diff --git a/requirements.txt b/requirements.txt
index c7ad49a..208fbbd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,5 +4,4 @@ git+https://github.com/tfiers/seaborn#seaborn
 git+https://github.com/tfiers/raincloud#raincloud
 
 # Closed source for now - download and install manually
-# git+https://bitbucket.org/kloostermannerflab/fklab-python-core/src/master
-#fklab
+# git+https://bitbucket.org/kloostermannerflab/fklab-python-core/src/master#fklab
diff --git a/sharp/config/default/tasks.py b/sharp/config/default/tasks.py
index 6584278..3e4fd35 100644
--- a/sharp/config/default/tasks.py
+++ b/sharp/config/default/tasks.py
@@ -99,6 +99,7 @@ def get_tasks_to_run():
     from sharp.tasks.plot.misc.training import PlotValidLoss
     from sharp.tasks.plot.paper.PR_curve import Plot_PR_Curve
     from sharp.tasks.plot.paper.signals import PlotSignals
+    from sharp.tasks.plot.paper.grid import RelativeLatencyGrid
 
     return (
         # *tasks_on_hold(),
@@ -107,9 +108,11 @@ def get_tasks_to_run():
         # AccuracyGrid(envelope_maker=ApplyOnlineBPF()),
         # LatencyGrid(envelope_maker=ApplyRNN()),
         # LatencyGrid(envelope_maker=ApplyOnlineBPF()),
-        Plot_PR_Curve(),
+        # RelativeLatencyGrid(envelope_maker=ApplyRNN()),
+        # RelativeLatencyGrid(envelope_maker=ApplyOnlineBPF()),
+        # Plot_PR_Curve(),
         PlotLatency(),
-        PlotSignals(),
+        # PlotSignals(),
     )
 
 
diff --git a/sharp/tasks/plot/paper/__init__.py b/sharp/tasks/plot/paper/__init__.py
index 348b581..b494f14 100644
--- a/sharp/tasks/plot/paper/__init__.py
+++ b/sharp/tasks/plot/paper/__init__.py
@@ -2,6 +2,7 @@
 
 from sharp.config.load import config
 from sharp.data.hardcoded.style import blue, pink
+from sharp.data.types.evaluation.sweep import ThresholdSweep
 from sharp.data.types.evaluation.threshold import ThresholdEvaluation
 from sharp.tasks.evaluate.sweep import ThresholdSweeper
 from sharp.tasks.neuralnet.apply import ApplyRNN
@@ -29,7 +30,7 @@
 labels = ("Band-pass filter", "Recurrent neural net")
 
 
-def get_sweeps() -> Tuple[ThresholdSweeper, ...]:
+def get_sweeps() -> Tuple[ThresholdSweep, ...]:
     return tuple([sweeper.output().read() for sweeper in sweepers])
 
 
diff --git a/sharp/tasks/plot/paper/grid.py b/sharp/tasks/plot/paper/grid.py
index 03251d3..b81f0cc 100644
--- a/sharp/tasks/plot/paper/grid.py
+++ b/sharp/tasks/plot/paper/grid.py
@@ -1,10 +1,11 @@
 from colorsys import rgb_to_hls
+from logging import getLogger
 
 from matplotlib.cm import get_cmap
 from matplotlib.colorbar import ColorbarBase
 from matplotlib.colors import Normalize
 from matplotlib.ticker import StrMethodFormatter
-from numpy import arange, max
+from numpy import arange, max, mean, percentile
 
 from sharp.config.load import config
 from sharp.data.files.figure import FigureTarget
@@ -19,6 +20,8 @@
 from sharp.tasks.signal.reference import MakeReference
 from sharp.util.misc import cached
 
+log = getLogger(__name__)
+
 
 class PaperGridPlotter(FigureMaker):
     envelope_maker: EnvelopeMaker = TaskParameter()
@@ -57,6 +60,13 @@ def filename(self):
         return f"{self.__class__.__name__} -- {self.envelope_maker.title}"
 
     def work(self):
+        log.info(
+            (
+                self.filename,
+                mean(self.data_matrix),
+                percentile(self.data_matrix, [25, 50, 100]),
+            )
+        )
         self.plot_grid()
         self.plot_colorbar()
 
@@ -182,7 +192,6 @@ def get_data(self, sweep):
 
 
 class LatencyGrid(PaperGridPlotter):
-    # cmap_range = (0.15, 0.48)
     cmap_range = (8, 32)
     cmap = get_cmap("viridis_r")
     colorbar_label = "Detection latency (ms)"
@@ -190,3 +199,13 @@ class LatencyGrid(PaperGridPlotter):
 
     def get_data(self, sweep):
         return sweep.at_max_F2().abs_delays_median * 1000
+
+
+class RelativeLatencyGrid(PaperGridPlotter):
+    cmap_range = (0.15, 0.48)
+    cmap = get_cmap("viridis_r")
+    colorbar_label = "Detection latency"
+    fstring = "{x:.0%}"
+
+    def get_data(self, sweep):
+        return sweep.at_max_F2().rel_delays_median