From 5edfe6c5ec28b114c8a47bf5cc9c3e3a4a08634c Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:19:52 +0300 Subject: [PATCH 01/11] Apply ruff/Pyflakes rule F401 to notebooks F401 imported but unused --- notebooks/dask_2d_subset.ipynb | 1 - notebooks/dask_copy.ipynb | 6 +----- notebooks/dask_count_alleles.ipynb | 11 +---------- notebooks/object_arrays.ipynb | 4 +--- 4 files changed, 3 insertions(+), 19 deletions(-) diff --git a/notebooks/dask_2d_subset.ipynb b/notebooks/dask_2d_subset.ipynb index 6e88b510d5..f675e7e09b 100644 --- a/notebooks/dask_2d_subset.ipynb +++ b/notebooks/dask_2d_subset.ipynb @@ -26,7 +26,6 @@ "source": [ "import zarr; print('zarr', zarr.__version__)\n", "import dask; print('dask', dask.__version__)\n", - "import dask.array as da\n", "import numpy as np" ] }, diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index ba4391737a..b11c5685d0 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -137,7 +137,6 @@ "sys.path.insert(0, '..')\n", "import zarr\n", "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", "import numpy as np\n", "import h5py\n", "import bcolz\n", @@ -145,12 +144,9 @@ "# see whether dask can make good use of multiple CPUs\n", "bcolz.set_nthreads(1)\n", "import multiprocessing\n", - "import dask\n", "import dask.array as da\n", - "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n", + "from dask.diagnostics import Profiler, ResourceProfiler\n", "from dask.diagnostics.profile_visualize import visualize\n", - "from cachey import nbytes\n", - "import bokeh\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] diff --git a/notebooks/dask_count_alleles.ipynb b/notebooks/dask_count_alleles.ipynb index 8b9b7cec6e..72e4154c7e 100644 --- a/notebooks/dask_count_alleles.ipynb +++ b/notebooks/dask_count_alleles.ipynb @@ -130,20 +130,11 @@ "sys.path.insert(0, '..')\n", "import zarr\n", "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", - "import numpy as np\n", "import h5py\n", - "import multiprocessing\n", - "import dask\n", - "import dask.array as da\n", - "from dask.diagnostics import Profiler, ResourceProfiler, CacheProfiler\n", + "from dask.diagnostics import Profiler, ResourceProfiler\n", "from dask.diagnostics.profile_visualize import visualize\n", - "from cachey import nbytes\n", - "import bokeh\n", "from bokeh.io import output_notebook\n", "output_notebook()\n", - "from functools import reduce\n", - "import operator\n", "import allel" ] }, diff --git a/notebooks/object_arrays.ipynb b/notebooks/object_arrays.ipynb index 714d024907..a648553ff6 100644 --- a/notebooks/object_arrays.ipynb +++ b/notebooks/object_arrays.ipynb @@ -14,9 +14,7 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "import numpy as np" - ] + "source": [] }, { "cell_type": "code", From cb398b07c92e513cf27a956924a6567b36dff13d Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:21:21 +0300 Subject: [PATCH 02/11] Apply ruff/Pyflakes rule F811 to notebooks F811 Redefinition of unused --- notebooks/advanced_indexing.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb index eba6b5880b..a58d2173b4 100644 --- a/notebooks/advanced_indexing.ipynb +++ b/notebooks/advanced_indexing.ipynb @@ -1438,7 +1438,6 @@ "outputs": [], "source": [ "import tempfile\n", - "import cProfile\n", "import pstats\n", "\n", "def profile(statement, sort='time', restrictions=(7,)):\n", @@ -2637,8 +2636,7 @@ "metadata": {}, "outputs": [], "source": [ - "import h5py\n", - "import tempfile" + "import h5py" ] }, { From 396a1655997825828048b2cc93e5ffe82751f215 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:26:07 +0300 Subject: [PATCH 03/11] Apply ruff/pycodestyle rule W291 rule to notebooks W291 Trailing whitespace --- notebooks/advanced_indexing.ipynb | 4 ++-- notebooks/dask_copy.ipynb | 10 +++++----- notebooks/genotype_benchmark_compressors.ipynb | 12 ++++++------ 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb index a58d2173b4..ceb0cbd7bc 100644 --- a/notebooks/advanced_indexing.ipynb +++ b/notebooks/advanced_indexing.ipynb @@ -975,7 +975,7 @@ "source": [ "a = np.array([(b'aaa', 1, 4.2),\n", " (b'bbb', 2, 8.4),\n", - " (b'ccc', 3, 12.6)], \n", + " (b'ccc', 3, 12.6)],\n", " dtype=[('foo', 'S3'), ('bar', 'i4'), ('baz', 'f8')])\n", "za = zarr.array(a, chunks=2, fill_value=None)\n", "za[:]" @@ -2731,7 +2731,7 @@ "metadata": {}, "outputs": [], "source": [ - "# # this is pathological, takes minutes \n", + "# # this is pathological, takes minutes\n", "# %time hc[ix_dense_bool]" ] }, diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index b11c5685d0..507980240c 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -140,7 +140,7 @@ "import numpy as np\n", "import h5py\n", "import bcolz\n", - "# don't let bcolz use multiple threads internally, we want to \n", + "# don't let bcolz use multiple threads internally, we want to\n", "# see whether dask can make good use of multiple CPUs\n", "bcolz.set_nthreads(1)\n", "import multiprocessing\n", @@ -563,7 +563,7 @@ } ], "source": [ - "z1 = zarr.array(a1, chunks=chunks, compression='blosc', \n", + "z1 = zarr.array(a1, chunks=chunks, compression='blosc',\n", " compression_opts=dict(cname='lz4', clevel=1, shuffle=2))\n", "z1" ] @@ -930,8 +930,8 @@ } ], "source": [ - "h2 = h5f.create_dataset('h2', shape=h1.shape, chunks=h1.chunks, \n", - " compression=h1.compression, compression_opts=h1.compression_opts, \n", + "h2 = h5f.create_dataset('h2', shape=h1.shape, chunks=h1.chunks,\n", + " compression=h1.compression, compression_opts=h1.compression_opts,\n", " shuffle=h1.shuffle)\n", "h5d_diagnostics(h2)" ] @@ -1214,7 +1214,7 @@ } ], "source": [ - "c2 = bcolz.zeros(a1.shape, chunklen=chunks[0], dtype=a1.dtype, \n", + "c2 = bcolz.zeros(a1.shape, chunklen=chunks[0], dtype=a1.dtype,\n", " cparams=bcolz.cparams(cname='lz4', clevel=1, shuffle=2))\n", "c2" ] diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index b262e63fa0..986d08c4cc 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -133,8 +133,8 @@ ], "source": [ "# 1M chunks of first dimension\n", - "chunks = (int(2**20 / (genotype_sample.shape[1] * genotype_sample.shape[2])), \n", - " genotype_sample.shape[1], \n", + "chunks = (int(2**20 / (genotype_sample.shape[1] * genotype_sample.shape[2])),\n", + " genotype_sample.shape[1],\n", " genotype_sample.shape[2])\n", "chunks" ] @@ -284,7 +284,7 @@ "def compression_ratios():\n", " x = list()\n", " for compression, compression_opts in compression_configs:\n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", + " z = zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", " compression_opts=compression_opts)\n", " ratio = z.nbytes / z.nbytes_stored\n", " x.append(ratio)\n", @@ -311,7 +311,7 @@ } ], "source": [ - "ratios = compression_ratios() \n", + "ratios = compression_ratios()\n", "labels = ['%s - %s' % (c, o)\n", " for c, o in compression_configs]\n", "\n", @@ -354,14 +354,14 @@ " for compression, compression_opts in compression_configs:\n", " \n", " def compress():\n", - " zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", + " zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", " compression_opts=compression_opts)\n", " \n", " t = timeit.Timer(stmt=compress, globals=locals())\n", " compress_times = t.repeat(repeat=repeat, number=number)\n", " c.append(compress_times)\n", " \n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression, \n", + " z = zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", " compression_opts=compression_opts)\n", " \n", " def decompress():\n", From 12f6df899c24ac12a109d33b435d7f7cbc8c90fb Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:27:00 +0300 Subject: [PATCH 04/11] Appply ruff/pycodestyle rule W293 to notebooks W293 Blank line contains whitespace --- notebooks/dask_copy.ipynb | 8 +++----- notebooks/genotype_benchmark_compressors.ipynb | 17 ++++++++--------- notebooks/store_benchmark.ipynb | 14 ++++++-------- 3 files changed, 17 insertions(+), 22 deletions(-) diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index 507980240c..772e85540a 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -184,7 +184,7 @@ "\n", "def h5d_diagnostics(d):\n", " \"\"\"Print some diagnostics on an HDF5 dataset.\"\"\"\n", - " \n", + "\n", " print(d)\n", " nbytes = reduce(operator.mul, d.shape) * d.dtype.itemsize\n", " cbytes = d._id.get_storage_size()\n", @@ -199,8 +199,7 @@ " r += '; nbytes_stored: %s' % human_readable_size(cbytes)\n", " r += '; ratio: %.1f' % ratio\n", " r += '; chunks: %s' % str(d.chunks)\n", - " print(r)\n", - " " + " print(r)\n" ] }, { @@ -215,8 +214,7 @@ " dsrc = da.from_array(src, chunks=chunks)\n", " with Profiler() as prof, ResourceProfiler(dt=dt) as rprof:\n", " da.store(dsrc, dst, num_workers=num_workers, lock=lock)\n", - " visualize([prof, rprof], min_border_top=60, min_border_bottom=60)\n", - " " + " visualize([prof, rprof], min_border_top=60, min_border_bottom=60)\n" ] }, { diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index 986d08c4cc..d766bfe204 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -352,29 +352,28 @@ " c = list()\n", " d = list()\n", " for compression, compression_opts in compression_configs:\n", - " \n", + "\n", " def compress():\n", " zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", " compression_opts=compression_opts)\n", - " \n", + "\n", " t = timeit.Timer(stmt=compress, globals=locals())\n", " compress_times = t.repeat(repeat=repeat, number=number)\n", " c.append(compress_times)\n", - " \n", + "\n", " z = zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", " compression_opts=compression_opts)\n", - " \n", + "\n", " def decompress():\n", " z[:]\n", - " \n", + "\n", " t = timeit.Timer(stmt=decompress, globals=locals())\n", " decompress_times = t.repeat(repeat=repeat, number=number)\n", " d.append(decompress_times)\n", - " \n", + "\n", " log(compression, compression_opts, compress_times, decompress_times)\n", - " \n", - " return c, d\n", - " " + "\n", + " return c, d\n" ] }, { diff --git a/notebooks/store_benchmark.ipynb b/notebooks/store_benchmark.ipynb index 869e7df608..b053f5357b 100644 --- a/notebooks/store_benchmark.ipynb +++ b/notebooks/store_benchmark.ipynb @@ -112,10 +112,10 @@ " shutil.rmtree(bench_dir)\n", " os.makedirs(bench_dir)\n", "\n", - " \n", + "\n", "def setup(a, name='foo/bar'):\n", " global fdict_z, hdict_z, lmdb_z, gdbm_z, ndbm_z, bdbm_btree_z, bdbm_hash_z, zip_z, dir_z\n", - " \n", + "\n", " clean()\n", " fdict_root = zarr.group(store=dict())\n", " hdict_root = zarr.group(store=zarr.DictStore())\n", @@ -140,8 +140,7 @@ " # check compression ratio\n", " fdict_z[:] = a\n", " return fdict_z.info\n", - " \n", - " " + "\n" ] }, { @@ -164,8 +163,8 @@ " z[:] = a\n", " if hasattr(z.store, 'flush'):\n", " z.store.flush()\n", - " \n", - " \n", + "\n", + "\n", "def load(z, a):\n", " z.get_basic_selection(out=a)\n" ] @@ -933,8 +932,7 @@ " result = (d // 2) * 2\n", " da.store(result, sink, lock=False)\n", " if hasattr(sink.store, 'flush'):\n", - " sink.store.flush()\n", - " " + " sink.store.flush()\n" ] }, { From 04ee41917f4cc6aff6ce775d25bd436ff32b222f Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:29:47 +0300 Subject: [PATCH 05/11] Apply ruff/isort rule I001 to notebooks I001 Import block is un-sorted or un-formatted --- notebooks/advanced_indexing.ipynb | 9 +++++++-- notebooks/blosc_microbench.ipynb | 7 ++++++- notebooks/dask_2d_subset.ipynb | 1 + notebooks/dask_copy.ipynb | 14 ++++++++++---- notebooks/dask_count_alleles.ipynb | 5 ++++- notebooks/genotype_benchmark_compressors.ipynb | 7 ++++++- notebooks/object_arrays.ipynb | 3 +++ notebooks/repr_tree.ipynb | 1 + notebooks/store_benchmark.ipynb | 4 ++++ notebooks/zip_benchmark.ipynb | 4 ++++ 10 files changed, 46 insertions(+), 9 deletions(-) diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb index ceb0cbd7bc..e86d04338a 100644 --- a/notebooks/advanced_indexing.ipynb +++ b/notebooks/advanced_indexing.ipynb @@ -25,11 +25,15 @@ ], "source": [ "import sys\n", + "\n", "sys.path.insert(0, '..')\n", - "import zarr\n", "import numpy as np\n", + "\n", + "import zarr\n", + "\n", "np.random.seed(42)\n", "import cProfile\n", + "\n", "zarr.__version__" ] }, @@ -1437,8 +1441,9 @@ "metadata": {}, "outputs": [], "source": [ - "import tempfile\n", "import pstats\n", + "import tempfile\n", + "\n", "\n", "def profile(statement, sort='time', restrictions=(7,)):\n", " with tempfile.NamedTemporaryFile() as f:\n", diff --git a/notebooks/blosc_microbench.ipynb b/notebooks/blosc_microbench.ipynb index 9361d8e95b..fbcfaa6743 100644 --- a/notebooks/blosc_microbench.ipynb +++ b/notebooks/blosc_microbench.ipynb @@ -20,7 +20,9 @@ ], "source": [ "import numpy as np\n", + "\n", "import zarr\n", + "\n", "zarr.__version__" ] }, @@ -101,10 +103,13 @@ } ], "source": [ - "import numpy as np\n", "import sys\n", + "\n", + "import numpy as np\n", + "\n", "sys.path.insert(0, '..')\n", "import zarr\n", + "\n", "zarr.__version__" ] }, diff --git a/notebooks/dask_2d_subset.ipynb b/notebooks/dask_2d_subset.ipynb index f675e7e09b..98dd126f29 100644 --- a/notebooks/dask_2d_subset.ipynb +++ b/notebooks/dask_2d_subset.ipynb @@ -366,6 +366,7 @@ "source": [ "# what's taking so long?\n", "import cProfile\n", + "\n", "cProfile.run('gd[dim0_condition][:, dim1_indices]', sort='time')" ] }, diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index 772e85540a..ab87746e7b 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -134,20 +134,25 @@ ], "source": [ "import sys\n", + "\n", "sys.path.insert(0, '..')\n", "import zarr\n", + "\n", "print('zarr', zarr.__version__)\n", - "import numpy as np\n", - "import h5py\n", "import bcolz\n", + "import h5py\n", + "import numpy as np\n", + "\n", "# don't let bcolz use multiple threads internally, we want to\n", "# see whether dask can make good use of multiple CPUs\n", "bcolz.set_nthreads(1)\n", "import multiprocessing\n", + "\n", "import dask.array as da\n", + "from bokeh.io import output_notebook\n", "from dask.diagnostics import Profiler, ResourceProfiler\n", "from dask.diagnostics.profile_visualize import visualize\n", - "from bokeh.io import output_notebook\n", + "\n", "output_notebook()" ] }, @@ -159,9 +164,10 @@ }, "outputs": [], "source": [ - "import tempfile\n", "import operator\n", + "import tempfile\n", "from functools import reduce\n", + "\n", "from zarr.util import human_readable_size\n", "\n", "\n", diff --git a/notebooks/dask_count_alleles.ipynb b/notebooks/dask_count_alleles.ipynb index 72e4154c7e..3ae12cc7f6 100644 --- a/notebooks/dask_count_alleles.ipynb +++ b/notebooks/dask_count_alleles.ipynb @@ -127,13 +127,16 @@ ], "source": [ "import sys\n", + "\n", "sys.path.insert(0, '..')\n", "import zarr\n", + "\n", "print('zarr', zarr.__version__)\n", "import h5py\n", + "from bokeh.io import output_notebook\n", "from dask.diagnostics import Profiler, ResourceProfiler\n", "from dask.diagnostics.profile_visualize import visualize\n", - "from bokeh.io import output_notebook\n", + "\n", "output_notebook()\n", "import allel" ] diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index d766bfe204..94898ddda5 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -18,15 +18,20 @@ ], "source": [ "import sys\n", + "\n", "sys.path.insert(0, '..')\n", "import functools\n", "import timeit\n", + "\n", "import zarr\n", + "\n", "print('zarr', zarr.__version__)\n", "from zarr import blosc\n", + "\n", "print('blosc', blosc.version())\n", - "import numpy as np\n", "import h5py\n", + "import numpy as np\n", + "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] diff --git a/notebooks/object_arrays.ipynb b/notebooks/object_arrays.ipynb index a648553ff6..e649656153 100644 --- a/notebooks/object_arrays.ipynb +++ b/notebooks/object_arrays.ipynb @@ -34,6 +34,7 @@ ], "source": [ "import zarr\n", + "\n", "zarr.__version__" ] }, @@ -55,6 +56,7 @@ ], "source": [ "import numcodecs\n", + "\n", "numcodecs.__version__" ] }, @@ -291,6 +293,7 @@ ], "source": [ "from numcodecs.tests.common import greetings\n", + "\n", "z = zarr.array(greetings, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", "z[:]" ] diff --git a/notebooks/repr_tree.ipynb b/notebooks/repr_tree.ipynb index dde8bbb9a0..9ec0b5f552 100644 --- a/notebooks/repr_tree.ipynb +++ b/notebooks/repr_tree.ipynb @@ -18,6 +18,7 @@ ], "source": [ "import zarr\n", + "\n", "zarr.__version__" ] }, diff --git a/notebooks/store_benchmark.ipynb b/notebooks/store_benchmark.ipynb index b053f5357b..a95a5d3da7 100644 --- a/notebooks/store_benchmark.ipynb +++ b/notebooks/store_benchmark.ipynb @@ -32,6 +32,7 @@ ], "source": [ "import zarr\n", + "\n", "zarr.__version__" ] }, @@ -53,6 +54,7 @@ ], "source": [ "import bsddb3\n", + "\n", "bsddb3.__version__" ] }, @@ -74,6 +76,7 @@ ], "source": [ "import lmdb\n", + "\n", "lmdb.__version__" ] }, @@ -104,6 +107,7 @@ "source": [ "import os\n", "import shutil\n", + "\n", "bench_dir = '../data/bench'\n", "\n", "\n", diff --git a/notebooks/zip_benchmark.ipynb b/notebooks/zip_benchmark.ipynb index 6805552422..d14a7c2ea0 100644 --- a/notebooks/zip_benchmark.ipynb +++ b/notebooks/zip_benchmark.ipynb @@ -20,8 +20,10 @@ ], "source": [ "import sys\n", + "\n", "sys.path.insert(0, '..')\n", "import zarr\n", + "\n", "zarr.