From 78e39b30e1a0ea5427d2c0eeb267bc2e987ae00b Mon Sep 17 00:00:00 2001
From: mikf <mikf@dtu.dk>
Date: Wed, 27 Jun 2018 11:44:52 +0200
Subject: [PATCH] Changed 1.1 to a variable; pad with a default value of 1.1.
---
topfarm/utils.py | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
diff --git a/topfarm/utils.py b/topfarm/utils.py
index f5256c7e..df16d39d 100644
--- a/topfarm/utils.py
+++ b/topfarm/utils.py
@@ -46,29 +46,30 @@ def latest_id(case_recorder_dir):
return latest
-def random_positions(boundary, n_wt, n_iter, step_size, min_space,
- plot=False, verbose=True):
+def random_positions(boundary, n_wt, n_iter, step_size, min_space,
+ pad = 1.1, plot=False, verbose=True):
'''
Input:
boundary: list of tuples, e.g.: [(0, 0), (6, 1), (7, -11), (-1, -10)]
n_wt: number of wind turbines
n_iter: number of iterations allowed to try and satisfy the minimum
spacing constraint
- step_size: the multiplier on the gradient that the turbines are moved
- in each step
+ step_size: the multiplier on the spacing gradient that the turbines
+ are moved in each step
min_space: the minimum spacing between turbines
+ pad: the multiplier on the boundary gradient
plot: plot the generated random layout
verbose: print helpful text to the console
Returns an array of xy coordinates of the wind turbines
'''
x, y = map(_random, np.array(boundary).T)
turbines = _random_positions(x, y, boundary, n_wt, n_iter, step_size,
- min_space, plot, verbose)
+ min_space, pad, plot, verbose)
return turbines
def _random_positions(x, y, boundary, n_wt, n_iter, step_size, min_space,
- plot, verbose):
+ pad, plot, verbose):
boundary_comp = PolygonBoundaryComp(boundary, n_wt)
spacing_comp = SpacingComp(nTurbines=n_wt)
turbineX = copy(x)
@@ -89,7 +90,7 @@ def _random_positions(x, y, boundary, n_wt, n_iter, step_size, min_space,
turbineX += dx[index]*step_size
turbineY += dy[index]*step_size
turbineX, turbineY = _contain(n_wt, turbineX, turbineY,
- boundary_comp)
+ boundary_comp, pad)
else:
if verbose:
print('Obtained required spacing after {} iterations'.format(
@@ -110,7 +111,7 @@ def _random(b):
return np.random.rand(n_wt) * (max(b) - min(b)) + min(b)
-def _contain(n_wt, turbineX, turbineY, boundary_comp):
+def _contain(n_wt, turbineX, turbineY, boundary_comp, pad):
for i in range(0, n_wt):
dng = boundary_comp.calc_distance_and_gradients(turbineX,
turbineY)
@@ -118,8 +119,8 @@ def _contain(n_wt, turbineX, turbineY, boundary_comp):
if dist < 0:
dx = dng[1][i]
dy = dng[2][i]
- turbineX[i] -= dx*dist*1.1
- turbineY[i] -= dy*dist*1.1
+ turbineX[i] -= dx*dist*pad
+ turbineY[i] -= dy*dist*pad
return turbineX, turbineY
--
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