Remove deprecation warnings. (#235)

* Remove deprecation warnings.  Remove references to err_gehrels.  Clean fpavg code so it
doesn't divide by zero when calculating the conversion from counts to flux

* Ensure numpy < 2.0, put devdeps back in for pytest

.github/workflows/python_testing.yml

@@ -38,4 +38,4 @@ jobs:
           pytest-results-summary: true
         - macos: py3-xdist
           pytest-results-summary: true
-        - linux: py3-devdeps-xdist
+        - linux: py3-devdeps-xdist

calcos/concurrent.py

@@ -864,7 +864,7 @@ def shift1Corr(self, n, i0, i1, extrapolate=False):
         for segment in self.segment_list:
 
             # Restrict the correction to the applicable regions.
-            shift_flags = np.zeros(i1 - i0, dtype=np.bool8)
+            shift_flags = np.zeros(i1 - i0, dtype=np.bool_)
             locn_list = self.regions[segment]
             for region in locn_list:
                 if region[0] is None:
@@ -1323,7 +1323,7 @@ def setShiftKeywords(self, avg_dx, avg_dy):
             self.ofd[1].header[key] = sum_ndf
 
             # use self.regions for dpixel1[abc]
-            shift_flags = np.zeros(len(self.eta), dtype=np.bool8)
+            shift_flags = np.zeros(len(self.eta), dtype=np.bool_)
             locn_list = self.regions[segment]
             # if NUV, take the region for the PSA (lower pixel numbers)
             region = locn_list[0]
@@ -1699,7 +1699,7 @@ def shift2Corr(self, n, i0, i1, extrapolate=False):
 
         # Restrict the correction to the applicable region.  Note that the
         # limits of the region (the active area) are not adjusted by shift2.
-        shift_flags = np.zeros(i1 - i0, dtype=np.bool8)
+        shift_flags = np.zeros(i1 - i0, dtype=np.bool_)
         region = self.regions[self.segment_list[0]][0]
         shift_flags |= np.logical_and(self.eta[i0:i1] >= region[0],
                                       self.eta[i0:i1] <= region[1])
@@ -1953,13 +1953,13 @@ def findShifts(self):
 
             # Now find the mean values of xi and of eta, but restrict
             # the range to the median plus or minus DX or DY.
-            select = np.zeros(len(self.xi), dtype=np.bool8)
+            select = np.zeros(len(self.xi), dtype=np.bool_)
             select[i0:i1] = 1
             select = np.where(self.xi < x_median-DX, False, select)
             select = np.where(self.xi > x_median+DX, False, select)
             select = np.where(self.eta < y_median-DY, False, select)
             select = np.where(self.eta > y_median+DY, False, select)
-            select = select.astype(np.bool8)
+            select = select.astype(np.bool_)
 
             x = self.xi[select].mean(dtype=np.float64)
             y = self.eta[select].mean(dtype=np.float64)

calcos/cosutil.py

@@ -3734,31 +3734,6 @@ def centerOfQuartic(x, coeff):
 
     return x_min
 
-def errGehrels(counts):
-    """Compute error estimate.
-
-    The error estimate is computed using the Gehrels approximation for the
-    upper confidence limit.
-
-    Parameters
-    ----------
-    counts: array_like or float
-        Number of counts (not necessarily integer values).
-
-    Returns
-    -------
-    tuple of 2 array_like or float
-        (The lower error estimate for counts,
-         the upper error estimate for counts)
-    """
-    icounts = (counts + .5).astype(int)
-    upper = (1. + np.sqrt(icounts + 0.75))
-    lower = np.where(icounts > 0., Gehrels_lower(icounts), 0.)
-    return (lower.astype(np.float32), upper.astype(np.float32))
-
-def Gehrels_lower(counts):
-    return counts - counts * (1.0 - 1.0 / (9.0 * counts) - 1.0 / (3.0 * np.sqrt(counts)))**3
-
 def errFrequentist(counts):
     """Compute errors using the 'frequentist-confidence' option of astropy's poisson_conf_interval
 

