Merge pull request #54 from axiom-data-science/glg

GetLegendGraphic support (base support, nothing dataset specific). Fixes #53.

Author: kwilcox

wms/glg_handler.py

@@ -0,0 +1,177 @@
+# -*- coding: utf-8 -*-
+from django.http import HttpResponse
+
+import numpy as np
+
+import matplotlib
+
+from matplotlib.pyplot import get_cmap, colorbar, legend
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_agg import FigureCanvasAgg
+
+from wms import logger
+
+from matplotlib import rcParams
+rcParams['font.family'] = 'sans-serif'
+rcParams['font.sans-serif'] = ['Bitstream Vera Sans']
+rcParams['font.serif'] = ['Bitstream Vera Sans']
+rcParams['font.size'] = '10'
+rcParams['figure.autolayout'] = True
+rcParams['savefig.dpi'] = 72.
+
+
+def create_axis(request, position=None):
+    position = position or [0, 0, 1, 1]
+    # Create figure
+    plt.close('all')
+    dpi = 72.
+    width = int(request.GET['width'])
+    height = int(request.GET['height'])
+    fig = plt.figure(dpi=dpi, figsize=(width / dpi, height / dpi), facecolor=None, edgecolor=None, frameon=False, tight_layout=True)
+    fig.set_alpha(0)
+    ax = fig.add_axes(position)
+
+    csr = request.GET['colorscalerange']
+    if request.GET['logscale'] is True:
+        norm = matplotlib.colors.LogNorm(vmin=csr.min, vmax=csr.max, clip=False)
+    else:
+        norm = matplotlib.colors.Normalize(vmin=csr.min, vmax=csr.max, clip=False)
+
+    return fig, ax, norm
+
+
+def figure_response(fig, request, adjust=None, **kwargs):
+
+    canvas = FigureCanvasAgg(fig)
+    response = HttpResponse(content_type='image/png')
+    canvas.print_png(response, bbox_inches='tight', pad_inches=0.1, **kwargs)
+    return response
+
+
+def get_position(request):
+    if request.GET['horizontal'] is True:
+        base = [0.08, 0.5, 0.8, 0.4]
+        if request.GET['showlabel'] is False:
+            base[3] += 0.1
+        if request.GET['showvalues'] is False:
+            base[3] += 0.2
+    else:
+        base = [0.05, 0.1, 0.4, 0.8]
+        if request.GET['showlabel'] is False:
+            base[2] += 0.1
+        if request.GET['showvalues'] is False:
+            base[2] += 0.2
+
+    return base
+
+
+def filledcontour(request):
+    # Create figure
+    fig, ax, norm = create_axis(request, get_position(request))
+
+    orientation = 'vertical'
+    if request.GET['horizontal'] is True:
+        orientation = 'horizontal'
+    csr = request.GET['colorscalerange']
+
+    if request.GET['logscale'] is True:
+        levs = np.hstack(([csr.min-3], np.linspace(csr.min, csr.max, request.GET['numcontours']), [csr.max+40]))
+        x, y = np.meshgrid(np.arange(1), np.arange(1))
+        cs = ax.contourf(x, y, x, levels=levs, norm=norm, cmap=get_cmap(request.GET['colormap']))
+        cb = colorbar(mappable=cs, cax=ax, orientation=orientation, spacing='proportional', extendrect=False, use_gridspec=True)
+        if request.GET['showvalues'] is False:
+            cb.set_ticks([])
+        else:
+            cb.set_ticks(levs[1:-1])
+            cb.set_ticklabels([ "%.1f" % x for x in levs[1:-1] ])
+
+    else:
+        levs = np.linspace(csr.min, csr.max, request.GET['numcontours'])
+        x, y = np.meshgrid(np.arange(1), np.arange(1))
+        cs = ax.contourf(x, y, x, levels=levs, norm=norm, cmap=get_cmap(request.GET['colormap']), extend='both')
+        cb = colorbar(mappable=cs, cax=ax, orientation=orientation, spacing='proportional', extendrect=False, use_gridspec=True)
+        if request.GET['showvalues'] is False:
+            cb.set_ticks([])
+        else:
+            cb.set_ticks(levs)
+            cb.set_ticklabels([ "%.1f" % x for x in levs ])
+
+    if request.GET['showlabel'] is True:
+        cb.set_label(request.GET['units'])
+
+    # Return HttpResponse
+    return figure_response(fig, request)
+
+
+def contour(request):
+
+    # Create figure
+    fig, ax, norm = create_axis(request)
+    ax.set_axis_off()
+
+    csr = request.GET['colorscalerange']
+
+    if request.GET['logscale'] is True:
+        levs = np.hstack(([csr.min-1], np.linspace(csr.min, csr.max, request.GET['numcontours']), [csr.max+1]))
+        levs_labels = [ "%.1f" % x for x in levs[1:-1] ]
+        if request.GET['showvalues'] is False:
+            levs_labels = [ '' for x in range(levs.size-2) ]
+        x, y = np.meshgrid(np.arange(1), np.arange(1))
+        cs = ax.contourf(x, y, x, levels=levs, norm=norm, cmap=get_cmap(request.GET['colormap']))
+        proxy = [plt.Rectangle((0, 0), 0, 0, fc=pc.get_facecolor()[0]) for pc in cs.collections]
+
+    else:
+        levs = np.linspace(csr.min, csr.max, request.GET['numcontours'])
+        levs_labels = [ "%.1f" % x for x in levs ]
+        if request.GET['showvalues'] is False:
+            levs_labels = [ '' for x in range(levs.size) ]
+        x, y = np.meshgrid(np.arange(1), np.arange(1))
+        cs = ax.contourf(x, y, x, levels=levs, norm=norm, cmap=get_cmap(request.GET['colormap']), extend='max')
+        proxy = [plt.Rectangle((0, 0), 0, 0, fc=pc.get_facecolor()[0]) for pc in cs.collections]
+
+    params = dict()
+    if request.GET['horizontal'] is True:
+        columns = 5
+        if request.GET['numcontours'] > 20:
+            columns = request.GET['numcontours'] / 10
+        params = dict(labelspacing=0, mode="expand", ncol=columns)
+
+    cb = legend(proxy, levs_labels, loc=10, borderaxespad=0., frameon=False, **params)
+
+    if request.GET['showlabel'] is True:
+        cb.set_title(request.GET['units'])
+
+    # Return HttpResponse
+    return figure_response(fig, request, bbox_extra_artists=(cb,))
+
+
+def vector(request):
+    raise NotImplementedError
+
+
+def barb(request):
+    raise NotImplementedError
+
+
+def gradiant(request):
+    # Create figure
+    fig, ax, norm = create_axis(request, get_position(request))
+
+    orientation = 'vertical'
+    if request.GET['horizontal'] is True:
+        orientation = 'horizontal'
+    cb = matplotlib.colorbar.ColorbarBase(ax, cmap=get_cmap(request.GET['colormap']), norm=norm, orientation=orientation)
+
+    if request.GET['showvalues'] is False:
+        cb.set_ticks([])
+    else:
+        csr = request.GET['colorscalerange']
+        ticks = np.linspace(csr.min, csr.max, 5)
+        cb.set_ticks(ticks)
+        cb.set_ticklabels([ "%.1f" % x for x in ticks ])
+
+    if request.GET['showlabel'] is True:
+        cb.set_label(request.GET['units'])
+
+    # Return HttpResponse
+    return figure_response(fig, request)

