def encode_as_pil(obj):
"""Attempt to convert PIL.Image.Image to base64 data uri"""
image = get_module("PIL.Image")
if image is not None and isinstance(obj, image.Image):
return ImageUriValidator.pil_image_to_uri(obj)
else:
raise NotEncodable
def test_validator_coercion_PIL(validator: ImageUriValidator):
# Single pixel black png (http://png-pixel.com/)
img_path = '_plotly_utils/tests/resources/1x1-black.png'
with open(img_path, 'rb') as f:
hex_bytes = base64.b64encode(f.read()).decode('ascii')
expected_uri = 'data:image/png;base64,' + hex_bytes
img = Image.open(img_path)
coerce_val = validator.validate_coerce(img)
assert coerce_val == expected_uri
def validator():
return ImageUriValidator('prop', 'parent')
def clean_to_json_compatible(obj, **kwargs):
# Try handling value as a scalar value that we have a conversion for.
# Return immediately if we know we've hit a primitive value
# Bail out fast for simple scalar types
if isinstance(obj, (int, float, string_types)):
return obj
if isinstance(obj, dict):
return {str(k): clean_to_json_compatible(v, **kwargs) for k, v in obj.items()}
elif isinstance(obj, (list, tuple)):
if obj:
# Must process list recursively even though it may be slow
return [clean_to_json_compatible(v, **kwargs) for v in obj]
# unpack kwargs
numpy_allowed = kwargs.get("numpy_allowed", False)
datetime_allowed = kwargs.get("datetime_allowed", False)
modules = kwargs.get("modules", {})
sage_all = modules["sage_all"]
np = modules["np"]
pd = modules["pd"]
image = modules["image"]
# Sage
if sage_all is not None:
if obj in sage_all.RR:
return float(obj)
elif obj in sage_all.ZZ:
return int(obj)
# numpy
if np is not None:
if obj is np.ma.core.masked:
return float("nan")
elif isinstance(obj, np.ndarray):
if numpy_allowed and obj.dtype.kind in ("b", "i", "u", "f"):
return np.ascontiguousarray(obj)
elif obj.dtype.kind == "M":
# datetime64 array
return np.datetime_as_string(obj).tolist()
elif obj.dtype.kind == "U":
return obj.tolist()
elif obj.dtype.kind == "O":
# Treat object array as a lists, continue processing
obj = obj.tolist()
elif isinstance(obj, np.datetime64):
return str(obj)
# pandas
if pd is not None:
if obj is pd.NaT:
return None
elif isinstance(obj, (pd.Series, pd.DatetimeIndex)):
if numpy_allowed and obj.dtype.kind in ("b", "i", "u", "f"):
return np.ascontiguousarray(obj.values)
elif obj.dtype.kind == "M":
if isinstance(obj, pd.Series):
dt_values = obj.dt.to_pydatetime().tolist()
else: # DatetimeIndex
dt_values = obj.to_pydatetime().tolist()
if not datetime_allowed:
# Note: We don't need to handle dropping timezones here because
# numpy's datetime64 doesn't support them and pandas's tz_localize
# above drops them.
for i in range(len(dt_values)):
dt_values[i] = dt_values[i].isoformat()
return dt_values
# datetime and date
try:
# Need to drop timezone for scalar datetimes. Don't need to convert
# to string since engine can do that
obj = obj.to_pydatetime()
except (TypeError, AttributeError):
pass
if not datetime_allowed:
try:
return obj.isoformat()
except (TypeError, AttributeError):
pass
elif isinstance(obj, datetime.datetime):
return obj
# Try .tolist() convertible, do not recurse inside
try:
return obj.tolist()
except AttributeError:
pass
# Do best we can with decimal
if isinstance(obj, decimal.Decimal):
return float(obj)
# PIL
if image is not None and isinstance(obj, image.Image):
return ImageUriValidator.pil_image_to_uri(obj)
# Plotly
try:
obj = obj.to_plotly_json()
except AttributeError:
pass
# Recurse into lists and dictionaries
if isinstance(obj, dict):
return {k: clean_to_json_compatible(v, **kwargs) for k, v in obj.items()}
elif isinstance(obj, (list, tuple)):
if obj:
# Must process list recursively even though it may be slow
return [clean_to_json_compatible(v, **kwargs) for v in obj]
return obj
def test_validator_acceptance(val, validator: ImageUriValidator):
assert validator.validate_coerce(val) == val
def validator():
return ImageUriValidator("prop", "parent")