From 48ce33971bb00a2ac3aa8ce330b1c8eb0d9d6f63 Mon Sep 17 00:00:00 2001 From: Andre Marquand Date: Tue, 5 Jan 2021 14:46:20 +0100 Subject: [PATCH] updated documentation --- doc/build/doctrees/environment.pickle | Bin 185153 -> 206155 bytes doc/build/doctrees/modindex.doctree | Bin 424927 -> 459345 bytes doc/build/html/_modules/fileio.html | 30 +- doc/build/html/_modules/normative.html | 56 ++-- .../html/_modules/normative_parallel.html | 296 +++++++++++++++++- doc/build/html/_modules/utils.html | 169 +++++++++- doc/build/html/genindex.html | 16 +- doc/build/html/modindex.html | 143 ++++++++- doc/build/html/objects.inv | Bin 1125 -> 1166 bytes doc/build/html/searchindex.js | 2 +- 10 files changed, 659 insertions(+), 53 deletions(-) diff --git a/doc/build/doctrees/environment.pickle b/doc/build/doctrees/environment.pickle index 1607ba72e6f8b9807c4da379e84e2335cef7461a..8e93c8cefa83e3f5a935c7f0d2afb363938136f8 100644 GIT binary patch delta 18920 zcmeHu33yb;kucp7LP!GL2RhJ`ggm2bba04EfDo4$MhFlHA;jou=8a}Xb1`oQB*)Ic zCdR?5*d{HfbJ&jk$A1pTUW{=v-dwhmIEOD_cfC%W#370C#%ntc@n*g8I?i9!@6BjN z95(s)|G)jdjXvmpud2GLy1Kf$Usul)U&ws=sjS&YQt$ZpgQsTSR#omkMrY01-1)+v z%J$9rUt8Tb$KI?@E_;)mu3C{HK(|d9CuMf9dIj6DsDOQLQ4!0osr*FsDzX-UE7M)$ zrohD1e;su6iYQG?1TQx;XSK=lyo zujwFGpz37xizH^Msbj0E7qF?CKK9i`i`lN~@`Bsd$#j>`<_X&s`;_|>DD8fPy}PJ3 zO*}r#47C;H7~5F8jy%tf)vir@5hx~&Y++Uvd%ZS~{Fwc@cEI5NXMl!BL+6H06GY^){781cNM#-yZfTQn$~pj8C~=1_Jl*p$L!2?h8<3G&(;}A zTapwtx}OL7$+;ev8nU`Vig$`l&FB!j#jrFxsge76_MD-lAQ|=&@`)KMoqek|eKiue ze~pFcbWzGRbecWeP!_>nO=ieGo6{fk$zEm3spxhn0A3o`+-uPqhK2Oql0w16B`e4#1#F*Z@xLOR&}bz=o$IGaJ_WSZ&^2W`sK zQ6kD#tzgMMuf#0g<^k!o1{WR%{*G)lxy`g>n!)&IFB@;hC2hQB>Tk7lXE3R z;gt|CF}Xn^FG5+eSku!gWPHG`qI1AZeYL?D5oVnBhW*#V=JMV!a4jq%i9BXn?3%4U z%Z&pU3oe)VW6a(O1JRl6lz+)cK%mlQ>K~ym+F(pxiwSTr*b_~KGl$~%wJ=FE=3I34pv482JOu*@-n-(`K?9Y0ebhx(f;3sr+5g)AkgFf z0f2hkKV*-#EGd0TJU=bUXP`XpeillQc!s65u7EkQu62UE#lGI!1?79K8`Itb^yEVJ zc&o%*ZRK;tYk9!0vNLUE?8|MXK=!@1F7hTzUt9yvjfxALvxk%UEPp7E@QCxZ75&&(h4JjE5AT zogG|N%HCeF08qIrmqWRA<$5STzH(27W;UxL>a8M4gi>t3=%4!4hS-ls^S61(VvZG_K#?yFE4UY(-Cp*youXgllI@PNq za>(Wc)XFOD(dzTU`qvWz*AX&I8aOV6bmSK&z!J&Xnl*i(9$SM(cybLYoU<1DwsmD0 z^Q}egKDl-Sls{d&63U`=8-RLnT`fExU55^FYTa5WFRsJ!S=@8eccAM<)|pX zv|;4}(L-Y(sMuY$kn50Qb@@U{@R00b>$-}YN^xGf{HDv`lT5Tgl@BS{f}QP3XPFzb zKt{<%lr$j9&xrD;8%3u#p~?fBme11$`;gz$c1W>_*6(eE4b7%(=;X7T>VWF)P3=%N zZ|>6OQ}Lz^JK3q;qWrnq))ijkYOwn@uL9chn>)3MP{AJGQpq-LK?nT0xssjPlEYp# zXR!kjZtAIIC6)?J@&{D4TsE$2mE@i8Fa@m(G=t*ZSn>9p}P-9ikY5T(;r%55Z zeFsj^AMHSjqMhhw)}82h_wDR~@~xeFDume@(9NulprWYZphF2-y^0J^3XYbcLL03s zkW7!@)?IDD^Tu7M)1$jk%Aap(P0n-5s#Q4{mP0PT zPv3pBdArMjs($xca*?@r*V8|wuutx8q5qM>ez?1h{$~n%7vA1WVWvGMLfFurHunA9 zc{y}6Fy;QzY*-e=u6_E1l{>fSQ8 zVQ(%g9H=C%Z0SHNy9c1-14i}&)DI67vD5+NJ%^ni$Y)Ci^4RQsWy}F}+rAoh57eXk z^4Sl8=5GQ17Q8>YubgaS&+ltsyZ7a>yulK(lQj%BvM<1!Yp{syWk&`Z$so(!pU)m2 ztRx4-(_4cTM24p(V%IU5Y-oQ0dtk7D-L$`sxCQh(`^#7kKrilJ$ksqzGgQM4LjCw) zCj0rSxy(0|Ne=1lGZ-H#Bopkxq2=TTCLb`cc?XKvrlA7XexQy`K>bJf|FHu_}sC2S6UT zR+76k9juw`J%Bs_|Bnx6vew~3@_B*TH(bW@hAY|Ba3R|?T)@6HoI@U9&kZjkC)r!W z&E#QLBe#;Tvfc7hRxRhTJLQF}59(*+a&`;U@5q(x>rgk0azE1^V-B_BC5B zv)QWHZ=k*d;O_xVp*^4dz*bOpl{n1aut~tYgcRb6KCgihU94V^IGD>PG?h zp1q9yE7V6u(%Dj_m@QBW*sxN{wn2TXQpT=_`ddmKnZ?d3Mv}t*sI-za*5ELZxh&V4 z&m4{llEvO}ma)${3Q0C=aFw%jjso_GBaeAq7+4Ef_TEf(0p7n1?`uYjNeTPiSUFqW zUGfQcCfhxl$G+v6!~SlxfGlL+9W{|E_O!EtWjk}pB39*GK@802E@y|Gg>0WQkKOHT zWRp<;%vnKNKyV9L3_@F2y$c1_vq7l8?5buQ>Yuwz>}jYg+zZKiw%XlFHUh6!=5^<> zQ|<<0WqYm~vLSJ%gCogJYGzDU)S+YRC|4@wAg+ z=Ja&2Q4eZfMjXNruX`4;AA1Uz-#>>ncuQE0w}AC~YgiZ5w|WiidZ=Ibq_cD0a`sht zCcbL+2Gs37iPiWD*qE=D^+SE1uZ|o=jtj3IAWuKx^OML$2zpM%sT>bY9i4K24`bB} zb7svl>Jck75!;-WjNO}r-8Ta}k%T=u1IsmRL>GML4DH_*FoH~@`LR;ZzIe9z~YZsyu)5P zcwOWV@Mv@=qb8G2hlCN6Dcm%D)~uVSj!li+JOxJuiX5~#r`+iPgwv+Wvdr8ih@@V5 zRGFIQm^H1>+;nzZa7WAUQ8z4{%CRZ;EMVy-f-k(G09^{aW#d~8%Taqs>1nI_-Nnt# z05NYdkAhO^>$mMR!6yYAa0sZ&d;*{u2=(|!J+3i;vmIY7Ag1G3Zid~s|w&}F?`1*Pfbk}|<@skt3!v%36;*2}75vfKQJkf;1|fo%=yQE6Tc z_-w#$bNNhG*etq2R;#W~mw6+qqa(GZ*l4qP2P&Y$b)JaWW$pt+nueHiuVFuad7-Aq zuvrES>apwK#_@njW5#kXlt1zWU{QU*>MDFmz_K>? 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 from __future__ import print_function
 
