def load_workspace_from_file(self,
filename,
remember_uri=True,
default_content=None):
if remember_uri:
self.last_workspace_uri = pycam.Utils.URIHandler(filename)
self.settings.get("set_last_filename")(filename)
log.info("Loading workspace from file: %s", filename)
try:
with open_file_context(filename, "r", True) as in_file:
content = in_file.read()
except OSError as exc:
if default_content:
content = default_content
else:
log.error("Failed to read workspace file (%s): %s", filename,
exc)
return False
try:
return self.load_workspace_from_description(content)
except PycamBaseException as exc:
log.warning(
"Failed to load workspace description from file (%s): %s",
filename, exc)
if default_content:
log.info("Falling back to default workspace due to load error")
self.load_workspace_from_description(default_content)
return False
def run(self):
if self._is_running:
log.warning(
"Refusing to run main loop again, while we are running")
return
self._is_running = True
try:
self._gtk.main()
except KeyboardInterrupt:
pass
self._is_running = False
def get_mainloop(use_gtk=False):
"""create new or return an existing mainloop
@param use_gtk: supply Gtk with timeslots for event handling (active if this parameter is True
at least once)
"""
try:
mainloop = __mainloop[0]
except IndexError:
try:
mainloop = GtkMainLoop()
except ImportError:
log.warning("No event loop is available")
mainloop = None
__mainloop.append(mainloop)
return mainloop
def load_preferences(self):
""" load all settings (see Preferences window) from a file in the user's home directory """
config = ConfigParser()
try:
with pycam.Gui.Settings.open_preferences_file() as in_file:
config.read_file(in_file)
except FileNotFoundError as exc:
log.info(
"No preferences file found (%s). Starting with default preferences.",
exc)
except OSError as exc:
log.error("Failed to read preferences: %s", exc)
return
# report any ignored (obsolete) preference keys present in the file
for item, value in config.items("DEFAULT"):
if item not in PREFERENCES_DEFAULTS.keys():
log.warn("Skipping obsolete preference item: %s", str(item))
for item in PREFERENCES_DEFAULTS:
if not config.has_option("DEFAULT", item):
# a new preference setting is missing in the (old) file
continue
value_json = config.get("DEFAULT", item)
try:
value = json.loads(value_json)
except ValueError as exc:
log.warning("Failed to parse configuration setting '%s': %s",
item, exc)
value = PREFERENCES_DEFAULTS[item]
wanted_type = type(PREFERENCES_DEFAULTS[item])
if wanted_type is float:
# int is accepted for floats, too
wanted_type = (float, int)
if not isinstance(value, wanted_type):
log.warning(
"Falling back to default configuration setting for '%s' due to "
"an invalid value type being parsed: %s != %s", item,
type(value), wanted_type)
value = PREFERENCES_DEFAULTS[item]
self.settings.set(item, value)
def generate_toolpath(model, tool_settings=None,
bounds=None, direction="x",
path_generator="DropCutter", path_postprocessor="ZigZagCutter",
material_allowance=0, overlap_percent=0, step_down=0, engrave_offset=0,
milling_style="ignore", pocketing_type="none",
support_grid_type=None, support_grid_distance_x=None,
support_grid_distance_y=None, support_grid_thickness=None,
support_grid_height=None, support_grid_offset_x=None,
support_grid_offset_y=None, support_grid_adjustments_x=None,
support_grid_adjustments_y=None, support_grid_average_distance=None,
support_grid_minimum_bridges=None, support_grid_length=None,
calculation_backend=None, callback=None):
""" abstract interface for generating a toolpath
@type model: pycam.Geometry.Model.Model
@value model: a model contains surface triangles or a contour
@type tool_settings: dict
@value tool_settings: contains at least the following keys (depending on
the tool type):
"shape": any of possible cutter shape (see "pycam.Cutters")
"tool_radius": main radius of the tools
"torus_radius": (only for ToroidalCutter) second toroidal radius
@type bounds_low: tuple(float) | list(float)
@value bounds_low: the lower processing boundary (used for the center of
the tool) (order: minx, miny, minz)
@type bounds_high: tuple(float) | list(float)
@value bounds_high: the lower processing boundary (used for the center of
the tool) (order: maxx, maxy, maxz)
@type direction: str
@value direction: any member of the DIRECTIONS set (e.g. "x", "y" or "xy")
@type path_generator: str
@value path_generator: any member of the PATH_GENERATORS set
@type path_postprocessor: str
@value path_postprocessor: any member of the PATH_POSTPROCESSORS set
@type material_allowance: float
@value material_allowance: the minimum distance between the tool and the model
@type overlap_percent: int
@value overlap_percent: the overlap between two adjacent tool paths (0..100) given in percent
@type step_down: float
@value step_down: maximum height of each layer (for PushCutter)
@type engrave_offset: float
@value engrave_offset: toolpath distance to the contour model
@type support_grid_distance_x: float
@value support_grid_distance_x: distance between support grid lines along x
@type support_grid_distance_y: float
@value support_grid_distance_y: distance between support grid lines along y
@type support_grid_thickness: float
@value support_grid_thickness: thickness of the support grid
@type support_grid_height: float
@value support_grid_height: height of the support grid
@type support_grid_offset_x: float
@value support_grid_offset_x: shift the support grid by this value along x
@type support_grid_offset_y: float
@value support_grid_offset_y: shift the support grid by this value along y
@type support_grid_adjustments_x: list(float)
@value support_grid_adjustments_x: manual adjustment of each x-grid bar
@type support_grid_adjustments_y: list(float)
@value support_grid_adjustments_y: manual adjustment of each y-grid bar
@type calculation_backend: str | None
@value calculation_backend: any member of the CALCULATION_BACKENDS set
The default is the triangular collision detection.
