Merge branch 'master' into byebye_cython

demos/wiener_filter_harmonic - Kopie.py

+from nifty import *
+from mpi4py import MPI
+import plotly.offline as py
+import plotly.graph_objs as go
+
+
+comm = MPI.COMM_WORLD
+rank = comm.rank
+
+
+def plot_maps(x, name):
+
+    trace = [None]*len(x)
+
+    keys = x.keys()
+    field = x[keys[0]]
+    domain = field.domain[0]
+    shape = len(domain.shape)
+    max_n = domain.shape[0]*domain.distances[0]
+    step = domain.distances[0]
+    x_axis = np.arange(0, max_n, step)
+
+    if shape == 1:
+        for ii in xrange(len(x)):
+            trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
+        fig = go.Figure(data=trace)
+
+        py.plot(fig, filename=name)
+
+    elif shape == 2:
+        for ii in xrange(len(x)):
+            py.plot([go.Heatmap(z=x[keys[ii]].val.get_full_data().real)], filename=keys[ii])
+    else:
+        raise TypeError("Only 1D and 2D field plots are supported")
+
+def plot_power(x, name):
+
+    layout = go.Layout(
+        xaxis=dict(
+            type='log',
+            autorange=True
+        ),
+        yaxis=dict(
+            type='log',
+            autorange=True
+        )
+    )
+
+    trace = [None]*len(x)
+
+    keys = x.keys()
+    field = x[keys[0]]
+    domain = field.domain[0]
+    x_axis = domain.kindex
+
+    for ii in xrange(len(x)):
+        trace[ii] = go.Scatter(x= x_axis, y=x[keys[ii]].val.get_full_data(), name=keys[ii])
+
+    fig = go.Figure(data=trace, layout=layout)
+    py.plot(fig, filename=name)
+
+np.random.seed(42)
+
+
+
+if __name__ == "__main__":
+
+    distribution_strategy = 'not'
+
+    # setting spaces
+    npix = np.array([500])  # number of pixels
+    total_volume = 1.  # total length
+
+    # setting signal parameters
+    lambda_s = .05  # signal correlation length
+    sigma_s = 10.  # signal variance
+
+
+    #setting response operator parameters
+    length_convolution = .025
+    exposure = 1.
+
+    # calculating parameters
+    k_0 = 4. / (2 * np.pi * lambda_s)
+    a_s = sigma_s ** 2. * lambda_s * total_volume
+
+    # creation of spaces
+#    x1 = RGSpace([npix,npix], distances=total_volume / npix,
+#                 zerocenter=False)
+#    k1 = RGRGTransformation.get_codomain(x1)
+    x1 = HPSpace(32)
+    k1 = HPLMTransformation.get_codomain(x1)
+
+    p1 = PowerSpace(harmonic_partner=k1, logarithmic=False)
+
+
+    # creating Power Operator with given spectrum
+    spec = (lambda k: a_s / (1 + (k / k_0) ** 2) ** 2)
+    p_field = Field(p1, val=spec)
+    S_op = create_power_operator(k1, spec)
+
+    # creating FFT-Operator and Response-Operator with Gaussian convolution
+    Fft_op = FFTOperator(domain=x1, target=k1,
+                        domain_dtype=np.float64,
+                        target_dtype=np.complex128)
+    R_op = ResponseOperator(x1, sigma=[length_convolution],
+                            exposure=[exposure])
+
+    # drawing a random field
+    sk = p_field.power_synthesize(real_signal=True, mean=0.)
+    s = Fft_op.adjoint_times(sk)
+
+    signal_to_noise = 1
+    N_op = DiagonalOperator(R_op.target, diagonal=s.var()/signal_to_noise, bare=True)
+    n = Field.from_random(domain=R_op.target,
+                          random_type='normal',
+                          std=s.std()/np.sqrt(signal_to_noise),
+                          mean=0.)
+
+    d = R_op(s) + n
+
+    # Wiener filter
+    j = Fft_op.times(R_op.adjoint_times(N_op.inverse_times(d)))
+    D = HarmonicPropagatorOperator(S=S_op, N=N_op, R=R_op)
+
+    mk = D(j)
+
+    m = Fft_op.adjoint_times(mk)
+
+#    z={}
+#    z["signal"] = s
+#    z["reconstructed_map"] = m
+#    z["data"] = d
+#    z["lambda"] = R_op(s)
+#    z["j"] = j
+#
+#    plot_maps(z, "Wiener_filter.html")
+
+

