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WIP: Rewrite and simplify postprocessing using Dask #236

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WIP: Rewrite and simplify postprocessing using Dask #236

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126 changes: 51 additions & 75 deletions nanshe_ipython.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1201,15 +1201,7 @@
"### 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 @@ -1219,87 +1211,71 @@
"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",
"noise_threshold = 2.5\n",
"\n",
"alignment_min_threshold = 0.6\n",
"overlap_min_threshold = 0.6\n",
"\n",
"\n",
"for k in zarr_store.get(subgroup_post, {}).keys():\n",
"for k in [\"post/zscore\", \"post/noise\", \"post/mask\"]:\n",
" with suppress(KeyError):\n",
" del dask_store[subgroup_post + \"/\" + k]\n",
" del dask_store[k]\n",
"with suppress(KeyError):\n",
" del zarr_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",
" del zarr_store[\"post\"]\n",
"zarr_store.require_group(\"post\")\n",
"\n",
"\n",
"da_imgs = dask_store[subgroup_dict]\n",
"\n",
"da_imgs_means = da_imgs.mean(axis=tuple(irange(1, da_imgs.ndim)), keepdims=True)\n",
"da_imgs_stds = da_imgs.std(axis=tuple(irange(1, da_imgs.ndim)), keepdims=True)\n",
"\n",
"da_imgs_zscore = (da_imgs - da_imgs_means) / da_imgs_stds\n",
"da_imgs_zscore_mag = abs(da_imgs_zscore)\n",
"\n",
"da_imgs_insignificant_mask = (da_imgs_zscore_mag < significance_threshold)\n",
"da_imgs_noise = da.atop(\n",
" lambda a, m: np.stack([np.nan_to_num(np.std(e_a[e_m])) for e_a, e_m in zip(a, m)]), (0,),\n",
" da_imgs, tuple(irange(da_imgs.ndim)),\n",
" da_imgs_insignificant_mask, tuple(irange(da_imgs.ndim)),\n",
" dtype=float,\n",
" concatenate=True\n",
")\n",
"da_imgs_noise = da_imgs_noise[(slice(None),) + (da_imgs.ndim - 1) * (None,)]\n",
"da_imgs_significant_mask = (da_imgs_zscore_mag >= (noise_threshold * da_imgs_noise / da_imgs_stds))\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",
"\n",
"# dask_store[\"post2_mask\"] = da_imgs_significant_mask\n",
"dask_store.update({\n",
" \"post/noise\": da_imgs_noise,\n",
" \"post/zscore\": da_imgs_zscore,\n",
" \"post/mask\": da_imgs_significant_mask\n",
"})\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",
" dask_store[\"post/noise\"],\n",
" dask_store[\"post/zscore\"],\n",
" dask_store[\"post/mask\"],\n",
" ]),\n",
" notebook=False\n",
")\n",
"print(\"\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"da_imgs = dask_store[\"post/mask\"].astype(np.uint8)\n",
"\n",
"mplsv = plt.figure(FigureClass=MPLViewer)\n",
"mplsv.set_images(\n",
" da_imgs,\n",
" vmin=0,\n",
" vmax=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
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