Updated changes

docs/src/conf.py

@@ -32,9 +32,20 @@
 
 
 # -- General configuration ---------------------------------------------------
+os.environ["SPHINX_BUILD"] = "1"
 
 # If your documentation needs a minimal Sphinx version, state it here.
 # needs_sphinx = "1.0"
+# conf.py
+
+# ... other configuration options ...
+
+# Global setup for doctests: this code is executed before any doctest example.
+doctest_global_setup = """
+from spectrum_utils import spectrum, proforma
+import matplotlib
+"""
+
 
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
@@ -44,13 +55,18 @@
     "sphinx_markdown_tables",  # Support tables in Markdown.
     "sphinx.ext.autodoc",  # Include documentation from docstrings.
     # "sphinx.ext.autosummary",  # Generate documentation summary one-liners.
-    # "sphinx.ext.doctest",  # Test code in the documentation.
+    "sphinx.ext.doctest",  # Test code in the documentation.
     # "sphinx.ext.coverage",  # Collect documentation coverage statistics.
     "sphinx.ext.napoleon",  # Support NumPy and Google style docstrings.
     "sphinx.ext.viewcode",  # Add links to the source code.
     "sphinx_rtd_theme",  # Read-the-docs theme.
 ]
 
+myst_enable_extensions = [
+    "dollarmath",
+    "amsmath",
+]
+
 # Generate documentation from all docstrings.
 autodoc_default_options = {
     "member-order": "bysource",  # Sort by order in the source.

docs/src/quickstart.md

@@ -13,36 +13,37 @@ Here we briefly introduce spectrum_utils' spectrum processing and visualization
 IO functionality to read spectra from MS data files is not directly included in spectrum_utils.
 Instead you can use excellent libraries to read a variety of mass spectrometry data formats such as [Pyteomics](https://pyteomics.readthedocs.io/) or [pymzML](https://pymzml.readthedocs.io/).
 
-```python
-import matplotlib.pyplot as plt
-import spectrum_utils.plot as sup
-import spectrum_utils.spectrum as sus
+
+
+```{doctest}
+>>> import matplotlib.pyplot as plt
+>>> import spectrum_utils.plot as sup
+>>> import spectrum_utils.spectrum as sus
 
 
 # Retrieve the spectrum by its USI.
-usi = "mzspec:PXD004732:01650b_BC2-TUM_first_pool_53_01_01-3xHCD-1h-R2:scan:41840"
-peptide = "WNQLQAFWGTGK"
-spectrum = sus.MsmsSpectrum.from_usi(usi)
+>>> usi = "mzspec:PXD000561:Adult_Frontalcortex_bRP_Elite_85_f09:scan:17555"
+>>> peptide = "VLHPLEGAVVIIFK"
+>>> spectrum = sus.MsmsSpectrum.from_usi(usi)
 
 # Process the spectrum.
-fragment_tol_mass, fragment_tol_mode = 10, "ppm"
-spectrum = (
-    spectrum.set_mz_range(min_mz=100, max_mz=1400)
-    .remove_precursor_peak(fragment_tol_mass, fragment_tol_mode)
-    .filter_intensity(min_intensity=0.05, max_num_peaks=50)
-    .scale_intensity("root")
-    .annotate_proforma(
-        peptide, fragment_tol_mass, fragment_tol_mode, ion_types="aby"
-    )
-)
+>>> fragment_tol_mass, fragment_tol_mode = 10, "ppm"
+>>> spectrum = (
+...    spectrum.set_mz_range(min_mz=100, max_mz=1400)
+...    .remove_precursor_peak(fragment_tol_mass, fragment_tol_mode)
+...    .filter_intensity(min_intensity=0.05, max_num_peaks=50)
+...    .scale_intensity("root")
+...    .annotate_proforma(
+...        peptide, fragment_tol_mass, fragment_tol_mode, ion_types="aby")
+... )
 