wms/migrations/0028_auto_20150710_1637.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+from __future__ import unicode_literals
+
+from django.db import models, migrations
+
+from pyaxiom.netcdf import EnhancedDataset, EnhancedMFDataset
+
+
+def forward(apps, schema_editor):
+    Layer = apps.get_model('wms', 'Layer')
+    Dataset = apps.get_model('wms', 'Dataset')
+    for d in Dataset.objects.all():
+        try:
+            nc = EnhancedDataset(d.uri)
+        except:
+            try:
+                nc = EnhancedMFDataset(d.uri, aggdim='time')
+            except:
+                pass
+
+        for v in nc.variables:
+            nc_var = nc.variables[v]
+            l, _ = Layer.objects.get_or_create(dataset_id=d.id, var_name=v)
+            if hasattr(nc_var, 'units'):
+                l.units = nc_var.units
+                l.save()
+
+
+def reverse(apps, schema_editor):
+    pass
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('wms', '0027_auto_20150602_1603'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='layer',
+            name='units',
+            field=models.CharField(help_text=b"The 'units' from the dataset variable", max_length=200, blank=True),
+            preserve_default=True,
+        ),
+        migrations.AddField(
+            model_name='virtuallayer',
+            name='units',
+            field=models.CharField(help_text=b"The 'units' from the dataset variable", max_length=200, blank=True),
+            preserve_default=True,
+        ),
+        migrations.RunPython(forward, reverse_code=reverse),
+    ]

