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I have an image with width: 1980 and height: 1080.
Ultimately, I want to place various shapes within the image, but at random locations and in such a way that they do not overlap. The 0,0 coordinates of the image are in the center.
Before rendering the shapes into the image (I don't need help with this), I need to write an algorithm to generate the XY points/locations. I want to be able to specify the minimum distance any given point is allowed to get to any other points.
How can do this?
All I have been able to do, is to generate points at equally spaced locations and then add a bit of randomness to each point. But this is not ideal, because it means points just vary within some 'cell' within a grid, and if the randomness value is too high, they will appear outside of the rectangle. Here is my code:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from random import randrange
def is_square(integer):
root = np.sqrt(integer)
return integer == int(root + 0.5) ** 2
def perfect_sqr(n):
nextN = np.floor(np.sqrt(n)) + 1
return int(nextN * nextN)
def generate_cells(width = 1920, height = 1080, n = 9, show_plot=False):
# If the number is not a perfect square, we need to find the next number which is
# so that we can get the root N, which will be used to determine the number of rows/columns
if not is_square(n):
n = perfect_sqr(n)
N = np.sqrt(n)
# generate x and y lists, where each represents an array of points evenly spaced between 0 and the width/height
x = np.array(list(range(0, width, int(width/N))))
y = np.array(list(range(0, height, int(height/N))))
# center the points within each 'cell'
x_centered = x+int(width/N)/2
y_centered = y+int(height/N)/2
x_centered = [a+randrange(50) for a in x_centered]
y_centered = [a+randrange(50) for a in y_centered]
# generate a grid with the points
xv, yv = np.meshgrid(x_centered, y_centered)
if(show_plot):
plt.scatter(xv,yv)
plt.gca().add_patch(Rectangle((0,0),width, height,edgecolor='red', facecolor='none', lw=1))
plt.show()
# convert the arrays to 1D
xx = xv.flatten()
yy = yv.flatten()
# Merge them side-by-side
zips = zip(xx, yy)
# convert to set of points/tuples and return
return set(zips)
coords = generate_cells(width=1920, height=1080, n=15, show_plot=True)
print(coords)
Assuming you simply want to randomly define non-overlapping coordinates within the confines of a maximum image size subject to not having images overlap, this might be a good solution.
import numpy as np
def locateImages(field_height: int, field_width: int, min_sep: int, points: int)-> np.array:
h_range = np.array(range(min_sep//2, field_height - (min_sep//2), min_sep))
w_range = np.array(range(min_sep//2, field_width-(min_sep//2), min_sep))
mx_len = max(len(h_range), len(w_range))
if len(h_range) < mx_len:
xtra = np.random.choice(h_range, mx_len - len(h_range))
h_range = np.append(h_range, xtra)
if len(w_range) < mx_len:
xtra = np.random.choice(w_range, mx_len - len(w_range))
w_range = np.append(w_range, xtra)
h_points = np.random.choice(h_range, points, replace=False)
w_points = np.random.choice(w_range, points, replace=False)
return np.concatenate((np.vstack(h_points), np.vstack(w_points)), axis= 1)
Then given:
field_height = the vertical coordinate of the Image space
field_width = the maximum horizontal coordinate of the Image space
min_sep = the minimum spacing between images
points = number of coordinates to be selected
Then:
locateImages(15, 8, 2, 5) will yield:
array([[13, 1],
[ 7, 3],
[ 1, 5],
[ 5, 5],
[11, 5]])
Render the output:
points = locateImages(1080, 1920, 100, 15)
x,y= zip(*points)
plt.scatter(x,x)
plt.gca().add_patch(Rectangle((0,0),1920, 1080,edgecolor='red', facecolor='none', lw=1))
plt.show()
I want to draw a volume in x1,x2,x3-space. The volume is an isocurve found by the marching cubes algorithm in skimage. The function generating the volume is pdf_grid = f(x1,x2,x3) and
I want to draw the volume where pdf = 60% max(pdf).
My issue is that the marching cubes algorithm generates vertices and faces, but how do I map those back to the x1, x2, x3-space?
My (rather limited) understanding of marching cubes is that "vertices" refer to the indices in the volume (pdf_grid in my case). If "vertices" contained only the exact indices in the grid this would have been easy, but "vertices" contains floats and not integers. It seems like marching cubes do some interpolation between grid points (according to https://www.cs.carleton.edu/cs_comps/0405/shape/marching_cubes.html), so the question is then how to recover exactly the values of x1,x2,x3?