 # Plot the spectrum.
-fig, ax = plt.subplots(figsize=(12, 6))
-sup.spectrum(spectrum, grid=False, ax=ax)
-ax.spines["right"].set_visible(False)
-ax.spines["top"].set_visible(False)
-plt.savefig("quickstart.png", bbox_inches="tight", dpi=300, transparent=True)
-plt.close()
+>>> fig, ax = plt.subplots(figsize=(12, 6))
+>>> sup.spectrum(spectrum, grid=False, ax=ax)
+>>> ax.spines["right"].set_visible(False)
+>>> ax.spines["top"].set_visible(False)
+>>> plt.savefig("quickstart.png", bbox_inches="tight", dpi=300, transparent=True)
+>>> plt.close()
 ```
 
 As demonstrated, each of the processing steps can be achieved using a single, high-level function call.

spectrum_utils/fragment_annotation.py

@@ -408,7 +408,7 @@ def get_theoretical_fragments(
                     (
                         fragment_sequence,
                         "b",
-                        f"{start_i+1}:{stop_i+1}",
+                        f"{start_i + 1}:{stop_i + 1}",
                         mod_mass,
                     )
                 )

spectrum_utils/proforma.py

@@ -400,12 +400,12 @@ def aa(self, tree) -> None:
         self._sequence.append(tree[0])
         # An amino acid token can be followed by (i) a modification on that
         # residue, or (ii) a label (linking it to another modified residue).
-        if isinstance(tree[1], Label):
+        if len(tree) > 1 and isinstance(tree[1], Label):
             # noinspection PyArgumentList
             self._modifications.append(
                 Modification(position=position, label=tree[1])
             )
-        elif isinstance(tree[1], Modification):
+        elif len(tree) > 1 and isinstance(tree[1], Modification):
             tree[1].position = position
             self._modifications.append(tree[1])
 
@@ -879,10 +879,8 @@ def _parse_obo(
                 elif (
                     cv_id == "XLMOD"
                     and isinstance(clause, fastobo.term.PropertyValueClause)
-                    and (
-                        clause.property_value.relation.prefix
-                        == "monoIsotopicMass"
-                    )
+                    and "monoIsotopicMass"
+                    in str(clause.property_value.relation)
                 ):
                     term_mass = float(clause.property_value.value)
                 elif cv_id == "GNO" and isinstance(

spectrum_utils/spectrum.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 import copy
 import functools
 import urllib.parse
@@ -22,7 +23,13 @@ def __init__(self, **kwargs):
 
 
 # Reload the Pyteomics PROXI aggregator to also include GNPS.
-pyteomics.usi._proxies["gnps"] = GnpsBackend
+# Only perform the assignment if not building docs.
+if not os.environ.get("SPHINX_BUILD"):
+    try:
+        pyteomics.usi._proxies["gnps"] = GnpsBackend
+    except Exception:
+        pass
+
 pyteomics.usi.AGGREGATOR = pyteomics.usi.PROXIAggregator()
 
 
@@ -75,7 +82,7 @@ def intensity(self) -> np.ndarray:
     def round(
         self, decimals: int = 0, combine: str = "sum"
     ) -> "MsmsSpectrumJit":
-        mz_round = np.round_(self._mz, decimals, np.empty_like(self._mz))
+        mz_round = np.round(self._mz, decimals, np.empty_like(self._mz))
         mz_unique = np.unique(mz_round)
         if len(mz_unique) == len(mz_round):
             self._mz = mz_unique
@@ -646,13 +653,24 @@ def _annotate_proteoforms(
         (since parsing the sequence is a lot slower than annotating the
         peaks).
 
+        >>> import spectrum_utils.spectrum as sus
+        >>> identifier = "test_spec"
+        >>> precursor_mz = 500.0
+        >>> precursor_charge = 2
+        >>> mz_array = np.array([100.0, 200.0, 300.0])
+        >>> intensity_array = np.array([10.0, 20.0, 30.0])
+
+        >>> spec = sus.MsmsSpectrum(identifier, precursor_mz, precursor_charge, mz_array, intensity_array)
         >>> proforma_sequence = "MYPEPTIDEK/2"
-        >>> spectrum.annotate_proforma(proforma_sequence, ...)
+        >>> _ = spec.annotate_proforma(proforma_str =proforma_sequence, fragment_tol_mass=10.0, 
+        ...                                 fragment_tol_mode ="ppm", ion_types="by")
 
-        or
+        --- or
 
         >>> parsed_proforma = proforma.parse(proforma_sequence)
-        >>> spectrum._annotate_proteoforms(parsed_proforma, proforma_sequence, ...)
+        >>> _ = spec._annotate_proteoforms(proteoforms=parsed_proforma, proforma_str =proforma_sequence,
+        ...                                 fragment_tol_mass=10.0, fragment_tol_mode ="ppm", ion_types="by")
+
 
         WARN:
             This function does not check that the passed sequence
@@ -704,6 +722,7 @@ def _annotate_proteoforms(
                     > fragment_tol_mass
                 ):
                     fragment_i += 1
+
                 i = 0
                 while (
                     fragment_i + i < len(fragments)

tests/fragment_annotation_test.py

@@ -326,8 +326,10 @@ def test_get_theoretical_fragments_neutral_loss():
         assert fragment_mz == pytest.approx(
             fragments[
                 f"""{annotation.ion_type}^{annotation.charge}{
-                annotation.neutral_loss if annotation.neutral_loss is not None
-                else ''}"""
+                    annotation.neutral_loss
+                    if annotation.neutral_loss is not None
+                    else ""
+                }"""
             ]
         )
 
@@ -390,8 +392,10 @@ def test_get_theoretical_fragments_mod_neutral_loss():
         assert fragment_mz == pytest.approx(
             fragments[
                 f"""{annotation.ion_type}^{annotation.charge}{
-                annotation.neutral_loss if annotation.neutral_loss is not None
-                else ''}"""
+                    annotation.neutral_loss
+                    if annotation.neutral_loss is not None
+                    else ""
+                }"""
             ]
         )
 

tests/spectrum_test.py

@@ -49,7 +49,7 @@ def test_mz_array():
     mz = np.random.uniform(100, 1400, num_peaks).tolist()
     intensity = np.random.lognormal(0, 1, num_peaks)
     spec = spectrum.MsmsSpectrum("test_spectrum", 500, 2, mz, intensity)
-    assert type(spec.mz) == np.ndarray
+    assert isinstance(spec.mz, np.ndarray)
     with pytest.raises(AttributeError):
         spec.mz = np.random.uniform(100, 1400, num_peaks)
 
@@ -59,7 +59,7 @@ def test_intensity_array():
     mz = np.random.uniform(100, 1400, num_peaks)
     intensity = np.random.lognormal(0, 1, num_peaks).tolist()
     spec = spectrum.MsmsSpectrum("test_spectrum", 500, 2, mz, intensity)
-    assert type(spec.intensity) == np.ndarray
+    assert isinstance(spec.intensity, np.ndarray)
     with pytest.raises(AttributeError):
         spec.intensity = np.random.lognormal(0, 1, num_peaks)