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WIP: Simplify and replace postprocessing #199

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WIP: Simplify and replace postprocessing #199

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c03e84e
Import dask_ndmeasure
jakirkham Apr 3, 2018
192d030
WIP: Rewrite post-processing
jakirkham Apr 3, 2018
8ce5237
Try mapping label and convert to masks
jakirkham Apr 3, 2018
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178 changes: 104 additions & 74 deletions nanshe_ipython.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -190,6 +190,7 @@
"import dask_imread\n",
"import dask_ndfilters\n",
"import dask_ndfourier\n",
"import dask_ndmeasure\n",
"\n",
"import zarr\n",
"\n",
Expand Down Expand Up @@ -1158,15 +1159,7 @@
"### Postprocessing\n",
"\n",
"* `significance_threshold` (`float`): number of standard deviations below which to include in \"noise\" estimate\n",
"* `wavelet_scale` (`int`): scale of wavelet transform to apply (should be the same as the one used above)\n",
"* `noise_threshold` (`float`): number of units of \"noise\" above which something needs to be to be significant\n",
"* `accepted_region_shape_constraints` (`dict`): if ROIs don't match this, reduce the `wavelet_scale` once.\n",
"* `percentage_pixels_below_max` (`float`): upper bound on ratio of ROI pixels not at max intensity vs. all ROI pixels\n",
"* `min_local_max_distance` (`float`): minimum allowable euclidean distance between two ROIs maximum intensities\n",
"* `accepted_neuron_shape_constraints` (`dict`): shape constraints for ROI to be kept.\n",
"\n",
"* `alignment_min_threshold` (`float`): similarity measure of the intensity of two ROIs images used for merging.\n",
"* `overlap_min_threshold` (`float`): similarity measure of the masks of two ROIs used for merging."
"* `noise_threshold` (`float`): number of units of \"noise\" above which something needs to be to be significant"
]
},
{
Expand All @@ -1175,83 +1168,120 @@
"metadata": {},
"outputs": [],
"source": [
"significance_threshold = 3.0\n",
"wavelet_scale = 3\n",
"noise_threshold = 3.0\n",
"percentage_pixels_below_max = 0.8\n",
"min_local_max_distance = 16.0\n",
"def np_label(a):\n",
" return np.array(\n",
" scipy.ndimage.label(a),\n",
" dtype=[\n",
" (\"label\", np.int32, da_imgs_thrd.shape),\n",
" (\"num\", int, ()),\n",
" ]\n",
" )\n",
"\n",
"alignment_min_threshold = 0.6\n",
"overlap_min_threshold = 0.6\n",
"def label_chunk(a):\n",
" return np.stack([np_label(e) for e in a])\n",
"\n",
"def label(d):\n",
" d_lbld = d.map_blocks(\n",
" label_chunk,\n",
" dtype=[\n",
" (\"label\", np.int32, d.shape[1:]),\n",
" (\"num\", int, ()),\n",
" ],\n",
" drop_axis=tuple(irange(1, d.ndim))\n",
" )\n",
"\n",
" return d_lbld[\"label\"], d_lbld[\"num\"]\n",
"\n",
"def np_labels_to_masks_chunk(a, num):\n",
" r = np.empty((0,) + a.shape[1:], dtype=bool)\n",
" if num:\n",
" r = np.concatenate([a == i for i in irange(1, 1 + num)])\n",
" return r\n",
"\n",
"def labels_to_masks_chunk(a, num):\n",
" r = np.empty((0,) + a.shape[2:], dtype=bool)\n",
" if len(num):\n",
" r = np.concatenate([np_labels_to_masks_chunk(e0, e1) for e0, e1 in zip(a, num)])\n",
" return r\n",
"\n",
"def labels_to_masks(d, nums):\n",
" out = da.atop(\n",
" labels_to_masks_chunk, tuple(irange(d.ndim)),\n",
" d, tuple(irange(d.ndim)),\n",
" nums, tuple(irange(nums.ndim)),\n",
" dtype=bool\n",
" )\n",
" out._chunks = (len(out.chunks[0]) * (np.nan,),) + out.chunks[1:]\n",
"\n",