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
#Make some random data
cov = np.array([[1, .2, -.5],
[.2, 1.2, .1],
[-.5, .1, .8]])
dist = scipy.stats.multivariate_normal(mean = [1., 3., 2], cov = cov)
N = 500
x_samples = dist.rvs(size=N).T
#Create the kernel density estimator - approximation of a pdf
kernel = scipy.stats.gaussian_kde(x_samples)
x_mean = x_samples.mean(axis=1)
#Find the mode
res = scipy.optimize.minimize(lambda x: -kernel.logpdf(x),
x_mean #x0, initial guess
)
x_mode = res["x"]
num_el = 50 #number of elements in the grid
x_min = np.min(x_samples, axis = 1)
x_max = np.max(x_samples, axis = 1)
x1g, x2g, x3g = np.mgrid[x_min[0]:x_max[0]:num_el*1j,
x_min[1]:x_max[1]:num_el*1j,
x_min[2]:x_max[2]:num_el*1j
]
pdf_grid = np.zeros(x1g.shape) #implicit function/grid for the marching cubes
for an in range(x1g.shape[0]):
for b in range(x1g.shape[1]):
for c in range(x1g.shape[2]):
pdf_grid[a,b,c] = kernel(np.array([x1g[a,b,c],
x2g[a,b,c],
x3g[a,b,c]]
))
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from skimage import measure
iso_level = .6 #draw a volume which contains pdf_val(mode)*60%
verts, faces, normals, values = measure.marching_cubes(pdf_grid, kernel(x_mode)*iso_level)
#How to convert the figure back to x1,x2,x3 space? I just draw the output as it was done in the skimage example here https://scikit-image.org/docs/0.16.x/auto_examples/edges/plot_marching_cubes.html#sphx-glr-auto-examples-edges-plot-marching-cubes-py so you can see the volume
# Fancy indexing: `verts[faces]` to generate a collection of triangles
mesh = Poly3DCollection(verts[faces],
alpha = .5,
label = f"KDE = {iso_level}"+r"$x_{mode}$",
linewidth = .1)
mesh.set_edgecolor('k')
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
c1 = ax.add_collection3d(mesh)
c1._facecolors2d=c1._facecolor3d
c1._edgecolors2d=c1._edgecolor3d
#Plot the samples. Marching cubes volume does not capture these samples
pdf_val = kernel(x_samples) #get density value for each point (for color-coding)
x1, x2, x3 = x_samples
scatter_plot = ax.scatter(x1, x2, x3, c=pdf_val, alpha = .2, label = r" samples")
ax.scatter(x_mode[0], x_mode[1], x_mode[2], c = "r", alpha = .2, label = r"$x_{mode}$")
ax.set_xlabel(r"$x_1$")
ax.set_ylabel(r"$x_2$")
ax.set_zlabel(r"$x_3$")
# ax.set_box_aspect([np.ptp(i) for me in x_samples]) # equal aspect ratio
cbar = fig.color bar(scatter_plot, ax=ax)
cbar.set_label(r"$KDE(w) \approx pdf(w)$")
ax.legend()
#Make the axis limit so that the volume and samples are shown.
ax.set_xlim(- 5, np.max(verts, axis=0)[0] + 3)
ax.set_ylim(- 5, np.max(verts, axis=0)[1] + 3)
ax.set_zlim(- 5, np.max(verts, axis=0)[2] + 3)
This is probably way too late of an answer to help OP, but in case anyone else comes across this post looking for a solution to this problem, the issue stems from the marching cubes algorithm outputting the relevant vertices in array space. This space is defined by the number of elements per dimension of the mesh grid and the marching cubes algorithm does indeed do some interpolation in this space (explaining the presence of floats).
Anyways, in order to transform the vertices back into x1,x2,x3 space you just need to scale and shift them by the appropriate quantities. These quantities are defined by the range, number of elements of the mesh grid, and the minimum value in each dimension respectively. So using the variables defined in the OP, the following will provide the actual location of the vertices:
verts_actual = verts*((x_max-x_min)/pdf_grid.shape) + x_min
I have a big number of screenshots that need to be cropped. All the images look similar - there is a rectangular window with blue border, containing some graphical elements inside. This window is contained inside another one but I need to crop only the inner window. Across all images the dimensions of the inner window are different and so is the content. The content in most cases includes elements with rectangular form and sometimes - blue border, the same border as the inner window. I am mentioning this because I am thinking of the following flow:
A script that goes through all images in the target directory. For each of them:
Find the area to be cropped (inner window)
Crop the area
Save the file
How can this be done? Python is not compulsory, can be any other too also.
