I am trying to plot the segment of a circle (2D) as an arc in matplotlib. I have written a class which will provide the maths for the segment such as chord length, height of arc etc. I wish to plot the x y values between (0,0) and (0, chord length).
I am currently representing the X values as numpy linspace array (0, chordLength, 200). I am a bit stumped as to how to plot the y values as a similar linspace array so that I can plot these points using matplotlib. The idea behind this is to display the curvature of the earth between two points of a known arc length (great circle distance). I have been reading around sine cosine etc however outside of using cookie cutter formulas for my geometry calculations, I am somewhat lost as to how to apply it to gain my y values.
First, the circle class
import numpy as np
class Circle:
def __init__(self,radiusOfCircle,lengthOfArc):
self.radius = radiusOfCircle
self.circumference = 2 * np.pi * self.radius
self.diameter = self.radius * 2
self.arcLength = lengthOfArc
self.degrees = self.calcDegrees()
self.radians = self.calcRadians()
self.chordLength = self.calcChordLength()
self.sagitta = self.calcSagitta()
self.segmentArea = self.calcSegmentArea()
self.arcHeight = self.calcArcHeight()
#Setters and getters for the Circle class (TODO: setters)
def getRadius(self):
return self.radius
def getCircumference(self):
return self.circumference
def getDiameter(self):
return self.diameter
def getArcLength(self):
return self.arcLength
def getRadians(self):
return self.radians
def getDegrees(self):
return self.degrees
def getChordLength(self):
return self.chordLength
def getSagitta(self):
return self.sagitta
def getSegmentArea(self):
return self.segmentArea
def getArcHeight(self):
return self.arcHeight
#Define Circle class methods
#Calculate the central angle, in degrees, by using the arcLength
def calcDegrees(self):
self.degrees = (self.arcLength / (np.pi * self.diameter)) * 360 #Gives angle in degrees at centre of the circle between the two points (beginning and end points of arcLength)
return self.degrees
#Calculate the central angle in radians, between two points on the circle
def calcRadians(self):#Where theta is the angle between both points at the centre of the circle
self.radians = np.radians(self.degrees) # Convert degrees to radians to work with ChordLength formula
return self.radians
#Returns the chord lengths of the arc, taking theta (angle in radians) as it's argument
#The chord is the horizontal line which separates the arc segment from the rest of the circle
def calcChordLength(self):
self.chordLength = 2*self.radius*np.sin(self.radians/2) #formula works for theta (radians) only, not degrees #confirmed using http://www.ambrsoft.com/TrigoCalc/Sphere/Arc_.htm
return self.chordLength
#Calculates the length of arc, taking theta (angle in radians) as its argument.
def calcArcLength(self):
self.arcLength = (self.degrees/360)*self.diameter*np.pi #confirmed using http://www.ambrsoft.com/TrigoCalc/Sphere/Arc_.htm
return self.arcLength
#Calculates the sagitta of the arc segment. The sagitta is the horizontal line which extends from the bottom
#of the circle to the chord of the segment
def calcSagitta(self):
self.sagitta = self.radius - (np.sqrt((self.radius**2)-((self.chordLength/2)**2))) #Confirmed correct against online calculator https://www.liutaiomottola.com/formulae/sag.htm
return self.sagitta
#Calculates the area of the circular segment/arc).
def calcSegmentArea(self):
self.segmentArea = (self.radians - np.sin(self.radians) / 2) * self.radius**2
return self.segmentArea
#Calculate the height of the arc
#Radius - sagitta of the segment
def calcArcHeight(self):
self.arcHeight = self.radius - self.sagitta
return self.arcHeight
I have not progressed very far with the main program as one of the first tasks im aiming to do is create the y values. This is what I have so far -
from circle import Circle
import numpy as np
import matplotlib.pyplot as plt
def main():
#define centre point
#Circle(radius,arc length)
c1 = Circle(3440.065,35) #Nautical miles radius with 35Nm arc length
chordLength = c1.getChordLength()
arcHeight = c1.getArcHeight()
centerX = chordLength/2
centerY = 0
if __name__ == "__main__":
main()
For context, I wish to use this 'arc' to add elevation data to, akin to - https://link.ui.com/#. I hope to simulate increased curvature over distance which I can use for rough line of sight analysis.
However, first step is getting the y values.
Here is the final solution, I'm not 100% about the maths and how it all works but if anyone is struggling with the same problem - I hope this helps.
The circle class can be found in the original question, located below. Find attached the final code which provides me with what I was after - simulating the curvature of the earth on a graph based upon the arc length (great circle distance).
