Merge pull request #19 from chiahaoliu/REF_plot_update

REF: waterfall plot update

xpdview/tests/test_viwers.py

@@ -0,0 +1,26 @@
+"""module to start pure mpl-backend viewer"""
+import matplotlib.pyplot as plt
+import numpy as np
+from xpdview.cross_2d import StackViewer, CrossSection
+from xpdview.waterfall import Waterfall
+
+# def data list to test
+img_data_list = []
+key_list = []
+int_data_list = []
+x = np.linspace(0, 2*np.pi, 200)
+for i in range(5):
+    key_list.append(str(i))
+    img_data_list.append(np.random.rand(50, 50))
+    int_data_list.append((x, np.sin(x)))
+
+# init
+viewer_fig = plt.figure('test_viewer')  # let mpl decide backend
+cross_section = CrossSection(viewer_fig)
+stack_viewer = StackViewer(cross_section, img_data_list=img_data_list,
+                           key_list=key_list)
+waterfall_fig = plt.figure('test_waterfall')  # let mpl decide backend
+waterfall = Waterfall(fig=waterfall_fig, key_list=key_list,
+                      int_data_list=int_data_list,
+                      unit=('x_unit', 'y_unit'))
+plt.show()

xpdview/tests/test_waterfall.py

@@ -0,0 +1,19 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from xpdview.waterfall import Waterfall
+
+fig = plt.figure('test_title')
+unit = ('x unit', 'y unit')
+int_data_list = []
+key_list = []
+
+x = np.linspace(0, 4*np.pi, 200)
+for i in range(5):
+    key_list.append(str(i))
+    int_data_list.append((x, np.sin(x)))
+
+p1 = Waterfall()
+p2 = Waterfall(fig, key_list=key_list, int_data_list=int_data_list,
+               unit=unit)
+
+plt.show()

xpdview/utils.py

@@ -2,6 +2,19 @@
 import os
 import numpy as np
 
+
+def conf_label_size(ax, label_size):
+    ax.xaxis.label.set_size(label_size)
+    ax.yaxis.label.set_size(label_size)
+
+
+def conf_tick_size(ax, tick_size):
+    for tick in ax.xaxis.get_major_ticks():
+                tick.label.set_fontsize(tick_size)
+    for tick in ax.yaxis.get_major_ticks():
+                tick.label.set_fontsize(tick_size)
+
+
 def chi_read(fn, skiprows=4):
     """wrapper for reading .chi files
 

xpdview/waterfall.py

@@ -1,12 +1,14 @@
 import numpy as np
+import matplotlib as mpl
 import matplotlib.pyplot as plt
 from matplotlib.widgets import Slider
-
-
-def _normalize(self, array, max_val, min_val):
-    """core function to normalize a ndarray"""
-    return np.subtract(array, min_val) / np.subtract(max_val, min_val)
-
+from .utils import conf_label_size, conf_tick_size
+from cycler import cycler
+simonCycle2 = ["#0B3C5D", "#B82601",  "#1c6b0a",
+               "#328CC1", "#062F4F", "#D9B310",
+               "#984B43", "#76323F", "#626E60",
+               "#AB987A", "#C09F80", "#b0b0b0ff"]
+mpl.rcParams['axes.prop_cycle'] = cycler(color=simonCycle2)
 
 class Waterfall:
     """class holds data and generate watefall plot
@@ -24,11 +26,18 @@ class Waterfall:
         format. default to None
     unit : tuple, optional
         a tuple containing strings of x and y labels
+    label_size : int, optional
+        size of x-, y-label. default is 16
+    tick_size : int, optional
+        size of x-, y-tick. default is 14
+    kwargs :
+        keyword arguments for plotting
     """
 
     def __init__(self, fig=None, canvas=None,
                  key_list=None, int_data_list=None,
-                 *, unit=None):
+                 *, unit=None, label_size=16, tick_size=14,
+                 **kwargs):
         if int_data_list is None:
             int_data_list = []
         if key_list is None:
@@ -39,14 +48,16 @@ def __init__(self, fig=None, canvas=None,
         if not canvas:
             canvas = self.fig.canvas
         self.canvas = canvas
+        self.kwargs = kwargs
 
