From 965cae72a79e870f68c6bc28231d701f3518f246 Mon Sep 17 00:00:00 2001
From: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Date: Sat, 2 May 2020 03:32:00 +0300
Subject: utils: raspberrypi: ctt: Fix pycodestyle E251
E251 unexpected spaces around keyword / parameter equals
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
---
utils/raspberrypi/ctt/ctt.py | 2 +-
utils/raspberrypi/ctt/ctt_alsc.py | 2 +-
utils/raspberrypi/ctt/ctt_geq.py | 2 +-
utils/raspberrypi/ctt/ctt_macbeth_locator.py | 4 ++--
utils/raspberrypi/ctt/ctt_noise.py | 2 +-
utils/raspberrypi/ctt/ctt_ransac.py | 6 +++---
6 files changed, 9 insertions(+), 9 deletions(-)
(limited to 'utils/raspberrypi')
diff --git a/utils/raspberrypi/ctt/ctt.py b/utils/raspberrypi/ctt/ctt.py
index 743ee3bc..46cf92cd 100755
--- a/utils/raspberrypi/ctt/ctt.py
+++ b/utils/raspberrypi/ctt/ctt.py
@@ -474,7 +474,7 @@ class Camera:
run calibration on all images and sort by slope.
"""
plot = "rpi.noise" in self.plot
- noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key = lambda x: x[0])
+ noise_out = sorted([noise(self, Img, plot) for Img in self.imgs], key=lambda x: x[0])
self.log += '\nFinished processing images'
"""
take the average of the interquartile
diff --git a/utils/raspberrypi/ctt/ctt_alsc.py b/utils/raspberrypi/ctt/ctt_alsc.py
index cd99495f..a006f7ff 100644
--- a/utils/raspberrypi/ctt/ctt_alsc.py
+++ b/utils/raspberrypi/ctt/ctt_alsc.py
@@ -131,7 +131,7 @@ def alsc(Cam, Img, do_alsc_colour, plot=False):
"""
average the green channels into one
"""
- av_ch_g = np.mean((channels[1:2]), axis = 0)
+ av_ch_g = np.mean((channels[1:2]), axis=0)
if do_alsc_colour:
"""
obtain 16x12 grid of intensities for each channel and subtract black level
diff --git a/utils/raspberrypi/ctt/ctt_geq.py b/utils/raspberrypi/ctt/ctt_geq.py
index 17598640..1ffa4e91 100644
--- a/utils/raspberrypi/ctt/ctt_geq.py
+++ b/utils/raspberrypi/ctt/ctt_geq.py
@@ -27,7 +27,7 @@ def geq_fit(Cam, plot):
data is sorted by green difference and top half is selected since higher
green difference data define the decision boundary.
"""
- geqs = np.array(sorted(geqs, key = lambda r: np.abs((r[1]-r[0])/r[0])))
+ geqs = np.array(sorted(geqs, key=lambda r: np.abs((r[1]-r[0])/r[0])))
length = len(geqs)
g0 = geqs[length//2:, 0]
diff --git a/utils/raspberrypi/ctt/ctt_macbeth_locator.py b/utils/raspberrypi/ctt/ctt_macbeth_locator.py
index 949e3686..55ab4fdb 100644
--- a/utils/raspberrypi/ctt/ctt_macbeth_locator.py
+++ b/utils/raspberrypi/ctt/ctt_macbeth_locator.py
@@ -487,8 +487,8 @@ def get_macbeth_chart(img, ref_data):
"""
clustering = cluster.AgglomerativeClustering(
n_clusters=None,
- compute_full_tree = True,
- distance_threshold = side*2
+ compute_full_tree=True,
+ distance_threshold=side*2
)
mac_mids_list = [x[0] for x in mac_mids]
diff --git a/utils/raspberrypi/ctt/ctt_noise.py b/utils/raspberrypi/ctt/ctt_noise.py
index f258bc6d..2d021e55 100644
--- a/utils/raspberrypi/ctt/ctt_noise.py
+++ b/utils/raspberrypi/ctt/ctt_noise.py
@@ -102,7 +102,7 @@ def noise(Cam, Img, plot):
plt.plot(x, fit_plot_2, label='fit 0 intercept', color='green', ls='--')
plt.plot(0, 0)
plt.title('Noise Plot\nImg: {}'.format(Img.str))
- plt.legend(loc = 'upper left')
+ plt.legend(loc='upper left')
plt.xlabel('Sqrt Pixel Value')
plt.ylabel('Noise Standard Deviation')
plt.grid()
diff --git a/utils/raspberrypi/ctt/ctt_ransac.py b/utils/raspberrypi/ctt/ctt_ransac.py
index a1513625..f7a722e0 100644
--- a/utils/raspberrypi/ctt/ctt_ransac.py
+++ b/utils/raspberrypi/ctt/ctt_ransac.py
@@ -11,7 +11,7 @@ scale = 2
"""
constructs normalised macbeth chart corners for ransac algorithm
"""
-def get_square_verts(c_err = 0.05, scale = scale):
+def get_square_verts(c_err=0.05, scale=scale):
"""
define macbeth chart corners
"""
@@ -57,13 +57,13 @@ def get_square_verts(c_err = 0.05, scale = scale):
# print(square_verts)
return np.array(square_verts, np.float32), mac_norm
-def get_square_centres(c_err = 0.05, scale=scale):
+def get_square_centres(c_err=0.05, scale=scale):
"""
define macbeth square centres
"""
verts, mac_norm = get_square_verts(c_err, scale=scale)
- centres = np.mean(verts, axis = 1)
+ centres = np.mean(verts, axis=1)
# print('centres')
# print(centres)
return np.array(centres, np.float32)
--
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