 import os
+import sys
 import numpy as np
 import nibabel as nib
 import tempfile
 import pandas as pd
 import re
 
+try:  # run as a package if installed
+    from pcntoolkit import configs
+except ImportError:
+    pass
+
+    path = os.path.abspath(os.path.dirname(__file__))
+    if path not in sys.path:
+        sys.path.append(path)
+    del path
+    import configs
+
 CIFTI_MAPPINGS = ('dconn', 'dtseries', 'pconn', 'ptseries', 'dscalar',
                   'dlabel', 'pscalar', 'pdconn', 'dpconn',
                   'pconnseries', 'pconnscalar')
 
 CIFTI_VOL_ATLAS = 'Atlas_ROIs.2.nii.gz'
 
+PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
+
 # ------------------------
 # general utility routines
 # ------------------------
 
+
[docs]def predictive_interval(s2_forward,
+                        cov_forward,
+                        multiplicator):
+  # calculates a predictive interval
+  
+    PI=np.zeros(len(cov_forward))
+    for i,xdot in enumerate(cov_forward):
+        s=np.sqrt(s2_forward[i])
+        PI[i]=multiplicator*s
+    return PI

 
 
[docs]def create_mask(data_array, mask, verbose=False):
     # create a (volumetric) mask either from an input nifti or the nifti itself
@@ -170,8 +194,8 @@ 
Source code for fileio
     img = nib.load(datafile)
     dat = img.get_data()
 
-#    if mask is not None:
-#        mask=load_nifti(mask, vol=True)
+    if mask is not None:
+        mask=load_nifti(mask, vol=True)
 
     if not vol:
         dat = vol2vec(dat, mask)
@@ -390,7 +414,7 @@ 
Source code for fileio
         save_ascii(data, filename)
     elif file_type(filename) == 'binary':
         data = pd.DataFrame(data)
-        data.to_pickle(filename)

+        data.to_pickle(filename, protocol=PICKLE_PROTOCOL)

 
 
 
[docs]def load(filename, mask=None, text=False, vol=True):
diff --git a/doc/build/html/_modules/normative.html b/doc/build/html/_modules/normative.html
index 0f80f4a8..1d53a8ed 100644
--- a/doc/build/html/_modules/normative.html
+++ b/doc/build/html/_modules/normative.html
@@ -70,6 +70,7 @@ 
Source code for normative
 from sklearn.model_selection import KFold
 try:  # run as a package if installed
     from pcntoolkit import fileio
+    from pcntoolkit import configs
     from pcntoolkit.normative_model.norm_utils import norm_init
     from pcntoolkit.utils import compute_pearsonr, CustomCV, explained_var, compute_MSLL
 except ImportError:
@@ -82,9 +83,11 @@ 
Source code for normative
     del path
 
     import fileio
+    import configs
     from utils import compute_pearsonr, CustomCV, explained_var, compute_MSLL
     from normative_model.norm_utils import norm_init
 
+PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
 
[docs]def load_response_vars(datafile, maskfile=None, vol=True):
     """ load response variables (of any data type)"""
@@ -445,7 +448,7 @@ 
Source code for normative
                 yhat, s2 = nm.predict(Xz[te, :], Xz[tr, :], Yz[tr, nz[i]], **kwargs)
                 
                 if savemodel:
-                    nm.save('Models/NM_' + str(fold) + '_' + str(nz[i]) + '.pkl' )
+                    nm.save('Models/NM_' + str(fold) + '_' + str(nz[i]) + outputsuffix + '.pkl' )
                 
                 if standardize:
                     Yhat[te, nz[i]] = yhat * sY[i] + mY[i]
@@ -498,7 +501,7 @@ 
Source code for normative
             pickle.dump({'valid_voxels':nz, 'fold_num':cvfolds, 
                          'mean_resp':mean_resp, 'std_resp':std_resp, 
                          'mean_cov':mean_cov, 'std_cov':std_cov, 
-                         'regressor':alg, 'standardize':standardize}, file)    
+                         'regressor':alg, 'standardize':standardize}, file, protocol=PICKLE_PROTOCOL)    
 
     # compute performance metrics
     if (run_cv or testresp is not None):
@@ -539,6 +542,7 @@ 
Source code for normative
     alg = kwargs.pop('alg','gpr')
     savemodel = kwargs.pop('savemodel','True')=='True'
     standardize = kwargs.pop('standardize',True)
+    outputsuffix = kwargs.pop('outputsuffix','_fit')
     
     if savemodel and not os.path.isdir('Models'):
         os.mkdir('Models')
@@ -588,7 +592,7 @@ 
Source code for normative
         nm = nm.estimate(Xz, Yz[:, nz[i]], **kwargs)     
             
         if savemodel:
-            nm.save('Models/NM_' + str(0) + '_' + str(nz[i]) + '.pkl' )
+            nm.save('Models/NM_' + str(0) + '_' + str(nz[i]) + outputsuffix + '.pkl' )
 
     if savemodel:
         print('Saving model meta-data...')
@@ -596,7 +600,7 @@ 
Source code for normative
             pickle.dump({'valid_voxels':nz,
                          'mean_resp':mean_resp, 'std_resp':std_resp, 
                          'mean_cov':mean_cov, 'std_cov':std_cov, 
-                         'regressor':alg, 'standardize':standardize}, file)
+                         'regressor':alg, 'standardize':standardize}, file, protocol=PICKLE_PROTOCOL)
         
