUdseOLBnRE/9JRsXBtYiDzcLz84zti1f583xdRe2eONB/OA6+Npx5K7BSc4XVZjL19Zdw6IuVLT9aLT7rxpT++QdNviX/c9kQOx5xP3Upj5tL7YmpJ8peXXL9IpB5v9fq7C/MFr/yWWBhP5PmFpvljyXm85bfEupTuv/nUpcVrMIe6PNV/XcYaD5r+YDw2uW/TL94+ALulM87oEWVl/1ec9/86ryjH99mfP4vKyv9ffPlljckC0nwPy/W7Xmu+h2X63M9/3y31/oGu8wm9553fWMcAAAAAQFEZ/I1vKgJdw5Ab46X6Dr1Va+KGztbttDOZuCQq7xkdES3r8tyiOc2pc29hTHp4Syyqu2l9mx4fQOG+0cXNLzwbM+tyIbl07i3054HPXgDoHjomI9NNJf2+aAtzFd1UUl5kxzMxpQUN7ihuhfq9Ytc0c0f7zFcuunkH4L4x9aGVsW7tLTE2wyvG3r4y1qUJ11Yua7svCJt/tTP5iZKr47G0YxwVC9emH19zx7wwn+6xAy+KfomPq9+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oczLNxsBbCQAAgLSTqQTUxNQZs+K1N96JM04/ucH28bEnx8XyFasa9DgWFRXFy6++Ee++NynOOev0OOXEUeWGb+tK48aN41OnnhSf/tQpkZOdXeY2W7duixdfeT3efvf92LlzZ72uea+ePdxAoJp2Fe+qv23fucsAAgAAAAB1oq7+JveE2w8M/656/pfx7QOCvbuDwhembNvpvG/Gl1fcGf/7fsXtPei/KV42Lr59+7h6NT61P17T4556Htg81H9b33AMi+FDSh6unhYTllVQu9UvxQ/KDGEOi6/88bp9K7XGoLjlR6NjWVoFNhvGfbq8sVny1M/ixqcaUm+7xOUXDtr73ZT/uz/eTav2TY7/vX1ytcauLl+XajNb8+4f70yzWtNgLRsXf3h+cPzovA67XzduHxbv/nHyIW9Gnb9fWPhK/LysEGW7EXHrtUOj7Z7v2wyNmy7aUPa21MkYz3rmnrDMS4p+R0ffPY83TIn7n/mgQb6fTh6C19JDfd9JHqZ++O8yAACA+ksAmBob+9wLsStZHGd96tQG1a9kMhmPPTku3n1/0hEzllu2boknxz4Xb779blx43lmRN2RQnZ8zIyMjRg3Pi/PP+XS0bNmizG2KinbGm2+Pj5dfeyu2FRamfR0Lt2+vN2NeH+oJe6xatabetn3lmjUGEAAAAACoG8k6+LPckTfHrUNSvl/9Yvzg++UFApfH49//ajzebXT8x/fOiU4lzw698dtx+fKfxePLym347rYfSX9VnKyl8dr/GMlkw5rDSX9qXrXrdMje4O6q/PxYekAdy7jeDjAp/ve2SdH9ku/Ej84vSfF3PDsuGfFMmQF+zOnK5+X5+z4QYvWL8dSEetb3ZB2/xlbpuoT0t/TJ52PKeSUfIDHk3Li866QK3vMdqgv2EJ1j3ftxz/+8H8dddFuM6VPyXJ9hcXq7hfHGOnOjFt80K0MVdWibu/fx+gULY02Dq93+8yF5mO4Fh2IJ4HR+UwQAAEA6yFACavyf+slkjHvuxbj/wYdi7br1DaJPS5cuj7v+cF+8896R+VvN9RsK4s8PPRq/uusPsXDhojo5RyKRiIEDjo1vfv0rcdUVY8oM/yaTyZgwMT/+4z9/Hc/886V6E1Zdv6Gg3oz1uvUb3MSoN6bNmBVbt26rd+0uLi6OiZPyDSAAAAAAUCeStf5veHz5ppQPiS0J/y6pbL9l4+Lff/JirNq7Y8e48KLhB2xX9+1Pr3911d+GVMcjbU7U1r/uXfamLGPypOUHVdslTz0Qz67et93QYcPV2Lyuwb+ucfnofa8f+eOq8NqR5mNnbvjnX3n/JsVT/1xd4Xu+urpmDtkFWsm2s8e+EvP2btw6+vVtY2K4OardoepTNKAX7HQau3SeRwAAAESEFYCpBTNmzYnZc+fFccf2i+OP6x9H9egerVq2iCY5OWnf9q1bt8XGTZviww8/immzZsf8BR9E0ietxpJlK+J39/4p+vc9Ji668Jzo0qljrRy351HdY8yF50Wvnj3K3Wb23Pkx7rkXY8Wq1fWubrPnzIvLx1xYL9o6Z94CNy/qjY2bNscDf304rvnsFdGyRfN60eYdO3bEY0+OiyXLVhhAAAAAAKBu1PavtEYNiby936yOZ+8eF0uqeo6l4+IP/xy8byXRIUPixOSkeLe89u79Y97h8ZV7rks572759381/nfC/icpe9vyt0/t283xp9Rw8x6r9oWcK1e185/4xbtKr6IcEZ3O/2b86fz995oe99x6X+kaVXfMa2MOdBsdP//+vhWcq9W+cvetuL49Lk1ZdXaPIdfFn+6+br8tV8ezP/5ZPLasa1zx42/GhZ2qNt6pY3DAtqltLtW+0uNb7jkOci6V2fdqzYeuMXJoyf6rpsX7S6swZyqcJ8tj+cqI6HjgvEpt66p//jL+/cnlZdSgvHaXHrMDx7T8/u3bL3Xb6twrqln7qX+OL/x+UuVzvKL58sSL0eWy/a+FQXHr3XfFrftPlT21TK1jpfOnvHpWtQbl379KjW2NXz9SVv9d9WI8PaGq+5VzPVU6T2ryulC9+0h5183+9/h99UttS0r7y+pjx3PiR3efU8OxrGRuHzCfU9uVcr2W0a4yz1/Wfb6iaybKfy2scV/Lea3ZfYyU/tX5dVRLyruvlNXPqr5Pqfa1VM682K9G5d0fljz5QuSfX7L/kHPjiq6TqnC9pu9b4Oq/xdoQ6zZE9Guz//Zt4vQbroyT20REfBzv/PnhvSsDD7jothhzTMnGC16J/3hmYRnH7ROXf/3M6HfA84vi6V+9GLPKbU/qfinbthsRt12XF21LbVu6XRVL7U9p8567Ox6fW85u/c+Jb1/Q68C+lmrPx/HOmwVx8mm99jtlXtz09bxyz9fh5Kvi5lGtK6ljZfWsYg3KrF8V+l9t5dW5/HaWqkOJtqOujH8fVXq79RMejbvf2VAHbavKsWs4fyp5K5s2awBXMD923yby474HJsaaat2Dyq5ZtcYx9fqrYXvK/8+H8q6p2phrAAAAVEYAmFqxa1dxzJg5J2bMnKMYDUQymYw58+bH3PkLYuSwoXH+uWdG61Yta3SsDu3bxUXnnxMDjz+23G2WLFsRY597vs5WHj4U1m8oiCnTZsbQwcendTs3b9ka7x+hq5JuLyqKhR9Uf47tKCqq0nbFu3bV6Ph79q2KDxYvicZZWTXqe302f+EH8fP//E30PaZ3dOzQPjIyElXed9OmTQd1L3z+pVersUci1q1fHwsWfhifbNwUAAAAAAB1JVnLf+584rCU4MiqaTFhWfXO8NGTz0f++deXhEkGxfBRyRg/oezWJiMZJ3zxf+O2IWUfK++mu+JPF74Y3//+Mymhl2T1q9HtovhFRQHVTufEj+45J2Lqg3FDuWGmrnHFj78VozuVf/a8m+6KP90Useqfv4inqzmGyYOcAzXff3h89Z7rywzORckY3nrPXXHrqv3HoWS+VDB++9d31T9/Ed9KCRAlq93H/fuZrGQ2VGfb8ubifvsd7FyqbP899Y6I/Pu/EneVF0TrNjSG7QlmTcmPj6pQzcrmybKVqyOGdDxg6wPrOKzMObP/8SueGx3jwu/fFRfG9Lj71nvLCA7vf6/oEpdXcP2Vfa/Yz6hb4oEyg3ElhlwXf7rnugPmafXmy8FfueWNU2XX2u77z+oY9+OflhnAq2z/vR9QsKqSOlbx9SP/2Wfio4O+/+yZJ1HOuFTvvrx7/+rcR8oejR6Xfjcl1Lp/zZO1cH+u2T294jPv/9Pyr6n9r6dy586Q6+JP9wwp+xqu9F6XOj7l3QeqNk/2jPGhuI7q9v1cBfe5kteW8l8XanotHThrul/63fjxAR+SUF5FJ8bEqddF3pDd5xg2oks8umz5ISxass5HpeJzrI916yOizf7bJw9sZ79z49sX9qrk+G3i9C98psyg5G69YszXb4sxG/Ljvj+9X0aAb//z9o7Lv3FWmSG9iNZx8nW3xckLXo7/GFt+eHbAxbfvCyyXod8Ft8W3L/g43nnwHwcGVJP79zW3kv5VccyTVR2nyupZUoNYFE//9wtlBKvbVNre3f2PiErqWJmK61xZO2tjLkeN50DbUVfGt0eVXYPamz/7X+/JKsyL2rkFVHTPqax/u6dRXtz89bxYP+GRuPvtDZXO7w4nX31AqLt0rSuZB+1Gxm3XVxBILmlP2XO28rpWeT6Ue58CAADgYAkAAxVKJpMxYVJ+5E+dHqefcmKc9enTIycnu8r7jxg2JM4/59ORSJQd1lu/oSDG/fPFmDp9VoNYffnJsc9Gr549ahyWPhTj+dAjj8f27TuOyPlcUPBx/Pbu/6uz4xcVFdXp8SMi7vm/vxyx96NthYUxbcasQ37NPP/Sa14MAAAAAID0U6u/VhkeI4bs+27VlPyqr/6718pYsSoiryTAkpc3POK9skK1HWP09/+38sN1Oid+fMfKCoK5+9Vi//aOuiUeuHlPKK0k1LO0dJ+/em9JaGbI9fHAHVHGuVK2qYtxSR6mOdC9dEAr/779g0Vd44qflISROp0TP/5xxPe/t18wsBrn7nT+t+IX8Yv41hPLa6efla1qm6zqtoPji/eUE1RL3e9g51L3qgXiqtLmHiMG7z3OiuXLy96umkujde3UsfJzD/1CPFDmysWp26fMm0oNitvu+W50PaCW+98rvnVQ94oel6UG2qbH3bfsFzZMGZvK52kF86Umc7rScdqvnlMODJfv61/HGP3970bsV88TqxtWrtE9JfX1Y3pMfK/y7Q/uvlpL9+WKlhQsY3xKz6Wy7gPVu5ZrZftqH2NQ3HbPoEqvpy9emh9Pd/pWJXNnUNx2x/B4tyqv0RXeB26JuKWsEHANxrmOrqM61emc+PE951S6Wd5Nt8SJ7+1fp9p6j9Ixxvz4f6NTp+rdF96dND1uG7J7PnUamhc9nlheow8QqJ/aRLu2lW/V/6LbqxB67VNBWHf/0+bFzd/IrSQM2ivGfKNX5cc65qy47ZQNZQQT9wu/lhEW7HDKnqBi6zj5+qsjygpx7tF2eNz2jdbR9pCNTel6lhW+3Bdk7BVjvnFuRKl6tjn4sHIV51DVz9Mrxnzj6mhXUZ0PW9uibudPA9B21Gfitqg4BNzvwturcA/oFWO+MDLWI31ACwAAZAZJREFUlhWu7Z/6QQNlBatTrotjzopvXxzVCq5XKfAMAABAnRMABqqkaOfOePn1t+LdiZPj7DNOi1NPPiEaNWpU6X7ty/m/3lu2bI0XXnkt3nl3Yuyq4sqn9cHGTZvjd3f/X9zyhWuiQ/t2adW2nTt3xkMPPxGz58w3oQEAAAAAoJ6r1RWAu3eOznu/WR2TJi6rwdGXxYQpq2P0noBU587RPZIlYZByjjblgbi+VBCna1z5k3/fF9QZen18ddTE+M2EiIiJ8ZtbJpa93QHVGB537g1sTou7b7k3xh9w8onxm1tW7DvO0PPiiu4T49GlqW3ZL1iz6sX43vfGlg64jLolHrx58N5WjP/9l2N8RJz0xd/GbUN3b7Lynz+vefj1gHFPfVSTWdA1rrx1T4hxdYz78U9S+rxvLB/93pdj+Z4+dDonLhk1tmQcSsu/78tlPh8xPO6894a99es0NC+6P7EslkTER0/8JK5/IqLHZd+Ln+yZLwfMhbJ7Xf3Znyw/Z9mpYyV1qJ25dNJFqaHRss9Vqhbl9q9rjBzacW9bJk4ob7vqrEQ6PEYM3ffdylUrylwBuNOekHBZ18DePnyhdPi3jG1L97NjjL71ophQapuyW3rgNVTRvWLPfe3iuOP8Stq9dGx885YVe+dqp/PPjxOfKD3GVZ4v/xwb0f3i+MX3zym5n5Y3X6o2Tqn1LO8e8tETP4nrl++5B3WM0RcNi0f3XEfdL46Lh1Z0vz3wOq3RqrWprx+rVsWySo7R47LzSt1Xy7yH7Hdf3XfEmt2Xk+UkrpPljkfpSvS47Hv7hX/Lul+Uc/wJ98T1E0q3qaLr6OBeF8oaxbJHo3TdS19Pnc7/VtxWzrWX+tp24OtmyhkrvFek3gcGxYhRyRi/3xw46Yv7jXOZ83f/9wJ1cB3V2Xu4Kryeps6ZGBQXX9Ylxqe0/+CupdQWdNwb/i3/Nb0ME/Ij/+ZBu9vQqWN0jWQVVv+uu/rV9jErPEe7PtE/JRy5bt2GMrZvHW1Ltlk/4ZH4YznhvwEX7xf+XfBy/KyM1VQv2Ru+6xVjLu4TM6sQ3pv37B/jsbmpz/SJK1LCsW1HnROnz/1HvJ4SFOxwyjl7w5vltXv12/+In607N75zYa+IaB0nn9QnXi+vPW32hH8XxVNlBJdfnxgR/fccKyI25Me9FaweWtk4pdbzwP7vNnPsH2PNKVfHLaNaR0SvOOWUNjFzTz/7jygVfC2zBu1Gxu0pK63WZD6m1rm8fnfY28bd8+nki0bG7JJtVr/9j/jZ26W3qWieVceAi/cP/5YxduXUoNbnT1Tvc3/q8p5T1itreXPsgGvtmD7R/u3S41tmu8uYB6Wu/zZ5cekpC/era5+4Ym/4t+zrLGJhPPbfG+JTe8LZxwyP09stLHXtl1vXdiPjlGMqvkft39/kIRoXAACAI40AMFAtW7ZsjafGPR9vvjMhLjjvzBg+dHC19i/auTNef3N8vPL6m1FYuL1B1mjtuvXxn7/+fZx28gkxcnhedOxweIPA2woLY/rM2fHiy6/H+g0FJjEAAAAAADQEyTo8Vk3/areq6YlVL8b3vltWMGh5PPrdn0f8dF+gJ29YeSsJl3+uk760J9C2Osb96N4YX25flsej46bF6L2hn+Hx6O9KznXCBaVCRSufKyfE+969cf17EXHCLfGLLlG1lSZrZZwGx233/nZvSKtCUx6I60v61eOyG/f2K/++n8SjFSTQxo99MS4eujvMmDf64ujx3r4xG/+7L1cQbIyImBS/uTnizvtKxqLT4BjVbWwsWVqD+XKw87+iLFxKbcpy8HMpNWC7Osb9qOyg8ZLHfxLXP747GHdpef3rPiyG7V3FckoFbalqrbrGlT+9ISVANi3GPr683P3y7/ty/Ka81V27XxxfTFkhuLzrZcnjP4nr3784fvGDkpBsp3Pi0lFjyz9uueNT2b1id9B9bxD3uxWFLSfFk8+dH3kXdIyIwXHxpV33heyqOV9qbU6n1nPKA/Gtxyv4AIH3notxoweXhM/Pjyu7TYpHl0b0GD54XzC33HZPit/cvHue3vnTLjW7T3XpuPc8K/MnV7J6fNcYNbRj5XOqvPtqbd6XKxufvee8JSWsWv41XO37fl2/hkd1rqnl8ejdL8awPdflnuvm5gMD7ON/9/Pouve66xhdukSUuriWjI1v3Ty2wuYuefwn8b3k9+InF3Qs+3X+hFv2hYwrvPeU0e5avo4Oyfu38q7P9+6N73XZV6fOQ4dFj8f3rLJ7kNdSld+XVfw6P3HKDZE3NCKiU3TtFhGHauXkZLKOj5ms4Bx94oqU4GNsmBxvzilv2w/jqf+qYLXe/uemBHsj5o37Q5lhwllP/yFm9T83vjP66N1PHDMsPtVuQby+tuxJtf69R+KPb68v44QL4rH/SsYV/3p2SVCvdfTv1yZeX1uybfuRcdmewOmCl+KPb60vv15zJsY7J/YqCRPu155kVdtT3dpXsm1KPde/90g8Nqf846x5a2LMG7W7Dm1HDY8Bb+0epwF9e1Xe7rUT4o//NSGi/ci4/aRk9edjap0rOM+at/4eP5s7Mm6/ftju+dYmL07rN6H0HEk9d7IWro32I+PUUmHPl+JnTy+sWg3qZP5UYT4k45DfD2Y9/YcKVuEu41pr0yuOazch1qyNat8rZj39h4gxd+yd222P6R0d3lq/NyQ8YMye4G1BvPPg8zGr3Hqsj9fHfxgnjz46dgeve8fre8c2eeAbmJKnOvTrte9+V958iAXx2H8t2H1/vLFN3dyjAQAAEAAGamZDQUH89e+Pxetvjo+LLzwn+vbpXeH2yWQyJkzMj3+++Ep8snFTg69PUVFRvPL6W/HK629Fs2ZNo1XLFpFZhRWTa1MyIjZv2RKffLIpiouLTVoAAAAAAGhAanUF4K4dU0I/q2LZ0pod/aMVqyKiJJBSajW41KOtjnF3P13BKnHL4pG9QcqIGDo0ToyJ+4WQKlrvcEQM3xMcWjU13qusL8tXxcooCcqlrFp80rCUD8Gd8kB884llFXf+vXvim1Vs4cGM+sHNl64xKm/fCrLvv1dJq5auiBURJUHNmqzutyJWrIrIKwmKde6ajOTS8nuTrGJKr7JqJivcNuW7VS/Ed383sYJj1c5cSlbjyv3oiR/Hr8v5WY8R+wKdkydPrOJsSJZdrxNujT/fXPqDnlc+92y8s98a0/t+9h/x6/fKP2Nq22LVC/G7JypYRXzp0/H0lHPi9pLa5g0bHsn3JpZx1tUxbmxF/azgXtE9r1RY+p1KqpV67+rcuXMkY1kN5svBXPmlx6l6Y70slq2MiP2us+qtmz0xfv3dmt1benTZl8hdsWJZ7dZnv/vqwd2Xq3sfSe53nayOcT/6cTyytDq9q+Z1WUuvDOXf9abFH8ubw0snxeRV55QEe1fHuB/dE+9UOt8iOnfpEsmo/gr3pd4vpNwvyxrniu49Zd3xa/M6qqt3cFW9r3w0cWqsvODcMl6HD+5aOmBefPfpGq3eu3Tl6oihHQ9BzWrr3VB1rqMDdTjl6rj1hNxSz80d/36sLnO/gnj7wRdiZgXnHNDv6H3fLHgpHp1bwcZzJ8XbJx0dp7SJiMiN/v3axmtr15dx3g/jzbfXV1CjhfHGe8OjX0k/2vbtHe3f3h0m7NDv6L1Bv7nzFlZS5/WxZn1ElLSnbduIZFnhxgUvxR8qCv+WMxbJGozTvnoWxJx56ytpf0Gs3RDRr6T97drvbn+1Psdj7fvxh6erP9dS6xwbJsfjFY3X2vfjzQXD4tKS8Ge/fn0iOXdhtebtwbTtnqcXVrkGdTJ/ovzVdyubK7V9f6j+eUrPsf37mHq8ueMqvlfMHD85Tj1mTxD86Di2/fuxem1ERJ/ovyewveHDmLW2knauL4j1EbuP07ZNtI/YGyQur67Vm2ML49H/8/9pAAAA6sohDgAnIyKh6rVWS+NiXA6/ZctXxB/ufTD6HdM7Lrrg3Oia8gu+PWbNnhfP/PPFWLV6zRE5Llu2bI0tW7a6PFwvxoU0GRcAAAAAoEE4XKsH1sZxKvsr6nenxOSbB8ewiIjoFN0qW1Uu9XgnDC3ZLyJWrqxkNcqIWLIyJeTaKbolI5bEiJRVWyMmT5p48PVOHuYxT0bpFWRXrYpllR5vRaxcFSWBqMrGYUTcef8N+2pfk3Gvzoq2Nd22OnO9VuZSag07xuiLR8Qjv51YgwHsGiekhLcnvlvFOnY6N35637mVH37KA/HNx5fXeP5165yy+m+lq8BGjH/6hRgztCTUNnRonJScWPaK0jW8V/QYMWTfqrQrV1Q+X5aVDm/3SJYsalrde2N1ti9329SxXh0rl1V+3mV7Q3gRXbp0jUgujyXLUwKWQ8+Pz3SbWGF4tab2jX1V2rq8VHh02OiLo8e7VV11tJbvy5XdG7pdHL9MDf/+8MfVq19N7z3dL45f/vDclA/lKMOqF+K736mgbgd9n+0Ynfdf2bec+VaVY/a4/Pvx0ws6VvHaqeY41/F1dEjev9WofwdzLdXe+4vU+0yd1uxQO+bs+O6/nl3pZuvfe6TMFXurpm20b7vvu7nzFlZ2tnh9/IdxSskqwG379o4Ob+9bBbQ61sz7MNafsCdMmFsSAmwbx/XdE24uiHVVyOyuXV8Qcczufdq3axsR6w/joPWJY/euXFsQa9dWOnqxNiWA2q5tRKwt3ae2JwyPAW+/UMlKr9XXvu2+EPn6+R9UOoazxk+O0/aEP4/pHQNiYa23ac+c3DcHqta2svetj/Pn4OffFf96dvSvq8Ov/SDmbBi29wMA9szX6N973znXb6h8vNZuiLUR+137ldwv1hVERMnYHjM8PtV+Ycrq4wAAABxKhzQAXLRjR2Q1zlb1WqqlcTEu6SKZTMbc+Qtj3oIPYsigAZE3eFA0b94s1q5fH+9NmBwfLv7IuGBcjAtpMi4AAAAAQMNQqysAp65cGp2ia/eareLWo2vKB8WuWhVLU1pZ8aqs+9u9cuywClbiK2+lxVJtGHpD/Pn+G6pf2e6do8ve76fFxPeStZD/re0PepwWf7zp7nJWaCxH1077QmWdzo2f3n9utftQqhfdx8T/qyyoVkEVqrN+YbLG1ax8lc9kVeZzTedSLIt381fH6D3ht5TjTL73i/Hr96p6rM7Ree+KtvmVrmhbHSuf+4/4/x5fVknNK6pkt+iaMglWLF9WeetSV5fe755TG/eK1EBy5wu+HX++IA5urle5PTW/8vcdO2Wso2OM/uHvYnRN7jfv5cfkW/aEo1OOs+qF+M53nq55ULCG19Me70yaFrcPHXzAfai8ebjvfnPw9+Uq30c6nx+//GHHkvm5Op754Y+qEP6t/grDyVqqdcVnPpi11qu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+ "text/html": [ + "\"\"" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nbscreenshot(viewer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "114ae356", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "napari-skimage-regionprops-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + }, + "vscode": { + "interpreter": { + "hash": "89194614874d20ddf8746d6fd6c19366af8b6d367ee602507dbc652a381a6ce7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/demo/measure_things_inside_things_functions.ipynb b/demo/measure_things_inside_things_functions.ipynb deleted file mode 100644 index 0e8fd4b..0000000 --- a/demo/measure_things_inside_things_functions.ipynb +++ /dev/null @@ -1,1471 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "46649d66-03c2-4f41-ad06-a5b5c5159b57", - "metadata": {}, - "source": [ - "# Measure Things Inside Things by Code" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c2ad93cc-d06e-4bd5-bfda-1ae52e07249b", - "metadata": {}, - "source": [ - "This notebook shows how to measure features of segmentated objects arising from multichannel images. It shows the similar steps done via the \"Regionprops Measure Things Inside Things\" plugin module, but here these are applied by calling different functions. \n", - "\n", - "It shows how to do that in the following ways (the first 2 are a wrapper arounf regular \"Regionprops\" plugin module):\n", - "\n", - "1. **measure_labels**: Get features from on one segmented image (it needs one label image);\n", - " \n", - "2. **measure_labels_with_intensity**: Get features (including intensity) from one segmented image and one intensity image (it needs one label image and one intensity image);\n", - "\n", - "3. **link_two_label_images**: Get which objects from one segmented image \"belong\" or \"are inside\" another reference image, returning a relationship table;\n", - " \n", - "4. **measure_labels_in_labels**: Get features from two segmented images, relating objects from one image to another image (it needs two label images, one being a reference), returning a (list of) relationship table(s) with features;\n", - " \n", - "5. **measure_labels_in_labels_with_intensity**: Get features (including intensity features) from two segmented images and two intensity images (it needs two label images and two intensity images), returning a (list of) relationship table(s) with features;\n", - "\n", - "6. **measure_summary_statistics**: Transforms a (list of) relationship table(s) to return a single summary statistics table instead of individual relationships.\n", - "\n", - "We start by loading the same cropped images from the [BBBC007 dataset](https://bbbc.broadinstitute.org/BBBC007): two label images and two intensity images." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b2aa6b54-0cb3-4550-b9c4-37796d455c28", - "metadata": {}, - "outputs": [], - "source": [ - "from skimage import io\n", - "import numpy as np\n", - "import pandas as pd\n", - "import napari\n", - "from napari.utils import nbscreenshot\n", - "from napari_skimage_regionprops import measure_labels, measure_labels_with_intensity\n", - "from napari_skimage_regionprops import measure_labels_in_labels, measure_labels_in_labels_with_intensity\n", - "from napari_skimage_regionprops import link_two_label_images, make_summary_table\n", - "from napari_skimage_regionprops import regionprops_table" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "60af03d0-e028-4b52-9ead-d0001e3d29fc", - "metadata": {}, - "outputs": [], - "source": [ - "image_channel1 = io.imread('../data/actin.tif')\n", - "image_channel2 = io.imread('../data/dna.tif')\n", - "cell_labels = io.imread('../data/cell_labels.tif')\n", - "dna_labels = io.imread('../data/dna_labels.tif')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cdab0f3-1e65-479a-b7c8-df460265d443", - "metadata": {}, - "outputs": [], - "source": [ - "viewer = napari.Viewer()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a4f84587-009a-4f7b-86da-c9649091e717", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "viewer.add_image(image_channel1, name = 'cell', colormap='green')\n", - "viewer.add_image(image_channel2, name = 'dna', colormap='magenta', blending='additive')\n", - "cell_labels_layer = viewer.add_labels(cell_labels)\n", - "cell_labels_layer.contour = 1\n", - "dna_labels_layer = viewer.add_labels(dna_labels)\n", - "nbscreenshot(viewer, canvas_only=True)" - ] - }, - { - "cell_type": "markdown", - "id": "8ed80a5b-67fd-40fd-8ccd-8c46682dd5c1", - "metadata": {}, - "source": [ - "## 1. Measure Lables" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "872e57d6-c438-42cf-9a3d-e65454206e10", - "metadata": {}, - "source": [ - "To measure cell size features from one label image, we can use the function `measure_labels` providing the labeled image as an argument. By doing that, we get a table with size and shape properties." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5143dd10-6885-48c0-9ed0-8a93146d85fe", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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labelareabbox_areaequivalent_diameterconvex_area
0144264823.722813461
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" - ], - "text/plain": [ - " label area bbox_area equivalent_diameter convex_area\n", - "0 1 442 648 23.722813 461\n", - "1 2 910 1240 34.038919 972\n", - "2 3 1407 1892 42.325501 1452\n", - "3 4 911 1320 34.057616 941\n", - "4 5 1185 1680 38.843131 1231\n", - "5 6 1803 2400 47.912951 1919" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements = measure_labels(label_image_reference=cell_labels)\n", - "measurements" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0e994fab", - "metadata": {}, - "source": [ - "Extra properties can be provided to get other features like `shape`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "cca15459", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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labelareabbox_areaequivalent_diameterconvex_areaextentlocal_centroid-0local_centroid-1solidityferet_diameter_maxmajor_axis_lengthminor_axis_lengthorientationeccentricityaspect_ratioroundnesscircularity
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" - ], - "text/plain": [ - " label area bbox_area equivalent_diameter convex_area extent \\\n", - "0 1 442 648 23.722813 461 0.682099 \n", - "1 2 910 1240 34.038919 972 0.733871 \n", - "2 3 1407 1892 42.325501 1452 0.743658 \n", - "3 4 911 1320 34.057616 941 0.690152 \n", - "4 5 1185 1680 38.843131 1231 0.705357 \n", - "5 6 1803 2400 47.912951 1919 0.751250 \n", - "\n", - " local_centroid-0 local_centroid-1 solidity feret_diameter_max \\\n", - "0 12.248869 11.158371 0.958785 28.792360 \n", - "1 15.034066 21.548352 0.936214 40.607881 \n", - "2 21.185501 21.783937 0.969008 49.040799 \n", - "3 15.396268 18.670692 0.968119 42.154478 \n", - "4 22.182278 20.661603 0.962632 45.486262 \n", - "5 23.469773 24.153078 0.939552 53.851648 \n", - "\n", - " major_axis_length minor_axis_length orientation eccentricity \\\n", - "0 26.534396 21.946451 1.114467 0.562063 \n", - "1 38.480267 30.581397 1.219138 0.606964 \n", - "2 47.789939 37.707490 0.739053 0.614360 \n", - "3 41.248159 28.712921 1.186024 0.717943 \n", - "4 41.268687 37.257503 -0.896798 0.430054 \n", - "5 52.248724 44.392437 0.893456 0.527368 \n", - "\n", - " aspect_ratio roundness circularity \n", - "0 1.209052 0.799308 0.884261 \n", - "1 1.258290 0.782484 0.820115 \n", - "2 1.267386 0.784389 0.863983 \n", - "3 1.436571 0.681741 0.856081 \n", - "4 1.107661 0.885905 0.833792 \n", - "5 1.176974 0.840920 0.770883 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements = measure_labels(label_image_reference=cell_labels, shape=True)\n", - "measurements" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "79a08c47-8a7a-4e62-9d72-d3cf38cd9c9d", - "metadata": {}, - "source": [ - "## 2. Measure cell intensity features\n", - "\n", - "To also include intensity features, we use the `measure_labels_with_intensity` function, which needs a label image and an intensity single-channel image." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "b0631c10-2a51-4514-b299-b57a74b2ba75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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labelareabbox_areaequivalent_diameterconvex_areamax_intensitymean_intensitymin_intensitystandard_deviation_intensity