calcos/fpavg.py

@@ -1200,10 +1200,12 @@ def normalizeSums(self, data, sumweight):
         error_frequentist_lower, error_frequentist_upper = cosutil.errFrequentist(variance)
         error_frequentist_lower[zeroweight] = 0.0
         error_frequentist_upper[zeroweight] = 0.0
-        conversion = data.field("flux") / data.field("net")
+        bad = np.where(data.field("net") == 0.0)
+        good = np.where(data.field("net") != 0.0)
+        nelements = len(data.field("flux"))
+        conversion = np.zeros(nelements)
+        conversion[good] = data.field("flux")[good] / data.field("net")[good]
 #       Clean out NaNs from where flux and net are zero
-        good = np.where(~np.isnan(conversion))
-        bad = np.where(np.isnan(conversion))
         wavelength = data.field("wavelength")
         interpolated_values = np.interp(wavelength[bad], wavelength[good], conversion[good])
         conversion[bad] = interpolated_values

calcos/timeline.py

@@ -125,7 +125,7 @@ def createTimeline(input, fd, info, reffiles,
                                  (key.upper(), x0, x1+1, y0, y1+1), VERBOSE)
             # A value of 1 (True) in region_flags means the corresponding
             # event is within the area that includes the airglow line.
-            region_flags = np.ones(len(xfull), dtype=np.bool8)
+            region_flags = np.ones(len(xfull), dtype=np.bool_)
             region_flags = np.where(xfull > x1, False, region_flags)
             region_flags = np.where(xfull < x0,  False, region_flags)
             if isinstance(y0, (list, tuple)):
@@ -139,7 +139,7 @@ def createTimeline(input, fd, info, reffiles,
                 region_flags = np.where(yfull > y1, False, region_flags)
                 region_flags = np.where(yfull < y0,  False, region_flags)
                 npixels = 1.
-            region_flags = region_flags.astype(np.bool8)
+            region_flags = region_flags.astype(np.bool_)
             # scratch array for counts per second within each time bin
             temp = np.zeros(len(tl_time), dtype=np.float32)
             if time[-1] - time[0] < 1.:         # e.g. ACCUM data

calcos/timetag.py

@@ -389,7 +389,7 @@ def setActiveArea(events, info, brftab):
 
     xi  = events.field(xcorr)
     eta = events.field(ycorr)
-    active_area = np.ones(len(xi), dtype=np.bool8)
+    active_area = np.ones(len(xi), dtype=np.bool_)
 
     # A value of 1 (True) in active_area means the corresponding event
     # is within the active area.
@@ -401,7 +401,7 @@ def setActiveArea(events, info, brftab):
         active_area = np.where(eta > b_high, False, active_area)
         active_area = np.where(eta < b_low,  False, active_area)
         # Make sure the data type is still boolean.
-        active_area = active_area.astype(np.bool8)
+        active_area = active_area.astype(np.bool_)
 
 def mkHeaders(phdr, events_header, extver=1):
     """Create a list of four headers for creating the flt and counts files.
@@ -3879,7 +3879,8 @@ def writeImages(x, y, epsilon, dq,
     # E_rate = E_counts / t
     # reciprocal_flat_field = E_counts / C_counts
     # the corresponding error arrays are:
-    # errC_rate = errGehrels(C_counts) / t
+    # err_lower, err_upper from cosutil.errFrequentist(C_counts)
+    # errC_rate = err_upper / t
     # errE_rate = errC_rate * reciprocal_flat_field
 
     if outcounts is not None:

setup.py

@@ -37,7 +37,7 @@ def c_includes(parent, depth=1):
     setup_requires=['setuptools_scm'],
     install_requires=[
         'astropy>=5.0.4',
-        'numpy',
+        'numpy<2.0',
         'scipy',
         'stsci.tools>=4.0.0',
     ],

tests/helpers.py

@@ -75,8 +75,6 @@ def calref_from_image(input_image):
 @pytest.mark.bigdata
 @pytest.mark.usefixtures('_jail', 'envopt')
 class BaseCOS:
-    # Timeout in seconds for file downloads.
-    timeout = 30
 
     instrument = 'cos'
     ignore_keywords = ['DATE', 'CAL_VER']
@@ -164,7 +162,7 @@ def get_input_files(self, filenames):
                         os.environ[var] = ref_path  # hacky hack hack
 
