Fixed all other occurrences of scape.fitting that I could find and conformed to the scikits.fitting as fit convention.

Author: james-smith-za

AR1/reductions/tipping_curve/jupyter_analysis/Tipping curve for SKA prototype report.ipynb

@@ -418,7 +418,7 @@
     "            for val_el,ra,dec,el in zip(sort_ind,self.ra,self.dec,self.elevation):\n",
     "                self.T_sky.append( T_sky(ra,dec))\n",
     "                self.Tsys_sky[pol].append(tipping_mu[val_el]-T_sky(ra,dec))\n",
-    "        TmpSky = scape.fitting.PiecewisePolynomial1DFit()\n",
+    "        TmpSky = fit.PiecewisePolynomial1DFit()\n",
     "        TmpSky.fit(self.elevation, self.T_sky)\n",
     "        self.Tsky = TmpSky\n",
     "\n",
@@ -661,7 +661,7 @@
     "    else:   \n",
     "        tau = 0.01078\n",
     "    print(\"atmospheric_opacity = %f  at  %f MHz\"%(tau,freqs))\n",
-    "    tip = scape.fitting.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars\n",
+    "    tip = fit.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars\n",
     "    def know_quant(x):\n",
     "        rx = T_rx.rec[pol](freqs)\n",
     "        sky = T_sys.Tsky(x)\n",

AR1/reductions/tipping_curve/red_tipping_curve_AR1.py

@@ -269,7 +269,7 @@ def __init__(self,d,freqs=1822,freq_index=0,elevation=None,ra=None,dec=None ,sur
             for val_el,ra,dec,el in zip(sort_ind,self.ra,self.dec,self.elevation):
                 self.T_sky.append( T_sky(ra,dec))
                 self.Tsys_sky[pol].append(tipping_mu[val_el]-T_sky(ra,dec))
-        TmpSky = scape.fitting.PiecewisePolynomial1DFit()
+        TmpSky = fit.PiecewisePolynomial1DFit()
         TmpSky.fit(self.elevation, self.T_sky)
         self.Tsky = TmpSky
 
@@ -421,7 +421,7 @@ def fit_tipping(T_sys,SpillOver,pol,freqs,T_rx,fixopacity=False):
     else:
         tau = 0.01078
     print("atmospheric_opacity = %f  at  %f MHz"%(tau,freqs))
-    tip = scape.fitting.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars
+    tip = fit.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars
     def know_quant(x):
         rx = T_rx.rec[pol](freqs)
         sky = T_sys.Tsky(x)
@@ -673,7 +673,7 @@ def find_nearest(array,value):
             #print ('Chi square for HH  at %s MHz is: %6f ' % (np.mean(d.freqs),fit_H['chisq'],))
             #print ('Chi square for VV  at %s MHz is: %6f ' % (np.mean(d.freqs),fit_V['chisq'],))
             length = len(T_SysTemp.elevation)
-            Tsky_spec = 2.725 + 1.6*(d.freqs[i]/1e3)**-2.75 # T_SysTemp.Tsys_sky  is Tsys-(Tsky-cmb) . We then add the spec sky aproxx (T_gal+Tcmb) 
+            Tsky_spec = 2.725 + 1.6*(d.freqs[i]/1e3)**-2.75 # T_SysTemp.Tsys_sky  is Tsys-(Tsky-cmb) . We then add the spec sky aproxx (T_gal+Tcmb)
             tsys[0:length,i,0] = (np.array(T_SysTemp.Tsys_sky['HH'])+Tsky_spec)/aperture_efficiency.eff['HH'](d.freqs[i])
             tsys[0:length,i,1] = (np.array(T_SysTemp.Tsys_sky['VV'])+Tsky_spec)/aperture_efficiency.eff['VV'](d.freqs[i])
             tsys[0:length,i,2] = T_SysTemp.elevation
@@ -728,7 +728,7 @@ def find_nearest(array,value):
 according to ITU-R P.676-9. $T_{\mathrm{ant}}$ is the excess temperature since the other components are
 known. """
 