     return nm

 
@@ -617,7 +621,9 @@ 
Source code for normative
     :param covfile: test covariates used to predict the response variable
     :param respfile: test response variables for the normative model
     :param maskfile: mask used to apply to the data (nifti only)
-    :param model_path: Directory containing the normative model and metadata
+    :param model_path: Directory containing the normative model and metadata.
+     When using parallel prediction, do not pass the model path. It will be automatically
+     decided.
     :param output_path: Directory to store the results
     :param outputsuffix: Text string to add to the output filenames
     :param batch_size: batch size (for use with normative_parallel)
@@ -636,6 +642,8 @@ 
Source code for normative
     batch_size = kwargs.pop('batch_size', None)
     output_path = kwargs.pop('output_path', '')
     outputsuffix = kwargs.pop('outputsuffix', '_predict')
+    inputsuffix = kwargs.pop('inputsuffix', '_estimate')
+    alg = kwargs.pop('alg')
         
     if respfile is not None and not os.path.exists(respfile):
         print("Response file does not exist. Only returning predictions")
@@ -669,7 +677,7 @@ 
Source code for normative
         X = X[:, np.newaxis]
     
     sample_num = X.shape[0]
-    feature_num = len(glob.glob(os.path.join(model_path, 'NM_*.pkl')))
+    feature_num = len(glob.glob(os.path.join(model_path, 'NM_*' + inputsuffix + '.pkl')))
 
     Yhat = np.zeros([sample_num, feature_num])
     S2 = np.zeros([sample_num, feature_num])
@@ -686,8 +694,13 @@ 
Source code for normative
         print("Prediction by model ", i+1, "of", feature_num)      
         nm = norm_init(Xz)
         nm = nm.load(os.path.join(model_path, 'NM_' + str(0) + '_' + 
-                                  str(i) + '.pkl'))
-        yhat, s2 = nm.predict(Xz, **kwargs)
+                                  str(i) + inputsuffix + '.pkl'))
+        if (alg!='hbr' or nm.configs['transferred']==False):
+            yhat, s2 = nm.predict(Xz, **kwargs)
+        else:
+            tsbefile = kwargs.pop('tsbefile') 
+            batch_effects_test = fileio.load(tsbefile)
+            yhat, s2 = nm.predict_on_new_sites(Xz, batch_effects_test)
         
         if standardize:
             Yhat[:, i] = yhat.squeeze() * sY[0][i] + mY[0][i]
@@ -771,6 +784,7 @@ 
Source code for normative
         batch_effects_train = fileio.load(trbefile)
     
     outputsuffix = kwargs.pop('outputsuffix', '_transfer')
+    inputsuffix = kwargs.pop('inputsuffix', '_estimate')
     tsbefile = kwargs.pop('tsbefile', None)
     
     job_id = kwargs.pop('job_id', None)
@@ -821,20 +835,22 @@ 
Source code for normative
               
         nm = norm_init(X)
         if batch_size is not None: # when using normative_parallel
-            print("Transferting model ", job_id*batch_size+i)
+            print("Transferring model ", job_id*batch_size+i)
             nm = nm.load(os.path.join(model_path, 'NM_0_' + 
-                                      str(job_id*batch_size+i) + '.pkl'))
+                                      str(job_id*batch_size+i) + inputsuffix + '.pkl'))
         else:
-            print("Transferting model ", i+1, "of", feature_num)
-            nm = nm.load(os.path.join(model_path, 'NM_0_' + str(i) + '.pkl'))
+            print("Transferring model ", i+1, "of", feature_num)
+            nm = nm.load(os.path.join(model_path, 'NM_0_' + str(i) + inputsuffix + '.pkl'))
         
         nm = nm.estimate_on_new_sites(X, Y[:,i], batch_effects_train)
         if batch_size is not None: 
             nm.save(os.path.join(output_path, 'NM_0_' + 
-                             str(job_id*batch_size+i) + '.pkl'))
+                             str(job_id*batch_size+i) + outputsuffix + '.pkl'))
+            nm.save(os.path.join('Models', 'NM_0_' + 
+                             str(i) + outputsuffix + '.pkl'))
         else:
             nm.save(os.path.join(output_path, 'NM_0_' + 
-                             str(i) + '.pkl'))
+                             str(i) + outputsuffix + '.pkl'))
         
         if testcov is not None:
             yhat, s2 = nm.predict_on_new_sites(Xte, batch_effects_test)
@@ -876,6 +892,8 @@ 
Source code for normative
         dummycovfile = kwargs.pop('dummycovfile')
         dummybefile = kwargs.pop('dummybefile')
     
+    outputsuffix = kwargs.pop('outputsuffix', '_extend')
+    inputsuffix = kwargs.pop('inputsuffix', '_estimate')
     informative_prior = kwargs.pop('job_id', 'False') == 'True'
     generation_factor = int(kwargs.pop('generation_factor', '10'))
     job_id = kwargs.pop('job_id', None)
@@ -910,20 +928,22 @@ 
Source code for normative
         if batch_size is not None: # when using nirmative_parallel
             print("Extending model ", job_id*batch_size+i)
             nm = nm.load(os.path.join(model_path, 'NM_0_' + 
-                                      str(job_id*batch_size+i) + '.pkl'))
+                                      str(job_id*batch_size+i) + inputsuffix + '.pkl'))
         else:
             print("Extending model ", i+1, "of", feature_num)
-            nm = nm.load(os.path.join(model_path, 'NM_0_' + str(i) + '.pkl'))
+            nm = nm.load(os.path.join(model_path, 'NM_0_' + str(i) + inputsuffix +'.pkl'))
         
         nm = nm.extend(X, Y[:,i:i+1], batch_effects_train, X_dummy, batch_effects_dummy, 
                samples=generation_factor, informative_prior=informative_prior)
         
         if batch_size is not None: 
             nm.save(os.path.join(output_path, 'NM_0_' + 
-                             str(job_id*batch_size+i) + '.pkl'))
+                             str(job_id*batch_size+i) + outputsuffix + '.pkl'))
+            nm.save(os.path.join('Models', 'NM_0_' + 
+                             str(i) + outputsuffix + '.pkl'))
         else:
             nm.save(os.path.join(output_path, 'NM_0_' + 
-                             str(i) + '.pkl'))

+                             str(i) + outputsuffix + '.pkl'))

 
 
 
[docs]def main(*args):
diff --git a/doc/build/html/_modules/normative_parallel.html b/doc/build/html/_modules/normative_parallel.html
index e1439e1d..0e78f723 100644
--- a/doc/build/html/_modules/normative_parallel.html
+++ b/doc/build/html/_modules/normative_parallel.html
@@ -72,12 +72,15 @@ 
Source code for normative_parallel
 import glob
 import shutil
 import pickle
+import fileinput
 import numpy as np
 import pandas as pd
 from subprocess import call
 
 try:
+    import pcntoolkit as ptk
     import pcntoolkit.fileio as fileio
+    from pcntoolkit import configs
 except ImportError:
     pass
     path = os.path.abspath(os.path.dirname(__file__))
@@ -85,17 +88,20 @@ 
Source code for normative_parallel
         sys.path.append(path)
         del path
     import fileio
+    import configs
+    
+PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
 
