def test_hash_omit_style(self):
class N(Node):
my_attr = tl.Int().tag(attr=True)
n1 = N(my_attr=1, style=Style(name="a"))
n2 = N(my_attr=1, style=Style(name="b"))
# json has style in it
assert n1.json != n2.json
# but hash does not
assert n1.hash == n2.hash
def test_eq_ignore_style(self):
class N(Node):
my_attr = tl.Int().tag(attr=True)
n1 = N(my_attr=1, style=Style(name="a"))
n2 = N(my_attr=1, style=Style(name="b"))
# json has style in it
assert n1.json != n2.json
# but == and != don't care
assert n1 == n2
assert not n1 != n2
def test_enumeration(self):
# matplotlib enumeration tuples
style = Style(
enumeration_colors={
1: "r",
3: "o"
},
enumeration_legend={
1: "apples",
3: "oranges"
},
default_enumeration_color="k",
)
assert style.full_enumeration_colors == ("r", "k", "o")
assert style.full_enumeration_legend == ("apples", "unknown",
"oranges")
# negative key
style = Style(
enumeration_colors={
-1: "r",
1: "o"
},
enumeration_legend={
-1: "apples",
1: "oranges"
},
default_enumeration_color="k",
)
assert style.full_enumeration_colors == ("r", "k", "o")
assert style.full_enumeration_legend == ("apples", "unknown",
"oranges")
# invalid
with pytest.raises(
ValueError,
match=
"Style enumeration_legend keys must match enumeration_colors keys"
):
style = Style(enumeration_colors={
1: "r",
3: "o"
},
enumeration_legend={1: "apples"})
with pytest.raises(
TypeError,
match="Style enumeration_legend requires enumeration_colors"):
style = Style(enumeration_legend={-1: "apples", 3: "oranges"})
def test_eq(self):
style1 = Style(name="test")
style2 = Style(name="test")
style3 = Style(name="other")
assert style1 is not style2
assert style1 is not style3
assert style2 is not style3
assert style1 == style1
assert style2 == style2
assert style3 == style3
assert style1 == style2
assert style1 != style3
def test_cmap(self):
style = Style()
assert style.cmap.name == "viridis"
style = Style(colormap="cividis")
assert style.cmap.name == "cividis"
style = Style(enumeration_colors=("c", "k"))
assert style.cmap.name == "from_list"
assert style.cmap.colors == ("c", "k")
with pytest.raises(
TypeError,
match="Style can have a colormap or enumeration_colors"):
style = Style(colormap="cividis", enumeration_colors=("c", "k"))
def test_serialization_deserialization(self):
n_lats = 3
n_lons = 4
n_alts = 2
a = UnitsDataArray(
np.arange(n_lats * n_lons * n_alts).reshape(
(n_lats, n_lons, n_alts)),
dims=["lat", "lon", "alt"],
attrs={
"units": ureg.meter,
"layer_style": Style()
},
)
f = a.to_netcdf()
b = UnitsDataArray(xr.open_dataarray(f))
assert a.attrs["units"] == b.attrs["units"]
assert a.attrs["layer_style"].json == b.attrs["layer_style"].json
def test_serialization(self):
# default
style = Style()
d = style.definition
assert isinstance(d, OrderedDict)
assert len(d.keys()) == 0
s = Style.from_json(style.json)
assert isinstance(s, Style)
# with traits
style = Style(name="test",
units="meters",
colormap="cividis",
clim=(-1, 1))
d = style.definition
assert isinstance(d, OrderedDict)
assert set(d.keys()) == {"name", "units", "colormap", "clim"}
assert d["name"] == "test"
assert d["units"] == "meters"
assert d["colormap"] == "cividis"
assert d["clim"] == [-1, 1]
s = Style.from_json(style.json)
assert s.name == style.name
assert s.units == style.units
assert s.colormap == style.colormap
assert s.clim == style.clim
# enumeration traits
style = Style(enumeration_legend=({
0: "apples",
1: "oranges"
}),
enumeration_colors=({
0: "r",
1: "o"
}))
d = style.definition
assert isinstance(d, OrderedDict)
assert set(d.keys()) == {"enumeration_legend", "enumeration_colors"}
assert d["enumeration_legend"] == {0: "apples", 1: "oranges"}
assert d["enumeration_colors"] == {0: "r", 1: "o"}
s = Style.from_json(style.json)
assert s.enumeration_legend == style.enumeration_legend
assert s.enumeration_colors == style.enumeration_colors
assert s.cmap.colors == style.cmap.colors
def test_style(self):
node = podpac.data.Array(
source=[10, 20, 30],
coordinates=podpac.Coordinates([[0, 1, 2]], dims=["lat"]),
style=Style(name="test", units="m"),
)
d = node.definition
assert "style" in d[node.base_ref]
node2 = Node.from_definition(d)
assert node2 is not node
assert isinstance(node2, podpac.data.Array)
assert node2.style is not node.style
assert node2.style == node.style
assert node2.style.name == "test"
assert node2.style.units == "m"
# default style
node = podpac.data.Array(source=[10, 20, 30],
coordinates=podpac.Coordinates([[0, 1, 2]],
dims=["lat"]))
d = node.definition
assert "style" not in d[node.base_ref]
def _default_style(self):
return Style(clim=[-1.0, 1.0], colormap="jet")
def test_init(self):
s = Style()
def from_definition(cls, definition):
"""
Create podpac Node from a dictionary definition.
