Clean PythonCodePrinter and add dtype property (#1260)

Remove the duplicate `_print_NumpyArray` function from PythonCodePrinter. The original `_print_NumpyArray` function is more accurate as it specifies the `dtype` etc explicitly. Specifying the `dtype` explicitly makes issue #1377 reproducible. To fix this problem support is added for `a.dtype` expressions (Fixes #1421) using the similar function [`np.result_type`](https://numpy.org/doc/stable/reference/generated/numpy.result_type.html). The initialisation dtype is saved and used in the Python printer. This is a work-around until #1334 is resolved well enough to define a clear issue. Additionally a warning is raised if the Python code is not consistent. Fixes #1377. **Additional commits** - Simplify bot linux/windows/macosx output to reduce duplication. Change regex for C now that the test is no longer verbose. - Stop generating `numpy.bool` in code as it has been deprecated by NumPy. Instead use the default bool (with precision `-1`). - Add support for NumPy's short type names. - Reduce duplication in the Python printer. - Remove outdated skips on tests. - Stop array error tests failing in developer mode.
pyccel · Aug 15, 2023 · 3964466 · 3964466
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -11,6 +11,8 @@ All notable changes to this project will be documented in this file.
 -   Add Python support for a simple class.
 -   #1430 : Add conjugate support to booleans.
 -   #1452 : Add C printing support for a class containing only functions.
+-   #1260 : Add support for NumPy `dtype` property: `a.dtype`.
+-   #1260 : Add support for NumPy `result_type` function.
 
 ### Fixed
 
@@ -31,6 +33,7 @@ All notable changes to this project will be documented in this file.
 
 ### Deprecated
 
+-   Stop generating `numpy.bool` (deprecated from NumPy) in code.
 -   \[INTERNALS\] Removed `obsolete` folder.
 -   \[INTERNALS\] Removed out of date `samples` folder.
 -   \[INTERNALS\] Removed out of date `doc` folder.

diff --git a/ci_tools/json_pytest_output.py b/ci_tools/json_pytest_output.py
@@ -18,7 +18,7 @@
 import re
 import sys
 
-def     mini_md_summary(title, outcome, c, f, py):
+def     mini_md_summary(title, outcome, failed_tests):
     """
     Generate Markdown.
 
@@ -30,12 +30,8 @@ def     mini_md_summary(title, outcome, c, f, py):
         The title of the summary.
     outcome : str
         The result of a completed step, Possible values are success, failure, cancelled, or skipped.
-    c : list
-        A list of C test summaries, (Failed tests).
-    f : list
-        A list of Fortran test summaries, (Failed tests).
-    py : list
-        A list of Python test summaries, (Failed tests).
+    failed_tests : dict
+        A dictionary whose keys are languages and whose values are lists of failed test summaries.
 
     Returns
     -------
@@ -44,21 +40,12 @@ def     mini_md_summary(title, outcome, c, f, py):
     """
     md = f"## {title} - {outcome} "
     if outcome == "failure":
-        if len(c) != 0:
-            md = md + '\n' + "### C Test summary: "
-            md = md + '\n'
-            for i in c:
-                md = md + i + "\n"
-        if len(f) != 0:
-            md = md + '\n' + "### Fortran Test summary: "
-            md = md + '\n'
-            for i in f:
-                md = md + i + "\n"
-        if len(py) != 0:
-            md = md + '\n' + "### Python Test summary: "
-            md = md + '\n'
-            for i in py:
-                md = md + i + "\n"
+        for lang, errs in failed_tests.items():
+            if len(errs) != 0:
+                md = md + '\n' + f"### {lang.capitalize()} Test summary: "
+                md = md + '\n'
+                for i in errs:
+                    md = md + i + "\n"
     md = md + "\n"
     return(md)
 
@@ -73,6 +60,10 @@ def     mini_md_summary(title, outcome, c, f, py):
     output_file = 'test_json_result.json'
     summary = ""
 
+    failed_pattern = re.compile(r".*FAILED.*")
+    languages = ('c', 'fortran', 'python')
+    pattern = {lang: re.compile(r".*\["+lang+r"\]\ \_.*") for lang in languages}
+
     for i in p_args.tests:
         values = i.split(':')
         mini_title = values[0] if len(values) >= 1 else None
@@ -86,28 +77,18 @@ def     mini_md_summary(title, outcome, c, f, py):
             c_tests = []
             f_tests = []
             py_tests = []
-            failed_pattern = r".*FAILED.*"
-            c_pattern = r".*\[c\].*"
-            f_pattern = r".*\[fortran\]\ \_.*"
-            py_pattern = r".*\[python\]\ \_.*"
 
