Merge branch 'flint-window-average' into 'master'

Flint: Mean filter on sliding window

Closes #3371

See merge request !4673

CHANGELOG.md

@@ -11,6 +11,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Flint
    - Added widget in the regulation plot to specify a time to life for the data
+   - Added sliding window mean filter to the live curve plot
 
 ### Changed
 

bliss/flint/filters/sliding_window_mean.py

+# -*- coding: utf-8 -*-
+#
+# This file is part of the bliss project
+#
+# Copyright (c) 2015-2022 Beamline Control Unit, ESRF
+# Distributed under the GNU LGPLv3. See LICENSE for more info.
+
+"""
+Implementation of a mean filter based on a sliding window.
+"""
+from __future__ import annotations
+from typing import Optional
+from typing import NamedTuple
+from typing import Dict
+from typing import Any
+
+import numpy
+from numpy.lib.stride_tricks import sliding_window_view
+import logging
+
+from ..model import scan_model
+from ..model import plot_model
+from ..model import plot_item_model
+from ..model.plot_item_model import ComputedCurveItem
+
+_logger = logging.getLogger(__name__)
+
+
+class SlidingWindowData(NamedTuple):
+    xx: numpy.ndarray
+    yy: numpy.ndarray
+    nb_points: int
+
+
+class SlidingWindowMeanItem(ComputedCurveItem, plot_model.IncrementalComputableMixIn):
+    """This item use the scan data to process result before displaying it."""
+
+    NAME = "Sliding window mean"
+    ICON_NAME = "flint:icons/item-func"
+
+    DEFAULT_EXTRA_POINTS = 5
+    """Extra points used on each side of the actual position"""
+
+    def __init__(self, parent=None):
+        ComputedCurveItem.__init__(self, parent=parent)
+        plot_model.IncrementalComputableMixIn.__init__(self)
+        self.__extra_points = self.DEFAULT_EXTRA_POINTS
+
+    def name(self) -> str:
+        nb = self.__extra_points * 2 + 1
+        return f"MeanOnWindow{nb}"
+
+    def setHalfWindowSize(self, nbPoints: int):
+        """Set the number of point for the half window.
+
+        - `0` means the mean is computed with a single point.
+        - `5` means the mean is computed with 5+1+5 points
+        """
+        self.__extra_points = nbPoints
+
+    def halfWindowSize(self) -> int:
+        return self.__extra_points
+
+    def __getstate__(self):
+        state: Dict[str, Any] = {}
+        state.update(plot_model.ChildItem.__getstate__(self))
+        state.update(plot_item_model.CurveMixIn.__getstate__(self))
+        return state
+
+    def __setstate__(self, state):
+        plot_model.ChildItem.__setstate__(self, state)
+        plot_item_model.CurveMixIn.__setstate__(self, state)
+
+    def compute(self, scan: scan_model.Scan) -> Optional[SlidingWindowData]:
+        sourceItem = self.source()
+
+        xx = sourceItem.xArray(scan)
+        yy = sourceItem.yArray(scan)
+        if xx is None or yy is None:
+            return None
+
+        window_size = self.__extra_points * 2 + 1
+        nb_points = min(len(xx), len(yy))
+        if nb_points < window_size:
+            return None
+
+        xx, yy = xx[:nb_points], yy[:nb_points]
+
+        xwindow = xx[self.__extra_points : -self.__extra_points]
+        ywindow = sliding_window_view(yy, window_size)
+        ywindow = numpy.mean(ywindow, axis=1)
+
+        return SlidingWindowData(xwindow, ywindow, nb_points)
+
+    def incrementalCompute(
+        self, previousResult: SlidingWindowData, scan: scan_model.Scan
+    ) -> SlidingWindowData:
+        """Compute a data using the previous value as basis.
+
+        The function expect extra points before and after the
+        each points it can compute.
+        """
+        if previousResult is None:
+            return self.compute(scan)
+
+        sourceItem = self.source()
+        xx = sourceItem.xArray(scan)
+        yy = sourceItem.yArray(scan)
+        if xx is None or yy is None:
+            raise ValueError("Non empty data expected")
+
+        nb_points = min(len(xx), len(yy))
+        if nb_points <= previousResult.nb_points:
+            # Obviously nothing to compute
+            return previousResult
+
+        window_size = self.__extra_points * 2 + 1
+        nb_points = min(len(xx), len(yy))
+        if nb_points < window_size:
+            # There is still not enough data
+            return SlidingWindowData(previousResult.xx, previousResult.yy, nb_points)
+
+        # There is necessary something new to compute
+
+        start = len(previousResult.xx) - self.__extra_points
+        xx, yy = xx[:nb_points], yy[start:nb_points]
+
+        xwindow = xx[self.__extra_points : -self.__extra_points]
+        ywindow = sliding_window_view(yy, window_size)
+        ywindow = numpy.mean(ywindow, axis=1)
+        ywindow = numpy.append(previousResult.yy, ywindow)
+
+        return SlidingWindowData(xwindow, ywindow, nb_points)
+
+    def displayName(self, axisName, scan: scan_model.Scan) -> str:
+        """Helper to reach the axis display name"""
+        sourceItem = self.source()
+        if axisName == "x":
+            return sourceItem.displayName("x", scan)
+        elif axisName == "y":
+            return "mean(%s)" % sourceItem.displayName("y", scan)
+        else:
+            assert False

bliss/flint/widgets/curve_plot_property.py

@@ -36,6 +36,7 @@ from bliss.flint.filters.negative import NegativeItem
 from bliss.flint.filters.normalized_zero_one import NormalizedZeroOneItem
 from bliss.flint.filters.gaussian_fit import GaussianFitItem
 from bliss.flint.filters.normalized import NormalizedCurveItem
+from bliss.flint.filters.sliding_window_mean import SlidingWindowMeanItem
 from . import delegates
 from . import data_views
 from . import _property_tree_helper
@@ -278,6 +279,7 @@ class _AddItemAction(qt.QWidgetAction):
             GaussianFitItem: None,
             NormalizedCurveItem: self.__editNormalizedParams,
             NormalizedZeroOneItem: None,
+            SlidingWindowMeanItem: self.__editSlidingWindowMeanParams,
         }
 
         item = self.parent().selectedPlotItem()
@@ -355,6 +357,27 @@ class _AddItemAction(qt.QWidgetAction):
         item.setMonitorChannel(channel)
         return True
 