0144264823.72281346164.023.9886889.010.084142
12910124034.03891997296.042.54285716.014.954136
231407189242.3255011452128.072.48045524.016.704404
34911132034.057616941105.045.80351314.016.341103
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561803240047.9129511919136.056.30116516.024.383326
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" - ], - "text/plain": [ - " label area bbox_area equivalent_diameter convex_area max_intensity \\\n", - "0 1 442 648 23.722813 461 64.0 \n", - "1 2 910 1240 34.038919 972 96.0 \n", - "2 3 1407 1892 42.325501 1452 128.0 \n", - "3 4 911 1320 34.057616 941 105.0 \n", - "4 5 1185 1680 38.843131 1231 135.0 \n", - "5 6 1803 2400 47.912951 1919 136.0 \n", - "\n", - " mean_intensity min_intensity standard_deviation_intensity \n", - "0 23.988688 9.0 10.084142 \n", - "1 42.542857 16.0 14.954136 \n", - "2 72.480455 24.0 16.704404 \n", - "3 45.803513 14.0 16.341103 \n", - "4 51.278481 20.0 19.006819 \n", - "5 56.301165 16.0 24.383326 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements_with_intensity = measure_labels_with_intensity(\n", - " label_image_reference=cell_labels,\n", - " intensity_image_reference=image_channel1\n", - ")\n", - "measurements_with_intensity" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "d0e803bc-8ccc-4144-b603-3d387240559c", - "metadata": {}, - "source": [ - "## 3. Link each cell to objects in another channel" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a282dc12", - "metadata": {}, - "source": [ - "To get the relationship of each labeled object in one label image (reference) to other labeled objects in a second (target) label image, we can use the `link_two_label_images` function." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "4e52d594-4768-4a30-8f9f-f0462162bc65", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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label_referencelabel_target
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" - ], - "text/plain": [ - " label_reference label_target\n", - "0 0 8\n", - "1 1 7\n", - "2 2 6\n", - "3 3 5\n", - "4 4 4\n", - "5 5 3\n", - "6 6 1\n", - "7 6 2" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "labels_relationship = link_two_label_images(\n", - " label_image_reference=cell_labels,\n", - " labels_to_measure=dna_labels\n", - ")\n", - "labels_relationship" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a89c68c9", - "metadata": {}, - "source": [ - "This tells us the object with label `8` from the DNA labels 'belongs' to the background of the cell labels (label `0`).\n", - "\n", - "Also, that the object `7` from the DNA labels 'belongs' to the cell with label `1`.\n", - "\n", - "Finally, cell label `6` appears twice because it contains DNA labels `1` and `2`.\n", - "\n", - "To understand in more detail how an object is considered to belong to another, please refer to the [´intersection_over_target_area_explained´ notebook](https://github.com/haesleinhuepf/napari-skimage-regionprops/tree/master/demo/intersection_over_target_area_explained.ipynb)." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "bef36a5b-b5f1-431c-99aa-4d11ee98fe54", - "metadata": {}, - "source": [ - "## 4. Link each cell with its features to objects in another channel, also with their features\n", - "\n", - "To get not only the label relationships shown above, but also to get the corresponding properties in a same table, we can use the `measure_labels_in_labels` function. We can optionally provide `suffixes` to better identify the sources, otherwise '_reference' and increasing numbers will be used.\n", - "\n", - "*Notice that the return output of this function is a list of tables (each relationship of a channel to the reference yields a new table with possibly different shapes).*\n", - "*In this example, we get the single element of the list `[0]` since we have a single extra channel besides the reference.*" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "dacc2b95-9b45-4d62-9e89-7dea1a9781a0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:87: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", - " output_table = pd.merge(table_reference_labels_properties,\n" - ] - }, - { - "data": { - "text/html": [ - "
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label_cellsarea_cellsbbox_area_cellsequivalent_diameter_cellsconvex_area_cellslabel_dnaarea_dnabbox_area_dnaequivalent_diameter_dnaconvex_area_dna
01442.0648.023.722813461.0713015612.865502135
12910.01240.034.038919972.0628335718.982276293
231407.01892.042.3255011452.0521043716.351768271
34911.01320.034.057616941.0427534218.712052284
451185.01680.038.8431311231.0332946220.466944345
561803.02400.047.9129511919.0141355122.931374437
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70NaNNaNNaNNaN866889.16699670
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" - ], - "text/plain": [ - " label_cells area_cells bbox_area_cells equivalent_diameter_cells \\\n", - "0 1 442.0 648.0 23.722813 \n", - "1 2 910.0 1240.0 34.038919 \n", - "2 3 1407.0 1892.0 42.325501 \n", - "3 4 911.0 1320.0 34.057616 \n", - "4 5 1185.0 1680.0 38.843131 \n", - "5 6 1803.0 2400.0 47.912951 \n", - "6 6 1803.0 2400.0 47.912951 \n", - "7 0 NaN NaN NaN \n", - "\n", - " convex_area_cells label_dna area_dna bbox_area_dna \\\n", - "0 461.0 7 130 156 \n", - "1 972.0 6 283 357 \n", - "2 1452.0 5 210 437 \n", - "3 941.0 4 275 342 \n", - "4 1231.0 3 329 462 \n", - "5 1919.0 1 413 551 \n", - "6 1919.0 2 28 36 \n", - "7 NaN 8 66 88 \n", - "\n", - " equivalent_diameter_dna convex_area_dna \n", - "0 12.865502 135 \n", - "1 18.982276 293 \n", - "2 16.351768 271 \n", - "3 18.712052 284 \n", - "4 20.466944 345 \n", - "5 22.931374 437 \n", - "6 5.970821 29 \n", - "7 9.166996 70 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements_linked = measure_labels_in_labels(label_image_reference=cell_labels,\n", - " labels_to_measure=dna_labels,\n", - " suffixes=['_cells', '_dna'])\n", - "measurements_linked[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "a61f5f53", - "metadata": {}, - "source": [ - "*Currently, background feature calculation is not implemented, so NaN are returned for it.*\n", - "\n", - "Similarly to the ouput of `link_two_label_images`, this table provides direct relationships, so some labels may show up duplicated. To have unique labels in the first column, we should later apply the `make_summary_table` function explained in the last section of this notebook." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "6fff90c6-c411-4b8c-96ea-4a0d99d81cd4", - "metadata": {}, - "source": [ - "## 5. Link each cell with its features (including intensity features) to objects in another channel, also with their features\n", - "\n", - "To also include intensity features, we use the function `measure_labels_in_labels_with_intensity`, which needs the corresponding intensity images as extra parameters.\n", - "\n", - "*Similarly, this funtion also returns a list of tables.*" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "6c60bf3b-2e13-4b3f-8ad5-93c240b39baf", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:87: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", - " output_table = pd.merge(table_reference_labels_properties,\n" - ] - }, - { - "data": { - "text/html": [ - "
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label_cellsarea_cellsbbox_area_cellsequivalent_diameter_cellsconvex_area_cellsmax_intensity_cellsmean_intensity_cellsmin_intensity_cellsstandard_deviation_intensity_cellslabel_dnaarea_dnabbox_area_dnaequivalent_diameter_dnaconvex_area_dnamax_intensity_dnamean_intensity_dnamin_intensity_dnastandard_deviation_intensity_dna
01442.0648.023.722813461.064.023.9886889.010.084142713015612.865502135183.0104.34615419.040.762674
12910.01240.034.038919972.096.042.54285716.014.954136628335718.982276293182.099.56890528.034.877849
231407.01892.042.3255011452.0128.072.48045524.016.704404521043716.351768271247.0127.58571424.055.615558
34911.01320.034.057616941.0105.045.80351314.016.341103427534218.712052284173.096.65818223.037.846574
451185.01680.038.8431311231.0135.051.27848120.019.006819332946220.466944345161.079.70820710.032.925170
561803.02400.047.9129511919.0136.056.30116516.024.383326141355122.931374437132.058.98789314.024.761279
661803.02400.047.9129511919.0136.056.30116516.024.383326228365.9708212956.035.60714313.011.564537
70NaNNaNNaNNaNNaNNaNNaNNaN866889.1669967094.046.03030322.017.076450