__version__" ] }, @@ -135,6 +137,7 @@ ], "source": [ "import cProfile\n", + "\n", "cProfile.run('z[:10]', sort='cumtime')" ] }, @@ -159,6 +162,7 @@ "source": [ "import dask\n", "import dask.array as da\n", + "\n", "dask.__version__" ] }, From bd2d489ae9365bdf51df3a8703ef9cd0aa2c141e Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:31:40 +0300 Subject: [PATCH 06/11] Apply ruff/pyupgrde rule UP031 to notebooks UP031 Use format specifiers instead of percent format --- notebooks/dask_copy.ipynb | 14 +++++++------- notebooks/genotype_benchmark_compressors.ipynb | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index ab87746e7b..c5af37c92d 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -198,13 +198,13 @@ " ratio = nbytes / cbytes\n", " else:\n", " ratio = np.inf\n", - " r = ' compression: %s' % d.compression\n", - " r += '; compression_opts: %s' % d.compression_opts\n", - " r += '; shuffle: %s' % d.shuffle\n", - " r += '\\n nbytes: %s' % human_readable_size(nbytes)\n", - " r += '; nbytes_stored: %s' % human_readable_size(cbytes)\n", - " r += '; ratio: %.1f' % ratio\n", - " r += '; chunks: %s' % str(d.chunks)\n", + " r = ' compression: {}'.format(d.compression)\n", + " r += '; compression_opts: {}'.format(d.compression_opts)\n", + " r += '; shuffle: {}'.format(d.shuffle)\n", + " r += '\\n nbytes: {}'.format(human_readable_size(nbytes))\n", + " r += '; nbytes_stored: {}'.format(human_readable_size(cbytes))\n", + " r += '; ratio: {:.1f}'.format(ratio)\n", + " r += '; chunks: {}'.format(str(d.chunks))\n", " print(r)\n" ] }, diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index 94898ddda5..8b99959fde 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -317,7 +317,7 @@ ], "source": [ "ratios = compression_ratios()\n", - "labels = ['%s - %s' % (c, o)\n", + "labels = ['{} - {}'.format(c, o)\n", " for c, o in compression_configs]\n", "\n", "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", From 43606ce33a06479176324cd1dadec6e65a7a05e3 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:32:34 +0300 Subject: [PATCH 07/11] Apply ruff/pyupgrade rule UP032 to notebooks UP032 Use f-string instead of `format` call --- notebooks/dask_copy.ipynb | 14 +++++++------- notebooks/genotype_benchmark_compressors.ipynb | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index c5af37c92d..9068d903b4 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -198,13 +198,13 @@ " ratio = nbytes / cbytes\n", " else:\n", " ratio = np.inf\n", - " r = ' compression: {}'.format(d.compression)\n", - " r += '; compression_opts: {}'.format(d.compression_opts)\n", - " r += '; shuffle: {}'.format(d.shuffle)\n", - " r += '\\n nbytes: {}'.format(human_readable_size(nbytes))\n", - " r += '; nbytes_stored: {}'.format(human_readable_size(cbytes))\n", - " r += '; ratio: {:.1f}'.format(ratio)\n", - " r += '; chunks: {}'.format(str(d.chunks))\n", + " r = f' compression: {d.compression}'\n", + " r += f'; compression_opts: {d.compression_opts}'\n", + " r += f'; shuffle: {d.shuffle}'\n", + " r += f'\\n nbytes: {human_readable_size(nbytes)}'\n", + " r += f'; nbytes_stored: {human_readable_size(cbytes)}'\n", + " r += f'; ratio: {ratio:.1f}'\n", + " r += f'; chunks: {str(d.chunks)}'\n", " print(r)\n" ] }, diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index 8b99959fde..584cee454e 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -317,7 +317,7 @@ ], "source": [ "ratios = compression_ratios()\n", - "labels = ['{} - {}'.format(c, o)\n", + "labels = [f'{c} - {o}'\n", " for c, o in compression_configs]\n", "\n", "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", From 5141321ee3bc24f7fe6b96560462b7f1aa1c8053 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:33:33 +0300 Subject: [PATCH 08/11] Apply ruff/pyupgrade rule UP033 to notebooks UP033 Use `@functools.cache` instead of `@functools.lru_cache(maxsize=None)` --- notebooks/genotype_benchmark_compressors.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index 584cee454e..73ade8b95d 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -285,7 +285,7 @@ }, "outputs": [], "source": [ - "@functools.lru_cache(maxsize=None)\n", + "@functools.cache\n", "def compression_ratios():\n", " x = list()\n", " for compression, compression_opts in compression_configs:\n", @@ -352,7 +352,7 @@ }, "outputs": [], "source": [ - "@functools.lru_cache(maxsize=None)\n", + "@functools.cache\n", "def compression_decompression_times(repeat=3, number=1):\n", " c = list()\n", " d = list()\n", From f10e623991a98a650fee95e61b8fc05ad7b211c9 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Sat, 24 Aug 2024 17:37:10 +0300 Subject: [PATCH 09/11] Apply ruff rule RUF010 to notebooks RUF010 Use explicit conversion flag --- notebooks/dask_copy.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index 9068d903b4..33ec017f01 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -204,7 +204,7 @@ " r += f'\\n nbytes: {human_readable_size(nbytes)}'\n", " r += f'; nbytes_stored: {human_readable_size(cbytes)}'\n", " r += f'; ratio: {ratio:.1f}'\n", - " r += f'; chunks: {str(d.chunks)}'\n", + " r += f'; chunks: {d.chunks}'\n", " print(r)\n" ] }, From c3c3a991fad25f0b4269fd90a0bcd379b8ef4c9f Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Wed, 28 Aug 2024 12:36:04 +0300 Subject: [PATCH 10/11] Apply ruff/pycodestyle rule E402 to notebooks E402 Module level import not at top of cell --- notebooks/advanced_indexing.ipynb | 5 +++-- notebooks/dask_2d_subset.ipynb | 10 +++++++--- notebooks/dask_copy.ipynb | 19 +++++++++--------- notebooks/dask_count_alleles.ipynb | 13 ++++++------ .../genotype_benchmark_compressors.ipynb | 20 +++++++++---------- 5 files changed, 37 insertions(+), 30 deletions(-) diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb index e86d04338a..eb2365a112 100644 --- a/notebooks/advanced_indexing.ipynb +++ b/notebooks/advanced_indexing.ipynb @@ -24,15 +24,16 @@ } ], "source": [ + "import cProfile\n", "import sys\n", "\n", - "sys.path.insert(0, '..')\n", "import numpy as np\n", "\n", + "sys.path.insert(0, '..')\n", + "\n", "import zarr\n", "\n", "np.random.seed(42)\n", - "import cProfile\n", "\n", "zarr.__version__" ] diff --git a/notebooks/dask_2d_subset.ipynb b/notebooks/dask_2d_subset.ipynb index 98dd126f29..839e306627 100644 --- a/notebooks/dask_2d_subset.ipynb +++ b/notebooks/dask_2d_subset.ipynb @@ -24,9 +24,13 @@ } ], "source": [ - "import zarr; print('zarr', zarr.__version__)\n", - "import dask; print('dask', dask.__version__)\n", - "import numpy as np" + "import dask\n", + "import numpy as np\n", + "\n", + "import zarr\n", + "\n", + "print('zarr', zarr.__version__)\n", + "print('dask', dask.__version__)\n" ] }, { diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb index 33ec017f01..41cc07ae68 100644 --- a/notebooks/dask_copy.ipynb +++ b/notebooks/dask_copy.ipynb @@ -133,25 +133,26 @@ } ], "source": [ + "import multiprocessing\n", "import sys\n", "\n", + "import bcolz\n", + "import dask.array as da\n", + "import h5py\n", + "import numpy as np\n", + "from bokeh.io import output_notebook\n", + "from dask.diagnostics import Profiler, ResourceProfiler\n", + "from dask.diagnostics.profile_visualize import visualize\n", + "\n", "sys.path.insert(0, '..')\n", + "\n", "import zarr\n", "\n", "print('zarr', zarr.__version__)\n", - "import bcolz\n", - "import h5py\n", - "import numpy as np\n", "\n", "# don't let bcolz use multiple threads internally, we want to\n", "# see whether dask can make good use of multiple CPUs\n", "bcolz.set_nthreads(1)\n", - "import multiprocessing\n", - "\n", - "import dask.array as da\n", - "from bokeh.io import output_notebook\n", - "from dask.diagnostics import Profiler, ResourceProfiler\n", - "from dask.diagnostics.profile_visualize import visualize\n", "\n", "output_notebook()" ] diff --git a/notebooks/dask_count_alleles.ipynb b/notebooks/dask_count_alleles.ipynb index 3ae12cc7f6..64312c572c 100644 --- a/notebooks/dask_count_alleles.ipynb +++ b/notebooks/dask_count_alleles.ipynb @@ -128,17 +128,18 @@ "source": [ "import sys\n", "\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "\n", - "print('zarr', zarr.__version__)\n", + "import allel\n", "import h5py\n", "from bokeh.io import output_notebook\n", "from dask.diagnostics import Profiler, ResourceProfiler\n", "from dask.diagnostics.profile_visualize import visualize\n", "\n", - "output_notebook()\n", - "import allel" + "sys.path.insert(0, '..')\n", + "\n", + "import zarr\n", + "\n", + "print('zarr', zarr.__version__)\n", + "output_notebook()" ] }, { diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb index 73ade8b95d..e575bd0477 100644 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ b/notebooks/genotype_benchmark_compressors.ipynb @@ -17,23 +17,23 @@ } ], "source": [ - "import sys\n", - "\n", - "sys.path.insert(0, '..')\n", "import functools\n", + "import sys\n", "import timeit\n", "\n", - "import zarr\n", - "\n", - "print('zarr', zarr.__version__)\n", - "from zarr import blosc\n", - "\n", - "print('blosc', blosc.version())\n", "import h5py\n", "import numpy as np\n", "\n", "%matplotlib inline\n", - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "\n", + "sys.path.insert(0, '..')\n", + "\n", + "import zarr\n", + "from zarr import blosc\n", + "\n", + "print('zarr', zarr.__version__)\n", + "print('blosc', blosc.version())" ] }, { From a72301143a755293f7bd3598e47dd155dcd95af0 Mon Sep 17 00:00:00 2001 From: Dimitri Papadopoulos <3234522+DimitriPapadopoulos@users.noreply.github.com> Date: Wed, 25 Sep 2024 09:22:42 +0200 Subject: [PATCH 11/11] Remove notebooks Many of them are referencing data that we don't have access to. --- notebooks/advanced_indexing.ipynb | 2802 ----------------- notebooks/blosc_microbench.ipynb | 205 -- notebooks/dask_2d_subset.ipynb | 873 ----- notebooks/dask_copy.ipynb | 1519 --------- notebooks/dask_count_alleles.ipynb | 643 ---- .../genotype_benchmark_compressors.ipynb | 552 ---- notebooks/object_arrays.ipynb | 351 --- notebooks/repr_info.ipynb | 365 --- notebooks/repr_tree.ipynb | 1389 -------- notebooks/store_benchmark.ipynb | 1305 -------- notebooks/zip_benchmark.ipynb | 347 -- pyproject.toml | 1 - 12 files changed, 10352 deletions(-) delete mode 100644 notebooks/advanced_indexing.ipynb delete mode 100644 notebooks/blosc_microbench.ipynb delete mode 100644 notebooks/dask_2d_subset.ipynb delete mode 100644 notebooks/dask_copy.ipynb delete mode 100644 notebooks/dask_count_alleles.ipynb delete mode 100644 notebooks/genotype_benchmark_compressors.ipynb delete mode 100644 notebooks/object_arrays.ipynb delete mode 100644 notebooks/repr_info.ipynb delete mode 100644 notebooks/repr_tree.ipynb delete mode 100644 notebooks/store_benchmark.ipynb delete mode 100644 notebooks/zip_benchmark.ipynb diff --git a/notebooks/advanced_indexing.ipynb b/notebooks/advanced_indexing.ipynb deleted file mode 100644 index eb2365a112..0000000000 --- a/notebooks/advanced_indexing.ipynb +++ /dev/null @@ -1,2802 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Advanced indexing" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.1.5.dev144'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import cProfile\n", - "import sys\n", - "\n", - "import numpy as np\n", - "\n", - "sys.path.insert(0, '..')\n", - "\n", - "import zarr\n", - "\n", - "np.random.seed(42)\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Functionality and API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a Boolean (mask) array\n", - "\n", - "Supported via ``get/set_mask_selection()`` and ``.vindex[]``. Also supported via ``get/set_orthogonal_selection()`` and ``.oindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = [False, True, False, True, False, True, False, True, False, True]" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.oindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 100, 2, 300, 4, 500, 6, 700, 8, 900])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[ix] = a[ix] * 100\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# if using .oindex, indexing array can be any array-like, e.g., Zarr array\n", - "zix = zarr.array(ix, chunks=2)\n", - "za = zarr.array(a, chunks=2)\n", - "za.oindex[zix] # will not load all zix into memory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a 1D integer (coordinate) array\n", - "\n", - "Supported via ``get/set_coordinate_selection()`` and ``.vindex[]``. Also supported via ``get/set_orthogonal_selection()`` and ``.oindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = [1, 3, 5, 7, 9]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.oindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 100, 2, 300, 4, 500, 6, 700, 8, 900])" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[ix] = a[ix] * 100\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Indexing a 1D array with a multi-dimensional integer (coordinate) array\n", - "\n", - "Supported via ``get/set_coordinate_selection()`` and ``.vindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)\n", - "ix = np.array([[1, 3, 5], [2, 4, 6]])" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[1, 3, 5],\n", - " [2, 4, 6]])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 20, 30, 40, 50, 60, 7, 8, 9])" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.vindex[ix] = a[ix] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Slicing a 1D array with step > 1\n", - "\n", - "Slices with step > 1 are supported via ``get/set_basic_selection()``, ``get/set_orthogonal_selection()``, ``__getitem__`` and ``.oindex[]``. Negative steps are not supported." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "a = np.arange(10)\n", - "za = zarr.array(a, chunks=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 3, 5, 7, 9])" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "za[1::2]" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 0, 10, 2, 30, 4, 50, 6, 70, 8, 90])" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.oindex[1::2] = a[1::2] * 10\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Orthogonal (outer) indexing of multi-dimensional arrays\n", - "\n", - "Orthogonal (a.k.a. outer) indexing is supported with either Boolean or integer arrays, in combination with integers and slices. This functionality is provided via the ``get/set_orthogonal_selection()`` methods. For convenience, this functionality is also available via the ``.oindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# orthogonal indexing with Boolean arrays\n", - "ix0 = [False, True, False, True, False]\n", - "ix1 = [True, False, True]\n", - "za.get_orthogonal_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# orthogonal indexing with integer arrays\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "za.get_orthogonal_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 5],\n", - " [ 9, 11]])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 3, 4, 5],\n", - " [ 9, 10, 11]])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# combine with slice\n", - "za.oindex[[1, 3], :]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 2],\n", - " [ 3, 5],\n", - " [ 6, 8],\n", - " [ 9, 11],\n", - " [12, 14]])" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# combine with slice\n", - "za.oindex[:, [0, 2]]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 42],\n", - " [ 6, 7, 8],\n", - " [42, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items via Boolean selection\n", - "ix0 = [False, True, False, True, False]\n", - "ix1 = [True, False, True]\n", - "selection = ix0, ix1\n", - "value = 42\n", - "za.set_orthogonal_selection(selection, value)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 44],\n", - " [ 6, 7, 8],\n", - " [44, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [46, 4, 46],\n", - " [ 6, 7, 8],\n", - " [46, 10, 46],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items via integer selection\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "selection = ix0, ix1\n", - "value = 46\n", - "za.set_orthogonal_selection(selection, value)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [48, 4, 48],\n", - " [ 6, 7, 8],\n", - " [48, 10, 48],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.oindex[ix0, ix1] = 48\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Coordinate indexing of multi-dimensional arrays\n", - "\n", - "Selecting arbitrary points from a multi-dimensional array by indexing with integer (coordinate) arrays is supported. This functionality is provided via the ``get/set_coordinate_selection()`` methods. For convenience, this functionality is also available via the ``.vindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# get items\n", - "ix0 = [1, 3]\n", - "ix1 = [0, 2]\n", - "za.get_coordinate_selection((ix0, ix1))" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.vindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# set items\n", - "za.set_coordinate_selection((ix0, ix1), 42)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# alternative API\n", - "za.vindex[ix0, ix1] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Mask indexing of multi-dimensional arrays\n", - "\n", - "Selecting arbitrary points from a multi-dimensional array by a Boolean array is supported. This functionality is provided via the ``get/set_mask_selection()`` methods. For convenience, this functionality is also available via the ``.vindex[]`` property." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(15).reshape(5, 3)\n", - "za = zarr.array(a, chunks=(3, 2))\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix = np.zeros_like(a, dtype=bool)\n", - "ix[1, 0] = True\n", - "ix[3, 2] = True\n", - "za.get_mask_selection(ix)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 3, 11])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.vindex[ix]" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [42, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 42],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.set_mask_selection(ix, 42)\n", - "za[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[ 0, 1, 2],\n", - " [44, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 44],\n", - " [12, 13, 14]])" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "za.vindex[ix] = 44\n", - "za[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Selecting fields from arrays with a structured dtype\n", - "\n", - "All ``get/set_selection_...()`` methods support a ``fields`` argument which allows retrieving/replacing data for a specific field or fields. Also h5py-like API is supported where fields can be provided within ``__getitem__``, ``.oindex[]`` and ``.vindex[]``." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(b'aaa', 1, 4.2), (b'bbb', 2, 8.4), (b'ccc', 3, 12.6)],\n", - " dtype=[('foo', 'S3'), ('bar', '\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'baz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices" - ] - } - ], - "source": [ - "a['foo', 'baz']" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([(b'aaa', 4.2), (b'bbb', 8.4), (b'ccc', 12.6)],\n", - " dtype=[('foo', 'S3'), ('baz', '", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mza\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'foo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'baz'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m 537\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 538\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 539\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 541\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEllipsis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mget_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 661\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 662\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 663\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_basic_selection_nd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 664\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 665\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[0;31m# setup indexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 705\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/indexing.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, selection, array)\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 276\u001b[0m raise IndexError('unsupported selection item for basic indexing; expected integer '\n\u001b[0;32m--> 277\u001b[0;31m 'or slice, got {!r}'.format(type(dim_sel)))\n\u001b[0m\u001b[1;32m 278\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 279\u001b[0m \u001b[0mdim_indexers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdim_indexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mIndexError\u001b[0m: unsupported selection item for basic indexing; expected integer or slice, got " - ] - } - ], - "source": [ - "za[['foo', 'baz']]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1D Benchmarking" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "800000000" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c = np.arange(100000000)\n", - "c.nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 480 ms, sys: 16 ms, total: 496 ms\n", - "Wall time: 141 ms\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (100000000,)\n", - "Chunk shape : (97657,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 800000000 (762.9M)\n", - "No. bytes stored : 11854081 (11.3M)\n", - "Storage ratio : 67.5\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time zc = zarr.array(c)\n", - "zc.info" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "121 ms ± 1.49 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "254 ms ± 942 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc[:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool dense selection" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9997476" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# relatively dense selection - 10%\n", - "ix_dense_bool = np.random.binomial(1, 0.1, size=c.shape[0]).astype(bool)\n", - "np.count_nonzero(ix_dense_bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "243 ms ± 5.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "433 ms ± 6.49 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "548 ms ± 5.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [], - "source": [ - "import pstats\n", - "import tempfile\n", - "\n", - "\n", - "def profile(statement, sort='time', restrictions=(7,)):\n", - " with tempfile.NamedTemporaryFile() as f:\n", - " cProfile.run(statement, filename=f.name)\n", - " pstats.Stats(f.name).sort_stats(sort).print_stats(*restrictions)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:17:48 2017 /tmp/tmpruua2rs_\n", - "\n", - " 98386 function calls in 0.483 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 83 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1025 0.197 0.000 0.197 0.000 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.149 0.000 0.159 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.044 0.000 0.231 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1024 0.009 0.000 0.009 0.000 {built-in method numpy.core.multiarray.count_nonzero}\n", - " 1025 0.007 0.000 0.238 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.006 0.000 0.207 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/core.py:337()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Method ``nonzero`` is being called internally within numpy to convert bool to int selections, no way to avoid." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:18:06 2017 /tmp/tmp7_bautep\n", - "\n", - " 52382 function calls in 0.592 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 2 0.219 0.110 0.219 0.110 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.096 0.000 0.101 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.094 0.047 0.094 0.047 ../zarr/indexing.py:630()\n", - " 1024 0.044 0.000 0.167 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.029 0.029 0.029 0.029 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 1 0.021 0.021 0.181 0.181 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "``.vindex[]`` is a bit slower, possibly because internally it converts to a coordinate array first." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int dense selection" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000000" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix_dense_int = np.random.choice(c.shape[0], size=c.shape[0]//10, replace=True)\n", - "ix_dense_int_sorted = ix_dense_int.copy()\n", - "ix_dense_int_sorted.sort()\n", - "len(ix_dense_int)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "62.2 ms ± 2.36 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "355 ms ± 3.53 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "351 ms ± 3.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "128 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.71 s ± 5.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.68 s ± 3.87 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_dense_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:09 2017 /tmp/tmpgmu5btr_\n", - "\n", - " 95338 function calls in 0.424 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 89 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.141 0.141 0.184 0.184 ../zarr/indexing.py:369(__init__)\n", - " 1024 0.099 0.000 0.106 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.046 0.000 0.175 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.027 0.000 0.027 0.000 ../zarr/indexing.py:424(__iter__)\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 1 0.010 0.010 0.010 0.010 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/function_base.py:1848(diff)\n", - " 1025 0.006 0.000 0.059 0.000 ../zarr/indexing.py:541(__iter__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_int_sorted]')" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:13 2017 /tmp/tmpay1gvnx8\n", - "\n", - " 52362 function calls in 0.398 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 85 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 2 0.107 0.054 0.107 0.054 ../zarr/indexing.py:630()\n", - " 1024 0.091 0.000 0.096 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.041 0.000 0.160 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.040 0.040 0.213 0.213 ../zarr/indexing.py:603(__init__)\n", - " 1 0.029 0.029 0.029 0.029 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - " 2048 0.011 0.000 0.011 0.000 ../zarr/indexing.py:695()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_int_sorted]')" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:20 2017 /tmp/tmpngsf6zpp\n", - "\n", - " 120946 function calls in 1.793 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 92 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.128 1.128 1.128 1.128 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 1024 0.139 0.000 0.285 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1 0.132 0.132 1.422 1.422 ../zarr/indexing.py:369(__init__)\n", - " 1 0.120 0.120 0.120 0.120 {method 'take' of 'numpy.ndarray' objects}\n", - " 1024 0.116 0.000 0.123 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.034 0.000 0.034 0.000 ../zarr/indexing.py:424(__iter__)\n", - " 1 0.023 0.023 0.023 0.023 {built-in method numpy.core.multiarray.bincount}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_dense_int]')" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:19:22 2017 /tmp/tmpbskhj8de\n", - "\n", - " 50320 function calls in 1.730 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 86 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.116 1.116 1.116 1.116 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 1024 0.133 0.000 0.275 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2 0.121 0.060 0.121 0.060 ../zarr/indexing.py:654()\n", - " 1024 0.113 0.000 0.119 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.100 0.050 0.100 0.050 ../zarr/indexing.py:630()\n", - " 1 0.030 0.030 0.030 0.030 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.024 0.024 1.427 1.427 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_dense_int]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When indices are not sorted, zarr needs to partially sort them so the occur in chunk order, so we only have to visit each chunk once. This sorting dominates the processing time and is unavoidable AFAIK." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool sparse selection" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "9932" - ] - }, - "execution_count": 75, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# relatively sparse selection\n", - "ix_sparse_bool = np.random.binomial(1, 0.0001, size=c.shape[0]).astype(bool)\n", - "np.count_nonzero(ix_sparse_bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15.7 ms ± 38.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "156 ms ± 2.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "133 ms ± 2.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:20:09 2017 /tmp/tmpb7nqc9ax\n", - "\n", - " 98386 function calls in 0.191 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 83 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.093 0.000 0.098 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.017 0.000 0.017 0.000 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1024 0.007 0.000 0.007 0.000 {built-in method numpy.core.multiarray.count_nonzero}\n", - " 1024 0.007 0.000 0.129 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.005 0.000 0.052 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.005 0.000 0.025 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_sparse_bool]')" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:20:09 2017 /tmp/tmphsko8nvh\n", - "\n", - " 52382 function calls in 0.160 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.093 0.000 0.098 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2 0.017 0.008 0.017 0.008 {method 'nonzero' of 'numpy.ndarray' objects}\n", - " 1025 0.008 0.000 0.014 0.000 ../zarr/indexing.py:674(__iter__)\n", - " 1024 0.006 0.000 0.127 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/indexing.py:695()\n", - " 2054 0.003 0.000 0.003 0.000 ../zarr/core.py:337()\n", - " 1024 0.002 0.000 0.005 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/core/arrayprint.py:381(wrapper)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_sparse_bool]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int sparse selection" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ix_sparse_int = np.random.choice(c.shape[0], size=c.shape[0]//10000, replace=True)\n", - "ix_sparse_int_sorted = ix_sparse_int.copy()\n", - "ix_sparse_int_sorted.sort()\n", - "len(ix_sparse_int)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "18.9 µs ± 392 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "20.3 µs ± 155 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit c[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "125 ms ± 296 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "109 ms ± 428 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_int_sorted]" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "132 ms ± 489 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "108 ms ± 579 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc.vindex[ix_sparse_int]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:21:12 2017 /tmp/tmp0b0o2quo\n", - "\n", - " 120946 function calls in 0.196 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 92 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.105 0.000 0.111 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 2048 0.006 0.000 0.013 0.000 /home/aliman/pyenv/zarr_20171023/lib/python3.6/site-packages/numpy/lib/index_tricks.py:26(ix_)\n", - " 1025 0.006 0.000 0.051 0.000 ../zarr/indexing.py:541(__iter__)\n", - " 1024 0.006 0.000 0.141 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/core.py:337()\n", - " 15373 0.004 0.000 0.010 0.000 {built-in method builtins.isinstance}\n", - " 1025 0.004 0.000 0.005 0.000 ../zarr/indexing.py:424(__iter__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.oindex[ix_sparse_int]')" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:21:19 2017 /tmp/tmpdwju98kn\n", - "\n", - " 50320 function calls in 0.167 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 86 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.105 0.000 0.111 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1025 0.009 0.000 0.017 0.000 ../zarr/indexing.py:674(__iter__)\n", - " 1024 0.006 0.000 0.142 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 2048 0.005 0.000 0.005 0.000 ../zarr/indexing.py:695()\n", - " 2054 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - " 1 0.003 0.003 0.162 0.162 ../zarr/core.py:591(_get_selection)\n", - " 1027 0.003 0.000 0.003 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc.vindex[ix_sparse_int]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For sparse selections, processing time is dominated by decompression, so we can't do any better." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### sparse bool selection as zarr array" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Typezarr.core.Array
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" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : bool\n", - "Shape : (100000000,)\n", - "Chunk shape : (390625,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 100000000 (95.4M)\n", - "No. bytes stored : 507131 (495.2K)\n", - "Storage ratio : 197.2\n", - "Chunks initialized : 256/256" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zix_sparse_bool = zarr.array(ix_sparse_bool)\n", - "zix_sparse_bool.info" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "387 ms ± 5.47 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zc.oindex[zix_sparse_bool]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### slice with step" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "80.3 ms ± 377 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit np.array(c[::2])" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "168 ms ± 837 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::2]" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "136 ms ± 1.56 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::10]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "104 ms ± 1.86 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::100]" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "100 ms ± 1.47 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit zc[::1000]" - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:22:44 2017 /tmp/tmpg9dxqcpg\n", - "\n", - " 49193 function calls in 0.211 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 55 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1024 0.104 0.000 0.110 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1024 0.067 0.000 0.195 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1025 0.005 0.000 0.013 0.000 ../zarr/indexing.py:278(__iter__)\n", - " 2048 0.004 0.000 0.004 0.000 ../zarr/core.py:337()\n", - " 2050 0.003 0.000 0.003 0.000 ../zarr/indexing.py:90(ceildiv)\n", - " 1025 0.003 0.000 0.006 0.000 ../zarr/indexing.py:109(__iter__)\n", - " 1024 0.003 0.000 0.003 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zc[::2]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2D Benchmarking" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000000,)" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(100000, 1000)" - ] - }, - "execution_count": 100, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = c.reshape(-1, 1000)\n", - "d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Storage ratio20.4
