Merge pull request #3365 from snbianco/ASB-31576-filter-integer

bsipocz · web-flow · commit 6fccfe98fca2 · 2025-08-16T20:21:46.000-08:00
diff --git a/CHANGES.rst b/CHANGES.rst
@@ -118,6 +118,9 @@ mast
 
 - Improved ``MastMissions`` queries to accept lists for query critieria values, in addition to comma-delimited strings. [#3319]
 
+- Enhanced ``filter_products`` methods in ``MastMissions`` and ``Observations`` to support advanced filtering expressions
+  for numeric columns. [#3365]
+
 
 Infrastructure, Utility and Other Changes and Additions
 -------------------------------------------------------
diff --git a/astroquery/mast/missions.py b/astroquery/mast/missions.py
@@ -22,7 +22,7 @@
 from astroquery.utils import commons, async_to_sync
 from astroquery.utils.class_or_instance import class_or_instance
 from astropy.utils.console import ProgressBarOrSpinner
-from astroquery.exceptions import InvalidQueryError, MaxResultsWarning, InputWarning, NoResultsWarning
+from astroquery.exceptions import InvalidQueryError, MaxResultsWarning, NoResultsWarning
 
 from astroquery.mast import utils
 from astroquery.mast.core import MastQueryWithLogin
@@ -472,11 +472,19 @@ def filter_products(self, products, *, extension=None, **filters):
         extension : string or array, optional
             Default is None. Filters by file extension(s), matching any specified extensions.
         **filters :
-            Column-based filters to be applied.
+            Column-based filters to apply to the products table.
+
             Each keyword corresponds to a column name in the table, with the argument being one or more
             acceptable values for that column. AND logic is applied between filters, OR logic within
-            each filter set.
-            For example: type="science", extension=["fits","jpg"]
+            each filter set. For example: type="science", extension=["fits", "jpg"]
+
+            For columns with numeric data types (int or float), filter values can be expressed
+            in several ways:
+
+            - A single number: ``size=100``
+            - A range in the form "start..end": ``size="100..1000"``
+            - A comparison operator followed by a number: ``size=">=1000"``
+            - A list of expressions (OR logic): ``size=[100, "500..1000", ">=1500"]``
 
         Returns
         -------
@@ -497,17 +505,10 @@ def filter_products(self, products, *, extension=None, **filters):
             )
             filter_mask &= ext_mask
 
-        # Applying column-based filters
-        for colname, vals in filters.items():
-            if colname not in products.colnames:
-                warnings.warn(f"Column '{colname}' not found in product table.", InputWarning)
-                continue
-
-            vals = [vals] if isinstance(vals, str) else vals
-            col_mask = np.isin(products[colname], vals)
-            filter_mask &= col_mask
+        # Apply column-based filters
+        col_mask = utils.apply_column_filters(products, filters)
+        filter_mask &= col_mask
 
-        # Return filtered products
         return products[filter_mask]
 
     def download_file(self, uri, *, local_path=None, cache=True, verbose=True):
diff --git a/astroquery/mast/observations.py b/astroquery/mast/observations.py
@@ -545,7 +545,7 @@ def get_product_list_async(self, observations):
 
     def filter_products(self, products, *, mrp_only=False, extension=None, **filters):
         """
-        Takes an `~astropy.table.Table` of MAST observation data products and filters it based on given filters.
+        Filters an `~astropy.table.Table` of data products based on given filters.
 
         Parameters
         ----------
@@ -556,47 +556,50 @@ def filter_products(self, products, *, mrp_only=False, extension=None, **filters
         extension : string or array, optional
             Default None. Option to filter by file extension.
         **filters :
-            Filters to be applied.  Valid filters are all products fields listed
+            Column-based filters to apply to the products table. Valid filters are all products fields listed
             `here <https://masttest.stsci.edu/api/v0/_productsfields.html>`__.
-            The column name is the keyword, with the argument being one or more acceptable values
-            for that parameter.
-            Filter behavior is AND between the filters and OR within a filter set.
-            For example: productType="SCIENCE",extension=["fits","jpg"]
+
+            Each keyword corresponds to a column name in the table, with the argument being one or more
+            acceptable values for that column. AND logic is applied between filters, OR logic within
+            each filter set.
+
+            For example: type="science", extension=["fits", "jpg"]
+
+            For columns with numeric data types (int or float), filter values can be expressed
+            in several ways:
+
+            - A single number: ``size=100``
+            - A range in the form "start..end": ``size="100..1000"``
+            - A comparison operator followed by a number: ``size=">=1000"``
+            - A list of expressions (OR logic): ``size=[100, "500..1000", ">=1500"]``
 
