Add features in BigFileGrid DataSource.

These shall probably go into the painter ... But the transfer function
syntax we currently have looks more clumsy that this..

examples/paint/test_grid.params

@@ -6,6 +6,9 @@ DataSource:
    plugin: BigFileGrid
    path: ${NBKIT_CACHE}/data/bigfile_grid
    dataset: PaintGrid
+   normalize : true
+   fk : exp(- 0.5 * (k * 8) ** 2)
+   frho : (rho - 1) ** 2
 Painter:
    plugin: DefaultPainter
 

nbodykit/plugins/datasource/BigFileGrid.py

@@ -13,7 +13,7 @@ class BigFileGridSource(GridSource):
     """
     plugin_name = "BigFileGrid"
 
-    def __init__(self, path, dataset):
+    def __init__(self, path, dataset, frho=None, normalize=False, fk=None):
         import bigfile
         f = bigfile.BigFileMPI(self.comm, self.path)
         self.dataset = dataset
@@ -39,6 +39,9 @@ def register(cls):
 
         s.add_argument("path", type=str, help="the file path to load the data from")
         s.add_argument("dataset", type=str, help="the file path to load the data from")
+        s.add_argument("frho", type=str, help="A python expresion for transforming the real space density field. variables: rho. example: 1 + (rho - 1)**2")
+        s.add_argument("fk", type=str, help="A python expresion for transforming the fourier space density field. variables: k. example: exp(-(k * 0.5)**2) ")
+        s.add_argument("normalize", type=bool, help="Normalize the field to set mean == 1")
 
     def read(self, pm):
         import bigfile
@@ -48,4 +51,37 @@ def read(self, pm):
         f = bigfile.BigFileMPI(self.comm, self.path)
         with f[self.dataset] as ds:
             resampler.read(pm, ds, self.Nmesh, self.isfourier)
+        mean = self.comm.allreduce(pm.real.sum(dtype='f8')) / pm.Nmesh ** 3.
+
+        if self.comm.rank == 0:
+            self.logger.info("Mean = %g" % mean)
+
+        if self.normalize:
+            pm.real *= 1. / mean
+            mean = self.comm.allreduce(pm.real.sum(dtype='f8')) / pm.Nmesh ** 3.
+            if self.comm.rank == 0:
+                self.logger.info("Renormalized mean = %g" % mean)
+
+        if self.fk:
+            if self.comm.rank == 0:
+                self.logger.info("applying transformation fk %s" % self.fk)
+
+            def function(rho):
+                return eval(self.frho)
+            pm.r2c()
+            k = (pm.k[0] ** 2 + pm.k[1] ** 2 + pm.k[2] ** 2) ** 0.5
+            pm.complex[...] *= function(k)
+            pm.c2r()
+
+        if self.frho:
+            if self.comm.rank == 0:
+                self.logger.info("applying transformation frho %s" % self.frho)
+
+            def function(rho):
+                return eval(self.frho)
+            if self.comm.rank == 0:
+                self.logger.info("example value before frho %g" % pm.real.flat[0])
+            pm.real[...] = function(pm.real)
+            if self.comm.rank == 0:
+                self.logger.info("example value after frho %g" % pm.real.flat[0])