 
[docs]def execute_nm(processing_dir,
                python_path,
-               normative_path,
                job_name,
                covfile_path,
                respfile_path,
                batch_size,
                memory,
                duration,
+               normative_path=None,
                func='estimate',
                **kwargs):
 
@@ -107,7 +113,9 @@ 
Source code for normative_parallel
     :Parameters:
         * processing_dir     -> Full path to the processing dir
         * python_path        -> Full path to the python distribution
-        * normative_path     -> Full path to the normative.py
+        * normative_path     -> Full path to the normative.py. If None (default)
+                                then it will automatically retrieves the path from 
+                                the installed packeage.
         * job_name           -> Name for the bash script that is the output of
                                 this function
         * covfile_path       -> Full path to a .txt file that contains all
@@ -133,6 +141,9 @@ 
Source code for normative_parallel
     written by (primarily) T Wolfers, (adapted) SM Kia
     """
     
+    if normative_path is None:
+        normative_path = ptk.__path__[0] + '/normative.py'
+        
     cv_folds = kwargs.get('cv_folds', None)
     testcovfile_path = kwargs.get('testcovfile_path', None)
     testrespfile_path= kwargs.get('testrespfile_path', None)
@@ -174,7 +185,7 @@ 
Source code for normative_parallel
                                            'testresp_batch_' +
                                            str(n) + file_extentions)
                 batch_job_path = batch_processing_dir + batch_job_name
-                if cluster_spec is 'torque':
+                if cluster_spec == 'torque':
                     # update the response file 
                     kwargs.update({'testrespfile_path': \
                                    batch_testrespfile_path})
@@ -190,11 +201,27 @@ 
Source code for normative_parallel
                             log_path=log_path,
                             memory=memory,
                             duration=duration)
-                elif cluster_spec is 'new':
-                    # this part requires addition in different envioronment [
-                    bashwrap_nm(processing_dir=batch_processing_dir, func=func,
+                elif cluster_spec == 'sbatch':
+                    # update the response file 
+                    kwargs.update({'testrespfile_path': \
+                                   batch_testrespfile_path})
+                    sbatchwrap_nm(batch_processing_dir,
+                                python_path,
+                                normative_path,
+                                batch_job_name,
+                                covfile_path,
+                                batch_respfile_path,
+                                func=func,
+                                memory=memory,
+                                duration=duration,
                                 **kwargs)
-                    qsub_nm(processing_dir=batch_processing_dir)
+                    sbatch_nm(job_path=batch_job_path,
+                            log_path=log_path)
+                elif cluster_spec == 'new':
+                    # this part requires addition in different envioronment [
+                    sbatchwrap_nm(processing_dir=batch_processing_dir, func=func,
+                                  **kwargs)
+                    sbatch_nm(processing_dir=batch_processing_dir)
                     # ]
         if testrespfile_path is None:
             if testcovfile_path is not None:
@@ -204,7 +231,7 @@ 
Source code for normative_parallel
                 batch_respfile_path = (batch_processing_dir + 'resp_batch_' +
                                        str(n) + file_extentions)
                 batch_job_path = batch_processing_dir + batch_job_name
-                if cluster_spec is 'torque':
+                if cluster_spec == 'torque':
                     bashwrap_nm(batch_processing_dir,
                                 python_path,
                                 normative_path,
@@ -217,7 +244,20 @@ 
Source code for normative_parallel
                             log_path=log_path,
                             memory=memory,
                             duration=duration)
-                elif cluster_spec is 'new':
+                elif cluster_spec == 'sbatch':
+                    sbatchwrap_nm(batch_processing_dir,
+                                python_path,
+                                normative_path,
+                                batch_job_name,
+                                covfile_path,
+                                batch_respfile_path,
+                                func=func,
+                                memory=memory,
+                                duration=duration,
+                                **kwargs)
+                    sbatch_nm(job_path=batch_job_path,
+                              log_path=log_path)
+                elif cluster_spec == 'new':
                     # this part requires addition in different envioronment [
                     bashwrap_nm(processing_dir=batch_processing_dir, func=func,
                                 **kwargs)
@@ -232,7 +272,7 @@ 
Source code for normative_parallel
                                        'resp_batch_' + str(n) +
                                        file_extentions)
                 batch_job_path = batch_processing_dir + batch_job_name
-                if cluster_spec is 'torque':
+                if cluster_spec == 'torque':
                     bashwrap_nm(batch_processing_dir,
                                 python_path,
                                 normative_path,
@@ -245,7 +285,20 @@ 
Source code for normative_parallel
                             log_path=log_path,
                             memory=memory,
                             duration=duration)
-                elif cluster_spec is 'new':
+                elif cluster_spec == 'sbatch':
+                    sbatchwrap_nm(batch_processing_dir,
+                                python_path,
+                                normative_path,
+                                batch_job_name,
+                                covfile_path,
+                                batch_respfile_path,
+                                func=func,
+                                memory=memory,
+                                duration=duration,
+                                **kwargs)
+                    sbatch_nm(job_path=batch_job_path,
+                            log_path=log_path)
+                elif cluster_spec == 'new':
                     # this part requires addition in different envioronment [
                     bashwrap_nm(processing_dir=batch_processing_dir, func=func,
                                 **kwargs)
@@ -312,13 +365,14 @@ 
Source code for normative_parallel
             batch = str('batch_' + str(n+1))
             if not os.path.exists(processing_dir + batch):
                 os.makedirs(processing_dir + batch)
+                os.makedirs(processing_dir + batch + '/Models/')
             if (binary==False):
                 fileio.save_pd(resp_batch,
                                processing_dir + batch + '/' +
                                resp + '.txt')
             else:
                 resp_batch.to_pickle(processing_dir + batch + '/' +
-                                     resp + '.pkl')
+                                     resp + '.pkl', protocol=PICKLE_PROTOCOL)
 
     # splits response and test responsefile into batches
     else:
@@ -355,6 +409,7 @@ 
Source code for normative_parallel
             batch = str('batch_' + str(n+1))
             if not os.path.exists(processing_dir + batch):
                 os.makedirs(processing_dir + batch)
+                os.makedirs(processing_dir + batch + '/Models/')
             if (binary==False):
                 fileio.save_pd(resp_batch,
                                processing_dir + batch + '/' +
@@ -364,9 +419,9 @@ 
Source code for normative_parallel
                                '.txt')
             else:
                 resp_batch.to_pickle(processing_dir + batch + '/' +
-                                     resp + '.pkl')
+                                     resp + '.pkl', protocol=PICKLE_PROTOCOL)
                 testresp_batch.to_pickle(processing_dir + batch + '/' +
-                                         testresp + '.pkl')

+                                         testresp + '.pkl', protocol=PICKLE_PROTOCOL)