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" - ], - "text/plain": [ - " label_cells area_cells bbox_area_cells equivalent_diameter_cells \\\n", - "0 1 442.0 648.0 23.722813 \n", - "1 2 910.0 1240.0 34.038919 \n", - "2 3 1407.0 1892.0 42.325501 \n", - "3 4 911.0 1320.0 34.057616 \n", - "4 5 1185.0 1680.0 38.843131 \n", - "5 6 1803.0 2400.0 47.912951 \n", - "6 6 1803.0 2400.0 47.912951 \n", - "7 0 NaN NaN NaN \n", - "\n", - " convex_area_cells max_intensity_cells mean_intensity_cells \\\n", - "0 461.0 64.0 23.988688 \n", - "1 972.0 96.0 42.542857 \n", - "2 1452.0 128.0 72.480455 \n", - "3 941.0 105.0 45.803513 \n", - "4 1231.0 135.0 51.278481 \n", - "5 1919.0 136.0 56.301165 \n", - "6 1919.0 136.0 56.301165 \n", - "7 NaN NaN NaN \n", - "\n", - " min_intensity_cells standard_deviation_intensity_cells label_dna \\\n", - "0 9.0 10.084142 7 \n", - "1 16.0 14.954136 6 \n", - "2 24.0 16.704404 5 \n", - "3 14.0 16.341103 4 \n", - "4 20.0 19.006819 3 \n", - "5 16.0 24.383326 1 \n", - "6 16.0 24.383326 2 \n", - "7 NaN NaN 8 \n", - "\n", - " area_dna bbox_area_dna equivalent_diameter_dna convex_area_dna \\\n", - "0 130 156 12.865502 135 \n", - "1 283 357 18.982276 293 \n", - "2 210 437 16.351768 271 \n", - "3 275 342 18.712052 284 \n", - "4 329 462 20.466944 345 \n", - "5 413 551 22.931374 437 \n", - "6 28 36 5.970821 29 \n", - "7 66 88 9.166996 70 \n", - "\n", - " max_intensity_dna mean_intensity_dna min_intensity_dna \\\n", - "0 183.0 104.346154 19.0 \n", - "1 182.0 99.568905 28.0 \n", - "2 247.0 127.585714 24.0 \n", - "3 173.0 96.658182 23.0 \n", - "4 161.0 79.708207 10.0 \n", - "5 132.0 58.987893 14.0 \n", - "6 56.0 35.607143 13.0 \n", - "7 94.0 46.030303 22.0 \n", - "\n", - " standard_deviation_intensity_dna \n", - "0 40.762674 \n", - "1 34.877849 \n", - "2 55.615558 \n", - "3 37.846574 \n", - "4 32.925170 \n", - "5 24.761279 \n", - "6 11.564537 \n", - "7 17.076450 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements_with_intensity_linked = measure_labels_in_labels_with_intensity(label_image_reference=cell_labels,\n", - " intensity_image_reference=image_channel1,\n", - " labels_to_measure=dna_labels,\n", - " intensity_image_of_labels_to_measure=image_channel2,\n", - " suffixes=['_cells', '_dna'])\n", - "measurements_with_intensity_linked[0]" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0ffd12fa-4b94-48c8-bdaa-cdebd07f1ae3", - "metadata": {}, - "source": [ - "## 6. Get individual cell summary statistics for each feature\n", - "\n", - "To get summary statistics instead of individual direct relationships, we can use the `make_linked_summary` function. We must provide as input the list of direct relationships tables and a list of desired statistics. \n", - "\n", - "Below, we provide 'count' and 'average'. So, for each cell label, we get the number of objects 'inside' it and the average of `size` features, like average area of the dna objects inside." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "8e968d8b-6816-4084-bfc3-ab7f3fb58d89", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " label_cells counts_dna area_dna average bbox_area_dna average \\\n", - "0 0 1 66.0 88.0 \n", - "1 1 1 130.0 156.0 \n", - "2 2 1 283.0 357.0 \n", - "3 3 1 210.0 437.0 \n", - "4 4 1 275.0 342.0 \n", - "5 5 1 329.0 462.0 \n", - "6 6 2 220.5 293.5 \n", - "\n", - " equivalent_diameter_dna average convex_area_dna average \n", - "0 9.166996 70.0 \n", - "1 12.865502 135.0 \n", - "2 18.982276 293.0 \n", - "3 16.351768 271.0 \n", - "4 18.712052 284.0 \n", - "5 20.466944 345.0 \n", - "6 14.451098 233.0 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "measurements_linked_summary = make_summary_table(measurements_linked,\n", - " statistics_list=['count', 'average'])\n", - "measurements_linked_summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "91c84b11", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "napari-skimage-regionprops-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "89194614874d20ddf8746d6fd6c19366af8b6d367ee602507dbc652a381a6ce7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/demo/measure_things_inside_things_plugin.ipynb b/demo/measure_things_inside_things_plugin.ipynb deleted file mode 100644 index 9f33c7d..0000000 --- a/demo/measure_things_inside_things_plugin.ipynb +++ /dev/null @@ -1,418 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "id": "46649d66-03c2-4f41-ad06-a5b5c5159b57", - "metadata": {}, - "source": [ - "# Measure things inside things\n", - "This notebook shows an example of how to use the \"Regionprops Measure Things Inside Things\" module from napari-skimage-regionprops.\n", - "\n", - "The goal is to measure properties from two labeled images produced from a multi-channel image and generate a table that, not only displays these properties, but also shows how properties from objects in a \"target\" channel relate to objects in a \"reference\" channel.\n", - "\n", - "Thus, in this notebook we:\n", - " - count how many objects from a DNA channel belong or are 'inside' an actin channel (cell bodies)\n", - " - get the average area of DNA which are 'inside' each cell\n", - " - get the average of intensity features of DNA which are 'inside' each cell\n", - "\n", - "*This results in new features for the reference labels, some of which may display a odd looking name at first, like `mean_intensity average`. That's because `mean_intensity` is an intensity feature and `mean_intensity average` rpresents the average of this feature inside a cell/object.*" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b2aa6b54-0cb3-4550-b9c4-37796d455c28", - "metadata": {}, - "outputs": [], - "source": [ - "from skimage import io\n", - "import numpy as np\n", - "import pandas as pd\n", - "import napari\n", - "from napari.utils import nbscreenshot\n", - "# from napari_skimage_regionprops import x\n", - "from napari_skimage_regionprops import regionprops_measure_things_inside_things" - ] - }, - { - "cell_type": "markdown", - "id": "6232e6bf", - "metadata": {}, - "source": [ - "## Open images in napari" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "c3aec745", - "metadata": {}, - "source": [ - "In this small example, we open two cropped images from the [BBBC007 dataset](https://bbbc.broadinstitute.org/BBBC007), and two label images generated from the intensity images.\n", - "\n", - "Thus, we have two intensity images (a multichannel image) and two labels images. The cell labels are shown as contours and the DNA labels are shown as filled regions for better differentiation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "60af03d0-e028-4b52-9ead-d0001e3d29fc", - "metadata": {}, - "outputs": [], - "source": [ - "image_channel1 = io.imread('../data/actin.tif')\n", - "image_channel2 = io.imread('../data/dna.tif')\n", - "cell_labels = io.imread('../data/cell_labels.tif')\n", - "dna_labels = io.imread('../data/dna_labels.tif')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cdab0f3-1e65-479a-b7c8-df460265d443", - "metadata": {}, - "outputs": [], - "source": [ - "viewer = napari.Viewer()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a4f84587-009a-4f7b-86da-c9649091e717", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "viewer.add_image(image_channel1, name = 'cell', colormap='green')\n", - "viewer.add_image(image_channel2, name = 'dna', colormap='magenta', blending='additive')\n", - "cell_labels_layer = viewer.add_labels(cell_labels)\n", - "cell_labels_layer.contour = 1\n", - "dna_labels_layer = viewer.add_labels(dna_labels)\n", - "nbscreenshot(viewer)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "85cc716c", - "metadata": {}, - "source": [ - "## Open the plugin\n", - "We open the plugin by the Tools menu (Tools -> Measurement tables -> Regionprops Measure Things Inside Things (scikit-image, nsr))." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "4d2d9ee6", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbscreenshot(viewer)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "b07b3a6c-1337-41fa-b5db-b2fc5708c003", - "metadata": {}, - "source": [ - "## Count number of objects 'inside' objects in another channel\n", - "\n", - "If we want to count how many labels from one labels layer can be considered 'inside' another labels layer, we have to:\n", - " 1. ensure the `things_inside_things` checkbox is enabled;\n", - " 2. click on the `+` button in front of `Label Image(s) to Measure`;\n", - " 3. assign the second labels layer to it (in this case, the 'dna_labels');\n", - " 4. click on `configure summary statistics`, (extra options show up, notice that `average` is enabled by default), uncheck `average` and check `count`.\n", - "\n", - "It should look like this:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "87b01f8b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PROPERTIES = ['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area']\n", - "PROPERTIES = ['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area']\n", - "Napari status bar display of label properties disabled because https://github.com/napari/napari/issues/5417 and https://github.com/napari/napari/issues/4342\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:87: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", - " output_table = pd.merge(table_reference_labels_properties,\n" - ] - } - ], - "source": [ - "nbscreenshot(viewer)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "dbc6f513", - "metadata": {}, - "source": [ - "Then hit the 'Run' button.\n", - "\n", - "This returns a small table in a new tab indicating how many labels (`counts`) from the second labels layer are 'inside' the reference labels layer, i.e., how many DNA objects are inside each cell.\n", - "\n", - "The row where label is `0` means background." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d008795a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PROPERTIES = ['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area']\n", - "PROPERTIES = ['label', 'area', 'bbox_area', 'equivalent_diameter', 'convex_area']\n", - "Napari status bar display of label properties disabled because https://github.com/napari/napari/issues/5417 and https://github.com/napari/napari/issues/4342\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\mazo260d\\documents\\github\\napari-skimage-regionprops\\napari_skimage_regionprops\\_process_tables.py:87: FutureWarning: In a future version, the Index constructor will not infer numeric dtypes when passed object-dtype sequences (matching Series behavior)\n", - " output_table = pd.merge(table_reference_labels_properties,\n" - ] - } - ], - "source": [ - "nbscreenshot(viewer)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "7ca27d12-89dd-4408-9f2d-81b8be577352", - "metadata": {}, - "source": [ - "# Get average area of objects 'inside' objects in another channel" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ff54a8a8", - "metadata": {}, - "source": [ - "Let's say we want to do more than count objects. We are also interested in knowing the average area of the objects that are 'inside' objects in another channel. To do that, we should:\n", - "\n", - " 1. click on `configure summary statistics`, check `average`;\n", - " 2. hit the Run button.\n", - "\n", - "We now get a larger table, with all the average values (related to `size`) of the objects 'inside'.\n", - "\n", - "If we check other features, like `perimeter`, `shape`, `position` and `moments`, we will also get average values for each property involved in these features.\n", - "\n", - "If we check other statistics, like `std`, `minimum`, etc; we will get each of these statistics for every property selected. \n", - "\n", - "*Keep in mind that the table may become **very** wide if you select everything!*" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "41e9f6a9", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbscreenshot(viewer)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "39640685-eb60-4353-9a5b-965adc576396", - "metadata": {}, - "source": [ - "## Include intensity features\n", - "\n", - "We can also measure intensity features if we enable the `intensity` checkbox. Of course, we need intensity images to get these features from. Therefore, one (if `things_inside_things` if disabled) or two (if `things_inside_things` is enabled) extra inputs will be added to the interface. We should select an `Intensity Image Reference` (related to the reference labels) and, if `things_inside_things`, another `Intensity Image(s) to Measure` (related to the other labels layer)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2013c75d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbscreenshot(viewer)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "114ae356", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "napari-skimage-regionprops-dev", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - }, - "vscode": { - "interpreter": { - "hash": "89194614874d20ddf8746d6fd6c19366af8b6d367ee602507dbc652a381a6ce7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/napari_skimage_regionprops/__init__.py b/napari_skimage_regionprops/__init__.py index b9c4488..6ee4da5 100644 --- a/napari_skimage_regionprops/__init__.py +++ b/napari_skimage_regionprops/__init__.py @@ -2,7 +2,7 @@ from ._regionprops import regionprops, regionprops_table, regionprops_table_all_frames from ._multichannel import link_two_label_images, measure_labels, measure_labels_with_intensity from ._multichannel import measure_labels_in_labels_with_intensity, measure_labels_in_labels -from ._multichannel import regionprops_measure_things_inside_things +from ._multichannel import regionprops_measure_relationship_to_other_channels from ._process_tables import merge_measurements_to_reference, make_summary_table from ._parametric_images import visualize_measurement_on_labels, relabel, map_measurements_on_labels from ._measure_points import measure_points diff --git a/napari_skimage_regionprops/_multichannel.py b/napari_skimage_regionprops/_multichannel.py index d90043c..b8cc441 100644 --- a/napari_skimage_regionprops/_multichannel.py +++ b/napari_skimage_regionprops/_multichannel.py @@ -1,12 +1,6 @@ -import pandas -import numpy as np from typing import List from magicgui import magic_factory from napari_tools_menu import register_dock_widget -from ._regionprops import regionprops_table -from ._process_tables import merge_measurements_to_reference -from ._process_tables import make_summary_table - def make_element_wise_dict(list_of_keys, list_of_values): @@ -32,21 +26,21 @@ def make_element_wise_dict(list_of_keys, list_of_values): def _connect_events(widget): def toggle_intensity_widgets(event): widget.intensity_image_reference.visible = event - # If things_inside_things is True when enabling intensity, then enable + # If relate_to_other_channels is True when enabling intensity, then enable # intensity image inputs - if (event == True) & (widget.things_inside_things.value == True): - widget.intensity_images_to_measure.visible = True + if (event == True) & (widget.relate_to_other_channels.value == True): + widget.intensity_images_other_channels.visible = True else: - widget.intensity_images_to_measure.visible = False + widget.intensity_images_other_channels.visible = False - def toggle_things_inside_things_widgets(event): - widget.label_images_to_measure.visible = event - widget.intersection_over_target_area.visible = event + def toggle_relate_to_other_channels_widgets(event): + widget.label_images_other_channels.visible = event + widget.intersection_over_target_obj_area.visible = event widget.configure_summary_statistics.visible = event if (event == True) & (widget.intensity.value == True): - widget.intensity_images_to_measure.visible = True + widget.intensity_images_other_channels.visible = True else: - widget.intensity_images_to_measure.visible = False + widget.intensity_images_other_channels.visible = False if event == True: if widget.configure_summary_statistics.value == True: toggle_summary_statistics_widgets(True) @@ -65,17 +59,17 @@ def toggle_summary_statistics_widgets(event): widget.maximum.visible = event widget.intensity.changed.connect(toggle_intensity_widgets) - widget.things_inside_things.changed.connect(toggle_things_inside_things_widgets) + widget.relate_to_other_channels.changed.connect(toggle_relate_to_other_channels_widgets) widget.configure_summary_statistics.changed.connect( toggle_summary_statistics_widgets ) # Intial visibility states widget.intensity_image_reference.visible = False - widget.intensity_images_to_measure.visible = False - widget.label_images_to_measure.visible = False + widget.intensity_images_other_channels.visible = False + widget.label_images_other_channels.visible = False widget.configure_summary_statistics.visible = False - widget.intersection_over_target_area.visible = False + widget.intersection_over_target_obj_area.visible = False widget.counts.visible = False widget.average.visible = False widget.std.visible = False @@ -93,16 +87,16 @@ def toggle_summary_statistics_widgets(event): 'intensity_image_reference': { 'label': 'Intensity Image Reference' }, - 'label_images_to_measure': { + 'label_images_other_channels': { 'label': 'Label Image(s) from other Channel(s)' }, - 'intensity_images_to_measure': { + 'intensity_images_other_channels': { 'label': 'Intensity Image(s) from other Channel(s)' }, - 'things_inside_things': { + 'relate_to_other_channels': { 'label': 'relate to other channel(s)' }, - 'intersection_over_target_area': { + 'intersection_over_target_obj_area': { 'widget_type': 'FloatSlider', 'min': 0, 'max': 1, @@ -134,30 +128,30 @@ def toggle_summary_statistics_widgets(event): 'label_image_reference'], intensity_image_reference=widgets_layout_settings[ 'intensity_image_reference'], - label_images_to_measure=widgets_layout_settings[ - 'label_images_to_measure'], - intensity_images_to_measure=widgets_layout_settings[ - 'intensity_images_to_measure'], - intersection_over_target_area=widgets_layout_settings[ - 'intersection_over_target_area'], - things_inside_things=widgets_layout_settings[ - 'things_inside_things'], + label_images_other_channels=widgets_layout_settings[ + 'label_images_other_channels'], + intensity_images_other_channels=widgets_layout_settings[ + 'intensity_images_other_channels'], + intersection_over_target_obj_area=widgets_layout_settings[ + 'intersection_over_target_obj_area'], + relate_to_other_channels=widgets_layout_settings[ + 'relate_to_other_channels'], std=widgets_layout_settings['std'], percentile_25=widgets_layout_settings['percentile_25'], percentile_75=widgets_layout_settings['percentile_75']) -def regionprops_measure_things_inside_things( +def regionprops_measure_relationship_to_other_channels( label_image_reference: "napari.types.LabelsData", intensity_image_reference: "napari.types.ImageData", - label_images_to_measure: List["napari.types.LabelsData"], - intensity_images_to_measure: List["napari.types.ImageData"], + label_images_other_channels: List["napari.types.LabelsData"], + intensity_images_other_channels: List["napari.types.ImageData"], intensity: bool = False, - things_inside_things: bool = False, + relate_to_other_channels: bool = False, size: bool = True, perimeter: bool = False, shape: bool = False, position: bool = False, moments: bool = False, - intersection_over_target_area: float = 0.5, + intersection_over_target_obj_area: float = 0.5, configure_summary_statistics: bool = False, counts: bool = False, average: bool = True, @@ -183,19 +177,19 @@ def regionprops_measure_things_inside_things( a label image used as a reference. intensity_image_reference : array_like an intensity image, matching the `label_image_reference` shape. - label_images_to_measure : List[array_like] + label_images_other_channels : List[array_like] a list of label images, whose measured features will be related to labels from `label_image_reference`. - intensity_images_to_measure : List[array_like] + intensity_images_other_channels : List[array_like] a list of label images, whose measured intensity features will be related to labels from `label_image_reference`. intensity : bool a flag indicating that intensity features should be measured. If `True`, `intensity_image_reference` must be given. - things_inside_things : bool - a flag indicating that things_inside_things analysis should be performed, + relate_to_other_channels : bool + a flag indicating that relate_to_other_channels analysis should be performed, relating labels from `label_image_reference` to - `label_images_to_measure`. If `True`, `label_images_to_measure` + `label_images_other_channels`. If `True`, `label_images_other_channels` must be provided. size : bool a flag indicating to measure size features. By default True. @@ -207,7 +201,7 @@ def regionprops_measure_things_inside_things( a flag indicating to measure position features. By default False. moments : bool a flag indicating to measure moments features. By default False. - intersection_over_target_area : float + intersection_over_target_obj_area : float a ratio of areas that indicates whether an object is considered 'inside' another. It is the intersection area divided by the target object area. It goes from 0 to 1. 0 indicates object always belongs to @@ -250,7 +244,9 @@ def regionprops_measure_things_inside_things( in other channels. """ from napari.utils import notifications + from ._process_tables import make_summary_table import napari + import numpy as np statistics_list = [] if counts: @@ -279,31 +275,31 @@ def regionprops_measure_things_inside_things( reference_labels_layer = layer reference_suffix = '_' + layer.name suffixes.insert(0, reference_suffix) - for i, labels in enumerate(label_images_to_measure): + for i, labels in enumerate(label_images_other_channels): for layer in napari_viewer.layers: if isinstance(layer, napari.layers.Labels) and np.array_equal(layer.data, labels): new_suffix = '_' + layer.name if new_suffix not in suffixes: suffixes.append(new_suffix) else: - # labels_to_measure layer is a duplicated reference - n_leading_zeros = len(label_images_to_measure) // 10 + # label_image_other_channel layer is a duplicated reference + n_leading_zeros = len(label_images_other_channels) // 10 suffixes.append(new_suffix + str(i+1).zfill(1+n_leading_zeros)) # Ensures proper iterable inputs else: - if not isinstance(label_images_to_measure, list): - label_images_to_measure = [label_images_to_measure] - if not isinstance(intensity_images_to_measure, list): - intensity_images_to_measure = [intensity_images_to_measure] + if not isinstance(label_images_other_channels, list): + label_images_other_channels = [label_images_other_channels] + if not isinstance(intensity_images_other_channels, list): + intensity_images_other_channels = [intensity_images_other_channels] # in case function is used without viewer and suffixes are missing if len(suffixes) == 0: - n_leading_zeros = len(label_images_to_measure) // 10 + n_leading_zeros = len(label_images_other_channels) // 10 suffixes = ['_reference'] + ['_' + str(i+1).zfill( 1+n_leading_zeros) - for i in range(len(label_images_to_measure))] + for i in range(len(label_images_other_channels))] # Single image measurements - if things_inside_things == False: + if relate_to_other_channels == False: # Without intensity measurements if intensity == False: table = measure_labels( @@ -331,8 +327,8 @@ def regionprops_measure_things_inside_things( shape=shape, position=position, moments=moments) else: - # Check if user provided 'label_images_to_measure' - if len(label_images_to_measure) == 0: + # Check if user provided 'label_images_other_channels' + if len(label_images_other_channels) == 0: notifications.show_warning(('Error! Add a labels layer to ' '\'Label Image(s) from other ' 'Channel(s)\' when \'relate ' @@ -344,19 +340,19 @@ def regionprops_measure_things_inside_things( if intensity == False: table = measure_labels_in_labels( label_image_reference=label_image_reference, - labels_to_measure=label_images_to_measure, + label_image_other_channel=label_images_other_channels, size=size, perimeter=perimeter, shape=shape, position=position, moments=moments, - intersection_over_target_area=intersection_over_target_area, + intersection_over_target_obj_area=intersection_over_target_obj_area, suffixes=suffixes) # Or with intensity measurements else: - # Check if user provided 'intensity_images_to_measure' - if len(intensity_images_to_measure) == 0: + # Check if user provided 'intensity_images_other_channels' + if len(intensity_images_other_channels) == 0: notifications.show_warning(('Error! Add an image layer to ' '\'Intensity Image(s) from other ' 'Channel(s)\' when both \'relate ' @@ -366,13 +362,13 @@ def regionprops_measure_things_inside_things( return table = measure_labels_in_labels_with_intensity( label_image_reference=label_image_reference, - labels_to_measure=label_images_to_measure, + label_image_other_channel=label_images_other_channels, intensity_image_reference=intensity_image_reference, - intensity_image_of_labels_to_measure=intensity_images_to_measure, + intensity_image_other_channel=intensity_images_other_channels, size=size, perimeter=perimeter, shape=shape, position=position, moments=moments, - intersection_over_target_area=intersection_over_target_area, + intersection_over_target_obj_area=intersection_over_target_obj_area, suffixes=suffixes) # Compute summary statistics instead of individual relationships # This guarantees no repeated numbers in the 'label' column @@ -395,8 +391,8 @@ def regionprops_measure_things_inside_things( def link_two_label_images(label_image_reference: "napari.types.LabelsData", - labels_to_measure: "napari.types.LabelsData", - intersection_over_target_area: float = 0.5 + label_image_other_channel: "napari.types.LabelsData", + intersection_over_target_obj_area: float = 0.5 ) -> "pandas.DataFrame": """ Associate each label from a reference to a target label image. @@ -410,14 +406,14 @@ def link_two_label_images(label_image_reference: "napari.types.LabelsData", ---------- label_image_reference : napari.types.LabelsData a label image to be used as reference labels. - labels_to_measure : napari.types.LabelsData + label_image_other_channel : napari.types.LabelsData a label image to be used as target labels. - intersection_over_target_area : float, optional + intersection_over_target_obj_area : float, optional an area ratio threshold value that determines if a label in the target image belongs to another in the reference image. The area ratio is calculated by the intersection area divided by the target label area. If this area ratio is bigger - or equal to `intersection_over_target_area`, the target object + or equal to `intersection_over_target_obj_area`, the target object gets associated to the reference object, otherwise, it gets associated to the background. This parameter goes from 0 to 1, by default 0.5. @@ -434,7 +430,7 @@ def link_two_label_images(label_image_reference: "napari.types.LabelsData", from skimage.measure import regionprops_table as sk_regionprops_table def highest_overlap(regionmask, label_image_reference, - overlap_threshold=intersection_over_target_area): + overlap_threshold=intersection_over_target_obj_area): """ Get the label number with highest overlap with label in another image. @@ -484,7 +480,7 @@ def highest_overlap(regionmask, label_image_reference, # Create table that links labels from scanning channel to reference channel table_linking_labels = pd.DataFrame( - sk_regionprops_table(label_image=np.asarray(labels_to_measure).astype(int), + sk_regionprops_table(label_image=np.asarray(label_image_other_channel).astype(int), intensity_image=np.asarray(label_image_reference).astype(int), properties=['label', ], extra_properties=[highest_overlap] @@ -554,6 +550,9 @@ def measure_labels(label_image_reference: "napari.types.LabelsData", pandas.DataFrame A table containing labels and corresponding measured features. """ + from ._regionprops import regionprops_table + import numpy as np + table = regionprops_table(image=np.zeros_like(label_image_reference), labels=label_image_reference, size=size, @@ -609,6 +608,8 @@ def measure_labels_with_intensity( pandas.DataFrame A table containing labels and corresponding measured features. """ + from ._regionprops import regionprops_table + table = regionprops_table(image=intensity_image_reference, labels=label_image_reference, size=size, @@ -621,13 +622,13 @@ def measure_labels_with_intensity( def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", - labels_to_measure: List["napari.types.LabelsData"], + label_image_other_channel: List["napari.types.LabelsData"], size: bool = True, perimeter: bool = False, shape: bool = False, position: bool = False, moments: bool = False, - intersection_over_target_area: float = 0.5, + intersection_over_target_obj_area: float = 0.5, suffixes: List[str] = None, napari_viewer: "napari.Viewer" = None ) -> List["pandas.DataFrame"]: @@ -638,13 +639,13 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", one or more target