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Doing the Mars Rover coding problem and am stuck at level 2. Trying to debug but I just can't see it and it wont let me progress until current level is finished.
Problem Description as follows:
Calculate the position and the direction of the rover after driving a certain distance with a certain steering angle.
Input: WheelBase, Distance, SteeringAngle (2 decimal floats)
Output: X, Y, NewDirection Angle
Example:
In: 1.00 1.00 30.00
Out: 0.24 0.96 28.65
Anybody know of any links to some walk throughs, solutions etc or more examples?
There is an image link to the coding problem at the bottom
Thanks
https://catcoder.codingcontest.org/training/1212/play
## Level 1 - calculate the turn radius ##
## level1 2 - calculate new position and angle
import math
## solution works for this data
WHEELBASE = 1.00
DISTANCE = 1.00
STEERINGANGLE = 30.00
#WHEELBASE = 1.75
#DISTANCE = 3.14
#STEERINGANGLE = -23.00
def calculateTurnRadius(wheelbase, steeringangle):
return round(wheelbase / math.sin(math.radians(steeringangle)), 2)
def calculateNewDirection(wheelbase, steeringangle, distance):
turnRadius = calculateTurnRadius(wheelbase, steeringangle)
theta = distance / turnRadius
#brings theta to within a 180 arc
while theta >= math.pi * 2:
theta -= math.pi * 2
while theta < 0:
theta += math.pi * 2
# calculate theta with basic sin and cos trig
x = turnRadius - (math.cos(theta) * turnRadius)
y = math.sin(theta) * turnRadius
x = abs(round(x, 2))
y = round(y, 2)
theta = math.degrees(theta)
theta = round(theta, 2)
return x, y, theta
print(f"Turn Radius = {calculateTurnRadius(WHEELBASE, STEERINGANGLE)}")
print(f"{calculateNewDirection(WHEELBASE, STEERINGANGLE, DISTANCE)}")
Turn Radius = 2.0
(0.24, 0.96, 28.65)
[1]: https://i.stack.imgur.com/tDY2u.jpg
I'm also stuck, but what I have so far works for the first 2 inputs:
import math
WheelBase, Distance, SteeringAngle = 1, 1, 30.00
WheelBase, Distance, SteeringAngle = 2.13, 4.30, 23.00
WheelBase = float(WheelBase)
Distance = float(Distance)
SteeringAngle = float(SteeringAngle)
TurnRadius = abs(WheelBase / math.sin(math.pi * (SteeringAngle) / 180))
NewDirection = 180 * (Distance / TurnRadius) / math.pi
chord = 2 * TurnRadius * math.sin(math.pi * NewDirection / 360)
y = TurnRadius * math.sin(math.pi * (NewDirection) / 180)
x = math.sqrt(chord ** 2 - y ** 2)
print(round(x, 2), round(y, 2), round(NewDirection, 2))
print(chord)
print(math.sqrt(x ** 2 + y ** 2))
I haven't really looked at your code, but I got the solutions from level 1 to 4. So here's my code for level 2.
import math
def turn_radius(wheel_base, steering_angle):
radius: float = wheel_base/math.sin(math.radians(steering_angle))
print (f"{radius:.2f}")
return radius
def get_position(distance, steering_angle, radius):
angle = (distance*180)/(math.pi*radius)
y = (math.sin(math.radians(angle)) * radius)
x = radius - (math.cos(math.radians(angle)) * radius)
while angle < 0:
angle = 360 + angle
if angle == 360:
angle = 0
print(f"{x:.2f} {y:.2f} {angle:.2f}")
if __name__ == "__main__":
wheel_base = 2.7
distance = 45
steering_angle = -34
if steering_angle == 0:
steering_angle = 360
radius = turn_radius(wheel_base ,steering_angle)
get_position(distance, steering_angle, radius)
I want to use Python/Matplotlib/Basemap to draw a map and shade a circle that lies within a given distance of a specified point, similar to this (Map generated by the Great Circle Mapper - copyright © Karl L. Swartz.):
I can get the map to generate as follows:
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
# create new figure, axes instances.
fig,ax = plt.subplots()
