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Rename all objects like "name_1" to avoid conflicts. Objects are
only renamed if necessary.
This method produces more readable GLSL, but is rather slow.
def _rename_objects_pretty(self):
""" Rename all objects like "name_1" to avoid conflicts. Objects are
only renamed if necessary.
This method produces more readable GLSL, but is rather slow.
"""
#
# 1. For each object, add its static names to the global namespace
# and make a list of the shaders used by the object.
#
# {name: obj} mapping for finding unique names
# initialize with reserved keywords.
self._global_ns = dict([(kwd, None) for kwd in gloo.util.KEYWORDS])
# functions are local per-shader
self._shader_ns = dict([(shader, {}) for shader in self.shaders])
# for each object, keep a list of shaders the object appears in
obj_shaders = {}
for shader_name, deps in self._shader_deps.items():
for dep in deps:
# Add static names to namespace
for name in dep.static_names():
self._global_ns[name] = None
obj_shaders.setdefault(dep, []).append(shader_name)
#
# 2. Assign new object names
#
name_index = {}
for obj, shaders in obj_shaders.items():
name = obj.name
if self._name_available(obj, name, shaders):
# hooray, we get to keep this name
self._assign_name(obj, name, shaders)
else:
# boo, find a new name
while True:
index = name_index.get(name, 0) + 1
name_index[name] = index
ext = '_%d' % index
new_name = name[:32-len(ext)] + ext
if self._name_available(obj, new_name, shaders):
self._assign_name(obj, new_name, shaders)
break |
Return True if *name* is available for *obj* in *shaders*.
def _name_available(self, obj, name, shaders):
""" Return True if *name* is available for *obj* in *shaders*.
"""
if name in self._global_ns:
return False
shaders = self.shaders if self._is_global(obj) else shaders
for shader in shaders:
if name in self._shader_ns[shader]:
return False
return True |
Assign *name* to *obj* in *shaders*.
def _assign_name(self, obj, name, shaders):
""" Assign *name* to *obj* in *shaders*.
"""
if self._is_global(obj):
assert name not in self._global_ns
self._global_ns[name] = obj
else:
for shader in shaders:
ns = self._shader_ns[shader]
assert name not in ns
ns[name] = obj
self._object_names[obj] = name |
Rebuilds the shaders, and repositions the objects
that are used internally by the ColorBarVisual
def _update(self):
"""Rebuilds the shaders, and repositions the objects
that are used internally by the ColorBarVisual
"""
x, y = self._pos
halfw, halfh = self._halfdim
# test that width and height are non-zero
if halfw <= 0:
raise ValueError("half-width must be positive and non-zero"
", not %s" % halfw)
if halfh <= 0:
raise ValueError("half-height must be positive and non-zero"
", not %s" % halfh)
# test that the given width and height is consistent
# with the orientation
if (self._orientation == "bottom" or self._orientation == "top"):
if halfw < halfh:
raise ValueError("half-width(%s) < half-height(%s) for"
"%s orientation,"
" expected half-width >= half-height" %
(halfw, halfh, self._orientation, ))
else: # orientation == left or orientation == right
if halfw > halfh:
raise ValueError("half-width(%s) > half-height(%s) for"
"%s orientation,"
" expected half-width <= half-height" %
(halfw, halfh, self._orientation, ))
# Set up the attributes that the shaders require
vertices = np.array([[x - halfw, y - halfh],
[x + halfw, y - halfh],
[x + halfw, y + halfh],
# tri 2
[x - halfw, y - halfh],
[x + halfw, y + halfh],
[x - halfw, y + halfh]],
dtype=np.float32)
self.shared_program['a_position'] = vertices |
Rebuilds the shaders, and repositions the objects
that are used internally by the ColorBarVisual
def _update(self):
"""Rebuilds the shaders, and repositions the objects
that are used internally by the ColorBarVisual
"""
self._colorbar.halfdim = self._halfdim
self._border.halfdim = self._halfdim
self._label.text = self._label_str
self._ticks[0].text = str(self._clim[0])
self._ticks[1].text = str(self._clim[1])
self._update_positions()
self._colorbar._update()
self._border._update() |
updates the positions of the colorbars and labels
def _update_positions(self):
"""
updates the positions of the colorbars and labels
"""
self._colorbar.pos = self._pos
self._border.pos = self._pos
if self._orientation == "right" or self._orientation == "left":
self._label.rotation = -90
x, y = self._pos
halfw, halfh = self._halfdim
label_anchors = \
ColorBarVisual._get_label_anchors(center=self._pos,
halfdim=self._halfdim,
orientation=self._orientation,
transforms=self.label.transforms)
self._label.anchors = label_anchors
ticks_anchors = \
ColorBarVisual._get_ticks_anchors(center=self._pos,
halfdim=self._halfdim,
orientation=self._orientation,
transforms=self.label.transforms)
self._ticks[0].anchors = ticks_anchors
self._ticks[1].anchors = ticks_anchors
(label_pos, ticks_pos) = \
ColorBarVisual._calc_positions(center=self._pos,
halfdim=self._halfdim,
border_width=self.border_width,
orientation=self._orientation,
transforms=self.transforms)
self._label.pos = label_pos
self._ticks[0].pos = ticks_pos[0]
self._ticks[1].pos = ticks_pos[1] |
Calculate the text centeritions given the ColorBar
parameters.
Note
----
This is static because in principle, this
function does not need access to the state of the ColorBar
at all. It's a computation function that computes coordinate
transforms
Parameters
----------
center: tuple (x, y)
Center of the ColorBar
halfdim: tuple (halfw, halfh)
Half of the dimensions measured from the center
border_width: float
Width of the border of the ColorBar
orientation: "top" | "bottom" | "left" | "right"
Position of the label with respect to the ColorBar
transforms: TransformSystem
the transforms of the ColorBar
def _calc_positions(center, halfdim, border_width,
orientation, transforms):
"""
Calculate the text centeritions given the ColorBar
parameters.
Note
----
This is static because in principle, this
function does not need access to the state of the ColorBar
at all. It's a computation function that computes coordinate
transforms
Parameters
----------
center: tuple (x, y)
Center of the ColorBar
halfdim: tuple (halfw, halfh)
Half of the dimensions measured from the center
border_width: float
Width of the border of the ColorBar
orientation: "top" | "bottom" | "left" | "right"
Position of the label with respect to the ColorBar
transforms: TransformSystem
the transforms of the ColorBar
"""
(x, y) = center
(halfw, halfh) = halfdim
visual_to_doc = transforms.get_transform('visual', 'document')
doc_to_visual = transforms.get_transform('document', 'visual')
# doc_widths = visual_to_doc.map(np.array([halfw, halfh, 0, 0],
# dtype=np.float32))
doc_x = visual_to_doc.map(np.array([halfw, 0, 0, 0], dtype=np.float32))
doc_y = visual_to_doc.map(np.array([0, halfh, 0, 0], dtype=np.float32))
if doc_x[0] < 0:
doc_x *= -1
if doc_y[1] < 0:
doc_y *= -1
# doc_halfw = np.abs(doc_widths[0])
# doc_halfh = np.abs(doc_widths[1])
if orientation == "top":
doc_perp_vector = -doc_y
elif orientation == "bottom":
doc_perp_vector = doc_y
elif orientation == "left":
doc_perp_vector = -doc_x
if orientation == "right":
doc_perp_vector = doc_x
perp_len = np.linalg.norm(doc_perp_vector)
doc_perp_vector /= perp_len
perp_len += border_width
perp_len += 5 # pixels
perp_len *= ColorBarVisual.text_padding_factor
doc_perp_vector *= perp_len
doc_center = visual_to_doc.map(np.array([x, y, 0, 0],
dtype=np.float32))
doc_label_pos = doc_center + doc_perp_vector
visual_label_pos = doc_to_visual.map(doc_label_pos)[:3]
# next, calculate tick positions
if orientation in ["top", "bottom"]:
doc_ticks_pos = [doc_label_pos - doc_x,
doc_label_pos + doc_x]
else:
doc_ticks_pos = [doc_label_pos + doc_y,
doc_label_pos - doc_y]
visual_ticks_pos = []
visual_ticks_pos.append(doc_to_visual.map(doc_ticks_pos[0])[:3])
visual_ticks_pos.append(doc_to_visual.map(doc_ticks_pos[1])[:3])
return (visual_label_pos, visual_ticks_pos) |
The size of the ColorBar
Returns
-------
size: (major_axis_length, minor_axis_length)
major and minor axis are defined by the
orientation of the ColorBar
def size(self):
""" The size of the ColorBar
Returns
-------
size: (major_axis_length, minor_axis_length)
major and minor axis are defined by the
orientation of the ColorBar
"""
(halfw, halfh) = self._halfdim
if self.orientation in ["top", "bottom"]:
return (halfw * 2., halfh * 2.)
else:
return (halfh * 2., halfw * 2.) |
Add 'pseudo' fields (e.g non-displayed fields) to the display.
def add_pseudo_fields(self):
"""Add 'pseudo' fields (e.g non-displayed fields) to the display."""
fields = []
if self.backlight_on != enums.BACKLIGHT_ON_NEVER:
fields.append(
display_fields.BacklightPseudoField(ref='0', backlight_rule=self.backlight_on)
)
fields.append(
display_fields.PriorityPseudoField(
ref='0',
priority_playing=self.priority_playing,
priority_not_playing=self.priority_not_playing,
)
)
self.pattern.add_pseudo_fields(fields, self.screen) |
Return a new Rect padded (smaller) by padding on all sides
Parameters
----------
padding : float
The padding.
Returns
-------
rect : instance of Rect
The padded rectangle.
def padded(self, padding):
"""Return a new Rect padded (smaller) by padding on all sides
Parameters
----------
padding : float
The padding.
Returns
-------
rect : instance of Rect
The padded rectangle.
"""
return Rect(pos=(self.pos[0]+padding, self.pos[1]+padding),
size=(self.size[0]-2*padding, self.size[1]-2*padding)) |
Return a Rect covering the same area, but with height and width
guaranteed to be positive.
def normalized(self):
"""Return a Rect covering the same area, but with height and width
guaranteed to be positive."""
return Rect(pos=(min(self.left, self.right),
min(self.top, self.bottom)),
size=(abs(self.width), abs(self.height))) |
Return a Rect with the same bounds but with axes inverted
Parameters
----------
x : bool
Flip the X axis.
y : bool
Flip the Y axis.
Returns
-------
rect : instance of Rect
The flipped rectangle.
def flipped(self, x=False, y=True):
"""Return a Rect with the same bounds but with axes inverted
Parameters
----------
x : bool
Flip the X axis.
y : bool
Flip the Y axis.
Returns
-------
rect : instance of Rect
The flipped rectangle.
"""
pos = list(self.pos)
size = list(self.size)
for i, flip in enumerate((x, y)):
if flip:
pos[i] += size[i]
size[i] *= -1
return Rect(pos, size) |
Return array of coordinates that can be mapped by Transform
classes.
def _transform_in(self):
"""Return array of coordinates that can be mapped by Transform
classes."""
return np.array([
[self.left, self.bottom, 0, 1],
[self.right, self.top, 0, 1]]) |
Automatically configure the TransformSystem:
* canvas_transform maps from the Canvas logical pixel
coordinate system to the framebuffer coordinate system, taking into
account the logical/physical pixel scale factor, current FBO
position, and y-axis inversion.
* framebuffer_transform maps from the current GL viewport on the
framebuffer coordinate system to clip coordinates (-1 to 1).
Parameters
==========
viewport : tuple or None
The GL viewport rectangle (x, y, w, h). If None, then it
is assumed to cover the entire canvas.
fbo_size : tuple or None
The size of the active FBO. If None, then it is assumed to have the
same size as the canvas's framebuffer.
fbo_rect : tuple or None
The position and size (x, y, w, h) of the FBO in the coordinate
system of the canvas's framebuffer. If None, then the bounds are
assumed to cover the entire active framebuffer.
canvas : Canvas instance
Optionally set the canvas for this TransformSystem. See the
`canvas` property.
def configure(self, viewport=None, fbo_size=None, fbo_rect=None,
canvas=None):
"""Automatically configure the TransformSystem:
* canvas_transform maps from the Canvas logical pixel
coordinate system to the framebuffer coordinate system, taking into
account the logical/physical pixel scale factor, current FBO
position, and y-axis inversion.
* framebuffer_transform maps from the current GL viewport on the
framebuffer coordinate system to clip coordinates (-1 to 1).
Parameters
==========
viewport : tuple or None
The GL viewport rectangle (x, y, w, h). If None, then it
is assumed to cover the entire canvas.
fbo_size : tuple or None
The size of the active FBO. If None, then it is assumed to have the
same size as the canvas's framebuffer.
fbo_rect : tuple or None
The position and size (x, y, w, h) of the FBO in the coordinate
system of the canvas's framebuffer. If None, then the bounds are
assumed to cover the entire active framebuffer.
canvas : Canvas instance
Optionally set the canvas for this TransformSystem. See the
`canvas` property.
"""
# TODO: check that d2f and f2r transforms still contain a single
# STTransform (if the user has modified these, then auto-config should
# either fail or replace the transforms)
if canvas is not None:
self.canvas = canvas
canvas = self._canvas
if canvas is None:
raise RuntimeError("No canvas assigned to this TransformSystem.")
# By default, this should invert the y axis--canvas origin is in top
# left, whereas framebuffer origin is in bottom left.
map_from = [(0, 0), canvas.size]
map_to = [(0, canvas.physical_size[1]), (canvas.physical_size[0], 0)]
self._canvas_transform.transforms[1].set_mapping(map_from, map_to)
if fbo_rect is None:
self._canvas_transform.transforms[0].scale = (1, 1, 1)
self._canvas_transform.transforms[0].translate = (0, 0, 0)
else:
# Map into FBO coordinates
map_from = [(fbo_rect[0], fbo_rect[1]),
(fbo_rect[0] + fbo_rect[2], fbo_rect[1] + fbo_rect[3])]
map_to = [(0, 0), fbo_size]
self._canvas_transform.transforms[0].set_mapping(map_from, map_to)
if viewport is None:
if fbo_size is None:
# viewport covers entire canvas
map_from = [(0, 0), canvas.physical_size]
else:
# viewport covers entire FBO
map_from = [(0, 0), fbo_size]
else:
map_from = [viewport[:2],
(viewport[0] + viewport[2], viewport[1] + viewport[3])]
map_to = [(-1, -1), (1, 1)]
self._framebuffer_transform.transforms[0].set_mapping(map_from, map_to) |
Physical resolution of the document coordinate system (dots per
inch).
def dpi(self):
""" Physical resolution of the document coordinate system (dots per
inch).
"""
if self._dpi is None:
if self._canvas is None:
return None
else:
return self.canvas.dpi
else:
return self._dpi |
Return a transform mapping between any two coordinate systems.
Parameters
----------
map_from : str
The starting coordinate system to map from. Must be one of: visual,
scene, document, canvas, framebuffer, or render.
map_to : str
The ending coordinate system to map to. Must be one of: visual,
scene, document, canvas, framebuffer, or render.
def get_transform(self, map_from='visual', map_to='render'):
"""Return a transform mapping between any two coordinate systems.
Parameters
----------
map_from : str
The starting coordinate system to map from. Must be one of: visual,
scene, document, canvas, framebuffer, or render.
map_to : str
The ending coordinate system to map to. Must be one of: visual,
scene, document, canvas, framebuffer, or render.
"""
tr = ['visual', 'scene', 'document', 'canvas', 'framebuffer', 'render']
ifrom = tr.index(map_from)
ito = tr.index(map_to)
if ifrom < ito:
trs = [getattr(self, '_' + t + '_transform')
for t in tr[ifrom:ito]][::-1]
else:
trs = [getattr(self, '_' + t + '_transform').inverse
for t in tr[ito:ifrom]]
return self._cache.get(trs) |
Helper to calculate the delta position
def _calculate_delta_pos(adjacency_arr, pos, t, optimal):
"""Helper to calculate the delta position"""
# XXX eventually this should be refactored for the sparse case to only
# do the necessary pairwise distances
delta = pos[:, np.newaxis, :] - pos
# Distance between points
distance2 = (delta*delta).sum(axis=-1)
# Enforce minimum distance of 0.01
distance2 = np.where(distance2 < 0.0001, 0.0001, distance2)
distance = np.sqrt(distance2)
# Displacement "force"
displacement = np.zeros((len(delta), 2))
for ii in range(2):
displacement[:, ii] = (
delta[:, :, ii] *
((optimal * optimal) / (distance*distance) -
(adjacency_arr * distance) / optimal)).sum(axis=1)
length = np.sqrt((displacement**2).sum(axis=1))
length = np.where(length < 0.01, 0.1, length)
delta_pos = displacement * t / length[:, np.newaxis]
return delta_pos |
You can insert arbitrary business logic code here
def get_recipe_intent_handler(request):
"""
You can insert arbitrary business logic code here
"""
# Get variables like userId, slots, intent name etc from the 'Request' object
ingredient = request.slots["Ingredient"] # Gets an Ingredient Slot from the Request object.
if ingredient == None:
return alexa.create_response("Could not find an ingredient!")
