Merge pull request #728 from ChristosT/add-hdf5-support

Add fch5 an hdf5-based format for storing framecaches

Author: ChristosT

conda.recipe/meta.yaml

@@ -42,6 +42,7 @@ requirements:
     - lxml >=4.9.2
     - fast-histogram
     - h5py
+    - hdf5plugin
     - lmfit
     - matplotlib-base
     - numba

hexrd/imageseries/load/framecache.py

@@ -6,10 +6,17 @@
 import numpy as np
 from scipy.sparse import csr_matrix
 import yaml
+import h5py
 
 from . import ImageSeriesAdapter
 from ..imageseriesiter import ImageSeriesIterator
 from .metadata import yamlmeta
+from hexrd.utils.hdf5 import unwrap_h5_to_dict
+from hexrd.utils.compatibility import h5py_read_string
+
+import multiprocessing
+from concurrent.futures import ThreadPoolExecutor
+
 
 class FrameCacheImageSeriesAdapter(ImageSeriesAdapter):
     """collection of images in HDF5 format"""
@@ -26,13 +33,25 @@ def __init__(self, fname, style='npz', **kwargs):
         self._framelist = []
         self._framelist_was_loaded = False
         self._load_framelist_lock = Lock()
+        # TODO extract style from filename ?
+        self._style = style.lower()
+
+        ncpus = multiprocessing.cpu_count()
+        self._max_workers = kwargs.get('max_workers', ncpus)
 
-        if style.lower() in ('yml', 'yaml', 'test'):
+        if self._style in ('yml', 'yaml', 'test'):
             self._from_yml = True
             self._load_yml()
-        else:
+        elif self._style == "npz":
             self._from_yml = False
             self._load_cache()
+        elif self._style == "fch5":
+            self._from_yml = False
+            self._load_cache()
+        else:
+            raise TypeError(f"Unknown style format for loading data: {style}."
+                            "Known style formats: 'npz', 'fch5' 'yml', ",
+                            "'yaml', 'test'")
 
     def _load_yml(self):
         with open(self._fname, "r") as f:
@@ -45,6 +64,29 @@ def _load_yml(self):
         self._meta = yamlmeta(d['meta'], path=self._cache)
 
     def _load_cache(self):
+        if self._style == 'fch5':
+            self._load_cache_fch5()
+        else:
+            self._load_cache_npz()
+
+    def _load_cache_fch5(self):
+        with h5py.File(self._fname, "r") as file:
+            if 'HEXRD_FRAMECACHE_VERSION' not in file.attrs.keys():
+                raise NotImplementedError("Unsupported file. "
+                                          "HEXRD_FRAMECACHE_VERSION "
+                                          "is missing!")
+            version = file.attrs.get('HEXRD_FRAMECACHE_VERSION', 0)
+            if version != 1:
+                raise NotImplementedError("Framecache version is not "
+                                          f"supported: {version}")
+
+            self._shape = file["shape"][()]
+            self._nframes = file["nframes"][()]
+            self._dtype = np.dtype(h5py_read_string(file["dtype"]))
+            self._meta = {}
+            unwrap_h5_to_dict(file["metadata"], self._meta)
+
+    def _load_cache_npz(self):
         arrs = np.load(self._fname)
         # HACK: while the loaded npz file has a getitem method
         # that mimicks a dict, it doesn't have a "pop" method.
@@ -79,6 +121,41 @@ def _load_cache(self):
 
     def _load_framelist(self):
         """load into list of csr sparse matrices"""
+        if self._style == 'fch5':
+            self._load_framelist_fch5()
+        else:
+            self._load_framelist_npz()
+
+    def _load_framelist_fch5(self):
+        self._framelist = [None] * self._nframes
+        with h5py.File(self._fname, "r") as file:
+            frame_id = file["frame_ids"]
+            data = file["data"]
+            indices = file["indices"]
+
+            def read_list_arrays_method_thread(i):
+                frame_data = data[frame_id[2*i]: frame_id[2*i+1]]
+                frame_indices = indices[frame_id[2*i]: frame_id[2*i+1]]
+                row = frame_indices[:, 0]
+                col = frame_indices[:, 1]
+                mat_data = frame_data[:, 0]
+                frame = csr_matrix((mat_data, (row, col)),
+                                   shape=self._shape,
+                                   dtype=self._dtype)
+                self._framelist[i] = frame
+                return
+
+            kwargs = {
+                "max_workers": self._max_workers,
+            }
+            with ThreadPoolExecutor(**kwargs) as executor:
+                # Evaluate the results via `list()`, so that if an exception is
+                # raised in a thread, it will be re-raised and visible to the
+                # user.
+                list(executor.map(read_list_arrays_method_thread,
+                                  range(self._nframes)))
+
+    def _load_framelist_npz(self):
         self._framelist = []
         if self._from_yml:
             bpath = os.path.dirname(self._fname)
@@ -149,6 +226,6 @@ def __getitem__(self, key):
     def __iter__(self):
         return ImageSeriesIterator(self)
 
-    #@memoize
+    # @memoize
     def __len__(self):
         return self._nframes