Chunks initialized1024/1024
" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (100000, 1000)\n", - "Chunk shape : (3125, 32)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 800000000 (762.9M)\n", - "No. bytes stored : 39228864 (37.4M)\n", - "Storage ratio : 20.4\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zd = zarr.array(d)\n", - "zd.info" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### bool orthogonal selection" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [], - "source": [ - "ix0 = np.random.binomial(1, 0.5, size=d.shape[0]).astype(bool)\n", - "ix1 = np.random.binomial(1, 0.5, size=d.shape[1]).astype(bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "101 ms ± 577 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit d[np.ix_(ix0, ix1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "373 ms ± 5.45 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### int orthogonal selection" - ] - }, - { - "cell_type": "code", - "execution_count": 105, - "metadata": {}, - "outputs": [], - "source": [ - "ix0 = np.random.choice(d.shape[0], size=int(d.shape[0] * .5), replace=True)\n", - "ix1 = np.random.choice(d.shape[1], size=int(d.shape[1] * .5), replace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "174 ms ± 4.13 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" - ] - } - ], - "source": [ - "%timeit d[np.ix_(ix0, ix1)]" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "566 ms ± 12.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.oindex[ix0, ix1]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### coordinate (point) selection" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10000000" - ] - }, - "execution_count": 108, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "n = int(d.size * .1)\n", - "ix0 = np.random.choice(d.shape[0], size=n, replace=True)\n", - "ix1 = np.random.choice(d.shape[1], size=n, replace=True)\n", - "n" - ] - }, - { - "cell_type": "code", - "execution_count": 109, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "243 ms ± 3.37 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit d[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 110, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.03 s ± 17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit zd.vindex[ix0, ix1]" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Wed Nov 8 17:24:31 2017 /tmp/tmp7c68z70p\n", - "\n", - " 62673 function calls in 2.065 seconds\n", - "\n", - " Ordered by: internal time\n", - " List reduced from 88 to 7 due to restriction <7>\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 1.112 1.112 1.112 1.112 {method 'argsort' of 'numpy.ndarray' objects}\n", - " 3 0.244 0.081 0.244 0.081 ../zarr/indexing.py:654()\n", - " 3 0.193 0.064 0.193 0.064 ../zarr/indexing.py:630()\n", - " 1024 0.170 0.000 0.350 0.000 ../zarr/core.py:849(_chunk_getitem)\n", - " 1024 0.142 0.000 0.151 0.000 ../zarr/core.py:1028(_decode_chunk)\n", - " 1 0.044 0.044 0.044 0.044 {built-in method numpy.core.multiarray.ravel_multi_index}\n", - " 1 0.043 0.043 1.676 1.676 ../zarr/indexing.py:603(__init__)\n", - "\n", - "\n" - ] - } - ], - "source": [ - "profile('zd.vindex[ix0, ix1]')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Points need to be partially sorted so all points in the same chunk are grouped and processed together. This requires ``argsort`` which dominates time." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## h5py comparison\n", - "\n", - "N.B., not really fair because using slower compressor, but for interest..." - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "import h5py" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [], - "source": [ - "h5f = h5py.File(tempfile.mktemp(), driver='core', backing_store=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hc = h5f.create_dataset('c', data=c, compression='gzip', compression_opts=1, chunks=zc.chunks, shuffle=True)\n", - "hc" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.16 s, sys: 172 ms, total: 1.33 s\n", - "Wall time: 1.32 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 0, 1, 2, ..., 99999997, 99999998, 99999999])" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time hc[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.11 s, sys: 0 ns, total: 1.11 s\n", - "Wall time: 1.11 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 1063, 28396, 37229, ..., 99955875, 99979354, 99995791])" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time hc[ix_sparse_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [], - "source": [ - "# # this is pathological, takes minutes\n", - "# %time hc[ix_dense_bool]" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 38.3 s, sys: 136 ms, total: 38.4 s\n", - "Wall time: 38.1 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([ 0, 1000, 2000, ..., 99997000, 99998000, 99999000])" - ] - }, - "execution_count": 83, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is pretty slow\n", - "%time hc[::1000]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/blosc_microbench.ipynb b/notebooks/blosc_microbench.ipynb deleted file mode 100644 index fbcfaa6743..0000000000 --- a/notebooks/blosc_microbench.ipynb +++ /dev/null @@ -1,205 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 110 ms per loop\n", - "1 loop, best of 3: 235 ms per loop\n", - "Array((100000000,), int64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 11.2M; ratio: 67.8; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='i8')\n", - "data = np.arange(100000000, dtype='i8')\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 3: 331 ms per loop\n", - "1 loop, best of 3: 246 ms per loop\n", - "Array((100000000,), float64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 724.8M; ratio: 1.1; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='f8')\n", - "data = np.random.normal(size=100000000)\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.2.dev0+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "\n", - "import numpy as np\n", - "\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "10 loops, best of 3: 92.7 ms per loop\n", - "1 loop, best of 3: 230 ms per loop\n", - "Array((100000000,), int64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 11.2M; ratio: 67.8; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='i8')\n", - "data = np.arange(100000000, dtype='i8')\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 loop, best of 3: 338 ms per loop\n", - "1 loop, best of 3: 253 ms per loop\n", - "Array((100000000,), float64, chunks=(200000,), order=C)\n", - " nbytes: 762.9M; nbytes_stored: 724.8M; ratio: 1.1; initialized: 500/500\n", - " compressor: Blosc(cname='lz4', clevel=5, shuffle=1)\n", - " store: dict\n" - ] - } - ], - "source": [ - "z = zarr.empty(shape=100000000, chunks=200000, dtype='f8')\n", - "data = np.random.normal(size=100000000)\n", - "%timeit z[:] = data\n", - "%timeit z[:]\n", - "print(z)\n", - "assert np.all(z[:] == data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/dask_2d_subset.ipynb b/notebooks/dask_2d_subset.ipynb deleted file mode 100644 index 839e306627..0000000000 --- a/notebooks/dask_2d_subset.ipynb +++ /dev/null @@ -1,873 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook has some profiling of Dask used to make a selection along both first and second axes of a large-ish multidimensional array. The use case is making selections of genotype data, e.g., as required for making a web-browser for genotype data as in www.malariagen.net/apps/ag1000g." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 2.1.1\n", - "dask 0.11.0\n" - ] - } - ], - "source": [ - "import dask\n", - "import numpy as np\n", - "\n", - "import zarr\n", - "\n", - "print('zarr', zarr.__version__)\n", - "print('dask', dask.__version__)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Real data" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Group(/, 8)\n", - " arrays: 1; samples\n", - " groups: 7; 2L, 2R, 3L, 3R, UNKN, X, Y_unplaced\n", - " store: DirectoryStore" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# here's the real data\n", - "callset = zarr.open_group('/kwiat/2/coluzzi/ag1000g/data/phase1/release/AR3.1/variation/main/zarr2/zstd/ag1000g.phase1.ar3',\n", - " mode='r')\n", - "callset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3R/calldata/genotype, (22632425, 765, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 32.2G; nbytes_stored: 1.0G; ratio: 31.8; initialized: 34540/34540\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: DirectoryStore" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# here's the array we're going to work with\n", - "g = callset['3R/calldata/genotype']\n", - "g" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n", - "Wall time: 5.13 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# wrap as dask array with very simple chunking of first dim only\n", - "%time gd = da.from_array(g, chunks=(g.chunks[0], None, None))\n", - "gd" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((22632425,), dtype('bool'), 13167162)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load condition used to make selection on first axis\n", - "dim0_condition = callset['3R/variants/FILTER_PASS'][:]\n", - "dim0_condition.shape, dim0_condition.dtype, np.count_nonzero(dim0_condition)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# invent a random selection for second axis\n", - "dim1_indices = sorted(np.random.choice(765, size=100, replace=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.3 s, sys: 256 ms, total: 15.5 s\n", - "Wall time: 15.5 s\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# setup the 2D selection - this is the slow bit\n", - "%time gd_sel = gd[dim0_condition][:, dim1_indices]\n", - "gd_sel" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.21 s, sys: 152 ms, total: 1.36 s\n", - "Wall time: 316 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " ..., \n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 1],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]],\n", - "\n", - " [[0, 0],\n", - " [0, 0],\n", - " [0, 0],\n", - " ..., \n", - " [0, 0],\n", - " [0, 0],\n", - " [0, 0]]], dtype=int8)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# now load a slice from this new selection - quick!\n", - "%time gd_sel[1000000:1100000].compute(optimize_graph=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 105406881 function calls (79072145 primitive calls) in 26.182 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "13167268/6 6.807 0.000 9.038 1.506 slicing.py:623(check_index)\n", - " 2 4.713 2.356 5.831 2.916 slicing.py:398(partition_by_size)\n", - "13167270/2 4.470 0.000 8.763 4.382 slicing.py:540(posify_index)\n", - " 52669338 4.118 0.000 4.119 0.000 {built-in method builtins.isinstance}\n", - " 2 2.406 1.203 8.763 4.382 slicing.py:563()\n", - " 1 0.875 0.875 0.875 0.875 slicing.py:44()\n", - " 13182474 0.600 0.000 0.600 0.000 {built-in method builtins.len}\n", - " 2 0.527 0.264 0.527 0.264 slicing.py:420(issorted)\n", - " 13189168 0.520 0.000 0.520 0.000 {method 'append' of 'list' objects}\n", - " 2 0.271 0.136 0.271 0.136 slicing.py:479()\n", - " 2 0.220 0.110 0.220 0.110 {built-in method builtins.sorted}\n", - " 1 0.162 0.162 0.162 0.162 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.113 0.056 26.071 13.035 core.py:1024(__getitem__)\n", - " 2 0.112 0.056 6.435 3.217 slicing.py:441(take_sorted)\n", - " 1 0.111 0.111 26.182 26.182 :1()\n", - " 2 0.060 0.030 24.843 12.422 slicing.py:142(slice_with_newaxes)\n", - " 106/3 0.039 0.000 1.077 0.359 slicing.py:15(sanitize_index)\n", - " 3 0.037 0.012 0.037 0.012 {built-in method _hashlib.openssl_md5}\n", - " 6726 0.012 0.000 0.017 0.000 slicing.py:567(insert_many)\n", - " 3364 0.004 0.000 0.021 0.000 slicing.py:156()\n", - " 20178 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 8 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 2 0.000 0.000 25.920 12.960 slicing.py:60(slice_array)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 106/4 0.000 0.000 0.037 0.009 utils.py:502(__call__)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 2 0.000 0.000 0.037 0.019 base.py:343(tokenize)\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 2 0.000 0.000 24.763 12.381 slicing.py:170(slice_wrap_lists)\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 6.962 3.481 slicing.py:487(take)\n", - " 1 0.000 0.000 26.182 26.182 {built-in method builtins.exec}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 1 0.000 0.000 0.037 0.037 base.py:314(normalize_array)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 2 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 4 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# what's taking so long?\n", - "import cProfile\n", - "\n", - "cProfile.run('gd[dim0_condition][:, dim1_indices]', sort='time')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 105406881 function calls (79072145 primitive calls) in 25.630 seconds\n", - "\n", - " Ordered by: cumulative time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 25.630 25.630 {built-in method builtins.exec}\n", - " 1 0.107 0.107 25.630 25.630 :1()\n", - " 2 0.102 0.051 25.523 12.761 core.py:1024(__getitem__)\n", - " 2 0.001 0.000 25.381 12.691 slicing.py:60(slice_array)\n", - " 2 0.049 0.024 24.214 12.107 slicing.py:142(slice_with_newaxes)\n", - " 2 0.000 0.000 24.147 12.073 slicing.py:170(slice_wrap_lists)\n", - "13167268/6 6.664 0.000 8.855 1.476 slicing.py:623(check_index)\n", - "13167270/2 4.354 0.000 8.466 4.233 slicing.py:540(posify_index)\n", - " 2 2.277 1.139 8.465 4.233 slicing.py:563()\n", - " 2 0.000 0.000 6.826 3.413 slicing.py:487(take)\n", - " 2 0.111 0.056 6.331 3.165 slicing.py:441(take_sorted)\n", - " 2 4.628 2.314 5.704 2.852 slicing.py:398(partition_by_size)\n", - " 52669338 4.026 0.000 4.026 0.000 {built-in method builtins.isinstance}\n", - " 106/3 0.071 0.001 1.167 0.389 slicing.py:15(sanitize_index)\n", - " 1 0.943 0.943 0.943 0.943 slicing.py:44()\n", - " 13182474 0.581 0.000 0.581 0.000 {built-in method builtins.len}\n", - " 13189168 0.497 0.000 0.497 0.000 {method 'append' of 'list' objects}\n", - " 2 0.495 0.248 0.495 0.248 slicing.py:420(issorted)\n", - " 2 0.281 0.141 0.281 0.141 slicing.py:479()\n", - " 2 0.234 0.117 0.234 0.117 {built-in method builtins.sorted}\n", - " 1 0.152 0.152 0.152 0.152 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.039 0.020 base.py:343(tokenize)\n", - " 106/4 0.000 0.000 0.039 0.010 utils.py:502(__call__)\n", - " 1 0.000 0.000 0.039 0.039 base.py:314(normalize_array)\n", - " 3 0.039 0.013 0.039 0.013 {built-in method _hashlib.openssl_md5}\n", - " 3364 0.003 0.000 0.019 0.000 slicing.py:156()\n", - " 6726 0.012 0.000 0.016 0.000 slicing.py:567(insert_many)\n", - " 20178 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 8 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 4 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 2 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "cProfile.run('gd[dim0_condition][:, dim1_indices]', sort='cumtime')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array((20000000, 200, 2), int8, chunks=(10000, 100, 2), order=C)\n", - " nbytes: 7.5G; nbytes_stored: 2.7G; ratio: 2.8; initialized: 4000/4000\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: dict" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# create a synthetic dataset for profiling\n", - "a = zarr.array(np.random.randint(-1, 4, size=(20000000, 200, 2), dtype='i1'),\n", - " chunks=(10000, 100, 2), compressor=zarr.Blosc(cname='zstd', clevel=1, shuffle=2))\n", - "a" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# create a synthetic selection for first axis\n", - "c = np.random.randint(0, 2, size=a.shape[0], dtype=bool)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# create a synthetic selection for second axis\n", - "s = sorted(np.random.choice(a.shape[1], size=100, replace=False))" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 208 ms, sys: 0 ns, total: 208 ms\n", - "Wall time: 