Remove ismrmrd dependency

.circleci/config.yml

@@ -12,7 +12,6 @@ jobs:
       - run:
           name: Preinstallation Packages
           command: |
-            pip install pyxb
             pip install wheel
             pip install pytest
       - python/install-packages:

experimental/zero_filled/run_zero_filled.py

@@ -5,25 +5,31 @@
 LICENSE file in the root directory of this source tree.
 """
 
-import pathlib
+import xml.etree.ElementTree as etree
 from argparse import ArgumentParser
+from pathlib import Path
 
 import fastmri
 import h5py
-import ismrmrd
 from fastmri.data import transforms
+from fastmri.data.mri_data import et_query
+from tqdm import tqdm
 
 
 def save_zero_filled(data_dir, out_dir, which_challenge):
     reconstructions = {}
 
-    for f in data_dir.iterdir():
-        with h5py.File(f, "r") as hf:
-            enc = ismrmrd.xsd.CreateFromDocument(hf["ismrmrd_header"][()]).encoding[0]
+    for fname in tqdm(list(data_dir.glob("*.h5"))):
+        with h5py.File(fname, "r") as hf:
+            et_root = etree.fromstring(hf["ismrmrd_header"][()])
             masked_kspace = transforms.to_tensor(hf["kspace"][()])
 
             # extract target image width, height from ismrmrd header
-            crop_size = (enc.reconSpace.matrixSize.x, enc.reconSpace.matrixSize.y)
+            enc = ["encoding", "encodedSpace", "matrixSize"]
+            crop_size = (
+                int(et_query(et_root, enc + ["x"])),
+                int(et_query(et_root, enc + ["y"])),
+            )
 
             # inverse Fourier Transform to get zero filled solution
             image = fastmri.ifft2c(masked_kspace)
@@ -42,20 +48,20 @@ def save_zero_filled(data_dir, out_dir, which_challenge):
             if which_challenge == "multicoil":
                 image = fastmri.rss(image, dim=1)
 
-            reconstructions[f.name] = image
+            reconstructions[fname.name] = image
 
     fastmri.save_reconstructions(reconstructions, out_dir)
 
 
 def create_arg_parser():
-    parser = ArgumentParser(add_help=False)
+    parser = ArgumentParser()
 
     parser.add_argument(
-        "--data_path", type=pathlib.Path, required=True, help="Path to the data",
+        "--data_path", type=Path, required=True, help="Path to the data",
     )
     parser.add_argument(
         "--out_path",
-        type=pathlib.Path,
+        type=Path,
         required=True,
         help="Path to save the reconstructions to",
     )

fastmri/data/mri_data.py

@@ -9,15 +9,41 @@
 import pathlib
 import pickle
 import random
+import xml.etree.ElementTree as etree
 from warnings import warn
 
 import h5py
-import ismrmrd
 import numpy as np
 import torch
 import yaml
 
 
+def et_query(root, qlist, namespace="http://www.ismrm.org/ISMRMRD"):
+    """
+    ElementTree query function.
+
+    This can be used to query an xml document via ElementTree. It uses qlist
+    for nexted queries.
+
+    Args:
+        root (xml.etree.ElementTree.Element): Root of the xml.
+        qlist (Sequence): A list of strings for nested searches.
+        namespace (str): xml namespace.
+
+    Returns:
+        str: The retrieved data.
+    """
+    s = "."
+    prefix = "ismrmrd_namespace"
+
+    ns = {prefix: namespace}
+
+    for el in qlist:
+        s = s + f"//{prefix}:{el}"
+
+    return root.find(s, ns).text
+
+
 def fetch_dir(key, data_config_file=pathlib.Path("fastmri_dirs.yaml")):
     """
     Data directory fetcher.
@@ -162,24 +188,25 @@ def __init__(
             files = list(pathlib.Path(root).iterdir())
             for fname in sorted(files):
                 with h5py.File(fname, "r") as hf:
-                    hdr = ismrmrd.xsd.CreateFromDocument(hf["ismrmrd_header"][()])
-                    enc = hdr.encoding[0]
+                    et_root = etree.fromstring(hf["ismrmrd_header"][()])
 
+                    enc = ["encoding", "encodedSpace", "matrixSize"]
                     enc_size = (
-                        enc.encodedSpace.matrixSize.x,
-                        enc.encodedSpace.matrixSize.y,
-                        enc.encodedSpace.matrixSize.z,
+                        int(et_query(et_root, enc + ["x"])),
+                        int(et_query(et_root, enc + ["y"])),
+                        int(et_query(et_root, enc + ["z"])),
                     )
+                    rec = ["encoding", "reconSpace", "matrixSize"]
                     recon_size = (
-                        enc.reconSpace.matrixSize.x,
-                        enc.reconSpace.matrixSize.y,
-                        enc.reconSpace.matrixSize.z,
+                        int(et_query(et_root, rec + ["x"])),
+                        int(et_query(et_root, rec + ["y"])),
+                        int(et_query(et_root, rec + ["z"])),
                     )
 
-                    enc_limits_center = enc.encodingLimits.kspace_encoding_step_1.center
-                    enc_limits_max = (
-                        enc.encodingLimits.kspace_encoding_step_1.maximum + 1
-                    )
+                    lims = ["encoding", "encodingLimits", "kspace_encoding_step_1"]
+                    enc_limits_center = int(et_query(et_root, lims + ["center"]))
+                    enc_limits_max = int(et_query(et_root, lims + ["maximum"])) + 1
+
                     padding_left = enc_size[1] // 2 - enc_limits_center
                     padding_right = padding_left + enc_limits_max
 

requirements.txt

@@ -6,4 +6,3 @@ runstats>=1.8.0
 pytorch_lightning>=1.0.0
 h5py>=2.10.0
 PyYAML>=5.3.1
-git+https://github.com/ismrmrd/ismrmrd-python.git

setup.py

@@ -16,8 +16,6 @@
     "pytorch_lightning",
     "h5py",
     "PyYAML",
-    "pytest",
-    "ismrmrd @ git+https://github.com/ismrmrd/ismrmrd-python.git",
 ]
 
 setup(