Arguments
---------
d : dict
node definition
Returns
-------
:class:`Node`
podpac Node
See Also
--------
definition : node definition as a dictionary
from_json : create podpac node from a JSON definition
load : create a node from file
"""
if "podpac_version" in definition and definition[
"podpac_version"] != podpac.__version__:
warnings.warn("node definition version mismatch "
"(this node was created with podpac version '%s', "
"but your current podpac version is '%s')" %
(definition["podpac_version"], podpac.__version__))
if len(definition) == 0:
raise ValueError("Invalid definition: definition cannot be empty.")
# parse node definitions in order
nodes = OrderedDict()
for name, d in definition.items():
if name == "podpac_version":
continue
if "node" not in d:
raise ValueError(
"Invalid definition for node '%s': 'node' property required"
% name)
# get node class
module_root = d.get("plugin", "podpac")
node_string = "%s.%s" % (module_root, d["node"])
module_name, node_name = node_string.rsplit(".", 1)
try:
module = importlib.import_module(module_name)
except ImportError:
raise ValueError(
"Invalid definition for node '%s': no module found '%s'" %
(name, module_name))
try:
node_class = getattr(module, node_name)
except AttributeError:
raise ValueError(
"Invalid definition for node '%s': class '%s' not found in module '%s'"
% (name, node_name, module_name))
# parse and configure kwargs
kwargs = {}
for k, v in d.get("attrs", {}).items():
kwargs[k] = v
for k, v in d.get("inputs", {}).items():
kwargs[k] = _lookup_input(nodes, name, v)
for k, v in d.get("lookup_attrs", {}).items():
kwargs[k] = _lookup_attr(nodes, name, v)
if "style" in d:
kwargs["style"] = Style.from_definition(d["style"])
for k in d:
if k not in [
"node", "inputs", "attrs", "lookup_attrs", "plugin",
"style"
]:
raise ValueError(
"Invalid definition for node '%s': unexpected property '%s'"
% (name, k))
nodes[name] = node_class(**kwargs)
return list(nodes.values())[-1]
def test_init_from_node(self):
from podpac.core.node import Node
node = Node()
s = Style(node)
def test_base_definition_style(self):
node = Node(style=Style(name="test"))
d = node._base_definition
assert "style" in node._base_definition
def test_basic_creation(self):
s = Style()
def to_image(data, format="png", vmin=None, vmax=None, return_base64=False):
"""Return a base64-encoded image of data
Parameters
----------
data : array-like
data to output, usually a UnitsDataArray
format : str, optional
Default is 'png'. Type of image.
vmin : number, optional
Minimum value of colormap
vmax : vmax, optional
Maximum value of colormap
return_base64: bool, optional
Default is False. Normally this returns an io.BytesIO, but if True, will return a base64 encoded string.
Returns
-------
BytesIO/str
Binary or Base64 encoded image.