-    text += r"""The green solid lines in the figures reflect the modelled $T_{\mathrm{sys}}(\mathrm{el})$ or $T_{\mathrm{sys}}(\mathrm{freq})$, with the 
+    text += r"""The green solid lines in the figures reflect the modelled $T_{\mathrm{sys}}(\mathrm{el})$ or $T_{\mathrm{sys}}(\mathrm{freq})$, with the
 broken green lines indicating a $\pm10\%$ margin."""
 
     params = {'font.size': 10}

RTS/2.1-Tipping_Curve/red_tipping_curve.py

@@ -277,7 +277,7 @@ def __init__(self,d,freqs=1822,freq_index=0,elevation=None,ra=None,dec=None ,sur
             for val_el,ra,dec,el in zip(sort_ind,self.ra,self.dec,self.elevation):
                 self.T_sky.append( T_sky(ra,dec))
                 self.Tsys_sky[pol].append(tipping_mu[val_el]-T_sky(ra,dec))
-        TmpSky = scape.fitting.PiecewisePolynomial1DFit()
+        TmpSky = fit.PiecewisePolynomial1DFit()
         TmpSky.fit(self.elevation, self.T_sky)
         self.Tsky = TmpSky
 
@@ -307,16 +307,16 @@ def remove_rfi(d,width=3,sigma=5,axis=1):
 def load_cal(filename, baseline, nd_models, freq_channel=None,channel_bw=10.0,channel_mask='',n_chan = 4096,channel_range=None,band_input=None):
     """ Load the dataset into memory """
     print('Loading noise diode models')
-    
+
     try:
         d = scape.DataSet(filename, baseline=baseline, nd_models=nd_models,band=band_input)
     except IOError:
         nd = scape.gaincal.NoiseDiodeModel(freq=[800,2000],temp=[20,20])
         warnings.warn('Warning: Failed to load/find Noise Diode Models, setting models to 20K ')
         print('Warning: Failed to load/find Noise Diode Models, setting models to 20K ')
         d = scape.DataSet(filename, baseline=baseline,  nd_h_model = nd, nd_v_model=nd ,band=band_input)
-        
-        
+
+
     if not channel_range is None :
         start_freq_channel = int(channel_range.split(',')[0])
         end_freq_channel = int(channel_range.split(',')[1])
@@ -438,7 +438,7 @@ def fit_tipping(T_sys,SpillOver,pol,freqs,T_rx,fixopacity=False):
     else:
         tau = 0.01078
     print("atmospheric_opacity = %f  at  %f MHz"%(tau,freqs))
-    tip = scape.fitting.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars
+    tip = fit.NonLinearLeastSquaresFit(None, [0, 0.00]) # nonsense Vars
     def know_quant(x):
         rx = T_rx.rec[pol](freqs)
         sky = T_sys.Tsky(x)
@@ -480,7 +480,7 @@ def plot_data_el(Tsys,Tant,title='',units='K',line=42,aperture_efficiency=None,f
         for error_margin in [0.9,1.1]:
             plt.hlines(recLim_apEffH*error_margin,elevation.min(), elevation.max(), lw=1.1,colors='g',linestyle='--')
             plt.hlines(recLim_apEffV*error_margin,elevation.min(), elevation.max(), lw=1.1,colors='g',linestyle='--')
-        
+
     plt.grid()
     plt.ylabel('$T_{sys}/\eta_{ap}$  (K)')
     return fig
@@ -524,7 +524,7 @@ def plot_data_freq(frequency,Tsys,Tant,title='',aperture_efficiency=None):
         plt.plot(frequency,recLim_apEffV,lw=1.1,color='limegreen',linestyle='-')
         for error_margin in [0.9,1.1]:
             plt.plot(frequency,recLim_apEffH*error_margin, lw=1.1,color='g',linestyle='--')
-            plt.plot(frequency,recLim_apEffV*error_margin, lw=1.1,color='g',linestyle='--')   
+            plt.plot(frequency,recLim_apEffV*error_margin, lw=1.1,color='g',linestyle='--')
 