         # callback for showing legend
         self.canvas.mpl_connect('pick_event', self.on_plot_hover)
         self.key_list = key_list
         self.int_data_list = int_data_list
         self.ax = self.fig.add_subplot(111)
         self.unit = unit
-
+        self.label_size = label_size
+        self.tick_size = tick_size
         # flag to prevent update
         self.halt = False
         # add sliders, which store information
@@ -64,6 +75,7 @@ def __init__(self, fig=None, canvas=None,
         # init
         self.update(self.key_list, self.int_data_list, refresh=True)
 
+
     def update(self, key_list=None, int_data_list=None, refresh=False):
         """top method to update information carried by class and plot
 
@@ -88,6 +100,7 @@ def update(self, key_list=None, int_data_list=None, refresh=False):
             self.int_data_list = []
         self.key_list.extend(key_list)
         self.int_data_list.extend(int_data_list)
+        self._adapt_data_list(int_data_list)
         # generate plot
         self.halt = False
         self._update_plot()  # use current value of x,y offset
@@ -96,17 +109,15 @@ def _adapt_data_list(self, int_data_list):
         """method to return statefull information of 1D data list"""
         x_array_list = []
         y_array_list = []
+        # parse
         for x, y in int_data_list:
             x_array_list.append(x)
             y_array_list.append(y)
-        y_max = np.max(y_array_list)
-        y_min = np.min(y_array_list)
-        y_dist = y_max - y_min
-        x_max = np.max(x_array_list)
-        x_min = np.min(x_array_list)
-        x_dist = x_max - x_min
-        return (x_array_list, y_array_list, y_min, y_max,
-                y_dist, x_min, x_max, x_dist)
+        self.x_array_list = x_array_list
+        self.y_array_list = y_array_list
+        # stateful information
+        self.y_dist = np.max(y_array_list) - np.min(y_array_list)
+        self.x_dist = np.max(x_array_list) - np.min(x_array_list)
 
     def on_plot_hover(self, event):
         """callback to show legend when click on one of curves"""
@@ -120,24 +131,26 @@ def _update_plot(self, x_offset_val=None, y_offset_val=None):
         """core method to update x-, y-offset sliders"""
         self.ax.set_facecolor('w')
         self.ax.cla()
+        # remain current offset
         if not x_offset_val:
             x_offset_val = self.x_offset_slider.val
         if not y_offset_val:
             y_offset_val = self.y_offset_slider.val
         # get stateful info
-        state = self._adapt_data_list(self.int_data_list)
-        x_array_list, y_array_list, \
-        y_min, y_max, y_dist, x_min, x_max, x_dist = state
-        for ind, el in enumerate(zip(x_array_list, y_array_list)):
+        for ind, el in enumerate(zip(self.x_array_list,
+                                     self.y_array_list)):
             x, y = el
-            self.ax.plot(x + x_dist * ind * x_offset_val,
-                         y + y_dist * ind * y_offset_val,
-                         label=self.key_list[ind], picker=5)
+            self.ax.plot(x + self.x_dist * ind * x_offset_val,
+                         y + self.y_dist * ind * y_offset_val,
+                         label=self.key_list[ind], picker=5,
+                         **self.kwargs)
         self.ax.autoscale()
         if self.unit:
             xlabel, ylabel = self.unit
-            self.ax.set_xlabel = xlabel
-            self.ax.set_ylabel = ylabel
+            self.ax.set_xlabel(xlabel)
+            self.ax.set_ylabel(ylabel)
+        conf_tick_size(self.ax, self.tick_size)
+        conf_label_size(self.ax, self.label_size)
         self.canvas.draw_idle()
 
     def update_y_offset(self, val):
@@ -155,10 +168,10 @@ def no_int_data_plot(self, ax, canvas):
         """
         ax.cla()
         ax.text(.5, .5,
-                '{}'.format("We couldn't find reduced data in directory "
-                            "currently set.\nReducded data are generated "
-                            "by proper calibration and integration.\n"
-                            "Please go to our documentation for more details:\n"
+                '{}'.format("No reduced data was generated.\n"
+                            "Proper calibration is required for "
+                            "data reduction.\n"
+                            "Please refer to our documentation for more details:\n"
                             "http://xpdacq.github.io/quickstart.html"),
                 ha='center', va='center', color='w',
                 transform=ax.transAxes, size=11)