nifty/__init__.py

@@ -50,8 +50,8 @@ from spaces import *
 
 from operators import *
 
-from plotting import *
-
 from probing import *
 
 from sugar import *
+
+import plotting

nifty/domain_object.py

@@ -24,6 +24,21 @@ from keepers import Loggable,\
 
 
 class DomainObject(Versionable, Loggable, object):
+    """The abstract class that can be used as a domain for a field.
+
+    This holds all the information and functionality a field needs to know
+    about its domain and how the data of the field are stored.
+
+    Attributes
+    ----------
+    dim : int
+        Number of pixel-dimensions of the underlying data object.
+    shape : tuple
+        Shape of the array that stores the degrees of freedom for any field
+        on this domain.
+
+    """
+
     __metaclass__ = NiftyMeta
 
     def __init__(self):

nifty/plotting/Plotter/hp_plotter.py

-from plotter import Plotter

nifty/plotting/Plotter/plotter.py

-# -*- coding: utf-8 -*-
-
-import abc
-import os
-
-import plotly
-from plotly import tools
-import plotly.offline as ply
-
-
-from keepers import Loggable
-
-from nifty.spaces.space import Space
-from nifty.field_types.field_type import FieldType
-import nifty.nifty_utilities as utilities
-
-plotly.offline.init_notebook_mode()
-
-
-class Plotter(Loggable, object):
-    __metaclass__ = abc.ABCMeta
-
-    def __init__(self, interactive=False, path='.', stack_subplots=False,
-                 color_scale=None):
-        self.interactive = interactive
-        self.path = path
-        self.stack_subplots = stack_subplots
-        self.color_scale = color_scale
-        self.title = 'uiae'
-
-    @abc.abstractproperty
-    def domain(self):
-        return (Space,)
-
-    @abc.abstractproperty
-    def field_type(self):
-        return (FieldType,)
-
-    @property
-    def interactive(self):
-        return self._interactive
-
-    @interactive.setter
-    def interactive(self, interactive):
-        self._interactive = bool(interactive)
-
-    @property
-    def path(self):
-        return self._path
-
-    @path.setter
-    def path(self, new_path):
-        self._path = os.path.normpath(new_path)
-
-    @property
-    def stack_subplots(self):
-        return self._stack_subplots
-
-    @stack_subplots.setter
-    def stack_subplots(self, stack_subplots):
-        self._stack_subplots = bool(stack_subplots)
-
-    def plot(self, field, spaces=None, types=None, slice=None):
-        data = self._get_data_from_field(field, spaces, types, slice)
-        figures = self._create_individual_plot(data)
-        self._finalize_figure(figures)
-
-    @abc.abstractmethod
-    def _get_data_from_field(self, field, spaces=None, types=None, slice=None):
-        # if fields is a list, create a new field with appended
-        # field_type = field_array and copy individual parts into the new field
-
-        spaces = utilities.cast_axis_to_tuple(spaces, len(field.domain))
-        types = utilities.cast_axis_to_tuple(types, len(field.field_type))
-        if field.domain[spaces] != self.domain:
-            raise AttributeError("Given space(s) of input field-domain do not "
-                                 "match the plotters domain.")
-        if field.field_type[spaces] != self.field_type:
-            raise AttributeError("Given field_type(s) of input field-domain "
-                                 "do not match the plotters field_type.")
-
-            # iterate over the individual slices in order to compose the figure
-            # -> make a d2o.get_full_data() (for rank==0 only?)
-            # add
-        return [1,2,3]
-
-    def _create_individual_plot(self, data):
-        pass
-
-    def _finalize_figure(self, figure):
-        pass
-        # is there a use for ply.plot when one has no interest in
-        # saving a file?
-
-        # -> check for different file types
-        # -> store the file to disk (MPI awareness?)
-
-
-
-