         Returns
         -------
         response : `~astropy.table.Table`
+            Filtered table of data products.
         """
 
         filter_mask = np.full(len(products), True, dtype=bool)
 
-        # Applying the special filters (mrp_only and extension)
+        # Filter by minimum recommended products (MRP) if specified
         if mrp_only:
             filter_mask &= (products['productGroupDescription'] == "Minimum Recommended Products")
 
+        # Filter by file extension, if provided
         if extension:
-            if isinstance(extension, str):
-                extension = [extension]
-
-            mask = np.full(len(products), False, dtype=bool)
-            for elt in extension:
-                mask |= [False if isinstance(x, np.ma.core.MaskedConstant) else x.endswith(elt)
-                         for x in products["productFilename"]]
-            filter_mask &= mask
-
-        # Applying the rest of the filters
-        for colname, vals in filters.items():
-
-            if isinstance(vals, str):
-                vals = [vals]
-
-            mask = np.full(len(products), False, dtype=bool)
-            for elt in vals:
-                mask |= (products[colname] == elt)
-
-            filter_mask &= mask
-
-        return products[np.where(filter_mask)]
+            extensions = [extension] if isinstance(extension, str) else extension
+            ext_mask = np.array(
+                [not isinstance(x, np.ma.core.MaskedConstant) and any(x.endswith(ext) for ext in extensions)
+                 for x in products["productFilename"]],
+                dtype=bool
+            )
+            filter_mask &= ext_mask
+
+        # Apply column-based filters
+        col_mask = utils.apply_column_filters(products, filters)
+        filter_mask &= col_mask
+
+        return products[filter_mask]
 
     def download_file(self, uri, *, local_path=None, base_url=None, cache=True, cloud_only=False, verbose=True):
         """
diff --git a/astroquery/mast/tests/test_mast.py b/astroquery/mast/tests/test_mast.py
@@ -367,15 +367,51 @@ def test_missions_get_unique_product_list(patch_post, caplog):
 def test_missions_filter_products(patch_post):
     # Filter products list by column
     products = mast.MastMissions.get_product_list('Z14Z0104T')
-    filtered = mast.MastMissions.filter_products(products,
-                                                 category='CALIBRATED')
+    filtered = mast.MastMissions.filter_products(products, category='CALIBRATED')
     assert isinstance(filtered, Table)
     assert all(filtered['category'] == 'CALIBRATED')
 
-    # Filter by non-existing column
-    with pytest.warns(InputWarning):
-        mast.MastMissions.filter_products(products,
-                                          invalid=True)
+    # Filter by extension
+    filtered = mast.MastMissions.filter_products(products, extension='fits')
+    assert len(filtered) > 0
+
+    # Numeric filtering
+    # Single integer value
+    filtered = mast.MastMissions.filter_products(products, size=11520)
+    assert all(filtered['size'] == 11520)
+
+    # Single string value
+    filtered = mast.MastMissions.filter_products(products, size='11520')
+    assert all(filtered['size'] == 11520)
+
+    # Comparison operators
+    filtered = mast.MastMissions.filter_products(products, size='<15000')
+    assert all(filtered['size'] < 15000)
+
+    filtered = mast.MastMissions.filter_products(products, size='>15000')
+    assert all(filtered['size'] > 15000)
+
+    filtered = mast.MastMissions.filter_products(products, size='>=14400')
+    assert all(filtered['size'] >= 14400)
+
+    filtered = mast.MastMissions.filter_products(products, size='<=14400')
+    assert all(filtered['size'] <= 14400)
+
+    # Range operator
+    filtered = mast.MastMissions.filter_products(products, size='14400..17280')
+    assert all((filtered['size'] >= 14400) & (filtered['size'] <= 17280))
+
+    # List of expressions
+    filtered = mast.MastMissions.filter_products(products, size=[14400, '>20000'])
+    assert all((filtered['size'] == 14400) | (filtered['size'] > 20000))
+
+    with pytest.raises(InvalidQueryError, match="Could not parse numeric filter 'invalid' for column 'size'"):
+        # Invalid filter value
+        mast.MastMissions.filter_products(products, size='invalid')
+
+    # Error when filtering by non-existing column
+    with pytest.raises(InvalidQueryError, match="Column 'non_existing' not found in product table."):
+        mast.MastMissions.filter_products(products, non_existing='value')
 