" return out"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"significance_threshold = 3.0\n",
"noise_threshold = 1.0\n",
"\n",
"\n",
"with suppress(KeyError):\n",
" del dask_store[subgroup_post]\n",
"zarr_store.require_group(subgroup_post)\n",
"\n",
"\n",
"imgs = dask_store._diskstore[subgroup_dict]\n",
"da_imgs = da.from_array(imgs, chunks=((1,) + imgs.shape[1:]))\n",
"\n",
"result = block_postprocess_data_parallel(client)(da_imgs,\n",
" **{\n",
" \"wavelet_denoising\" : {\n",
" \"estimate_noise\" : {\n",
" \"significance_threshold\" : significance_threshold\n",
" },\n",
" \"wavelet.transform\" : {\n",
" \"scale\" : wavelet_scale\n",
" },\n",
" \"significant_mask\" : {\n",
" \"noise_threshold\" : noise_threshold\n",
" },\n",
" \"accepted_region_shape_constraints\" : {\n",
" \"major_axis_length\" : {\n",
" \"min\" : 0.0,\n",
" \"max\" : 25.0\n",
" }\n",
" },\n",
" \"remove_low_intensity_local_maxima\" : {\n",
" \"percentage_pixels_below_max\" : percentage_pixels_below_max\n",
" },\n",
" \"remove_too_close_local_maxima\" : {\n",
" \"min_local_max_distance\" : min_local_max_distance\n",
" },\n",
" \"accepted_neuron_shape_constraints\" : {\n",
" \"area\" : {\n",
" \"min\" : 25,\n",
" \"max\" : 600\n",
" },\n",
" \"eccentricity\" : {\n",
" \"min\" : 0.0,\n",
" \"max\" : 0.9\n",
" }\n",
" }\n",
" },\n",
" \"merge_neuron_sets\" : {\n",
" \"alignment_min_threshold\" : alignment_min_threshold,\n",
" \"overlap_min_threshold\" : overlap_min_threshold,\n",
" \"fuse_neurons\" : {\n",
" \"fraction_mean_neuron_max_threshold\" : 0.01\n",
" }\n",
" }\n",
" }\n",
"da_imgs = dask_store[subgroup_dict]\n",
"da_imgs = da_imgs.rechunk(((1,) + da_imgs.shape[1:]))\n",
"\n",
"da_imgs_thrd = (da_imgs - noise_threshold * (da_imgs - significance_threshold * da_imgs.std()).std()) > 0\n",
"\n",
"da_lbl_img, da_num_lbls = label(da_imgs_thrd)\n",
"\n",
"da_result = labels_to_masks(da_lbl_img, da_num_lbls)\n",
"da_result = da_result.astype(np.uint8)\n",
"\n",
"da_result = client.persist(da_result)\n",
"\n",
"dask.distributed.progress(da_result, notebook=False)\n",
"\n",
"\n",
"# Make chunks concrete\n",
"\n",
"da_result_chunks_0 = tuple(\n",
" da_result[:, 0, 0].map_blocks(lambda e: np.atleast_1d(np.ones_like(e).astype(int).sum())).compute()\n",
")\n",
"\n",
"# Store projections\n",
"dask_store.update(dict(zip(\n",
" [\"%s/%s\" % (subgroup_post, e) for e in result.dtype.names],\n",
" [result[e] for e in result.dtype.names]\n",
")))\n",
"da_result_keys_0, da_result_chunks_0 = list(zip(*[[k, c] for k, c in zip(dask.core.flatten(da_result.__dask_keys__()), da_result_chunks_0) if c]))\n",
"\n",
"dask.distributed.progress(\n",
" dask.distributed.futures_of([\n",
" dask_store[\"%s/%s\" % (subgroup_post, e)]\n",
" for e in result.dtype.names\n",
" ]),\n",
" notebook=False\n",
"da_result_chunks = (\n",
" (da_result_chunks_0,) + da_result.chunks[1:]\n",
")\n",
"print(\"\")"
"\n",
"# (cls, dask, name, chunks, dtype, shape=None):\n",
"da_result_2 = da.Array(\n",
" dask.sharedict.merge(dask.optimization.cull(da_result.__dask_graph__(), list(da_result_keys_0))[0]),\n",
" da_result.name,\n",
" da_result_chunks,\n",
" da_result.dtype\n",
")\n",
"\n",
"dask_store[subgroup_post_mask] = da_result_2\n",
"\n",
"dask.distributed.progress(dask_store[subgroup_post_mask], notebook=False)\n",
"print(\"\")\n",
"\n",
"\n",
"# View results\n",
"da_imgs = dask_store[subgroup_post_mask]\n",
"da_imgs = da_imgs.astype(np.uint8)\n",
"\n",
"mplsv = plt.figure(FigureClass=MPLViewer)\n",
"mplsv.set_images(\n",
" da_imgs,\n",
" vmin=0,\n",
" vmax=1\n",
")"
]
},
{
Expand Down