Grab 'values' attribute from Series in iterate_jit

- Resolves #1468 - if attribute passed to 'high level' function is a Series, grab the underlying 'values' attribute, which is enough like an ndarray to satisfy Numba (so that the jit operation can succeed)
PSLmodels · Jul 12, 2017 · 4c88306 · 4c88306
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commit 4c88306
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Showing 2 changed files with 23 additions and 3 deletions.
diff --git a/taxcalc/decorators.py b/taxcalc/decorators.py
@@ -127,15 +127,22 @@ def hl_func(x_0, x_1, x_2, ...):
     fstr.write("):\n")
     fstr.write("    from pandas import DataFrame\n")
     fstr.write("    import numpy as np\n")
+    fstr.write("    import pandas as pd\n")
+    fstr.write("    def get_values(x):\n")
+    fstr.write("        if isinstance(x, pd.Series):\n")
+    fstr.write("            return x.values\n")
+    fstr.write("        else:\n")
+    fstr.write("            return x\n")
     fstr.write("    outputs = \\\n")
+
     outs = []
     for ppp, attr in zip(pm_or_pf, args_out + args_in):
         outs.append(ppp + "." + attr + ", ")
     outs = [m_or_f + "." + arg for m_or_f, arg in zip(pm_or_pf, args_out)]
     fstr.write("        (" + ", ".join(outs) + ") = \\\n")
     fstr.write("        " + "applied_f(")
     for ppp, attr in zip(pm_or_pf, args_out + args_in):
-        fstr.write(ppp + "." + attr + ", ")
+        fstr.write("get_values(" + ppp + "." + attr + ")" + ", ")
     fstr.write(")\n")
     fstr.write("    header = [")
     col_headers = ["'" + out + "'" for out in args_out]

diff --git a/taxcalc/tests/test_decorators.py b/taxcalc/tests/test_decorators.py
@@ -32,9 +32,16 @@ def test_create_toplevel_function_string_mult_outputs():
     exp = ("def hl_func(pm, pf):\n"
            "    from pandas import DataFrame\n"
            "    import numpy as np\n"
+           "    import pandas as pd\n"
+           "    def get_values(x):\n"
+           "        if isinstance(x, pd.Series):\n"
+           "            return x.values\n"
+           "        else:\n"
+           "            return x\n"
            "    outputs = \\\n"
            "        (pm.a, pm.b) = \\\n"
-           "        applied_f(pm.a, pm.b, pf.d, pm.e, )\n"
+           "        applied_f(get_values(pm.a), get_values(pm.b), "
+           "get_values(pf.d), get_values(pm.e), )\n"
            "    header = ['a', 'b']\n"
            "    return DataFrame(data=np.column_stack(outputs),"
            "columns=header)")
@@ -49,9 +56,15 @@ def test_create_toplevel_function_string():
     exp = ("def hl_func(pm, pf):\n"
            "    from pandas import DataFrame\n"
            "    import numpy as np\n"
+           "    import pandas as pd\n"
+           "    def get_values(x):\n"
+           "        if isinstance(x, pd.Series):\n"
+           "            return x.values\n"
+           "        else:\n"
+           "            return x\n"
            "    outputs = \\\n"
            "        (pm.a) = \\\n"
-           "        applied_f(pm.a, pf.d, pm.e, )\n"
+           "        applied_f(get_values(pm.a), get_values(pf.d), get_values(pm.e), )\n"
            "    header = ['a']\n"
            "    return DataFrame(data=outputs,"
            "columns=header)")