-            failed_matches = re.findall(failed_pattern, outfile, re.MULTILINE)
+            failed_matches = failed_pattern.findall(outfile, re.MULTILINE)
             failed_matches = [re.sub(r'.*FAILED ', "- ``", string) for string in failed_matches]
 
-            r = re.compile(c_pattern)
-            c_failed = list(filter(r.match, failed_matches))
-            c_failed = [re.sub(r'\[c\]', "`` :heavy_multiplication_x:", string) for string in c_failed]
-
-            failed_matches = re.findall(f_pattern, outfile, re.MULTILINE)
-            failed_matches = ["- ``" + string.strip('_') for string in failed_matches]
-            f_failed = [re.sub(r'\[fortran\]', "`` :heavy_multiplication_x:", string) for string in failed_matches]
-
-            failed_matches = re.findall(py_pattern, outfile, re.MULTILINE)
-            failed_matches = ["- ``" + string.strip('_') for string in failed_matches]
-            py_failed = [re.sub(r'\[python\]', "`` :heavy_multiplication_x:", string) for string in failed_matches]
+            fails = {}
 
+            for lang in languages:
+                failed_matches = pattern[lang].findall(outfile, re.MULTILINE)
+                failed_matches = ["- ``" + string.strip('_') for string in failed_matches]
+                fails[lang] = [re.sub(r'\['+lang+r'\]', "`` :heavy_multiplication_x:", string) for string in failed_matches]
 
-        summary = summary + mini_md_summary(mini_title, outcome, c_failed, f_failed, py_failed)
+        summary = summary + mini_md_summary(mini_title, outcome, fails)
 
     print(summary)
     json_ouput = {

diff --git a/docs/numpy-functions.md b/docs/numpy-functions.md
@@ -570,6 +570,6 @@ In Pyccel we try to support the NumPy functions which developers use the most..
 
 -   others:
 
-    -   `amax`, `amin`, `sum`, `shape`, `size`, `floor`, `sign`
+    -   `amax`, `amin`, `sum`, `shape`, `size`, `floor`, `sign`, `result_type`
 
 If discrepancies beyond round-off error are found between [NumPy](https://numpy.org/doc/stable/reference/)'s and [Pyccel](https://github.com/pyccel/pyccel)'s results, please create an issue at <https://github.com/pyccel/pyccel/issues> and provide a small example of your problem. Do not forget to specify your target language.
diff --git a/pyccel/ast/class_defs.py b/pyccel/ast/class_defs.py
@@ -11,7 +11,7 @@
                         NativeComplex, NativeString, NativeNumeric)
 from .numpyext  import (NumpyShape, NumpySum, NumpyAmin, NumpyAmax,
                         NumpyImag, NumpyReal, NumpyTranspose,
-                        NumpyConjugate, NumpySize)
+                        NumpyConjugate, NumpySize, NumpyResultType)
 
 __all__ = ('BooleanClass',
         'IntegerClass',
@@ -158,7 +158,9 @@
             PyccelFunctionDef('conj', func_class = NumpyConjugate,
                 decorators = {'numpy_wrapper': 'numpy_wrapper'}),
             PyccelFunctionDef('conjugate', func_class = NumpyConjugate,
-                decorators = {'numpy_wrapper': 'numpy_wrapper'})
+                decorators = {'numpy_wrapper': 'numpy_wrapper'}),
+            PyccelFunctionDef('dtype', func_class = NumpyResultType,
+                decorators = {'property': 'property', 'numpy_wrapper': 'numpy_wrapper'}),
         ]
 )
 

diff --git a/pyccel/ast/cwrapper.py b/pyccel/ast/cwrapper.py
@@ -108,7 +108,7 @@ def arg_names(self):
     (NativeFloat(), 4)         : 'f',
     (NativeComplex(), 4)       : 'O',
     (NativeComplex(), 8)       : 'O',
-    (NativeBool(), 4)          : 'p',
+    (NativeBool(), -1)         : 'p',
     (NativeString(), 0)        : 's',
     (PyccelPyObject(), 0)      : 'O',
     }
@@ -366,7 +366,7 @@ def Python_to_C(c_object):
 