# All manipulations to the request's session object are automatically reflected in the request returned to Amazon.
# For e.g. This statement adds a new session attribute (automatically returned with the response) storing the
# Last seen ingredient value in the 'last_ingredient' key.
request.session['last_ingredient'] = ingredient # Automatically returned as a sessionAttribute
# Modifying state like this saves us from explicitly having to return Session objects after every response
# alexa can also build cards which can be sent as part of the response
card = alexa.create_card(title="GetRecipeIntent activated", subtitle=None,
content="asked alexa to find a recipe using {}".format(ingredient))
return alexa.create_response("Finding a recipe with the ingredient {}".format(ingredient),
end_session=False, card_obj=card) |
Set the usage options for vispy
Specify what app backend and GL backend to use.
Parameters
----------
app : str
The app backend to use (case insensitive). Standard backends:
* 'PyQt4': use Qt widget toolkit via PyQt4.
* 'PyQt5': use Qt widget toolkit via PyQt5.
* 'PySide': use Qt widget toolkit via PySide.
* 'PyGlet': use Pyglet backend.
* 'Glfw': use Glfw backend (successor of Glut). Widely available
on Linux.
* 'SDL2': use SDL v2 backend.
* 'osmesa': Use OSMesa backend
Additional backends:
* 'ipynb_vnc': render in the IPython notebook via a VNC approach
(experimental)
gl : str
The gl backend to use (case insensitive). Options are:
* 'gl2': use Vispy's desktop OpenGL API.
* 'pyopengl2': use PyOpenGL's desktop OpenGL API. Mostly for
testing.
* 'es2': (TO COME) use real OpenGL ES 2.0 on Windows via Angle.
Availability of ES 2.0 is larger for Windows, since it relies
on DirectX.
* 'gl+': use the full OpenGL functionality available on
your system (via PyOpenGL).
Notes
-----
If the app option is given, ``vispy.app.use_app()`` is called. If
the gl option is given, ``vispy.gloo.use_gl()`` is called.
If an app backend name is provided, and that backend could not be
loaded, an error is raised.
If no backend name is provided, Vispy will first check if the GUI
toolkit corresponding to each backend is already imported, and try
that backend first. If this is unsuccessful, it will try the
'default_backend' provided in the vispy config. If still not
succesful, it will try each backend in a predetermined order.
See Also
--------
vispy.app.use_app
vispy.gloo.gl.use_gl
def use(app=None, gl=None):
""" Set the usage options for vispy
Specify what app backend and GL backend to use.
Parameters
----------
app : str
The app backend to use (case insensitive). Standard backends:
* 'PyQt4': use Qt widget toolkit via PyQt4.
* 'PyQt5': use Qt widget toolkit via PyQt5.
* 'PySide': use Qt widget toolkit via PySide.
* 'PyGlet': use Pyglet backend.
* 'Glfw': use Glfw backend (successor of Glut). Widely available
on Linux.
* 'SDL2': use SDL v2 backend.
* 'osmesa': Use OSMesa backend
Additional backends:
* 'ipynb_vnc': render in the IPython notebook via a VNC approach
(experimental)
gl : str
The gl backend to use (case insensitive). Options are:
* 'gl2': use Vispy's desktop OpenGL API.
* 'pyopengl2': use PyOpenGL's desktop OpenGL API. Mostly for
testing.
* 'es2': (TO COME) use real OpenGL ES 2.0 on Windows via Angle.
Availability of ES 2.0 is larger for Windows, since it relies
on DirectX.
* 'gl+': use the full OpenGL functionality available on
your system (via PyOpenGL).
Notes
-----
If the app option is given, ``vispy.app.use_app()`` is called. If
the gl option is given, ``vispy.gloo.use_gl()`` is called.
If an app backend name is provided, and that backend could not be
loaded, an error is raised.
If no backend name is provided, Vispy will first check if the GUI
toolkit corresponding to each backend is already imported, and try
that backend first. If this is unsuccessful, it will try the
'default_backend' provided in the vispy config. If still not
succesful, it will try each backend in a predetermined order.
See Also
--------
vispy.app.use_app
vispy.gloo.gl.use_gl
"""
if app is None and gl is None:
raise TypeError('Must specify at least one of "app" or "gl".')
# Example for future. This wont work (yet).
if app == 'ipynb_webgl':
app = 'headless'
gl = 'webgl'
if app == 'osmesa':
from ..util.osmesa_gl import fix_osmesa_gl_lib
fix_osmesa_gl_lib()
if gl is not None:
raise ValueError("Do not specify gl when using osmesa")
# Apply now
if gl:
from .. import gloo, config
config['gl_backend'] = gl
gloo.gl.use_gl(gl)
if app:
from ..app import use_app
use_app(app) |
Run command using subprocess.Popen
Run command and wait for command to complete. If the return code was zero
then return, otherwise raise CalledProcessError.
By default, this will also add stdout= and stderr=subproces.PIPE
to the call to Popen to suppress printing to the terminal.
Parameters
----------
command : list of str
Command to run as subprocess (see subprocess.Popen documentation).
return_code : bool
If True, the returncode will be returned, and no error checking
will be performed (so this function should always return without
error).
**kwargs : dict
Additional kwargs to pass to ``subprocess.Popen``.
Returns
-------
stdout : str
Stdout returned by the process.
stderr : str
Stderr returned by the process.
code : int
The command exit code. Only returned if ``return_code`` is True.
def run_subprocess(command, return_code=False, **kwargs):
"""Run command using subprocess.Popen
Run command and wait for command to complete. If the return code was zero
then return, otherwise raise CalledProcessError.
By default, this will also add stdout= and stderr=subproces.PIPE
to the call to Popen to suppress printing to the terminal.
Parameters
----------
command : list of str
Command to run as subprocess (see subprocess.Popen documentation).
return_code : bool
If True, the returncode will be returned, and no error checking
will be performed (so this function should always return without
error).
**kwargs : dict
Additional kwargs to pass to ``subprocess.Popen``.
Returns
-------
stdout : str
Stdout returned by the process.
stderr : str
Stderr returned by the process.
code : int
The command exit code. Only returned if ``return_code`` is True.
"""
# code adapted with permission from mne-python
use_kwargs = dict(stderr=subprocess.PIPE, stdout=subprocess.PIPE)
use_kwargs.update(kwargs)
p = subprocess.Popen(command, **use_kwargs)
output = p.communicate()
# communicate() may return bytes, str, or None depending on the kwargs
# passed to Popen(). Convert all to unicode str:
output = ['' if s is None else s for s in output]
output = [s.decode('utf-8') if isinstance(s, bytes) else s for s in output]
output = tuple(output)
if not return_code and p.returncode:
print(output[0])
print(output[1])
err_fun = subprocess.CalledProcessError.__init__
if 'output' in inspect.getargspec(err_fun).args:
raise subprocess.CalledProcessError(p.returncode, command, output)
else:
raise subprocess.CalledProcessError(p.returncode, command)
if return_code:
output = output + (p.returncode,)
return output |
Start the timer.
A timeout event will be generated every *interval* seconds.
If *interval* is None, then self.interval will be used.
If *iterations* is specified, the timer will stop after
emitting that number of events. If unspecified, then
the previous value of self.iterations will be used. If the value is
negative, then the timer will continue running until stop() is called.
If the timer is already running when this function is called, nothing
happens (timer continues running as it did previously, without
changing the interval, number of iterations, or emitting a timer
start event).
def start(self, interval=None, iterations=None):
"""Start the timer.
A timeout event will be generated every *interval* seconds.
If *interval* is None, then self.interval will be used.
If *iterations* is specified, the timer will stop after
emitting that number of events. If unspecified, then
the previous value of self.iterations will be used. If the value is
negative, then the timer will continue running until stop() is called.
If the timer is already running when this function is called, nothing
happens (timer continues running as it did previously, without
changing the interval, number of iterations, or emitting a timer
start event).
"""
if self.running:
return # don't do anything if already running
self.iter_count = 0
if interval is not None:
self.interval = interval
if iterations is not None:
self.max_iterations = iterations
self._backend._vispy_start(self.interval)
self._running = True
self._first_emit_time = precision_time()
self._last_emit_time = precision_time()
self.events.start(type='timer_start') |
Stop the timer.
def stop(self):
"""Stop the timer."""
self._backend._vispy_stop()
self._running = False
self.events.stop(type='timer_stop') |
Returns a numpy array of all the HEALPix indexes contained in the MOC at its max order.
Returns
-------
result : `~numpy.ndarray`
The array of HEALPix at ``max_order``
def _best_res_pixels(self):
"""
Returns a numpy array of all the HEALPix indexes contained in the MOC at its max order.
Returns
-------
result : `~numpy.ndarray`
The array of HEALPix at ``max_order``
"""
factor = 2 * (AbstractMOC.HPY_MAX_NORDER - self.max_order)
pix_l = []
for iv in self._interval_set._intervals:
for val in range(iv[0] >> factor, iv[1] >> factor):
pix_l.append(val)
return np.asarray(pix_l) |
Returns a boolean mask array of the positions lying inside (or outside) the MOC instance.
Parameters
----------
ra : `astropy.units.Quantity`
Right ascension array
dec : `astropy.units.Quantity`
Declination array
keep_inside : bool, optional
True by default. If so the mask describes coordinates lying inside the MOC. If ``keep_inside``
is false, contains will return the mask of the coordinates lying outside the MOC.
Returns
-------
array : `~np.ndarray`
A mask boolean array
def contains(self, ra, dec, keep_inside=True):
"""
Returns a boolean mask array of the positions lying inside (or outside) the MOC instance.
Parameters
----------
ra : `astropy.units.Quantity`
Right ascension array
dec : `astropy.units.Quantity`
Declination array
keep_inside : bool, optional
True by default. If so the mask describes coordinates lying inside the MOC. If ``keep_inside``
is false, contains will return the mask of the coordinates lying outside the MOC.
Returns
-------
array : `~np.ndarray`
A mask boolean array
"""
depth = self.max_order
m = np.zeros(nside2npix(1 << depth), dtype=bool)
pix_id = self._best_res_pixels()
m[pix_id] = True
if not keep_inside:
m = np.logical_not(m)
hp = HEALPix(nside=(1 << depth), order='nested')
pix = hp.lonlat_to_healpix(ra, dec)
return m[pix] |
Extends the MOC instance so that it includes the HEALPix cells touching its border.
The depth of the HEALPix cells added at the border is equal to the maximum depth of the MOC instance.
Returns
-------
moc : `~mocpy.moc.MOC`
self extended by one degree of neighbours.
def add_neighbours(self):
"""
Extends the MOC instance so that it includes the HEALPix cells touching its border.
The depth of the HEALPix cells added at the border is equal to the maximum depth of the MOC instance.
Returns
-------
moc : `~mocpy.moc.MOC`
self extended by one degree of neighbours.
"""
# Get the pixels array of the MOC at the its max order.
ipix = self._best_res_pixels()
hp = HEALPix(nside=(1 << self.max_order), order='nested')
# Get the HEALPix array containing the neighbors of ``ipix``.
# This array "extends" ``ipix`` by one degree of neighbors.
extend_ipix = AbstractMOC._neighbour_pixels(hp, ipix)
# Compute the difference between ``extend_ipix`` and ``ipix`` to get only the neighboring pixels
# located at the border of the MOC.
neigh_ipix = np.setdiff1d(extend_ipix, ipix)
shift = 2 * (AbstractMOC.HPY_MAX_NORDER - self.max_order)
neigh_itv = np.vstack((neigh_ipix << shift, (neigh_ipix + 1) << shift)).T
# This array of HEALPix neighbors are added to the MOC to get an ``extended`` MOC at its max order.
self._interval_set = self._interval_set.union(IntervalSet(neigh_itv))
return self |
Removes from the MOC instance the HEALPix cells located at its border.
The depth of the HEALPix cells removed is equal to the maximum depth of the MOC instance.
Returns
-------
moc : `~mocpy.moc.MOC`
self minus its HEALPix cells located at its border.
def remove_neighbours(self):
"""
Removes from the MOC instance the HEALPix cells located at its border.
The depth of the HEALPix cells removed is equal to the maximum depth of the MOC instance.
Returns
-------
moc : `~mocpy.moc.MOC`
self minus its HEALPix cells located at its border.
"""
# Get the HEALPix cells of the MOC at its max depth
ipix = self._best_res_pixels()
hp = HEALPix(nside=(1 << self.max_order), order='nested')
# Extend it to include the max depth neighbor cells.
extend_ipix = AbstractMOC._neighbour_pixels(hp, ipix)
# Get only the max depth HEALPix cells lying at the border of the MOC
neigh_ipix = np.setxor1d(extend_ipix, ipix)
# Remove these pixels from ``ipix``
border_ipix = AbstractMOC._neighbour_pixels(hp, neigh_ipix)
reduced_ipix = np.setdiff1d(ipix, border_ipix)
# Build the reduced MOC, i.e. MOC without its pixels which were located at its border.
shift = 2 * (AbstractMOC.HPY_MAX_NORDER - self.max_order)
reduced_itv = np.vstack((reduced_ipix << shift, (reduced_ipix + 1) << shift)).T
self._interval_set = IntervalSet(reduced_itv)
return self |
Draws the MOC on a matplotlib axis.
This performs the projection of the cells from the world coordinate system to the pixel image coordinate system.
You are able to specify various styling kwargs for `matplotlib.patches.PathPatch`
(see the `list of valid keywords <https://matplotlib.org/api/_as_gen/matplotlib.patches.PathPatch.html#matplotlib.patches.PathPatch>`__).
Parameters
----------
ax : `matplotlib.axes.Axes`
Matplotlib axis.
wcs : `astropy.wcs.WCS`
WCS defining the World system <-> Image system projection.
kw_mpl_pathpatch
Plotting arguments for `matplotlib.patches.PathPatch`.
Examples
--------
>>> from mocpy import MOC, WCS
>>> from astropy.coordinates import Angle, SkyCoord
>>> import astropy.units as u
>>> # Load a MOC, e.g. the MOC of GALEXGR6-AIS-FUV
>>> filename = './../resources/P-GALEXGR6-AIS-FUV.fits'
>>> moc = MOC.from_fits(filename)
>>> # Plot the MOC using matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure(111, figsize=(15, 15))
>>> # Define a WCS as a context
>>> with WCS(fig,
... fov=50 * u.deg,
... center=SkyCoord(0, 20, unit='deg', frame='icrs'),
... coordsys="icrs",
... rotation=Angle(0, u.degree),
... projection="AIT") as wcs:
... ax = fig.add_subplot(1, 1, 1, projection=wcs)
... # Call fill giving the matplotlib axe and the `~astropy.wcs.WCS` object.
... # We will set the matplotlib keyword linewidth to 0 so that it does not plot
... # the border of each HEALPix cell.
... # The color can also be specified along with an alpha value.
... moc.fill(ax=ax, wcs=wcs, linewidth=0, alpha=0.5, fill=True, color="green")
>>> plt.xlabel('ra')
>>> plt.ylabel('dec')
>>> plt.grid(color="black", linestyle="dotted")
def fill(self, ax, wcs, **kw_mpl_pathpatch):
"""
Draws the MOC on a matplotlib axis.
This performs the projection of the cells from the world coordinate system to the pixel image coordinate system.
You are able to specify various styling kwargs for `matplotlib.patches.PathPatch`
(see the `list of valid keywords <https://matplotlib.org/api/_as_gen/matplotlib.patches.PathPatch.html#matplotlib.patches.PathPatch>`__).
Parameters
----------
ax : `matplotlib.axes.Axes`
Matplotlib axis.
wcs : `astropy.wcs.WCS`
WCS defining the World system <-> Image system projection.
kw_mpl_pathpatch
Plotting arguments for `matplotlib.patches.PathPatch`.
Examples
--------
>>> from mocpy import MOC, WCS
>>> from astropy.coordinates import Angle, SkyCoord
>>> import astropy.units as u
>>> # Load a MOC, e.g. the MOC of GALEXGR6-AIS-FUV
>>> filename = './../resources/P-GALEXGR6-AIS-FUV.fits'
>>> moc = MOC.from_fits(filename)
>>> # Plot the MOC using matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure(111, figsize=(15, 15))
>>> # Define a WCS as a context
>>> with WCS(fig,
... fov=50 * u.deg,
... center=SkyCoord(0, 20, unit='deg', frame='icrs'),
... coordsys="icrs",
... rotation=Angle(0, u.degree),
... projection="AIT") as wcs:
... ax = fig.add_subplot(1, 1, 1, projection=wcs)
... # Call fill giving the matplotlib axe and the `~astropy.wcs.WCS` object.