 
 
 
[docs]def collect_nm(processing_dir,
@@ -620,20 +675,20 @@ 
Source code for normative_parallel
                     
                 with open(os.path.join(processing_dir, 'Models', 'meta_data.md'), 
                           'wb') as file:
-                    pickle.dump(meta_data, file)
+                    pickle.dump(meta_data, file, protocol=PICKLE_PROTOCOL)
             
             batch_dirs = glob.glob(processing_dir + 'batch_*/')
             if batch_dirs:
                 batch_dirs = fileio.sort_nicely(batch_dirs)
                 for b, batch_dir in enumerate(batch_dirs):
-                    src_files = glob.glob(batch_dir + 'Models/*.pkl')
+                    src_files = glob.glob(batch_dir + 'Models/NM*' + outputsuffix + '.pkl')
                     if src_files:
                         src_files = fileio.sort_nicely(src_files)
                         for f, full_file_name in enumerate(src_files):
                             if os.path.isfile(full_file_name):
                                 file_name = full_file_name.split('/')[-1]
                                 n = file_name.split('_')
-                                n[-1] = str(b * batch_size + f) + '.pkl'
+                                n[-2] = str(b * batch_size + f)
                                 n = '_'.join(n)
                                 shutil.copy(full_file_name, processing_dir + 'Models/' + n)
                     elif func=='fit':
@@ -863,6 +918,213 @@ 
Source code for normative_parallel
 
 # COPY the rotines above here and aadapt those to your cluster
 # bashwarp_nm; qsub_nm; rerun_nm
+
+
[docs]def sbatchwrap_nm(processing_dir,
+                  python_path,
+                  normative_path,
+                  job_name,
+                  covfile_path,
+                  respfile_path,
+                  memory,
+                  duration,
+                  func='estimate',
+                  **kwargs):
+
+    """ This function wraps normative modelling into a bash script to run it
+    on a torque cluster system.
+
+    :Parameters:
+        * processing_dir     -> Full path to the processing dir
+        * python_path        -> Full path to the python distribution
+        * normative_path     -> Full path to the normative.py
+        * job_name           -> Name for the bash script that is the output of
+                                this function
+        * covfile_path       -> Full path to a .txt file that contains all
+                                covariats (subjects x covariates) for the
+                                responsefile
+        * respfile_path      -> Full path to a .txt that contains all features
+                                (subjects x features)
+        * cv_folds           -> Number of cross validations
+        * testcovfile_path   -> Full path to a .txt file that contains all
+                                covariats (subjects x covariates) for the
+                                testresponse file
+        * testrespfile_path  -> Full path to a .txt file that contains all
+                                test features
+        * alg                -> which algorithm to use
+        * configparam        -> configuration parameters for this algorithm
+
+    :outputs:
+        * A bash.sh file containing the commands for normative modelling saved
+          to the processing directory (written to disk)
+
+    written by (primarily) T Wolfers
+    """
+    
+    # here we use pop not get to remove the arguments as they used 
+    cv_folds = kwargs.pop('cv_folds',None)
+    testcovfile_path = kwargs.pop('testcovfile_path', None)
+    testrespfile_path = kwargs.pop('testrespfile_path', None)
+    alg = kwargs.pop('alg', None)
+    configparam = kwargs.pop('configparam', None)
+    standardize = kwargs.pop('standardize', True)
+    
+    # change to processing dir
+    os.chdir(processing_dir)
+    output_changedir = ['cd ' + processing_dir + '\n']
+
+    sbatch_init='#!/bin/bash\n'
+    sbatch_jobname='#SBATCH --job-name=' + processing_dir + '\n'
+    sbatch_account='#SBATCH --account=p33_norment\n'
+    sbatch_nodes='#SBATCH --nodes=1\n'
+    sbatch_tasks='#SBATCH --ntasks=1\n'
+    sbatch_time='#SBATCH --time=' + str(duration) + '\n'
+    sbatch_memory='#SBATCH --mem-per-cpu=' + str(memory) + '\n'
+    sbatch_module='module purge\n'
+    sbatch_anaconda='module load anaconda3\n'
+    sbatch_exit='set -o errexit\n'
+
+    #echo -n "This script is running on "
+    #hostname
+    
+    bash_environment = [sbatch_init + 
+                        sbatch_jobname +
+                        sbatch_account +
+                        sbatch_nodes +
+                        sbatch_tasks +
+                        sbatch_time +
+                        sbatch_module +
+                        sbatch_anaconda]
+
+    # creates call of function for normative modelling
+    if (testrespfile_path is not None) and (testcovfile_path is not None):
+        job_call = [python_path + ' ' + normative_path + ' -c ' +
+                    covfile_path + ' -t ' + testcovfile_path + ' -r ' +
+                    testrespfile_path + ' -f ' + func]
+    elif (testrespfile_path is None) and (testcovfile_path is not None):
+        job_call = [python_path + ' ' + normative_path + ' -c ' +
+                    covfile_path + ' -t ' + testcovfile_path + ' -f ' + func]
+    elif cv_folds is not None:
+        job_call = [python_path + ' ' + normative_path + ' -c ' +
+                    covfile_path + ' -k ' + str(cv_folds) +  ' -f ' + func]
+    elif func != 'estimate':
+        job_call = [python_path + ' ' + normative_path + ' -c ' +
+                    covfile_path +  ' -f ' + func]
+    else:
+        raise(ValueError, """For 'estimate' function either testcov or cvfold
+              must be specified.""")
+        
+    # add algorithm-specific parameters
+    if alg is not None:
+        job_call = [job_call[0] + ' -a ' + alg]
+        if configparam is not None:
+            job_call = [job_call[0] + ' -x ' + str(configparam)]
+    
+    # add standardization flag if it is false
+    if not standardize:
+        job_call = [job_call[0] + ' -s']
+    
+    # add responses file
+    job_call = [job_call[0] + ' ' + respfile_path]
+    
+    # add in optional arguments. 
+    for k in kwargs:
+        job_call = [job_call[0] + ' ' + k + '=' + kwargs[k]]
+
+    # writes bash file into processing dir
+    with open(processing_dir+job_name, 'w') as bash_file:
+        bash_file.writelines(bash_environment + output_changedir + \
+                             job_call + ["\n"] + [sbatch_exit])
+
+    # changes permissoins for bash.sh file
+    os.chmod(processing_dir + job_name, 0o700)