Big thank you to all who took the time to answer me and help me along my way.
from circle import Circle
import numpy as np
import matplotlib.pyplot as plt
def calcStartAngle(startY,centreY,startX,centreX):
startAngle = np.arctan2(startY-centreY, startX-centreX)
return startAngle
def calcEndAngle(endY,centreY,endX,centreX):
endAngle = np.arctan2(endY-centreY, endX-centreX)
return endAngle
def main():
distance = 200
radius = 3440.065
#create circle object
c1 = Circle(radius,distance)
angle = c1.getDegrees()
xc = c1.getXc()
yc = c1.getYc()
#set start and end points
x1,y1 = 0,0
x2,y2 = distance,0
#get start and end angles
startAngle = calcStartAngle(y1,yc,x1,xc)
endAngle = calcEndAngle(y2,yc,x2,xc)
angleList = np.linspace(startAngle,endAngle,distance)
x_values = np.linspace(x1,x2,distance)
y_valuesList = []
for i in range(len(x_values)):
y = radius*np.sin(angleList[i]) - c1.getArcHeight()
y_valuesList.append(y)
#Create numpy array to hold y values
y_values = np.array(y_valuesList)
plt.ylim(0,50)
plt.plot(x_values,y_values)
plt.show()
if __name__ == "__main__":
main()
Here is an example of the finished product -
Related
Here is the code that should calulate the motion but it is producing a line instead of a parabola, any help is appreciate.
import math as m
import matplotlib.pyplot as plt
import numpy as np
class Projectile_motion:
def __init__(self, V_x, V_y, g, delta_time):
self.gravity = g
self.V_x = V_x
self.V_y = V_y
self.delta_time = delta_time
def velocity(self, angle):
self.angle = angle
self.V_x= m.ceil(self.V_x *m.cos(self.angle))
self.V_y = m.ceil((self.V_y * m.sin(self.angle))) - (self.gravity * self.delta_time)
def distance(self, x, y):
self.x = x
self.y = y
self.x = self.x + (self.V_x * self.delta_time)
self.y = self.y + (self.V_y * m.sin(self.angle)) + (0.5 * self.gravity * ((self.delta_time)**2))
return self.x, self.y
ww = np.linspace(0, 50, num=5)
for i in ww:
attempt1 = Projectile_motion(30, 30, 9.8, i)
attempt1.velocity(1.042)
ss=attempt1.distance(0, 0)
plt.plot(ss)
plt.show()
Output:
You are off to a good start here, but you need to clean up some of the physics in your model.
If you have a V_x and V_y, it isn’t clear what you are doing with an angle, because that is already defined by the relationship of V_x and V_y.
I would suggest you get rid of the angle for a starter. Then, you just need to do a couple things with V_y:
Update V_y repeatedly when you are running. Gravity should be increasing V_y, right? Right now your V_y does not update. It should increase by 9.8m/s^2, so that is your update every time step
Use this updated V_y to calculate the distance. So After you update V_y, just use that to change y position. You should not be doing any more math there, just y += V_y*dt
If you get that working, you can transition back to using an angle (which I assume is your initial direction) and calculate V_x and V_y by standard application of cosine and sine on the angle. Also, remember, the math module wants angular inputs in radians.
I am trying to solve an issue when two rectangles intersect/overlap each other. when this happens, i want to know if intersection is True or False. I found a solution, however it is written in C or C++. I want to write these code in Python.
Here is the source: http://www.jeffreythompson.org/collision-detection/rect-rect.php
This is literally the first line of python code I've ever written (I do know C++ however)
def rectRect(r1x, r1y, r1w, r1h, r2x, r2y, r2w, r2h):
# are the sides of one rectangle touching the other?
return r1x + r1w >= r2x and \ # r1 right edge past r2 left
r1x <= r2x + r2w and \ # r1 left edge past r2 right
r1y + r1h >= r2y and \ # r1 top edge past r2 bottom
r1y <= r2y + r2h # r1 bottom edge past r2 top
IMHO rectRect is a really bad name for the function, I kept it from the linked code however.
Following is simple class that can perform both rectangle-rectangle intersection as well as point to rectangle intersection. The difference between earlier solution is that following snippet allows even detection of rotated rectangles.
import numpy as np
import matplotlib.pyplot as plt
class Rectangle:
def __init__(self, center: np.ndarray, dims: np.ndarray, angle: float):
self.corners = self.get_rect_points(center, dims, angle)
self.area = dims[0] * dims[1]
#staticmethod
def get_rect_points(center: np.ndarray, dims: np.ndarray, angle: float):
"""
returns four corners of the rectangle.
bottom left is the first conrner, from there it goes
counter clockwise.
"""
center = np.asarray(center)
length, breadth = dims
angle = np.deg2rad(angle)
corners = np.array([[-length/2, -breadth/2],
[length/2, -breadth/2],
[length/2, breadth/2],
[-length/2, breadth/2]])
rot = np.array([[np.cos(angle), np.sin(angle)], [-np.sin(angle), np.cos(angle)]])
corners = rot.dot(corners.T) + center[:, None]
return corners.T
def is_point_in_collision(self, p: np.ndarray):
"""
check if a point is in collision with the rectangle.
