ezdxf "bulge" to ARC conversion - ezdxf

Here is the problem description:
parse a dxf for all the available entity's (no issues here, straight forward with ezdxf)
establish the envelope area and perimeter (done by math not via ezdxf)
establish all the contained entities area and perimeter (done by math not via ezdxf)
Option 1
parse all the entities for start/end coordinates and match the points, from there figure out the Area and Perimeter
# helper function
def print_entity(line):
print("LINE on layer: %s\n" % line.dxf.layer)
print("start point: %s\n" % line.dxf.start)
print("end point: %s\n" % line.dxf.end)
# iterate over all entities in modelspace
msp = dxf.modelspace()
for line in msp:
if line.dxftype() == "LINE":
print(line)
print_entity(line)
for arc in msp.query('ARC'):
print('ARC:')
# Get all attributes
attribs = arc.dxfattribs()
for key in arc.dxfattribs():
print(f'\t{key}: {attribs[key]}')`
Option 1 - while should work seems that is the long and problematic rout to take hence the Option 2
Option 2
parse all the LWPOLYLINE and extract (start, end, bulge)
from bulge convert to arc (start_point, end_point, start_angle, end_angle, radius, center (x,y))
No matter what I do I cant extract the bulge information's, should be easy using this available functions:
midpoint, radius, start_angle, end_angle = ezdxf.math.bulge_to_arc(bulge, start, end)
center = ezdxf.math.bulge_center(bulge, start, end)
This iterates trough all LWPOLYLINE
from ezdxf.math import bulge_to_arc, bulge_center
from ezdxf.math.bulge import signed_bulge_radius
from ezdxf.acc.vector import Vec2
import math
# Get the LWPOLYLINE entities from the DXF file
entities = dwg.entities
lwpolylines = [e for e in entities if e.dxftype() == 'LWPOLYLINE']
# Iterate over each LWPOLYLINE entity
for pline in lwpolylines:
# Get the bulge value, start and end points of each vertex in the polyline
for x, y, start_width, end_width, bulge in pline:
print("Start Point: ({}, {})".format(x, y))
print("End Point: ({}, {})".format(x, y))
print("Bulge value: ", bulge)
print("Start Width: ", start_width)
print("End Width: ", end_width)
print("\n")
This should extract all the "bulge" informations:
import ezdxf
# Load the DXF file
dwg = ezdxf.readfile("example.dxf")
# Query all LWPOLYLINE entities in the DXF file
for num, line in enumerate(dwg.query('LWPOLYLINE'), start=1):
print("\n{}. POLYLINE:".format(num))
# Get the points of the LWPOLYLINE
with line.points() as points:
for x, y, start_width, end_width, bulge in points:
if bulge:
# Convert bulge to arc information
midpoint, radius, start_angle, end_angle = ezdxf.math.bulge_to_arc(x, y, start_width, end_width, bulge)
center = ezdxf.math.bulge_center(x, y, start_width, end_width, bulge)
# Print the arc information
print("\tMidpoint:", midpoint)
print("\tRadius:", radius)
print("\tStart Angle:", start_angle)
print("\tEnd Angle:", end_angle)
print("\tCenter:", center)
This is the error I get:
/usr/local/lib/python3.8/dist-packages/ezdxf/math/bulge.py in signed_bulge_radius(start_point, end_point, bulge)
133 ) -> float:
134 return (
--> 135 Vec2(start_point).distance(Vec2(end_point))
136 * (1.0 + (bulge * bulge))
137 / 4.0
src/ezdxf/acc/vector.pyx in ezdxf.acc.vector.Vec2.__cinit__()
TypeError: object of type 'float' has no len()
How would one determine all the Areas and Perimeters available for all CLOSED entities in the DXF file?
CLOSED is defined as two variants:
a closed Polyline (LWPOLYLINE) and can have any shape (line, splines and arcs)
closed as in each point would start from the previous entity end point (exploded contour)
Is this relevant to my problem?
