New plotting functions for MHOU

doc/sphinx/source/vp/theorycov/examples.rst

@@ -154,39 +154,6 @@ a comprehensive set of plots and tables describing the covariance matrices.
       {@plot_diag_cov_comparison@}
    {@endwith@}
 
-   Experimental $\chi^2$
-   ---------------------
-   {@with default_theory@}
-      {@total_experiments_chi2@}
-
-   Total (exp. + th.) $\chi^2$
-   ---------------------------
-      {@chi2_impact_custom@}
-
-   Experimental $\chi^2$ by dataset
-   --------------------------------
-      {@experiments_chi2_table@}
-
-   Total (exp. + th.) $\chi^2$ by dataset
-   --------------------------------------
-      {@experiments_chi2_table_theory@}
-
-   $\chi^2$ including only diagonal theory elements
-   ------------------------------------------------
-      {@chi2_diag_only@}
-
-   Impact of theory covariance matrix on $\chi^2$s 
-   -----------------------------------------------
-      {@plot_datasets_chi2_theory@}
-   {@endwith@}
-
-   Scale variations as a function of the kinematics
-   ------------------------------------------------
-   {@with matched_datasets_from_dataspecs@}
-      [Plots for {@dataset_name@}]({@dataset_report report@})
-   {@endwith@}
-
-
 Validation report
 ----------------- 
 

validphys2/examples/theory_covariance/template_matrix_plots.md

@@ -18,35 +18,3 @@ Diagonal elements of covariance matrices
 {@with default_theory@}
    {@plot_diag_cov_comparison@}
 {@endwith@}
-
-Experimental $\chi^2$
----------------------
-{@with default_theory@}
-   {@total_chi2_per_point_data@}
-
-Total (exp. + th.) $\chi^2$
----------------------------
-   {@chi2_impact_custom@}
-
-Experimental $\chi^2$ by dataset
---------------------------------
-   {@procs_chi2_table@}
-
-Total (exp. + th.) $\chi^2$ by dataset
---------------------------------------
-   {@procs_chi2_table_theory@}
-
-$\chi^2$ including only diagonal theory elements
-------------------------------------------------
-   {@chi2_diag_only@}
-
-Impact of theory covariance matrix on $\chi^2$s
------------------------------------------------
-   {@plot_datasets_chi2_theory@}
-{@endwith@}
-
-Scale variations as a function of the kinematics
-------------------------------------------------
-{@with matched_datasets_from_dataspecs@}
-   [Plots for {@dataset_name@}]({@dataset_report report@})
-{@endwith@}

validphys2/src/validphys/pdfgrids.py

@@ -346,7 +346,7 @@ def distance_grids(pdfs, xplotting_grids, normalize_to: (int, str, type(None)) =
     This method returns distance grids where the relative distance between both PDF
     set is computed. At least one grid will be identical to zero.
     """
-
+    
     gr2_stats = xplotting_grids[normalize_to].grid_values
     cv2 = gr2_stats.central_value()
     sg2 = gr2_stats.std_error()
@@ -374,6 +374,46 @@ def distance_grids(pdfs, xplotting_grids, normalize_to: (int, str, type(None)) =
 
     return newgrids
 
+@check_pdf_normalize_to
+def pull_grids(pdfs, xplotting_grids, normalize_to: (int, str, type(None)) = None):
+    """Return an object containing the value of the distance PDF at the specified values
+    of x and flavour.
+
+    The parameter ``normalize_to`` identifies the reference PDF set with respect to the
+    distance is computed.
+
+    This method returns distance grids where the relative distance between both PDF
+    set is computed. At least one grid will be identical to zero.
+    """
+
+    gr2_stats = xplotting_grids[normalize_to].grid_values
+    cv2 = gr2_stats.central_value()
+    sg2 = gr2_stats.std_error()
+    N2 = pdfs[normalize_to].get_members()
+
+    newgrids = []
+    for grid, pdf in zip(xplotting_grids, pdfs):
+        if pdf == pdfs[normalize_to]:
+            # Zero the PDF we are normalizing against
+            pdf_zero = pdf.stats_class(np.zeros_like(gr2_stats.data[0:1]))
+            newgrid = grid.copy_grid(grid_values=pdf_zero)
+            newgrids.append(newgrid)
+            continue
+
+        g_stats = grid.grid_values
+        cv1 = g_stats.central_value()
+        sg1 = g_stats.std_error()
+        N1 = pdf.get_members()
+
+        # Wrap the distance into a Stats (1, flavours, points)
+        distance = Stats([np.sqrt((cv1 - cv2) ** 2 / (sg1**2 + sg2**2))])
+
+        newgrid = grid.copy_grid(grid_values=distance)
+        newgrids.append(newgrid)
+
+    return newgrids
+
+pull_grids_list = collect(pull_grids, ('pdfs_list',))
 