"""
def area_triangle(a, b, c):
return abs((b[0] * a[1] - a[0] * b[1]) + (c[0] * b[1] - b[0] * c[1]) + (a[0] * c[1] - c[0] * a[1])) / 2
area = 0
area += area_triangle(self.corners[0], p, self.corners[3])
area += area_triangle(self.corners[3], p, self.corners[2])
area += area_triangle(self.corners[2], p, self.corners[1])
area += area_triangle(self.corners[1], p, self.corners[0])
return area > self.area
def is_intersect(self, rect_2: Rectangle):
"""
check if any of the four corners of the
rectangle is in collision
"""
if not np.all([self.is_point_in_collision(c) for c in rect_2.corners]):
return True
return False
def plot_rect(p1, p2, p3, p4, color='r'):
ax.plot([p1[0], p2[0]], [p1[1], p2[1]], color)
ax.plot([p2[0], p3[0]], [p2[1], p3[1]], color)
ax.plot([p3[0], p4[0]], [p3[1], p4[1]], color)
ax.plot([p4[0], p1[0]], [p4[1], p1[1]], color)
mid_point = 0.5 * (p1 + p3)
plt.scatter(mid_point[0], mid_point[1], marker='*')
plt.xlim([-1, 1])
plt.ylim([-1, 1])
Following are two samples:
Sample 1:
ax = plt.subplot(111)
st = Rectangle((0.067, 0.476),(0.61, 0.41), 90)
gripper = Rectangle((-0.367, 0.476),(0.21,0.16), 45)
plot_rect(*st.corners)
plot_rect(*gripper.corners)
plt.show()
print(f"gripper and rectangle intersect: {st.is_intersect(gripper)}")
Sample 2:
ax = plt.subplot(111)
st = Rectangle((0.067, 0.476),(0.61, 0.41), 90)
gripper = Rectangle((-0.167, 0.476),(0.21,0.16), 45)
plot_rect(*st.corners)
plot_rect(*gripper.corners)
plt.show()
print(f"gripper and rectangle intersect: {st.is_intersect(gripper)}")
I want to make a program which plots a Sierpinsky triangle (of any modulo). In order to do it I've used TkInter. The program generates the fractal by moving a point randomly, always keeping it in the sides. After repeating the process many times, the fractal appears.
However, there's a problem. I don't know how to plot points on a canvas in TkInter. The rest of the program is OK, but I had to "cheat" in order to plot the points by drawing small lines instead of points. It works more or less, but it doesn't have as much resolution as it could have.
Is there a function to plot points on a canvas, or another tool to do it (using Python)? Ideas for improving the rest of the program are also welcome.
Thanks. Here's what I have:
from tkinter import *
import random
import math
def plotpoint(x, y):
global canvas
point = canvas.create_line(x-1, y-1, x+1, y+1, fill = "#000000")
x = 0 #Initial coordinates
y = 0
#x and y will always be in the interval [0, 1]
mod = int(input("What is the modulo of the Sierpinsky triangle that you want to generate? "))
points = int(input("How many points do you want the triangle to have? "))
tkengine = Tk() #Window in which the triangle will be generated
window = Frame(tkengine)
window.pack()
canvas = Canvas(window, height = 700, width = 808, bg = "#FFFFFF") #The dimensions of the canvas make the triangle look equilateral
canvas.pack()
for t in range(points):
#Procedure for placing the points
while True:
#First, randomly choose one of the mod(mod+1)/2 triangles of the first step. a and b are two vectors which point to the chosen triangle. a goes one triangle to the right and b one up-right. The algorithm gives the same probability to every triangle, although it's not efficient.
a = random.randint(0,mod-1)
b = random.randint(0,mod-1)
if a + b < mod:
break
#The previous point is dilated towards the origin of coordinates so that the big triangle of step 0 becomes the small one at the bottom-left of step one (divide by modulus). Then the vectors are added in order to move the point to the same place in another triangle.
x = x / mod + a / mod + b / 2 / mod
y = y / mod + b / mod
#Coordinates [0,1] converted to pixels, for plotting in the canvas.
X = math.floor(x * 808)
Y = math.floor((1-y) * 700)
plotpoint(X, Y)
tkengine.mainloop()
If you are wanting to plot pixels, a canvas is probably the wrong choice. You can create a PhotoImage and modify individual pixels. It's a little slow if you plot each individual pixel, but you can get dramatic speedups if you only call the put method once for each row of the image.