label images. It returns a table where each column is a feature and each row associates a label (and its features) from `label_image_reference` - to a target label (and its features) from `labels_to_measure`. + to a target label (and its features) from `label_image_other_channel`. Parameters ---------- label_image_reference : napari.types.LabelsData a reference label image to measure features. - labels_to_measure : List[napari.types.LabelsData] + label_image_other_channel : List[napari.types.LabelsData] a list of target label images to measure features. size : bool, optional measure size related features. @@ -661,12 +662,12 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", moments : bool, optional measure moments related features. By default False. - intersection_over_target_area : float, optional + intersection_over_target_obj_area : float, optional an area ratio threshold value that determines if a label in the target image belongs to another in the reference image. The area ratio is calculated by the intersection area divided by the target label area. If this area ratio is bigger - or equal to `intersection_over_target_area`, the target object + or equal to `intersection_over_target_obj_area`, the target object gets associated to the reference object, otherwise, it gets associated to the background. This parameter goes from 0 to 1, by default 0.5. @@ -683,6 +684,8 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", A list of tables containing labels and corresponding measured features. Each row associates reference label features to target label features. """ + from ._process_tables import merge_measurements_to_reference + # Get reference properties reference_labels_properties = measure_labels( label_image_reference=label_image_reference, @@ -694,16 +697,16 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", napari_viewer=napari_viewer ) list_table_linking_labels = [] - list_table_labels_to_measure_properties = [] - # Make labels_to_measure iterable - if not isinstance(labels_to_measure, list): - labels_to_measure = [labels_to_measure] - # Link each labels_to_measure image to reference and get their properties - for label_image in labels_to_measure: + list_table_other_channel_labels_properties = [] + # Make label_image_other_channel iterable + if not isinstance(label_image_other_channel, list): + label_image_other_channel = [label_image_other_channel] + # Link each label_image_other_channel image to reference and get their properties + for label_image in label_image_other_channel: table_linking_labels = link_two_label_images( label_image_reference=label_image_reference, - labels_to_measure=label_image, - intersection_over_target_area=intersection_over_target_area + label_image_other_channel=label_image, + intersection_over_target_obj_area=intersection_over_target_obj_area ) list_table_linking_labels.append(table_linking_labels) @@ -716,7 +719,7 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", moments=moments, napari_viewer=napari_viewer ) - list_table_labels_to_measure_properties.append( + list_table_other_channel_labels_properties.append( labels_to_measure_properties) # Merge each table with target label properties to table with reference @@ -724,22 +727,22 @@ def measure_labels_in_labels(label_image_reference: "napari.types.LabelsData", table_list = merge_measurements_to_reference( table_reference_labels_properties=reference_labels_properties, table_linking_labels=list_table_linking_labels, - table_labels_to_measure_properties=list_table_labels_to_measure_properties, + table_other_channel_labels_properties=list_table_other_channel_labels_properties, suffixes=suffixes) return table_list def measure_labels_in_labels_with_intensity( label_image_reference: "napari.types.LabelsData", - labels_to_measure: List["napari.types.LabelsData"], + label_image_other_channel: List["napari.types.LabelsData"], intensity_image_reference: "napari.types.ImageData", - intensity_image_of_labels_to_measure: List["napari.types.ImageData"] = None, + intensity_image_other_channel: List["napari.types.ImageData"] = None, size: bool = True, perimeter: bool = False, shape: bool = False, position: bool = False, moments: bool = False, - intersection_over_target_area: float = 0.5, + intersection_over_target_obj_area: float = 0.5, suffixes: List[str] = None, napari_viewer: "napari.Viewer" = None) -> List["pandas.DataFrame"]: """ @@ -751,17 +754,17 @@ def measure_labels_in_labels_with_intensity( images. It returns a table where each column is a feature and each row associates a label (and its features) from `label_image_reference` - to a target label (and its features) from `labels_to_measure`. + to a target label (and its features) from `label_image_other_channel`. Parameters ---------- label_image_reference : napari.types.LabelsData a reference label image to measure features. - labels_to_measure : List[napari.types.LabelsData], array_like + label_image_other_channel : List[napari.types.LabelsData], array_like a list of target label images to measure features. intensity_image_reference : napari.types.ImageData, array_like a reference intensity image to measure intensity features. - intensity_image_of_labels_to_measure : List[napari.types.ImageData], + intensity_image_other_channel : List[napari.types.ImageData], array_like, optional a list of target intensity images to measure intensity features. If None (default), intensity features are extracted from @@ -781,12 +784,12 @@ def measure_labels_in_labels_with_intensity( moments : bool, optional measure moments related features. By default False. - intersection_over_target_area : float, optional + intersection_over_target_obj_area : float, optional an area ratio threshold value that determines if a label in the target image belongs to another in the reference image. The area ratio is calculated by the intersection area divided by the target label area. If this area ratio is bigger - or equal to `intersection_over_target_area`, the target object + or equal to `intersection_over_target_obj_area`, the target object gets associated to the reference object, otherwise, it gets associated to the background. This parameter goes from 0 to 1, by default 0.5. @@ -803,6 +806,8 @@ def measure_labels_in_labels_with_intensity( A list of tables containing labels and corresponding measured features. Each row associates reference label features to target label features. """ + from ._process_tables import merge_measurements_to_reference + # Get reference properties reference_labels_properties = measure_labels_with_intensity( label_image_reference=label_image_reference, @@ -815,33 +820,33 @@ def measure_labels_in_labels_with_intensity( napari_viewer=napari_viewer ) list_table_linking_labels = [] - list_table_labels_to_measure_properties = [] - # Make labels_to_measure iterable - if not isinstance(labels_to_measure, list): - labels_to_measure = [labels_to_measure] - # If no intensity_image_of_labels_to_measure provided, use reference + list_table_other_channel_labels_properties = [] + # Make label_image_other_channel iterable + if not isinstance(label_image_other_channel, list): + label_image_other_channel = [label_image_other_channel] + # If no intensity_image_other_channel provided, use reference # intensity - if intensity_image_of_labels_to_measure is None: - intensity_image_of_labels_to_measure = [intensity_image_reference] * \ - len(labels_to_measure) - # If intensity_image_of_labels_to_measure provided, check if sizes match + if intensity_image_other_channel is None: + intensity_image_other_channel = [intensity_image_reference] * \ + len(label_image_other_channel) + # If intensity_image_other_channel provided, check if sizes match else: - # Make intensity_image_of_labels_to_measure iterable - if not isinstance(intensity_image_of_labels_to_measure, list): - intensity_image_of_labels_to_measure = \ - [intensity_image_of_labels_to_measure] - if len(intensity_image_of_labels_to_measure) != len(labels_to_measure): - print(('Error! Length of intensity_image_of_labels_to_measure and' - 'labels_to_measure must match.')) + # Make intensity_image_other_channel iterable + if not isinstance(intensity_image_other_channel, list): + intensity_image_other_channel = \ + [intensity_image_other_channel] + if len(intensity_image_other_channel) != len(label_image_other_channel): + print(('Error! Length of intensity_image_other_channel and' + 'label_image_other_channel must match.')) return - # Link each labels_to_measure image to reference and get their properties + # Link each label_image_other_channel image to reference and get their properties for label_image, intensity_image in zip( - labels_to_measure, intensity_image_of_labels_to_measure): + label_image_other_channel, intensity_image_other_channel): table_linking_labels = link_two_label_images( label_image_reference=label_image_reference, - labels_to_measure=label_image, - intersection_over_target_area=intersection_over_target_area + label_image_other_channel=label_image, + intersection_over_target_obj_area=intersection_over_target_obj_area ) list_table_linking_labels.append(table_linking_labels) @@ -855,7 +860,7 @@ def measure_labels_in_labels_with_intensity( moments=moments, napari_viewer=napari_viewer ) - list_table_labels_to_measure_properties.append( + list_table_other_channel_labels_properties.append( labels_to_measure_properties) # Merge each table with