@rtype: pycam.Toolpath.Toolpath | str
@return: the resulting toolpath object or an error string in case of invalid
arguments
"""
log.debug("Starting toolpath generation")
step_down = number(step_down)
engrave_offset = number(engrave_offset)
if bounds is None:
# no bounds were given - we use the boundaries of the model
bounds = pycam.Toolpath.Bounds(pycam.Toolpath.Bounds.TYPE_CUSTOM,
(model.minx, model.miny, model.minz),
(model.maxx, model.maxy, model.maxz))
bounds_low, bounds_high = bounds.get_absolute_limits()
minx, miny, minz = [number(value) for value in bounds_low]
maxx, maxy, maxz = [number(value) for value in bounds_high]
# trimesh model or contour model?
if isinstance(model, pycam.Geometry.Model.ContourModel):
# contour model
trimesh_models = []
contour_model = model
else:
# trimesh model
trimesh_models = [model]
contour_model = None
# Due to some weirdness the height of the drill must be bigger than the
# object's size. Otherwise some collisions are not detected.
cutter_height = 4 * abs(maxz - minz)
cutter = pycam.Cutters.get_tool_from_settings(tool_settings, cutter_height)
if isinstance(cutter, basestring):
return cutter
if not path_generator in ("EngraveCutter", "ContourFollow"):
# material allowance is not available for these two strategies
cutter.set_required_distance(material_allowance)
# create the grid model if requested
if (support_grid_type == "grid") \
and (((not support_grid_distance_x is None) \
or (not support_grid_distance_y is None)) \
and (not support_grid_thickness is None)):
# grid height defaults to the thickness
if support_grid_height is None:
support_grid_height = support_grid_thickness
if (support_grid_distance_x < 0) or (support_grid_distance_y < 0):
return "The distance of the support grid must be a positive value"
if not ((support_grid_distance_x > 0) or (support_grid_distance_y > 0)):
return "Both distance values for the support grid may not be " \
+ "zero at the same time"
if support_grid_thickness <= 0:
return "The thickness of the support grid must be a positive value"
if support_grid_height <= 0:
return "The height of the support grid must be a positive value"
if not callback is None:
callback(text="Preparing support grid model ...")
support_grid_model = pycam.Toolpath.SupportGrid.get_support_grid(
minx, maxx, miny, maxy, minz, support_grid_distance_x,
support_grid_distance_y, support_grid_thickness,
support_grid_height, offset_x=support_grid_offset_x,
offset_y=support_grid_offset_y,
adjustments_x=support_grid_adjustments_x,
adjustments_y=support_grid_adjustments_y)
trimesh_models.append(support_grid_model)
elif (support_grid_type in ("distributed_edges", "distributed_corners")) \
and (not support_grid_average_distance is None) \
and (not support_grid_thickness is None) \
and (not support_grid_length is None):
if support_grid_height is None:
support_grid_height = support_grid_thickness
if support_grid_minimum_bridges is None:
support_grid_minimum_bridges = 2
if support_grid_average_distance <= 0:
return "The average support grid distance must be a positive value"
if support_grid_minimum_bridges <= 0:
return "The minimum number of bridged per polygon must be a " \
+ "positive value"
if support_grid_thickness <= 0:
return "The thickness of the support grid must be a positive value"
if support_grid_height <= 0:
return "The height of the support grid must be a positive value"
if not callback is None:
callback(text="Preparing support grid model ...")
# check which model to choose
if contour_model:
model = contour_model
else:
model = trimesh_models[0]
start_at_corners = (support_grid_type == "distributed_corners")
support_grid_model = pycam.Toolpath.SupportGrid.get_support_distributed(
model, minz, support_grid_average_distance,
support_grid_minimum_bridges, support_grid_thickness,
support_grid_height, support_grid_length,
bounds, start_at_corners=start_at_corners)
trimesh_models.append(support_grid_model)
elif (not support_grid_type) or (support_grid_type == "none"):
pass
else:
return "Invalid support grid type selected: %s" % support_grid_type
# Adapt the contour_model to the engraving offset. This offset is
# considered to be part of the material_allowance.
if contour_model and (engrave_offset != 0):
if not callback is None:
callback(text="Preparing contour model with offset ...")