Here's a complete example:
from tkinter import *
import random
import math
def plotpoint(x, y):
global the_image
the_image.put(('#000000',), to=(x,y))
x = 0
y = 0
mod = 3
points = 100000
tkengine = Tk() #Window in which the triangle will be generated
window = Frame(tkengine)
window.pack()
the_image = PhotoImage(width=809, height=700)
label = Label(window, image=the_image, borderwidth=2, relief="raised")
label.pack(fill="both", expand=True)
for t in range(points):
while True:
a = random.randint(0,mod-1)
b = random.randint(0,mod-1)
if a + b < mod:
break
x = x / mod + a / mod + b / 2 / mod
y = y / mod + b / mod
X = math.floor(x * 808)
Y = math.floor((1-y) * 700)
plotpoint(X, Y)
tkengine.mainloop()
You can use canvas.create_oval with the same coordinates for the two corners of the bounding box:
from tkinter import *
import random
import math
def plotpoint(x, y):
global canvas
# point = canvas.create_line(x-1, y-1, x+1, y+1, fill = "#000000")
point = canvas.create_oval(x, y, x, y, fill="#000000", outline="#000000")
x = 0 #Initial coordinates
y = 0
#x and y will always be in the interval [0, 1]
mod = int(input("What is the modulo of the Sierpinsky triangle that you want to generate? "))
points = int(input("How many points do you want the triangle to have? "))
tkengine = Tk() #Window in which the triangle will be generated
window = Frame(tkengine)
window.pack()
canvas = Canvas(window, height = 700, width = 808, bg = "#FFFFFF") #The dimensions of the canvas make the triangle look equilateral
canvas.pack()
for t in range(points):
#Procedure for placing the points
while True:
#First, randomly choose one of the mod(mod+1)/2 triangles of the first step. a and b are two vectors which point to the chosen triangle. a goes one triangle to the right and b one up-right. The algorithm gives the same probability to every triangle, although it's not efficient.
a = random.randint(0,mod-1)
b = random.randint(0,mod-1)
if a + b < mod:
break
#The previous point is dilated towards the origin of coordinates so that the big triangle of step 0 becomes the small one at the bottom-left of step one (divide by modulus). Then the vectors are added in order to move the point to the same place in another triangle.
x = x / mod + a / mod + b / 2 / mod
y = y / mod + b / mod
#Coordinates [0,1] converted to pixels, for plotting in the canvas.
X = math.floor(x * 808)
Y = math.floor((1-y) * 700)
plotpoint(X, Y)
tkengine.mainloop()
with a depth of 3 and 100,000 points, this gives:
Finally found a solution: if a 1x1 point is to be placed in pixel (x,y), a command which does it exactly is:
point = canvas.create_line(x, y, x+1, y+1, fill = "colour")
The oval is a good idea for 2x2 points.
Something remarkable about the original program is that it uses a lot of RAM if every point is treated as a separate object.
I have gotten as far as making a set of rays, but I need to connect them. Any help? My code is as follows
from math import *
from graphics import *
i = 1
segments = 15
lastPoint = Point(100,0)
print("Begin")
win = GraphWin("Trigonometry", 1500, 1500)
while i<=segments:
angle =i*pi/segments
y = int(sin(angle)*100)
x = int(cos(angle)*100)
i = i+1
p = Point(x,y)
l = Line(p, lastPoint)
l.draw(win)
print(p.x, p.y)
print("End")
OP code draws only "rays" because, while point p lays on the circle, lastPoint doesn't change between iterations.
We have to update the value of lastPoint to literally the last point calculated in order to draw the arc as a series of consecutive segments.
Here is a modified code, with further explanations as asked by OP in his comment:
from math import *
from graphics import *
# Function to calculate the integer coordinates of a Point on a circle
# given the center (c, a Point), the radius (r) and the angle (a, radians)
def point_on_circle( c, r, a ) :
return Point( int(round(c.x + r*cos(a))), int(round(c.y + r*sin(a))) )
# Define the graphical output window, I'll set the coordinates system so
# that the origin is the bottom left corner of the windows, y axis is going
# upwards and 1 unit corresponds to 1 pixel
win = GraphWin("Trigonometry", 800, 600)
win.setCoords(0,0,800,600)
# Arc data. Angles are in degrees (more user friendly, but later will be
# transformed in radians for calculations), 0 is East, positive values
# are counterclockwise. A value of 360 for angle_range_deg gives a complete
# circle (polygon).
angle_start_deg = 0
angle_range_deg = 90
center = Point(10,10)
radius = 200
segments = 16
angle_start = radians(angle_start_deg)
angle_step = radians(angle_range_deg) / segments
# Initialize lastPoint with the position corresponding to angle_start
# (or i = 0). Try different values of all the previous variables
lastPoint = point_on_circle(center, radius, angle_start)
print("Begin")
i = 1
while i <= segments :
# update the angle to calculate a new point on the circle
angle = angle_start + i * angle_step
p = point_on_circle(center, radius, angle)
# draw a line between the last two points
l = Line(p, lastPoint)
l.draw(win)
print(p.x, p.y)
# update the variables to move on to the next segment which share an edge
# (the last point) with the previous segment
i = i + 1
lastPoint = p
print("End")
I am trying to modify this custom-projection example:
http://matplotlib.org/examples/api/custom_projection_example.html
to display a Schmidt plot. The mathematics behind the projection are explained e.g. here:
https://bearspace.baylor.edu/Vince_Cronin/www/StructGeol/StructLabBk3.html
I made some modifications of the example which brought me closer to the solution but I am still doing something wrong. Anything I change within the function transform_non_affine makes the plot look worse. It would be great if somebody could explain to me how this function can be modified.