206 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d = da.from_array(a, chunks=(a.chunks[0], None, None))\n", - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 12 s, sys: 200 ms, total: 12.2 s\n", - "Wall time: 12.2 s\n" - ] - } - ], - "source": [ - "%time ds = d[c][:, s]" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 80095589 function calls (60091843 primitive calls) in 19.467 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "10001773/6 4.872 0.000 6.456 1.076 slicing.py:623(check_index)\n", - " 2 3.517 1.758 4.357 2.179 slicing.py:398(partition_by_size)\n", - "10001775/2 3.354 0.000 6.484 3.242 slicing.py:540(posify_index)\n", - " 40007358 2.965 0.000 2.965 0.000 {built-in method builtins.isinstance}\n", - " 2 1.749 0.875 6.484 3.242 slicing.py:563()\n", - " 1 0.878 0.878 0.878 0.878 slicing.py:44()\n", - " 10019804 0.451 0.000 0.451 0.000 {built-in method builtins.len}\n", - " 10027774 0.392 0.000 0.392 0.000 {method 'append' of 'list' objects}\n", - " 2 0.363 0.181 0.363 0.181 slicing.py:420(issorted)\n", - " 2 0.270 0.135 4.786 2.393 slicing.py:441(take_sorted)\n", - " 1 0.207 0.207 0.207 0.207 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 2 0.158 0.079 0.158 0.079 {built-in method builtins.sorted}\n", - " 1 0.094 0.094 19.467 19.467 :1()\n", - " 2 0.079 0.040 19.373 9.686 core.py:1024(__getitem__)\n", - " 2 0.035 0.017 18.147 9.074 slicing.py:142(slice_with_newaxes)\n", - " 3 0.033 0.011 0.033 0.011 {built-in method _hashlib.openssl_md5}\n", - " 106/3 0.028 0.000 1.112 0.371 slicing.py:15(sanitize_index)\n", - " 8002 0.015 0.000 0.020 0.000 slicing.py:567(insert_many)\n", - " 4002 0.004 0.000 0.023 0.000 slicing.py:156()\n", - " 24006 0.003 0.000 0.003 0.000 {method 'pop' of 'list' objects}\n", - " 8 0.001 0.000 0.001 0.000 {method 'update' of 'dict' objects}\n", - " 2 0.001 0.000 0.001 0.000 slicing.py:479()\n", - " 2 0.000 0.000 19.259 9.630 slicing.py:60(slice_array)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 106/4 0.000 0.000 0.034 0.008 utils.py:502(__call__)\n", - " 2 0.000 0.000 18.089 9.044 slicing.py:170(slice_wrap_lists)\n", - " 100 0.000 0.000 0.000 0.000 arrayprint.py:340(array2string)\n", - " 100 0.000 0.000 0.000 0.000 {built-in method builtins.repr}\n", - " 108 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 2 0.000 0.000 5.149 2.574 slicing.py:487(take)\n", - " 2 0.000 0.000 0.034 0.017 base.py:343(tokenize)\n", - " 1 0.000 0.000 0.033 0.033 base.py:314(normalize_array)\n", - " 116 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2/1 0.000 0.000 0.000 0.000 base.py:270(normalize_seq)\n", - " 6 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 100 0.000 0.000 0.000 0.000 numeric.py:1835(array_str)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:47()\n", - " 1 0.000 0.000 19.467 19.467 {built-in method builtins.exec}\n", - " 100 0.000 0.000 0.000 0.000 inspect.py:441(getmro)\n", - " 8 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 4 0.000 0.000 0.001 0.000 dicttoolz.py:19(merge)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 100 0.000 0.000 0.000 0.000 {method 'item' of 'numpy.ndarray' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 2 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 3 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 2 0.000 0.000 0.001 0.000 exceptions.py:15(merge)\n", - " 7 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 4 0.000 0.000 0.001 0.000 functoolz.py:217(__call__)\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 4 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 5 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 2 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 5 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 8 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 2 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 1 0.000 0.000 0.000 0.000 functoolz.py:11(identity)\n", - " 4 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 2 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "cProfile.run('d[c][:, s]', sort='time')" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 452 ms, sys: 8 ms, total: 460 ms\n", - "Wall time: 148 ms\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[[ 2, -1],\n", - " [ 2, 3],\n", - " [ 3, 0],\n", - " ..., \n", - " [ 1, 3],\n", - " [-1, -1],\n", - " [ 1, 1]],\n", - "\n", - " [[ 1, -1],\n", - " [ 2, 2],\n", - " [-1, 2],\n", - " ..., \n", - " [ 2, -1],\n", - " [ 1, 3],\n", - " [-1, -1]],\n", - "\n", - " [[ 1, -1],\n", - " [ 2, 0],\n", - " [ 0, 3],\n", - " ..., \n", - " [ 2, 2],\n", - " [ 3, 2],\n", - " [ 0, 2]],\n", - "\n", - " ..., \n", - " [[ 1, 2],\n", - " [ 3, -1],\n", - " [ 2, 1],\n", - " ..., \n", - " [ 1, 2],\n", - " [ 1, 0],\n", - " [ 2, 0]],\n", - "\n", - " [[ 1, 2],\n", - " [ 1, 0],\n", - " [ 2, 3],\n", - " ..., \n", - " [-1, 2],\n", - " [ 3, 3],\n", - " [ 1, -1]],\n", - "\n", - " [[-1, 3],\n", - " [ 2, 2],\n", - " [ 1, 1],\n", - " ..., \n", - " [ 3, 3],\n", - " [ 0, 0],\n", - " [ 0, 2]]], dtype=int8)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time ds[1000000:1100000].compute(optimize_graph=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 80055494 function calls (60052157 primitive calls) in 19.425 seconds\n", - "\n", - " Ordered by: internal time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - "10001670/3 5.032 0.000 6.671 2.224 slicing.py:623(check_index)\n", - " 1 3.459 3.459 4.272 4.272 slicing.py:398(partition_by_size)\n", - "10001671/1 3.287 0.000 6.378 6.378 slicing.py:540(posify_index)\n", - " 40006704 2.999 0.000 2.999 0.000 {built-in method builtins.isinstance}\n", - " 1 1.731 1.731 6.378 6.378 slicing.py:563()\n", - " 1 0.849 0.849 0.849 0.849 slicing.py:44()\n", - " 10011685 0.433 0.000 0.433 0.000 {built-in method builtins.len}\n", - " 10015670 0.381 0.000 0.381 0.000 {method 'append' of 'list' objects}\n", - " 1 0.355 0.355 0.355 0.355 slicing.py:420(issorted)\n", - " 1 0.196 0.196 0.196 0.196 {method 'tolist' of 'numpy.ndarray' objects}\n", - " 1 0.193 0.193 0.193 0.193 slicing.py:479()\n", - " 1 0.157 0.157 0.157 0.157 {built-in method builtins.sorted}\n", - " 1 0.085 0.085 4.707 4.707 slicing.py:441(take_sorted)\n", - " 1 0.085 0.085 19.425 19.425 :1()\n", - " 1 0.079 0.079 19.341 19.341 core.py:1024(__getitem__)\n", - " 1 0.034 0.034 18.157 18.157 slicing.py:142(slice_with_newaxes)\n", - " 2 0.033 0.017 0.033 0.017 {built-in method _hashlib.openssl_md5}\n", - " 1 0.026 0.026 1.071 1.071 slicing.py:15(sanitize_index)\n", - " 4001 0.007 0.000 0.009 0.000 slicing.py:567(insert_many)\n", - " 2001 0.002 0.000 0.011 0.000 slicing.py:156()\n", - " 12003 0.001 0.000 0.001 0.000 {method 'pop' of 'list' objects}\n", - " 4 0.000 0.000 0.000 0.000 {method 'update' of 'dict' objects}\n", - " 1 0.000 0.000 19.228 19.228 slicing.py:60(slice_array)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:464()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:162()\n", - " 1 0.000 0.000 0.033 0.033 base.py:314(normalize_array)\n", - " 1 0.000 0.000 18.111 18.111 slicing.py:170(slice_wrap_lists)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:465()\n", - " 1 0.000 0.000 5.062 5.062 slicing.py:487(take)\n", - " 1 0.000 0.000 0.033 0.033 base.py:343(tokenize)\n", - " 1 0.000 0.000 19.425 19.425 {built-in method builtins.exec}\n", - " 2 0.000 0.000 0.000 0.000 functoolz.py:217(__call__)\n", - " 3 0.000 0.000 0.000 0.000 {built-in method builtins.sum}\n", - " 2 0.000 0.000 0.000 0.000 abc.py:178(__instancecheck__)\n", - " 1 0.000 0.000 0.000 0.000 core.py:1455(normalize_chunks)\n", - " 2 0.000 0.000 0.000 0.000 dicttoolz.py:19(merge)\n", - " 4 0.000 0.000 0.000 0.000 _weakrefset.py:70(__contains__)\n", - " 2 0.000 0.000 0.000 0.000 dicttoolz.py:11(_get_factory)\n", - " 1 0.000 0.000 0.000 0.000 exceptions.py:15(merge)\n", - " 1 0.000 0.000 0.000 0.000 core.py:794(__init__)\n", - " 4 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:467()\n", - " 1 0.000 0.000 0.000 0.000 {method 'view' of 'numpy.ndarray' objects}\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:183()\n", - " 2 0.000 0.000 0.000 0.000 {method 'hexdigest' of '_hashlib.HASH' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:606(replace_ellipsis)\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:192()\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:207()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:475()\n", - " 2 0.000 0.000 0.033 0.017 utils.py:502(__call__)\n", - " 2 0.000 0.000 0.000 0.000 slicing.py:125()\n", - " 2 0.000 0.000 0.000 0.000 core.py:1043()\n", - " 4 0.000 0.000 0.000 0.000 slicing.py:197()\n", - " 1 0.000 0.000 0.000 0.000 core.py:826(_get_chunks)\n", - " 2 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 1 0.000 0.000 0.000 0.000 {method 'ravel' of 'numpy.ndarray' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - " 1 0.000 0.000 0.000 0.000 {method 'encode' of 'str' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:613()\n", - " 1 0.000 0.000 0.000 0.000 core.py:1452()\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:149()\n", - " 2 0.000 0.000 0.000 0.000 {method 'pop' of 'dict' objects}\n", - " 2 0.000 0.000 0.000 0.000 {method 'items' of 'dict' objects}\n", - " 1 0.000 0.000 0.000 0.000 slicing.py:150()\n", - " 1 0.000 0.000 0.000 0.000 {method 'count' of 'tuple' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "# problem is in fact just the dim0 selection\n", - "cProfile.run('d[c]', sort='time')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/dask_copy.ipynb b/notebooks/dask_copy.ipynb deleted file mode 100644 index 41cc07ae68..0000000000 --- a/notebooks/dask_copy.ipynb +++ /dev/null @@ -1,1519 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Profile array copy via dask threaded scheduler" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notebook profiles a very simple array copy operation, using synthetic data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.0.1.dev18+dirty\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#d4821cb3-378c-411d-a941-d0708c0c532b\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import multiprocessing\n", - "import sys\n", - "\n", - "import bcolz\n", - "import dask.array as da\n", - "import h5py\n", - "import numpy as np\n", - "from bokeh.io import output_notebook\n", - "from dask.diagnostics import Profiler, ResourceProfiler\n", - "from dask.diagnostics.profile_visualize import visualize\n", - "\n", - "sys.path.insert(0, '..')\n", - "\n", - "import zarr\n", - "\n", - "print('zarr', zarr.__version__)\n", - "\n", - "# don't let bcolz use multiple threads internally, we want to\n", - "# see whether dask can make good use of multiple CPUs\n", - "bcolz.set_nthreads(1)\n", - "\n", - "output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import operator\n", - "import tempfile\n", - "from functools import reduce\n", - "\n", - "from zarr.util import human_readable_size\n", - "\n", - "\n", - "def h5fmem(**kwargs):\n", - " \"\"\"Convenience function to create an in-memory HDF5 file.\"\"\"\n", - "\n", - " # need a file name even tho nothing is ever written\n", - " fn = tempfile.mktemp()\n", - "\n", - " # file creation args\n", - " kwargs['mode'] = 'w'\n", - " kwargs['driver'] = 'core'\n", - " kwargs['backing_store'] = False\n", - "\n", - " # open HDF5 file\n", - " h5f = h5py.File(fn, **kwargs)\n", - "\n", - " return h5f\n", - "\n", - "\n", - "def h5d_diagnostics(d):\n", - " \"\"\"Print some diagnostics on an HDF5 dataset.\"\"\"\n", - "\n", - " print(d)\n", - " nbytes = reduce(operator.mul, d.shape) * d.dtype.itemsize\n", - " cbytes = d._id.get_storage_size()\n", - " if cbytes > 0:\n", - " ratio = nbytes / cbytes\n", - " else:\n", - " ratio = np.inf\n", - " r = f' compression: {d.compression}'\n", - " r += f'; compression_opts: {d.compression_opts}'\n", - " r += f'; shuffle: {d.shuffle}'\n", - " r += f'\\n nbytes: {human_readable_size(nbytes)}'\n", - " r += f'; nbytes_stored: {human_readable_size(cbytes)}'\n", - " r += f'; ratio: {ratio:.1f}'\n", - " r += f'; chunks: {d.chunks}'\n", - " print(r)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def profile_dask_copy(src, dst, chunks, num_workers=multiprocessing.cpu_count(), dt=0.1, lock=True):\n", - " dsrc = da.from_array(src, chunks=chunks)\n", - " with Profiler() as prof, ResourceProfiler(dt=dt) as rprof:\n", - " da.store(dsrc, dst, num_workers=num_workers, lock=lock)\n", - " visualize([prof, rprof], min_border_top=60, min_border_bottom=60)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NumPy arrays" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1314, 2727, 2905, ..., 1958, 1325, 1971], dtype=uint16)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# a1 = np.arange(400000000, dtype='i4')\n", - "a1 = np.random.normal(2000, 1000, size=200000000).astype('u2')\n", - "a1" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'381.5M'" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "human_readable_size(a1.nbytes)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a2 = np.empty_like(a1)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "chunks = 2**20, # 4M" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 56 ms, sys: 36 ms, total: 92 ms\n", - "Wall time: 91.7 ms\n" - ] - } - ], - "source": [ - "%time a2[:] = a1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(a1, a2, chunks, lock=True, dt=.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(a1, a2, chunks, lock=False, dt=.01)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Zarr arrays (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((200000000,), uint16, chunks=(1048576,), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 381.5M; nbytes_stored: 318.2M; ratio: 1.2; initialized: 191/191\n", - " store: builtins.dict" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z1 = zarr.array(a1, chunks=chunks, compression='blosc',\n", - " compression_opts=dict(cname='lz4', clevel=1, shuffle=2))\n", - "z1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((200000000,), uint16, chunks=(1048576,), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 381.5M; nbytes_stored: 294; ratio: 1360544.2; initialized: 0/191\n", - " store: builtins.dict" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z2 = zarr.empty_like(z1)\n", - "z2" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(z1, z2, chunks, lock=True, dt=.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(z1, z2, chunks, lock=False, dt=0.02)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 251 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison, using blosc internal threads\n", - "%timeit -n3 -r5 z2[:] = z1" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " " - ] - } - ], - "source": [ - "%prun z2[:] = z1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Without the dask lock, we get better CPU utilisation. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## HDF5 datasets (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "h5f = h5fmem()\n", - "h5f" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " compression: lzf; compression_opts: None; shuffle: True\n", - " nbytes: 381.5M; nbytes_stored: 357.4M; ratio: 1.1; chunks: (1048576,)\n" - ] - } - ], - "source": [ - "h1 = h5f.create_dataset('h1', data=a1, chunks=chunks, compression='lzf', shuffle=True)\n", - "h5d_diagnostics(h1)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " compression: lzf; compression_opts: None; shuffle: True\n", - " nbytes: 762.9M; nbytes_stored: 0; ratio: inf; chunks: (1048576,)\n" - ] - } - ], - "source": [ - "h2 = h5f.create_dataset('h2', shape=h1.shape, chunks=h1.chunks,\n", - " compression=h1.compression, compression_opts=h1.compression_opts,\n", - " shuffle=h1.shuffle)\n", - "h5d_diagnostics(h2)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(h1, h2, chunks, lock=True, dt=0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(h1, h2, chunks, lock=False, dt=0.1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bcolz carrays (in-memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "carray((200000000,), uint16)\n", - " nbytes := 381.47 MB; cbytes := 318.98 MB; ratio: 1.20\n", - " cparams := cparams(clevel=1, shuffle=2, cname='lz4', quantize=0)\n", - " chunklen := 1048576; chunksize: 2097152; blocksize: 16384\n", - "[1314 2727 2905 ..., 1958 1325 1971]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c1 = bcolz.carray(a1, chunklen=chunks[0],\n", - " cparams=bcolz.cparams(cname='lz4', clevel=1, shuffle=2))\n", - "c1" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "carray((200000000,), uint16)\n", - " nbytes := 381.47 MB; cbytes := 2.00 MB; ratio: 190.73\n", - " cparams := cparams(clevel=1, shuffle=2, cname='lz4', quantize=0)\n", - " chunklen := 1048576; chunksize: 2097152; blocksize: 4096\n", - "[0 0 0 ..., 0 0 0]" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "c2 = bcolz.zeros(a1.shape, chunklen=chunks[0], dtype=a1.dtype,\n", - " cparams=bcolz.cparams(cname='lz4', clevel=1, shuffle=2))\n", - "c2" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "profile_dask_copy(c1, c2, chunks, lock=True, dt=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# not sure it's safe to use bcolz without a lock, but what the heck...