You can access virtual LINE and ARC entities of LWPOLYLINE entities: https://ezdxf.mozman.at/docs/dxfentities/lwpolyline.html#ezdxf.entities.LWPolyline.virtual_entities
Virtual entity means, the ARC and LINE entities are not assigned to any layout.
arc_and_lines = list(pline.virtual_entities())
For approximation (flattening) you can convert the LWPOLYLINE into a Path object: https://ezdxf.mozman.at/docs/path.html
from ezdxf import path
p = path.make_path(pline)
vertices = p.flattening(0.01)
I appreciate any help I can get! Thanks

from ezdxf.math import bulge_to_arc, bulge_center
from ezdxf.math.bulge import signed_bulge_radius
from ezdxf.acc.vector import Vec2
from ezdxf import math
dwg = ezdxf.readfile('your_dxf_file.dxf')
modelspace = dwg.modelspace()
# Get the LWPOLYLINE entities from the DXF file
entities = dwg.entities
lwpolylines = [e for e in entities if e.dxftype() == 'LWPOLYLINE']
# Iterate through the polylines and pass the bulge value to the function
for pline in lwpolylines:
for index, (x, y, start_width, end_width, bulge) in enumerate(pline):
start_pline = pline[index+0]
start_point = (start_pline[0], start_pline[1])
if index+1 >= len(pline):
break
next_pline = pline[index+1]
end_point = (next_pline[0], next_pline[1])
# Check if the start & end points are the same
if start_point == end_point:
bulge = 0.0 # Set the bulge value to 0
# Print the updated values
print("Start Point:", start_point)
print("End Point:", end_point)
print("Bulge value: ", bulge)
elif bulge != 0:
center_point, start_angle, end_angle, radius = math.bulge_to_arc(start_point, end_point, bulge)
# Print the updated values
print("Start Point:", start_point)
print("End Point:", end_point)
print("Center Point: {}".format(center_point))
print("Bulge value: ", bulge)
print("Start Angle: {}".format(start_angle))
print("End Angle: {}".format(end_angle))
print("Radius: {}".format(radius))

Related

create grid with turtles in python

This is the current code I have. It only makes the vertical lines of the grid. I need help making the horizontal lines. I'm not sure how to go about with the directions.
import math
import turtle
GRID_SIZE = 600
sub_divisions = int(input("Enter the number of sub-divisions: "))
cell_size = GRID_SIZE / sub_divisions
#print subdivisions
scn = turtle.Screen()
dan = turtle.Turtle()
dan.pu()
dan.forward(GRID_SIZE/2)
dan.right(90)
dan.forward(GRID_SIZE/2)
dan.pd()
for i in range(4):
dan.right(90)
dan.forward(GRID_SIZE)
for z in range(1,sub_divisions):
dan.pu()
dan.goto(-GRID_SIZE/2,GRID_SIZE/2)
dan.pd()
dan.left(90)
dan.forward(cell_size * z)
dan.right(90)
dan.forward(GRID_SIZE)
scn.exitonclick()
If you can draw your vertical lines in relative terms, not absolute terms where you always go back to a fixed point like dan.goto(-GRID_SIZE/2,GRID_SIZE/2), then drawing horizontal lines is simply a mater of rerunning the same code, changing the turtle starting point and orientation:
from turtle import Screen, Turtle
GRID_SIZE = 600
sub_divisions = int(input("Enter the number of sub-divisions: "))
cell_size = GRID_SIZE / float(sub_divisions) # force float for Python 2
screen = Screen()
turtle = Turtle()
turtle.penup()
turtle.goto(-GRID_SIZE/2, GRID_SIZE/2)
turtle.pendown()
angle = 90
for _ in range(4):
turtle.forward(GRID_SIZE)
turtle.right(angle)
for _ in range(2):
for _ in range(1, sub_divisions):
turtle.forward(cell_size)
turtle.right(angle)
turtle.forward(GRID_SIZE)
turtle.left(angle)
angle = -angle
turtle.forward(cell_size)
turtle.right(angle)
screen.exitonclick()
Note how the turtle is always moving forward, not backing up over something it has just drawn.

AttributeError: draw_artist can only be used after an initial draw which caches the render

My requirement is to plot the data in polar graph. However I need to keep polar graph in particular angle to looks like "V" shape and data need to plotted in between the particular angle.
In python I don't find a solution to keep the polar graph in particular angle, Example : Graph should be display in between -60 to 60 degree radius. To achieve that I have looked into couple of existing examples and creating required polar graph with FloatingSubplot functions. However I am hitting the issue , when we try to use along with function animation function with blit=True. Error message is displayed is "AttributeError: draw_artist can only be used after an initial draw which caches the render"
Here is my code.