contour_model = contour_model.get_offset_model(engrave_offset,
callback=callback)
if not contour_model:
return "Failed to calculate offset polygons"
if not callback is None:
# reset percentage counter after the contour model calculation
callback(percent=0)
if callback(text="Checking contour model with offset for " \
+ "collisions ..."):
# quit requested
return None
progress_callback = ProgressCounter(
len(contour_model.get_polygons()), callback).increment
else:
progress_callback = None
result = contour_model.check_for_collisions(callback=progress_callback)
if result is None:
return None
elif result:
warning = "The contour model contains colliding line groups. " + \
"This can cause problems with an engraving offset.\n" + \
"A collision was detected at (%.2f, %.2f, %.2f)." % \
(result.x, result.y, result.z)
log.warning(warning)
else:
# no collisions and no user interruption
pass
# check the pocketing type
if contour_model and (pocketing_type != "none"):
if not callback is None:
callback(text="Generating pocketing polygons ...")
pocketing_offset = cutter.radius * 1.8
# TODO: this is an arbitrary limit to avoid infinite loops
pocketing_limit = 1000
base_polygons = []
other_polygons = []
if pocketing_type == "holes":
# fill polygons with negative area
for poly in contour_model.get_polygons():
if poly.is_closed and not poly.is_outer():
base_polygons.append(poly)
else:
other_polygons.append(poly)
elif pocketing_type == "enclosed":
# fill polygons with positive area
pocketing_offset *= -1
for poly in contour_model.get_polygons():
if poly.is_closed and poly.is_outer():
base_polygons.append(poly)
else:
other_polygons.append(poly)
else:
return "Unknown pocketing type given (not one of 'none', " + \
"'holes', 'enclosed'): %s" % str(pocketing_type)
# For now we use only the polygons that do not surround eny other
# polygons. Sorry - the pocketing is currently very simple ...
base_filtered_polygons = []
for candidate in base_polygons:
if callback and callback():
return "Interrupted"
for other in other_polygons:
if candidate.is_polygon_inside(other):
break
else:
base_filtered_polygons.append(candidate)
# start the pocketing for all remaining polygons
pocket_polygons = []
for base_polygon in base_filtered_polygons:
current_queue = [base_polygon]
next_queue = []
pocket_depth = 0
while current_queue and (pocket_depth < pocketing_limit):
if callback and callback():
return "Interrupted"
for poly in current_queue:
result = poly.get_offset_polygons(pocketing_offset)
pocket_polygons.extend(result)
next_queue.extend(result)
pocket_depth += 1
current_queue = next_queue
next_queue = []
# use a copy instead of the original
contour_model = contour_model.get_copy()
for pocket in pocket_polygons:
contour_model.append(pocket)
# limit the contour model to the bounding box
if contour_model:
# use minz/maxz of the contour model (in other words: ignore z)
contour_model = contour_model.get_cropped_model(minx, maxx, miny, maxy,
contour_model.minz, contour_model.maxz)
if not contour_model:
return "No part of the contour model is within the bounding box."
physics = _get_physics(trimesh_models, cutter, calculation_backend)
if isinstance(physics, basestring):
return physics
generator = _get_pathgenerator_instance(trimesh_models, contour_model,
cutter, path_generator, path_postprocessor, physics,
milling_style)
if isinstance(generator, basestring):
return generator
overlap = overlap_percent / 100.0
if (overlap < 0) or (overlap >= 1):
return "Invalid overlap value (%f): should be greater or equal 0 " \
+ "and lower than 1"
# factor "2" since we are based on radius instead of diameter
line_stepping = 2 * number(tool_settings["tool_radius"]) * (1 - overlap)
if path_generator == "PushCutter":
step_width = None
else:
# the step_width is only used for the DropCutter
step_width = tool_settings["tool_radius"] / 4
if path_generator == "DropCutter":
layer_distance = None
else:
layer_distance = step_down
direction_dict = {"x": pycam.Toolpath.MotionGrid.GRID_DIRECTION_X,
"y": pycam.Toolpath.MotionGrid.GRID_DIRECTION_Y,
"xy": pycam.Toolpath.MotionGrid.GRID_DIRECTION_XY}
milling_style_grid = {
"ignore": pycam.Toolpath.MotionGrid.MILLING_STYLE_IGNORE,
"conventional": pycam.Toolpath.MotionGrid.MILLING_STYLE_CONVENTIONAL,
"climb": pycam.Toolpath.MotionGrid.MILLING_STYLE_CLIMB}
if path_generator in ("DropCutter", "PushCutter"):
motion_grid = pycam.Toolpath.MotionGrid.get_fixed_grid(bounds,
layer_distance, line_stepping, step_width=step_width,
grid_direction=direction_dict[direction],
milling_style=milling_style_grid[milling_style])
if path_generator == "DropCutter":
toolpath = generator.GenerateToolPath(motion_grid, minz, maxz,
callback)
else:
toolpath = generator.GenerateToolPath(motion_grid, callback)
elif path_generator == "EngraveCutter":
if step_down > 0:
dz = step_down
else:
dz = maxz - minz
toolpath = generator.GenerateToolPath(minz, maxz, step_width, dz,
callback)
elif path_generator == "ContourFollow":
if step_down > 0:
