Implemented extract functions for data_grid

DataModule/data_grid.py

@@ -12,7 +12,8 @@ hv.extension('bokeh')
 import holoviews.operation.datashader as hd
 import numpy as np
 import xarray as xr
-
+from .data_xy import data_xy
+from IPython.display import display
 
 class data_grid(data_module_base):
     """Class for grid like data in multiple dimensions. Powered by the
@@ -45,6 +46,7 @@ class data_grid(data_module_base):
                                        zip(data_names[:-1], data_array[:-1])},
                                name=data_names[-1])
 
+    # Helpful data functions ###################################################
     def return_coord(self, coord_name):
         return np.array(self.df.coords[coord_name])
 
@@ -65,35 +67,77 @@ class data_grid(data_module_base):
         """Default for two dim grid: Return values"""
         return self.df.values
 
-    # TODO Implement This
-    def import_data(self, data_arrays, data_names=None):
-        """Import data from new arrays. Naming highly recommencd!
-        Assumes first given array is the array of the independant variable.
+    def extract_x(self, x0, plot=True):
+        """Extract z values along  axis for specified x value x0.
+
+        This function will return the data at the line corresponding at
+        the specified value of the x-axis.
+        If the value is not exact, it will take the closest one above the
+        value.
+
         Parameters
         -----------
-        data_arrays : list, array, np.array
-            List of data arrays. Structure
-            [[x, x, x, x, ....], [y, y, y,....], ... ]
-        name_list : list, array, np.array, None
-            List of names for arrays:
-            ['x1', 'y', 'Resistances', ... ]
+        x0 : float
+            y value for which the data should be extracted
+        plot : bool
+            Plot the extracted datamodule directly
+
+        Returns
+        --------
+        DataModule
+            data_line module
         """
-        if data_names:
-            order_names = data_names
-        else:
-            order_names = df.columns
-
-        for d, idx in zip(data_arrays[0],
-                          list(self.df[order_names[0]]
-                                       .index.reindex(data_arrays[0])[1])):
-            if idx != -1:
-                for i in range(1, len(data_arrays)):
-                    # Use mean
-                    self.df[order_names[i]][idx] += data_arrays[i]
-                    self.df[order_names[i]][idx] /= 2
-            else:
-                self.df = self.df.append({key: value[idx]} for key, value
-                                         in zip(order_names, data_arrays))
+        x, y = self.df.dims[:2]
+        ex = self.df.sel(x=x0, method='nearest')
+        data = data_xy(np.array(self.df.coords[y]), np.array(ex),
+                       x, y)
+        data.par = self.par
+        data.comments = self.comments
+        data.temp_start = self.temp_start
+        data.temp_stop = self.temp_stop
+        data.time_start = self.time_start
+        data.time_stop = self.time_stop
+
+        if plot:
+            display(data.plot_hv(title='{} = {:.6e}'.format(x, x0)))
+
+        return data
+
+    def extract_y(self, y0, plot=True):
+        """Extract z values along  axis for specified y value y0.
+
+        This function will return the data at the line corresponding at
+        the specified value of the x-axis.
+        If the value is not exact, it will take the closest one above the
+        value.
+
+        Parameters
+        -----------
+        y0 : float
+            y value for which the data should be extracted
+        plot : bool
+            Plot the extracted datamodule directly
+
+        Returns
+        --------
+        DataModule
+            data_line module
+        """
+        x, y = self.df.dims[:2]
+        ex = self.df.sel(x=x0, method='nearest')
+        data = data_xy(np.array(self.df.coords[y]), np.array(ex),
+                       x, y)
+        data.par = self.par
+        data.comments = self.comments
+        data.temp_start = self.temp_start
+        data.temp_stop = self.temp_stop
+        data.time_start = self.time_start
+        data.time_stop = self.time_stop
+
+        if plot:
+            display(data.plot_hv(title='{} = {:.6e}'.format(x, x0)))
+
+        return data
 
     def plot_hv(self, name_x=None, name_y=None, cmap='magma', height=400,
                 width=800):
@@ -125,3 +169,4 @@ class data_grid(data_module_base):
                            'style': {'cmap': cmap}}})
         ds = hv.Dataset(self.df)
         return hd.regrid(ds.to(hv.Image, [x_vals, y_vals]))
+