It's not straightforward but this is a possible recipe:
import matplotlib.pyplot as plt
import numpy as np
def synthimage():
w,h = 300,200
im = np.random.randint(0,255,(w,h,3))/255
xa = np.random.randint(50,w-60)
xb = xa + np.random.randint(50,90)
ya = np.random.randint(50,h-60)
yb = ya + np.random.randint(20,50)
im[xa:xb,ya] = np.array([1,0,0])
im[xa:xb,yb] = np.array([1,0,0])
im[xa,ya:yb] = np.array([1,0,0])
im[xb,ya:yb] = np.array([1,0,0])
return im
def getRectPoints(im):
x,y = [],[]
for i in range(im.shape[0]):
for j in range(im.shape[1]):
if (im[i,j]-np.array([1,0,0])).sum()==0:
x.append(i)
y.append(j)
return np.array(x),np.array(y)
def denoise(x,y):
nx,ny = [],[]
for i in range(x.shape[0]):
d = np.sqrt((x[i]-x)**2+(y[i]-y)**2)
m = d<2
if len(m.nonzero()[0])>2:
nx.append(x[i])
ny.append(y[i])
return np.array(nx),np.array(ny)
im = synthimage()
plt.imshow(np.swapaxes(im,0,1),origin='lower',interpolation='nearest')
plt.show()
x,y = getRectPoints(im)
plt.scatter(x,y,c='red')
plt.xlim(0,300)
plt.ylim(0,200)
plt.show()
nx,ny = denoise(x,y)
plt.scatter(nx,ny,c='red')
plt.xlim(0,300)
plt.ylim(0,200)
plt.show()
#Assuming rectangle has no rotation (otherwise check Scipy ConveHull)
xmi = nx.min()
xma = nx.max()
ymi = ny.min()
yma = ny.max()
new = np.ones(im.shape)
new[xmi:xma,ymi:yma] = im[xmi:xma,ymi:yma]
plt.imshow(np.swapaxes(new,0,1),origin='lower',interpolation='nearest')
plt.show()
, the name of the functions should be self-explaining. Synthetic data was generated for the purpose of this exercise. The results are (in order):
Obviously each one of this steps can be changed depending on the requirements but this would be a functional solution for the majority of case-studies.
I have figured out a method to cluster disperse point data into structured 2-d array(like rasterize function). And I hope there are some better ways to achieve that target.
My work
1. Intro
1000 point data has there dimensions of properties (lon, lat, emission) whicn represent one factory located at (x,y) emit certain amount of CO2 into atmosphere
grid network: predefine the 2-d array in the shape of 20x20
http://i4.tietuku.com/02fbaf32d2f09fff.png
The code reproduced here:
#### define the map area
xc1,xc2,yc1,yc2 = 113.49805889531724,115.5030664238035,37.39995194888143,38.789235929357105
map = Basemap(llcrnrlon=xc1,llcrnrlat=yc1,urcrnrlon=xc2,urcrnrlat=yc2)
#### reading the point data and scatter plot by their position
df = pd.read_csv("xxxxx.csv")
px,py = map(df.lon, df.lat)
map.scatter(px, py, color = "red", s= 5,zorder =3)
#### predefine the grid networks
lon_grid,lat_grid = np.linspace(xc1,xc2,21), np.linspace(yc1,yc2,21)
lon_x,lat_y = np.meshgrid(lon_grid,lat_grid)
grids = np.zeros(20*20).reshape(20,20)
plt.pcolormesh(lon_x,lat_y,grids,cmap = 'gray', facecolor = 'none',edgecolor = 'k',zorder=3)
2. My target
Finding the nearest grid point for each factory
Add the emission data into this grid number
3. Algorithm realization
3.1 Raster grid
note: 20x20 grid points are distributed in this area represented by blue dot.
