From 11afb7158d9d93f22e0da53914eae99178324ea4 Mon Sep 17 00:00:00 2001
From: Jeremy Fee <jmfee@usgs.gov>
Date: Mon, 25 Jan 2016 16:17:28 -0700
Subject: [PATCH] Fix remaining flake8 issues
---
geomagio/algorithm/SQDistAlgorithm.py | 19 ++++++++++---------
1 file changed, 10 insertions(+), 9 deletions(-)
diff --git a/geomagio/algorithm/SQDistAlgorithm.py b/geomagio/algorithm/SQDistAlgorithm.py
index 89c74cc85..abc1569ca 100644
--- a/geomagio/algorithm/SQDistAlgorithm.py
+++ b/geomagio/algorithm/SQDistAlgorithm.py
@@ -185,9 +185,9 @@ def additive(yobs, m, alpha=None, beta=None, gamma=None, phi=1,
initial_values = np.array(initial_values)
method = 'additive'
- parameters = fmin_l_bfgs_b(RMSE, x0 = initial_values,
- args = (yobs, method, m, s, l, b, hstep, zthresh),
- bounds = boundaries, approx_grad = True)
+ parameters = fmin_l_bfgs_b(RMSE, x0=initial_values,
+ args=(yobs, method, m, s, l, b, hstep, zthresh),
+ bounds=boundaries, approx_grad=True)
alpha, beta, gamma = parameters[0]
rmse = parameters[1]
@@ -207,9 +207,9 @@ def additive(yobs, m, alpha=None, beta=None, gamma=None, phi=1,
sumc2_H = 1
phiHminus1 = 0
for h in range(1, hstep):
- phiHminus1 = phiHminus1 + phi**(h - 1)
+ phiHminus1 = phiHminus1 + phi ** (h - 1)
sumc2_H = sumc2_H + (alpha * (1 + phiHminus1 * beta) +
- gamma * (1 if (h % m == 0) else 0))**2
+ gamma * (1 if (h % m == 0) else 0)) ** 2
phiJminus1 = phiHminus1
sumc2 = sumc2_H
jstep = hstep
@@ -254,9 +254,9 @@ def additive(yobs, m, alpha=None, beta=None, gamma=None, phi=1,
# when forecasting, grow sigma=sqrt(var) like a prediction
# interval; sumc2 and jstep will be reset with the next
# valid observation
- phiJminus1 = phiJminus1 + phi**jstep
+ phiJminus1 = phiJminus1 + phi ** jstep
sumc2 = sumc2 + (alpha * (1 + phiJminus1 * beta) +
- gamma * (1 if (jstep % m == 0) else 0))**2
+ gamma * (1 if (jstep % m == 0) else 0)) ** 2
jstep = jstep + 1
else:
@@ -315,7 +315,8 @@ def additive(yobs, m, alpha=None, beta=None, gamma=None, phi=1,
b,
sigma[len(yobs) + fc:])
-def rmse(params, *args):
+
+def RMSE(params, *args):
"""Wrapper function passed to scipy.optimize.fmin_l_bfgs_b in
order to find optimal Holt-Winters prediction coefficients.
@@ -348,7 +349,7 @@ def rmse(params, *args):
raise Exception
# calculate root-mean-squared-error of predictions
- rmse = np.sqrt(np.nanmean([(m - n) ** 2 for m, n in zip(yobs, yhat)]))
+ rmse = np.sqrt(np.nanmean([(x - y) ** 2 for x, y in zip(yobs, yhat)]))
return rmse
--
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