dz = step_down
else:
dz = maxz - minz
if dz <= 0:
dz = 1
toolpath = generator.GenerateToolPath(minx, maxx, miny, maxy, minz,
maxz, dz, callback)
elif path_generator == "Contour2dCutter": # JULIEN
toolpath = generator.GenerateToolPath(callback)
else:
return "Invalid path generator (%s): not one of %s" \
% (path_generator, PATH_GENERATORS)
return toolpath
def generate_toolpath(model,
tool_settings=None,
bounds=None,
direction="x",
path_generator="DropCutter",
path_postprocessor="ZigZagCutter",
material_allowance=0,
overlap_percent=0,
step_down=0,
engrave_offset=0,
milling_style="ignore",
pocketing_type="none",
support_model=None,
calculation_backend=None,
callback=None):
""" abstract interface for generating a toolpath
@type model: pycam.Geometry.Model.Model
@value model: a model contains surface triangles or a contour
@type tool_settings: dict
@value tool_settings: contains at least the following keys (depending on
the tool type):
"shape": any of possible cutter shape (see "pycam.Cutters")
"tool_radius": main radius of the tools
"torus_radius": (only for ToroidalCutter) second toroidal radius
@type bounds_low: tuple(float) | list(float)
@value bounds_low: the lower processing boundary (used for the center of
the tool) (order: minx, miny, minz)
@type bounds_high: tuple(float) | list(float)
@value bounds_high: the lower processing boundary (used for the center of
the tool) (order: maxx, maxy, maxz)
@type direction: str
@value direction: any member of the DIRECTIONS set (e.g. "x", "y" or "xy")
@type path_generator: str
@value path_generator: any member of the PATH_GENERATORS set
@type path_postprocessor: str
@value path_postprocessor: any member of the PATH_POSTPROCESSORS set
@type material_allowance: float
@value material_allowance: the minimum distance between the tool and the model
@type overlap_percent: int
@value overlap_percent: the overlap between two adjacent tool paths (0..100) given in percent
@type step_down: float
@value step_down: maximum height of each layer (for PushCutter)
@type engrave_offset: float
@value engrave_offset: toolpath distance to the contour model
@type calculation_backend: str | None
@value calculation_backend: any member of the CALCULATION_BACKENDS set
The default is the triangular collision detection.
@rtype: pycam.Toolpath.Toolpath | str
@return: the resulting toolpath object or an error string in case of invalid
arguments
"""
log.debug("Starting toolpath generation")
step_down = number(step_down)
engrave_offset = number(engrave_offset)
if bounds is None:
# no bounds were given - we use the boundaries of the model
bounds = pycam.Toolpath.Bounds(pycam.Toolpath.Bounds.TYPE_CUSTOM,
(model.minx, model.miny, model.minz),
(model.maxx, model.maxy, model.maxz))
bounds_low, bounds_high = bounds.get_absolute_limits()
minx, miny, minz = [number(value) for value in bounds_low]
maxx, maxy, maxz = [number(value) for value in bounds_high]
# trimesh model or contour model?
if isinstance(model, pycam.Geometry.Model.ContourModel):
# contour model
trimesh_models = []
contour_model = model
else:
# trimesh model
trimesh_models = [model]
contour_model = None
# Due to some weirdness the height of the drill must be bigger than the
# object's size. Otherwise some collisions are not detected.
cutter_height = 4 * abs(maxz - minz)
cutter = pycam.Cutters.get_tool_from_settings(tool_settings, cutter_height)
if isinstance(cutter, basestring):
return cutter
if not path_generator in ("EngraveCutter", "ContourFollow"):
# material allowance is not available for these two strategies
cutter.set_required_distance(material_allowance)
# create the grid model if requested
if support_model:
trimesh_models.append(support_model)
# Adapt the contour_model to the engraving offset. This offset is
# considered to be part of the material_allowance.
if contour_model and (engrave_offset != 0):
if not callback is None:
callback(text="Preparing contour model with offset ...")
contour_model = contour_model.get_offset_model(engrave_offset,
callback=callback)
if contour_model:
return "Failed to calculate offset polygons"
if not callback is None:
# reset percentage counter after the contour model calculation
callback(percent=0)
if callback(text="Checking contour model with offset for " \
+ "collisions ..."):
# quit requested
return None
progress_callback = ProgressCounter(
len(contour_model.get_polygons()), callback).increment
else:
progress_callback = None
result = contour_model.check_for_collisions(callback=progress_callback)
if result is None:
return None
elif result:
warning = "The contour model contains colliding line groups. " + \
"This can cause problems with an engraving offset.\n" + \
"A collision was detected at (%.2f, %.2f, %.2f)." % \
(result.x, result.y, result.z)
log.warning(warning)
else:
# no collisions and no user interruption
pass
# check the pocketing type
if contour_model and (pocketing_type != "none"):
if not callback is None:
callback(text="Generating pocketing polygons ...")
