FastBox-mod

README.md

@@ -27,6 +27,21 @@ The following optional dependencies are needed for some of the foreground modell
 * `multiprocessing`
 * `GPy`
 
+INSTALLATION IN A CONDA ENVIRONMENT
+
+conda create -n fast-env python=3.8 pyccl=2.3.0 scikit-learn scikit-image healpy lmfit -c bccp -c conda-forge -y
+
+git clone https://github.com/philbull/FastBox.git
+
+cd Fastbox
+
+python setup.py install
+
+If want to use katbeam model:
+
+pip install git+https://github.com/ska-sa/katbeam.git
+
+
 ## Current features
 
  - Gaussian and log-normal density fields for any cosmology

docs/power.md

@@ -0,0 +1 @@
+::: fastbox.power

examples/example_box.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pylab as plt
 from fastbox.box import CosmoBox, default_cosmo
-from nbodykit.algorithms.fftpower import FFTPower
-from nbodykit.lab import ArrayMesh
 from numpy import fft
 import sys
 
@@ -29,7 +27,7 @@
 plt.colorbar()
 plt.show()
 
-sys.exit(0)
+#sys.exit(0)
 
 
 # Trim negative values
@@ -65,7 +63,7 @@
 plt.show()
 
 
-sys.exit(0)
+#sys.exit(0)
 
 # Gaussian box with beam smoothing and foreground cut
 transfer_fn = lambda k_perp, k_par: \
@@ -103,7 +101,7 @@
 plt.show()
 
 
-sys.exit(0)
+#sys.exit(0)
 
 
 # Log-normal box
@@ -127,7 +125,7 @@
 #plt.errorbar(re_k, re_pk, yerr=re_stddev, fmt=".", color='r')
 plt.plot(re_k, re_pk, 'r.', label="P(k) from density field")
 plt.plot(lnre_k, lnre_pk, 'gx', label="P(k) from log-normal")
-plt.xscale('log')
+plt.xscale('log')ples
 plt.yscale('log')
 plt.legend(loc='lower left', frameon=False)
 

examples/example_endtoend.py

@@ -11,10 +11,10 @@
 import fastbox
 from fastbox.box import CosmoBox, default_cosmo
 from fastbox.foregrounds import ForegroundModel
-from nbodykit.lab import ArrayMesh
-from nbodykit.algorithms.fftcorr import FFTCorr
-from nbodykit.algorithms.fftpower import FFTPower
-import time, sys
+#from nbodykit.algorithms.fftpower import FFTPower
+#import time, sys
+from fastbox.beams import KatBeamModel
+import time
 
 #-------------------------------------------------------------------------------
 # Realise density field in redshift space
@@ -72,27 +72,32 @@
 
 print("\t(2) Adding foregrounds complete (%3.3f sec)" % (time.time()-t0))
 
+#-------------------------------------------------------------------------------
+# Beam convolution
+#-------------------------------------------------------------------------------
+print("(3) Beam convolving...")
+
+freqs = box.freq_array(redshift=0.8)
+kbm = KatBeamModel(box, model='L')
+data_cube = kbm.convolve_fft(data_cube,pol='I')
+
+print("Beam convolved succesfully!")
 #-------------------------------------------------------------------------------
 # Add noise
 #-------------------------------------------------------------------------------
-print("(3) Adding noise...")
+print("(4) Adding noise...")
 t0 = time.time()
 
 # Generate homogeneous radiometer noise
 noise_model = fastbox.noise.NoiseModel(box)
 noise_cube = noise_model.realise_radiometer_noise(Tinst=18., tp=2., fov=1., 
-                                                  Ndish=64) # FIXME: Long integration time!
+                                                  Ndish=64)
 
 # Add to data cube
 data_cube += noise_cube
 
 print("\t(3) Adding noise complete (%3.3f sec)" % (time.time()-t0))
 
-#-------------------------------------------------------------------------------
-# Beam convolution
-#-------------------------------------------------------------------------------
-
-# FIXME
 
 
 #-------------------------------------------------------------------------------
@@ -109,7 +114,6 @@
 cleaned_cube8, U_fg, amp_fg = fastbox.filters.pca_filter(data_cube, 
                                                          nmodes=12, 
                                                          return_filter=True)
-#cleaned_cube = data_cube # FIXME
 
 print("\t(4) Cleaning foregrounds complete (%3.3f sec)" % (time.time()-t0))
 #-------------------------------------------------------------------------------
@@ -196,26 +200,7 @@
 
 plt.xscale('log')
 plt.yscale('log')
-#plt.show()
+plt.show()
 
 #sys.exit(0)
 
-# Plot correlation functions and vanilla theoretical prediction
-plt.figure()
-plt.subplot(111)
-r = corr_true['r']
-h = box.cosmo['h']
-
-rr = np.linspace(2., 200., 300)
-xi = ccl.correlation_multipole(box.cosmo, a=box.scale_factor, l=0, s=rr, beta=0.)
-
-plt.subplot(111)
-plt.plot(rr, rr**2. * xi * tracer.signal_amplitude()**2., 'k-')
-plt.plot(r, r**2. * corr_true['corr'], 'r.', label="True field")
-plt.plot(r, r**2. * corr_proc4['corr'], 'bx', label="4 modes")
-plt.plot(r, r**2. * corr_proc4_hp['corr'], 'ys', label="4 modes (high-pass)")
-plt.plot(r, r**2. * corr_proc8['corr'], 'g+', label="4 modes")
-plt.xlabel("r", fontsize=16)
-plt.ylabel(r"$r^2 \xi(r)$", fontsize=16)
-
-plt.show()