     low_lim = (r_lim(Tsys[:,0:2]),r_lim(Tant[:,0:2]) )
     low_lim = np.min(low_lim)
@@ -536,7 +536,7 @@ def tmp(x):
     high_lim = np.max((high_lim , 46*1.3))
     plt.ylim(low_lim,high_lim)
     plt.vlines(900,low_lim,high_lim,lw=1.1,color='darkviolet',linestyle='--')
-    plt.vlines(1680,low_lim,high_lim,lw=1.1,color='darkviolet',linestyle='--')    
+    plt.vlines(1680,low_lim,high_lim,lw=1.1,color='darkviolet',linestyle='--')
     if np.min(frequency) <= 1420 :
         plt.hlines(42, np.min((frequency.min(),1420)), 1420, colors='k')
     if np.max(frequency) >=1420 :
@@ -569,7 +569,7 @@ def tmp(x):
 
 parser.add_option( "--fix-opacity",action="store_true", default=False,
                   help="The opacity is fixed to  0.01078 (Van Zee et al.,1997) or it is calculated according to ITU-R P.676-9.")
-parser.add_option("-c", "--channel-mask", default='/var/kat/katsdpscripts/RTS/rfi_mask.pickle', 
+parser.add_option("-c", "--channel-mask", default='/var/kat/katsdpscripts/RTS/rfi_mask.pickle',
                   help="Optional pickle file with boolean array specifying channels to mask (default is no mask)")
 
 (opts, args) = parser.parse_args()
@@ -610,9 +610,9 @@ def find_nearest(array,value):
         Band,SN = h5.receivers.get(ant.name,'l.4').split('.') # A safe Default
     else:
         Band = 'L'
-        SN = h5.sensor['Antennas/'+ant.name+'/rsc_rxl_serial_number'][0] # Try get the serial no. only used for noise&recever model 
-        
-        
+        SN = h5.sensor['Antennas/'+ant.name+'/rsc_rxl_serial_number'][0] # Try get the serial no. only used for noise&recever model
+
+
     receiver_model_H = str("{}/Rx{}_SN{:0>4d}_calculated_noise_H_chan.dat".format(opts.receiver_models,str.upper(Band),int(SN)))
     receiver_model_V = str("{}/Rx{}_SN{:0>4d}_calculated_noise_V_chan.dat".format(opts.receiver_models,str.upper(Band),int(SN)))
     aperture_efficiency_h = "%s/ant_eff_%s_H_AsBuilt.csv"%(opts.aperture_efficiency,str.upper(Band))
@@ -625,8 +625,8 @@ def find_nearest(array,value):
     freq_list = np.zeros((len(chunks)))
     for j,chunk in enumerate(chunks):freq_list[j] = h5.channel_freqs[chunk].mean()/1e6
     print("Selecting channel data to form %f MHz Channels"%(channel_bw) )
-    d = load_cal(filename, "%s" % (ant.name,), nd_models, chunks,channel_mask=channel_mask,n_chan=n_chans,channel_range=freq_chans,band_input=Band.lower())    
-    
+    d = load_cal(filename, "%s" % (ant.name,), nd_models, chunks,channel_mask=channel_mask,n_chan=n_chans,channel_range=freq_chans,band_input=Band.lower())
+
     for j in xrange(len(d.freqs)):freq_list[j] = d.freqs[j]
 