+
+
[docs]def sbatch_nm(job_path,
+              log_path):
+    """
+    This function submits a job.sh scipt to the torque custer using the qsub
+    command.
+
+    ** Input:
+        * job_path      -> Full path to the job.sh file
+        * log_path      -> The logs are currently stored in the working dir
+
+    ** Output:
+        * Submission of the job to the (torque) cluster
+
+    witten by (primarily) T Wolfers
+    """
+
+    # created qsub command
+    sbatch_call = ['sbatch ' + job_path]
+
+    # submits job to cluster
+    call(sbatch_call, shell=True)
+    
+    def rerun_nm(processing_dir,
+                 memory,
+                 duration,
+                 new_memory=False,
+                 new_duration=False,
+                 binary=False,
+                 **kwargs):
+        """
+        This function reruns all failed batched in processing_dir after collect_nm
+        has identified he failed batches
+    
+        * Input:
+            * processing_dir        -> Full path to the processing directory
+            * memory                -> Memory requirements written as string
+                                       for example 4gb or 500mb
+            * duration              -> The approximate duration of the job, a
+                                       string with HH:MM:SS for example 01:01:01
+            * new_memory            -> If you want to change the memory 
+                                        you have to indicate it here.
+            * new_duration          -> If you want to change the duration 
+                                        you have to indicate it here.
+        * Outputs:
+            * Reruns failed batches. 
+    
+        written by (primarily) T Wolfers
+        """
+        log_path = kwargs.pop('log_path', None)
+    
+        if binary:
+            file_extentions = '.pkl'
+            failed_batches = fileio.load(processing_dir +
+                                         'failed_batches' + 
+                                         file_extentions)
+            shape = failed_batches.shape
+            for n in range(0, shape[0]):
+                jobpath = failed_batches[n, 0]
+                print(jobpath)
+                if new_duration != False:
+                    with fileinput.FileInput(jobpath, inplace=True) as file:
+                        for line in file:
+                            print(line.replace(duration, new_duration), end='')
+                if new_memory != False:
+                    with fileinput.FileInput(jobpath, inplace=True) as file:
+                        for line in file:
+                            print(line.replace(memory, new_memory), end='')
+                sbatch_nm(jobpath,
+                          log_path)
+        else:
+            file_extentions = '.txt'
+            failed_batches = fileio.load_pd(processing_dir +
+                                           'failed_batches' + file_extentions)
+            shape = failed_batches.shape
+            for n in range(0, shape[0]):
+                jobpath = failed_batches.iloc[n, 0]
+                print(jobpath)
+                if new_duration != False:
+                    with fileinput.FileInput(jobpath, inplace=True) as file:
+                        for line in file:
+                            print(line.replace(duration, new_duration), end='')
+                if new_memory != False:
+                    with fileinput.FileInput(jobpath, inplace=True) as file:
+                        for line in file:
+                            print(line.replace(memory, new_memory), end='')
+                sbatch_nm(jobpath,
+                          log_path)

 

 
             

diff --git a/doc/build/html/_modules/utils.html b/doc/build/html/_modules/utils.html
index c0ea6533..d669eaff 100644
--- a/doc/build/html/_modules/utils.html
+++ b/doc/build/html/_modules/utils.html
@@ -47,6 +47,7 @@ 
Source code for utils
 from __future__ import print_function
 
 import os
+import sys
 import numpy as np
 from scipy import stats
 from subprocess import call
@@ -60,6 +61,20 @@ 
Source code for utils
 from bspline import splinelab
 from sklearn.datasets import make_regression
 import pymc3 as pm
+from io import StringIO
+
+try:  # run as a package if installed
+    from pcntoolkit import configs
+except ImportError:
+    pass
+
+    path = os.path.abspath(os.path.dirname(__file__))
+    if path not in sys.path:
+        sys.path.append(path)
+    del path
+    import configs
+    
+PICKLE_PROTOCOL = configs.PICKLE_PROTOCOL
 
 # -----------------
 # Utility functions
@@ -368,6 +383,22 @@ 
Source code for utils
     """ Sin-hyperbolic arcsin warp having two parameters (a, b) and defined by 
     
         y = sinh(b *  arcsinh(x) - a)
+        
+        Using the parametrisation of Rios et al, Neural Networks 118 (2017)
+        where a controls skew and b controls kurtosis, such that:
+            a = 0 : symmetric
+            a > 0 : positive skew
+            a < 0 : negative skew
+            b = 1 : mesokurtic
+            b > 1 : leptokurtic
+            b < 1 : platykurtic
+        
+        where b > 0. However, it is more convenentent to use an alternative 
+        parameterisation, where
+
+        y = sinh(b * arcsinh(x) + epsilon * b)
+        
+        and a = -epsilon*b
     
         see Jones and Pewsey A (2009) Biometrika, 96 (4) (2009)
     """
@@ -379,7 +410,12 @@ 
Source code for utils
         if len(param) != self.n_params:
             raise(ValueError, 
                   'number of parameters must be ' + str(self.n_params))
-        return param[0], param[1]
+
+        epsilon = param[0]
+        b = np.exp(param[1])
+        a = -epsilon*b
+        
+        return a, b
 
 
[docs]    def f(self, x, params):
         a, b = self._get_params(params)
@@ -757,17 +793,17 @@ 
Source code for utils
         if not os.path.isdir(working_dir):
             os.mkdir(working_dir)
         with open(os.path.join(working_dir ,'trbefile.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(grp_id_train),file)
+            pickle.dump(pd.DataFrame(grp_id_train),file, protocol=PICKLE_PROTOCOL)
         with open(os.path.join(working_dir ,'tsbefile.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(grp_id_test),file)
+            pickle.dump(pd.DataFrame(grp_id_test),file, protocol=PICKLE_PROTOCOL)
         with open(os.path.join(working_dir ,'X_train.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(X_train),file)
+            pickle.dump(pd.DataFrame(X_train),file, protocol=PICKLE_PROTOCOL)
         with open(os.path.join(working_dir ,'X_test.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(X_test),file)
+            pickle.dump(pd.DataFrame(X_test),file, protocol=PICKLE_PROTOCOL)
         with open(os.path.join(working_dir ,'Y_train.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(Y_train),file)
+            pickle.dump(pd.DataFrame(Y_train),file, protocol=PICKLE_PROTOCOL)
         with open(os.path.join(working_dir ,'Y_test.pkl'), 'wb') as file:
-            pickle.dump(pd.DataFrame(Y_test),file)
+            pickle.dump(pd.DataFrame(Y_test),file, protocol=PICKLE_PROTOCOL)
         
     return X_train, Y_train, grp_id_train, X_test, Y_test, grp_id_test, coef

 
@@ -794,6 +830,125 @@ 
Source code for utils
     plt.xticks(rotation=90, fontsize=7)
     plt.tight_layout()
     plt.show()