pocketing_offset = cutter.radius * 1.8
# TODO: this is an arbitrary limit to avoid infinite loops
pocketing_limit = 1000
base_polygons = []
other_polygons = []
if pocketing_type == "holes":
# fill polygons with negative area
for poly in contour_model.get_polygons():
if poly.is_closed and not poly.is_outer():
base_polygons.append(poly)
else:
other_polygons.append(poly)
elif pocketing_type == "enclosed":
# fill polygons with positive area
pocketing_offset *= -1
for poly in contour_model.get_polygons():
if poly.is_closed and poly.is_outer():
base_polygons.append(poly)
else:
other_polygons.append(poly)
else:
return "Unknown pocketing type given (not one of 'none', " + \
"'holes', 'enclosed'): %s" % str(pocketing_type)
# For now we use only the polygons that do not surround eny other
# polygons. Sorry - the pocketing is currently very simple ...
base_filtered_polygons = []
for candidate in base_polygons:
if callback and callback():
return "Interrupted"
for other in other_polygons:
if candidate.is_polygon_inside(other):
break
else:
base_filtered_polygons.append(candidate)
# start the pocketing for all remaining polygons
pocket_polygons = []
for base_polygon in base_filtered_polygons:
current_queue = [base_polygon]
next_queue = []
pocket_depth = 0
while current_queue and (pocket_depth < pocketing_limit):
if callback and callback():
return "Interrupted"
for poly in current_queue:
result = poly.get_offset_polygons(pocketing_offset)
pocket_polygons.extend(result)
next_queue.extend(result)
pocket_depth += 1
current_queue = next_queue
next_queue = []
# use a copy instead of the original
contour_model = contour_model.get_copy()
for pocket in pocket_polygons:
contour_model.append(pocket)
# limit the contour model to the bounding box
if contour_model:
# use minz/maxz of the contour model (in other words: ignore z)
contour_model = contour_model.get_cropped_model(
minx, maxx, miny, maxy, contour_model.minz, contour_model.maxz)
if contour_model:
return "No part of the contour model is within the bounding box."
physics = _get_physics(trimesh_models, cutter, calculation_backend)
if isinstance(physics, basestring):
return physics
generator = _get_pathgenerator_instance(trimesh_models, contour_model,
cutter, path_generator,
path_postprocessor, physics,
milling_style)
if isinstance(generator, basestring):
return generator
overlap = overlap_percent / 100.0
if (overlap < 0) or (overlap >= 1):
return "Invalid overlap value (%f): should be greater or equal 0 " \
+ "and lower than 1"
# factor "2" since we are based on radius instead of diameter
line_stepping = 2 * number(tool_settings["tool_radius"]) * (1 - overlap)
if path_generator == "PushCutter":
step_width = None
else:
# the step_width is only used for the DropCutter
step_width = tool_settings["tool_radius"] / 4
if path_generator == "DropCutter":
layer_distance = None
else:
layer_distance = step_down
direction_dict = {
"x": pycam.Toolpath.MotionGrid.GRID_DIRECTION_X,
"y": pycam.Toolpath.MotionGrid.GRID_DIRECTION_Y,
"xy": pycam.Toolpath.MotionGrid.GRID_DIRECTION_XY
}
milling_style_grid = {
"ignore": pycam.Toolpath.MotionGrid.MILLING_STYLE_IGNORE,
"conventional": pycam.Toolpath.MotionGrid.MILLING_STYLE_CONVENTIONAL,
"climb": pycam.Toolpath.MotionGrid.MILLING_STYLE_CLIMB
}
if path_generator in ("DropCutter", "PushCutter"):
motion_grid = pycam.Toolpath.MotionGrid.get_fixed_grid(
(bounds_low, bounds_high),
layer_distance,
line_stepping,
step_width=step_width,
grid_direction=direction_dict[direction],
milling_style=milling_style_grid[milling_style])
if path_generator == "DropCutter":
toolpath = generator.GenerateToolPath(motion_grid, minz, maxz,
callback)
else:
toolpath = generator.GenerateToolPath(motion_grid, callback)
elif path_generator == "EngraveCutter":
if step_down > 0:
dz = step_down
else:
dz = maxz - minz
toolpath = generator.GenerateToolPath(minz, maxz, step_width, dz,
callback)
elif path_generator == "ContourFollow":
if step_down > 0:
dz = step_down
else:
dz = maxz - minz
if dz <= 0:
dz = 1
toolpath = generator.GenerateToolPath(minx, maxx, miny, maxy, minz,
maxz, dz, callback)
else:
return "Invalid path generator (%s): not one of %s" \
% (path_generator, PATH_GENERATORS)
return toolpath
def init_threading(number_of_processes=None,
enable_server=False,
remote=None,
run_server=False,
server_credentials="",
local_port=DEFAULT_PORT):
global __multiprocessing, __num_of_processes, __manager, __closing, \
__task_source_uuid
if __multiprocessing:
# kill the manager and clean everything up for a re-initialization
cleanup()
if (not is_server_mode_available()) and (enable_server or run_server):
# server mode is disabled for the Windows pyinstaller standalone
# due to "pickle errors". How to reproduce: run the standalone binary
# with "--enable-server --server-auth-key foo".
feature_matrix_text = "Take a look at the wiki for a matrix of " + \
"platforms and available features: " + \
"http://sf.net/apps/mediawiki/pycam/?title=" + \
"Parallel_Processing_on_different_Platforms"
if enable_server:
log.warn("Unable to enable server mode with your current " + \
"setup.\n" + feature_matrix_text)
elif run_server:
log.warn("Unable to run in server-only mode with the Windows " + \
"standalone executable.\n" + feature_matrix_text)
else:
# no further warnings required
pass
enable_server = False
run_server = False
# only local -> no server settings allowed
if (not enable_server) and (not run_server):
remote = None
run_server = None
server_credentials = ""
try:
import multiprocessing
mp_is_available = True
except ImportError:
mp_is_available = False
if not mp_is_available:
