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        "description": "# convolutional network metric scripts\n- Code for fast watersheds. Code is based around code from https://bitbucket.org/poozh/watershed described in http://arxiv.org/abs/1505.00249.  For use in https://github.com/naibaf7/PyGreentea. \n\n# building \n\n### conda\n- `conda install -c conda-forge zwatershed`\n\n### pip [<img src=\"https://img.shields.io/pypi/v/zwatershed.svg?maxAge=2592000\">](https://pypi.python.org/pypi/zwatershed/)\n- `pip install zwatershed`\n\n### from source\n- clone the repository\n- run ./make.sh\n\n### requirements\n- numpy, h5py, cython\n- if using parallel watershed, also requires multiprocessing or pyspark\n- in order to build the cython, requires a c++ compiler and boost\n\n# function api\n- `(segs, rand) = zwatershed_and_metrics(segTrue, aff_graph, eval_thresh_list, seg_save_thresh_list)`\n\t- *returns segmentations and metrics*\n\t- `segs`: list of segmentations\n\t\t- `len(segs) == len(seg_save_thresh_list)`\n\t- `rand`: dict\n\t\t- `rand['V_Rand']`:  V_Rand score (scalar)\n\t\t- `rand['V_Rand_split']`: list of score values\n\t\t\t- `len(rand['V_Rand_split']) == len(eval_thresh_list)`\n\t\t- `rand['V_Rand_merge']`: list of score values, \n\t\t\t- `len(rand['V_Rand_merge']) == len(eval_thresh_list)`\n- `segs = zwatershed(aff_graph, seg_save_thresh_list)` \n\t\t- *returns segmentations*\n\t- `segs`: list of segmentations\n\t\t- `len(segs) == len(seg_save_thresh_list)`\n\n##### These methods have versions which save the segmentations to hdf5 files instead of returning them\n- `rand = zwatershed_and_metrics_h5(segTrue, aff_graph, eval_thresh_list, seg_save_thresh_list, seg_save_path)`\n- `zwatershed_h5(aff_graph, eval_thresh_list, seg_save_path)`\n\n##### All 4 methods have versions which take an edgelist representation of the affinity graph\n- `(segs, rand) = zwatershed_and_metrics_arb(segTrue, node1, node2, edgeWeight, eval_thresh_list, seg_save_thresh_list)`\n- `segs = zwatershed_arb(seg_shape, node1, node2, edgeWeight, seg_save_thresh_list)`\n- `rand = zwatershed_and_metrics_h5_arb(segTrue, node1, node2, edgeWeight, eval_thresh_list, seg_save_thresh_list, seg_save_path)`\n- `zwatershed_h5_arb(seg_shape, node1, node2, edgeWeight, eval_thresh_list, seg_save_path)`\n\n# parallel watershed - 4 steps\n- *a full example is given in par_ex.ipynb*\n\n1. Partition the subvolumes\n\t- `partition_data = partition_subvols(pred_file,out_folder,max_len)`\n\t\t- evenly divides the data in *pred_file* with the constraint that no dimension of any subvolume is longer than max_len\n2. Zwatershed the subvolumes\n\t1. `eval_with_spark(partition_data[0])`\n\t\t- *with spark*\n\t2. `eval_with_par_map(partition_data[0],NUM_WORKERS)`\n\t\t- *with python multiprocessing map*\n\t- after evaluating, subvolumes will be saved into the out\\_folder directory named based on their smallest indices in each dimension (ex. path/to/out\\_folder/0\\_0\\_0\\_vol)\n3. Stitch the subvolumes together\n\t- `stitch_and_save(partition_data,outname)`\n\t\t- stitch together the subvolumes in partition_data\n\t\t- save to the hdf5 file outname\n\t\t\t- outname['starts'] = list of min_indices of each subvolume\n\t\t\t- outname['ends'] = list of max_indices of each subvolume\n\t\t\t- outname['seg'] = full stitched segmentation\n\t\t\t- outname['seg_sizes'] = array of size of each segmentation\n\t\t\t- outname['rg_i'] = region graph for ith subvolume\n4. Threshold individual subvolumes by merging\n\t- `seg_merged = merge_by_thresh(seg,seg_sizes,rg,thresh)`\n\t\t- load in these areguments from outname\n",
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