#
import matplotlib
matplotlib.use('Qt4Agg')
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
import matplotlib.animation as animation
import mpl_toolkits.axisartist.floating_axes as floating_axes
from matplotlib.transforms import Affine2D
from matplotlib.projections import PolarAxes
from mpl_toolkits.axisartist import angle_helper
from mpl_toolkits.axisartist.grid_finder import MaxNLocator, DictFormatter
from mpl_toolkits.axisartist.floating_axes import GridHelperCurveLinear, FloatingSubplot
plt.close('all')
fig = plt.figure('Practice', dpi=100) # To set the fig title as pratice
ax1 = fig.add_subplot(2, 2, 1) # subplot for 1st plot
plt.ion()
ax1.grid(True)
def fractional_polar_axes(f, thlim=(0, 120), rlim=(0, 20), step=(30, 0.25),
thlabel='theta', rlabel='r', ticklabels=True, theta_offset=0, rlabels=None):
'''Return polar axes that adhere to desired theta (in deg) and r limits. steps for theta
and r are really just hints for the locators.'''
th0, th1 = thlim # deg
r0, r1 = rlim
thstep, rstep = step
tr_rotate = Affine2D().translate(theta_offset, 0)
# scale degrees to radians:
tr_scale = Affine2D().scale(np.pi / 180., 1.)
# pa = axes(polar="true") # Create a polar axis
pa = PolarAxes
tr = tr_rotate + tr_scale + pa.PolarTransform()
theta_grid_locator = angle_helper.LocatorDMS((th1 - th0) // thstep)
r_grid_locator = MaxNLocator((r1 - r0) // rstep)
theta_tick_formatter = angle_helper.FormatterDMS()
if rlabels:
rlabels = DictFormatter(rlabels)
grid_helper = GridHelperCurveLinear(tr,
extremes=(th0, th1, r0, r1),
grid_locator1=theta_grid_locator,
grid_locator2=r_grid_locator,
tick_formatter1=theta_tick_formatter,
tick_formatter2=rlabels)
a = FloatingSubplot(f, 222, grid_helper=grid_helper)
# a = Subplot(f,753, grid_helper=grid_helper)
# f.add_subplot(7,5,(3,34))
f.add_subplot(a)
# adjust x axis (theta):
print(a)
a.axis["bottom"].set_visible(False)
a.axis["top"].set_axis_direction("bottom") # tick direction
a.axis["top"].toggle(ticklabels=ticklabels, label=bool(thlabel))
a.axis["top"].major_ticklabels.set_axis_direction("top")
a.axis["top"].label.set_axis_direction("top")
a.axis["top"].major_ticklabels.set_pad(10)
# adjust y axis (r):
a.axis["left"].set_axis_direction("bottom") # tick direction
a.axis["right"].set_axis_direction("top") # tick direction
a.axis["left"].toggle(ticklabels=True, label=bool(rlabel))
# add labels:
a.axis["top"].label.set_text(thlabel)
a.axis["left"].label.set_text(rlabel)
# create a parasite axes whose transData is theta, r:
auxa = a.get_aux_axes(tr)
print(auxa)
# make aux_ax to have a clip path as in a?:
auxa.patch = a.patch
# this has a side effect that the patch is drawn twice, and possibly over some other
# artists. So, we decrease the zorder a bit to prevent this:
a.patch.zorder = -2
# add sector lines for both dimensions:
thticks = grid_helper.grid_info['lon_info'][0]
rticks = grid_helper.grid_info['lat_info'][0]
print(grid_helper.grid_info['lat_info'])
for th in thticks[1:-1]: # all but the first and last
auxa.plot([th, th], [r0, r1], ':', c='grey', zorder=-1, lw=0.5)
for ri, r in enumerate(rticks):
# plot first r line as axes border in solid black only if it isn't at r=0
if ri == 0 and r != 0:
ls, lw, color = 'solid', 1, 'black'
else:
ls, lw, color = 'dashed', 0.5, 'grey'
# From http://stackoverflow.com/a/19828753/2020363
auxa.add_artist(plt.Circle([0, 0], radius=r, ls=ls, lw=lw, color=color, fill=False,
transform=auxa.transData._b, zorder=-1))
return auxa
def animate(i):
global loopcount, th, r
th = th+.1
r = r+.1
datapoints.set_offsets(np.vstack((th,r)).T)
#print("in animate")
return datapoints,
if __name__ == '__main__':
r_locs = [0,5,10, 15, 20]
r_labels = ['0', '5', '10', '15', '20']
r_ticks = {loc: label for loc, label in zip(r_locs, r_labels)}
a1 = fractional_polar_axes(fig, thlim=(-60, 60), step=(20, 5),
theta_offset=90, rlabels=r_ticks)
th= 20
r=10
a1.scatter(th,r , c = 'r', alpha = 0.5, linewidths = '.2', s = 20) # plotting the line at thetha 20 and radius 10
datapoints = a1.scatter([], [], c='b', alpha = 0.5, linewidths = '.2', s = 20) # creating scatter line with given instruction,
ani = animation.FuncAnimation(fig, animate, frames=30, interval=20, blit=True)
plt.show(block=True)
#
"""
Above code is working perfectly fine with blit=False and also same solution working fine with line and scatter plotting in normal graph.