# setup Mercator map projection.
m = Basemap(
llcrnrlat=47.0,
llcrnrlon=-126.62,
urcrnrlat=50.60,
urcrnrlon=-119.78,
rsphere=(6378137.00,6356752.3142),
resolution='i',
projection='merc',
lat_0=49.290,
lon_0=-123.117,
)
# Latitudes and longitudes of locations of interest
coords = dict()
coords['SEA'] = [47.450, -122.309]
# Plot markers and labels on map
for key in coords:
lon, lat = coords[key]
x,y = m(lat, lon)
m.plot(x, y, 'bo', markersize=5)
plt.text(x+10000, y+5000, key, color='k')
# Draw in coastlines
m.drawcoastlines()
m.fillcontinents()
m.fillcontinents(color='grey',lake_color='aqua')
m.drawmapboundary(fill_color='aqua')
plt.show()
which generates the map:
Now I would like to create a great circle around a specified point, such as the top map.
My attempt is a function that takes the map object, a center coordinate pair and a distance, and creates two curves and then shade between them, something like:
def shaded_great_circle(map_, lat_0, lon_0, dist=100, alpha=0.2): # dist specified in nautical miles
dist = dist * 1852 # Convert distance to nautical miles
lat = np.linspace(lat_0-dist/2, lat_0+dist/2,50)
lon = # Somehow find these points
# Create curve for longitudes above lon_0
# Create curve for longitudes below lon_0
# Shade region between above two curves
where I have commented what I want to do, but am not sure how to do it.
I have tried a few ways to do this, but what has me confused is that all inputs to the map are coordinates measured in degrees, whereas I want to specify points in length, and have that converted to latitude/longitude points to plot. I think this is related to data as lat/lon in degrees versus map projection coordinates.
Any nudges in the right direction would be appreciated
Thanks
In the end I had to implement this manually.
In short, I used an equation given here to calculate the coordinates given an
initial starting point and a radial to calculate points around 360 degrees, and then plot a line through these points. I don't really need the shading part, so I haven't implemented that yet.
I thought this is a useful feature so here is how I implemented it:
from mpl_toolkits.basemap import Basemap
import numpy as np
import matplotlib.pyplot as plt
def calc_new_coord(lat1, lon1, rad, dist):
"""
Calculate coordinate pair given starting point, radial and distance
Method from: http://www.geomidpoint.com/destination/calculation.html
"""
flat = 298.257223563
a = 2 * 6378137.00
b = 2 * 6356752.3142
# Calculate the destination point using Vincenty's formula
f = 1 / flat
sb = np.sin(rad)
cb = np.cos(rad)
tu1 = (1 - f) * np.tan(lat1)
cu1 = 1 / np.sqrt((1 + tu1*tu1))
su1 = tu1 * cu1
s2 = np.arctan2(tu1, cb)
sa = cu1 * sb
csa = 1 - sa * sa
us = csa * (a * a - b * b) / (b * b)
A = 1 + us / 16384 * (4096 + us * (-768 + us * (320 - 175 * us)))
B = us / 1024 * (256 + us * (-128 + us * (74 - 47 * us)))
s1 = dist / (b * A)
s1p = 2 * np.pi
while (abs(s1 - s1p) > 1e-12):
cs1m = np.cos(2 * s2 + s1)
ss1 = np.sin(s1)
cs1 = np.cos(s1)
ds1 = B * ss1 * (cs1m + B / 4 * (cs1 * (- 1 + 2 * cs1m * cs1m) - B / 6 * \
cs1m * (- 3 + 4 * ss1 * ss1) * (-3 + 4 * cs1m * cs1m)))
s1p = s1
s1 = dist / (b * A) + ds1
t = su1 * ss1 - cu1 * cs1 * cb
lat2 = np.arctan2(su1 * cs1 + cu1 * ss1 * cb, (1 - f) * np.sqrt(sa * sa + t * t))
l2 = np.arctan2(ss1 * sb, cu1 * cs1 - su1 * ss1 * cb)
c = f / 16 * csa * (4 + f * (4 - 3 * csa))
l = l2 - (1 - c) * f * sa * (s1 + c * ss1 * (cs1m + c * cs1 * (-1 + 2 * cs1m * cs1m)))
d = np.arctan2(sa, -t)
finaltc = d + 2 * np.pi
backtc = d + np.pi
lon2 = lon1 + l
return (np.rad2deg(lat2), np.rad2deg(lon2))
def shaded_great_circle(m, lat_0, lon_0, dist=100, alpha=0.2, col='k'): # dist specified in nautical miles
dist = dist * 1852 # Convert distance to nautical miles
theta_arr = np.linspace(0, np.deg2rad(360), 100)
lat_0 = np.deg2rad(lat_0)
lon_0 = np.deg2rad(lon_0)
coords_new = []
for theta in theta_arr:
coords_new.append(calc_new_coord(lat_0, lon_0, theta, dist))
lat = [item[0] for item in coords_new]
lon = [item[1] for item in coords_new]
x, y = m(lon, lat)
m.plot(x, y, col)