__multiprocessing = False
# Maybe a multiprocessing feature was explicitely requested?
# Issue some warnings if necessary.
multiprocessing_missing_text = "Failed to enable server mode due to " \
+ "a lack of 'multiprocessing' capabilities. Please use " \
+ "Python2.6 or install the 'python-multiprocessing' package."
if enable_server:
log.warn("Failed to enable server mode due to a lack of " \
+ "'multiprocessing' capabilities. " \
+ multiprocessing_missing_text)
elif run_server:
log.warn("Failed to run in server-only mode due to a lack of " \
+ "'multiprocessing' capabilities. " \
+ multiprocessing_missing_text)
else:
# no further warnings required
pass
else:
if number_of_processes is None:
# use defaults
# don't enable threading for a single cpu
if (multiprocessing.cpu_count() > 1) or remote or run_server or \
enable_server:
__multiprocessing = multiprocessing
__num_of_processes = multiprocessing.cpu_count()
else:
__multiprocessing = False
elif (number_of_processes < 1) and (remote is None) and \
(enable_server is None):
# Zero processes are allowed if we use a remote server or offer a
# server.
__multiprocessing = False
else:
__multiprocessing = multiprocessing
__num_of_processes = number_of_processes
# initialize the manager
if not __multiprocessing:
__manager = None
log.info("Disabled parallel processing")
elif not enable_server and not run_server:
__manager = None
log.info("Enabled %d parallel local processes" % __num_of_processes)
else:
# with multiprocessing
log.info("Enabled %d parallel local processes" % __num_of_processes)
log.info("Allow remote processing")
# initialize the uuid list for all workers
worker_uuid_list = [
str(uuid.uuid1()) for index in range(__num_of_processes)
]
__task_source_uuid = str(uuid.uuid1())
if remote is None:
# try to guess an appropriate interface for binding
if pycam.Utils.get_platform() == pycam.Utils.PLATFORM_WINDOWS:
# Windows does not support a wildcard interface listener
all_ips = pycam.Utils.get_all_ips()
if all_ips:
address = (all_ips[0], local_port)
log.info("Binding to local interface with IP %s" % \
str(all_ips[0]))
else:
return "Failed to find any local IP"
else:
# empty hostname -> wildcard interface
# (this does not work with Windows - see above)
address = ('', local_port)
else:
if ":" in remote:
host, port = remote.split(":", 1)
try:
port = int(port)
except ValueError:
log.warning(("Invalid port specified: '%s' - using " + \
"default port (%d) instead") % \
(port, DEFAULT_PORT))
port = DEFAULT_PORT
else:
host = remote
port = DEFAULT_PORT
address = (host, port)
if remote is None:
tasks_queue = multiprocessing.Queue()
results_queue = multiprocessing.Queue()
statistics = ProcessStatistics()
cache = ProcessDataCache()
pending_tasks = PendingTasks()
info = ManagerInfo(tasks_queue, results_queue, statistics, cache,
pending_tasks)
TaskManager.register("tasks", callable=info.get_tasks_queue)
TaskManager.register("results", callable=info.get_results_queue)
TaskManager.register("statistics", callable=info.get_statistics)
TaskManager.register("cache", callable=info.get_cache)
TaskManager.register("pending_tasks",
callable=info.get_pending_tasks)
else:
TaskManager.register("tasks")
TaskManager.register("results")
TaskManager.register("statistics")
TaskManager.register("cache")
TaskManager.register("pending_tasks")
__manager = TaskManager(address=address, authkey=server_credentials)
# run the local server, connect to a remote one or begin serving
try:
if remote is None:
__manager.start()
log.info("Started a local server.")
else:
__manager.connect()
log.info("Connected to a remote task server.")