\n", - "profile_dask_copy(c1, c2, chunks, lock=False, dt=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 649 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison\n", - "%timeit -n3 -r5 c2[:] = c1" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3 loops, best of 5: 557 ms per loop\n" - ] - } - ], - "source": [ - "# for comparison\n", - "%timeit -n3 -r5 c1.copy()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/dask_count_alleles.ipynb b/notebooks/dask_count_alleles.ipynb deleted file mode 100644 index 64312c572c..0000000000 --- a/notebooks/dask_count_alleles.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Profile allele count from genotype data via dask.array" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.0.1.dev18+dirty\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " Loading BokehJS ...\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", - " window._bokeh_onload_callbacks = [];\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.0.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#b153ad5f-436a-4afb-945c-87790add89c8\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.0.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.0.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import sys\n", - "\n", - "import allel\n", - "import h5py\n", - "from bokeh.io import output_notebook\n", - "from dask.diagnostics import Profiler, ResourceProfiler\n", - "from dask.diagnostics.profile_visualize import visualize\n", - "\n", - "sys.path.insert(0, '..')\n", - "\n", - "import zarr\n", - "\n", - "print('zarr', zarr.__version__)\n", - "output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "callset = h5py.File('/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/ag1000g.phase1.ar3.pass.h5',\n", - " mode='r')\n", - "genotype = callset['3R/calldata/genotype']\n", - "genotype" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((13167162, 765, 2), int8, chunks=(6553, 200, 2), order=C)\n", - " compression: blosc; compression_opts: {'clevel': 1, 'cname': 'lz4', 'shuffle': 2}\n", - " nbytes: 18.8G; nbytes_stored: 683.2M; ratio: 28.1; initialized: 8040/8040\n", - " store: builtins.dict" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# copy into a zarr array\n", - "# N.B., chunks in HDF5 are too small really, use something bigger\n", - "chunks = (genotype.chunks[0], genotype.chunks[1] * 20, genotype.chunks[2])\n", - "genotype_zarr = zarr.array(genotype, chunks=chunks, compression='blosc',\n", - " compression_opts=dict(cname='lz4', clevel=1, shuffle=2))\n", - "genotype_zarr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We want to perform an allele count. Compare serial and parallel implementations, and compare working direct from HDF5 versus from Zarr." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 50s, sys: 512 ms, total: 1min 51s\n", - "Wall time: 1min 50s\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))
nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;
compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);
data: bcolz.carray_ext.carray
0123
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...

" - ], - "text/plain": [ - "AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))\n", - " nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;\n", - " compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);\n", - " data: bcolz.carray_ext.carray" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# linear implementation from HDF5 on disk\n", - "allel.GenotypeChunkedArray(genotype).count_alleles()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 2min 27s, sys: 2.14 s, total: 2min 29s\n", - "Wall time: 1min 23s\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))
nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;
compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);
data: bcolz.carray_ext.carray
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" - ], - "text/plain": [ - "AlleleCountsChunkedArray((13167162, 4), int32, chunks=(65536, 4))\n", - " nbytes: 200.9M; cbytes: 38.3M; cratio: 5.2;\n", - " compression: blosc; compression_opts: cparams(clevel=5, shuffle=1, cname='lz4', quantize=0);\n", - " data: bcolz.carray_ext.carray" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# linear implementation from zarr in memory\n", - "# (although blosc can use multiple threads internally)\n", - "allel.GenotypeChunkedArray(genotype_zarr).count_alleles()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# multi-threaded implementation from HDF5 on disk\n", - "gd = allel.model.dask.GenotypeDaskArray.from_array(genotype, chunks=chunks)\n", - "ac = gd.count_alleles(max_allele=3)\n", - "with Profiler() as prof, ResourceProfiler(dt=1) as rprof:\n", - " ac.compute(num_workers=8)\n", - "visualize([prof, rprof], min_border_bottom=60, min_border_top=60);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "
\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# multi-threaded implementation from zarr in memory\n", - "gdz = allel.model.dask.GenotypeDaskArray.from_array(genotype_zarr, chunks=chunks)\n", - "acz = gdz.count_alleles(max_allele=3)\n", - "with Profiler() as prof, ResourceProfiler(dt=1) as rprof:\n", - " acz.compute(num_workers=8)\n", - "visualize([prof, rprof], min_border_bottom=60, min_border_top=60);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/genotype_benchmark_compressors.ipynb b/notebooks/genotype_benchmark_compressors.ipynb deleted file mode 100644 index e575bd0477..0000000000 --- a/notebooks/genotype_benchmark_compressors.ipynb +++ /dev/null @@ -1,552 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "zarr 1.1.1.dev7+dirty\n", - "blosc ('1.10.0.dev', '$Date:: 2016-07-20 #$')\n" - ] - } - ], - "source": [ - "import functools\n", - "import sys\n", - "import timeit\n", - "\n", - "import h5py\n", - "import numpy as np\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "sys.path.insert(0, '..')\n", - "\n", - "import zarr\n", - "from zarr import blosc\n", - "\n", - "print('zarr', zarr.__version__)\n", - "print('blosc', blosc.version())" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "callset = h5py.File('/data/coluzzi/ag1000g/data/phase1/release/AR3/variation/main/hdf5/ag1000g.phase1.ar3.pass.h5',\n", - " mode='r')\n", - "genotype = callset['3R/calldata/genotype']\n", - "genotype" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "n_variants = 500000" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(500000, 765, 2)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genotype_sample = genotype[1000000:1000000+n_variants, ...]\n", - "genotype_sample.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "765000000" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nbytes = genotype_sample.nbytes\n", - "nbytes" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(685, 765, 2)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 1M chunks of first dimension\n", - "chunks = (int(2**20 / (genotype_sample.shape[1] * genotype_sample.shape[2])),\n", - " genotype_sample.shape[1],\n", - " genotype_sample.shape[2])\n", - "chunks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "blosc.get_nthreads()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((500000, 765, 2), int8, chunks=(685, 765, 2), order=C)\n", - " compression: blosc; compression_opts: {'cname': 'lz4', 'clevel': 1, 'shuffle': 2}\n", - " nbytes: 729.6M; nbytes_stored: 23.0M; ratio: 31.7; initialized: 730/730\n", - " store: builtins.dict" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zarr.array(genotype_sample, chunks=chunks, compression_opts=dict(cname='lz4', clevel=1, shuffle=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zarr.core.Array((500000, 765, 2), int8, chunks=(685, 765, 2), order=C)\n", - " compression: blosc; compression_opts: {'cname': 'zstd', 'clevel': 1, 'shuffle': 2}\n", - " nbytes: 729.6M; nbytes_stored: 12.0M; ratio: 60.7; initialized: 730/730\n", - " store: builtins.dict" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zarr.array(genotype_sample, chunks=chunks, compression_opts=dict(cname='zstd', clevel=1, shuffle=2))" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "compression_configs = (\n", - " (None, None),\n", - " ('zlib', 1),\n", - " ('bz2', 1),\n", - " ('lzma', dict(preset=1)),\n", - " ('blosc', dict(cname='snappy', clevel=0, shuffle=0)),\n", - " ('blosc', dict(cname='snappy', clevel=0, shuffle=2)),\n", - " ('blosc', dict(cname='snappy', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='snappy', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='blosclz', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='blosclz', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='lz4', clevel=9, shuffle=0)),\n", - " ('blosc', dict(cname='lz4', clevel=9, shuffle=2)),\n", - " ('blosc', dict(cname='lz4hc', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='lz4hc', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='lz4hc', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='lz4hc', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zstd', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='zstd', clevel=5, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=1, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=1, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=3, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=3, shuffle=2)),\n", - " ('blosc', dict(cname='zlib', clevel=5, shuffle=0)),\n", - " ('blosc', dict(cname='zlib', clevel=5, shuffle=2)),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def log(*msg):\n", - " print(*msg, file=sys.stdout)\n", - " sys.stdout.flush()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "@functools.cache\n", - "def compression_ratios():\n", - " x = list()\n", - " for compression, compression_opts in compression_configs:\n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", - " compression_opts=compression_opts)\n", - " ratio = z.nbytes / z.nbytes_stored\n", - " x.append(ratio)\n", - " log(compression, compression_opts, ratio)\n", - " return x\n" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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diDzlq1H0cI+BWTYNkfRaiOuXRMMQ00NEewD5erJyXXtZrwEze4GokbUk1PVK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F2V6vG9BvuXretxkh6VtCpfB0lHSXpE0hnNxG1mZmZm5bVyDlZf/BPmYcC8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bKunNlUMC31SxtqUT2L5UtyIilqTth4G70vaDwM5p+8MUHZSz0uNNKN7AL6+cNCLmA59qcmnz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\nJMnHk9yU5GOt11yV5P1Jbmj237w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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ratios = compression_ratios()\n", - "labels = [f'{c} - {o}'\n", - " for c, o in compression_configs]\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Compression ratio', fontsize=14, y=1.01)\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = [ratios[i] for i in y]\n", - "ax.barh(bottom=np.array(y)+.2, width=np.array(x), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = [ratios[i] for i in y]\n", - "ax.barh(bottom=np.array(y)+.2, width=np.array(x), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "ax.set_xlim(0, max(ratios)+3)\n", - "ax.set_ylim(0, len(ratios))\n", - "ax.set_xlabel('compression ratio')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "@functools.cache\n", - "def compression_decompression_times(repeat=3, number=1):\n", - " c = list()\n", - " d = list()\n", - " for compression, compression_opts in compression_configs:\n", - "\n", - " def compress():\n", - " zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", - " compression_opts=compression_opts)\n", - "\n", - " t = timeit.Timer(stmt=compress, globals=locals())\n", - " compress_times = t.repeat(repeat=repeat, number=number)\n", - " c.append(compress_times)\n", - "\n", - " z = zarr.array(genotype_sample, chunks=chunks, compression=compression,\n", - " compression_opts=compression_opts)\n", - "\n", - " def decompress():\n", - " z[:]\n", - "\n", - " t = timeit.Timer(stmt=decompress, globals=locals())\n", - " decompress_times = t.repeat(repeat=repeat, number=number)\n", - " d.append(decompress_times)\n", - "\n", - " log(compression, compression_opts, compress_times, decompress_times)\n", - "\n", - " return c, d\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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fchPRg0AWK3oIxc/TM0g6IqxNyuVKomllLyp6iEJFnnrnEk13W0g0zW9+Wp37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Cl9cZ1WSRotabGkublta9qRSy2S9pM0o4m4Nem/NWtdFb+1pMclfTt/DknDW8itYa3S6zu6zv4T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\nSnVAnbYeCewDfDL3Ok+sihmUb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CVwmdL6pHZg+0zdcjNb4sdPAgWn4XFgBz8+BDhS0jl+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\nv8Oa74cD32y2vwIc17r++a32Y1v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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "co_t, de_t = compression_decompression_times()\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Compression speed', fontsize=14, y=1.01)\n", - "\n", - "\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = (nbytes / 1000000) / np.array([co_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = (nbytes / 1000000) / np.array([co_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "xlim = (0, np.max((nbytes / 1000000) / np.array(co_t)) + 100)\n", - "ax.set_xlim(*xlim)\n", - "ax.set_ylim(0, len(co_t))\n", - "ax.set_xlabel('speed (Mb/s)')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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5nDL1h5gHwo3kZXFK3gvkmUKWxT+BdpkS4pS+WwmdvPnA+GS8yazx76PAETGe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\nrieTpTPWk2+K3P6oAWT34LXp87aSZtZpO5Lsvrie7LCM0omI1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AHmSTobuAw4KvU7mJ62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\nN8xHONpM4+Pjkybeqh/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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "co_t, de_t = compression_decompression_times()\n", - "\n", - "fig = plt.figure(figsize=(12, len(compression_configs)*.3))\n", - "fig.suptitle('Decompression speed', fontsize=14, y=1.01)\n", - "\n", - "\n", - "ax = fig.add_subplot(1, 1, 1)\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c == 'blosc' and o['shuffle'] == 2]\n", - "x = (nbytes / 1000000) / np.array([de_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='bit shuffle', color='b')\n", - "\n", - "y = [i for i, (c, o) in enumerate(compression_configs) if c != 'blosc' or o['shuffle'] == 0]\n", - "x = (nbytes / 1000000) / np.array([de_t[i] for i in y])\n", - "ax.barh(bottom=np.array(y)+.2, width=x.max(axis=1), height=.6, label='no shuffle', color='g')\n", - "\n", - "ax.set_yticks(np.arange(len(labels))+.5)\n", - "ax.set_yticklabels(labels, rotation=0)\n", - "\n", - "xlim = (0, np.max((nbytes / 1000000) / np.array(de_t)) + 100)\n", - "ax.set_xlim(*xlim)\n", - "ax.set_ylim(0, len(de_t))\n", - "ax.set_xlabel('speed (Mb/s)')\n", - "ax.grid(axis='x')\n", - "ax.legend(loc='upper right')\n", - "\n", - "fig.tight_layout();" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import cpuinfo" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Vendor ID: GenuineIntel\n", - "Hardware Raw: \n", - "Brand: Intel(R) Xeon(R) CPU E3-1505M v5 @ 2.80GHz\n", - "Hz Advertised: 2.8000 GHz\n", - "Hz Actual: 1.1000 GHz\n", - "Hz Advertised Raw: (2800000000, 0)\n", - "Hz Actual Raw: (1100000000, 0)\n", - "Arch: X86_64\n", - "Bits: 64\n", - "Count: 8\n", - "Raw Arch String: x86_64\n", - "L2 Cache Size: 8192 KB\n", - "L2 Cache Line Size: 0\n", - "L2 Cache Associativity: 0\n", - "Stepping: 3\n", - "Model: 94\n", - "Family: 6\n", - "Processor Type: 0\n", - "Extended Model: 0\n", - "Extended Family: 0\n", - "Flags: 3dnowprefetch, abm, acpi, adx, aes, aperfmperf, apic, arat, arch_perfmon, avx, avx2, bmi1, bmi2, bts, clflush, clflushopt, cmov, constant_tsc, cx16, cx8, de, ds_cpl, dtes64, dtherm, dts, eagerfpu, epb, ept, erms, est, f16c, flexpriority, fma, fpu, fsgsbase, fxsr, hle, ht, hwp, hwp_act_window, hwp_epp, hwp_noitfy, ida, invpcid, lahf_lm, lm, mca, mce, mmx, monitor, movbe, mpx, msr, mtrr, nonstop_tsc, nopl, nx, pae, pat, pbe, pcid, pclmulqdq, pdcm, pdpe1gb, pebs, pge, pln, pni, popcnt, pse, pse36, pts, rdrand, rdseed, rdtscp, rep_good, rtm, sep, smap, smep, smx, ss, sse, sse2, sse4_1, sse4_2, ssse3, syscall, tm, tm2, tpr_shadow, tsc, tsc_adjust, tsc_deadline_timer, vme, vmx, vnmi, vpid, x2apic, xgetbv1, xsave, xsavec, xsaveopt, xtopology, xtpr\n" - ] - } - ], - "source": [ - "cpuinfo.main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/object_arrays.ipynb b/notebooks/object_arrays.ipynb deleted file mode 100644 index e649656153..0000000000 --- a/notebooks/object_arrays.ipynb +++ /dev/null @@ -1,351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Object arrays\n", - "\n", - "See [#212](https://github.com/alimanfoo/zarr/pull/212) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.2.0a2.dev82+dirty'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.5.0'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numcodecs\n", - "\n", - "numcodecs.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## API changes in Zarr version 2.2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Creation of an object array requires providing new ``object_codec`` argument:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To maintain backwards compatibility with previously-created data, the object codec is treated as a filter and inserted as the first filter in the chain:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Typezarr.core.Array
