From b8ff2944267a8ecaca101d2a72925c4c17f1bb57 Mon Sep 17 00:00:00 2001 From: zqfang Date: Wed, 20 Mar 2024 10:10:03 -0700 Subject: [PATCH] optimize #244 --- gseapy/plot.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/gseapy/plot.py b/gseapy/plot.py index d86955c..1defe89 100644 --- a/gseapy/plot.py +++ b/gseapy/plot.py @@ -686,7 +686,7 @@ def process(self, df: pd.DataFrame): ## impute the 0s in pval, fdr for visualization purpose if self.colname in ["Adjusted P-value", "P-value", "NOM p-val", "FDR q-val"]: # if all values are zeros, raise error - if not all(df[self.colname].abs() > 0): + if not any(df[self.colname].abs() > 0): raise ValueError( f"Can not detetermine colormap. All values in {self.colname} are 0s" ) @@ -700,7 +700,11 @@ def process(self, df: pd.DataFrame): self.cbar_title = r"$\log_{10} \frac{1}{ " + _t + " }$" # get top terms; sort ascending - if (self.x is not None) and (self.x in df.columns): + if ( + (self.x is not None) + and (self.x in df.columns) + and (not all(df[self.x].map(self.isfloat))) + ): # if x is numeric column # get top term of each group df = ( @@ -834,7 +838,10 @@ def scatter( # if self.x is None: x, xlabel = self.set_x() y = self.y - # set x, y order + # if x axis is numberic, prettifiy the plot with the numberic order + if all(df[x].map(self.isfloat)): + df = df.sort_values(by=x) + # set x, y order if set xunits = UnitData(self.get_x_order()) if self.x_order else None yunits = UnitData(self.get_y_order()) if self.y_order else None @@ -1138,7 +1145,7 @@ def dotplot( :param title: Figure title. :param cutoff: Terms with `column` value < cut-off are shown. Work only for ("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val") - :param top_term: Number of enriched terms to show. + :param top_term: Number of enriched terms to show (based on values in the `column` (colormap)). :param size: float, scale the dot size to get proper visualization. :param figsize: tuple, matplotlib figure size. :param cmap: Matplotlib colormap for mapping the `column` semantic.