... # We will set the matplotlib keyword linewidth to 0 so that it does not plot
... # the border of each HEALPix cell.
... # The color can also be specified along with an alpha value.
... moc.fill(ax=ax, wcs=wcs, linewidth=0, alpha=0.5, fill=True, color="green")
>>> plt.xlabel('ra')
>>> plt.ylabel('dec')
>>> plt.grid(color="black", linestyle="dotted")
"""
fill.fill(self, ax, wcs, **kw_mpl_pathpatch) |
Draws the MOC border(s) on a matplotlib axis.
This performs the projection of the sky coordinates defining the perimeter of the MOC to the pixel image coordinate system.
You are able to specify various styling kwargs for `matplotlib.patches.PathPatch`
(see the `list of valid keywords <https://matplotlib.org/api/_as_gen/matplotlib.patches.PathPatch.html#matplotlib.patches.PathPatch>`__).
Parameters
----------
ax : `matplotlib.axes.Axes`
Matplotlib axis.
wcs : `astropy.wcs.WCS`
WCS defining the World system <-> Image system projection.
kw_mpl_pathpatch
Plotting arguments for `matplotlib.patches.PathPatch`
Examples
--------
>>> from mocpy import MOC, WCS
>>> from astropy.coordinates import Angle, SkyCoord
>>> import astropy.units as u
>>> # Load a MOC, e.g. the MOC of GALEXGR6-AIS-FUV
>>> filename = './../resources/P-GALEXGR6-AIS-FUV.fits'
>>> moc = MOC.from_fits(filename)
>>> # Plot the MOC using matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure(111, figsize=(15, 15))
>>> # Define a WCS as a context
>>> with WCS(fig,
... fov=50 * u.deg,
... center=SkyCoord(0, 20, unit='deg', frame='icrs'),
... coordsys="icrs",
... rotation=Angle(0, u.degree),
... projection="AIT") as wcs:
... ax = fig.add_subplot(1, 1, 1, projection=wcs)
... # Call border giving the matplotlib axe and the `~astropy.wcs.WCS` object.
... moc.border(ax=ax, wcs=wcs, alpha=0.5, color="red")
>>> plt.xlabel('ra')
>>> plt.ylabel('dec')
>>> plt.grid(color="black", linestyle="dotted")
def border(self, ax, wcs, **kw_mpl_pathpatch):
"""
Draws the MOC border(s) on a matplotlib axis.
This performs the projection of the sky coordinates defining the perimeter of the MOC to the pixel image coordinate system.
You are able to specify various styling kwargs for `matplotlib.patches.PathPatch`
(see the `list of valid keywords <https://matplotlib.org/api/_as_gen/matplotlib.patches.PathPatch.html#matplotlib.patches.PathPatch>`__).
Parameters
----------
ax : `matplotlib.axes.Axes`
Matplotlib axis.
wcs : `astropy.wcs.WCS`
WCS defining the World system <-> Image system projection.
kw_mpl_pathpatch
Plotting arguments for `matplotlib.patches.PathPatch`
Examples
--------
>>> from mocpy import MOC, WCS
>>> from astropy.coordinates import Angle, SkyCoord
>>> import astropy.units as u
>>> # Load a MOC, e.g. the MOC of GALEXGR6-AIS-FUV
>>> filename = './../resources/P-GALEXGR6-AIS-FUV.fits'
>>> moc = MOC.from_fits(filename)
>>> # Plot the MOC using matplotlib
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure(111, figsize=(15, 15))
>>> # Define a WCS as a context
>>> with WCS(fig,
... fov=50 * u.deg,
... center=SkyCoord(0, 20, unit='deg', frame='icrs'),
... coordsys="icrs",
... rotation=Angle(0, u.degree),
... projection="AIT") as wcs:
... ax = fig.add_subplot(1, 1, 1, projection=wcs)
... # Call border giving the matplotlib axe and the `~astropy.wcs.WCS` object.
... moc.border(ax=ax, wcs=wcs, alpha=0.5, color="red")
>>> plt.xlabel('ra')
>>> plt.ylabel('dec')
>>> plt.grid(color="black", linestyle="dotted")
"""
border.border(self, ax, wcs, **kw_mpl_pathpatch) |
Creates a `~mocpy.moc.MOC` from an image stored as a FITS file.
Parameters
----------
header : `astropy.io.fits.Header`
FITS header containing all the info of where the image is located (position, size, etc...)
max_norder : int
The moc resolution.
mask : `numpy.ndarray`, optional
A boolean array of the same size of the image where pixels having the value 1 are part of
the final MOC and pixels having the value 0 are not.
Returns
-------
moc : `~mocpy.moc.MOC`
The resulting MOC.
def from_image(cls, header, max_norder, mask=None):
"""
Creates a `~mocpy.moc.MOC` from an image stored as a FITS file.
Parameters
----------
header : `astropy.io.fits.Header`
FITS header containing all the info of where the image is located (position, size, etc...)
max_norder : int
The moc resolution.
mask : `numpy.ndarray`, optional
A boolean array of the same size of the image where pixels having the value 1 are part of
the final MOC and pixels having the value 0 are not.
Returns
-------
moc : `~mocpy.moc.MOC`
The resulting MOC.
"""
# load the image data
height = header['NAXIS2']
width = header['NAXIS1']
# use wcs from astropy to locate the image in the world coordinates
w = wcs.WCS(header)
if mask is not None:
# We have an array of pixels that are part of of survey
y, x = np.where(mask)
pix_crd = np.dstack((x, y))[0]
else:
# If we do not have a mask array we create the moc of all the image
#
step_pix = 1
"""
Coords returned by wcs_pix2world method correspond to pixel centers. We want to retrieve the moc pix
crossing the borders of the image so we have to add 1/2 to the pixels coords before computing the lonlat.
The step between two pix_crd is set to `step_pix` but can be diminished to have a better precision at the
borders so that all the image is covered (a too big step does not retrieve all
the moc pix crossing the borders of the image).
"""
x, y = np.mgrid[0.5:(width + 0.5 + step_pix):step_pix, 0.5:(height + 0.5 + step_pix):step_pix]
pix_crd = np.dstack((x.ravel(), y.ravel()))[0]
frame = wcs.utils.wcs_to_celestial_frame(w)
world_pix_crd = SkyCoord(w.wcs_pix2world(pix_crd, 1), unit='deg', frame=frame)
hp = HEALPix(nside=(1 << max_norder), order='nested', frame=ICRS())
ipix = hp.skycoord_to_healpix(world_pix_crd)
# remove doubles
ipix = np.unique(ipix)
shift = 2 * (AbstractMOC.HPY_MAX_NORDER - max_norder)
intervals_arr = np.vstack((ipix << shift, (ipix + 1) << shift)).T
# This MOC will be consistent when one will do operations on the moc (union, inter, ...) or
# simply write it to a fits or json file
interval_set = IntervalSet(intervals_arr)
return cls(interval_set=interval_set) |
Loads a MOC from a set of FITS file images.
Parameters
----------
path_l : [str]
A list of path where the fits image are located.
max_norder : int
The MOC resolution.
Returns
-------
moc : `~mocpy.moc.MOC`
The union of all the MOCs created from the paths found in ``path_l``.
def from_fits_images(cls, path_l, max_norder):
"""
Loads a MOC from a set of FITS file images.
Parameters
----------
path_l : [str]
A list of path where the fits image are located.
max_norder : int
The MOC resolution.
Returns
-------
moc : `~mocpy.moc.MOC`
The union of all the MOCs created from the paths found in ``path_l``.
"""
moc = MOC()
for path in path_l:
header = fits.getheader(path)
current_moc = MOC.from_image(header=header, max_norder=max_norder)
moc = moc.union(current_moc)
return moc |
Creates a `~mocpy.moc.MOC` object from a VizieR table.
**Info**: This method is already implemented in `astroquery.cds <https://astroquery.readthedocs.io/en/latest/cds/cds.html>`__. You can ask to get a `mocpy.moc.MOC` object
from a vizier catalog ID.
Parameters
----------
table_id : str
table index
nside : int, optional
256 by default
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
def from_vizier_table(cls, table_id, nside=256):
"""
Creates a `~mocpy.moc.MOC` object from a VizieR table.
**Info**: This method is already implemented in `astroquery.cds <https://astroquery.readthedocs.io/en/latest/cds/cds.html>`__. You can ask to get a `mocpy.moc.MOC` object
from a vizier catalog ID.
Parameters
----------
table_id : str
table index
nside : int, optional
256 by default
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
"""
nside_possible_values = (8, 16, 32, 64, 128, 256, 512)
if nside not in nside_possible_values:
raise ValueError('Bad value for nside. Must be in {0}'.format(nside_possible_values))
result = cls.from_ivorn('ivo://CDS/' + table_id, nside)
return result |
Creates a `~mocpy.moc.MOC` object from a given ivorn.
Parameters
----------
ivorn : str
nside : int, optional
256 by default
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
def from_ivorn(cls, ivorn, nside=256):
"""
Creates a `~mocpy.moc.MOC` object from a given ivorn.
Parameters
----------
ivorn : str
nside : int, optional
256 by default
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
"""
return cls.from_url('%s?%s' % (MOC.MOC_SERVER_ROOT_URL,
urlencode({
'ivorn': ivorn,
'get': 'moc',
'order': int(np.log2(nside))
}))) |
Creates a `~mocpy.moc.MOC` object from a given url.
Parameters
----------
url : str
The url of a FITS file storing a MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
def from_url(cls, url):
"""
Creates a `~mocpy.moc.MOC` object from a given url.
Parameters
----------
url : str
The url of a FITS file storing a MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC.
"""
path = download_file(url, show_progress=False, timeout=60)
return cls.from_fits(path) |
Creates a MOC from an `astropy.coordinates.SkyCoord`.
Parameters
----------
skycoords : `astropy.coordinates.SkyCoord`
The sky coordinates that will belong to the MOC.
max_norder : int
The depth of the smallest HEALPix cells contained in the MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
def from_skycoords(cls, skycoords, max_norder):
"""
Creates a MOC from an `astropy.coordinates.SkyCoord`.
Parameters
----------
skycoords : `astropy.coordinates.SkyCoord`
The sky coordinates that will belong to the MOC.
max_norder : int
The depth of the smallest HEALPix cells contained in the MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
"""
hp = HEALPix(nside=(1 << max_norder), order='nested')
ipix = hp.lonlat_to_healpix(skycoords.icrs.ra, skycoords.icrs.dec)
shift = 2 * (AbstractMOC.HPY_MAX_NORDER - max_norder)
intervals = np.vstack((ipix << shift, (ipix + 1) << shift)).T
interval_set = IntervalSet(intervals)
return cls(interval_set) |
Creates a MOC from astropy lon, lat `astropy.units.Quantity`.
Parameters
----------
lon : `astropy.units.Quantity`
The longitudes of the sky coordinates belonging to the MOC.
lat : `astropy.units.Quantity`
The latitudes of the sky coordinates belonging to the MOC.
max_norder : int
The depth of the smallest HEALPix cells contained in the MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
def from_lonlat(cls, lon, lat, max_norder):
"""
Creates a MOC from astropy lon, lat `astropy.units.Quantity`.
Parameters
----------
lon : `astropy.units.Quantity`
The longitudes of the sky coordinates belonging to the MOC.
lat : `astropy.units.Quantity`
The latitudes of the sky coordinates belonging to the MOC.
max_norder : int
The depth of the smallest HEALPix cells contained in the MOC.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
"""
hp = HEALPix(nside=(1 << max_norder), order='nested')
ipix = hp.lonlat_to_healpix(lon, lat)
shift = 2 * (AbstractMOC.HPY_MAX_NORDER - max_norder)
intervals = np.vstack((ipix << shift, (ipix + 1) << shift)).T
interval_set = IntervalSet(intervals)
return cls(interval_set) |
Creates a MOC from a polygon.
The polygon is given as an `astropy.coordinates.SkyCoord` that contains the
vertices of the polygon. Concave and convex polygons are accepted but
self-intersecting ones are currently not properly handled.
Parameters
----------
skycoord : `astropy.coordinates.SkyCoord`
The sky coordinates defining the vertices of a polygon. It can describe a convex or
concave polygon but not a self-intersecting one.
inside : `astropy.coordinates.SkyCoord`, optional
A point that will be inside the MOC is needed as it is not possible to determine the inside area of a polygon
on the unit sphere (there is no infinite area that can be considered as the outside because on the sphere,
a closed polygon delimits two finite areas).
Possible improvement: take the inside area as the one covering the smallest region on the sphere.
If inside=None (default behavior), the mean of all the vertices is taken as lying inside the polygon. That approach may not work for
concave polygons.
max_depth : int, optional
The resolution of the MOC. Set to 10 by default.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
def from_polygon_skycoord(cls, skycoord, inside=None, max_depth=10):
"""
Creates a MOC from a polygon.
The polygon is given as an `astropy.coordinates.SkyCoord` that contains the
vertices of the polygon. Concave and convex polygons are accepted but
self-intersecting ones are currently not properly handled.
Parameters
----------
skycoord : `astropy.coordinates.SkyCoord`
The sky coordinates defining the vertices of a polygon. It can describe a convex or
concave polygon but not a self-intersecting one.
inside : `astropy.coordinates.SkyCoord`, optional
A point that will be inside the MOC is needed as it is not possible to determine the inside area of a polygon
on the unit sphere (there is no infinite area that can be considered as the outside because on the sphere,
a closed polygon delimits two finite areas).
Possible improvement: take the inside area as the one covering the smallest region on the sphere.
If inside=None (default behavior), the mean of all the vertices is taken as lying inside the polygon. That approach may not work for
concave polygons.
max_depth : int, optional
The resolution of the MOC. Set to 10 by default.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
"""
return MOC.from_polygon(lon=skycoord.icrs.ra, lat=skycoord.icrs.dec,
inside=inside, max_depth=max_depth) |
Creates a MOC from a polygon
The polygon is given as lon and lat `astropy.units.Quantity` that define the
vertices of the polygon. Concave and convex polygons are accepted but
self-intersecting ones are currently not properly handled.
Parameters
----------
lon : `astropy.units.Quantity`
The longitudes defining the polygon. Can describe convex and
concave polygons but not self-intersecting ones.
lat : `astropy.units.Quantity`
The latitudes defining the polygon. Can describe convex and concave
polygons but not self-intersecting ones.
inside : `astropy.coordinates.SkyCoord`, optional
A point that will be inside the MOC is needed as it is not possible to determine the inside area of a polygon
on the unit sphere (there is no infinite area that can be considered as the outside because on the sphere,
a closed polygon delimits two finite areas).
Possible improvement: take the inside area as the one covering the smallest region on the sphere.
If inside=None (default behavior), the mean of all the vertices is taken as lying inside the polygon. That approach may not work for
concave polygons.
max_depth : int, optional
The resolution of the MOC. Set to 10 by default.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
def from_polygon(cls, lon, lat, inside=None, max_depth=10):
"""
Creates a MOC from a polygon
The polygon is given as lon and lat `astropy.units.Quantity` that define the
vertices of the polygon. Concave and convex polygons are accepted but
self-intersecting ones are currently not properly handled.
Parameters
----------
lon : `astropy.units.Quantity`
The longitudes defining the polygon. Can describe convex and
concave polygons but not self-intersecting ones.
lat : `astropy.units.Quantity`
The latitudes defining the polygon. Can describe convex and concave
polygons but not self-intersecting ones.
inside : `astropy.coordinates.SkyCoord`, optional
A point that will be inside the MOC is needed as it is not possible to determine the inside area of a polygon
on the unit sphere (there is no infinite area that can be considered as the outside because on the sphere,
a closed polygon delimits two finite areas).
Possible improvement: take the inside area as the one covering the smallest region on the sphere.
If inside=None (default behavior), the mean of all the vertices is taken as lying inside the polygon. That approach may not work for
concave polygons.
max_depth : int, optional
The resolution of the MOC. Set to 10 by default.