Please someone help me to resolve the issue.
"""

Interpolating 3d data at a single point in space (Python 2.7)

I have a point cloud in 4 dimensions, where each point in the cloud has a location and a value (x,y,z,Value). In addition, I have a 'special' point, S0, within the 3d point cloud; I've used this example to find the closest 10 points in the cloud, relative to S0. Now, I have a numpy array for each of the 10 closest points and their values. How can I interpolate these 10 points, to find the interpolated value at point S0? Example code is shown below:
import numpy as np
import matplotlib.pyplot as plt
numpoints = 20
linexs = 320
lineys = 40
linezs = 60
linexe = 20
lineye = 20
lineze = 0
# Create vectors of points
xpts = np.linspace(linexs, linexe, numpoints)
ypts = np.linspace(lineys, lineye, numpoints)
zpts = np.linspace(linezs, lineze, numpoints)
lin = np.dstack((xpts,ypts,zpts))
# Image line of points
fig = plt.figure()
ax = fig.add_subplot(211, projection='3d')
ax.set_xlim(0,365); ax.set_ylim(-85, 85); ax.set_zlim(0, 100)
ax.plot_wireframe(xpts, ypts, zpts)
ax.view_init(elev=12, azim=78)
def randrange(n, vmin, vmax):
return (vmax - vmin)*np.random.rand(n) + vmin
n = 10
for n in range(21):
xs = randrange(n, 0, 350)
ys = randrange(n, -75, 75)
zs = randrange(n, 0, 100)
ax.scatter(xs, ys, zs)
dat = np.dstack((xs,ys,zs))
ax.set_xlabel('X Label')
ax.set_xlim(0,350)
ax.set_ylabel('Y Label')
ax.set_ylim(-75,75)
ax.set_zlabel('Z Label')
ax.set_zlim(0,100)
ax = fig.add_subplot(212, projection='3d')
ax.set_xlim(0,365); ax.set_ylim(-85, 85); ax.set_zlim(0, 100)
ax.plot_wireframe(xpts,ypts,zpts)
ax.view_init(elev=12, azim=78)
plt.show()
dist = []
# Calculate distance from first point to all other points in cloud
for l in range(len(xpts)):
aaa = lin[0][0]-dat
dist.append(np.sqrt(aaa[0][l][0]**2+aaa[0][l][1]**2+aaa[0][l][2]**2))
full = np.dstack((dat,dist))
aaa = full[0][full[0][:,3].argsort()]
print(aaa[0:10])
A basic example. Note that the meshgrid is not needed for the interpolation, but only to make a fast ufunc to generate an example function A=f(x,y,z), here A=x+y+z.
from scipy.interpolate import interpn
import numpy as np
#make up a regular 3d grid
X=np.linspace(-5,5,11)
Y=np.linspace(-5,5,11)
Z=np.linspace(-5,5,11)
xv,yv,zv = np.meshgrid(X,Y,Z)
# make up a function
# see http://docs.scipy.org/doc/numpy/reference/ufuncs.html
A = np.add(xv,np.add(yv,zv))
#this one is easy enough for us to know what to expect at (.5,.5,.5)
# usage : interpn(points, values, xi, method='linear', bounds_error=True, fill_value=nan)
interpn((X,Y,Z),A,[0.5,0.5,0.5])
Output:
array([ 1.5])
If you pass in an array of points of interest, it will give you multiple answers.

Python Enthought Mayavi crashes on data update using mlab_source.reset

I'm trying to update data in a mayavi 3D plot. Some of the changes to the data don't affect the data shape so the mlab_source.set() method can be used (which updates underlying data and refreshes the display without resetting the camera, regenerating the VTK pipeline, regenerating the underlying data structure, etc. This is the best possible case for animation or quick plot updates.
If the underlying data changes shape, the documentation recommends using the mlab_source.reset() method, which while not recreating the entire pipeline or messing up the current scene's camera, does cause the data structure to be rebuilt, costing some performance overhead. This is causing crashes for me.
The worst way to go is completely deleting the plot source and generating a new one with a new call to mlab.mesh() or whatever function was used to plot the data. This recreates a new VTK pipeline, new data structure, and resets the scene's view (loses current zoom and camera settings, which can make smooth interactivity impossible depending on the application).
