From 4ed45dbc55b597c73783511293063a7bb14afe5d Mon Sep 17 00:00:00 2001 From: Michael Shirts Date: Mon, 26 Aug 2024 10:21:44 -0600 Subject: [PATCH] more linting. --- pymbar/confidenceintervals.py | 2 +- pymbar/mbar.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pymbar/confidenceintervals.py b/pymbar/confidenceintervals.py index d385ce31..7198a50e 100644 --- a/pymbar/confidenceintervals.py +++ b/pymbar/confidenceintervals.py @@ -149,7 +149,7 @@ def qq_plot(replicates, K, title="Generic Q-Q plot", filename="qq.pdf"): dim = len(np.shape(replicates[0]["error"])) xvals = scipy.stats.norm.ppf((np.arange(0, N) + 0.5) / N) # inverse pdf nplots = None # keep lint happy when variables are defined in conditionals - + if dim == 0: nplots = 1 elif dim == 1: diff --git a/pymbar/mbar.py b/pymbar/mbar.py index 447f3d20..bea47f16 100644 --- a/pymbar/mbar.py +++ b/pymbar/mbar.py @@ -851,8 +851,8 @@ def compute_expectations_inner( K = self.K N = self.N # N is total number of samples result_vals = dict() # dictionary we will store uncertainties in - Theta_ij = None # keep lint happy when variables are defined in conditionals. - + Theta_ij = None # keep lint happy when variables are defined in conditionals. + # make observables all positive, allowing us to take the logarithm, which is # required to prevent overflow in some examples. # WARNING: one issue to watch for is if one of the energies is extremely