nifty/plotting/__init__.py

 from descriptors import *
 from plots import *
 from figures import *
-from colormap import *
\ No newline at end of file
+from colormap import *
+from plotter import *

nifty/plotting/colormap/colormap.py

@@ -13,8 +13,9 @@ class Colormap(PlotlyWrapper):
             #TODO: implement validation
             pass
 
+    # no discontinuities only
     @staticmethod
-    def from_matplotlib_colormap_internal(name, mpl_cmap): # no discontinuities only
+    def from_matplotlib_colormap_internal(name, mpl_cmap):
         red = [(c[0], c[2]) for c in mpl_cmap['red']]
         green = [(c[0], c[2]) for c in mpl_cmap['green']]
         blue = [(c[0], c[2]) for c in mpl_cmap['blue']]
@@ -40,7 +41,8 @@ class Colormap(PlotlyWrapper):
                 green_val = self.green[g][1]
                 g += 1
             else:
-                slope = (self.green[g][1] - prev_g) / (self.green[g][0] - prev_split)
+                slope = ((self.green[g][1] - prev_g) /
+                         (self.green[g][0] - prev_split))
                 y = prev_g - slope * prev_split
                 green_val = slope * next_split + y
 
@@ -48,11 +50,16 @@ class Colormap(PlotlyWrapper):
                 blue_val = self.blue[b][1]
                 b += 1
             else:
-                slope = (self.blue[b][1] - prev_b) / (self.blue[b][0] - prev_split)
+                slope = ((self.blue[b][1] - prev_b) /
+                         (self.blue[b][0] - prev_split))
                 y = prev_r - slope * prev_split
                 blue_val = slope * next_split + y
 
-            prev_split, prev_r, prev_g, prev_b = next_split, red_val, green_val, blue_val
+            prev_split = next_split
+            prev_r = red_val
+            prev_g = green_val
+            prev_b = blue_val
+
             converted.append([next_split,
                               'rgb(' +
                               str(int(red_val*255)) + "," +

nifty/plotting/figures/multi_figure.py

@@ -31,29 +31,46 @@ class MultiFigure(FigureBase):
 
     def to_plotly(self):
         title_extractor = lambda z: z.title if z else ""
-        sub_titles = \
-            tuple(np.vectorize(title_extractor)(self.subfigures.flatten()))
+        sub_titles = tuple(np.vectorize(title_extractor)(self.subfigures.flatten()))
 
-        specs_setter = \
-            lambda z: {'is_3d': True} if isinstance(z, Figure3D) else {}
-        sub_specs = \
-            list(map(list, np.vectorize(specs_setter)(self.subfigures)))
+        specs_setter = lambda z: {'is_3d': True} if isinstance(z, Figure3D) else {}
+        sub_specs = list(map(list, np.vectorize(specs_setter)(self.subfigures)))
 
         multi_figure_plotly_object = plotly.tools.make_subplots(
                                                    self.rows,
                                                    self.columns,
                                                    subplot_titles=sub_titles,
                                                    specs=sub_specs)
-        # TODO resolve bug with titles and 3D subplots
+
+        multi_figure_plotly_object['layout'].update(height=self.height,
+                                                    width=self.width,
+                                                    title=self.title)
+
+        #TODO resolve bug with titles and 3D subplots
+
+        i = 1
         for index, fig in np.ndenumerate(self.subfigures):
             if fig:
                 for plot in fig.plots:
                     multi_figure_plotly_object.append_trace(plot.to_plotly(),
                                                             index[0]+1,
                                                             index[1]+1)
+                    if isinstance(fig, Figure3D):
+                        scene = dict()
+                        if fig.xaxis:
+                            scene['xaxis'] = fig.xaxis.to_plotly()
+                        if fig.yaxis:
+                            scene['yaxis'] = fig.yaxis.to_plotly()
+                        if fig.zaxis:
+                            scene['zaxis'] = fig.zaxis.to_plotly()
 