     tsys = np.zeros((len(d.scans),len(freq_list),5 ))#*np.NaN
@@ -659,7 +659,7 @@ def find_nearest(array,value):
             #print ('Chi square for HH  at %s MHz is: %6f ' % (np.mean(d.freqs),fit_H['chisq'],))
             #print ('Chi square for VV  at %s MHz is: %6f ' % (np.mean(d.freqs),fit_V['chisq'],))
             length = len(T_SysTemp.elevation)
-            Tsky_spec = 2.725 + 1.6*(d.freqs[i]/1e3)**-2.75 # T_SysTemp.Tsys_sky  is Tsys-(Tsky-cmb) . We then add the spec sky aproxx (T_gal+Tcmb) 
+            Tsky_spec = 2.725 + 1.6*(d.freqs[i]/1e3)**-2.75 # T_SysTemp.Tsys_sky  is Tsys-(Tsky-cmb) . We then add the spec sky aproxx (T_gal+Tcmb)
             tsys[0:length,i,0] = (np.array(T_SysTemp.Tsys_sky['HH'])+Tsky_spec)/aperture_efficiency.eff['HH'](d.freqs[i])
             tsys[0:length,i,1] = (np.array(T_SysTemp.Tsys_sky['VV'])+Tsky_spec)/aperture_efficiency.eff['VV'](d.freqs[i])
             tsys[0:length,i,2] = T_SysTemp.elevation
@@ -695,26 +695,26 @@ def find_nearest(array,value):
                 #break
 
     fig = plt.figure(None,figsize = (8,8))
-    text =r"""The 'tipping curve' is calculated according to the expression below, with the parameters 
-of $T_{\mathrm{ant}}$ and $\tau_{0}$, the Antenna temperature and the atmospheric opacity respectively. All the 
+    text =r"""The 'tipping curve' is calculated according to the expression below, with the parameters
+of $T_{\mathrm{ant}}$ and $\tau_{0}$, the Antenna temperature and the atmospheric opacity respectively. All the
 variables are also functions of frequency.
 
 $T_{\mathrm{sys}}(\mathrm{el}) = T_{\mathrm{cmb}}(\mathrm{ra,dec}) + T_{\mathrm{gal}}(\mathrm{ra,dec}) + T_{\mathrm{atm}}*(1-\exp\left(\frac{-\tau_{0}}{\sin(\mathrm{el})}\right)) + T_{\mathrm{spill}}(\mathrm{el}) + T_{\mathrm{ant}} + T_{\mathrm{rx}}$
 
 $T_{\mathrm{sys}}(\mathrm{el})$ is determined from the noise diode calibration so it is $\frac{T_{\mathrm{sys}}(\mathrm{el})}{\eta_{_{\mathrm{illum}}}}$
 
-We assume the opacity and $T_{\mathrm{ant}}$ is the residual after the tipping curve function is calculated. 
+We assume the opacity and $T_{\mathrm{ant}}$ is the residual after the tipping curve function is calculated.
 $T_{\mathrm{cmb}}$ + $T_{\mathrm{gal}}$ is obtained from the Sky model. """
     if fix_opacity :
-        text += r"""$\tau_{0}$, the zenith opacity, is set to 0.01078 
-(Van Zee et al., 1997). $T_{\mathrm{ant}}$ is the excess temperature since the other components are 
+        text += r"""$\tau_{0}$, the zenith opacity, is set to 0.01078
+(Van Zee et al., 1997). $T_{\mathrm{ant}}$ is the excess temperature since the other components are
 known. """
     else:
-        text += r"""$\tau_{0}$, the zenith opacity, is the calculated opacity 
-according to ITU-R P.676-9. $T_{\mathrm{ant}}$ is the excess temperature since the other components are 
+        text += r"""$\tau_{0}$, the zenith opacity, is the calculated opacity
+according to ITU-R P.676-9. $T_{\mathrm{ant}}$ is the excess temperature since the other components are
 known. """
 
-    text += r"""The green solid lines in the figures reflect the modelled $T_{\mathrm{sys}}(\mathrm{el})$ or $T_{\mathrm{sys}}(\mathrm{freq})$, with the 
+    text += r"""The green solid lines in the figures reflect the modelled $T_{\mathrm{sys}}(\mathrm{el})$ or $T_{\mathrm{sys}}(\mathrm{freq})$, with the
 broken green lines indicating a $\pm10\%$ margin."""
 
