Add default zero constructor for QuadPrecision

.github/workflows/big_endian.yml

@@ -137,7 +137,7 @@ jobs:
               python -m pip install --break-system-packages --no-deps . -v --no-build-isolation --force-reinstall &&
 
               # Install test dependencies separately
-              python -m pip install --break-system-packages pytest pytest-run-parallel pytest-timeout mpmath &&
+              python -m pip install --break-system-packages .[test] &&
 
               python -m pytest -vvv --color=yes --timeout=600 --tb=short tests/
             '"

.github/workflows/test_old_cpu.yml

@@ -54,9 +54,9 @@ jobs:
           # For Sandy Bridge (x86-64-v2), we need to disable FMA code paths
           # since FMA instructions are not available on that microarchitecture
           if [ "${{ matrix.cpu[0] }}" = "snb" ]; then
-            pip install . --no-build-isolation -v -Csetup-args=-Ddisable_fma=true
+            pip install .[test] --no-build-isolation -v -Csetup-args=-Ddisable_fma=true
           else
-            pip install . --no-build-isolation -v
+            pip install .[test] --no-build-isolation -v
           fi
 
       - name: Test import on ${{ matrix.cpu[1] }}
@@ -74,5 +74,4 @@ jobs:
 
       - name: Run tests on ${{ matrix.cpu[1] }}
         run: |
-          pip install pytest mpmath
-          sde -${{ matrix.cpu[0] }} -- python -m pytest tests/ -v --tb=short -v -s
+          sde -${{ matrix.cpu[0] }} -- python -m pytest tests/ -v --tb=short -v -s

pyproject.toml

@@ -36,7 +36,8 @@ dependencies = [
 test = [
     "pytest",
     "mpmath",
-    "pytest-run-parallel"
+    "pytest-run-parallel",
+    "pandas"
 ]
 
 docs = [

src/csrc/scalar.c

@@ -281,11 +281,11 @@ QuadPrecision_from_object(PyObject *value, QuadBackendType backend)
 static PyObject *
 QuadPrecision_new(PyTypeObject *cls, PyObject *args, PyObject *kwargs)
 {
-    PyObject *value;
+    PyObject *value = 0;
     const char *backend_str = "sleef";
     static char *kwlist[] = {"value", "backend", NULL};
 
-    if (!PyArg_ParseTupleAndKeywords(args, kwargs, "O|s", kwlist, &value, &backend_str)) {
+    if (!PyArg_ParseTupleAndKeywords(args, kwargs, "|Os", kwlist, &value, &backend_str)) {
         return NULL;
     }
 
@@ -298,6 +298,9 @@ QuadPrecision_new(PyTypeObject *cls, PyObject *args, PyObject *kwargs)
         return NULL;
     }
 
+    if (value == NULL) {
+      return (PyObject *)QuadPrecision_raw_new(backend);
+    }
     return (PyObject *)QuadPrecision_from_object(value, backend);
 }
 
@@ -747,4 +750,4 @@ init_quadprecision_scalar(void)
 {
     QuadPrecision_Type.tp_base = &PyFloatingArrType_Type;
     return PyType_Ready(&QuadPrecision_Type);
-}
+}

tests/test_quaddtype.py

@@ -5996,4 +5996,26 @@ def test_sleef_purecfma_symbols():
         for sym in purecfma_symbols[:5]:
             print(f"    {sym}")
         if len(purecfma_symbols) > 5:
-            print(f"    ... and {len(purecfma_symbols) - 5} more")
+            print(f"    ... and {len(purecfma_symbols) - 5} more")
+
+
+def test_empty_construct():
+    assert QuadPrecision() == QuadPrecision(0) == 0
+
+
+def test_pandas_strrep():
+    """Test that we can construct a pandas data frame with quad precision columns
+
+    Make sure the string representation can be generated
+    """
+    import pandas as pd
+
+    BIG_NUMBER=123456789098765432123456789
+    x = np.arange(500, dtype=np.float64) * BIG_NUMBER
+    y = np.arange(500, dtype=QuadPrecDType()) * BIG_NUMBER
+    df = pd.DataFrame({"col1": x, "col2": y})
+    assert isinstance(str(df), str) # Make sure this doesn't fail
+    assert df["col1"].dtype == np.float64
+    assert df["col2"].dtype == QuadPrecDType()
+    assert df["col1"].iloc[499] != QuadPrecision(499 * BIG_NUMBER)
+    assert df["col2"].iloc[499] == QuadPrecision(499 * BIG_NUMBER)