Returns
-------
result : `~mocpy.moc.MOC`
The resulting MOC
"""
from .polygon import PolygonComputer
polygon_computer = PolygonComputer(lon, lat, inside, max_depth)
# Create the moc from the python dictionary
moc = MOC.from_json(polygon_computer.ipix)
# We degrade it to the user-requested order
if polygon_computer.degrade_to_max_depth:
moc = moc.degrade_to_order(max_depth)
return moc |
Sky fraction covered by the MOC
def sky_fraction(self):
"""
Sky fraction covered by the MOC
"""
pix_id = self._best_res_pixels()
nb_pix_filled = pix_id.size
return nb_pix_filled / float(3 << (2*(self.max_order + 1))) |
Internal method to query Simbad or a VizieR table
for sources in the coverage of the MOC instance
def _query(self, resource_id, max_rows):
"""
Internal method to query Simbad or a VizieR table
for sources in the coverage of the MOC instance
"""
from astropy.io.votable import parse_single_table
if max_rows is not None and max_rows >= 0:
max_rows_str = str(max_rows)
else:
max_rows_str = str(9999999999)
tmp_moc = tempfile.NamedTemporaryFile(delete=False)
self.write(tmp_moc.name)
r = requests.post('http://cdsxmatch.u-strasbg.fr/QueryCat/QueryCat',
data={'mode': 'mocfile',
'catName': resource_id,
'format': 'votable',
'limit': max_rows_str},
files={'moc': open(tmp_moc.name, 'rb')},
headers={'User-Agent': 'MOCPy'},
stream=True)
tmp_vot = BytesIO()
tmp_vot.write(r.content)
table = parse_single_table(tmp_vot).to_table()
# finally delete temp files
os.unlink(tmp_moc.name)
return table |
Plot the MOC object using a mollweide projection.
**Deprecated**: New `fill` and `border` methods produce more reliable results and allow you to specify additional
matplotlib style parameters.
Parameters
----------
title : str
The title of the plot
frame : `astropy.coordinates.BaseCoordinateFrame`, optional
Describes the coordinate system the plot will be (ICRS, Galactic are the only coordinate systems supported).
def plot(self, title='MOC', frame=None):
"""
Plot the MOC object using a mollweide projection.
**Deprecated**: New `fill` and `border` methods produce more reliable results and allow you to specify additional
matplotlib style parameters.
Parameters
----------
title : str
The title of the plot
frame : `astropy.coordinates.BaseCoordinateFrame`, optional
Describes the coordinate system the plot will be (ICRS, Galactic are the only coordinate systems supported).
"""
frame = ICRS() if frame is None else frame
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
plot_order = 8
if self.max_order > plot_order:
plotted_moc = self.degrade_to_order(plot_order)
else:
plotted_moc = self
num_pixels_map = 1024
delta = 2. * np.pi / num_pixels_map
x = np.arange(-np.pi, np.pi, delta)
y = np.arange(-np.pi/2, np.pi/2, delta)
lon_rad, lat_rad = np.meshgrid(x, y)
hp = HEALPix(nside=(1 << plotted_moc.max_order), order='nested')
if frame and not isinstance(frame, BaseCoordinateFrame):
raise ValueError("Only Galactic/ICRS coordinate systems are supported."
"Please set `coord` to either 'C' or 'G'.")
pix_map = hp.lonlat_to_healpix(lon_rad * u.rad, lat_rad * u.rad)
m = np.zeros(nside2npix(1 << plotted_moc.max_order))
pix_id = plotted_moc._best_res_pixels()
# change the HEALPix cells if the frame of the MOC is not the same as the one associated with the plot method.
if isinstance(frame, Galactic):
lon, lat = hp.boundaries_lonlat(pix_id, step=2)
sky_crd = SkyCoord(lon, lat, unit='deg')
pix_id = hp.lonlat_to_healpix(sky_crd.galactic.l, sky_crd.galactic.b)
m[pix_id] = 1
z = np.flip(m[pix_map], axis=1)
plt.figure(figsize=(10, 10))
ax = plt.subplot(111, projection="mollweide")
ax.set_xticklabels(['150°', '120°', '90°', '60°', '30°', '0°', '330°', '300°', '270°', '240°', '210°', '180°'])
color_map = LinearSegmentedColormap.from_list('w2r', ['#eeeeee', '#aa0000'])
color_map.set_under('w')
color_map.set_bad('gray')
ax.pcolormesh(x, y, z, cmap=color_map, vmin=0, vmax=1)
ax.tick_params(labelsize=14, labelcolor='#000000')
plt.title(title)
plt.grid(True, linestyle='--', linewidth=1, color='#555555')
plt.show() |
The inverse of this transform.
def inverse(self):
""" The inverse of this transform.
"""
if self._inverse is None:
self._inverse = InverseTransform(self)
return self._inverse |
Tiles tick marks along the axis.
def _tile_ticks(self, frac, tickvec):
"""Tiles tick marks along the axis."""
origins = np.tile(self.axis._vec, (len(frac), 1))
origins = self.axis.pos[0].T + (origins.T*frac).T
endpoints = tickvec + origins
return origins, endpoints |
Get the major ticks, minor ticks, and major labels
def _get_tick_frac_labels(self):
"""Get the major ticks, minor ticks, and major labels"""
minor_num = 4 # number of minor ticks per major division
if (self.axis.scale_type == 'linear'):
domain = self.axis.domain
if domain[1] < domain[0]:
flip = True
domain = domain[::-1]
else:
flip = False
offset = domain[0]
scale = domain[1] - domain[0]
transforms = self.axis.transforms
length = self.axis.pos[1] - self.axis.pos[0] # in logical coords
n_inches = np.sqrt(np.sum(length ** 2)) / transforms.dpi
# major = np.linspace(domain[0], domain[1], num=11)
# major = MaxNLocator(10).tick_values(*domain)
major = _get_ticks_talbot(domain[0], domain[1], n_inches, 2)
labels = ['%g' % x for x in major]
majstep = major[1] - major[0]
minor = []
minstep = majstep / (minor_num + 1)
minstart = 0 if self.axis._stop_at_major[0] else -1
minstop = -1 if self.axis._stop_at_major[1] else 0
for i in range(minstart, len(major) + minstop):
maj = major[0] + i * majstep
minor.extend(np.linspace(maj + minstep,
maj + majstep - minstep,
minor_num))
major_frac = (major - offset) / scale
minor_frac = (np.array(minor) - offset) / scale
major_frac = major_frac[::-1] if flip else major_frac
use_mask = (major_frac > -0.0001) & (major_frac < 1.0001)
major_frac = major_frac[use_mask]
labels = [l for li, l in enumerate(labels) if use_mask[li]]
minor_frac = minor_frac[(minor_frac > -0.0001) &
(minor_frac < 1.0001)]
elif self.axis.scale_type == 'logarithmic':
return NotImplementedError
elif self.axis.scale_type == 'power':
return NotImplementedError
return major_frac, minor_frac, labels |
Interleave (colour) planes, e.g. RGB + A = RGBA.
Return an array of pixels consisting of the `ipsize` elements of
data from each pixel in `ipixels` followed by the `apsize` elements
of data from each pixel in `apixels`. Conventionally `ipixels`
and `apixels` are byte arrays so the sizes are bytes, but it
actually works with any arrays of the same type. The returned
array is the same type as the input arrays which should be the
same type as each other.
def interleave_planes(ipixels, apixels, ipsize, apsize):
"""
Interleave (colour) planes, e.g. RGB + A = RGBA.
Return an array of pixels consisting of the `ipsize` elements of
data from each pixel in `ipixels` followed by the `apsize` elements
of data from each pixel in `apixels`. Conventionally `ipixels`
and `apixels` are byte arrays so the sizes are bytes, but it
actually works with any arrays of the same type. The returned
array is the same type as the input arrays which should be the
same type as each other.
"""
itotal = len(ipixels)
atotal = len(apixels)
newtotal = itotal + atotal
newpsize = ipsize + apsize
# Set up the output buffer
# See http://www.python.org/doc/2.4.4/lib/module-array.html#l2h-1356
out = array(ipixels.typecode)
# It's annoying that there is no cheap way to set the array size :-(
out.extend(ipixels)
out.extend(apixels)
# Interleave in the pixel data
for i in range(ipsize):
out[i:newtotal:newpsize] = ipixels[i:itotal:ipsize]
for i in range(apsize):
out[i+ipsize:newtotal:newpsize] = apixels[i:atotal:apsize]
return out |
Create a PNG file by writing out the chunks.
def write_chunks(out, chunks):
"""Create a PNG file by writing out the chunks."""
out.write(_signature)
for chunk in chunks:
write_chunk(out, *chunk) |
Apply a scanline filter to a scanline. `type` specifies the
filter type (0 to 4); `line` specifies the current (unfiltered)
scanline as a sequence of bytes; `prev` specifies the previous
(unfiltered) scanline as a sequence of bytes. `fo` specifies the
filter offset; normally this is size of a pixel in bytes (the number
of bytes per sample times the number of channels), but when this is
< 1 (for bit depths < 8) then the filter offset is 1.
def filter_scanline(type, line, fo, prev=None):
"""Apply a scanline filter to a scanline. `type` specifies the
filter type (0 to 4); `line` specifies the current (unfiltered)
scanline as a sequence of bytes; `prev` specifies the previous
(unfiltered) scanline as a sequence of bytes. `fo` specifies the
filter offset; normally this is size of a pixel in bytes (the number
of bytes per sample times the number of channels), but when this is
< 1 (for bit depths < 8) then the filter offset is 1.
"""
assert 0 <= type < 5
# The output array. Which, pathetically, we extend one-byte at a
# time (fortunately this is linear).
out = array('B', [type])
def sub():
ai = -fo
for x in line:
if ai >= 0:
x = (x - line[ai]) & 0xff
out.append(x)
ai += 1
def up():
for i,x in enumerate(line):
x = (x - prev[i]) & 0xff
out.append(x)
def average():
ai = -fo
for i,x in enumerate(line):
if ai >= 0:
x = (x - ((line[ai] + prev[i]) >> 1)) & 0xff
else:
x = (x - (prev[i] >> 1)) & 0xff
out.append(x)
ai += 1
def paeth():
# http://www.w3.org/TR/PNG/#9Filter-type-4-Paeth
ai = -fo # also used for ci
for i,x in enumerate(line):
a = 0
b = prev[i]
c = 0
if ai >= 0:
a = line[ai]
c = prev[ai]
p = a + b - c
pa = abs(p - a)
pb = abs(p - b)
pc = abs(p - c)
if pa <= pb and pa <= pc:
Pr = a
elif pb <= pc:
Pr = b
else:
Pr = c
x = (x - Pr) & 0xff
out.append(x)
ai += 1
if not prev:
# We're on the first line. Some of the filters can be reduced
# to simpler cases which makes handling the line "off the top"
# of the image simpler. "up" becomes "none"; "paeth" becomes
# "left" (non-trivial, but true). "average" needs to be handled
# specially.
if type == 2: # "up"
type = 0
elif type == 3:
prev = [0]*len(line)
elif type == 4: # "paeth"
type = 1
if type == 0:
out.extend(line)
elif type == 1:
sub()
elif type == 2:
up()
elif type == 3:
average()
else: # type == 4
paeth()
return out |
Create a PNG :class:`Image` object from a 2- or 3-dimensional
array. One application of this function is easy PIL-style saving:
``png.from_array(pixels, 'L').save('foo.png')``.
.. note :
The use of the term *3-dimensional* is for marketing purposes
only. It doesn't actually work. Please bear with us. Meanwhile
enjoy the complimentary snacks (on request) and please use a
2-dimensional array.
Unless they are specified using the *info* parameter, the PNG's
height and width are taken from the array size. For a 3 dimensional
array the first axis is the height; the second axis is the width;
and the third axis is the channel number. Thus an RGB image that is
16 pixels high and 8 wide will use an array that is 16x8x3. For 2
dimensional arrays the first axis is the height, but the second axis
is ``width*channels``, so an RGB image that is 16 pixels high and 8
wide will use a 2-dimensional array that is 16x24 (each row will be
8*3==24 sample values).
*mode* is a string that specifies the image colour format in a
PIL-style mode. It can be:
``'L'``
greyscale (1 channel)
``'LA'``
greyscale with alpha (2 channel)
``'RGB'``
colour image (3 channel)
``'RGBA'``
colour image with alpha (4 channel)
The mode string can also specify the bit depth (overriding how this
function normally derives the bit depth, see below). Appending
``';16'`` to the mode will cause the PNG to be 16 bits per channel;
any decimal from 1 to 16 can be used to specify the bit depth.
When a 2-dimensional array is used *mode* determines how many
channels the image has, and so allows the width to be derived from
the second array dimension.
The array is expected to be a ``numpy`` array, but it can be any
suitable Python sequence. For example, a list of lists can be used:
``png.from_array([[0, 255, 0], [255, 0, 255]], 'L')``. The exact
rules are: ``len(a)`` gives the first dimension, height;
``len(a[0])`` gives the second dimension; ``len(a[0][0])`` gives the
third dimension, unless an exception is raised in which case a
2-dimensional array is assumed. It's slightly more complicated than
that because an iterator of rows can be used, and it all still
works. Using an iterator allows data to be streamed efficiently.
The bit depth of the PNG is normally taken from the array element's
datatype (but if *mode* specifies a bitdepth then that is used
instead). The array element's datatype is determined in a way which
is supposed to work both for ``numpy`` arrays and for Python
``array.array`` objects. A 1 byte datatype will give a bit depth of
8, a 2 byte datatype will give a bit depth of 16. If the datatype
does not have an implicit size, for example it is a plain Python
list of lists, as above, then a default of 8 is used.
The *info* parameter is a dictionary that can be used to specify
metadata (in the same style as the arguments to the
:class:``png.Writer`` class). For this function the keys that are
useful are:
height
overrides the height derived from the array dimensions and allows
*a* to be an iterable.
width
overrides the width derived from the array dimensions.
bitdepth
overrides the bit depth derived from the element datatype (but
must match *mode* if that also specifies a bit depth).
Generally anything specified in the
*info* dictionary will override any implicit choices that this
function would otherwise make, but must match any explicit ones.
For example, if the *info* dictionary has a ``greyscale`` key then
this must be true when mode is ``'L'`` or ``'LA'`` and false when
mode is ``'RGB'`` or ``'RGBA'``.
def from_array(a, mode=None, info={}):
"""Create a PNG :class:`Image` object from a 2- or 3-dimensional
array. One application of this function is easy PIL-style saving:
``png.from_array(pixels, 'L').save('foo.png')``.
.. note :
The use of the term *3-dimensional* is for marketing purposes
only. It doesn't actually work. Please bear with us. Meanwhile
enjoy the complimentary snacks (on request) and please use a
2-dimensional array.
Unless they are specified using the *info* parameter, the PNG's
height and width are taken from the array size. For a 3 dimensional
array the first axis is the height; the second axis is the width;
and the third axis is the channel number. Thus an RGB image that is
16 pixels high and 8 wide will use an array that is 16x8x3. For 2
dimensional arrays the first axis is the height, but the second axis
is ``width*channels``, so an RGB image that is 16 pixels high and 8
wide will use a 2-dimensional array that is 16x24 (each row will be
8*3==24 sample values).
*mode* is a string that specifies the image colour format in a
PIL-style mode. It can be:
``'L'``
greyscale (1 channel)
``'LA'``
greyscale with alpha (2 channel)
``'RGB'``
colour image (3 channel)
``'RGBA'``
colour image with alpha (4 channel)
The mode string can also specify the bit depth (overriding how this
function normally derives the bit depth, see below). Appending
``';16'`` to the mode will cause the PNG to be 16 bits per channel;
any decimal from 1 to 16 can be used to specify the bit depth.
When a 2-dimensional array is used *mode* determines how many
channels the image has, and so allows the width to be derived from
the second array dimension.
The array is expected to be a ``numpy`` array, but it can be any
suitable Python sequence. For example, a list of lists can be used:
``png.from_array([[0, 255, 0], [255, 0, 255]], 'L')``. The exact
rules are: ``len(a)`` gives the first dimension, height;
``len(a[0])`` gives the second dimension; ``len(a[0][0])`` gives the
third dimension, unless an exception is raised in which case a
2-dimensional array is assumed. It's slightly more complicated than
that because an iterator of rows can be used, and it all still
works. Using an iterator allows data to be streamed efficiently.
The bit depth of the PNG is normally taken from the array element's
datatype (but if *mode* specifies a bitdepth then that is used
instead). The array element's datatype is determined in a way which
is supposed to work both for ``numpy`` arrays and for Python
``array.array`` objects. A 1 byte datatype will give a bit depth of
8, a 2 byte datatype will give a bit depth of 16. If the datatype
does not have an implicit size, for example it is a plain Python
list of lists, as above, then a default of 8 is used.
The *info* parameter is a dictionary that can be used to specify
metadata (in the same style as the arguments to the
:class:``png.Writer`` class). For this function the keys that are
useful are:
height
overrides the height derived from the array dimensions and allows
*a* to be an iterable.
width
overrides the width derived from the array dimensions.
bitdepth
overrides the bit depth derived from the element datatype (but
must match *mode* if that also specifies a bit depth).
Generally anything specified in the
*info* dictionary will override any implicit choices that this
function would otherwise make, but must match any explicit ones.
For example, if the *info* dictionary has a ``greyscale`` key then
this must be true when mode is ``'L'`` or ``'LA'`` and false when
mode is ``'RGB'`` or ``'RGBA'``.
