add 'holm' function; remove save-to-disk lines

events.py

@@ -6,7 +6,7 @@ A suite of functions that assist in carrying out the event-detection analysis
 
 """
 # Created: Thu May 26, 2016  03:50PM
-# Last modified: Tue Aug 15, 2017  01:25pm
+# Last modified: Tue Jan 09, 2018  01:53PM
 # Copyright: Bedartha Goswami <goswami@pik-potsdam.de>
 
 import numpy as np
@@ -14,7 +14,7 @@ from scipy.stats import percentileofscore as percentile
 
 from lib import DATPATH
 from networks import link_density
-from utils import _printmsg, _savedat
+from utils import _printmsg
 from utils import _progressbar_start
 from utils import _progressbar_update
 from utils import _progressbar_finish
@@ -142,30 +142,10 @@ def community_strength_random_model(G, time, ld, wsize, wstep, nsurr,
     return Qsurr, LDsurr, tmid
 
 
-def pvalue(name, dtype, ld, wsize, wstep, nsurr,
-           verbose=False, save=True, pbar=False):
+def pvalues(Q, Qsurr, nwind, verbose=False, save=True, pbar=False):
     """
     Returns the p-value of obtaining intra-community link density.
     """
-    _printmsg("p-VALUE OF AN EVENT FOR %s" % name.upper(), verbose)
-    _printmsg("=========================================", verbose)
-    _printmsg("Loading data...", verbose)
-    prefix = "%s/%s/community_strength" % (DATPATH, dtype)
-    suffix = "LD%.2f/WSIZE%d/%s_WSTEP%d.npz" % (ld, wsize, name, wstep)
-    f_data = "%s_data/%s" % (prefix, suffix)
-    f_cfmd = "%s_random_model/%s" % (prefix, suffix)
-    d_data = np.load(f_data)
-    d_cfmd = np.load(f_cfmd)
-    # check for consistency
-    tmid1 = d_data["tmid"]
-    tmid2 = d_cfmd["tmid"]
-    assert all(tmid1 == tmid2), "Data & random model timescales don't match!"
-    assert nsurr == d_cfmd["nsurr"], "Incompatible no. of surrogates!"
-    # initiate relevant variables
-    time = tmid1
-    Q = d_data["Q"]
-    Qsurr = d_cfmd["Qsurr"]
-    nwind = d_data["nwind"]
     _printmsg("Estimating p-value...", verbose)
     pvalue = np.zeros(nwind)
     prog_bar = _progressbar_start(nwind, pbar)
@@ -174,12 +154,25 @@ def pvalue(name, dtype, ld, wsize, wstep, nsurr,
         pvalue[i] = 1. - percentile(Qnull, Q[i], kind="weak") / 100.
         _progressbar_update(prog_bar, i)
     _progressbar_finish(prog_bar)
-    p = "%s/%s/pvalues/" % (DATPATH, dtype)
-    o = "LD%.2f_WSIZE%d_%s_WSTEP%d" % (ld, wsize, name, wstep)
-    _savedat(p, o, verbose, save,
-             pvalue=pvalue,
-             Qsurr=Qsurr, Q=Q, time=time,
-             wsize=wsize, wstep=wstep,
-             nwind=nwind, nsurr=nsurr,
-             )
-    return None
+    return pvalues
+
+
+def holm(pvalues, alpha=0.05, corr_type="dunn"):
+    """
+    Returns indices of p-values using Holm's method for multiple testing.
+    """
+    n = len(pvalues)
+    sortidx = np.argsort(pvalues)
+    p_ = pvalues[sortidx]
+    j = np.arange(1, n + 1)
+    if corr_type == "bonf":
+            corr_factor = alpha / (n - j + 1)
+    elif corr_type == "dunn":
+        corr_factor = 1. - (1. - alpha) ** (1. / (n - j + 1))
+    try:
+        idx = np.where(p_ <= corr_factor)[0][-1]
+        idx = sortidx[:idx]
+    except IndexError:
+        idx = []
+    return idx
+