target label properties to table with reference @@ -863,6 +868,6 @@ def measure_labels_in_labels_with_intensity( table_list = merge_measurements_to_reference( table_reference_labels_properties=reference_labels_properties, table_linking_labels=list_table_linking_labels, - table_labels_to_measure_properties=list_table_labels_to_measure_properties, + table_other_channel_labels_properties=list_table_other_channel_labels_properties, suffixes=suffixes) return table_list diff --git a/napari_skimage_regionprops/_process_tables.py b/napari_skimage_regionprops/_process_tables.py index 0df74c7..96c0355 100644 --- a/napari_skimage_regionprops/_process_tables.py +++ b/napari_skimage_regionprops/_process_tables.py @@ -1,11 +1,10 @@ -import pandas from typing import List def merge_measurements_to_reference( table_reference_labels_properties: "pandas.DataFrame", table_linking_labels: List["pandas.DataFrame"], - table_labels_to_measure_properties: List["pandas.DataFrame"], + table_other_channel_labels_properties: List["pandas.DataFrame"], suffixes=None) -> List["pandas.DataFrame"]: """ Merge measurements from target to reference table through a linking table. @@ -19,7 +18,7 @@ def merge_measurements_to_reference( a list of tables. Each table should contain 2 columns, a label_reference' and a 'label_target'. Each table row associates a target label to a reference label. - table_labels_to_measure_properties : List["pandas.DataFrame"] + table_other_channel_labels_properties : List["pandas.DataFrame"] a list of tables to be used as targets with a column 'label' and other columns with features. suffixes : List[str], optional @@ -40,20 +39,20 @@ def merge_measurements_to_reference( list_table_linking_labels = [table_linking_labels] else: list_table_linking_labels = table_linking_labels - if not isinstance(table_labels_to_measure_properties, list): - list_table_labels_to_measure_properties = [ - table_labels_to_measure_properties] + if not isinstance(table_other_channel_labels_properties, list): + list_table_other_channel_labels_properties = [ + table_other_channel_labels_properties] else: - list_table_labels_to_measure_properties = \ - table_labels_to_measure_properties + list_table_other_channel_labels_properties = \ + table_other_channel_labels_properties # Build custom suffixes or check if provided suffixes match data size - n_measurement_tables = len(list_table_labels_to_measure_properties) + n_measurement_tables = len(list_table_other_channel_labels_properties) if suffixes is None: n_leading_zeros = n_measurement_tables // 10 suffixes = ['_reference'] + ['_' + str(i+1).zfill(1+n_leading_zeros) for i in range(n_measurement_tables)] else: - if len(suffixes) != len(table_labels_to_measure_properties) + 1: + if len(suffixes) != len(table_other_channel_labels_properties) + 1: print(('Error: List of suffixes must have the same length as the' 'number of tables containing measurements')) return @@ -70,19 +69,19 @@ def merge_measurements_to_reference( 'label_target': 'label' + suffixes[i+1]}, inplace=True) # Rename columns of tables with properties from other channels - for i, table_labels_to_measure_properties in enumerate( - list_table_labels_to_measure_properties): - table_labels_to_measure_properties.columns = [ + for i, table_other_channel_labels_properties in enumerate( + list_table_other_channel_labels_properties): + table_other_channel_labels_properties.columns = [ props + suffixes[i+1] - for props in table_labels_to_measure_properties.columns] + for props in table_other_channel_labels_properties.columns] output_table_list = [] # Consecutively merge linking_labels tables and properties from other # channels tables to the reference table - for i, table_linking_labels, table_labels_to_measure_properties in zip( + for i, table_linking_labels, table_other_channel_labels_properties in zip( range(n_measurement_tables), list_table_linking_labels, - list_table_labels_to_measure_properties): + list_table_other_channel_labels_properties): # Merge other labels to label_reference output_table = pd.merge(table_reference_labels_properties, table_linking_labels, @@ -93,12 +92,13 @@ def merge_measurements_to_reference( 'label' + suffixes[i+1]].fillna(0) # Merge other properties to output table based on new labels column output_table = pd.merge(output_table, - table_labels_to_measure_properties, + table_other_channel_labels_properties, how='outer', on='label' + suffixes[i+1]) # Ensure label columns type to be integer for column in output_table.columns: if column.startswith('label'): output_table[column] = output_table[column].astype(int) + output_table = output_table.sort_values(by='label' + suffixes[0]) # Append output table to list (each table may have different shapes) output_table_list.append(output_table) return output_table_list @@ -157,7 +157,7 @@ def make_summary_table(table: List["pandas.DataFrame"], print(('Could not infer suffixes from column names. Please ' 'provide a list of suffixes identifying different ' 'channels')) - if isinstance(table, pandas.DataFrame): + if isinstance(table, pd.DataFrame): table = [table] if 'count' in statistics_list: diff --git a/napari_skimage_regionprops/_tests/test_things_inside_things.py b/napari_skimage_regionprops/_tests/test_multichannel.py similarity index 85% rename from napari_skimage_regionprops/_tests/test_things_inside_things.py rename to napari_skimage_regionprops/_tests/test_multichannel.py index 6e3cb79..3ea4bff 100644 --- a/napari_skimage_regionprops/_tests/test_things_inside_things.py +++ b/napari_skimage_regionprops/_tests/test_multichannel.py @@ -1,7 +1,7 @@ def test_napari_regionprops_map_2channels_2D(make_napari_viewer): import numpy as np - from napari_skimage_regionprops import regionprops_measure_things_inside_things + from napari_skimage_regionprops import regionprops_measure_relationship_to_other_channels viewer = make_napari_viewer() @@ -25,16 +25,16 @@ def test_napari_regionprops_map_2channels_2D(make_napari_viewer): viewer.add_labels(ref_labels, name='ref_labels') viewer.add_labels(target_labels, name='target_labels') - widget = regionprops_measure_things_inside_things() - regionprops_measure_things_inside_things_function = widget._function + widget = regionprops_measure_relationship_to_other_channels() + regionprops_measure_relationship_to_other_channels_function = widget._function # Measure everything we can - table = regionprops_measure_things_inside_things_function( + table = regionprops_measure_relationship_to_other_channels_function( ref_labels, multichannel_image[..., 0], [target_labels], [multichannel_image[..., 1]], intensity = True, - things_inside_things = True, + relate_to_other_channels = True, size = True, perimeter = True, shape = True, @@ -63,7 +63,7 @@ def test_napari_regionprops_map_2channels_2D(make_napari_viewer): def test_napari_regionprops_map_3channels_2D(make_napari_viewer): import numpy as np - from napari_skimage_regionprops import regionprops_measure_things_inside_things + from napari_skimage_regionprops import regionprops_measure_relationship_to_other_channels viewer = make_napari_viewer() @@ -91,17 +91,17 @@ def test_napari_regionprops_map_3channels_2D(make_napari_viewer): viewer.add_labels(target_labels, name='target_labels') viewer.add_labels(target_labels_B, name='target_labels_B') - widget = regionprops_measure_things_inside_things() - regionprops_measure_things_inside_things_function = widget._function + widget = regionprops_measure_relationship_to_other_channels() + regionprops_measure_relationship_to_other_channels_function = widget._function # Measure everything we can - table = regionprops_measure_things_inside_things_function( + table = regionprops_measure_relationship_to_other_channels_function( ref_labels, multichannel_image[..., 0], [target_labels, target_labels_B], [multichannel_image[..., 1], multichannel_image[..., 2]], intensity = True, - things_inside_things = True, + relate_to_other_channels = True, size = True, perimeter = True, shape = True, @@ -134,7 +134,7 @@ def test_napari_regionprops_map_3channels_2D(make_napari_viewer): def test_napari_regionprops_map_2channels_3D(make_napari_viewer): import numpy as np - from napari_skimage_regionprops import regionprops_measure_things_inside_things + from napari_skimage_regionprops import regionprops_measure_relationship_to_other_channels viewer = make_napari_viewer() @@ -170,17 +170,17 @@ def test_napari_regionprops_map_2channels_3D(make_napari_viewer): viewer.add_labels(ref_labels_3D, name='ref_labels_3D') viewer.add_labels(target_labels_3D, name='target_labels_3D') - widget = regionprops_measure_things_inside_things() - regionprops_measure_things_inside_things_function = widget._function + widget = regionprops_measure_relationship_to_other_channels() + regionprops_measure_relationship_to_other_channels_function = widget._function # Measure everything we can - table = regionprops_measure_things_inside_things_function( + table = regionprops_measure_relationship_to_other_channels_function( ref_labels_3D, multichannel_image[0], [target_labels_3D], [multichannel_image[1]], intensity = True, - things_inside_things = True, + relate_to_other_channels = True, size = True, perimeter = True, shape = True, diff --git a/requirements.txt b/requirements.txt index 151dd4c..cbc8066 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ pandas napari-tools-menu>=0.1.19 napari-workflows imageio!=2.22.1 +Deprecated