+    def __editSlidingWindowMeanParams(self, item: SlidingWindowMeanItem):
+        """Edit SlidingWindowMeanItem parameters.
+
+        Returns true if the edition succeeded, else false.
+        """
+        parentWidget = self.parent()
+        value = item.halfWindowSize() * 2 + 1
+        value, result = qt.QInputDialog.getInt(
+            parentWidget,
+            "Edit sliding window",
+            "Nb elements for the window",
+            value,
+            1,
+            100,
+        )
+        if not result:
+            return False
+        nbPoints = (value - 1) // 2
+        item.setHalfWindowSize(nbPoints)
+        return True
+
 
 class _DataItem(_property_tree_helper.ScanRowItem):
 

doc/docs/flint/examples_mesh_rendering.md

@@ -18,7 +18,7 @@ This scans have to be created usually per beamline in a very specific way.
 
 For this examples we will use a function to simulate them.
 
-```
+```python
 import gevent
 import numpy
 from bliss.scanning.group import Sequence
@@ -53,7 +53,7 @@ This example create a regular scatter.
 
 ![Regular scatter in Flint](img/scan_info/regular-scatter.png)
 
-```
+```python
 from bliss.scanning.scan_info import ScanInfo
 import numpy
 
@@ -109,7 +109,7 @@ For now only Flint will only display the last frame.
 
 ![Regular scatter in Flint](img/scan_info/regular-scatter-3d.png)
 
-```
+```python
 from bliss.scanning.scan_info import ScanInfo
 import numpy
 
@@ -157,7 +157,7 @@ That's what we simulate here. `axis1` and `axis2` are also provided as
 
 ![Axis and encoder together](img/scan_info/scatter-encoder.png)
 
-```
+```python
 from bliss.scanning.scan_info import ScanInfo
 import numpy
 
@@ -217,7 +217,7 @@ used to know the amount of pixels to used per axis.
 
 ![Non-regular scatter in Flint](img/scan_info/irregular-scatter.png)
 
-```
+```python
 from bliss.scanning.scan_info import ScanInfo
 import numpy
 
@@ -255,7 +255,7 @@ This also can work for extra dimensions with steppers.
 
 ![Non-regular scatter in Flint](img/scan_info/irregular-scatter-3d.png)
 
-```
+```python
 from bliss.scanning.scan_info import ScanInfo
 import numpy
 

doc/docs/flint/flint_data_plotting.md

@@ -8,7 +8,7 @@ with the data to be displayed.
 
 ## Basic plot display
 
-A generic display is provided through the BLISS `plot` command.
+A generic display is provided through the BLISS `plot()` command.
 
 It checks the data dimensionality to select the kind of charts.
 
@@ -40,7 +40,7 @@ plot(cube)
 
 To use more features another way is provided.
 
-First a plot have to be created from Flint.
+First a plot has to be created from Flint.
 
 The first argument (here `curve`) is used to select the kind of expected plot.
 See the following documentation for example of each kind.
@@ -384,7 +384,7 @@ p.export_to_logbook()
 
 From a scripts Flint also can be used to display plot.
 
-The `plot` command can be imported the following way:
+The `plot()` command can be imported the following way:
 
 ```python
 from bliss.common.plot import plot

doc/docs/flint/index.md

@@ -55,7 +55,7 @@ From BLISS shell, few basic interaction with Flint are provided.
 
 To invoke or retrieve a proxy to interact with Flint you can use:
 
-```
+```python
 flint()
 ```
 
@@ -65,7 +65,7 @@ flint()
 
 The returned object provides few methods to close the application:
 
-```
+```python
 f = flint()
 f.close()    # request a normal close of the application
 f.kill()     # kill
@@ -77,7 +77,7 @@ f.kill9()    # kill -9
 If your window manager provides this feature, you can force the focus on the
 application this way:
 
-```
+```python
 f = flint()
 f.focus()
 ```

doc/docs/flint/live_curve.md

@@ -4,6 +4,33 @@ If the scan contains counters, the curve widget will be displayed automatically.
 
 ![Flint screenshot](img/flint-curve-widget.png)
 
+
+## Statistic tools
+
+Some statistics can be added to the graph as curve or markers.
+
+By clicking on the "Create new items in the plot" ![Flint
+screenshot](img/curve_icon_add_curve.png) icon, a new curve or marker can be
+added to represent a calculation based on the current curve.
+
+The statistical filters available are:
+
+* Max marker
+* Min marker
+* Derivative function
+* Negative function
+* Gaussian fit
+* Normalized function
+* Normalized range
+* Sliding windows mean
+
+For example, on this plot, min/max markers and a 11-points sliding window mean
+have been added.
+
+![Curve statistics](img/curve_statistics.png)
+
+
+
 ## Compare scans
 
 A tool provided in the property tool bar allow to compare scans.