I've illustrated a simple example from my application in which a Sphere class can have it's properties manipulated (position, size, and resolution). While changing position and size cause the coordinates to refresh, the data remains the same size. However changing the resolution affects the number of latitude and longitude subdivisions used to represent the sphere, which changes the number of coordinates. When attempting to use the "reset" function, the interpreter crashes completely. I'm pretty sure this is a C level segfault in the VTK code based on similar errors around the web. This thread seems to indicate the core developers dealing with the problem almost 5 years ago, but I can't tell if it was truly solved.
I am on Mayavi 4.3.1 which I got along with the Enthought Tool Suite with Python(x, y). I'm on Windows 7 64-bit with Python 2.7.5. I'm using PySide, but I removed those calls and let mlab work by itself for this example.
Here's my example that shows mlab_source.set() working but crashes on mlab_source.reset(). Any thoughts on why it's crashing? Can others duplicate it? I'm pretty sure there are other ways to update the data through the source (TVTK?) object, but I can't find it in the docs and the dozens of traits related attributes are very difficult to wade through.
Any help is appreciated!
#Numpy Imports
from time import sleep
import numpy as np
from numpy import sin, cos, pi
class Sphere(object):
"""
Class for a sphere
"""
def __init__(self, c=None, r=None, n=None):
#Initial defaults
self._coordinates = None
self._c = np.array([0.0, 0.0, 0.0])
self._r = 1.0
self._n = 20
self._hash = []
self._required_inputs = [('c', list),
('r', float)]
#Assign Inputs
if c is not None:
self.c = c
else:
self.c = self._c
if r is not None:
self.r = r
else:
self.r = self._r
if n is not None:
self.n = n
else:
self.n = self._n
#property
def c(self):
"""
Center point of sphere
- Point is specified as a cartesian coordinate triplet, [x, y, z]
- Coordinates are stored as a numpy array
- Coordinates input as a list will be coerced to a numpy array
"""
return self._c
#c.setter
def c(self, val):
if isinstance(val, list):
val = np.array(val)
self._c = val
#property
def r(self):
"""
Radius of sphere
"""
return self._r
#r.setter
def r(self, val):
if val < 0:
raise ValueError("Sphere radius input must be positive")
self._r = val
#property
def n(self):
"""
Resolution of curvature
- Number of points used to represent circles and arcs
- For a sphere, n is the number of subdivisions per hemisphere (in both latitude and longitude)
"""
return self._n
#n.setter
def n(self, val):
if val < 0:
raise ValueError("Sphere n-value for specifying arc/circle resolution must be positive")
self._n = val
#property
def coordinates(self):
"""
Returns x, y, z coordinate arrays to visualize the shape in 3D
"""
self._lazy_update()
return self._coordinates
def _lazy_update(self):
"""
Only update the coordinates data if necessary
"""
#Get a newly calculated hash based on the sphere's inputs
new_hash = self._get_hash()
#Get the old hash
old_hash = self._hash
#Check if the sphere's state has changed
if new_hash != old_hash:
#Something changed - update the coordinates
self._update_coordinates()
def _get_hash(self):
"""
Get the sphere's inputs as an immutable data structure
"""
return tuple(map(tuple, [self._c, [self._r, self._n]]))
def _update_coordinates(self):
"""
Calculate 3D coordinates to represent the sphere
"""
c, r, n = self._c, self._r, self._n
#Get the angular distance between latitude and longitude lines
dphi, dtheta = pi / n, pi / n
#Generate a latitude and longitude grid
[phi, theta] = np.mgrid[0:pi + dphi*1.0:dphi,
0:2 * pi + dtheta*1.0:dtheta]
#Map the latitude longitude grid into cartesian x, y, z coordinates
x = c[0] + r * cos(phi) * sin(theta)
y = c[1] + r * sin(phi) * sin(theta)
z = c[2] + r * cos(theta)
#Store the coordinates
self._coordinates = x, y, z
#Update the hash to coordinates to these coordinates
self._hash = self._get_hash()
if __name__ == '__main__':
from mayavi import mlab
#Make a sphere
sphere = Sphere()
#Plot the sphere
source = mlab.mesh(*sphere.coordinates, representation='wireframe')
#Get the mlab_source
ms = source.mlab_source
#Increase the sphere's radius by 2
sphere.r *= 2
#New coordinates (with larger radius)
x, y, z = sphere.coordinates
#Currently plotted coordinates
x_old, y_old, z_old = ms.x, ms.y, ms.z
#Verify that new x, y, z are all same shape as old x, y, z
data_is_same_shape = all([i.shape == j.shape for i, j in zip([x_old, y_old, z_old], [x, y, z])])
#Pause to see the old sphere
sleep(2)
#Check if data has changed shape... (shouldn't have)
if data_is_same_shape:
print "Updating same-shaped data"
ms.set(x=x, y=y, z=z)
else:
print "Updating with different shaped data"
ms.reset(x=x, y=y, z=z)
#Increase the arc resolution
sphere.n = 50
#New coordinates (with more points)
x, y, z = sphere.coordinates
#Currently plotted coordinates
x_old, y_old, z_old = ms.x, ms.y, ms.z
#Verify that new x, y, z are all same shape as old x, y, z
data_is_same_shape = all([i.shape == j.shape for i, j in zip([x_old, y_old, z_old], [x, y, z])])
#Pause to see the bigger sphere
sleep(2)
#Check if data has changed shape... (should have this time...)