 @check_pdf_normalize_to
 def variance_distance_grids(pdfs, xplotting_grids, normalize_to: (int, str, type(None)) = None):

validphys2/src/validphys/pdfplots.py

@@ -273,7 +273,106 @@ def plot_pdf_uncertainties(
     PDF's central value is plotted. Otherwise it is the absolute values."""
     yield from UncertaintyPDFPlotter(pdfs, xplotting_grids, xscale, normalize_to, ymin, ymax)
 
+class PullPDFPlotter(metaclass=abc.ABCMeta):
+    """Auxiliary class which groups multiple pulls in one plot."""
+
+    def __init__(self, pdfs_list, pull_grids_list, xscale, normalize_to, ymin, ymax):
+        self.pdfs_list = pdfs_list
+        self.pull_grids_list = pull_grids_list
+        self._xscale = xscale
+        self.normalize_to = normalize_to
+        self.ymin = ymin
+        self.ymax = ymax
+        self.firstgrid = pull_grids_list[0][0]
+
+    def legend(self, flstate):
+        return flstate.ax.legend()
+
+    def get_ylabel(self):
+        return "Pull"
+
+    @property
+    def Q(self):
+        return self.firstgrid.Q
+
+    @property
+    def xscale(self):
+        if self._xscale is None:
+            return scale_from_grid(self.firstgrid)
+        return self._xscale
+
+    def get_title(self, flstate):
+        return '$%s$' % flstate.parton_name + f', Q={self.Q : .1f} GeV'
+
+    def draw(self, pdfs, grid, flstate):
+        ax = flstate.ax
+        flindex = flstate.flindex
+        color = ax._get_lines.get_next_color()
+
+        # The grid for the distance is (1, flavours, points)
+        # take only the flavour we are interested in
+        gv = grid.select_flavour(flindex).grid_values.data.squeeze()
+
+        ax.plot(grid.xgrid, gv, color=color, label = f'{pdfs[0].label}-{pdfs[1].label} pull')
+
+        return gv
+
+    def plot_call(self):
+        basis = self.firstgrid.basis
+        for flindex, fl in enumerate(self.firstgrid.flavours):
+            fig, ax = plotutils.subplots()
+            parton_name = basis.elementlabel(fl)
+            flstate = FlavourState(flindex=flindex, fl=fl, fig=fig, ax=ax, parton_name=parton_name)
+            ax.set_title(self.get_title(flstate))
+
+            all_vals = []
+            for pdf, grids in zip(self.pdfs_list, self.pull_grids_list):
+                limits = self.draw([pdf['pdfs'][0],pdf['pdfs'][1]], grids[1], flstate)
+                if limits is not None:
+                    all_vals.append(np.atleast_2d(limits))
 
+            # Note these two lines do not conmute!
+            ax.set_xscale(self.xscale)
+            plotutils.frame_center(ax, self.firstgrid.xgrid, np.concatenate(all_vals))
+            if self.ymin is not None:
+                ax.set_ylim(ymin=self.ymin)
+            if self.ymax is not None:
+                ax.set_ylim(ymax=self.ymax)
+
+            ax.set_xlabel('$x$')
+            ax.set_xlim(self.firstgrid.xgrid[0])
+
+            ax.set_ylabel(self.get_ylabel())
+
+            ax.set_axisbelow(True)
+
+            self.legend(flstate)
+            yield fig, parton_name
+
+    def __call__(self):
+        for fig, parton_name in self.plot_call():
+            ax = fig.get_axes()[0]
+            ymin, _ = ax.get_ylim()
+            ax.set_ylim(max(0, ymin), None)
+            yield fig, parton_name
+
+
+@figuregen
+@check_scale('xscale', allow_none=True)
+def plot_pdf_pulls(
+    pdfs_list,
+    pull_grids_list,
+    xscale: (str, type(None)) = None,
+    normalize_to: (int, str, type(None)) = None,
+    ymin=None,
+    ymax=None,
+):
+    """Plot the PDF standard deviations as a function of x.
+    If normalize_to is set, the ratio to that
+    PDF's central value is plotted. Otherwise it is the absolute values."""
+    yield from PullPDFPlotter(pdfs_list, pull_grids_list, xscale, normalize_to, ymin, ymax)()
+
+
 class AllFlavoursPlotter(PDFPlotter):
     """Auxiliary class which groups multiple PDF flavours in one plot."""