Showing 1 of 1 files from the diff.

@@ -58,7 +58,7 @@
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def _colors_to_rgb(colorscale):
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    """ Ensure that the color scale is formatted in rgb strings. 
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    """ Ensure that the color scale is formatted in rgb strings.
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        If the colorscale is a hex string, then convert to rgb.
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    """
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    if colorscale[0][1][0] == "#":
@@ -266,9 +266,19 @@
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        if X_names.shape[0] == 0:
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            X_names = np.array(["f_%s" % (i) for i in range(X.shape[1])])
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        cluster_X_mean = np.mean(X[member_ids], axis=0)
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        X_mean = np.mean(X, axis=0)
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        X_std = np.std(X, axis=0)
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        # wrap cluster_X_mean, X_mean, and X_std in np.array(---).squeeze()
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        # to get the same treatment for dense and sparse arrays
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        cluster_X_mean = np.array(
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            np.mean(X[member_ids], axis=0)
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        ).squeeze()
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        X_mean = np.array(
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            np.mean(X, axis=0)
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        ).squeeze()
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        X_std = np.array(
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            # use StandardScaler as a way to get std for dense or sparse array
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            np.sqrt(preprocessing.StandardScaler(with_mean=False).fit(X).var_)
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        ).squeeze()
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        above_mean = cluster_X_mean > X_mean
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        std_m = np.sqrt((cluster_X_mean - X_mean) ** 2) / X_std
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@@ -276,10 +286,10 @@
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            zip(
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                std_m,
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                X_names,
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                np.mean(X, axis=0),
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                X_mean,
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                cluster_X_mean,
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                above_mean,
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                np.std(X, axis=0),
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                X_std,
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            )
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        )
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        stats = sorted(stat_zip, reverse=True)
Files Coverage
kmapper 79.09%
Project Totals (10 files) 79.09%
435.4
3.6=.6
TRAVIS_OS_NAME=linux
435.5
3.7=.7
TRAVIS_OS_NAME=linux
435.3
3.5=.5
TRAVIS_OS_NAME=linux
435.1
TRAVIS_OS_NAME=linux
2.7=.7
435.2
3.4=.4
TRAVIS_OS_NAME=linux

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