# setup Mercator map projection.
m = Basemap(
llcrnrlat=45.0,
llcrnrlon=-126.62,
urcrnrlat=50.60,
urcrnrlon=-119.78,
rsphere=(6378137.00,6356752.3142),
resolution='i',
projection='merc',
lat_0=49.290,
lon_0=-123.117,
)
# Latitudes and longitudes of locations of interest
coords = dict()
coords['SEA'] = [47.450, -122.309]
# Plot markers and labels on map
for key in coords:
lon, lat = coords[key]
x,y = m(lat, lon)
m.plot(x, y, 'bo', markersize=5)
plt.text(x+10000, y+5000, key, color='k')
# Draw in coastlines
m.drawcoastlines()
m.fillcontinents()
m.fillcontinents(color='grey',lake_color='aqua')
m.drawmapboundary(fill_color='aqua')
# Draw great circle
shaded_great_circle(m, 47.450, -122.309, 100, col='k') # Distance specified in nautical miles, i.e. 100 nmi in this case
plt.show()
Running this should give you (with 100 nautical mile circle around Seattle):
I have dataframe with measurements coordinates and cell coordinates.
I need to find for each row angle (azimuth angle) between a line that connects these two points and the north pole.
df:
id cell_lat cell_long meas_lat meas_long
1 53.543643 11.636235 53.44758 11.03720
2 52.988823 10.0421645 53.03501 9.04165
3 54.013442 9.100981 53.90384 10.62370
I have found some code online, but none if that really helps me get any closer to the solution.
I have used this function but not sure if get it right and I guess there is simplier solution.
Any help or hint is welcomed, thanks in advance.
The trickiest part of this problem is converting geodetic (latitude, longitude) coordinates to Cartesian (x, y, z) coordinates. If you look at https://en.wikipedia.org/wiki/Geographic_coordinate_conversion you can see how to do this, which involves choosing a reference system. Assuming we choose ECEF (https://en.wikipedia.org/wiki/ECEF), the following code calculates the angles you are looking for:
def vector_calc(lat, long, ht):
'''
Calculates the vector from a specified point on the Earth's surface to the North Pole.
'''
a = 6378137.0 # Equatorial radius of the Earth
b = 6356752.314245 # Polar radius of the Earth
e_squared = 1 - ((b ** 2) / (a ** 2)) # e is the eccentricity of the Earth
n_phi = a / (np.sqrt(1 - (e_squared * (np.sin(lat) ** 2))))
x = (n_phi + ht) * np.cos(lat) * np.cos(long)
y = (n_phi + ht) * np.cos(lat) * np.sin(long)
z = ((((b ** 2) / (a ** 2)) * n_phi) + ht) * np.sin(lat)
x_npole = 0.0
y_npole = 6378137.0
z_npole = 0.0
v = ((x_npole - x), (y_npole - y), (z_npole - z))
return v
def angle_calc(lat1, long1, lat2, long2, ht1=0, ht2=0):
'''
Calculates the angle between the vectors from 2 points to the North Pole.
'''
# Convert from degrees to radians
lat1_rad = (lat1 / 180) * np.pi
long1_rad = (long1 / 180) * np.pi
lat2_rad = (lat2 / 180) * np.pi
long2_rad = (long2 / 180) * np.pi
v1 = vector_calc(lat1_rad, long1_rad, ht1)
v2 = vector_calc(lat2_rad, long2_rad, ht2)
# The angle between two vectors, vect1 and vect2 is given by:
# arccos[vect1.vect2 / |vect1||vect2|]
dot = np.dot(v1, v2) # The dot product of the two vectors
v1_mag = np.linalg.norm(v1) # The magnitude of the vector v1
v2_mag = np.linalg.norm(v2) # The magnitude of the vector v2
theta_rad = np.arccos(dot / (v1_mag * v2_mag))
# Convert radians back to degrees
theta = (theta_rad / np.pi) * 180
return theta
angles = []
for row in range(df.shape[0]):
cell_lat = df.iloc[row]['cell_lat']
cell_long = df.iloc[row]['cell_long']
meas_lat = df.iloc[row]['meas_lat']
meas_long = df.iloc[row]['meas_long']
angle = angle_calc(cell_lat, cell_long, meas_lat, meas_long)
angles.append(angle)
This will read each row out of your dataframe, calculate the angle and append it to the list angles. Obviously you can do what you like with those angles after they've been calculated.