+    
+    
+
[docs]def load_freesurfer_measure(measure, data_path, subjects_list):
+    
+    """
+    This is a utility function to load different Freesurfer measures in a pandas
+    Dataframe.
+    
+    Inputs:
+        - measure: a string that defines the type of Freesurfer measure we want 
+        to load. The options include:
+            - 'NumVert': Number of Vertices in each cortical area based on Destrieux atlas.
+            - 'SurfArea: Surface area for each cortical area based on Destrieux atlas.
+            - 'GrayVol': Gary matter volume in each cortical area based on Destrieux atlas.
+            - 'ThickAvg': Average Cortical thinckness in each cortical area based on Destrieux atlas.
+            - 'ThickStd': STD of Cortical thinckness in each cortical area based on Destrieux atlas.
+            - 'MeanCurv': Integrated Rectified Mean Curvature in each cortical area based on Destrieux atlas.
+            - 'GausCurv': Integrated Rectified Gaussian Curvature in each cortical area based on Destrieux atlas.
+            - 'FoldInd': Folding Index in each cortical area based on Destrieux atlas.
+            - 'CurvInd': Intrinsic Curvature Index in each cortical area based on Destrieux atlas.
+            - 'brain': Brain Segmentation Statistics from aseg.stats file.
+            - 'subcortical_volumes': Subcortical areas volume.
+            
+        - data_path: a string that specifies the path to the main Freesurfer folder.
+        
+        - subjects_list: A Pythin list containing the list of subject names to load the data for. 
+        The subject names should match the folder name for each subject's Freesurfer data folder.
+        
+    Outputs:
+        - df: A pandas datafrmae containing the subject names as Index and target Freesurfer measures.
+        - missing_subs: A Python list of subject names that miss the target Freesurefr measures.
+            
+    """
+    
+    df = pd.DataFrame()
+    missing_subs = []
+    
+    if measure in ['NumVert', 'SurfArea', 'GrayVol', 'ThickAvg', 
+                   'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd']:
+        l = ['NumVert', 'SurfArea', 'GrayVol', 'ThickAvg', 
+                   'ThickStd', 'MeanCurv', 'GausCurv', 'FoldInd', 'CurvInd']
+        col = l.index(measure) + 1
+        for i, sub in enumerate(subjects_list):
+            try:
+                data = dict()
+           
+                a = pd.read_csv(data_path + sub + '/stats/lh.aparc.a2009s.stats', 
+                                delimiter='\s+', comment='#', header=None)
+                temp = dict(zip(a[0], a[col]))
+                for key in list(temp.keys()):
+                    temp['L_'+key] = temp.pop(key)
+                data.update(temp)
+               
+                a = pd.read_csv(data_path + sub + '/stats/rh.aparc.a2009s.stats', 
+                                delimiter='\s+', comment='#', header=None)
+                temp = dict(zip(a[0], a[col]))
+                for key in list(temp.keys()):
+                    temp['R_'+key] = temp.pop(key)
+                data.update(temp)
+                
+                df_temp = pd.DataFrame(data,index=[sub])         
+                df = pd.concat([df, df_temp])
+                print('%d / %d: %s is done!' %(i, len(subjects_list), sub))
+            except:
+                missing_subs.append(sub)
+                print('%d / %d: %s is missing!' %(i, len(subjects_list), sub))
+                continue
+    
+    elif measure == 'brain':
+        for i, sub in enumerate(subjects_list):
+            try:
+                data = dict()
+                s = StringIO()
+                with open(data_path + sub + '/stats/aseg.stats') as f:
+                    for line in f:
+                        if line.startswith('# Measure'):
+                            s.write(line)
+                s.seek(0) # "rewind" to the beginning of the StringIO object
+                a = pd.read_csv(s, header=None) # with further parameters?
+                data_brain = dict(zip(a[1], a[3]))
+                data.update(data_brain)
+                df_temp = pd.DataFrame(data,index=[sub])         
+                df = pd.concat([df, df_temp])
+                print('%d / %d: %s is done!' %(i, len(subjects_list), sub))
+            except:
+                missing_subs.append(sub)
+                print('%d / %d: %s is missing!' %(i, len(subjects_list), sub))
+                continue
+    
+    elif measure == 'subcortical_volumes':
+        for i, sub in enumerate(subjects_list):
+            try:
+                data = dict()
+                s = StringIO()
+                with open(data_path + sub + '/stats/aseg.stats') as f:
+                    for line in f:
+                        if line.startswith('# Measure'):
+                            s.write(line)
+                s.seek(0) # "rewind" to the beginning of the StringIO object
+                a = pd.read_csv(s, header=None) # with further parameters?
+                a = dict(zip(a[1], a[3]))
+                if ' eTIV' in a.keys():
+                    tiv = a[' eTIV']
+                else:
+                    tiv = a[' ICV']
+                a = pd.read_csv(data_path + sub + '/stats/aseg.stats', delimiter='\s+', comment='#', header=None)
+                data_vol = dict(zip(a[4]+'_mm3', a[3]))
+                for key in data_vol.keys():
+                    data_vol[key] = data_vol[key]/tiv
+                data.update(data_vol)
+                data = pd.DataFrame(data,index=[sub])         
+                df = pd.concat([df, data])
+                print('%d / %d: %s is done!' %(i, len(subjects_list), sub))
+            except:
+                missing_subs.append(sub)
+                print('%d / %d: %s is missing!' %(i, len(subjects_list), sub))
+                continue
+    
+    return df, missing_subs

 

 
             

diff --git a/doc/build/html/genindex.html b/doc/build/html/genindex.html
index 89417bed..ca00b338 100644
--- a/doc/build/html/genindex.html
+++ b/doc/build/html/genindex.html
@@ -335,10 +335,12 @@ 
L

 
       
  • load_data() (in module trendsurf)
 
    • 
-      
  • load_nifti() (in module fileio)
+      
  • load_freesurfer_measure() (in module utils)
 
    • 
   
   
-  
-  
+  
 
 
 
    Q
    
@@ -478,10 +482,14 @@ 
    S
    
       
  • save_nifti() (in module fileio)
 
    • 
       
  • save_pd() (in module fileio)
+
    • 
+      
  • save_results() (in module normative)
 
    • 
   
   
      
-      
    • save_results() (in module normative)
+      
    • sbatch_nm() (in module normative_parallel)
+
      • 
+      
    • sbatchwrap_nm() (in module normative_parallel)
 
      • 
       
    • simulate_data() (in module utils)
 
      • 
diff --git a/doc/build/html/modindex.html b/doc/build/html/modindex.html
index 95a14547..320674cc 100644
--- a/doc/build/html/modindex.html
+++ b/doc/build/html/modindex.html
@@ -498,7 +498,9 @@ 
      Navigation
      
 
    • covfile – test covariates used to predict the response variable
      • 
 
    • respfile – test response variables for the normative model
      • 
 
    • maskfile – mask used to apply to the data (nifti only)
      • 
-
    • model_path – Directory containing the normative model and metadata
      • 
+
    • model_path – Directory containing the normative model and metadata.
+When using parallel prediction, do not pass the model path. It will be automatically
+decided.
      • 
 
    • output_path – Directory to store the results
      • 
 
    • outputsuffix – Text string to add to the output filenames
      • 
 
    • batch_size – batch size (for use with normative_parallel)
      • 
@@ -659,7 +661,7 @@ 
      Navigation
      
 
 
      
 
      
-normative_parallel.execute_nm(processing_dir, python_path, normative_path, job_name, covfile_path, respfile_path, batch_size, memory, duration, func='estimate', **kwargs)[source]
      
+normative_parallel.execute_nm(processing_dir, python_path, job_name, covfile_path, respfile_path, batch_size, memory, duration, normative_path=None, func='estimate', **kwargs)[source]
 
      This function is a mother function that executes all parallel normative
 modelling routines. Different specifications are possible using the sub-
 functions.
      