I also looked at the example at
https://github.com/joferkington/mplstereonet/blob/master/mplstereonet/stereonet_transforms.py
but couldn't really figure out how to translate that into the example.
def transform_non_affine(self, ll):
"""
Override the transform_non_affine method to implement the custom
transform.
The input and output are Nx2 numpy arrays.
"""
longitude = ll[:, 0:1]
latitude = ll[:, 1:2]
# Pre-compute some values
half_long = longitude / 2.0
cos_latitude = np.cos(latitude)
sqrt2 = np.sqrt(2.0)
alpha = 1.0 + cos_latitude * np.cos(half_long)
x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha
y = (sqrt2 * np.sin(latitude)) / alpha
return np.concatenate((x, y), 1)
The whole code can be run and shows the result:
import matplotlib
from matplotlib.axes import Axes
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import NullLocator, Formatter, FixedLocator
from matplotlib.transforms import Affine2D, BboxTransformTo, Transform
from matplotlib.projections import register_projection, LambertAxes
import matplotlib.spines as mspines
import matplotlib.axis as maxis
import matplotlib.pyplot as plt
import numpy as np
class SchmidtProjection(Axes):
'''Class defines the new projection'''
name = 'SchmidtProjection'
def __init__(self, *args, **kwargs):
'''Call self, set aspect ratio and call default values'''
Axes.__init__(self, *args, **kwargs)
self.set_aspect(1.0, adjustable='box', anchor='C')
self.cla()
def _init_axis(self):
'''Initialize axis'''
self.xaxis = maxis.XAxis(self)
self.yaxis = maxis.YAxis(self)
# Do not register xaxis or yaxis with spines -- as done in
# Axes._init_axis() -- until HammerAxes.xaxis.cla() works.
# self.spines['hammer'].register_axis(self.yaxis)
self._update_transScale()
def cla(self):
'''Calls Axes.cla and overrides some functions to set new defaults'''
Axes.cla(self)
self.set_longitude_grid(10)
self.set_latitude_grid(10)
self.set_longitude_grid_ends(80)
self.xaxis.set_minor_locator(NullLocator())
self.yaxis.set_minor_locator(NullLocator())
self.xaxis.set_ticks_position('none')
self.yaxis.set_ticks_position('none')
# The limits on this projection are fixed -- they are not to
# be changed by the user. This makes the math in the
# transformation itself easier, and since this is a toy
# example, the easier, the better.
Axes.set_xlim(self, -np.pi, np.pi)
Axes.set_ylim(self, -np.pi, np.pi)
def _set_lim_and_transforms(self):
'''This is called once when the plot is created to set up all the
transforms for the data, text and grids.'''
# There are three important coordinate spaces going on here:
# 1. Data space: The space of the data itself
# 2. Axes space: The unit rectangle (0, 0) to (1, 1)
# covering the entire plot area.
# 3. Display space: The coordinates of the resulting image,
# often in pixels or dpi/inch.
# This function makes heavy use of the Transform classes in
# ``lib/matplotlib/transforms.py.`` For more information, see
# the inline documentation there.
# The goal of the first two transformations is to get from the
# data space (in this case longitude and latitude) to axes
# space. It is separated into a non-affine and affine part so
# that the non-affine part does not have to be recomputed when
# a simple affine change to the figure has been made (such as
# resizing the window or changing the dpi).
# 1) The core transformation from data space into
# rectilinear space defined in the SchmidtTransform class.
self.transProjection = self.SchmidtTransform()
#Plot should extend 180° = pi/2 NS and EW
xscale = np.pi/2
yscale = np.pi/2
#The radius of the circle (0.5) is divided by the scale.