Hope that helps!
okay, i have a circle and i want get the point on circle in 90 degree of origin.
def point_on_circle():
'''
Finding the x,y coordinates on circle, based on given angle
'''
from math import cos, sin
#center of circle, angle in degree and radius of circle
center = [0,0]
angle = 90
radius = 100
#x = offsetX + radius * Cosine(Degree)
x = center[0] + (radius * cos(angle))
#y = offsetY + radius * Sine(Degree)
y = center[1] + (radius * sin(angle))
return x,y
>>> print point_on_circle()
[-44.8073616129 , 89.3996663601]
since pi start from 3 o'clock, i expected to get x=0 and y=100but i have no idea why i'm getting that.
what am i doing wrong?
Edit: even i convert to radians, still i get weird result.
def point_on_circle():
'''
Finding the x,y coordinates on circle, based on given angle
'''
from math import cos, sin, radians
#center of circle, angle in degree and radius of circle
center = [0,0]
angle = radians(90)
radius = 100
#x = offsetX + radius * Cosine(radians)
x = center[0] + (radius * cos(angle))
#y = offsetY + radius * Sine(radians)
y = center[1] + (radius * sin(angle))
return x,y
>>> print point_on_circle()
[6.12323399574e-15 , 100.0]
any idea how to get accurate number?
math.cos and math.sin expect radians, not degrees. Simply replace 90 by pi/2:
def point_on_circle():
'''
Finding the x,y coordinates on circle, based on given angle
'''
from math import cos, sin, pi
#center of circle, angle in degree and radius of circle
center = [0,0]
angle = pi / 2
radius = 100
x = center[0] + (radius * cos(angle))
y = center[1] + (radius * sin(angle))
return x,y
You'll get (6.123233995736766e-15, 100.0) which is close to (0, 100).
If you want better precision, you can try SymPy online before installing it yourself:
>>> from sympy import pi, mpmath
>>> mpmath.cos(pi/2)
6.12323399573677e−17
We're getting closer, but this is still using floating-point. However, mpmath.cospi gets you the correct result:
>>> mpmath.cospi(1/2)
0.0
The sin() & cos() expect radians use:
x = center[0] + (radius * cos(angle*pi/180));
Two things need to be changed.
First, you need to change range = 90 to range = radians(90) which means you need to import radians.
Second you need to subtract the range from radian(360) so you get it starting in quadrant I instead of quadrant IV. Once you are done, your should have changed range = 90 to range = radians(360 - 90) and have imported radians.
Then if you want to stop your answer from having floating-point, you have return int(x), int(y) instead of return x,y at the end of your function. I made these changes and it worked.
Hi :)
i have the following python code that generates points lying on a sphere's surface
from math import sin, cos, pi
toRad = pi / 180
ox = 10
oy = -10
oz = 50
radius = 10.0
radBump = 3.0
angleMin = 0
angleMax = 360
angleOffset = angleMin * toRad
angleRange = (angleMax - angleMin) * toRad
steps = 48
angleStep = angleRange / steps
latMin = 0
latMax = 180
latOffset = latMin * toRad
if (latOffset < 0):
latOffset = 0;
latRange = (latMax - latMin) * toRad
if (latRange > pi):
latRange = pi - latOffset;
latSteps = 48
latAngleStep = latRange / latSteps
for lat in range(0, latSteps):
ang = lat * latAngleStep + latOffset
z = cos(ang) * radius + oz
radMod = sin(ang) * radius
for a in range(0, steps):
x = sin(a * angleStep + angleOffset) * radMod + ox
y = cos(a * angleStep + angleOffset) * radMod + oy
print "%f %f %f"%(x,y,z)
after that i plot the points with gnuplot using splot 'datafile'
can you give any hints on how to create deformations on that sphere?
like "mountains" or "spikes" on it?