@@ -668,7 +670,12 @@ 
      Navigation
      
 
        
 
      • processing_dir     -> Full path to the processing dir
        • 
 
      • python_path        -> Full path to the python distribution
        • 
-
      • normative_path     -> Full path to the normative.py
        • 
+
      • 
+
        normative_path     -> Full path to the normative.py. If None (default)
        then it will automatically retrieves the path from 
+the installed packeage.
        
+
        
+
        
+
        • 
 
      • 
 
        job_name           -> Name for the bash script that is the output of
        this function
        
 
        
@@ -775,6 +782,78 @@ 
        Navigation
        
 
        written by (primarily) T Wolfers, (adapted) SM Kia
        
 
      
 
+
      
+
      
+normative_parallel.sbatch_nm(job_path, log_path)[source]
      
+
      This function submits a job.sh scipt to the torque custer using the qsub
+command.
      
+
      
+
      ** Input:
        
+
      • job_path      -> Full path to the job.sh file
        • 
+
      • log_path      -> The logs are currently stored in the working dir
        • 
+
      
+
      
+
      ** Output:
        
+
      • Submission of the job to the (torque) cluster
        • 
+
      
+
      
+
      
+
      witten by (primarily) T Wolfers
      
+
      
+
+
      
+
      
+normative_parallel.sbatchwrap_nm(processing_dir, python_path, normative_path, job_name, covfile_path, respfile_path, memory, duration, func='estimate', **kwargs)[source]
      
+
      This function wraps normative modelling into a bash script to run it
+on a torque cluster system.
      
+
      
+
      Parameters
      
+
        
+
      • processing_dir     -> Full path to the processing dir
        • 
+
      • python_path        -> Full path to the python distribution
        • 
+
      • normative_path     -> Full path to the normative.py
        • 
+
      • 
+
        job_name           -> Name for the bash script that is the output of
        this function
        
+
        
+
        
+
        • 
+
      • 
+
        covfile_path       -> Full path to a .txt file that contains all
        covariats (subjects x covariates) for the
+responsefile
        
+
        
+
        
+
        • 
+
      • 
+
        respfile_path      -> Full path to a .txt that contains all features
        (subjects x features)
        
+
        
+
        
+
        • 
+
      • cv_folds           -> Number of cross validations
        • 
+
      • 
+
        testcovfile_path   -> Full path to a .txt file that contains all
        covariats (subjects x covariates) for the
+testresponse file
        
+
        
+
        
+
        • 
+
      • 
+
        testrespfile_path  -> Full path to a .txt file that contains all
        test features
        
+
        
+
        
+
        • 
+
      • alg                -> which algorithm to use
        • 
+
      • configparam        -> configuration parameters for this algorithm
        • 
+
      
+
      
+
      Outputs
      
+
        
+
      • A bash.sh file containing the commands for normative modelling saved
+to the processing directory (written to disk)
        • 
+
      
+
      
+
      
+
      written by (primarily) T Wolfers
      
+
      
+
 
      
 
      
 normative_parallel.split_nm(processing_dir, respfile_path, batch_size, binary, **kwargs)[source]
      
@@ -1009,6 +1088,11 @@ 
      Navigation
      
 fileio.load_pd(filename)[source]
 
      
 
+
      
+
      
+fileio.predictive_interval(s2_forward, cov_forward, multiplicator)[source]
      
+
      
+
 
      
 
      
 fileio.save(data, filename, example=None, mask=None, text=False)[source]
      
@@ -1338,6 +1422,20 @@ 
      Navigation
      
 
      Bases: utils.WarpBase
      
 
      Sin-hyperbolic arcsin warp having two parameters (a, b) and defined by
      
 
      y = sinh(b *  arcsinh(x) - a)
      
+
      Using the parametrisation of Rios et al, Neural Networks 118 (2017)
+where a controls skew and b controls kurtosis, such that:
      
+
      
+
      a = 0 : symmetric
+a > 0 : positive skew
+a < 0 : negative skew
+b = 1 : mesokurtic
+b > 1 : leptokurtic
+b < 1 : platykurtic
      
+
      
+
      where b > 0. However, it is more convenentent to use an alternative 
+parameterisation, where
      
+
      y = sinh(b * arcsinh(x) + epsilon * b)
      
+
      and a = -epsilon*b
      
 
      see Jones and Pewsey A (2009) Biometrika, 96 (4) (2009)
      
 
      
 
      
@@ -1538,6 +1636,45 @@ 
      Navigation
      
 utils.extreme_value_prob_fit(NPM, perc)[source]
      
 
      
 
+
      
+
      
+utils.load_freesurfer_measure(measure, data_path, subjects_list)[source]
      
+
      This is a utility function to load different Freesurfer measures in a pandas
+Dataframe.
      
+
      
+
      Inputs:
        
+
      • measure: a string that defines the type of Freesurfer measure we want
        • 
+
      
+
      
+
      to load. The options include:
        
+
      • ‘NumVert’: Number of Vertices in each cortical area based on Destrieux atlas.
        • 
+
      • ‘SurfArea: Surface area for each cortical area based on Destrieux atlas.
        • 
+
      • ‘GrayVol’: Gary matter volume in each cortical area based on Destrieux atlas.
        • 
+
      • ‘ThickAvg’: Average Cortical thinckness in each cortical area based on Destrieux atlas.
        • 
+
      • ‘ThickStd’: STD of Cortical thinckness in each cortical area based on Destrieux atlas.
        • 
+
      • ‘MeanCurv’: Integrated Rectified Mean Curvature in each cortical area based on Destrieux atlas.
        • 
+
      • ‘GausCurv’: Integrated Rectified Gaussian Curvature in each cortical area based on Destrieux atlas.
        • 
+
      • ‘FoldInd’: Folding Index in each cortical area based on Destrieux atlas.
        • 
+
      • ‘CurvInd’: Intrinsic Curvature Index in each cortical area based on Destrieux atlas.
        • 
+
      • ‘brain’: Brain Segmentation Statistics from aseg.stats file.
        • 
+
      • ‘subcortical_volumes’: Subcortical areas volume.
        • 
+
      
+
      
+
      
+
        
+
      • data_path: a string that specifies the path to the main Freesurfer folder.
        • 
+
      • subjects_list: A Pythin list containing the list of subject names to load the data for.
        • 
+
      
+
      The subject names should match the folder name for each subject’s Freesurfer data folder.
      
+
      
+
      Outputs:
        
+
      • df: A pandas datafrmae containing the subject names as Index and target Freesurfer measures.
        • 
+
      • missing_subs: A Python list of subject names that miss the target Freesurefr measures.
        • 
+
      
+
      
+
      
+
      
+
 
      
 
      
 utils.qsub(job_path, memory, duration, logdir=None)[source]
      
diff --git a/doc/build/html/objects.inv b/doc/build/html/objects.inv
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