http://i4.tietuku.com/8548554587b0cb3a.png
3.2 KD-tree
Find the nearest blue dot of each red point
sh = (20*20,2)
grids = np.zeros(20*20*2).reshape(*sh)
sh_emission = (20*20)
grids_em = np.zeros(20*20).reshape(sh_emission)
k = 0
for j in range(0,yy.shape[0],1):
for i in range(0,xx.shape[0],1):
grids[k] = np.array([lon_grid[i],lat_grid[j]])
k+=1
T = KDTree(grids)
x_delta = (lon_grid[2] - lon_grid[1])
y_delta = (lat_grid[2] - lat_grid[1])
R = np.sqrt(x_delta**2 + y_delta**2)
for i in range(0,len(df.lon),1):
idx = T.query_ball_point([df.lon.iloc[i],df.lat.iloc[i]], r=R)
# there are more than one blue dot which are founded sometimes,
# So I'll calculate the distances between the factory(red point)
# and all blue dots which are listed
if (idx > 1):
distance = []
for k in range(0,len(idx),1):
distance.append(np.sqrt((df.lon.iloc[i] - grids[k][0])**2 + (df.lat.iloc[i] - grids[k][1])**2))
pos_index = distance.index(min(distance))
pos = idx[pos_index]
# Only find 1 point
else:
pos = idx
grids_em[pos] += df.so2[i]
4. Result
co2 = grids_em.reshape(20,20)
plt.pcolormesh(lon_x,lat_y,co2,cmap =plt.cm.Spectral_r,zorder=3)
http://i4.tietuku.com/6ded65c4ac301294.png
5. My question
Can someone point out some drawbacks or error of this method?
Is there some algorithms more aligned with my target?
Thanks a lot!
There are many for-loop in your code, it's not the numpy way.
Make some sample data first:
import numpy as np
import pandas as pd
from scipy.spatial import KDTree
import pylab as pl
xc1, xc2, yc1, yc2 = 113.49805889531724, 115.5030664238035, 37.39995194888143, 38.789235929357105
N = 1000
GSIZE = 20
x, y = np.random.multivariate_normal([(xc1 + xc2)*0.5, (yc1 + yc2)*0.5], [[0.1, 0.02], [0.02, 0.1]], size=N).T
value = np.ones(N)
df_points = pd.DataFrame({"x":x, "y":y, "v":value})
For equal space grids you can use hist2d():
pl.hist2d(df_points.x, df_points.y, weights=df_points.v, bins=20, cmap="viridis");
Here is the output:
Here is the code to use KdTree:
X, Y = np.mgrid[x.min():x.max():GSIZE*1j, y.min():y.max():GSIZE*1j]
grid = np.c_[X.ravel(), Y.ravel()]
points = np.c_[df_points.x, df_points.y]
tree = KDTree(grid)
dist, indices = tree.query(points)
grid_values = df_points.groupby(indices).v.sum()
df_grid = pd.DataFrame(grid, columns=["x", "y"])
df_grid["v"] = grid_values
fig, ax = pl.subplots(figsize=(10, 8))
ax.plot(df_points.x, df_points.y, "kx", alpha=0.2)
mapper = ax.scatter(df_grid.x, df_grid.y, c=df_grid.v,
cmap="viridis",
linewidths=0,
s=100, marker="o")
pl.colorbar(mapper, ax=ax);
the output is:
In a project I'm doing, I have to take in a user input from a structured file (xml). The file contains road data of an area, which I have to plot on to the matplotlib canvas. The problem is that along with the road, I also have to render the road name, and most of the roads are curved. I know how to render text in an angle. But I was wondering whether it is possible to change the text angle midway through the string?
Something like this : Draw rotated text on curved path
But using matplotlib.
Here is my take on the problem:
In order to make the text robust to figure adjustments after drawing, I derive a child class, CurvedText, from matplotlib.text. The CurvedText object takes a string and a curve in the form of x- and y-value arrays. The text to be displayed itself is cut into separate characters, which each are added to the plot at the appropriate position. As matplotlib.text draws nothing if the string is empty, I replace all spaces by invisible 'a's. Upon figure adjustment, the overloaded draw() calls the update_positions() function, which takes care that the character positions and orientations stay correct. To assure the calling order (each character's draw() function will be called as well) the CurvedText object also takes care that the zorder of each character is higher than its own zorder. Following my example here, the text can have any alignment. If the text cannot be fit to the curve at the current resolution, the rest will be hidden, but will appear upon resizing. Below is the code with an example of application.
from matplotlib import pyplot as plt
from matplotlib import patches
from matplotlib import text as mtext
import numpy as np
import math
class CurvedText(mtext.Text):
"""
A text object that follows an arbitrary curve.