"""
# We abuse the *info* parameter by modifying it. Take a copy here.
# (Also typechecks *info* to some extent).
info = dict(info)
# Syntax check mode string.
bitdepth = None
try:
# Assign the 'L' or 'RGBA' part to `gotmode`.
if mode.startswith('L'):
gotmode = 'L'
mode = mode[1:]
elif mode.startswith('RGB'):
gotmode = 'RGB'
mode = mode[3:]
else:
raise Error()
if mode.startswith('A'):
gotmode += 'A'
mode = mode[1:]
# Skip any optional ';'
while mode.startswith(';'):
mode = mode[1:]
# Parse optional bitdepth
if mode:
try:
bitdepth = int(mode)
except (TypeError, ValueError):
raise Error()
except Error:
raise Error("mode string should be 'RGB' or 'L;16' or similar.")
mode = gotmode
# Get bitdepth from *mode* if possible.
if bitdepth:
if info.get('bitdepth') and bitdepth != info['bitdepth']:
raise Error("mode bitdepth (%d) should match info bitdepth (%d)." %
(bitdepth, info['bitdepth']))
info['bitdepth'] = bitdepth
# Fill in and/or check entries in *info*.
# Dimensions.
if 'size' in info:
# Check width, height, size all match where used.
for dimension,axis in [('width', 0), ('height', 1)]:
if dimension in info:
if info[dimension] != info['size'][axis]:
raise Error(
"info[%r] should match info['size'][%r]." %
(dimension, axis))
info['width'],info['height'] = info['size']
if 'height' not in info:
try:
l = len(a)
except TypeError:
raise Error(
"len(a) does not work, supply info['height'] instead.")
info['height'] = l
# Colour format.
if 'greyscale' in info:
if bool(info['greyscale']) != ('L' in mode):
raise Error("info['greyscale'] should match mode.")
info['greyscale'] = 'L' in mode
if 'alpha' in info:
if bool(info['alpha']) != ('A' in mode):
raise Error("info['alpha'] should match mode.")
info['alpha'] = 'A' in mode
planes = len(mode)
if 'planes' in info:
if info['planes'] != planes:
raise Error("info['planes'] should match mode.")
# In order to work out whether we the array is 2D or 3D we need its
# first row, which requires that we take a copy of its iterator.
# We may also need the first row to derive width and bitdepth.
a,t = itertools.tee(a)
row = t.next()
del t
try:
row[0][0]
threed = True
testelement = row[0]
except (IndexError, TypeError):
threed = False
testelement = row
if 'width' not in info:
if threed:
width = len(row)
else:
width = len(row) // planes
info['width'] = width
# Not implemented yet
assert not threed
if 'bitdepth' not in info:
try:
dtype = testelement.dtype
# goto the "else:" clause. Sorry.
except AttributeError:
try:
# Try a Python array.array.
bitdepth = 8 * testelement.itemsize
except AttributeError:
# We can't determine it from the array element's
# datatype, use a default of 8.
bitdepth = 8
else:
# If we got here without exception, we now assume that
# the array is a numpy array.
if dtype.kind == 'b':
bitdepth = 1
else:
bitdepth = 8 * dtype.itemsize
info['bitdepth'] = bitdepth
for thing in 'width height bitdepth greyscale alpha'.split():
assert thing in info
return Image(a, info) |
Create the byte sequences for a ``PLTE`` and if necessary a
``tRNS`` chunk. Returned as a pair (*p*, *t*). *t* will be
``None`` if no ``tRNS`` chunk is necessary.
def make_palette(self):
"""Create the byte sequences for a ``PLTE`` and if necessary a
``tRNS`` chunk. Returned as a pair (*p*, *t*). *t* will be
``None`` if no ``tRNS`` chunk is necessary.
"""
p = array('B')
t = array('B')
for x in self.palette:
p.extend(x[0:3])
if len(x) > 3:
t.append(x[3])
p = tostring(p)
t = tostring(t)
if t:
return p,t
return p,None |
Write a PNG image to the output file. `rows` should be
an iterable that yields each row in boxed row flat pixel
format. The rows should be the rows of the original image,
so there should be ``self.height`` rows of ``self.width *
self.planes`` values. If `interlace` is specified (when
creating the instance), then an interlaced PNG file will
be written. Supply the rows in the normal image order;
the interlacing is carried out internally.
.. note ::
Interlacing will require the entire image to be in working
memory.
def write(self, outfile, rows):
"""Write a PNG image to the output file. `rows` should be
an iterable that yields each row in boxed row flat pixel
format. The rows should be the rows of the original image,
so there should be ``self.height`` rows of ``self.width *
self.planes`` values. If `interlace` is specified (when
creating the instance), then an interlaced PNG file will
be written. Supply the rows in the normal image order;
the interlacing is carried out internally.
.. note ::
Interlacing will require the entire image to be in working
memory.
"""
if self.interlace:
fmt = 'BH'[self.bitdepth > 8]
a = array(fmt, itertools.chain(*rows))
return self.write_array(outfile, a)
nrows = self.write_passes(outfile, rows)
if nrows != self.height:
raise ValueError(
"rows supplied (%d) does not match height (%d)" %
(nrows, self.height)) |
Write a PNG image to the output file.
Most users are expected to find the :meth:`write` or
:meth:`write_array` method more convenient.
The rows should be given to this method in the order that
they appear in the output file. For straightlaced images,
this is the usual top to bottom ordering, but for interlaced
images the rows should have already been interlaced before
passing them to this function.
`rows` should be an iterable that yields each row. When
`packed` is ``False`` the rows should be in boxed row flat pixel
format; when `packed` is ``True`` each row should be a packed
sequence of bytes.
def write_passes(self, outfile, rows, packed=False):
"""
Write a PNG image to the output file.
Most users are expected to find the :meth:`write` or
:meth:`write_array` method more convenient.
The rows should be given to this method in the order that
they appear in the output file. For straightlaced images,
this is the usual top to bottom ordering, but for interlaced
images the rows should have already been interlaced before
passing them to this function.
`rows` should be an iterable that yields each row. When
`packed` is ``False`` the rows should be in boxed row flat pixel
format; when `packed` is ``True`` each row should be a packed
sequence of bytes.
"""
# http://www.w3.org/TR/PNG/#5PNG-file-signature
outfile.write(_signature)
# http://www.w3.org/TR/PNG/#11IHDR
write_chunk(outfile, 'IHDR',
struct.pack("!2I5B", self.width, self.height,
self.bitdepth, self.color_type,
0, 0, self.interlace))
# See :chunk:order
# http://www.w3.org/TR/PNG/#11gAMA
if self.gamma is not None:
write_chunk(outfile, 'gAMA',
struct.pack("!L", int(round(self.gamma*1e5))))
# See :chunk:order
# http://www.w3.org/TR/PNG/#11sBIT
if self.rescale:
write_chunk(outfile, 'sBIT',
struct.pack('%dB' % self.planes,
*[self.rescale[0]]*self.planes))
# :chunk:order: Without a palette (PLTE chunk), ordering is
# relatively relaxed. With one, gAMA chunk must precede PLTE
# chunk which must precede tRNS and bKGD.
# See http://www.w3.org/TR/PNG/#5ChunkOrdering
if self.palette:
p,t = self.make_palette()
write_chunk(outfile, 'PLTE', p)
if t:
# tRNS chunk is optional. Only needed if palette entries
# have alpha.
write_chunk(outfile, 'tRNS', t)
# http://www.w3.org/TR/PNG/#11tRNS
if self.transparent is not None:
if self.greyscale:
write_chunk(outfile, 'tRNS',
struct.pack("!1H", *self.transparent))
else:
write_chunk(outfile, 'tRNS',
struct.pack("!3H", *self.transparent))
# http://www.w3.org/TR/PNG/#11bKGD
if self.background is not None:
if self.greyscale:
write_chunk(outfile, 'bKGD',
struct.pack("!1H", *self.background))
else:
write_chunk(outfile, 'bKGD',
struct.pack("!3H", *self.background))
# http://www.w3.org/TR/PNG/#11IDAT
if self.compression is not None:
compressor = zlib.compressobj(self.compression)
else:
compressor = zlib.compressobj()
# Choose an extend function based on the bitdepth. The extend
# function packs/decomposes the pixel values into bytes and
# stuffs them onto the data array.
data = array('B')
if self.bitdepth == 8 or packed:
extend = data.extend
elif self.bitdepth == 16:
# Decompose into bytes
def extend(sl):
fmt = '!%dH' % len(sl)
data.extend(array('B', struct.pack(fmt, *sl)))
else:
# Pack into bytes
assert self.bitdepth < 8
# samples per byte
spb = int(8/self.bitdepth)
def extend(sl):
a = array('B', sl)
# Adding padding bytes so we can group into a whole
# number of spb-tuples.
l = float(len(a))
extra = math.ceil(l / float(spb))*spb - l
a.extend([0]*int(extra))
# Pack into bytes
l = group(a, spb)
l = map(lambda e: reduce(lambda x,y:
(x << self.bitdepth) + y, e), l)
data.extend(l)
if self.rescale:
oldextend = extend
factor = \
float(2**self.rescale[1]-1) / float(2**self.rescale[0]-1)
def extend(sl):
oldextend(map(lambda x: int(round(factor*x)), sl))
# Build the first row, testing mostly to see if we need to
# changed the extend function to cope with NumPy integer types
# (they cause our ordinary definition of extend to fail, so we
# wrap it). See
# http://code.google.com/p/pypng/issues/detail?id=44
enumrows = enumerate(rows)
del rows
# First row's filter type.
data.append(0)
# :todo: Certain exceptions in the call to ``.next()`` or the
# following try would indicate no row data supplied.
# Should catch.
i,row = enumrows.next()
try:
# If this fails...
extend(row)
except:
# ... try a version that converts the values to int first.
# Not only does this work for the (slightly broken) NumPy
# types, there are probably lots of other, unknown, "nearly"
# int types it works for.
def wrapmapint(f):
return lambda sl: f(map(int, sl))
extend = wrapmapint(extend)
del wrapmapint
extend(row)
for i,row in enumrows:
# Add "None" filter type. Currently, it's essential that
# this filter type be used for every scanline as we do not
# mark the first row of a reduced pass image; that means we
# could accidentally compute the wrong filtered scanline if
# we used "up", "average", or "paeth" on such a line.
data.append(0)
extend(row)
if len(data) > self.chunk_limit:
compressed = compressor.compress(tostring(data))
if len(compressed):
write_chunk(outfile, 'IDAT', compressed)
# Because of our very witty definition of ``extend``,
# above, we must re-use the same ``data`` object. Hence
# we use ``del`` to empty this one, rather than create a
# fresh one (which would be my natural FP instinct).
del data[:]
if len(data):
compressed = compressor.compress(tostring(data))
else:
compressed = strtobytes('')
flushed = compressor.flush()
if len(compressed) or len(flushed):
write_chunk(outfile, 'IDAT', compressed + flushed)
# http://www.w3.org/TR/PNG/#11IEND
write_chunk(outfile, 'IEND')
return i+1 |
Write PNG file to `outfile`. The pixel data comes from `rows`
which should be in boxed row packed format. Each row should be
a sequence of packed bytes.
Technically, this method does work for interlaced images but it
is best avoided. For interlaced images, the rows should be
presented in the order that they appear in the file.
This method should not be used when the source image bit depth
is not one naturally supported by PNG; the bit depth should be
1, 2, 4, 8, or 16.
def write_packed(self, outfile, rows):
"""
Write PNG file to `outfile`. The pixel data comes from `rows`
which should be in boxed row packed format. Each row should be
a sequence of packed bytes.
Technically, this method does work for interlaced images but it
is best avoided. For interlaced images, the rows should be
presented in the order that they appear in the file.
This method should not be used when the source image bit depth
is not one naturally supported by PNG; the bit depth should be
1, 2, 4, 8, or 16.
"""
if self.rescale:
raise Error("write_packed method not suitable for bit depth %d" %
self.rescale[0])
return self.write_passes(outfile, rows, packed=True) |
Convert a PNM file containing raw pixel data into a PNG file
with the parameters set in the writer object. Works for
(binary) PGM, PPM, and PAM formats.
def convert_pnm(self, infile, outfile):
"""
Convert a PNM file containing raw pixel data into a PNG file
with the parameters set in the writer object. Works for
(binary) PGM, PPM, and PAM formats.
"""
if self.interlace:
pixels = array('B')
pixels.fromfile(infile,
(self.bitdepth/8) * self.color_planes *
self.width * self.height)
self.write_passes(outfile, self.array_scanlines_interlace(pixels))
else:
self.write_passes(outfile, self.file_scanlines(infile)) |
Convert a PPM and PGM file containing raw pixel data into a
PNG outfile with the parameters set in the writer object.
def convert_ppm_and_pgm(self, ppmfile, pgmfile, outfile):
"""
Convert a PPM and PGM file containing raw pixel data into a
PNG outfile with the parameters set in the writer object.
"""
pixels = array('B')
pixels.fromfile(ppmfile,
(self.bitdepth/8) * self.color_planes *
self.width * self.height)
apixels = array('B')
apixels.fromfile(pgmfile,
(self.bitdepth/8) *
self.width * self.height)
pixels = interleave_planes(pixels, apixels,
(self.bitdepth/8) * self.color_planes,
(self.bitdepth/8))
if self.interlace:
self.write_passes(outfile, self.array_scanlines_interlace(pixels))
else:
self.write_passes(outfile, self.array_scanlines(pixels)) |
Generates boxed rows in flat pixel format, from the input file
`infile`. It assumes that the input file is in a "Netpbm-like"
binary format, and is positioned at the beginning of the first
pixel. The number of pixels to read is taken from the image
dimensions (`width`, `height`, `planes`) and the number of bytes
per value is implied by the image `bitdepth`.
def file_scanlines(self, infile):
"""
Generates boxed rows in flat pixel format, from the input file
`infile`. It assumes that the input file is in a "Netpbm-like"
binary format, and is positioned at the beginning of the first
pixel. The number of pixels to read is taken from the image
dimensions (`width`, `height`, `planes`) and the number of bytes
per value is implied by the image `bitdepth`.
"""
# Values per row
vpr = self.width * self.planes
row_bytes = vpr
if self.bitdepth > 8:
assert self.bitdepth == 16
row_bytes *= 2
fmt = '>%dH' % vpr
def line():
return array('H', struct.unpack(fmt, infile.read(row_bytes)))
else:
def line():
scanline = array('B', infile.read(row_bytes))
return scanline
for y in range(self.height):
yield line() |
Generator for interlaced scanlines from an array. `pixels` is
the full source image in flat row flat pixel format. The
generator yields each scanline of the reduced passes in turn, in
boxed row flat pixel format.
def array_scanlines_interlace(self, pixels):
"""
Generator for interlaced scanlines from an array. `pixels` is
the full source image in flat row flat pixel format. The
generator yields each scanline of the reduced passes in turn, in
boxed row flat pixel format.
"""
# http://www.w3.org/TR/PNG/#8InterlaceMethods
# Array type.
fmt = 'BH'[self.bitdepth > 8]
# Value per row
vpr = self.width * self.planes
for xstart, ystart, xstep, ystep in _adam7:
if xstart >= self.width:
continue
# Pixels per row (of reduced image)
ppr = int(math.ceil((self.width-xstart)/float(xstep)))
# number of values in reduced image row.
row_len = ppr*self.planes
for y in range(ystart, self.height, ystep):
if xstep == 1:
offset = y * vpr
yield pixels[offset:offset+vpr]
else:
row = array(fmt)
# There's no easier way to set the length of an array
row.extend(pixels[0:row_len])
offset = y * vpr + xstart * self.planes
end_offset = (y+1) * vpr
skip = self.planes * xstep
for i in range(self.planes):
row[i::self.planes] = \
pixels[offset+i:end_offset:skip]
yield row |
Undo the filter for a scanline. `scanline` is a sequence of
bytes that does not include the initial filter type byte.
`previous` is decoded previous scanline (for straightlaced
images this is the previous pixel row, but for interlaced
images, it is the previous scanline in the reduced image, which
in general is not the previous pixel row in the final image).
When there is no previous scanline (the first row of a
straightlaced image, or the first row in one of the passes in an
interlaced image), then this argument should be ``None``.
The scanline will have the effects of filtering removed, and the
result will be returned as a fresh sequence of bytes.
def undo_filter(self, filter_type, scanline, previous):
"""Undo the filter for a scanline. `scanline` is a sequence of
bytes that does not include the initial filter type byte.
`previous` is decoded previous scanline (for straightlaced
images this is the previous pixel row, but for interlaced
images, it is the previous scanline in the reduced image, which
in general is not the previous pixel row in the final image).
When there is no previous scanline (the first row of a
straightlaced image, or the first row in one of the passes in an
interlaced image), then this argument should be ``None``.