DataModule/data_table.py

@@ -72,7 +72,7 @@ class data_table(data_module_base):
                                          in zip(order_names, data_arrays))
 
     def plot_hv(self, x=None, y=None, height=400, width=800,
-                line_width=3):
+                title='', **kwargs):
         """Plot table with Holoviews
 
         Parameters
@@ -111,7 +111,8 @@ class data_table(data_module_base):
                 x_vals.append(self.df.keys()[0])
 
         # Plot FIRST element
-        s = hv.Scatter(self.df, x_vals[0], y_vals[0])
+        hv.opts({'Curve': {'style': kwargs}})
+        s = hv.Scatter(self.df, x_vals[0], y_vals[0], label=title)
         scatter_plots = hd.dynspread(hd.datashade(hv.Curve(s),
                                                   cmap=[color_scheme[0]]))
         # Plot the other elements

DataModule/data_xy.py

@@ -73,9 +73,6 @@ class data_xy(data_table):
 
         self.import_data([x, y], ['x', 'y'])
 
-    def return_ysel(self):
-        """Return currently selected y values"""
-        return self.y
 
     # Plotting ################################################################
     def plot(self, style='b-o', color=None, xscale=1, yscale=1,
@@ -612,6 +609,13 @@ class data_xy(data_table):
             Same as fit_func for compatibility to previous versions
         """
         return self.fit_func(x)
+
+    # Compatibility to lower versions
+    def return_xsel(self):
+        return self.x
+    def return_ysel(self):
+        """Return currently selected y values"""
+        return self.y
 ###############################################################################
 # Aliases (for compatibility to previous datamdoule versions) #################
 data_2d = data_xy

DataModule/functions.py

@@ -14,6 +14,8 @@ import pandas as pd
 from .data_xy import data_xy
 from .data_surface import data_surface
 from .data_complex import data_complex
+from .data_grid import data_grid
+from .data_table import data_table
 from .data_line import data_line
 from .data_IQ import data_IQ
 import matplotlib.pyplot as plt
@@ -22,7 +24,7 @@ from .version import __version__
 from bokeh.palettes import Category10_10
 import bokeh.plotting as bp
 
-
+current_types = (data_xy, data_grid, data_complex, data_table)
 # Function Library ############################################################
 def load_datamodule(filename, upgrade=True):
     """ Load a datamodule
@@ -544,7 +546,7 @@ def upgrade_dm(old_dm):
         print('Loaded Datamodule newer than your installed version\n' +
               'Please update python_repo (git pull)')
 
-    elif vers_f < new_vers:
+    elif vers_f < new_vers or not isinstance(old_dm, current_types):
         # Check if old datamodule is below current version and upgrade if
         # Initialization
         if isinstance(old_dm, data_xy) or isinstance(old_dm, data_line):
@@ -553,12 +555,10 @@ def upgrade_dm(old_dm):
             try:
                 data_new.xmin = old_dm.xmin
                 data_new.xmax = old_dm.xmax  # Selected
-                data_new.xerr = old_dm.xerr
-                data_new.yerr = old_dm.yerr
             except AttributeError:
                 pass
             # Sort data
-            # TODO sort data automatically
+            # TODO
             # Fit
             try:
                 tmp = old_dm._fit_function_code
@@ -569,21 +569,13 @@ def upgrade_dm(old_dm):
                 data_new._fit_par_errors = old_dm._fit_par_errors
                 data_new._fit_data_error = old_dm._fit_data_error
                 data_new._fit_labels = old_dm._fit_labels
-            except:
-                pass
-
-        elif isinstance(old_dm, data_surface):
-            data_new = data_surface(old_dm.x, old_dm.y, old_dm.z)
-            try:
-                data_new.xmin = old_dm.xmin
-                data_new.xmax = old_dm.xmax
-                data_new.ymin = old_dm.ymin
-                data_new.ymax = old_dm.ymax
-                data_new.xsellen = old_dm.xsellen
-                data_new.ysellen = old_dm.ysellen
             except AttributeError:
                 pass
 
+        elif isinstance(old_dm, (data_grid, data_surface)):
+            data_new = data_grid([old_dm.x, old_dm.y, old_dm.z.T],
+                                 ['x', 'y', 'z'])
+
         elif isinstance(old_dm, data_complex):
             data_new = data_complex(old_dm.x, old_dm.value)
             try:
@@ -642,6 +634,7 @@ def upgrade_dm(old_dm):
         return old_dm
 
 
+# TODO
 ###############################################################################
 # NEED RETHINKING #############################################################
 def data_stack_y(*args):