     params = {'font.size': 10}

katsdpscripts/reduction/analyse_point_source_scans.py

@@ -17,7 +17,7 @@
 import katpoint
 import logging
 import pickle
-import scikits.fitting as fitting
+import scikits.fitting as fit
 
 try:
     import matplotlib.pyplot as plt
@@ -33,7 +33,7 @@ def interp_sensor(compscan, quantity, default):
     except KeyError:
         return (lambda times: default)
     else:
-        interp = fitting.PiecewisePolynomial1DFit(max_degree=0)
+        interp = fit.PiecewisePolynomial1DFit(max_degree=0)
         interp.fit(sensor['timestamp'], sensor['value'])
         return interp
 
@@ -224,7 +224,7 @@ def reduce_and_plot(dataset, current_compscan, reduced_data, opts, fig=None, **k
             plt.close('all')
         #return the recarray
         to_keep=[]
-        for field in output_field_names: 
+        for field in output_field_names:
             to_keep.append([data[field] for data in reduced_data if data and data['keep']])
         output_data = np.rec.fromarrays(to_keep, dtype=zip(output_field_names,[np.array(tk).dtype for tk in to_keep]))
         return (dataset.antenna, output_data,)
@@ -505,14 +505,14 @@ def done_callback(event):
         plt.show()
 
 
-def batch_mode_analyse_point_source_scans(filename, outfilebase=None, keepfilename=None, baseline='sd', 
-        mc_iterations=1, time_offset=0.0, pointing_model=None, freq_chans=None, old_loader=None, nd_models=None, 
+def batch_mode_analyse_point_source_scans(filename, outfilebase=None, keepfilename=None, baseline='sd',
+        mc_iterations=1, time_offset=0.0, pointing_model=None, freq_chans=None, old_loader=None, nd_models=None,
         ku_band=False, channel_mask=None,keep_all=None,remove_spikes=False,freq_centre=None):
 
     class FakeOptsForBatch(object):
         batch = True #always batch
         plot_spectrum = False #never plot
-        def __init__(self, outfilebase, keepfilename, baseline, 
+        def __init__(self, outfilebase, keepfilename, baseline,
                         mc_iterations, time_offset, pointing_model, freq_chans, old_loader, nd_models, ku_band, channel_mask,keep_all,remove_spikes,freq_centre):
             self.outfilebase=outfilebase
             self.keepfilename=keepfilename
@@ -530,12 +530,12 @@ def __init__(self, outfilebase, keepfilename, baseline,
             self.remove_spikes=remove_spikes
             self.freq_centre = freq_centre
 
-    fake_opts = FakeOptsForBatch(outfilebase=outfilebase, keepfilename=keepfilename, baseline=baseline, 
+    fake_opts = FakeOptsForBatch(outfilebase=outfilebase, keepfilename=keepfilename, baseline=baseline,
     mc_iterations=mc_iterations, time_offset=time_offset, pointing_model=pointing_model, freq_chans=freq_chans,
     old_loader=old_loader, nd_models=nd_models, ku_band=ku_band, channel_mask=channel_mask,keep_all=keep_all,
     remove_spikes=remove_spikes,freq_centre=freq_centre)
     (dataset_antenna, output_data,) = analyse_point_source_scans(filename, fake_opts)
-    
+
     return dataset_antenna, output_data
 