 
 def test_missions_download_products(patch_post, tmp_path):
@@ -671,11 +707,31 @@ def test_observations_get_product_list(patch_post):
 
 def test_observations_filter_products(patch_post):
     products = mast.Observations.get_product_list('2003738726')
-    result = mast.Observations.filter_products(products,
-                                               productType=["SCIENCE"],
-                                               mrp_only=False)
-    assert isinstance(result, Table)
-    assert len(result) == 7
+    filtered = mast.Observations.filter_products(products,
+                                                 productType=["SCIENCE"],
+                                                 mrp_only=False)
+    assert isinstance(filtered, Table)
+    assert len(filtered) == 7
+
+    # Filter for minimum recommended products
+    filtered = mast.Observations.filter_products(products, mrp_only=True)
+    assert all(filtered['productGroupDescription'] == 'Minimum Recommended Products')
+
+    # Filter by extension
+    filtered = mast.Observations.filter_products(products, extension='fits')
+    assert len(filtered) > 0
+
+    # Numeric filtering
+    filtered = mast.Observations.filter_products(products, size='<50000')
+    assert all(filtered['size'] < 50000)
+
+    # Numeric filter that cannot be parsed
+    with pytest.raises(InvalidQueryError, match="Could not parse numeric filter 'invalid' for column 'size'"):
+        filtered = mast.Observations.filter_products(products, size='invalid')
+
+    # Filter by non-existing column
+    with pytest.raises(InvalidQueryError, match="Column 'invalid' not found in product table."):
+        mast.Observations.filter_products(products, invalid=True)
 
 
 def test_observations_download_products(patch_post, tmpdir):
@@ -703,8 +759,7 @@ def test_observations_download_products(patch_post, tmpdir):
 
     # passing row product
     products = mast.Observations.get_product_list('2003738726')
-    result1 = mast.Observations.download_products(products[0],
-                                                  download_dir=str(tmpdir))
+    result1 = mast.Observations.download_products(products[0], download_dir=str(tmpdir))
     assert isinstance(result1, Table)
 
 
diff --git a/astroquery/mast/tests/test_mast_remote.py b/astroquery/mast/tests/test_mast_remote.py
@@ -297,11 +297,6 @@ def test_missions_filter_products(self):
         assert isinstance(filtered, Table)
         assert all(filtered['category'] == 'CALIBRATED')
 
-        # Filter by non-existing column
-        with pytest.warns(InputWarning):
-            filtered = MastMissions.filter_products(products,
-                                                    invalid=True)
-
     def test_missions_download_products(self, tmp_path):
         def check_filepath(path):
             assert path.is_file()
diff --git a/astroquery/mast/utils.py b/astroquery/mast/utils.py
@@ -6,12 +6,12 @@
 Miscellaneous functions used throughout the MAST module.
 """
 
+import re
 import warnings
-import numpy as np
 
+import numpy as np
 import requests
 import platform
-
 from astropy.coordinates import SkyCoord
 from astropy.table import Table
 from astropy import units as u
@@ -345,3 +345,92 @@ def remove_duplicate_products(data_products, uri_key):
                  f"Only returning {len(unique_products)} unique product(s).")
 