The scanline will have the effects of filtering removed, and the
result will be returned as a fresh sequence of bytes.
"""
# :todo: Would it be better to update scanline in place?
# Yes, with the Cython extension making the undo_filter fast,
# updating scanline inplace makes the code 3 times faster
# (reading 50 images of 800x800 went from 40s to 16s)
result = scanline
if filter_type == 0:
return result
if filter_type not in (1,2,3,4):
raise FormatError('Invalid PNG Filter Type.'
' See http://www.w3.org/TR/2003/REC-PNG-20031110/#9Filters .')
# Filter unit. The stride from one pixel to the corresponding
# byte from the previous pixel. Normally this is the pixel
# size in bytes, but when this is smaller than 1, the previous
# byte is used instead.
fu = max(1, self.psize)
# For the first line of a pass, synthesize a dummy previous
# line. An alternative approach would be to observe that on the
# first line 'up' is the same as 'null', 'paeth' is the same
# as 'sub', with only 'average' requiring any special case.
if not previous:
previous = array('B', [0]*len(scanline))
def sub():
"""Undo sub filter."""
ai = 0
# Loop starts at index fu. Observe that the initial part
# of the result is already filled in correctly with
# scanline.
for i in range(fu, len(result)):
x = scanline[i]
a = result[ai]
result[i] = (x + a) & 0xff
ai += 1
def up():
"""Undo up filter."""
for i in range(len(result)):
x = scanline[i]
b = previous[i]
result[i] = (x + b) & 0xff
def average():
"""Undo average filter."""
ai = -fu
for i in range(len(result)):
x = scanline[i]
if ai < 0:
a = 0
else:
a = result[ai]
b = previous[i]
result[i] = (x + ((a + b) >> 1)) & 0xff
ai += 1
def paeth():
"""Undo Paeth filter."""
# Also used for ci.
ai = -fu
for i in range(len(result)):
x = scanline[i]
if ai < 0:
a = c = 0
else:
a = result[ai]
c = previous[ai]
b = previous[i]
p = a + b - c
pa = abs(p - a)
pb = abs(p - b)
pc = abs(p - c)
if pa <= pb and pa <= pc:
pr = a
elif pb <= pc:
pr = b
else:
pr = c
result[i] = (x + pr) & 0xff
ai += 1
# Call appropriate filter algorithm. Note that 0 has already
# been dealt with.
(None,
pngfilters.undo_filter_sub,
pngfilters.undo_filter_up,
pngfilters.undo_filter_average,
pngfilters.undo_filter_paeth)[filter_type](fu, scanline, previous, result)
return result |
Read raw pixel data, undo filters, deinterlace, and flatten.
Return in flat row flat pixel format.
def deinterlace(self, raw):
"""
Read raw pixel data, undo filters, deinterlace, and flatten.
Return in flat row flat pixel format.
"""
# Values per row (of the target image)
vpr = self.width * self.planes
# Make a result array, and make it big enough. Interleaving
# writes to the output array randomly (well, not quite), so the
# entire output array must be in memory.
fmt = 'BH'[self.bitdepth > 8]
a = array(fmt, [0]*vpr*self.height)
source_offset = 0
for xstart, ystart, xstep, ystep in _adam7:
if xstart >= self.width:
continue
# The previous (reconstructed) scanline. None at the
# beginning of a pass to indicate that there is no previous
# line.
recon = None
# Pixels per row (reduced pass image)
ppr = int(math.ceil((self.width-xstart)/float(xstep)))
# Row size in bytes for this pass.
row_size = int(math.ceil(self.psize * ppr))
for y in range(ystart, self.height, ystep):
filter_type = raw[source_offset]
source_offset += 1
scanline = raw[source_offset:source_offset+row_size]
source_offset += row_size
recon = self.undo_filter(filter_type, scanline, recon)
# Convert so that there is one element per pixel value
flat = self.serialtoflat(recon, ppr)
if xstep == 1:
assert xstart == 0
offset = y * vpr
a[offset:offset+vpr] = flat
else:
offset = y * vpr + xstart * self.planes
end_offset = (y+1) * vpr
skip = self.planes * xstep
for i in range(self.planes):
a[offset+i:end_offset:skip] = \
flat[i::self.planes]
return a |
Iterator that yields each scanline in boxed row flat pixel
format. `rows` should be an iterator that yields the bytes of
each row in turn.
def iterboxed(self, rows):
"""Iterator that yields each scanline in boxed row flat pixel
format. `rows` should be an iterator that yields the bytes of
each row in turn.
"""
def asvalues(raw):
"""Convert a row of raw bytes into a flat row. Result will
be a freshly allocated object, not shared with
argument.
"""
if self.bitdepth == 8:
return array('B', raw)
if self.bitdepth == 16:
raw = tostring(raw)
return array('H', struct.unpack('!%dH' % (len(raw)//2), raw))
assert self.bitdepth < 8
width = self.width
# Samples per byte
spb = 8//self.bitdepth
out = array('B')
mask = 2**self.bitdepth - 1
shifts = map(self.bitdepth.__mul__, reversed(range(spb)))
for o in raw:
out.extend(map(lambda i: mask&(o>>i), shifts))
return out[:width]
return imap(asvalues, rows) |
Convert serial format (byte stream) pixel data to flat row
flat pixel.
def serialtoflat(self, bytes, width=None):
"""Convert serial format (byte stream) pixel data to flat row
flat pixel.
"""
if self.bitdepth == 8:
return bytes
if self.bitdepth == 16:
bytes = tostring(bytes)
return array('H',
struct.unpack('!%dH' % (len(bytes)//2), bytes))
assert self.bitdepth < 8
if width is None:
width = self.width
# Samples per byte
spb = 8//self.bitdepth
out = array('B')
mask = 2**self.bitdepth - 1
shifts = map(self.bitdepth.__mul__, reversed(range(spb)))
l = width
for o in bytes:
out.extend([(mask&(o>>s)) for s in shifts][:l])
l -= spb
if l <= 0:
l = width
return out |
Iterator that undoes the effect of filtering, and yields
each row in serialised format (as a sequence of bytes).
Assumes input is straightlaced. `raw` should be an iterable
that yields the raw bytes in chunks of arbitrary size.
def iterstraight(self, raw):
"""Iterator that undoes the effect of filtering, and yields
each row in serialised format (as a sequence of bytes).
Assumes input is straightlaced. `raw` should be an iterable
that yields the raw bytes in chunks of arbitrary size.
"""
# length of row, in bytes
rb = self.row_bytes
a = array('B')
# The previous (reconstructed) scanline. None indicates first
# line of image.
recon = None
for some in raw:
a.extend(some)
while len(a) >= rb + 1:
filter_type = a[0]
scanline = a[1:rb+1]
del a[:rb+1]
recon = self.undo_filter(filter_type, scanline, recon)
yield recon
if len(a) != 0:
# :file:format We get here with a file format error:
# when the available bytes (after decompressing) do not
# pack into exact rows.
raise FormatError(
'Wrong size for decompressed IDAT chunk.')
assert len(a) == 0 |
Reads just enough of the input to determine the next
chunk's length and type, returned as a (*length*, *type*) pair
where *type* is a string. If there are no more chunks, ``None``
is returned.
def chunklentype(self):
"""Reads just enough of the input to determine the next
chunk's length and type, returned as a (*length*, *type*) pair
where *type* is a string. If there are no more chunks, ``None``
is returned.
"""
x = self.file.read(8)
if not x:
return None
if len(x) != 8:
raise FormatError(
'End of file whilst reading chunk length and type.')
length,type = struct.unpack('!I4s', x)
type = bytestostr(type)
if length > 2**31-1:
raise FormatError('Chunk %s is too large: %d.' % (type,length))
return length,type |
Read the PNG file and decode it. Returns (`width`, `height`,
`pixels`, `metadata`).
May use excessive memory.
`pixels` are returned in boxed row flat pixel format.
If the optional `lenient` argument evaluates to True,
checksum failures will raise warnings rather than exceptions.
def read(self, lenient=False):
"""
Read the PNG file and decode it. Returns (`width`, `height`,
`pixels`, `metadata`).
May use excessive memory.
`pixels` are returned in boxed row flat pixel format.
If the optional `lenient` argument evaluates to True,
checksum failures will raise warnings rather than exceptions.
"""
def iteridat():
"""Iterator that yields all the ``IDAT`` chunks as strings."""
while True:
try:
type, data = self.chunk(lenient=lenient)
except ValueError as e:
raise ChunkError(e.args[0])
if type == 'IEND':
# http://www.w3.org/TR/PNG/#11IEND
break
if type != 'IDAT':
continue
# type == 'IDAT'
# http://www.w3.org/TR/PNG/#11IDAT
if self.colormap and not self.plte:
warnings.warn("PLTE chunk is required before IDAT chunk")
yield data
def iterdecomp(idat):
"""Iterator that yields decompressed strings. `idat` should
be an iterator that yields the ``IDAT`` chunk data.
"""
# Currently, with no max_length parameter to decompress,
# this routine will do one yield per IDAT chunk: Not very
# incremental.
d = zlib.decompressobj()
# Each IDAT chunk is passed to the decompressor, then any
# remaining state is decompressed out.
for data in idat:
# :todo: add a max_length argument here to limit output
# size.
yield array('B', d.decompress(data))
yield array('B', d.flush())
self.preamble(lenient=lenient)
raw = iterdecomp(iteridat())
if self.interlace:
raw = array('B', itertools.chain(*raw))
arraycode = 'BH'[self.bitdepth>8]
# Like :meth:`group` but producing an array.array object for
# each row.
pixels = imap(lambda *row: array(arraycode, row),
*[iter(self.deinterlace(raw))]*self.width*self.planes)
else:
pixels = self.iterboxed(self.iterstraight(raw))
meta = dict()
for attr in 'greyscale alpha planes bitdepth interlace'.split():
meta[attr] = getattr(self, attr)
meta['size'] = (self.width, self.height)
for attr in 'gamma transparent background'.split():
a = getattr(self, attr, None)
if a is not None:
meta[attr] = a
if self.plte:
meta['palette'] = self.palette()
return self.width, self.height, pixels, meta |
Returns a palette that is a sequence of 3-tuples or 4-tuples,
synthesizing it from the ``PLTE`` and ``tRNS`` chunks. These
chunks should have already been processed (for example, by
calling the :meth:`preamble` method). All the tuples are the
same size: 3-tuples if there is no ``tRNS`` chunk, 4-tuples when
there is a ``tRNS`` chunk. Assumes that the image is colour type
3 and therefore a ``PLTE`` chunk is required.
If the `alpha` argument is ``'force'`` then an alpha channel is
always added, forcing the result to be a sequence of 4-tuples.
def palette(self, alpha='natural'):
"""Returns a palette that is a sequence of 3-tuples or 4-tuples,
synthesizing it from the ``PLTE`` and ``tRNS`` chunks. These
chunks should have already been processed (for example, by
calling the :meth:`preamble` method). All the tuples are the
same size: 3-tuples if there is no ``tRNS`` chunk, 4-tuples when
there is a ``tRNS`` chunk. Assumes that the image is colour type
3 and therefore a ``PLTE`` chunk is required.
If the `alpha` argument is ``'force'`` then an alpha channel is
always added, forcing the result to be a sequence of 4-tuples.
"""
if not self.plte:
raise FormatError(
"Required PLTE chunk is missing in colour type 3 image.")
plte = group(array('B', self.plte), 3)
if self.trns or alpha == 'force':
trns = array('B', self.trns or '')
trns.extend([255]*(len(plte)-len(trns)))
plte = map(operator.add, plte, group(trns, 1))
return plte |
Returns the image data as a direct representation of an
``x * y * planes`` array. This method is intended to remove the
need for callers to deal with palettes and transparency
themselves. Images with a palette (colour type 3)
are converted to RGB or RGBA; images with transparency (a
``tRNS`` chunk) are converted to LA or RGBA as appropriate.
When returned in this format the pixel values represent the
colour value directly without needing to refer to palettes or
transparency information.
Like the :meth:`read` method this method returns a 4-tuple:
(*width*, *height*, *pixels*, *meta*)
This method normally returns pixel values with the bit depth
they have in the source image, but when the source PNG has an
``sBIT`` chunk it is inspected and can reduce the bit depth of
the result pixels; pixel values will be reduced according to
the bit depth specified in the ``sBIT`` chunk (PNG nerds should
note a single result bit depth is used for all channels; the
maximum of the ones specified in the ``sBIT`` chunk. An RGB565
image will be rescaled to 6-bit RGB666).
The *meta* dictionary that is returned reflects the `direct`
format and not the original source image. For example, an RGB
source image with a ``tRNS`` chunk to represent a transparent
colour, will have ``planes=3`` and ``alpha=False`` for the
source image, but the *meta* dictionary returned by this method
will have ``planes=4`` and ``alpha=True`` because an alpha
channel is synthesized and added.
*pixels* is the pixel data in boxed row flat pixel format (just
like the :meth:`read` method).
All the other aspects of the image data are not changed.
def asDirect(self):
"""Returns the image data as a direct representation of an
``x * y * planes`` array. This method is intended to remove the
need for callers to deal with palettes and transparency
themselves. Images with a palette (colour type 3)
are converted to RGB or RGBA; images with transparency (a
``tRNS`` chunk) are converted to LA or RGBA as appropriate.
When returned in this format the pixel values represent the
colour value directly without needing to refer to palettes or
transparency information.
Like the :meth:`read` method this method returns a 4-tuple:
(*width*, *height*, *pixels*, *meta*)
This method normally returns pixel values with the bit depth
they have in the source image, but when the source PNG has an
``sBIT`` chunk it is inspected and can reduce the bit depth of
the result pixels; pixel values will be reduced according to
the bit depth specified in the ``sBIT`` chunk (PNG nerds should
note a single result bit depth is used for all channels; the
maximum of the ones specified in the ``sBIT`` chunk. An RGB565
image will be rescaled to 6-bit RGB666).
The *meta* dictionary that is returned reflects the `direct`
format and not the original source image. For example, an RGB
source image with a ``tRNS`` chunk to represent a transparent
colour, will have ``planes=3`` and ``alpha=False`` for the
source image, but the *meta* dictionary returned by this method
will have ``planes=4`` and ``alpha=True`` because an alpha
channel is synthesized and added.
*pixels* is the pixel data in boxed row flat pixel format (just
like the :meth:`read` method).
All the other aspects of the image data are not changed.
"""
self.preamble()
# Simple case, no conversion necessary.
if not self.colormap and not self.trns and not self.sbit:
return self.read()
x,y,pixels,meta = self.read()
if self.colormap:
meta['colormap'] = False
meta['alpha'] = bool(self.trns)
meta['bitdepth'] = 8
meta['planes'] = 3 + bool(self.trns)
plte = self.palette()
def iterpal(pixels):
for row in pixels:
row = map(plte.__getitem__, row)
yield array('B', itertools.chain(*row))
pixels = iterpal(pixels)
elif self.trns:
# It would be nice if there was some reasonable way
# of doing this without generating a whole load of
# intermediate tuples. But tuples does seem like the
# easiest way, with no other way clearly much simpler or
# much faster. (Actually, the L to LA conversion could
# perhaps go faster (all those 1-tuples!), but I still
# wonder whether the code proliferation is worth it)
it = self.transparent
maxval = 2**meta['bitdepth']-1
planes = meta['planes']
meta['alpha'] = True
meta['planes'] += 1
typecode = 'BH'[meta['bitdepth']>8]
def itertrns(pixels):
for row in pixels:
# For each row we group it into pixels, then form a
# characterisation vector that says whether each
# pixel is opaque or not. Then we convert
# True/False to 0/maxval (by multiplication),
# and add it as the extra channel.
row = group(row, planes)
opa = map(it.__ne__, row)
opa = map(maxval.__mul__, opa)
opa = zip(opa) # convert to 1-tuples
yield array(typecode,
itertools.chain(*map(operator.add, row, opa)))
pixels = itertrns(pixels)
targetbitdepth = None
if self.sbit:
sbit = struct.unpack('%dB' % len(self.sbit), self.sbit)
targetbitdepth = max(sbit)
if targetbitdepth > meta['bitdepth']:
raise Error('sBIT chunk %r exceeds bitdepth %d' %
(sbit,self.bitdepth))
if min(sbit) <= 0:
raise Error('sBIT chunk %r has a 0-entry' % sbit)
if targetbitdepth == meta['bitdepth']:
targetbitdepth = None
if targetbitdepth:
shift = meta['bitdepth'] - targetbitdepth
meta['bitdepth'] = targetbitdepth
def itershift(pixels):
for row in pixels:
yield map(shift.__rrshift__, row)
pixels = itershift(pixels)
return x,y,pixels,meta |
Return image pixels as per :meth:`asDirect` method, but scale
all pixel values to be floating point values between 0.0 and
*maxval*.
def asFloat(self, maxval=1.0):
"""Return image pixels as per :meth:`asDirect` method, but scale
all pixel values to be floating point values between 0.0 and
*maxval*.