-        multi_figure_plotly_object['layout'].update(height=self.height,
-                                                    width=self.width,
-                                                    title=self.title)
+                        multi_figure_plotly_object['layout']['scene'+str(i)] = scene
+                    else:
+                        if fig.xaxis:
+                            multi_figure_plotly_object['layout']['xaxis'+str(i)] = fig.xaxis.to_plotly()
+                        if fig.yaxis:
+                            multi_figure_plotly_object['layout']['yaxis'+str(i)] = fig.yaxis.to_plotly()
+
+                    i += 1
 
         return multi_figure_plotly_object

nifty/plotting/plots/heatmaps/heatmap.py

 # -*- coding: utf-8 -*-
+import numpy as np
 
+from nifty.plotting.colormap import Colormap
 from nifty.plotting.plotly_wrapper import PlotlyWrapper
 
 
 class Heatmap(PlotlyWrapper):
     def __init__(self, data, color_map=None, webgl=False,
                  smoothing=False):  # smoothing 'best', 'fast', False
-        self.data = data
+        if isinstance(data, list):
+            self.data = np.zeros((data[0].shape))
+            for arr in data:
+                self.data = np.add(self.data, arr)
+        else:
+            self.data = data
+
+        if color_map is not None:
+            if not isinstance(color_map, Colormap):
+                raise TypeError("Provided color_map must be an instance of "
+                                "the NIFTy Colormap class.")
         self.color_map = color_map
         self.webgl = webgl
         self.smoothing = smoothing
 
+    @property
+    def figure_dimension(self):
+        return 2
+
     def to_plotly(self):
         plotly_object = dict()
         plotly_object['z'] = self.data

nifty/plotting/plots/heatmaps/mollweide.py

@@ -14,8 +14,10 @@ class Mollweide(Heatmap):
             raise ImportError("The module pylab is needed but not available.")
         if 'healpy' not in gdi:
             raise ImportError("The module healpy is needed but not available.")
-
-        data = self._mollview(data)
+        if isinstance(data, list):
+            data = [self._mollview(d) for d in data]
+        else:
+            data = self._mollview(data)
         super(Mollweide, self).__init__(data, color_map, webgl, smoothing)
 
     def _mollview(self, x, xsize=800):

nifty/plotting/plots/scatter_plots/cartesian.py

@@ -4,14 +4,16 @@ from scatter_plot import ScatterPlot
 
 
 class Cartesian(ScatterPlot):
-    def __init__(self, x, y, label, line, marker):
+    def __init__(self, x, y, label, line, marker, showlegend=True):
         super(Cartesian, self).__init__(label, line, marker)
         self.x = x
         self.y = y
+        self.showlegend = showlegend
 
     @abstractmethod
     def to_plotly(self):
         plotly_object = super(Cartesian, self).to_plotly()
         plotly_object['x'] = self.x
         plotly_object['y'] = self.y
+        plotly_object['showlegend'] = self.showlegend
         return plotly_object

nifty/plotting/plots/scatter_plots/cartesian_2D.py

@@ -5,14 +5,20 @@ from cartesian import Cartesian
 
 class Cartesian2D(Cartesian):
     def __init__(self, x=None, y=None, x_start=0, x_step=1,
-                 label='', line=None, marker=None, webgl=True):
+                 label='', line=None, marker=None, showlegend=True,
+                 webgl=True):
         if y is None:
             raise Exception('Error: no y data to plot')
         if x is None:
             x = range(x_start, len(y) * x_step, x_step)
-        super(Cartesian2D, self).__init__(x, y, label, line, marker)
+        super(Cartesian2D, self).__init__(x, y, label, line, marker,
+                                          showlegend)
         self.webgl = webgl
 
