diff options
| -rw-r--r-- | src/aux.py | 19 |
1 files changed, 10 insertions, 9 deletions
@@ -49,7 +49,7 @@ def ramps_to_function(r, g, b): return functionise((fp(r), fp(g), fp(b))) -def linearly_interpolate_ramp(r, g, b): # TODO test, demo and document this +def linearly_interpolate_ramp(r, g, b): # TODO demo and document this ''' Linearly interpolate ramps to the size of the output axes @@ -61,7 +61,7 @@ def linearly_interpolate_ramp(r, g, b): # TODO test, demo and document this ''' C = lambda c : c[:] if len(c) >= o_size else ([None] * o_size) R, G, B = C(r), C(g), C(b) - for small, large in curves(R, G, B): + for small, large in ((r, R), (g, G), (b, B)): small_, large_ = len(small) - 1, len(large) - 1 # Only interpolate if scaling up if large_ > small_: @@ -75,7 +75,7 @@ def linearly_interpolate_ramp(r, g, b): # TODO test, demo and document this return (R, G, B) -def polynomially_interpolate_ramp(r, g, b): # TODO test, demo and document this +def polynomially_interpolate_ramp(r, g, b): # TODO Speedup, demo and document this ''' Polynomially interpolate ramps to the size of the output axes. @@ -92,13 +92,13 @@ def polynomially_interpolate_ramp(r, g, b): # TODO test, demo and document this ''' C = lambda c : c[:] if len(c) >= o_size else ([None] * o_size) R, G, B, linear = C(r), C(g), C(b), None - for small, (ci, large) in curves(*(enumerate((R, G, B)))): + for ci, (small, large) in enumerate(((r, R), (g, G), (b, B))): small_, large_ = len(small) - 1, len(large) - 1 # Only interpolate if scaling up if large_ > small_: - n = len(small) + 1 - ## Construct interpolation matrix - M = [[small[y] ** i for i in n] for y in range(n)] + n = len(small) + ## Construct interpolation matrix (TODO this is not working correctly) + M = [[small[y] ** i for i in range(n)] for y in range(n)] A = [x / small_ for x in range(n)] ## Eliminate interpolation matrix # (XXX this can be done faster by utilising the fact that we have a Vandermonde matrix) @@ -106,7 +106,7 @@ def polynomially_interpolate_ramp(r, g, b): # TODO test, demo and document this for k in range(n - 1): for i in range(k + 1, n): m = M[i][k] / M[k][k] - M[i][k + 1:] -= [M[i][j] - M[k][j] * m for j in range(k + 1, n)] + M[i][k + 1:] = [M[i][j] - M[k][j] * m for j in range(k + 1, n)] A[i] -= A[k] * m # Eliminiate upper right for k in reversed(range(n)): @@ -138,7 +138,8 @@ def polynomially_interpolate_ramp(r, g, b): # TODO test, demo and document this # Decreasing monotone = all(map(lambda x : large[x + 1] <= large[x], range(X1, X2))) and (Y2 < Y1) # If the monotonicity has been broken, - if not passed: + if not monotone: + print('failed') # replace the partition with linear interpolation. # If linear interpolation has not yet been calculated, if linear is None: |