self.transAffine = Affine2D() \
.scale(0.5 / xscale, 0.5 / yscale) \
.translate(0.5, 0.5)
# 3) This is the transformation from axes space to display
# space.
self.transAxes = BboxTransformTo(self.bbox)
# Now put these 3 transforms together -- from data all the way
# to display coordinates. Using the '+' operator, these
# transforms will be applied "in order". The transforms are
# automatically simplified, if possible, by the underlying
# transformation framework.
self.transData = \
self.transProjection + \
self.transAffine + \
self.transAxes
# The main data transformation is set up. Now deal with
# gridlines and tick labels.
# Longitude gridlines and ticklabels. The input to these
# transforms are in display space in x and axes space in y.
# Therefore, the input values will be in range (-xmin, 0),
# (xmax, 1). The goal of these transforms is to go from that
# space to display space. The tick labels will be offset 4
# pixels from the equator.
self._xaxis_pretransform = \
Affine2D() \
.scale(1.0, np.pi) \
.translate(0.0, -np.pi)
self._xaxis_transform = \
self._xaxis_pretransform + \
self.transData
self._xaxis_text1_transform = \
Affine2D().scale(1.0, 0.0) + \
self.transData + \
Affine2D().translate(0.0, 4.0)
self._xaxis_text2_transform = \
Affine2D().scale(1.0, 0.0) + \
self.transData + \
Affine2D().translate(0.0, -4.0)
# Now set up the transforms for the latitude ticks. The input to
# these transforms are in axes space in x and display space in
# y. Therefore, the input values will be in range (0, -ymin),
# (1, ymax). The goal of these transforms is to go from that
# space to display space. The tick labels will be offset 4
# pixels from the edge of the axes ellipse.
yaxis_stretch = Affine2D().scale(np.pi * 2.0, 1.0).translate(-np.pi, 0.0)
yaxis_space = Affine2D().scale(1.0, 1.1)
self._yaxis_transform = \
yaxis_stretch + \
self.transData
yaxis_text_base = \
yaxis_stretch + \
self.transProjection + \
(yaxis_space + \
self.transAffine + \
self.transAxes)
self._yaxis_text1_transform = \
yaxis_text_base + \
Affine2D().translate(-8.0, 0.0)
self._yaxis_text2_transform = \
yaxis_text_base + \
Affine2D().translate(8.0, 0.0)
def set_rotation(self, rotation):
"""Set the rotation of the stereonet in degrees clockwise from North."""
self._rotation = np.radians(90)
self._polar.set_theta_offset(self._rotation + np.pi / 2.0)
self.transData.invalidate()
self.transAxes.invalidate()
self._set_lim_and_transforms()
def get_xaxis_transform(self,which='grid'):
"""
Override this method to provide a transformation for the
x-axis grid and ticks.
"""
assert which in ['tick1','tick2','grid']
return self._xaxis_transform
def get_xaxis_text1_transform(self, pixelPad):
"""
Override this method to provide a transformation for the
x-axis tick labels.
Returns a tuple of the form (transform, valign, halign)
"""
return self._xaxis_text1_transform, 'bottom', 'center'
def get_xaxis_text2_transform(self, pixelPad):
"""
Override this method to provide a transformation for the
secondary x-axis tick labels.
Returns a tuple of the form (transform, valign, halign)
"""
return self._xaxis_text2_transform, 'top', 'center'
def get_yaxis_transform(self,which='grid'):
"""
Override this method to provide a transformation for the
y-axis grid and ticks.
"""
assert which in ['tick1','tick2','grid']
return self._yaxis_transform
def get_yaxis_text1_transform(self, pixelPad):
"""
Override this method to provide a transformation for the
y-axis tick labels.
Returns a tuple of the form (transform, valign, halign)
"""
return self._yaxis_text1_transform, 'center', 'right'
def get_yaxis_text2_transform(self, pixelPad):
"""
Override this method to provide a transformation for the
secondary y-axis tick labels.
Returns a tuple of the form (transform, valign, halign)
"""
return self._yaxis_text2_transform, 'center', 'left'
def _gen_axes_patch(self):
"""
Override this method to define the shape that is used for the
background of the plot. It should be a subclass of Patch.
In this case, it is a Circle (that may be warped by the axes
transform into an ellipse). Any data and gridlines will be
clipped to this shape.
"""
return Circle((0.5, 0.5), 0.5)
def _gen_axes_spines(self):
return {'SchmidtProjection':mspines.Spine.circular_spine(self,
(0.5, 0.5), 0.5)}
# Prevent the user from applying scales to one or both of the
# axes. In this particular case, scaling the axes wouldn't make
# sense, so we don't allow it.
def set_xscale(self, *args, **kwargs):
if args[0] != 'linear':
raise NotImplementedError
Axes.set_xscale(self, *args, **kwargs)
def set_yscale(self, *args, **kwargs):
if args[0] != 'linear':
raise NotImplementedError
Axes.set_yscale(self, *args, **kwargs)
# Prevent the user from changing the axes limits. In our case, we
# want to display the whole sphere all the time, so we override
# set_xlim and set_ylim to ignore any input. This also applies to
# interactive panning and zooming in the GUI interfaces.
def set_xlim(self, *args, **kwargs):
Axes.set_xlim(self, -np.pi, np.pi)
Axes.set_ylim(self, -np.pi / 2.0, np.pi / 2.0)
set_ylim = set_xlim
def format_coord(self, lon, lat):
"""
Override this method to change how the values are displayed in
the status bar.