if data_is_same_shape:
print "Updating same-shaped data"
ms.set(x=x, y=y, z=z)
else:
#This is where the segfault / crash occurs
print "Updating with different shaped data"
ms.reset(x=x, y=y, z=z)
mlab.show()
EDIT:
I just verified that all of these mlab_source tests pass for me which includes testing reset on an MGridSource. This does show some possible workarounds like accessing source.mlab_source.dataset.points ... maybe there's a way to update the data manually?
EDIT 2:
I tried this:
p = np.array([x.flatten(), y.flatten(), z.flatten()]).T
ms.dataset.points = p
ms.dataset.point_data.scalars = np.zeros(x.shape)
ms.dataset.points.modified()
#Regenerate the data structure
ms.reset(x=x, y=y, z=z)
It appears that modifying the TVTK Polydata object directly partly works. It appears that it's updating the points without also auto-fixing the connectivity, which is why I have to also run the mlab_source.reset(). I assume the reset() can work now because the data coming in has the same number of points and the mlab_source handles auto-generating the connectivity data. It still crashes when reducing the number of points, maybe because connectivity data exists for points that don't exist? I'm still very frustrated with this.
EDIT 3:
I've implemented the brute force method of just generating a new surface from mlab.mesh(). To prevent resetting the view I disable rendering and store the camera settings, then restore the camera settings after mlab.mesh() and then re-enable rendering. Seems to work quick enough - still wish underlying data could be updated with reset()
Here's the entire class I use to manage plotting objects (responds to GUI signals after an edit has been made).
class PlottablePrimitive(QtCore.QObject):
def __init__(self, parent=None, shape=None, scene=None, mlab=None):
super(PlottablePrimitive, self).__init__(parent=parent)
self._shape = None
self._scene = None
self._mlab = None
self._source = None
self._color = [0.706, 0.510, 0.196]
self._visible = True
self._opacity = 1.0
self._camera = {'position': None,
'focal_point': None,
'view_angle': None,
'view_up': None,
'clipping_range': None}
if shape is not None:
self._shape = shape
if scene is not None:
self._scene = scene
if mlab is not None:
self._mlab = mlab
#property
def shape(self):
return self._shape
#shape.setter
def shape(self, val):
self._shape = val
#property
def color(self):
return self._color
#color.setter
def color(self, color):
self._color = color
if self._source is not None:
surface = self._source.children[0].children[0].children[0]
surface.actor.mapper.scalar_visibility = False
surface.actor.property.color = tuple(color)
def plot(self):
x, y, z = self._shape.coordinates
self._source = self._mlab.mesh(x, y, z)
def update_plot(self):
ms = self._source.mlab_source
x, y, z = self._shape.coordinates
a, b, c = ms.x, ms.y, ms.z
data_is_same_shape = all([i.shape == j.shape for i, j in zip([a, b, c], [x, y, z])])
if data_is_same_shape:
print "Same Data Shape... updating"
#Update the data in-place
ms.set(x=x, y=y, z=z)
else:
print "New Data Shape... resetting data"
method = 'new_source'
if method == 'tvtk':
#Modify TVTK directly
p = np.array([x.flatten(), y.flatten(), z.flatten()]).T
ms.dataset.points = p
ms.dataset.point_data.scalars = np.zeros(x.shape)
ms.dataset.points.modified()
#Regenerate the data structure
ms.reset(x=x, y=y, z=z)
elif method == 'reset':
#Regenerate the data structure
ms.reset(x=x, y=y, z=z)
elif method == 'new_source':
scene = self._scene
#Save camera settings
self._save_camera()
#Disable rendering
self._scene.disable_render = True
#Delete old plot
self.delete_plot()
#Generate new mesh
self._source = self._mlab.mesh(x, y, z)
#Reset camera
self._restore_camera()
self._scene.disable_render = False
def _save_camera(self):
scene = self._scene
#Save camera settings
for setting in self._camera.keys():
self._camera[setting] = getattr(scene.camera, setting)
def _restore_camera(self):
scene = self._scene
#Save camera settings
for setting in self._camera.keys():
if self._camera[setting] is not None:
setattr(scene.camera, setting, self._camera[setting])
def delete_plot(self):
#Remove
if self._source is not None:
self._source.remove()
self._source = None

OpenStreetMap generate georeferenced image [closed]

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I'm new to Openstreetmap and mapnick,
I'm trying to export map image which will be geo-referenced
(So it can be used in other applications)
I've installed osm and mapnik inside ubuntu virtual machine
I've tried using generate_image.py script, but generated image is not equal to the bounding box. My python knowledge is not good enough for me to fix the script.