 

reduction/fit_tipping_curve.py

@@ -22,6 +22,7 @@
 import scape
 from katpoint import rad2deg, deg2rad,  construct_azel_target
 from math import *
+import skikits.fitting as fit
 
 class Sky_temp:
     """
@@ -101,8 +102,8 @@ def __init__(self,nu,path=''):
         elif (nu >= 1200) and (nu <= 1600):
             spilloverH_nu = (nu-1200)/(1600-1200)*spillover_1600H[1] + (1 - (nu-1200)/(1600-1200))*spillover_1200H[1]
             spilloverV_nu = (nu-1200)/(1600-1200)*spillover_1600V[1] + (1 - (nu-1200)/(1600-1200))*spillover_1200V[1]
-        T_HH = scape.fitting.PiecewisePolynomial1DFit()
-        T_VV = scape.fitting.PiecewisePolynomial1DFit()
+        T_HH = fit.PiecewisePolynomial1DFit()
+        T_VV = fit.PiecewisePolynomial1DFit()
         T_HH.fit(elH, spilloverH_nu[sort_ind])
         T_VV.fit(elV, spilloverV_nu[sort_ind])
         self.spill = {}
@@ -147,7 +148,7 @@ def __init__(self,d,path='~/comm/catalogues/TBGAL_CONVL.FITS'):#d, nu, pol
             for val_el,ra,dec,el in zip(sort_ind,self.ra,self.dec,self.elevation):
                 self.T_sky.append( T_sky(ra,dec))
                 self.Tsys_sky[pol].append(tipping_mu[val_el]-T_sky(ra,dec))
-        TmpSky = scape.fitting.PiecewisePolynomial1DFit()
+        TmpSky = fit.PiecewisePolynomial1DFit()
         TmpSky.fit(self.elevation, self.T_sky)
         self.Tsky = TmpSky
         T_skytemp.plot_sky(self.ra,self.dec)
@@ -189,7 +190,7 @@ def fit_tipping(T_sys,SpillOver,pol):
     #T_sky = np.average(T_sys.T_sky)# T_sys.Tsky(x)
     func = lambda p, x: p[0] +  T_sys.Tsky(x) + SpillOver.spill[pol](x) + T_atm * (1 - np.exp(-p[1] / np.sin(deg2rad(x))))
     # Initialise the fitter with the function and an initial guess of the parameter values
-    tip = scape.fitting.NonLinearLeastSquaresFit(func, [70, 0.005])
+    tip = fit.NonLinearLeastSquaresFit(func, [70, 0.005])
     tip.fit(T_sys.elevation, T_sys.Tsys[pol])
     logger.info('Fit results for %s polarisation:' % (pol,))
     logger.info('T_ant = %.2f K' % (tip.params[0],))

reduction/image_reduction.py

@@ -1046,12 +1046,12 @@ def save_fits_image(filename, x, y, Z, target_name='', coord_system='radec',
 #     vis, model_vis, uvd = vis_samples[n:n + solint_size], model_vis_samples[n:n + solint_size], uvdist[n:n + solint_size]
 #     good_uv = (uvd >= uv_dist_range[0]) & (uvd <= uv_dist_range[1])
 #     if selfcal_type == 'P':
-#         fitter = scape.fitting.NonLinearLeastSquaresFit(lambda p, x: apply_phases(p, x, model_vis[good_uv]), initial_phases)
+#         fitter = NonLinearLeastSquaresFit(lambda p, x: apply_phases(p, x, model_vis[good_uv]), initial_phases)
 #         fitter.fit(np.tile(input_pairs, bins_per_solint)[:, good_uv], np.vstack((vis.real, vis.imag))[:, good_uv])
 #         phase_params[phase_params_to_fit] = fitter.params
 #         gainsol = np.exp(1j * phase_params).astype(np.complex64)
 #     else:
-#         fitter = scape.fitting.NonLinearLeastSquaresFit(lambda p, x: apply_gains(p, x, model_vis[good_uv]), initial_gains)
+#         fitter = NonLinearLeastSquaresFit(lambda p, x: apply_gains(p, x, model_vis[good_uv]), initial_gains)
 #         fitter.fit(np.tile(input_pairs, bins_per_solint)[:, good_uv], np.vstack((vis.real, vis.imag))[:, good_uv])
 #         full_params[params_to_fit] = fitter.params * np.sign(fitter.params[2 * ref_input_index])
 #         gainsol = full_params.view(np.complex128).astype(np.complex64)