"""
import matplotlib
import matplotlib.cm
from matplotlib.image import imsave
with warnings.catch_warnings():
warnings.simplefilter("ignore")
matplotlib.use("agg")
if format != "png":
raise ValueError("Invalid image format '%s', must be 'png'" % format)
style = None
if isinstance(data, xr.DataArray):
style = data.attrs.get("layer_style", None)
if isinstance(style, string_types):
style = Style.from_json(style)
dims = data.squeeze().dims
y = data.coords[dims[0]]
x = data.coords[dims[1]]
data = data.data
if y[1] > y[0]:
data = data[::-1, :]
if x[1] < x[0]:
data = data[:, ::1]
data = data.squeeze()
if not np.any(np.isfinite(data)):
vmin = 0
vmax = 1
else:
if vmin is None or np.isnan(vmin):
if style is not None and style.clim[0] != None:
vmin = style.clim[0]
else:
vmin = np.nanmin(data)
if vmax is None or np.isnan(vmax):
if style is not None and style.clim[1] != None:
vmax = style.clim[1]
else:
vmax = np.nanmax(data)
if vmax == vmin:
vmax += 1e-15
# get the colormap
if style is None:
cmap = matplotlib.cm.viridis
else:
cmap = style.cmap
c = (data - vmin) / (vmax - vmin)
i = cmap(c, bytes=True)
i[np.isnan(c), 3] = 0
im_data = BytesIO()
imsave(im_data, i, format=format)
im_data.seek(0)
if return_base64:
return base64.b64encode(im_data.getvalue())
else:
return im_data
def test_get_default_cmap(self):
style = Style()
style.cmap
def test_create_with_node(self):
node = Node()
s = Style(node)
def _style_default(self):
return Style()
def from_definition(cls, definition):
"""
Create podpac Node from a dictionary definition.
Arguments
---------
d : dict
node definition
Returns
-------
:class:`Node`
podpac Node
See Also
--------
definition : node definition as a dictionary
from_json : create podpac node from a JSON definition
load : create a node from file
"""
if "podpac_version" in definition and definition["podpac_version"] != podpac.__version__:
warnings.warn(
"node definition version mismatch "
"(this node was created with podpac version '%s', "
"but your current podpac version is '%s')" % (definition["podpac_version"], podpac.__version__)
)
if len(definition) == 0:
raise ValueError("Invalid definition: definition cannot be empty.")
# parse node definitions in order
nodes = OrderedDict()
for name, d in definition.items():
if name == "podpac_version":
continue
if "node" not in d:
raise ValueError("Invalid definition for node '%s': 'node' property required" % name)
# get node class
module_root = d.get("plugin", "podpac")
node_string = "%s.%s" % (module_root, d["node"])
module_name, node_name = node_string.rsplit(".", 1)
try:
module = importlib.import_module(module_name)
except ImportError:
raise ValueError("Invalid definition for node '%s': no module found '%s'" % (name, module_name))
try:
node_class = getattr(module, node_name)
except AttributeError:
raise ValueError(
"Invalid definition for node '%s': class '%s' not found in module '%s'"
% (name, node_name, module_name)
)
# parse and configure kwargs
kwargs = {}
for k, v in d.get("attrs", {}).items():
kwargs[k] = v
for k, v in d.get("inputs", {}).items():
kwargs[k] = _lookup_input(nodes, name, v)
for k, v in d.get("lookup_attrs", {}).items():
kwargs[k] = _lookup_attr(nodes, name, v)
if "style" in d:
style_class = getattr(node_class, 'style', Style)
if isinstance(style_class, tl.TraitType):
# Now we actually have to look through the class to see
# if there is a custom initializer for style
for attr in dir(node_class):
atr = getattr(node_class, attr)
if not isinstance(atr, tl.traitlets.DefaultHandler) or atr.trait_name != 'style':
continue
try:
style_class = atr(node_class)
except Exception as e:
# print ("couldn't make style from class", e)
try:
style_class = atr(node_class())
except:
# print ("couldn't make style from class instance", e)
style_class = style_class.klass
try:
kwargs["style"] = style_class.from_definition(d["style"])
except Exception as e:
kwargs["style"] = Style.from_definition(d["style"])
# print ("couldn't make style from inferred style class", e)
for k in d:
if k not in ["node", "inputs", "attrs", "lookup_attrs", "plugin", "style"]:
raise ValueError("Invalid definition for node '%s': unexpected property '%s'" % (name, k))
nodes[name] = node_class(**kwargs)
return list(nodes.values())[-1]
def _default_style(self):
return Style()