"""
x,y,pixels,info = self.asDirect()
sourcemaxval = 2**info['bitdepth']-1
del info['bitdepth']
info['maxval'] = float(maxval)
factor = float(maxval)/float(sourcemaxval)
def iterfloat():
for row in pixels:
yield map(factor.__mul__, row)
return x,y,iterfloat(),info |
Helper used by :meth:`asRGB8` and :meth:`asRGBA8`.
def _as_rescale(self, get, targetbitdepth):
"""Helper used by :meth:`asRGB8` and :meth:`asRGBA8`."""
width,height,pixels,meta = get()
maxval = 2**meta['bitdepth'] - 1
targetmaxval = 2**targetbitdepth - 1
factor = float(targetmaxval) / float(maxval)
meta['bitdepth'] = targetbitdepth
def iterscale():
for row in pixels:
yield map(lambda x: int(round(x*factor)), row)
if maxval == targetmaxval:
return width, height, pixels, meta
else:
return width, height, iterscale(), meta |
Return image as RGB pixels. RGB colour images are passed
through unchanged; greyscales are expanded into RGB
triplets (there is a small speed overhead for doing this).
An alpha channel in the source image will raise an
exception.
The return values are as for the :meth:`read` method
except that the *metadata* reflect the returned pixels, not the
source image. In particular, for this method
``metadata['greyscale']`` will be ``False``.
def asRGB(self):
"""Return image as RGB pixels. RGB colour images are passed
through unchanged; greyscales are expanded into RGB
triplets (there is a small speed overhead for doing this).
An alpha channel in the source image will raise an
exception.
The return values are as for the :meth:`read` method
except that the *metadata* reflect the returned pixels, not the
source image. In particular, for this method
``metadata['greyscale']`` will be ``False``.
"""
width,height,pixels,meta = self.asDirect()
if meta['alpha']:
raise Error("will not convert image with alpha channel to RGB")
if not meta['greyscale']:
return width,height,pixels,meta
meta['greyscale'] = False
typecode = 'BH'[meta['bitdepth'] > 8]
def iterrgb():
for row in pixels:
a = array(typecode, [0]) * 3 * width
for i in range(3):
a[i::3] = row
yield a
return width,height,iterrgb(),meta |
Return image as RGBA pixels. Greyscales are expanded into
RGB triplets; an alpha channel is synthesized if necessary.
The return values are as for the :meth:`read` method
except that the *metadata* reflect the returned pixels, not the
source image. In particular, for this method
``metadata['greyscale']`` will be ``False``, and
``metadata['alpha']`` will be ``True``.
def asRGBA(self):
"""Return image as RGBA pixels. Greyscales are expanded into
RGB triplets; an alpha channel is synthesized if necessary.
The return values are as for the :meth:`read` method
except that the *metadata* reflect the returned pixels, not the
source image. In particular, for this method
``metadata['greyscale']`` will be ``False``, and
``metadata['alpha']`` will be ``True``.
"""
width,height,pixels,meta = self.asDirect()
if meta['alpha'] and not meta['greyscale']:
return width,height,pixels,meta
typecode = 'BH'[meta['bitdepth'] > 8]
maxval = 2**meta['bitdepth'] - 1
maxbuffer = struct.pack('=' + typecode, maxval) * 4 * width
def newarray():
return array(typecode, maxbuffer)
if meta['alpha'] and meta['greyscale']:
# LA to RGBA
def convert():
for row in pixels:
# Create a fresh target row, then copy L channel
# into first three target channels, and A channel
# into fourth channel.
a = newarray()
pngfilters.convert_la_to_rgba(row, a)
yield a
elif meta['greyscale']:
# L to RGBA
def convert():
for row in pixels:
a = newarray()
pngfilters.convert_l_to_rgba(row, a)
yield a
else:
assert not meta['alpha'] and not meta['greyscale']
# RGB to RGBA
def convert():
for row in pixels:
a = newarray()
pngfilters.convert_rgb_to_rgba(row, a)
yield a
meta['alpha'] = True
meta['greyscale'] = False
return width,height,convert(),meta |
Get a standard vispy demo data file
Parameters
----------
fname : str
The filename on the remote ``demo-data`` repository to download,
e.g. ``'molecular_viewer/micelle.npy'``. These correspond to paths
on ``https://github.com/vispy/demo-data/``.
directory : str | None
Directory to use to save the file. By default, the vispy
configuration directory is used.
force_download : bool | str
If True, the file will be downloaded even if a local copy exists
(and this copy will be overwritten). Can also be a YYYY-MM-DD date
to ensure a file is up-to-date (modified date of a file on disk,
if present, is checked).
Returns
-------
fname : str
The path to the file on the local system.
def load_data_file(fname, directory=None, force_download=False):
"""Get a standard vispy demo data file
Parameters
----------
fname : str
The filename on the remote ``demo-data`` repository to download,
e.g. ``'molecular_viewer/micelle.npy'``. These correspond to paths
on ``https://github.com/vispy/demo-data/``.
directory : str | None
Directory to use to save the file. By default, the vispy
configuration directory is used.
force_download : bool | str
If True, the file will be downloaded even if a local copy exists
(and this copy will be overwritten). Can also be a YYYY-MM-DD date
to ensure a file is up-to-date (modified date of a file on disk,
if present, is checked).
Returns
-------
fname : str
The path to the file on the local system.
"""
_url_root = 'http://github.com/vispy/demo-data/raw/master/'
url = _url_root + fname
if directory is None:
directory = config['data_path']
if directory is None:
raise ValueError('config["data_path"] is not defined, '
'so directory must be supplied')
fname = op.join(directory, op.normcase(fname)) # convert to native
if op.isfile(fname):
if not force_download: # we're done
return fname
if isinstance(force_download, string_types):
ntime = time.strptime(force_download, '%Y-%m-%d')
ftime = time.gmtime(op.getctime(fname))
if ftime >= ntime:
return fname
else:
print('File older than %s, updating...' % force_download)
if not op.isdir(op.dirname(fname)):
os.makedirs(op.abspath(op.dirname(fname)))
# let's go get the file
_fetch_file(url, fname)
return fname |
Download a file chunk by chunk and show advancement
Can also be used when resuming downloads over http.
Parameters
----------
response: urllib.response.addinfourl
Response to the download request in order to get file size.
local_file: file
Hard disk file where data should be written.
chunk_size: integer, optional
Size of downloaded chunks. Default: 8192
initial_size: int, optional
If resuming, indicate the initial size of the file.
def _chunk_read(response, local_file, chunk_size=65536, initial_size=0):
"""Download a file chunk by chunk and show advancement
Can also be used when resuming downloads over http.
Parameters
----------
response: urllib.response.addinfourl
Response to the download request in order to get file size.
local_file: file
Hard disk file where data should be written.
chunk_size: integer, optional
Size of downloaded chunks. Default: 8192
initial_size: int, optional
If resuming, indicate the initial size of the file.
"""
# Adapted from NISL:
# https://github.com/nisl/tutorial/blob/master/nisl/datasets.py
bytes_so_far = initial_size
# Returns only amount left to download when resuming, not the size of the
# entire file
total_size = int(response.headers['Content-Length'].strip())
total_size += initial_size
progress = ProgressBar(total_size, initial_value=bytes_so_far,
max_chars=40, spinner=True, mesg='downloading')
while True:
chunk = response.read(chunk_size)
bytes_so_far += len(chunk)
if not chunk:
sys.stderr.write('\n')
break
_chunk_write(chunk, local_file, progress) |
Write a chunk to file and update the progress bar
def _chunk_write(chunk, local_file, progress):
"""Write a chunk to file and update the progress bar"""
local_file.write(chunk)
progress.update_with_increment_value(len(chunk)) |
Load requested file, downloading it if needed or requested
Parameters
----------
url: string
The url of file to be downloaded.
file_name: string
Name, along with the path, of where downloaded file will be saved.
print_destination: bool, optional
If true, destination of where file was saved will be printed after
download finishes.
def _fetch_file(url, file_name, print_destination=True):
"""Load requested file, downloading it if needed or requested
Parameters
----------
url: string
The url of file to be downloaded.
file_name: string
Name, along with the path, of where downloaded file will be saved.
print_destination: bool, optional
If true, destination of where file was saved will be printed after
download finishes.
"""
# Adapted from NISL:
# https://github.com/nisl/tutorial/blob/master/nisl/datasets.py
temp_file_name = file_name + ".part"
local_file = None
initial_size = 0
# Checking file size and displaying it alongside the download url
n_try = 3
for ii in range(n_try):
try:
data = urllib.request.urlopen(url, timeout=15.)
except Exception as e:
if ii == n_try - 1:
raise RuntimeError('Error while fetching file %s.\n'
'Dataset fetching aborted (%s)' % (url, e))
try:
file_size = int(data.headers['Content-Length'].strip())
print('Downloading data from %s (%s)' % (url, sizeof_fmt(file_size)))
local_file = open(temp_file_name, "wb")
_chunk_read(data, local_file, initial_size=initial_size)
# temp file must be closed prior to the move
if not local_file.closed:
local_file.close()
shutil.move(temp_file_name, file_name)
if print_destination is True:
sys.stdout.write('File saved as %s.\n' % file_name)
except Exception as e:
raise RuntimeError('Error while fetching file %s.\n'
'Dataset fetching aborted (%s)' % (url, e))
finally:
if local_file is not None:
if not local_file.closed:
local_file.close() |
Update progressbar with current value of process
Parameters
----------
cur_value : number
Current value of process. Should be <= max_value (but this is not
enforced). The percent of the progressbar will be computed as
(cur_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
def update(self, cur_value, mesg=None):
"""Update progressbar with current value of process
Parameters
----------
cur_value : number
Current value of process. Should be <= max_value (but this is not
enforced). The percent of the progressbar will be computed as
(cur_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
"""
# Ensure floating-point division so we can get fractions of a percent
# for the progressbar.
self.cur_value = cur_value
progress = float(self.cur_value) / self.max_value
num_chars = int(progress * self.max_chars)
num_left = self.max_chars - num_chars
# Update the message
if mesg is not None:
self.mesg = mesg
# The \r tells the cursor to return to the beginning of the line rather
# than starting a new line. This allows us to have a progressbar-style
# display in the console window.
bar = self.template.format(self.progress_character * num_chars,
' ' * num_left,
progress * 100,
self.spinner_symbols[self.spinner_index],
self.mesg)
sys.stdout.write(bar)
# Increament the spinner
if self.spinner:
self.spinner_index = (self.spinner_index + 1) % self.n_spinner
# Force a flush because sometimes when using bash scripts and pipes,
# the output is not printed until after the program exits.
sys.stdout.flush() |
Update progressbar with the value of the increment instead of the
current value of process as in update()
Parameters
----------
increment_value : int
Value of the increment of process. The percent of the progressbar
will be computed as
(self.cur_value + increment_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
def update_with_increment_value(self, increment_value, mesg=None):
"""Update progressbar with the value of the increment instead of the
current value of process as in update()
Parameters
----------
increment_value : int
Value of the increment of process. The percent of the progressbar
will be computed as
(self.cur_value + increment_value / max_value) * 100
mesg : str
Message to display to the right of the progressbar. If None, the
last message provided will be used. To clear the current message,
pass a null string, ''.
"""
self.cur_value += increment_value
self.update(self.cur_value, mesg) |
Returns the default widget that occupies the entire area of the
canvas.
def central_widget(self):
""" Returns the default widget that occupies the entire area of the
canvas.
"""
if self._central_widget is None:
self._central_widget = Widget(size=self.size, parent=self.scene)
return self._central_widget |
Render the scene to an offscreen buffer and return the image array.
Parameters
----------
region : tuple | None
Specifies the region of the canvas to render. Format is
(x, y, w, h). By default, the entire canvas is rendered.
size : tuple | None
Specifies the size of the image array to return. If no size is
given, then the size of the *region* is used, multiplied by the
pixel scaling factor of the canvas (see `pixel_scale`). This
argument allows the scene to be rendered at resolutions different
from the native canvas resolution.
bgcolor : instance of Color | None
The background color to use.
Returns
-------
image : array
Numpy array of type ubyte and shape (h, w, 4). Index [0, 0] is the
upper-left corner of the rendered region.
def render(self, region=None, size=None, bgcolor=None):
"""Render the scene to an offscreen buffer and return the image array.
Parameters
----------
region : tuple | None
Specifies the region of the canvas to render. Format is
(x, y, w, h). By default, the entire canvas is rendered.
size : tuple | None
Specifies the size of the image array to return. If no size is
given, then the size of the *region* is used, multiplied by the
pixel scaling factor of the canvas (see `pixel_scale`). This
argument allows the scene to be rendered at resolutions different
from the native canvas resolution.
bgcolor : instance of Color | None
The background color to use.
Returns
-------
image : array
Numpy array of type ubyte and shape (h, w, 4). Index [0, 0] is the
upper-left corner of the rendered region.
"""
self.set_current()
# Set up a framebuffer to render to
offset = (0, 0) if region is None else region[:2]
csize = self.size if region is None else region[2:]
s = self.pixel_scale
size = tuple([x * s for x in csize]) if size is None else size
fbo = gloo.FrameBuffer(color=gloo.RenderBuffer(size[::-1]),
depth=gloo.RenderBuffer(size[::-1]))
self.push_fbo(fbo, offset, csize)
try:
self._draw_scene(bgcolor=bgcolor)
return fbo.read()
finally:
self.pop_fbo() |
Draw a visual and its children to the canvas or currently active
framebuffer.
Parameters
----------
visual : Visual
The visual to draw
event : None or DrawEvent
Optionally specifies the original canvas draw event that initiated
this draw.
def draw_visual(self, visual, event=None):
""" Draw a visual and its children to the canvas or currently active
framebuffer.
Parameters
----------
visual : Visual
The visual to draw
event : None or DrawEvent
Optionally specifies the original canvas draw event that initiated
this draw.
"""
prof = Profiler()
# make sure this canvas's context is active
self.set_current()
try:
self._drawing = True
# get order to draw visuals
if visual not in self._draw_order:
self._draw_order[visual] = self._generate_draw_order()
order = self._draw_order[visual]
# draw (while avoiding branches with visible=False)
stack = []
invisible_node = None
for node, start in order:
if start:
stack.append(node)
if invisible_node is None:
if not node.visible:
# disable drawing until we exit this node's subtree
invisible_node = node
else:
if hasattr(node, 'draw'):
node.draw()
prof.mark(str(node))
else:
if node is invisible_node:
invisible_node = None
stack.pop()
finally:
self._drawing = False |
Return a list giving the order to draw visuals.
Each node appears twice in the list--(node, True) appears before the
node's children are drawn, and (node, False) appears after.
def _generate_draw_order(self, node=None):
"""Return a list giving the order to draw visuals.
Each node appears twice in the list--(node, True) appears before the
node's children are drawn, and (node, False) appears after.
"""
if node is None:
node = self._scene
order = [(node, True)]
children = node.children
children.sort(key=lambda ch: ch.order)
for ch in children:
order.extend(self._generate_draw_order(ch))
order.append((node, False))
return order |
Return the visual at a given position
Parameters
----------
pos : tuple
The position in logical coordinates to query.
Returns
-------
visual : instance of Visual | None
The visual at the position, if it exists.
def visual_at(self, pos):
"""Return the visual at a given position
Parameters
----------
pos : tuple
The position in logical coordinates to query.
Returns
-------
visual : instance of Visual | None
The visual at the position, if it exists.
"""
tr = self.transforms.get_transform('canvas', 'framebuffer')
fbpos = tr.map(pos)[:2]
try:
id_ = self._render_picking(region=(fbpos[0], fbpos[1],
1, 1))
vis = VisualNode._visual_ids.get(id_[0, 0], None)
except RuntimeError:
# Don't have read_pixels() support for IPython. Fall back to
# bounds checking.
return self._visual_bounds_at(pos)
return vis |
Find a visual whose bounding rect encompasses *pos*.
def _visual_bounds_at(self, pos, node=None):
"""Find a visual whose bounding rect encompasses *pos*.
"""
if node is None:
node = self.scene
for ch in node.children:
hit = self._visual_bounds_at(pos, ch)
if hit is not None:
return hit
if (not isinstance(node, VisualNode) or not node.visible or
not node.interactive):
return None
bounds = [node.bounds(axis=i) for i in range(2)]
if None in bounds:
return None
tr = self.scene.node_transform(node).inverse
corners = np.array([
[bounds[0][0], bounds[1][0]],
[bounds[0][0], bounds[1][1]],
[bounds[0][1], bounds[1][0]],
[bounds[0][1], bounds[1][1]]])
bounds = tr.map(corners)
xhit = bounds[:, 0].min() < pos[0] < bounds[:, 0].max()
yhit = bounds[:, 1].min() < pos[1] < bounds[:, 1].max()
if xhit and yhit:
return node |
Return a list of visuals within *radius* pixels of *pos*.
