MAINT: remove hard dependency on odo

Odo is currently a hard dependency of warp_prism to convert the sqlalchemy types
into a numpy dtypes. Odo is no longer actively maintained and breaks with newer
versions of pandas. This change reimplements the needed functionality in
warp_prism directly without using odo. This PR does leave the odo edge
registration code so that existing users don't see a change in functionality.

setup.py

@@ -42,17 +42,16 @@
         ),
     ],
     install_requires=[
-        'datashape',
         'numpy',
         'pandas',
         'sqlalchemy',
         'psycopg2',
-        'odo',
         'toolz',
-        'networkx<=1.11',
     ],
     extras_require={
         'dev': [
+            'odo',
+            'networkx<=1.11',
             'flake8==3.3.0',
             'pycodestyle==2.3.1',
             'pyflakes==1.5.0',

warp_prism/__init__.py

@@ -1,13 +1,11 @@
 from io import BytesIO
+import numbers
 
-from datashape import discover
-from datashape.predicates import istabular
 import numpy as np
-from odo import convert
 import pandas as pd
 import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql as _postgresql
 from sqlalchemy.ext.compiler import compiles
-from toolz import keymap
 
 from ._warp_prism import (
     raw_to_arrays as _raw_to_arrays,
@@ -18,7 +16,7 @@
 __version__ = '0.1.1'
 
 
-_typeid_map = keymap(np.dtype, _raw_typeid_map)
+_typeid_map = {np.dtype(k): v for k, v in _raw_typeid_map.items()}
 _object_type_id = _raw_typeid_map['object']
 
 
@@ -66,14 +64,107 @@ def _compile_copy_to_binary_postgres(element, compiler, **kwargs):
     )
 
 
+types = {np.dtype(k): v for k, v in {
+    'i8': sa.BigInteger,
+    'i4': sa.Integer,
+    'i2': sa.SmallInteger,
+    'f4': sa.REAL,
+    'f8': sa.FLOAT,
+    'O': sa.Text,
+    'M8[D]': sa.Date,
+    'M8[us]': sa.DateTime,
+    '?': sa.Boolean,
+    "m8[D]": sa.Interval(second_precision=0, day_precision=9),
+    "m8[h]": sa.Interval(second_precision=0, day_precision=0),
+    "m8[m]": sa.Interval(second_precision=0, day_precision=0),
+    "m8[s]": sa.Interval(second_precision=0, day_precision=0),
+    "m8[ms]": sa.Interval(second_precision=3, day_precision=0),
+    "m8[us]": sa.Interval(second_precision=6, day_precision=0),
+    "m8[ns]": sa.Interval(second_precision=9, day_precision=0),
+}.items()}
+
+_revtypes = dict(map(reversed, types.items()))
+_revtypes.update({
+    sa.DATETIME: np.dtype('M8[us]'),
+    sa.TIMESTAMP: np.dtype('M8[us]'),
+    sa.FLOAT: np.dtype('f8'),
+    sa.DATE: np.dtype('M8[D]'),
+    sa.BIGINT: np.dtype('i8'),
+    sa.INTEGER: np.dtype('i4'),
+    sa.BIGINT: np.dtype('i8'),
+    sa.types.NullType: np.dtype('O'),
+    sa.REAL: np.dtype('f4'),
+    sa.Float: np.dtype('f8'),
+})
+
+_precision_types = {
+    sa.Float,
+    _postgresql.base.DOUBLE_PRECISION,
+}
+
+
+def _precision_to_dtype(precision):
+    if isinstance(precision, numbers.Integral):
+        if 1 <= precision <= 24:
+            return np.dtype('f4')
+        elif 25 <= precision <= 53:
+            return np.dtype('f8')
+    raise ValueError('%s is not a supported precision' % precision)
+
+
+_units_of_power = {
+    0: 's',
+    3: 'ms',
+    6: 'us',
+    9: 'ns'
+}
+
+
+def _discover_type(type_):
+    if isinstance(type_, sa.Interval):
+        if type_.second_precision is None and type_.day_precision is None:
+            return np.dtype('m8[us]')
+        elif type_.second_precision == 0 and type_.day_precision == 0:
+            return np.dtype('m8[s]')
+
+        if (type_.second_precision in _units_of_power and
+                not type_.day_precision):
+            unit = _units_of_power[type_.second_precision]
+        elif type_.day_precision > 0:
+            unit = 'D'
+        else:
+            raise ValueError(
+                'Cannot infer INTERVAL type_e with parameters'
+                'second_precision=%d, day_precision=%d' %
+                (type_.second_precision, type_.day_precision),
+            )
+        return np.dtype('m8[%s]' % unit)
+    if type(type_) in _precision_types and type_.precision is not None:
+        return _precision_to_dtype(type_.precision)
+    if type_ in _revtypes:
+        return _revtypes[type_]
+    if type(type_) in _revtypes:
+        return _revtypes[type(type_)]
+    if isinstance(type_, sa.Numeric):
+        raise ValueError('Cannot adapt numeric type to numpy dtype')
+    if isinstance(type_, (sa.String, sa.Unicode)):
+        return np.dtype('O')
+    else:
+        for k, v in _revtypes.items():
+            if isinstance(k, type) and (isinstance(type_, k) or
+                                        hasattr(type_, 'impl') and
+                                        isinstance(type_.impl, k)):
+                return v
+            if k == type_:
+                return v
+    raise NotImplementedError('No SQL-numpy match for type %s' % type_)
+
+
 def _warp_prism_types(query):
-    for name, dtype in discover(query).measure.fields:
+    for col in query.columns:
+        dtype = _discover_type(col.type)
         try:
-            np_dtype = getattr(dtype, 'ty', dtype).to_numpy_dtype()
-            if np_dtype.kind == 'U':
-                yield _object_type_id
-            else:
-                yield _typeid_map[np_dtype]
+            yield _typeid_map[dtype]
         except KeyError:
             raise TypeError(
                 'warp_prism cannot query columns of type %s' % dtype,
@@ -136,7 +227,7 @@ def to_arrays(query, *, bind=None):
     return {column_names[n]: v for n, v in enumerate(out)}
 
 
-null_values = keymap(np.dtype, {
+null_values = {np.dtype(k): v for k, v in {
     'float32': np.nan,
     'float64': np.nan,
     'int16': np.nan,
@@ -145,7 +236,7 @@ def to_arrays(query, *, bind=None):
     'bool': np.nan,
     'datetime64[ns]': np.datetime64('nat', 'ns'),
     'object': None,
-})
+}.items()}
 
 # alias because ``to_dataframe`` shadows this name
 _default_null_values_for_type = null_values
@@ -216,6 +307,9 @@ def register_odo_dataframe_edge():
     If the selectable is not in a postgres database, it will fallback to the
     default odo edge.
     """
+    from odo import convert
+    from datashape.predicates import istabular
+
     # estimating 8 times faster
     df_cost = convert.graph.edge[sa.sql.Select][pd.DataFrame]['cost'] / 8