Data typeobject
Shape(10,)
Chunk shape(5,)
OrderC
Read-onlyFalse
Filter [0]MsgPack(encoding='utf-8')
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes80
No. bytes stored396
Storage ratio0.2
Chunks initialized0/2
" - ], - "text/plain": [ - "Type : zarr.core.Array\n", - "Data type : object\n", - "Shape : (10,)\n", - "Chunk shape : (5,)\n", - "Order : C\n", - "Read-only : False\n", - "Filter [0] : MsgPack(encoding='utf-8')\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 80\n", - "No. bytes stored : 396\n", - "Storage ratio : 0.2\n", - "Chunks initialized : 0/2" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z.info" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['foo', 'bar', 1, list([2, 4, 6, 'baz']), {'a': 'b', 'c': 'd'}, None,\n", - " None, None, None, None], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "z[0] = 'foo'\n", - "z[1] = b'bar' # msgpack doesn't support bytes objects correctly\n", - "z[2] = 1\n", - "z[3] = [2, 4, 6, 'baz']\n", - "z[4] = {'a': 'b', 'c': 'd'}\n", - "a = z[:]\n", - "a" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If no ``object_codec`` is provided, a ``ValueError`` is raised:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "missing object_codec for object array", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/creation.py\u001b[0m in \u001b[0;36mempty\u001b[0;34m(shape, **kwargs)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \"\"\"\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/creation.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(shape, chunks, dtype, compressor, fill_value, order, store, synchronizer, overwrite, path, chunk_store, filters, cache_metadata, read_only, object_codec, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m init_array(store, shape=shape, chunks=chunks, dtype=dtype, compressor=compressor,\n\u001b[1;32m 113\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m chunk_store=chunk_store, filters=filters, object_codec=object_codec)\n\u001b[0m\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;31m# instantiate array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/storage.py\u001b[0m in \u001b[0;36minit_array\u001b[0;34m(store, shape, chunks, dtype, compressor, fill_value, order, overwrite, path, chunk_store, filters, object_codec)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moverwrite\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[0mchunk_store\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunk_store\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfilters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m object_codec=object_codec)\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/storage.py\u001b[0m in \u001b[0;36m_init_array_metadata\u001b[0;34m(store, shape, chunks, dtype, compressor, fill_value, order, overwrite, path, chunk_store, filters, object_codec)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfilters\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 347\u001b[0m \u001b[0;31m# there are no filters so we can be sure there is no object codec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 348\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'missing object_codec for object array'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 349\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0;31m# one of the filters may be an object codec, issue a warning rather\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: missing object_codec for object array" - ] - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For API backward-compatibility, if object codec is provided via filters, issue a warning but don't raise an error." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/aliman/src/github/alimanfoo/zarr/zarr/storage.py:353: FutureWarning: missing object_codec for object array; this will raise a ValueError in version 3.0\n", - " 'ValueError in version 3.0', FutureWarning)\n" - ] - } - ], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, filters=[numcodecs.MsgPack()])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If a user tries to subvert the system and create an object array with no object codec, a runtime check is added to ensure no object arrays are passed down to the compressor (which could lead to nasty errors and/or segfaults):" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "z = zarr.empty(10, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z._filters = None # try to live dangerously, manually wipe filters" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "cannot write object array without object codec", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, selection, value)\u001b[0m\n\u001b[1;32m 1094\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1095\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1096\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1097\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mset_basic_selection\u001b[0;34m(self, selection, value, fields)\u001b[0m\n\u001b[1;32m 1189\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1190\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_basic_selection_nd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mset_orthogonal_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_set_basic_selection_nd\u001b[0;34m(self, selection, value, fields)\u001b[0m\n\u001b[1;32m 1480\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1482\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1484\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_set_selection\u001b[0;34m(self, indexer, value, fields)\u001b[0m\n\u001b[1;32m 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1529\u001b[0m \u001b[0;31m# put data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1530\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_chunk_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_selection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1532\u001b[0m def _chunk_getitem(self, chunk_coords, chunk_selection, out, out_selection,\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_setitem\u001b[0;34m(self, chunk_coords, chunk_selection, value, fields)\u001b[0m\n\u001b[1;32m 1633\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mlock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1634\u001b[0m self._chunk_setitem_nosync(chunk_coords, chunk_selection, value,\n\u001b[0;32m-> 1635\u001b[0;31m fields=fields)\n\u001b[0m\u001b[1;32m 1636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1637\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_chunk_setitem_nosync\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_coords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_selection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_setitem_nosync\u001b[0;34m(self, chunk_coords, chunk_selection, value, fields)\u001b[0m\n\u001b[1;32m 1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1708\u001b[0m \u001b[0;31m# encode chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1709\u001b[0;31m \u001b[0mcdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encode_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1711\u001b[0m \u001b[0;31m# store\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_encode_chunk\u001b[0;34m(self, chunk)\u001b[0m\n\u001b[1;32m 1753\u001b[0m \u001b[0;31m# check object encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1754\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1755\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cannot write object array without object codec'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1757\u001b[0m \u001b[0;31m# compress\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: cannot write object array without object codec" - ] - } - ], - "source": [ - "z[0] = 'foo'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here is another way to subvert the system, wiping filters **after** storing some data. To cover this case a runtime check is added to ensure no object arrays are handled inappropriately during decoding (which could lead to nasty errors and/or segfaults)." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['¡Hola mundo!', 'Hej Världen!', 'Servus Woid!', 'Hei maailma!',\n", - " 'Xin chào thế giới', 'Njatjeta Botë!', 'Γεια σου κόσμε!', 'こんにちは世界',\n", - " '世界，你好！', 'Helló, világ!', 'Zdravo svete!', 'เฮลโลเวิลด์'], dtype=object)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from numcodecs.tests.common import greetings\n", - "\n", - "z = zarr.array(greetings, chunks=5, dtype=object, object_codec=numcodecs.MsgPack())\n", - "z[:]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "ename": "RuntimeError", - "evalue": "cannot read object array without object codec", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# try to live dangerously, manually wipe filters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpop_fields\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_basic_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEllipsis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36mget_basic_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 677\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 678\u001b[0m return self._get_basic_selection_nd(selection=selection, out=out,\n\u001b[0;32m--> 679\u001b[0;31m fields=fields)\n\u001b[0m\u001b[1;32m 680\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 681\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_basic_selection_zd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_basic_selection_nd\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m 719\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasicIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 721\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfields\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 723\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_orthogonal_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mselection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_get_selection\u001b[0;34m(self, indexer, out, fields)\u001b[0m\n\u001b[1;32m 1007\u001b[0m \u001b[0;31m# load chunk selection into output array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1008\u001b[0m self._chunk_getitem(chunk_coords, chunk_selection, out, out_selection,\n\u001b[0;32m-> 1009\u001b[0;31m drop_axes=indexer.drop_axes, fields=fields)\n\u001b[0m\u001b[1;32m 1010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1011\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_chunk_getitem\u001b[0;34m(self, chunk_coords, chunk_selection, out, out_selection, drop_axes, fields)\u001b[0m\n\u001b[1;32m 1597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1598\u001b[0m \u001b[0;31m# decode chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_decode_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1601\u001b[0m \u001b[0;31m# select data from chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/src/github/alimanfoo/zarr/zarr/core.py\u001b[0m in \u001b[0;36m_decode_chunk\u001b[0;34m(self, cdata)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cannot read object array without object codec'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1736\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.3.3.dev87'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "g1 = zarr.group()\n", - "g2 = g1.create_group('foo')\n", - "g3 = g1.create_group('bar')\n", - "g3.create_group('baz')\n", - "g3.create_dataset('xxx', shape=100)\n", - "g3.create_dataset('yyy', shape=(100, 100), dtype='i4')\n", - "g5 = g3.create_group('quux')\n", - "g5.create_dataset('aaa', shape=100)\n", - "g5.create_dataset('bbb', shape=(100, 100), dtype='i4')\n", - "g7 = g3.create_group('zoo')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate text (unicode) tree:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/\n", - " ├── bar\n", - " │ ├── baz\n", - " │ ├── quux\n", - " │ │ ├── aaa (100,) float64\n", - " │ │ └── bbb (100, 100) int32\n", - " │ ├── xxx (100,) float64\n", - " │ ├── yyy (100, 100) int32\n", - " │ └── zoo\n", - " └── foo\n" - ] - } - ], - "source": [ - "print(g1.tree())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The ``level`` parameter controls how deep the tree is." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/\n", - " ├── bar\n", - " └── foo\n" - ] - } - ], - "source": [ - "print(g1.tree(level=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/\n", - " ├── bar\n", - " │ ├── baz\n", - " │ ├── quux\n", - " │ ├── xxx (100,) float64\n", - " │ ├── yyy (100, 100) int32\n", - " │ └── zoo\n", - " └── foo\n" - ] - } - ], - "source": [ - "print(g1.tree(level=2))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternative plain ASCII tree:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/\n", - " +-- bar\n", - " | +-- baz\n", - " | +-- quux\n", - " | | +-- aaa (100,) float64\n", - " | | +-- bbb (100, 100) int32\n", - " | +-- xxx (100,) float64\n", - " | +-- yyy (100, 100) int32\n", - " | +-- zoo\n", - " +-- foo\n" - ] - } - ], - "source": [ - "print(bytes(g1.tree()).decode())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "HTML trees:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dc848c2195a54f1386f67cc631871449", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Tree(nodes=(Node(disabled=True, name='/', nodes=(Node(disabled=True, name='bar', nodes=(Node(disabled=True, na…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g1.tree()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Use ``expand=True`` to have all groups automatically expanded." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9459567637cf48cca67ba0145cc3a626", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Tree(nodes=(Node(disabled=True, name='/', nodes=(Node(disabled=True, name='bar', nodes=(Node(disabled=True, na…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "g1.tree(expand=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - 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"fda99f8dbbbe4636a0bab906c5267b81": { - "model_module": "ipytree", - "model_module_version": "0.1.3", - "model_name": "NodeModel", - "state": { - "_id": "093bd8d7-6f8d-4017-85eb-edf3260c4a30", - "_model_module_version": "0.1.3", - "_view_module_version": "0.1.3", - "disabled": true, - "name": "baz", - "opened": false - } - } - }, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/store_benchmark.ipynb b/notebooks/store_benchmark.ipynb deleted file mode 100644 index a95a5d3da7..0000000000 --- a/notebooks/store_benchmark.ipynb +++ /dev/null @@ -1,1305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are lies, damn lies and benchmarks..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.2.0a2.dev22+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'6.2.5'" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import bsddb3\n", - "\n", - "bsddb3.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.93'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import lmdb\n", - "\n", - "lmdb.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import dbm.gnu\n", - "import dbm.ndbm" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "\n", - "bench_dir = '../data/bench'\n", - "\n", - "\n", - "def clean():\n", - " if os.path.isdir(bench_dir):\n", - " shutil.rmtree(bench_dir)\n", - " os.makedirs(bench_dir)\n", - "\n", - "\n", - "def setup(a, name='foo/bar'):\n", - " global fdict_z, hdict_z, lmdb_z, gdbm_z, ndbm_z, bdbm_btree_z, bdbm_hash_z, zip_z, dir_z\n", - "\n", - " clean()\n", - " fdict_root = zarr.group(store=dict())\n", - " hdict_root = zarr.group(store=zarr.DictStore())\n", - " lmdb_root = zarr.group(store=zarr.LMDBStore(os.path.join(bench_dir, 'lmdb')))\n", - " gdbm_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'gdbm'), open=dbm.gnu.open))\n", - " ndbm_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'ndbm'), open=dbm.ndbm.open))\n", - " bdbm_btree_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'bdbm_btree'), open=bsddb3.btopen))\n", - " bdbm_hash_root = zarr.group(store=zarr.DBMStore(os.path.join(bench_dir, 'bdbm_hash'), open=bsddb3.hashopen))\n", - " zip_root = zarr.group(store=zarr.ZipStore(os.path.join(bench_dir, 'zip'), mode='w'))\n", - " dir_root = zarr.group(store=zarr.DirectoryStore(os.path.join(bench_dir, 'dir')))\n", - "\n", - " fdict_z = fdict_root.empty_like(name, a)\n", - " hdict_z = hdict_root.empty_like(name, a)\n", - " lmdb_z = lmdb_root.empty_like(name, a)\n", - " gdbm_z = gdbm_root.empty_like(name, a)\n", - " ndbm_z = ndbm_root.empty_like(name, a)\n", - " bdbm_btree_z = bdbm_btree_root.empty_like(name, a)\n", - " bdbm_hash_z = bdbm_hash_root.empty_like(name, a)\n", - " zip_z = zip_root.empty_like(name, a)\n", - " dir_z = dir_root.empty_like(name, a)\n", - "\n", - " # check compression ratio\n", - " fdict_z[:] = a\n", - " return fdict_z.info\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Main benchmarks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "def save(a, z):\n", - " if isinstance(z.store, zarr.ZipStore):\n", - " # needed for zip benchmarks to avoid duplicate entries\n", - " z.store.clear()\n", - " z[:] = a\n", - " if hasattr(z.store, 'flush'):\n", - " z.store.flush()\n", - "\n", - "\n", - "def load(z, a):\n", - " z.get_basic_selection(out=a)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## arange" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Name/foo/bar