+    @property
+    def figure_dimension(self):
+        return 2
+
     def to_plotly(self):
         plotly_object = super(Cartesian2D, self).to_plotly()
         if self.webgl:

nifty/plotting/plots/scatter_plots/cartesian_3D.py

@@ -4,10 +4,16 @@ from cartesian import Cartesian
 
 
 class Cartesian3D(Cartesian):
-    def __init__(self, x, y, z, label='', line=None, marker=None):
-        super(Cartesian3D, self).__init__(x, y, label, line, marker)
+    def __init__(self, x, y, z, label='', line=None, marker=None,
+                 showlegend=True):
+        super(Cartesian3D, self).__init__(x, y, label, line, marker,
+                                          showlegend)
         self.z = z
 
+    @property
+    def figure_dimension(self):
+        return 3
+
     def to_plotly(self):
         plotly_object = super(Cartesian3D, self).to_plotly()
         plotly_object['z'] = self.z

nifty/plotting/plots/scatter_plots/geo.py

+
 from scatter_plot import ScatterPlot
 
 
@@ -13,6 +14,10 @@ class Geo(ScatterPlot):
         self.lat = lat
         self.projection = proj
 
+    @property
+    def figure_dimension(self):
+        return 2
+
     def _to_plotly(self):
         plotly_object = super(Geo, self).to_plotly()
         plotly_object['type'] = 'scattergeo'

nifty/plotting/plots/scatter_plots/scatter_plot.py

 # -*- coding: utf-8 -*-
 
-from abc import abstractmethod
+import abc
 from nifty.plotting.plotly_wrapper import PlotlyWrapper
-from nifty.plotting.descriptors import Marker
+from nifty.plotting.descriptors import Marker,\
+                                       Line
 
 
 class ScatterPlot(PlotlyWrapper):
@@ -12,8 +13,13 @@ class ScatterPlot(PlotlyWrapper):
         self.marker = marker
         if not self.line and not self.marker:
             self.marker = Marker()
+            self.line = Line()
 
-    @abstractmethod
+    @abc.abstractproperty
+    def figure_dimension(self):
+        raise NotImplementedError
+
+    @abc.abstractmethod
     def to_plotly(self):
         ply_object = dict()
         ply_object['name'] = self.label

nifty/plotting/plotter/__init__.py

+
+from healpix_plotter import HealpixPlotter
+from power_plotter import PowerPlotter

nifty/plotting/plotter/healpix_plotter.py

+from nifty.spaces import HPSpace
+
+from nifty.plotting.figures import Figure2D
+from nifty.plotting.plots import Mollweide
+from .plotter import Plotter
+
+
+class HealpixPlotter(Plotter):
+    def __init__(self, interactive=False, path='.', title="", color_map=None):
+        super(HealpixPlotter, self).__init__(interactive, path, title)
+        self.color_map = color_map
+
+    @property
+    def domain_classes(self):
+        return (HPSpace, )
+
+    def _create_individual_figure(self, plots):
+        return Figure2D(plots)
+
+    def _create_individual_plot(self, data, plot_domain):
+        result_plot = Mollweide(data=data,
+                                color_map=self.color_map)
+        return result_plot

nifty/plotting/plotter/plotter.py

+# -*- coding: utf-8 -*-
+
+import abc
+import os
+
+import numpy as np
+
+import d2o
+
+from keepers import Loggable
+
+from nifty.config import dependency_injector as gdi
+
+from nifty.spaces.space import Space
+from nifty.field import Field
+import nifty.nifty_utilities as utilities
+
+from nifty.plotting.figures import MultiFigure
+
+plotly = gdi.get('plotly')
+
+try:
+    plotly.offline.init_notebook_mode()
+except AttributeError:
+    pass
+
+rank = d2o.config.dependency_injector[
+        d2o.configuration['mpi_module']].COMM_WORLD.rank
+