In this case, we want them to be displayed in degrees N/S/E/W.
"""
lon = lon * (180.0 / np.pi)
lat = lat * (180.0 / np.pi)
if lat >= 0.0:
ns = 'N'
else:
ns = 'S'
if lon >= 0.0:
ew = 'E'
else:
ew = 'W'
#return '%f°%s, %f°%s' % (abs(lat), ns, abs(lon), ew)
coord_string = ("{0} / {1}".format(round(lon, 2), round(lat,2)))
return coord_string
class LatitudeFormatter(Formatter):
"""
Custom formatter for Latitudes
"""
def __init__(self, round_to=1.0):
self._round_to = round_to
def __call__(self, x, pos=None):
degrees = np.degrees(x)
degrees = round(degrees / self._round_to) * self._round_to
return "%d°" % degrees
class LongitudeFormatter(Formatter):
"""
Custom formatter for Longitudes
"""
def __init__(self, round_to=1.0):
self._round_to = round_to
def __call__(self, x, pos=None):
degrees = np.degrees(x)
degrees = round(degrees / self._round_to) * self._round_to
return ""
def set_longitude_grid(self, degrees):
"""
Set the number of degrees between each longitude grid.
This is an example method that is specific to this projection
class -- it provides a more convenient interface to set the
ticking than set_xticks would.
"""
# Set up a FixedLocator at each of the points, evenly spaced
# by degrees.
number = (360.0 / degrees) + 1
self.xaxis.set_major_locator(
plt.FixedLocator(
np.linspace(-np.pi, np.pi, number, True)[1:-1]))
# Set the formatter to display the tick labels in degrees,
# rather than radians.
self.xaxis.set_major_formatter(self.LongitudeFormatter(degrees))
def set_latitude_grid(self, degrees):
"""
Set the number of degrees between each longitude grid.
This is an example method that is specific to this projection
class -- it provides a more convenient interface than
set_yticks would.
"""
# Set up a FixedLocator at each of the points, evenly spaced
# by degrees.
number = (180.0 / degrees) + 1
self.yaxis.set_major_locator(
FixedLocator(
np.linspace(-np.pi / 2.0, np.pi / 2.0, number, True)[1:-1]))
# Set the formatter to display the tick labels in degrees,
# rather than radians.
self.yaxis.set_major_formatter(self.LatitudeFormatter(degrees))
def set_longitude_grid_ends(self, degrees):
"""
Set the latitude(s) at which to stop drawing the longitude grids.
Often, in geographic projections, you wouldn't want to draw
longitude gridlines near the poles. This allows the user to
specify the degree at which to stop drawing longitude grids.
This is an example method that is specific to this projection
class -- it provides an interface to something that has no
analogy in the base Axes class.
"""
longitude_cap = np.radians(degrees)
# Change the xaxis gridlines transform so that it draws from
# -degrees to degrees, rather than -pi to pi.
self._xaxis_pretransform \
.clear() \
.scale(1.0, longitude_cap * 2.0) \
.translate(0.0, -longitude_cap)
def get_data_ratio(self):
"""
Return the aspect ratio of the data itself.
This method should be overridden by any Axes that have a
fixed data ratio.
"""
return 1.0
# Interactive panning and zooming is not supported with this projection,
# so we override all of the following methods to disable it.
def can_zoom(self):
"""
Return True if this axes support the zoom box
"""
return False
def start_pan(self, x, y, button):
pass
def end_pan(self):
pass
def drag_pan(self, button, key, x, y):
pass
# Now, the transforms themselves.
class SchmidtTransform(Transform):
"""
The base Hammer transform.
"""
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self):
"""
Create a new transform. Resolution is the number of steps to
interpolate between each input line segment to approximate its path in
projected space.
"""
Transform.__init__(self)
self._resolution = 10
self._center_longitude = 0
self._center_latitude = 0
def transform_non_affine(self, ll):
"""
Override the transform_non_affine method to implement the custom
transform.
The input and output are Nx2 numpy arrays.