                 # Download reference files, if needed only.
-                download_crds(ref_file, timeout=self.timeout, verbose=True)
+                download_crds(ref_file, verbose=True)
 
     def compare_outputs(self, outputs, atol=0, rtol=1e-7, raise_error=True,
                         ignore_keywords_overwrite=None):

tests/test_cosutil.py

@@ -180,51 +180,6 @@ def test_is_product():
     os.remove(raw_file)
 
 
-def test_gehrels_lower():
-    # Setup
-    counts = 4.0
-    actual = 1.9090363511659807
-    # Test
-    test_value = cosutil.Gehrels_lower(counts)
-    # Verify
-    assert actual == test_value
-
-
-def test_err_gehrels():
-    """
-    unit test for err_gehrels(counts)
-    test ran
-    - create 3 arrays, one should be random float values, the other two should be the lower (zero) and upper (one) error estimates
-    - following the math for calculating the upper limit by taking the sqrt of counts + 0.5 and then adding 1 to the result.
-    - similarly for the lower we add counts + 0.5 and then counts - counts * (1.0 - 1.0 / (9.0 * counts) - 1.0 / (3.0 * np.sqrt(counts))) ** 3
-      we will be able to get the lower array.
-    - finally assert the upper array and the lower array with the results obtained from err_gehrels().
-    """
-    # Setup
-    # values to be tested on
-    zeros = np.zeros(5)
-    ones = np.ones(5)
-    random_values = np.array([2.2400559, 0.85776844, 5.31731382, 8.98167105, 7.88191824]).astype(np.float32)
-    # Actual results expected
-    true_lower1 = np.random.uniform(low=0.0, high=0.0, size=(5,))
-    true_upper1 = np.array([1.8660254, 1.8660254, 1.8660254, 1.8660254, 1.8660254]).astype(np.float32)
-
-    true_lower2 = np.array([0.8285322, 0.8285322, 0.8285322, 0.8285322, 0.8285322]).astype(np.float32)
-    true_upper2 = np.array([2.3228757, 2.3228757, 2.3228757, 2.3228757, 2.3228757]).astype(np.float32)
-
-    true_lower3 = np.array([1.2879757, 0.8285322, 2.1544096, 2.9387457, 2.7635214]).astype(np.float32)
-    true_upper3 = np.array([2.6583123, 2.3228757, 3.3979158, 4.122499, 3.95804]).astype(np.float32)
-    # Test
-    lower1, upper1 = cosutil.errGehrels(zeros)  # should produce a warning
-    lower2, upper2 = cosutil.errGehrels(ones)
-    lower3, upper3 = cosutil.errGehrels(random_values)
-    # Verify
-    np.testing.assert_array_equal(true_lower1, lower1)
-    np.testing.assert_array_equal(true_upper1, upper1)
-    np.testing.assert_array_equal(true_lower2, lower2)
-    np.testing.assert_array_equal(true_upper2, upper2)
-    np.testing.assert_array_equal(true_lower3, lower3)
-    np.testing.assert_array_equal(true_upper3, upper3)
 
 
 def test_cmp_part_exception():
@@ -316,7 +271,7 @@ def test_return_gti():
 def test_err_frequentist():
     """
     unit test for err_frequentist(counts)
-    - create 3 arrays similar to the test in err_gehrels().
+    - create 3 arrays.
     - find the poisson confidence interval for each array.
     - assert the result with the expected err_lower and err_upper.
     """