I've also tried using nik2img.py script using verbose mode, for example:
nik2img.py osm.xml sarajevo.png --srs 900913 --bbox 18.227 43.93 18.511 43.765 --dimensions 10000 10000
and tried using the log bounding box
Step: 11 // --> Map long/lat bbox: Envelope(18.2164733537,43.765,18.5215266463,43.93)
Unfortunately generated image is not equal to the bounding box :(
How can I change scripts so I can georeference generated image?
Or do you know an easier way to accomplish this task?
Image i'm getting using the http://www.openstreetmap.org/ export is nicely geo-referenced, but it's not big enough :(
I've managed to change generate_tiles.py to generate 1024x1024 images together with correct bounding box
Changed script is available bellow
#!/usr/bin/python
from math import pi,cos,sin,log,exp,atan
from subprocess import call
import sys, os
from Queue import Queue
import mapnik
import threading
DEG_TO_RAD = pi/180
RAD_TO_DEG = 180/pi
# Default number of rendering threads to spawn, should be roughly equal to number of CPU cores available
NUM_THREADS = 4
def minmax (a,b,c):
a = max(a,b)
a = min(a,c)
return a
class GoogleProjection:
def __init__(self,levels=18):
self.Bc = []
self.Cc = []
self.zc = []
self.Ac = []
c = 1024
for d in range(0,levels):
e = c/2;
self.Bc.append(c/360.0)
self.Cc.append(c/(2 * pi))
self.zc.append((e,e))
self.Ac.append(c)
c *= 2
def fromLLtoPixel(self,ll,zoom):
d = self.zc[zoom]
e = round(d[0] + ll[0] * self.Bc[zoom])
f = minmax(sin(DEG_TO_RAD * ll[1]),-0.9999,0.9999)
g = round(d[1] + 0.5*log((1+f)/(1-f))*-self.Cc[zoom])
return (e,g)
def fromPixelToLL(self,px,zoom):
e = self.zc[zoom]
f = (px[0] - e[0])/self.Bc[zoom]
g = (px[1] - e[1])/-self.Cc[zoom]
h = RAD_TO_DEG * ( 2 * atan(exp(g)) - 0.5 * pi)
return (f,h)
class RenderThread:
def __init__(self, tile_dir, mapfile, q, printLock, maxZoom):
self.tile_dir = tile_dir
self.q = q
self.m = mapnik.Map(1024, 1024)
self.printLock = printLock
# Load style XML
mapnik.load_map(self.m, mapfile, True)
# Obtain <Map> projection
self.prj = mapnik.Projection(self.m.srs)
# Projects between tile pixel co-ordinates and LatLong (EPSG:4326)
self.tileproj = GoogleProjection(maxZoom+1)
def render_tile(self, tile_uri, x, y, z):
# Calculate pixel positions of bottom-left & top-right
p0 = (x * 1024, (y + 1) * 1024)
p1 = ((x + 1) * 1024, y * 1024)
# Convert to LatLong (EPSG:4326)
l0 = self.tileproj.fromPixelToLL(p0, z);
l1 = self.tileproj.fromPixelToLL(p1, z);
# Convert to map projection (e.g. mercator co-ords EPSG:900913)
c0 = self.prj.forward(mapnik.Coord(l0[0],l0[1]))
c1 = self.prj.forward(mapnik.Coord(l1[0],l1[1]))
# Bounding box for the tile
if hasattr(mapnik,'mapnik_version') and mapnik.mapnik_version() >= 800:
bbox = mapnik.Box2d(c0.x,c0.y, c1.x,c1.y)
else:
bbox = mapnik.Envelope(c0.x,c0.y, c1.x,c1.y)
render_size = 1024
self.m.resize(render_size, render_size)
self.m.zoom_to_box(bbox)
self.m.buffer_size = 128
# Render image with default Agg renderer
im = mapnik.Image(render_size, render_size)
mapnik.render(self.m, im)
im.save(tile_uri, 'png256')
print "Rendered: ", tile_uri, "; ", l0 , "; ", l1
# Write geo coding informations
file = open(tile_uri[:-4] + ".tab", 'w')
file.write("!table\n")
file.write("!version 300\n")
file.write("!charset WindowsLatin2\n")
file.write("Definition Table\n")