(something like the openbsd logo ;) : https://https.openbsd.org/images/tshirt-23.gif )
i know it is a trivial question :( but thanks for your time :)
DsP
The approach that springs to my mind, especially with the way you compute a set of points that are not explicitly connected, is to find where the point goes on the sphere's surface, then move it by a distance and direction determined by a set of control points. The control points could have smaller effects the further away they are. For example:
# we have already computed a points position on the sphere, and
# called it x,y,z
for p in controlPoints:
dx = p.x - x
dy = p.y - y
dz = p.z - z
xDisplace += 1/(dx*dx)
yDisplace += 1/(dy*dy)
zDisplace += 1/(dz*dz) # using distance^2 displacement
x += xDisplace
y += yDisplace
z += zDisplace
By changing the control points you can alter the sphere's shape
By changing the movement function, you can alter the way the points shape the sphere
You could get really tricky and have different functions for different points:
# we have already computed a points position on the sphere, and
# called it x,y,z
for p in controlPoints:
xDisplace += p.displacementFunction(x)
yDisplace += p.displacementFunction(y)
zDisplace += p.displacementFunction(z)
x += xDisplace
y += yDisplace
z += zDisplace
If you do not want all control points affecting every point in the sphere, just build that into the displacement function.
How's this?
from math import sin, cos, pi, radians, ceil
import itertools
try:
rng = xrange # Python 2.x
except NameError:
rng = range # Python 3.x
# for the following calculations,
# - all angles are in radians (unless otherwise specified)
# - latitude is in [-pi/2..pi/2]
# - longitude is in [-pi..pi)
MIN_LAT = -pi/2 # South Pole
MAX_LAT = pi/2 # North Pole
MIN_LON = -pi # Far West
MAX_LON = pi # Far East
def floatRange(start, end=None, step=1.0):
"Floating-point range generator"
start += 0.0 # cast to float
if end is None:
end = start
start = 0.0
steps = int(ceil((end-start)/step))
return (start + k*step for k in rng(0, steps+1))
def patch2d(xmin, xmax, ymin, ymax, step=1.0):
"2d rectangular grid generator"
if xmin>xmax:
xmin,xmax = xmax,xmin
xrange = floatRange(xmin, xmax, step)
if ymin>ymax:
ymin,ymax = ymax,ymin
yrange = floatRange(ymin, ymax, step)
return itertools.product(xrange, yrange)
def patch2d_to_3d(xyIter, zFn):
"Convert 2d field to 2.5d height-field"
mapFn = lambda a: (a[0], a[1], zFn(a[0],a[1]))
return itertools.imap(mapFn, xyIter)
#
# Representation conversion functions
#
def to_spherical(lon, lat, rad):
"Map from spherical to spherical coordinates (identity function)"
return lon, lat, rad
def to_cylindrical(lon, lat, rad):
"Map from spherical to cylindrical coordinates"
# angle, z, radius
return lon, rad*sin(lat), rad*cos(lat)
def to_cartesian(lon, lat, rad):
"Map from spherical to Cartesian coordinates"
# x, y, z
cos_lat = cos(lat)
return rad*cos_lat*cos(lon), rad*cos_lat*sin(lon), rad*sin(lat)
def bumpySphere(gridSize, radiusFn, outConv):
lonlat = patch2d(MIN_LON, MAX_LON, MIN_LAT, MAX_LAT, gridSize)
return list(outConv(*lonlatrad) for lonlatrad in patch2d_to_3d(lonlat, radiusFn))
# make a plain sphere of radius 10
sphere = bumpySphere(radians(5.0), lambda x,y: 10.0, to_cartesian)
# spiky-star-function maker
def starFnMaker(xWidth, xOffset, yWidth, yOffset, minRad, maxRad):
# make a spiky-star function:
# longitudinal and latitudinal triangular waveforms,
# joined as boolean intersection,
# resulting in a grid of positive square pyramids
def starFn(x, y, xWidth=xWidth, xOffset=xOffset, yWidth=yWidth, yOffset=yOffset, minRad=minRad, maxRad=maxRad):
xo = ((x-xOffset)/float(xWidth)) % 1.0 # xo in [0.0..1.0), progress across a single pattern-repetition
xh = 2 * min(xo, 1.0-xo) # height at xo in [0.0..1.0]
xHeight = minRad + xh*(maxRad-minRad)
yo = ((y-yOffset)/float(yWidth)) % 1.0
yh = 2 * min(yo, 1.0-yo)
yHeight = minRad + yh*(maxRad-minRad)
return min(xHeight, yHeight)
return starFn
# parameters to spike-star-function maker
width = 2*pi
horDivs = 20 # number of waveforms longitudinally
horShift = 0.0 # longitudinal offset in [0.0..1.0) of a wave
height = pi
verDivs = 10
verShift = 0.5 # leave spikes at the poles
minRad = 10.0
maxRad = 15.0
deathstarFn = starFnMaker(width/horDivs, width*horShift/horDivs, height/verDivs, height*verShift/verDivs, minRad, maxRad)
deathstar = bumpySphere(radians(2.0), deathstarFn, to_cartesian)
so i finally created the deformation using a set of control points that "pull" the spherical
surface. it is heavilly OO and ugly though ;)
thanks for all the help !!!