Visuals are sorted by their proximity to *pos*.
Parameters
----------
pos : tuple
(x, y) position at which to find visuals.
radius : int
Distance away from *pos* to search for visuals.
def visuals_at(self, pos, radius=10):
"""Return a list of visuals within *radius* pixels of *pos*.
Visuals are sorted by their proximity to *pos*.
Parameters
----------
pos : tuple
(x, y) position at which to find visuals.
radius : int
Distance away from *pos* to search for visuals.
"""
tr = self.transforms.get_transform('canvas', 'framebuffer')
pos = tr.map(pos)[:2]
id = self._render_picking(region=(pos[0]-radius, pos[1]-radius,
radius * 2 + 1, radius * 2 + 1))
ids = []
seen = set()
for i in range(radius):
subr = id[radius-i:radius+i+1, radius-i:radius+i+1]
subr_ids = set(list(np.unique(subr)))
ids.extend(list(subr_ids - seen))
seen |= subr_ids
visuals = [VisualNode._visual_ids.get(x, None) for x in ids]
return [v for v in visuals if v is not None] |
Render the scene in picking mode, returning a 2D array of visual
IDs.
def _render_picking(self, **kwargs):
"""Render the scene in picking mode, returning a 2D array of visual
IDs.
"""
try:
self._scene.picking = True
img = self.render(bgcolor=(0, 0, 0, 0), **kwargs)
finally:
self._scene.picking = False
img = img.astype('int32') * [2**0, 2**8, 2**16, 2**24]
id_ = img.sum(axis=2).astype('int32')
return id_ |
Resize handler
Parameters
----------
event : instance of Event
The resize event.
def on_resize(self, event):
"""Resize handler
Parameters
----------
event : instance of Event
The resize event.
"""
self._update_transforms()
if self._central_widget is not None:
self._central_widget.size = self.size
if len(self._vp_stack) == 0:
self.context.set_viewport(0, 0, *self.physical_size) |
Close event handler
Parameters
----------
event : instance of Event
The event.
def on_close(self, event):
"""Close event handler
Parameters
----------
event : instance of Event
The event.
"""
self.events.mouse_press.disconnect(self._process_mouse_event)
self.events.mouse_move.disconnect(self._process_mouse_event)
self.events.mouse_release.disconnect(self._process_mouse_event)
self.events.mouse_wheel.disconnect(self._process_mouse_event) |
Push a viewport (x, y, w, h) on the stack. Values must be integers
relative to the active framebuffer.
Parameters
----------
viewport : tuple
The viewport as (x, y, w, h).
def push_viewport(self, viewport):
""" Push a viewport (x, y, w, h) on the stack. Values must be integers
relative to the active framebuffer.
Parameters
----------
viewport : tuple
The viewport as (x, y, w, h).
"""
vp = list(viewport)
# Normalize viewport before setting;
if vp[2] < 0:
vp[0] += vp[2]
vp[2] *= -1
if vp[3] < 0:
vp[1] += vp[3]
vp[3] *= -1
self._vp_stack.append(vp)
try:
self.context.set_viewport(*vp)
except:
self._vp_stack.pop()
raise
self._update_transforms() |
Pop a viewport from the stack.
def pop_viewport(self):
""" Pop a viewport from the stack.
"""
vp = self._vp_stack.pop()
# Activate latest
if len(self._vp_stack) > 0:
self.context.set_viewport(*self._vp_stack[-1])
else:
self.context.set_viewport(0, 0, *self.physical_size)
self._update_transforms()
return vp |
Push an FBO on the stack.
This activates the framebuffer and causes subsequent rendering to be
written to the framebuffer rather than the canvas's back buffer. This
will also set the canvas viewport to cover the boundaries of the
framebuffer.
Parameters
----------
fbo : instance of FrameBuffer
The framebuffer object .
offset : tuple
The location of the fbo origin relative to the canvas's framebuffer
origin.
csize : tuple
The size of the region in the canvas's framebuffer that should be
covered by this framebuffer object.
def push_fbo(self, fbo, offset, csize):
""" Push an FBO on the stack.
This activates the framebuffer and causes subsequent rendering to be
written to the framebuffer rather than the canvas's back buffer. This
will also set the canvas viewport to cover the boundaries of the
framebuffer.
Parameters
----------
fbo : instance of FrameBuffer
The framebuffer object .
offset : tuple
The location of the fbo origin relative to the canvas's framebuffer
origin.
csize : tuple
The size of the region in the canvas's framebuffer that should be
covered by this framebuffer object.
"""
self._fb_stack.append((fbo, offset, csize))
try:
fbo.activate()
h, w = fbo.color_buffer.shape[:2]
self.push_viewport((0, 0, w, h))
except Exception:
self._fb_stack.pop()
raise
self._update_transforms() |
Pop an FBO from the stack.
def pop_fbo(self):
""" Pop an FBO from the stack.
"""
fbo = self._fb_stack.pop()
fbo[0].deactivate()
self.pop_viewport()
if len(self._fb_stack) > 0:
old_fbo = self._fb_stack[-1]
old_fbo[0].activate()
self._update_transforms()
return fbo |
Update the canvas's TransformSystem to correct for the current
canvas size, framebuffer, and viewport.
def _update_transforms(self):
"""Update the canvas's TransformSystem to correct for the current
canvas size, framebuffer, and viewport.
"""
if len(self._fb_stack) == 0:
fb_size = fb_rect = None
else:
fb, origin, fb_size = self._fb_stack[-1]
fb_rect = origin + fb_size
if len(self._vp_stack) == 0:
viewport = None
else:
viewport = self._vp_stack[-1]
self.transforms.configure(viewport=viewport, fbo_size=fb_size,
fbo_rect=fb_rect) |
Texture wrapping mode
def wrapping(self):
""" Texture wrapping mode """
value = self._wrapping
return value[0] if all([v == value[0] for v in value]) else value |
Set the texture size and format
Parameters
----------
shape : tuple of integers
New texture shape in zyx order. Optionally, an extra dimention
may be specified to indicate the number of color channels.
format : str | enum | None
The format of the texture: 'luminance', 'alpha',
'luminance_alpha', 'rgb', or 'rgba'. If not given the format
is chosen automatically based on the number of channels.
When the data has one channel, 'luminance' is assumed.
internalformat : str | enum | None
The internal (storage) format of the texture: 'luminance',
'alpha', 'r8', 'r16', 'r16f', 'r32f'; 'luminance_alpha',
'rg8', 'rg16', 'rg16f', 'rg32f'; 'rgb', 'rgb8', 'rgb16',
'rgb16f', 'rgb32f'; 'rgba', 'rgba8', 'rgba16', 'rgba16f',
'rgba32f'. If None, the internalformat is chosen
automatically based on the number of channels. This is a
hint which may be ignored by the OpenGL implementation.
def resize(self, shape, format=None, internalformat=None):
"""Set the texture size and format
Parameters
----------
shape : tuple of integers
New texture shape in zyx order. Optionally, an extra dimention
may be specified to indicate the number of color channels.
format : str | enum | None
The format of the texture: 'luminance', 'alpha',
'luminance_alpha', 'rgb', or 'rgba'. If not given the format
is chosen automatically based on the number of channels.
When the data has one channel, 'luminance' is assumed.
internalformat : str | enum | None
The internal (storage) format of the texture: 'luminance',
'alpha', 'r8', 'r16', 'r16f', 'r32f'; 'luminance_alpha',
'rg8', 'rg16', 'rg16f', 'rg32f'; 'rgb', 'rgb8', 'rgb16',
'rgb16f', 'rgb32f'; 'rgba', 'rgba8', 'rgba16', 'rgba16f',
'rgba32f'. If None, the internalformat is chosen
automatically based on the number of channels. This is a
hint which may be ignored by the OpenGL implementation.
"""
return self._resize(shape, format, internalformat) |
Internal method for resize.
def _resize(self, shape, format=None, internalformat=None):
"""Internal method for resize.
"""
shape = self._normalize_shape(shape)
# Check
if not self._resizable:
raise RuntimeError("Texture is not resizable")
# Determine format
if format is None:
format = self._formats[shape[-1]]
# Keep current format if channels match
if self._format and \
self._inv_formats[self._format] == self._inv_formats[format]:
format = self._format
else:
format = check_enum(format)
if internalformat is None:
# Keep current internalformat if channels match
if self._internalformat and \
self._inv_internalformats[self._internalformat] == shape[-1]:
internalformat = self._internalformat
else:
internalformat = check_enum(internalformat)
# Check
if format not in self._inv_formats:
raise ValueError('Invalid texture format: %r.' % format)
elif shape[-1] != self._inv_formats[format]:
raise ValueError('Format does not match with given shape. '
'(format expects %d elements, data has %d)' %
(self._inv_formats[format], shape[-1]))
if internalformat is None:
pass
elif internalformat not in self._inv_internalformats:
raise ValueError(
'Invalid texture internalformat: %r. Allowed formats: %r'
% (internalformat, self._inv_internalformats)
)
elif shape[-1] != self._inv_internalformats[internalformat]:
raise ValueError('Internalformat does not match with given shape.')
# Store and send GLIR command
self._shape = shape
self._format = format
self._internalformat = internalformat
self._glir.command('SIZE', self._id, self._shape, self._format,
self._internalformat) |
Set texture data
Parameters
----------
data : ndarray
Data to be uploaded
offset: int | tuple of ints
Offset in texture where to start copying data
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory. Asking explicitly
for a copy will prevent this behavior.
Notes
-----
This operation implicitely resizes the texture to the shape of
the data if given offset is None.
def set_data(self, data, offset=None, copy=False):
"""Set texture data
Parameters
----------
data : ndarray
Data to be uploaded
offset: int | tuple of ints
Offset in texture where to start copying data
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory. Asking explicitly
for a copy will prevent this behavior.
Notes
-----
This operation implicitely resizes the texture to the shape of
the data if given offset is None.
"""
return self._set_data(data, offset, copy) |
Internal method for set_data.
def _set_data(self, data, offset=None, copy=False):
"""Internal method for set_data.
"""
# Copy if needed, check/normalize shape
data = np.array(data, copy=copy)
data = self._normalize_shape(data)
# Maybe resize to purge DATA commands?
if offset is None:
self._resize(data.shape)
elif all([i == 0 for i in offset]) and data.shape == self._shape:
self._resize(data.shape)
# Convert offset to something usable
offset = offset or tuple([0 for i in range(self._ndim)])
assert len(offset) == self._ndim
# Check if data fits
for i in range(len(data.shape)-1):
if offset[i] + data.shape[i] > self._shape[i]:
raise ValueError("Data is too large")
# Send GLIR command
self._glir.command('DATA', self._id, offset, data) |
Set texture data
Parameters
----------
data : ndarray
Data to be uploaded
offset: int | tuple of ints
Offset in texture where to start copying data
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory. Asking explicitly
for a copy will prevent this behavior.
Notes
-----
This operation implicitely resizes the texture to the shape of
the data if given offset is None.
def set_data(self, data, offset=None, copy=False):
"""Set texture data
Parameters
----------
data : ndarray
Data to be uploaded
offset: int | tuple of ints
Offset in texture where to start copying data
copy: bool
Since the operation is deferred, data may change before
data is actually uploaded to GPU memory. Asking explicitly
for a copy will prevent this behavior.
Notes
-----
This operation implicitely resizes the texture to the shape of
the data if given offset is None.
"""
self._set_emulated_shape(data)
Texture2D.set_data(self, self._normalize_emulated_shape(data),
offset, copy)
self._update_variables() |
Set the texture size and format
Parameters
----------
shape : tuple of integers
New texture shape in zyx order. Optionally, an extra dimention
may be specified to indicate the number of color channels.
format : str | enum | None
The format of the texture: 'luminance', 'alpha',
'luminance_alpha', 'rgb', or 'rgba'. If not given the format
is chosen automatically based on the number of channels.
When the data has one channel, 'luminance' is assumed.
internalformat : str | enum | None
The internal (storage) format of the texture: 'luminance',
'alpha', 'r8', 'r16', 'r16f', 'r32f'; 'luminance_alpha',
'rg8', 'rg16', 'rg16f', 'rg32f'; 'rgb', 'rgb8', 'rgb16',
'rgb16f', 'rgb32f'; 'rgba', 'rgba8', 'rgba16', 'rgba16f',
'rgba32f'. If None, the internalformat is chosen
automatically based on the number of channels. This is a
hint which may be ignored by the OpenGL implementation.
def resize(self, shape, format=None, internalformat=None):
"""Set the texture size and format
Parameters
----------
shape : tuple of integers
New texture shape in zyx order. Optionally, an extra dimention
may be specified to indicate the number of color channels.
format : str | enum | None
The format of the texture: 'luminance', 'alpha',
'luminance_alpha', 'rgb', or 'rgba'. If not given the format
is chosen automatically based on the number of channels.
When the data has one channel, 'luminance' is assumed.
internalformat : str | enum | None
The internal (storage) format of the texture: 'luminance',
'alpha', 'r8', 'r16', 'r16f', 'r32f'; 'luminance_alpha',
'rg8', 'rg16', 'rg16f', 'rg32f'; 'rgb', 'rgb8', 'rgb16',
'rgb16f', 'rgb32f'; 'rgba', 'rgba8', 'rgba16', 'rgba16f',
'rgba32f'. If None, the internalformat is chosen
automatically based on the number of channels. This is a
hint which may be ignored by the OpenGL implementation.
"""
self._set_emulated_shape(shape)
Texture2D.resize(self, self._normalize_emulated_shape(shape),
format, internalformat)
self._update_variables() |
Get a free region of given size and allocate it
Parameters
----------
width : int
Width of region to allocate
height : int
Height of region to allocate
Returns
-------
bounds : tuple | None
A newly allocated region as (x, y, w, h) or None
(if failed).
def get_free_region(self, width, height):
"""Get a free region of given size and allocate it
Parameters
----------
width : int
Width of region to allocate
height : int
Height of region to allocate
Returns
-------
bounds : tuple | None
A newly allocated region as (x, y, w, h) or None
(if failed).
"""
best_height = best_width = np.inf
best_index = -1
for i in range(len(self._atlas_nodes)):
y = self._fit(i, width, height)
if y >= 0:
node = self._atlas_nodes[i]
if (y+height < best_height or
(y+height == best_height and node[2] < best_width)):
best_height = y+height
best_index = i
best_width = node[2]
region = node[0], y, width, height
if best_index == -1:
return None
node = region[0], region[1] + height, width
self._atlas_nodes.insert(best_index, node)
i = best_index+1
while i < len(self._atlas_nodes):
node = self._atlas_nodes[i]
prev_node = self._atlas_nodes[i-1]
if node[0] < prev_node[0]+prev_node[2]:
shrink = prev_node[0]+prev_node[2] - node[0]
x, y, w = self._atlas_nodes[i]
self._atlas_nodes[i] = x+shrink, y, w-shrink
if self._atlas_nodes[i][2] <= 0:
del self._atlas_nodes[i]
i -= 1
else:
break
else:
break
i += 1
# Merge nodes
i = 0
while i < len(self._atlas_nodes)-1:
node = self._atlas_nodes[i]
next_node = self._atlas_nodes[i+1]
if node[1] == next_node[1]:
self._atlas_nodes[i] = node[0], node[1], node[2]+next_node[2]
del self._atlas_nodes[i+1]
else:
i += 1
return region |
Test if region (width, height) fit into self._atlas_nodes[index]
def _fit(self, index, width, height):
"""Test if region (width, height) fit into self._atlas_nodes[index]"""
node = self._atlas_nodes[index]
x, y = node[0], node[1]
width_left = width
if x+width > self._shape[1]:
return -1
i = index
while width_left > 0:
node = self._atlas_nodes[i]
y = max(y, node[1])
if y+height > self._shape[0]:
return -1
width_left -= node[2]
i += 1
return y |
Convert an object to either a scalar or a row or column vector.
def _vector_or_scalar(x, type='row'):
"""Convert an object to either a scalar or a row or column vector."""
if isinstance(x, (list, tuple)):
x = np.array(x)
if isinstance(x, np.ndarray):
assert x.ndim == 1
if type == 'column':
x = x[:, None]
return x |
Convert an object to a row or column vector.
def _vector(x, type='row'):
"""Convert an object to a row or column vector."""
if isinstance(x, (list, tuple)):
x = np.array(x, dtype=np.float32)
elif not isinstance(x, np.ndarray):
x = np.array([x], dtype=np.float32)
assert x.ndim == 1
if type == 'column':
x = x[:, None]
return x |
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