except (multiprocessing.AuthenticationError, socket.error), err_msg:
__manager = None
return err_msg
except EOFError:
__manager = None
return "Failed to bind to socket for unknown reasons"
def init_threading(number_of_processes=None, enable_server=False, remote=None, run_server=False,
server_credentials="", local_port=DEFAULT_PORT):
global __multiprocessing, __num_of_processes, __manager, __closing, __task_source_uuid
if __multiprocessing:
# kill the manager and clean everything up for a re-initialization
cleanup()
if (not is_server_mode_available()) and (enable_server or run_server):
# server mode is disabled for the Windows pyinstaller standalone
# due to "pickle errors". How to reproduce: run the standalone binary
# with "--enable-server --server-auth-key foo".
feature_matrix_text = ("Take a look at the wiki for a matrix of platforms and available "
"features: http://pycam.sourceforge.net/parallel-processing")
if enable_server:
log.warn("Unable to enable server mode with your current setup.\n%s",
feature_matrix_text)
elif run_server:
log.warn("Unable to run in server-only mode with the Windows standalone "
"executable.\n%s", feature_matrix_text)
else:
# no further warnings required
pass
enable_server = False
run_server = False
# only local -> no server settings allowed
if (not enable_server) and (not run_server):
remote = None
run_server = None
server_credentials = ""
if not is_multiprocessing_available():
__multiprocessing = False
# Maybe a multiprocessing feature was explicitly requested?
# Issue some warnings if necessary.
multiprocessing_missing_text = (
"Failed to enable server mode due to a lack of 'multiprocessing' capabilities. Please "
"use Python2.6 or install the 'python-multiprocessing' package.")
if enable_server:
log.warn("Failed to enable server mode due to a lack of 'multiprocessing' "
"capabilities. %s", multiprocessing_missing_text)
elif run_server:
log.warn("Failed to run in server-only mode due to a lack of 'multiprocessing' "
"capabilities. %s", multiprocessing_missing_text)
else:
# no further warnings required
pass
else:
import multiprocessing
if number_of_processes is None:
# use defaults
# don't enable threading for a single cpu
if (multiprocessing.cpu_count() > 1) or remote or run_server or enable_server:
__multiprocessing = multiprocessing
__num_of_processes = multiprocessing.cpu_count()
else:
__multiprocessing = False
elif (number_of_processes < 1) and (remote is None) and (enable_server is None):
# Zero processes are allowed if we use a remote server or offer a
# server.
__multiprocessing = False
else:
__multiprocessing = multiprocessing
__num_of_processes = number_of_processes
# initialize the manager
if not __multiprocessing:
__manager = None
log.info("Disabled parallel processing")
elif not enable_server and not run_server:
__manager = None
log.info("Enabled %d parallel local processes", __num_of_processes)
else:
# with multiprocessing
log.info("Enabled %d parallel local processes", __num_of_processes)
log.info("Allow remote processing")
# initialize the uuid list for all workers
worker_uuid_list = [str(uuid.uuid1()) for index in range(__num_of_processes)]
__task_source_uuid = str(uuid.uuid1())
if remote is None:
# try to guess an appropriate interface for binding
if pycam.Utils.get_platform() == pycam.Utils.OSPlatform.WINDOWS:
# Windows does not support a wildcard interface listener
all_ips = pycam.Utils.get_all_ips()
if all_ips:
address = (all_ips[0], local_port)
log.info("Binding to local interface with IP %s", str(all_ips[0]))
else:
raise CommunicationError("Failed to find any local IP")
else:
# empty hostname -> wildcard interface
# (this does not work with Windows - see above)
address = ('', local_port)
else:
if ":" in remote:
host, port = remote.split(":", 1)
try:
port = int(port)
except ValueError:
log.warning("Invalid port specified: '%s' - using default port (%d) instead",
port, DEFAULT_PORT)
port = DEFAULT_PORT
else:
host = remote
port = DEFAULT_PORT
address = (host, port)
if remote is None:
tasks_queue = multiprocessing.Queue()
results_queue = multiprocessing.Queue()
statistics = ProcessStatistics()
cache = ProcessDataCache()
pending_tasks = PendingTasks()
info = ManagerInfo(tasks_queue, results_queue, statistics, cache, pending_tasks)
TaskManager.register("tasks", callable=info.get_tasks_queue)
TaskManager.register("results", callable=info.get_results_queue)
TaskManager.register("statistics", callable=info.get_statistics)
TaskManager.register("cache", callable=info.get_cache)
TaskManager.register("pending_tasks", callable=info.get_pending_tasks)
else:
TaskManager.register("tasks")
TaskManager.register("results")
TaskManager.register("statistics")
TaskManager.register("cache")
TaskManager.register("pending_tasks")
__manager = TaskManager(address=address, authkey=server_credentials)
# run the local server, connect to a remote one or begin serving
try:
if remote is None:
__manager.start()
log.info("Started a local server.")
else:
__manager.connect()
log.info("Connected to a remote task server.")
except (multiprocessing.AuthenticationError, socket.error) as err_msg:
__manager = None
return err_msg
except EOFError:
__manager = None
raise CommunicationError("Failed to bind to socket for unknown reasons")
# create the spawning process
__closing = __manager.Value("b", False)
if __num_of_processes > 0:
# only start the spawner, if we want to use local workers
spawner = __multiprocessing.Process(name="spawn", target=_spawn_daemon,
args=(__manager, __num_of_processes,
worker_uuid_list))
spawner.start()
else:
spawner = None
# wait forever - in case of a server
if run_server:
log.info("Running a local server and waiting for remote connections.")