to use it > afile and with gnuplot : splot 'afile' w l
DsP
from math import sin, cos, pi ,sqrt,exp
class Point:
"""a 3d point class"""
def __init__(self,x,y,z):
self.x = x
self.y = y
self.z = z
def __repr__(self):
return "%f %f %f\n"%(self.x,self.y,self.z)
def __str__(self):
return "point centered: %f %f %f\n"%(self.x,self.y,self.z)
def distance(self,b):
return sqrt((self.x - b.x)**2 +(self.y - b.y)**2 +(self.z -b.z)**2)
def displaceTowards(self,b):
self.x
class ControlPoint(Point):
"""a control point that deforms positions of other points"""
def __init__(self,p):
Point.__init__(self,p.x,p.y,p.z)
self.deformspoints=[]
def deforms(self,p):
self.deformspoints.append(p)
def deformothers(self):
self.deformspoints.sort()
#print self.deformspoints
for i in range(0,len(self.deformspoints)):
self.deformspoints[i].x += (self.x - self.deformspoints[i].x)/2
self.deformspoints[i].y += (self.y - self.deformspoints[i].y)/2
self.deformspoints[i].z += (self.z - self.deformspoints[i].z)/2
class Sphere:
"""returns points on a sphere"""
def __init__(self,radius,angleMin,angleMax,latMin,latMax,discrStep,ox,oy,oz):
toRad = pi/180
self.ox=ox
self.oy=oy
self.oz=oz
self.radius=radius
self.angleMin=angleMin
self.angleMax=angleMax
self.latMin=latMin
self.latMax=latMax
self.discrStep=discrStep
self.angleRange = (self.angleMax - self.angleMin)*toRad
self.angleOffset = self.angleMin*toRad
self.angleStep = self.angleRange / self.discrStep
self.latOffset = self.latMin*toRad
self.latRange = (self.latMax - self.latMin) * toRad
self.latAngleStep = self.latRange / self.discrStep
if(self.latOffset <0):
self.latOffset = 0
if(self.latRange > pi):
self.latRange = pi - latOffset
def CartesianPoints(self):
PointList = []
for lat in range(0,self.discrStep):
ang = lat * self.latAngleStep + self.latOffset
z = cos(ang) * self.radius + self.oz
radMod = sin(ang)*self.radius
for a in range(0,self.discrStep):
x = sin(a*self.angleStep+self.angleOffset)*radMod+self.ox
y = cos(a*self.angleStep+self.angleOffset)*radMod+self.oy
PointList.append(Point(x,y,z))
return PointList
mysphere = Sphere(10.0,0,360,0,180,50,10,10,10)
mylist = mysphere.CartesianPoints()
cpoints = [ControlPoint(Point(0.0,0.0,0.0)),ControlPoint(Point(20.0,0.0,0.0))]
deforpoints=[]
for cp in cpoints:
for p in mylist:
if(p.distance(cp) < 15.0):
cp.deforms(p)
"""print "cp ",cp,"deforms:"
for dp in cp.deformspoints:
print dp ,"at distance", dp.distance(cp)"""
cp.deformothers()
out= mylist.__repr__()
s = out.replace(","," ")
print s