file.write(" File \""+tile_uri[:-4]+".jpg\"\n")
file.write(" Type \"RASTER\"\n")
file.write(" ("+str(l0[0])+","+str(l1[1])+") (0,0) Label \"Pt 1\",\n")
file.write(" ("+str(l1[0])+","+str(l1[1])+") (1023,0) Label \"Pt 2\",\n")
file.write(" ("+str(l1[0])+","+str(l0[1])+") (1023,1023) Label \"Pt 3\",\n")
file.write(" ("+str(l0[0])+","+str(l0[1])+") (0,1023) Label \"Pt 4\"\n")
file.write(" CoordSys Earth Projection 1, 104\n")
file.write(" Units \"degree\"\n")
file.close()
def loop(self):
while True:
#Fetch a tile from the queue and render it
r = self.q.get()
if (r == None):
self.q.task_done()
break
else:
(name, tile_uri, x, y, z) = r
exists= ""
if os.path.isfile(tile_uri):
exists= "exists"
else:
self.render_tile(tile_uri, x, y, z)
bytes=os.stat(tile_uri)[6]
empty= ''
if bytes == 103:
empty = " Empty Tile "
self.printLock.acquire()
print name, ":", z, x, y, exists, empty
self.printLock.release()
self.q.task_done()
def render_tiles(bbox, mapfile, tile_dir, minZoom=1,maxZoom=18, name="unknown", num_threads=NUM_THREADS):
print "render_tiles(",bbox, mapfile, tile_dir, minZoom,maxZoom, name,")"
# Launch rendering threads
queue = Queue(32)
printLock = threading.Lock()
renderers = {}
for i in range(num_threads):
renderer = RenderThread(tile_dir, mapfile, queue, printLock, maxZoom)
render_thread = threading.Thread(target=renderer.loop)
render_thread.start()
#print "Started render thread %s" % render_thread.getName()
renderers[i] = render_thread
if not os.path.isdir(tile_dir):
os.mkdir(tile_dir)
gprj = GoogleProjection(maxZoom+1)
ll0 = (bbox[0],bbox[3])
ll1 = (bbox[2],bbox[1])
for z in range(minZoom,maxZoom + 1):
px0 = gprj.fromLLtoPixel(ll0,z)
px1 = gprj.fromLLtoPixel(ll1,z)
# check if we have directories in place
zoom = "%s" % z
if not os.path.isdir(tile_dir + zoom):
os.mkdir(tile_dir + zoom)
for x in range(int(px0[0]/1024.0),int(px1[0]/1024.0)+1):
# Validate x co-ordinate
if (x < 0) or (x >= 2**z):
continue
# check if we have directories in place
str_x = "%s" % x
if not os.path.isdir(tile_dir + zoom + '/' + str_x):
os.mkdir(tile_dir + zoom + '/' + str_x)
for y in range(int(px0[1]/1024.0),int(px1[1]/1024.0)+1):
# Validate x co-ordinate
if (y < 0) or (y >= 2**z):
continue
str_y = "%s" % y
tile_uri = tile_dir + zoom + '_' + str_x + '_' + str_y + '.png'
# Submit tile to be rendered into the queue
t = (name, tile_uri, x, y, z)
queue.put(t)
# Signal render threads to exit by sending empty request to queue
for i in range(num_threads):
queue.put(None)
# wait for pending rendering jobs to complete
queue.join()
for i in range(num_threads):
renderers[i].join()
if __name__ == "__main__":
home = os.environ['HOME']
try:
mapfile = "/home/emir/bin/mapnik/osm.xml" #os.environ['MAPNIK_MAP_FILE']
except KeyError:
mapfile = "/home/emir/bin/mapnik/osm.xml"
try:
tile_dir = os.environ['MAPNIK_TILE_DIR']
except KeyError:
tile_dir = home + "/osm/tiles/"
if not tile_dir.endswith('/'):
tile_dir = tile_dir + '/'
#-------------------------------------------------------------------------
#
# Change the following for different bounding boxes and zoom levels
#
#render sarajevo at 16 zoom level
bbox = (18.256, 43.785, 18.485, 43.907)
render_tiles(bbox, mapfile, tile_dir, 16, 16, "World")
Try Maperitive's export-bitmap command, it generates various georeferencing sidecar files
(worldfile, KML, OziExplorer .MAP file).