"""
longitude = ll[:, 0:1]
latitude = ll[:, 1:2]
# Pre-compute some values
half_long = longitude / 2.0
cos_latitude = np.cos(latitude)
sqrt2 = np.sqrt(2.0)
alpha = 1.0 + cos_latitude * np.cos(half_long)
x = (2.0 * sqrt2) * (cos_latitude * np.sin(half_long)) / alpha
y = (sqrt2 * np.sin(latitude)) / alpha
return np.concatenate((x, y), 1)
# This is where things get interesting. With this projection,
# straight lines in data space become curves in display space.
# This is done by interpolating new values between the input
# values of the data. Since ``transform`` must not return a
# differently-sized array, any transform that requires
# changing the length of the data array must happen within
# ``transform_path``.
def transform_path_non_affine(self, path):
ipath = path.interpolated(path._interpolation_steps)
return Path(self.transform(ipath.vertices), ipath.codes)
transform_path_non_affine.__doc__ = \
Transform.transform_path_non_affine.__doc__
if matplotlib.__version__ < '1.2':
# Note: For compatibility with matplotlib v1.1 and older, you'll
# need to explicitly implement a ``transform`` method as well.
# Otherwise a ``NotImplementedError`` will be raised. This isn't
# necessary for v1.2 and newer, however.
transform = transform_non_affine
# Similarly, we need to explicitly override ``transform_path`` if
# compatibility with older matplotlib versions is needed. With v1.2
# and newer, only overriding the ``transform_path_non_affine``
# method is sufficient.
transform_path = transform_path_non_affine
transform_path.__doc__ = Transform.transform_path.__doc__
def inverted(self):
return SchmidtProjection.InvertedSchmidtTransform()
inverted.__doc__ = Transform.inverted.__doc__
class InvertedSchmidtTransform(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def transform_non_affine(self, xy):
x = xy[:, 0:1]
y = xy[:, 1:2]
quarter_x = 0.25 * x
half_y = 0.5 * y
z = np.sqrt(1.0 - quarter_x*quarter_x - half_y*half_y)
longitude = 2 * np.arctan((z*x) / (2.0 * (2.0*z*z - 1.0)))
latitude = np.arcsin(y*z)
return np.concatenate((longitude, latitude), 1)
transform_non_affine.__doc__ = Transform.transform_non_affine.__doc__
# As before, we need to implement the "transform" method for
# compatibility with matplotlib v1.1 and older.
if matplotlib.__version__ < '1.2':
transform = transform_non_affine
def inverted(self):
return SchmidtProjection.SchmidtTransform()
inverted.__doc__ = Transform.inverted.__doc__
# Now register the projection with matplotlib so the user can select
# it.
register_projection(SchmidtProjection)
if __name__ == '__main__':
plt.subplot(111, projection="SchmidtProjection")
plt.grid(True)
plt.show()
Edit 1
This is the closest I get to the wanted solution:
With this code:
class SchmidtTransform(Transform):
input_dims = 2
output_dims = 2
is_separable = False
def __init__(self):
Transform.__init__(self)
self._resolution = 100
self._center_longitude = 0
self._center_latitude = 0
def transform_non_affine(self, ll):
longitude = ll[:, 0:1]
latitude = ll[:, 1:2]
clong = self._center_longitude
clat = self._center_latitude
cos_lat = np.cos(latitude)
sin_lat = np.sin(latitude)
diff_long = longitude - clong
cos_diff_long = np.cos(diff_long)
inner_k = (1.0 + np.sin(clat)*sin_lat + np.cos(clat)*cos_lat*cos_diff_long)
# Prevent divide-by-zero problems
inner_k = np.where(inner_k == 0.0, 1e-15, inner_k)
k = np.sqrt(2.0 / inner_k)
x = k*cos_lat*np.sin(diff_long)
y = k*(np.cos(clat)*sin_lat - np.sin(clat)*cos_lat*cos_diff_long)
return np.concatenate((x, y), 1)
Is there maybe a way to just do this with a regular transformation matrix? I can get the math to work with a transformation matrix, but I don't really understand at which place of the projection code I have to change what.
I could figure out the next step by reading the chapter about Lambert azimuthal equal-area projections in Map projections: A Working Manual - John Parr Snyder 1987 - Page 182 and following (http://pubs.er.usgs.gov/publication/pp1395).
The projection I was actually looking for was the one with Equatorial aspect.
The two formulas that are required for the transformation are (radius is not required for the later code):
y = R * k' * sin(phi)
x = R * k' * cos(phi) sin(lambda - lambda0)
With k being:
k = sqrt( 2 / (1 + cos(phi) cos(lambda - lambda0))
I got some errors, which turned out to be infinite values and divisions by zero, so I added some checks. Still getting some weird label placements, but that might be going off-topic in this question. The very rough code I have running now is:
def transform_non_affine(self, ll):
xi = ll[:, 0:1]
yi = ll[:, 1:2]
k = 1 + np.absolute(cos(yi) * cos(xi))
k = 2 / k
if np.isposinf(k[0]) == True:
k[0] = 1e+15
if np.isneginf(k[0]) == True:
k[0] = -1e+15
if k[0] == 0:
k[0] = 1e-15
k = sqrt(k)
x = k * cos(yi) * sin(xi)
y = k * sin(yi)
return np.concatenate((x, y), 1)