Typezarr.core.Array
Data typeint64
Shape(500000000,)
Chunk shape(488282,)
OrderC
Read-onlyFalse
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typebuiltins.dict
No. bytes4000000000 (3.7G)
No. bytes stored59269657 (56.5M)
Storage ratio67.5
Chunks initialized1024/1024
" - ], - "text/plain": [ - "Name : /foo/bar\n", - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (500000000,)\n", - "Chunk shape : (488282,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 4000000000 (3.7G)\n", - "No. bytes stored : 59269657 (56.5M)\n", - "Storage ratio : 67.5\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "a = np.arange(500000000)\n", - "setup(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "324 ms ± 60.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "302 ms ± 11.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "316 ms ± 12.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "938 ms ± 111 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "406 ms ± 8.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.43 s ± 156 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.24 s ± 260 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "519 ms ± 59.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "609 ms ± 48.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit save(a, dir_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "454 ms ± 56.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(fdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "428 ms ± 13.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(hdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "429 ms ± 19.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(lmdb_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "459 ms ± 10 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(gdbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "473 ms ± 5.71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(ndbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "504 ms ± 8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(bdbm_btree_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "519 ms ± 9.59 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(bdbm_hash_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "575 ms ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(zip_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "494 ms ± 10.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit load(dir_z, a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## randint" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Name/foo/bar
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Chunk shape(488282,)
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No. bytes4000000000 (3.7G)
No. bytes stored2020785466 (1.9G)
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" - ], - "text/plain": [ - "Name : /foo/bar\n", - "Type : zarr.core.Array\n", - "Data type : int64\n", - "Shape : (500000000,)\n", - "Chunk shape : (488282,)\n", - "Order : C\n", - "Read-only : False\n", - "Compressor : Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)\n", - "Store type : builtins.dict\n", - "No. bytes : 4000000000 (3.7G)\n", - "No. bytes stored : 2020785466 (1.9G)\n", - "Storage ratio : 2.0\n", - "Chunks initialized : 1024/1024" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.seed(42)\n", - "a = np.random.randint(0, 2**30, size=500000000)\n", - "setup(a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### save" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "670 ms ± 78.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "611 ms ± 6.11 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "846 ms ± 24 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.35 s ± 785 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "4.62 s ± 1.09 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7.84 s ± 1.66 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6.49 s ± 808 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.68 s ± 441 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3.55 s ± 1.24 s per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 save(a, dir_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### load" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "566 ms ± 72.8 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(fdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "521 ms ± 16.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(hdict_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "532 ms ± 16.1 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(lmdb_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.2 s ± 10.9 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(gdbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.18 s ± 13.2 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(ndbm_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.59 s ± 16.7 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(bdbm_btree_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.61 s ± 7.31 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(bdbm_hash_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2.33 s ± 19.8 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(zip_z, a)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "790 ms ± 56 ms per loop (mean ± std. dev. of 3 runs, 1 loop each)\n" - ] - } - ], - "source": [ - "%timeit -r3 load(dir_z, a)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### dask" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "import dask.array as da" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "def dask_op(source, sink, chunks=None):\n", - " if isinstance(sink.store, zarr.ZipStore):\n", - " sink.store.clear()\n", - " if chunks is None:\n", - " try:\n", - " chunks = sink.chunks\n", - " except AttributeError:\n", - " chunks = source.chunks\n", - " d = da.from_array(source, chunks=chunks, asarray=False, fancy=False, lock=False)\n", - " result = (d // 2) * 2\n", - " da.store(result, sink, lock=False)\n", - " if hasattr(sink.store, 'flush'):\n", - " sink.store.flush()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compare sources" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.6 s, sys: 1.8 s, total: 17.4 s\n", - "Wall time: 3.07 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.5 s, sys: 104 ms, total: 16.6 s\n", - "Wall time: 2.59 s\n" - ] - } - ], - "source": [ - "%time dask_op(hdict_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.1 s, sys: 524 ms, total: 15.6 s\n", - "Wall time: 3.02 s\n" - ] - } - ], - "source": [ - "%time dask_op(lmdb_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.5 s, sys: 712 ms, total: 17.2 s\n", - "Wall time: 3.13 s\n" - ] - } - ], - "source": [ - "%time dask_op(gdbm_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.3 s, sys: 604 ms, total: 16.9 s\n", - "Wall time: 3.22 s\n" - ] - } - ], - "source": [ - "%time dask_op(ndbm_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 19.6 s, sys: 1.24 s, total: 20.9 s\n", - "Wall time: 3.27 s\n" - ] - } - ], - "source": [ - "%time dask_op(bdbm_btree_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 20.3 s, sys: 1.08 s, total: 21.4 s\n", - "Wall time: 3.53 s\n" - ] - } - ], - "source": [ - "%time dask_op(bdbm_hash_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.7 s, sys: 700 ms, total: 16.4 s\n", - "Wall time: 3.12 s\n" - ] - } - ], - "source": [ - "%time dask_op(zip_z, fdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.4 s, sys: 1.08 s, total: 18.5 s\n", - "Wall time: 2.91 s\n" - ] - } - ], - "source": [ - "%time dask_op(dir_z, fdict_z)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Compare sinks" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.8 s, sys: 1.4 s, total: 17.2 s\n", - "Wall time: 3.04 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, hdict_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.2 s, sys: 1.6 s, total: 17.8 s\n", - "Wall time: 2.71 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, lmdb_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 16.8 s, sys: 3.05 s, total: 19.8 s\n", - "Wall time: 8.01 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, gdbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 17.9 s, sys: 3.01 s, total: 20.9 s\n", - "Wall time: 5.46 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, ndbm_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.8 s, sys: 3.39 s, total: 17.2 s\n", - "Wall time: 7.87 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, bdbm_btree_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.9 s, sys: 3.27 s, total: 17.2 s\n", - "Wall time: 6.73 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, bdbm_hash_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 13.9 s, sys: 2.5 s, total: 16.4 s\n", - "Wall time: 3.8 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, zip_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.7 s, sys: 3.72 s, total: 19.4 s\n", - "Wall time: 3.1 s\n" - ] - } - ], - "source": [ - "%time dask_op(fdict_z, dir_z)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "lmdb_z.store.close()\n", - "gdbm_z.store.close()\n", - "ndbm_z.store.close()\n", - "bdbm_btree_z.store.close()\n", - "bdbm_hash_z.store.close()\n", - "zip_z.store.close()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.6.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/zip_benchmark.ipynb b/notebooks/zip_benchmark.ipynb deleted file mode 100644 index d14a7c2ea0..0000000000 --- a/notebooks/zip_benchmark.ipynb +++ /dev/null @@ -1,347 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'2.0.2.dev0+dirty'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sys\n", - "\n", - "sys.path.insert(0, '..')\n", - "import zarr\n", - "\n", - "zarr.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3L/calldata/genotype, (7449486, 773, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 10.7G; nbytes_stored: 193.5M; ratio: 56.7; initialized: 11380/11380\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: ZipStore" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "store = zarr.ZipStore('/data/coluzzi/ag1000g/data/phase1/release/AR3.1/haplotypes/main/zarr2/zstd/ag1000g.phase1.ar3.1.haplotypes.zip',\n", - " mode='r')\n", - "grp = zarr.Group(store)\n", - "z = grp['3L/calldata/genotype']\n", - "z" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 1832 function calls in 0.024 seconds\n", - "\n", - " Ordered by: cumulative time\n", - "\n", - " ncalls tottime percall cumtime percall filename:lineno(function)\n", - " 1 0.000 0.000 0.024 0.024 {built-in method builtins.exec}\n", - " 1 0.000 0.000 0.024 0.024 :1()\n", - " 1 0.000 0.000 0.024 0.024 core.py:292(__getitem__)\n", - " 20 0.000 0.000 0.023 0.001 core.py:539(_chunk_getitem)\n", - " 20 0.000 0.000 0.020 0.001 core.py:679(_decode_chunk)\n", - " 20 0.000 0.000 0.020 0.001 codecs.py:355(decode)\n", - " 20 0.020 0.001 0.020 0.001 {zarr.blosc.decompress}\n", - " 20 0.000 0.000 0.002 0.000 storage.py:766(__getitem__)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:1235(open)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:821(read)\n", - " 20 0.000 0.000 0.001 0.000 zipfile.py:901(_read1)\n", - " 80 0.000 0.000 0.001 0.000 zipfile.py:660(read)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:854(_update_crc)\n", - " 40 0.000 0.000 0.000 0.000 {built-in method zlib.crc32}\n", - " 80 0.000 0.000 0.000 0.000 {method 'read' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:937(_read2)\n", - " 80 0.000 0.000 0.000 0.000 core.py:390()\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:953(close)\n", - " 20 0.000 0.000 0.000 0.000 {method 'reshape' of 'numpy.ndarray' objects}\n", - " 20 0.000 0.000 0.000 0.000 util.py:106(is_total_slice)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:708(__init__)\n", - " 20 0.000 0.000 0.000 0.000 {method 'decode' of 'bytes' objects}\n", - " 20 0.000 0.000 0.000 0.000 core.py:676(_chunk_key)\n", - " 80 0.000 0.000 0.000 0.000 {method 'seek' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.frombuffer}\n", - " 80 0.000 0.000 0.000 0.000 core.py:398()\n", - " 20 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects}\n", - " 20 0.000 0.000 0.000 0.000 core.py:386()\n", - " 20 0.000 0.000 0.000 0.000 {built-in method builtins.all}\n", - " 40 0.000 0.000 0.000 0.000 util.py:121()\n", - " 231 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}\n", - " 20 0.000 0.000 0.000 0.000 cp437.py:14(decode)\n", - " 80 0.000 0.000 0.000 0.000 {method 'tell' of '_io.BufferedReader' objects}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:667(close)\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _struct.unpack}\n", - " 140 0.000 0.000 0.000 0.000 {built-in method builtins.max}\n", - " 20 0.000 0.000 0.000 0.000 {function ZipExtFile.close at 0x7f8cd5ca2048}\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:1194(getinfo)\n", - " 140 0.000 0.000 0.000 0.000 {built-in method builtins.min}\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1224(current_thread)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:654(__init__)\n", - " 1 0.000 0.000 0.000 0.000 util.py:195(get_chunk_range)\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _codecs.charmap_decode}\n", - " 1 0.000 0.000 0.000 0.000 util.py:166(normalize_array_selection)\n", - " 1 0.000 0.000 0.000 0.000 util.py:198()\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:1715(_fpclose)\n", - " 20 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}\n", - " 63 0.000 0.000 0.000 0.000 {built-in method builtins.len}\n", - " 1 0.000 0.000 0.000 0.000 {built-in method numpy.core.multiarray.empty}\n", - " 2 0.000 0.000 0.000 0.000 util.py:182()\n", - " 20 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}\n", - " 20 0.000 0.000 0.000 0.000 {built-in method _thread.get_ident}\n", - " 1 0.000 0.000 0.000 0.000 util.py:130(normalize_axis_selection)\n", - " 20 0.000 0.000 0.000 0.000 zipfile.py:636(_get_decompressor)\n", - " 20 0.000 0.000 0.000 0.000 threading.py:1298(main_thread)\n", - " 4 0.000 0.000 0.000 0.000 core.py:373()\n", - " 3 0.000 0.000 0.000 0.000 util.py:187()\n", - " 1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}\n", - "\n", - "\n" - ] - } - ], - "source": [ - "import cProfile\n", - "\n", - "cProfile.run('z[:10]', sort='cumtime')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "'0.11.0'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import dask\n", - "import dask.array as da\n", - "\n", - "dask.__version__" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d = da.from_array(z, chunks=z.chunks)\n", - "d" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3min 35s, sys: 4.36 s, total: 3min 40s\n", - "Wall time: 29.5 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[3, 0],\n", - " [1, 0],\n", - " [2, 0],\n", - " ..., \n", - " [2, 8],\n", - " [8, 8],\n", - " [0, 1]])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d.sum(axis=1).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Array(/3L/calldata/genotype, (7449486, 773, 2), int8, chunks=(13107, 40, 2), order=C)\n", - " nbytes: 10.7G; nbytes_stored: 193.5M; ratio: 56.7; initialized: 11380/11380\n", - " compressor: Blosc(cname='zstd', clevel=1, shuffle=2)\n", - " store: DirectoryStore" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# compare with same data via directory store\n", - "store_dir = zarr.DirectoryStore('/data/coluzzi/ag1000g/data/phase1/release/AR3.1/haplotypes/main/zarr2/zstd/ag1000g.phase1.ar3.1.haplotypes')\n", - "grp_dir = zarr.Group(store_dir)\n", - "z_dir = grp_dir['3L/calldata/genotype']\n", - "z_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "dask.array" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "d_dir = da.from_array(z_dir, chunks=z_dir.chunks)\n", - "d_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 3min 39s, sys: 4.91 s, total: 3min 44s\n", - "Wall time: 31.1 s\n" - ] - }, - { - "data": { - "text/plain": [ - "array([[3, 0],\n", - " [1, 0],\n", - " [2, 0],\n", - " ..., \n", - " [2, 8],\n", - " [8, 8],\n", - " [0, 1]])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%time d_dir.sum(axis=1).compute()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "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": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/pyproject.toml b/pyproject.toml index dc0e4730eb..005e91805d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -258,7 +258,6 @@ extend-exclude = [ "buck-out", "build", "dist", - "notebooks", # temporary, until we achieve compatibility with ruff ≥ 0.6 "venv", "docs", "src/zarr/v2/",