     return unique_products
+
+
+def parse_numeric_product_filter(val):
+    """
+    Parses a numeric product filter value and returns a function that can be used to filter
+    a column of a product table.
+
+    Parameters
+    ----------
+    val : str or list of str
+        The filter value(s). Each entry can be:
+        - A single number (e.g., "100")
+        - A range in the form "start..end" (e.g., "100..200")
+        - A comparison operator followed by a number (e.g., ">=10", "<5", ">100.5")
+
+    Returns
+    -------
+    response : function
+        A function that takes a column of a product table and returns a boolean mask indicating
+        which rows satisfy the filter condition.
+    """
+    # Regular expression to match range patterns
+    range_pattern = re.compile(r'[+-]?(\d+(\.\d*)?|\.\d+)\.\.[+-]?(\d+(\.\d*)?|\.\d+)')
+
+    def single_condition(cond):
+        """Helper function to create a condition function for a single value."""
+        if isinstance(cond, (int, float)):
+            return lambda col: col == float(cond)
+        if cond.startswith('>='):
+            return lambda col: col >= float(cond[2:])
+        elif cond.startswith('<='):
+            return lambda col: col <= float(cond[2:])
+        elif cond.startswith('>'):
+            return lambda col: col > float(cond[1:])
+        elif cond.startswith('<'):
+            return lambda col: col < float(cond[1:])
+        elif range_pattern.fullmatch(cond):
+            start, end = map(float, cond.split('..'))
+            return lambda col: (col >= start) & (col <= end)
+        else:
+            return lambda col: col == float(cond)
+
+    if isinstance(val, list):
+        # If val is a list, create a condition for each value and combine them with logical OR
+        conditions = [single_condition(v) for v in val]
+        return lambda col: np.logical_or.reduce([cond(col) for cond in conditions])
+    else:
+        return single_condition(val)
+
+
+def apply_column_filters(products, filters):
+    """
+    Applies column-based filters to a product table.
+
+    Parameters
+    ----------
+    products : `~astropy.table.Table`
+        The product table to filter.
+    filters : dict
+        A dictionary where keys are column names and values are the filter values.
+
+    Returns
+    -------
+    col_mask : `numpy.ndarray`
+        A boolean mask indicating which rows of the product table satisfy the filter conditions.
+    """
+    col_mask = np.ones(len(products), dtype=bool)  # Start with all True mask
+
+    # Applying column-based filters
+    for colname, vals in filters.items():
+        if colname not in products.colnames:
+            raise InvalidQueryError(f"Column '{colname}' not found in product table.")
+
+        col_data = products[colname]
+        # If the column is an integer or float, accept numeric filters
+        if col_data.dtype.kind in ['i', 'f']:  # 'i' for integer, 'f' for float
+            try:
+                this_mask = parse_numeric_product_filter(vals)(col_data)
+            except ValueError:
+                raise InvalidQueryError(f"Could not parse numeric filter '{vals}' for column '{colname}'.")
+        else:  # Assume string or list filter
+            if isinstance(vals, str):
+                vals = [vals]
+            this_mask = np.isin(col_data, vals)
+
+        # Combine the current column mask with the overall mask
+        col_mask &= this_mask
+
+    return col_mask
diff --git a/docs/mast/mast_cut.rst b/docs/mast/mast_cut.rst
@@ -140,6 +140,8 @@ To access sector information for a particular coordinate, object, or moving targ
    -------------- ------ ------ ---
    tess-s0008-1-1      8      1   1
    tess-s0034-1-2     34      1   2
+   tess-s0061-1-2     61      1   2
+   tess-s0088-1-2     88      1   2
 
 The following example will request SPOC cutouts using the objectname argument, rather
 than a set of coordinates.
@@ -167,6 +169,7 @@ The following example requests SPOC cutouts for a moving target.
    tess-s0029-1-4     29      1   4
    tess-s0043-3-3     43      3   3
    tess-s0044-2-4     44      2   4
+   tess-s0092-4-3     92      4   3
 
 
 Zcut
diff --git a/docs/mast/mast_missions.rst b/docs/mast/mast_missions.rst
diff --git a/docs/mast/mast_obsquery.rst b/docs/mast/mast_obsquery.rst