wms/models/datasets/base.py

@@ -21,6 +21,7 @@
 
 from wms.utils import DotDict, find_appropriate_time
 from wms.data_handler import blank_canvas
+from wms import glg_handler
 
 from wms import logger
 
@@ -87,7 +88,18 @@ def getmap(self, layer, request):
         raise NotImplementedError
 
     def getlegendgraphic(self, layer, request):
-        raise NotImplementedError
+        try:
+            if 'filledcontours' in request.GET['image_type']:
+                return glg_handler.filledcontour(request)
+            elif 'contours' in request.GET['image_type']:
+                return glg_handler.contour(request)
+            elif 'vector' in request.GET['image_type']:
+                return glg_handler.vector(request)
+            else:
+                return glg_handler.gradiant(request)
+        except BaseException:
+            logger.exception("Could not process GetLegendGraphic request")
+            raise
 
     def setup_getfeatureinfo(self, ncd, variable_object, request, location=None):
 
@@ -253,6 +265,9 @@ def process_layers(self):
                 if hasattr(nc_var, 'long_name'):
                     l.description = nc_var.long_name
 
+                if hasattr(nc_var, 'units'):
+                    l.units = nc_var.units
+
                 # Set some standard styles
                 l.styles = Style.defaults()
                 l.save()

wms/models/datasets/sgrid.py

@@ -239,9 +239,6 @@ def getmap(self, layer, request):
         finally:
             nc.close()
 
-    def getlegendgraphic(self, layer, request):
-        return views.getLegendGraphic(request, self)
-
     def getfeatureinfo(self, layer, request):
         try:
             nc = self.netcdf4_dataset()

wms/models/datasets/ugrid.py

@@ -226,9 +226,6 @@ def getmap(self, layer, request):
         finally:
             nc.close()
 
-    def getlegendgraphic(self, layer, request):
-        raise NotImplementedError("GetLegendGraphic is not implemented for UGRID datasets")
-
     def getfeatureinfo(self, layer, request):
         try:
             nc = self.netcdf4_dataset()

wms/models/layer.py

@@ -11,6 +11,7 @@
 class LayerBase(models.Model):
     var_name    = models.CharField(max_length=200, help_text="Variable name from dataset")
     std_name    = models.CharField(max_length=200, blank=True, help_text="The 'standard_name' from the dataset variable")
+    units       = models.CharField(max_length=200, blank=True, help_text="The 'units' from the dataset variable")
     description = models.CharField(max_length=200, blank=True, help_text="Descriptive name of this layer, optional")
     dataset     = models.ForeignKey('Dataset')
     active      = models.BooleanField(default=False)