# the server can be stopped via CTRL-C - it is caught later
if spawner is not None:
spawner.join()
def init_threading(number_of_processes=None, enable_server=False, remote=None,
run_server=False, server_credentials="", local_port=DEFAULT_PORT):
global __multiprocessing, __num_of_processes, __manager, __closing, \
__task_source_uuid
if __multiprocessing:
# kill the manager and clean everything up for a re-initialization
cleanup()
if (not is_server_mode_available()) and (enable_server or run_server):
# server mode is disabled for the Windows pyinstaller standalone
# due to "pickle errors". How to reproduce: run the standalone binary
# with "--enable-server --server-auth-key foo".
feature_matrix_text = "Take a look at the wiki for a matrix of " + \
"platforms and available features: " + \
"http://sf.net/apps/mediawiki/pycam/?title=" + \
"Parallel_Processing_on_different_Platforms"
if enable_server:
log.warn("Unable to enable server mode with your current " + \
"setup.\n" + feature_matrix_text)
elif run_server:
log.warn("Unable to run in server-only mode with the Windows " + \
"standalone executable.\n" + feature_matrix_text)
else:
# no further warnings required
pass
enable_server = False
run_server = False
# only local -> no server settings allowed
if (not enable_server) and (not run_server):
remote = None
run_server = None
server_credentials = ""
try:
import multiprocessing
mp_is_available = True
except ImportError:
mp_is_available = False
if not mp_is_available:
__multiprocessing = False
# Maybe a multiprocessing feature was explicitely requested?
# Issue some warnings if necessary.
multiprocessing_missing_text = "Failed to enable server mode due to " \
+ "a lack of 'multiprocessing' capabilities. Please use " \
+ "Python2.6 or install the 'python-multiprocessing' package."
if enable_server:
log.warn("Failed to enable server mode due to a lack of " \
+ "'multiprocessing' capabilities. " \
+ multiprocessing_missing_text)
elif run_server:
log.warn("Failed to run in server-only mode due to a lack of " \
+ "'multiprocessing' capabilities. " \
+ multiprocessing_missing_text)
else:
# no further warnings required
pass
else:
if number_of_processes is None:
# use defaults
# don't enable threading for a single cpu
if (multiprocessing.cpu_count() > 1) or remote or run_server or \
enable_server:
__multiprocessing = multiprocessing
__num_of_processes = multiprocessing.cpu_count()
else:
__multiprocessing = False
elif (number_of_processes < 1) and (remote is None) and \
(enable_server is None):
# Zero processes are allowed if we use a remote server or offer a
# server.
__multiprocessing = False
else:
__multiprocessing = multiprocessing
__num_of_processes = number_of_processes
# initialize the manager
if not __multiprocessing:
__manager = None
log.info("Disabled parallel processing")
elif not enable_server and not run_server:
__manager = None
log.info("Enabled %d parallel local processes" % __num_of_processes)
else:
# with multiprocessing
log.info("Enabled %d parallel local processes" % __num_of_processes)
log.info("Allow remote processing")
# initialize the uuid list for all workers
worker_uuid_list = [str(uuid.uuid1())
for index in range(__num_of_processes)]
__task_source_uuid = str(uuid.uuid1())
if remote is None:
# try to guess an appropriate interface for binding
if pycam.Utils.get_platform() == pycam.Utils.PLATFORM_WINDOWS:
# Windows does not support a wildcard interface listener
all_ips = pycam.Utils.get_all_ips()
if all_ips:
address = (all_ips[0], local_port)
log.info("Binding to local interface with IP %s" % \
str(all_ips[0]))
else:
return "Failed to find any local IP"
else:
# empty hostname -> wildcard interface
# (this does not work with Windows - see above)
address = ('', local_port)
else:
if ":" in remote:
host, port = remote.split(":", 1)
try:
port = int(port)
except ValueError:
log.warning(("Invalid port specified: '%s' - using " + \
"default port (%d) instead") % \
(port, DEFAULT_PORT))
port = DEFAULT_PORT
else:
host = remote
port = DEFAULT_PORT
address = (host, port)
if remote is None:
tasks_queue = multiprocessing.Queue()
results_queue = multiprocessing.Queue()
statistics = ProcessStatistics()
cache = ProcessDataCache()
pending_tasks = PendingTasks()
info = ManagerInfo(tasks_queue, results_queue, statistics, cache,
pending_tasks)
TaskManager.register("tasks", callable=info.get_tasks_queue)
TaskManager.register("results", callable=info.get_results_queue)
TaskManager.register("statistics", callable=info.get_statistics)
TaskManager.register("cache", callable=info.get_cache)
TaskManager.register("pending_tasks",
callable=info.get_pending_tasks)
else:
TaskManager.register("tasks")
TaskManager.register("results")
TaskManager.register("statistics")
TaskManager.register("cache")
TaskManager.register("pending_tasks")
__manager = TaskManager(address=address, authkey=server_credentials)
# run the local server, connect to a remote one or begin serving
try:
if remote is None:
__manager.start()
log.info("Started a local server.")
else:
__manager.connect()
log.info("Connected to a remote task server.")
except (multiprocessing.AuthenticationError, socket.error), err_msg:
__manager = None
return err_msg
except EOFError:
__manager = None
return "Failed to bind to socket for unknown reasons"