"""
def __init__(self, x, y, text, axes, **kwargs):
super(CurvedText, self).__init__(x[0],y[0],' ', **kwargs)
axes.add_artist(self)
##saving the curve:
self.__x = x
self.__y = y
self.__zorder = self.get_zorder()
##creating the text objects
self.__Characters = []
for c in text:
if c == ' ':
##make this an invisible 'a':
t = mtext.Text(0,0,'a')
t.set_alpha(0.0)
else:
t = mtext.Text(0,0,c, **kwargs)
#resetting unnecessary arguments
t.set_ha('center')
t.set_rotation(0)
t.set_zorder(self.__zorder +1)
self.__Characters.append((c,t))
axes.add_artist(t)
##overloading some member functions, to assure correct functionality
##on update
def set_zorder(self, zorder):
super(CurvedText, self).set_zorder(zorder)
self.__zorder = self.get_zorder()
for c,t in self.__Characters:
t.set_zorder(self.__zorder+1)
def draw(self, renderer, *args, **kwargs):
"""
Overload of the Text.draw() function. Do not do
do any drawing, but update the positions and rotation
angles of self.__Characters.
"""
self.update_positions(renderer)
def update_positions(self,renderer):
"""
Update positions and rotations of the individual text elements.
"""
#preparations
##determining the aspect ratio:
##from https://stackoverflow.com/a/42014041/2454357
##data limits
xlim = self.axes.get_xlim()
ylim = self.axes.get_ylim()
## Axis size on figure
figW, figH = self.axes.get_figure().get_size_inches()
## Ratio of display units
_, _, w, h = self.axes.get_position().bounds
##final aspect ratio
aspect = ((figW * w)/(figH * h))*(ylim[1]-ylim[0])/(xlim[1]-xlim[0])
#points of the curve in figure coordinates:
x_fig,y_fig = (
np.array(l) for l in zip(*self.axes.transData.transform([
(i,j) for i,j in zip(self.__x,self.__y)
]))
)
#point distances in figure coordinates
x_fig_dist = (x_fig[1:]-x_fig[:-1])
y_fig_dist = (y_fig[1:]-y_fig[:-1])
r_fig_dist = np.sqrt(x_fig_dist**2+y_fig_dist**2)
#arc length in figure coordinates
l_fig = np.insert(np.cumsum(r_fig_dist),0,0)
#angles in figure coordinates
rads = np.arctan2((y_fig[1:] - y_fig[:-1]),(x_fig[1:] - x_fig[:-1]))
degs = np.rad2deg(rads)
rel_pos = 10
for c,t in self.__Characters:
#finding the width of c:
t.set_rotation(0)
t.set_va('center')
bbox1 = t.get_window_extent(renderer=renderer)
w = bbox1.width
h = bbox1.height
#ignore all letters that don't fit:
if rel_pos+w/2 > l_fig[-1]:
t.set_alpha(0.0)
rel_pos += w
continue
elif c != ' ':
t.set_alpha(1.0)
#finding the two data points between which the horizontal
#center point of the character will be situated
#left and right indices:
il = np.where(rel_pos+w/2 >= l_fig)[0][-1]
ir = np.where(rel_pos+w/2 <= l_fig)[0][0]
#if we exactly hit a data point:
if ir == il:
ir += 1
#how much of the letter width was needed to find il:
used = l_fig[il]-rel_pos
rel_pos = l_fig[il]
#relative distance between il and ir where the center
#of the character will be
fraction = (w/2-used)/r_fig_dist[il]
##setting the character position in data coordinates:
##interpolate between the two points:
x = self.__x[il]+fraction*(self.__x[ir]-self.__x[il])
y = self.__y[il]+fraction*(self.__y[ir]-self.__y[il])
#getting the offset when setting correct vertical alignment
#in data coordinates
t.set_va(self.get_va())
bbox2 = t.get_window_extent(renderer=renderer)
bbox1d = self.axes.transData.inverted().transform(bbox1)
bbox2d = self.axes.transData.inverted().transform(bbox2)
dr = np.array(bbox2d[0]-bbox1d[0])
#the rotation/stretch matrix
rad = rads[il]
rot_mat = np.array([
[math.cos(rad), math.sin(rad)*aspect],
[-math.sin(rad)/aspect, math.cos(rad)]
])
##computing the offset vector of the rotated character
drp = np.dot(dr,rot_mat)
#setting final position and rotation:
t.set_position(np.array([x,y])+drp)
t.set_rotation(degs[il])
t.set_va('center')
t.set_ha('center')
#updating rel_pos to right edge of character
rel_pos += w-used
if __name__ == '__main__':
Figure, Axes = plt.subplots(2,2, figsize=(7,7), dpi=100)
N = 100
curves = [
[
np.linspace(0,1,N),