 # Functions definitions are defined in pyccel/stdlib/cwrapper/cwrapper.c
 py_to_c_registry = {
-    (NativeBool(), 4)      : 'PyBool_to_Bool',
+    (NativeBool(), -1)     : 'PyBool_to_Bool',
     (NativeInteger(), 1)   : 'PyInt8_to_Int8',
     (NativeInteger(), 2)   : 'PyInt16_to_Int16',
     (NativeInteger(), 4)   : 'PyInt32_to_Int32',
@@ -412,7 +412,6 @@ def C_to_Python(c_object):
 # Functions definitions are defined in pyccel/stdlib/cwrapper/cwrapper.c
 c_to_py_registry = {
     (NativeBool(), -1)     : 'Bool_to_PyBool',
-    (NativeBool(), 4)      : 'Bool_to_PyBool',
     (NativeInteger(), -1)  : 'Int'+str(default_precision['int']*8)+'_to_PyLong',
     (NativeInteger(), 1)   : 'Int8_to_NumpyLong',
     (NativeInteger(), 2)   : 'Int16_to_NumpyLong',
@@ -510,7 +509,6 @@ def generate_datatype_error(variable):
 # Functions definitions are defined in pyccel/stdlib/cwrapper/cwrapper.c
 check_type_registry = {
     (NativeBool(), -1)     : 'PyIs_Bool',
-    (NativeBool(), 4)      : 'PyIs_Bool',
     (NativeInteger(), -1)  : 'PyIs_NativeInt',
     (NativeInteger(), 1)   : 'PyIs_Int8',
     (NativeInteger(), 2)   : 'PyIs_Int16',

diff --git a/pyccel/ast/datatypes.py b/pyccel/ast/datatypes.py
@@ -77,7 +77,7 @@
         8  : 'C_DOUBLE_COMPLEX',
         16 : 'C_LONG_DOUBLE_COMPLEX'},
     "logical" : {
-        4  : "C_BOOL"}
+        -1 : "C_BOOL"}
 }
 iso_c_binding_shortcut_mapping = {
     'C_INT8_T'              : 'i8',
@@ -91,31 +91,40 @@
     'C_FLOAT_COMPLEX'       : 'c32',
     'C_DOUBLE_COMPLEX'      : 'c64',
     'C_LONG_DOUBLE_COMPLEX' : 'c128',
-    'C_BOOL'                : 'b4'
+    'C_BOOL'                : 'b1'
 }
 default_precision = {'float': 8,
                     'int': numpy.dtype(int).alignment,
                     'integer': numpy.dtype(int).alignment,
                     'complex': 8,
-                    'bool':4}
+                    'bool':-1}
 dtype_and_precision_registry = { 'float':('float', -1),
                                  'double':('float', -1),
                                  'real':('float', -1),
                                  'pythonfloat':('float', -1), # built-in float
                                  'float32':('float',4),
                                  'float64':('float',8),
+                                 'f4':('float',4),
+                                 'f8':('float',8),
                                  'pythoncomplex':('complex', -1),
                                  'complex':('complex', -1),  # to create numpy array with dtype='complex'
                                  'complex64':('complex',4),
                                  'complex128':('complex',8),
+                                 'c8':('complex',4),
+                                 'c16':('complex',8),
                                  'int8' :('int',1),
                                  'int16':('int',2),
                                  'int32':('int',4),
                                  'int64':('int',8),
+                                 'i1' :('int',1),
+                                 'i2':('int',2),
+                                 'i4':('int',4),
+                                 'i8':('int',8),
                                  'int'  :('int', -1),
                                  'pythonint'  :('int', -1),
                                  'integer':('int',-1),
                                  'bool' :('bool',-1),
+                                 'b1' :('bool',-1),
                                  'pythonbool' :('bool',-1)}
 
 

diff --git a/pyccel/ast/numpy_wrapper.py b/pyccel/ast/numpy_wrapper.py
@@ -181,7 +181,7 @@ def get_numpy_max_acceptable_version_file():
         4 : np.dtype(np.int32).num,
         8 : np.dtype(np.int64).num}
 
-numpy_dtype_registry = {('bool',4)     : numpy_bool_type,
+numpy_dtype_registry = {('bool',-1)    : numpy_bool_type,
                         ('int',1)      : numpy_num_to_type[numpy_int_type_precision_map[1]],
                         ('int',2)      : numpy_num_to_type[numpy_int_type_precision_map[2]],
                         ('int',4)      : numpy_num_to_type[numpy_int_type_precision_map[4]],