np.linspace(0,1,N),
],
[
np.linspace(0,2*np.pi,N),
np.sin(np.linspace(0,2*np.pi,N)),
],
[
-np.cos(np.linspace(0,2*np.pi,N)),
np.sin(np.linspace(0,2*np.pi,N)),
],
[
np.cos(np.linspace(0,2*np.pi,N)),
np.sin(np.linspace(0,2*np.pi,N)),
],
]
texts = [
'straight lines work the same as rotated text',
'wavy curves work well on the convex side',
'you even can annotate parametric curves',
'changing the plotting direction also changes text orientation',
]
for ax, curve, text in zip(Axes.reshape(-1), curves, texts):
#plotting the curve
ax.plot(*curve, color='b')
#adjusting plot limits
stretch = 0.2
xlim = ax.get_xlim()
w = xlim[1] - xlim[0]
ax.set_xlim([xlim[0]-stretch*w, xlim[1]+stretch*w])
ylim = ax.get_ylim()
h = ylim[1] - ylim[0]
ax.set_ylim([ylim[0]-stretch*h, ylim[1]+stretch*h])
#adding the text
text = CurvedText(
x = curve[0],
y = curve[1],
text=text,#'this this is a very, very long text',
va = 'bottom',
axes = ax, ##calls ax.add_artist in __init__
)
plt.show()
The result looks like this:
There are still some problems, when the text follows the concave side of a sharply bending curve. This is because the characters are 'stitched together' along the curve without accounting for overlap. If I have time, I'll try to improve on that. Any comments are very welcome.
Tested on python 3.5 and 2.7
I found your problem quite interesting, so I made something which comes pretty close using the matplotlib text tool:
from __future__ import division
import itertools
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
# define figure and axes properties
fig, ax = plt.subplots(figsize=(8,6))
ax.set_xlim(left=0, right=10)
ax.set_ylim(bottom=-1.5, top=1.5)
(xmin, xmax), (ymin, ymax) = ax.get_xlim(), ax.get_ylim()
# calculate a shape factor, more explanation on usage further
# it is a representation of the distortion of the actual image compared to a
# cartesian space:
fshape = abs(fig.get_figwidth()*(xmax - xmin)/(ymax - ymin)/fig.get_figheight())
# the text you want to plot along your line
thetext = 'the text is flowing '
# generate a cycler, so that the string is cycled through
lettercycler = itertools.cycle(tuple(thetext))
# generate dummy river coordinates
xvals = np.linspace(1, 10, 300)
yvals = np.sin(xvals)**3
# every XX datapoints, a character is printed
markerevery = 10
# calculate the rotation angle for the labels (in degrees)
# the angle is calculated as the slope between two datapoints.
# it is then multiplied by a shape factor to get from the angles in a
# cartesian space to the angles in this figure
# first calculate the slope between two consecutive points, multiply with the
# shape factor, get the angle in radians with the arctangens functions, and
# convert to degrees
angles = np.rad2deg(np.arctan((yvals[1:]-yvals[:-1])/(xvals[1:]-xvals[:-1])*fshape))
# plot the 'river'
ax.plot(xvals, yvals, 'b', linewidth=3)
# loop over the data points, but only plot a character every XX steps
for counter in np.arange(0, len(xvals)-1, step=markerevery):
# plot the character in between two datapoints
xcoord = (xvals[counter] + xvals[counter+1])/2.
ycoord = (yvals[counter] + yvals[counter+1])/2.
# plot using the text method, set the rotation so it follows the line,
# aling in the center for a nicer look, optionally, a box can be drawn
# around the letter
ax.text(xcoord, ycoord, lettercycler.next(),
fontsize=25, rotation=angles[counter],
horizontalalignment='center', verticalalignment='center',
bbox=dict(facecolor='white', edgecolor='white', alpha=0.5))
The implementation is far from perfect, but it is a good starting point in my opinion.
Further, it seems that there is some development in matplotlib on having a scatterplot with rotation of the markers, which would be ideal for this case. However, my programming skills are nearly not as hardcore as they need to be to tackle this issue, so I cannot help here.
matplotlib on github: pull request
matplotlib on github: issue