diff --git a/example.py b/example.py
deleted file mode 100644
index 1cd77f40b7bdd781cfab8a1358204f5f8fd7c768..0000000000000000000000000000000000000000
--- a/example.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import numpy
-import eagle
-import sys
-import os
-
-
-sim='/cosma5/data/Eagle/ScienceRuns/Planck1/L0100N1504/PE/Z0p10_W1p00_E_3p0_0p3_ALPHA1p0e6_rhogas1_reposlim3p0soft_100mgas_cosma/data/'
-tag='028_z000p000'
-
-length_EA = eagle.readArray("FOF", sim, tag, "FOF/GroupLength")
-#lengthType_EA = eagle.readArray("FOF", sim_EA, tag, "FOF/GroupLengthType")
-offset_EA = eagle.readArray("FOF", sim, tag, "FOF/GroupOffset")
-
-#print min(offset_EA), max(offset_EA)
-
-offset_2 = numpy.cumsum(length_EA)
-
-print offset_2
-
-offset_2 = numpy.insert(offset_2, 0, 0)
-
-print offset_2
-#print min(offset_EA), max(offset_EA)
-
-
-a[b] = c
-
-
-R_500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_R_Crit500", numThreads=64)
-R_2500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_R_Crit2500", numThreads=64)
-M_500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_M_Crit500", numThreads=64) * 1e10
-
-ratio = R_500 / R_2500
-
-massBins = numpy.logspace(8.1, 15.9, 40)
-ratio_count = numpy.zeros(numpy.size(massBins))
-ratio_mean = numpy.zeros(numpy.size(massBins))
-ratio2_mean = numpy.zeros(numpy.size(massBins))
-
-for i in range(numpy.size(R_500)):
-    if M_500[i] > 1e6 and R_500[i] > 0.001 and R_2500[i] > 0.001:
-        bin = -1
-        for j in range(numpy.size(massBins)-1):
-            if M_500[i] > massBins[j] and M_500[i] < massBins[j+1]:
-                bin = j
-                break
-
-        if bin != -1:
-            ratio_count[bin] = ratio_count[bin] + 1
-            ratio_mean[bin] = ratio_mean[bin] + ratio[i]
-            ratio2_mean[bin] = ratio2_mean[bin] + ratio[i]*ratio[i]
-
-
-
-for j in range(numpy.size(massBins)):
-    if ratio_count[j] != 0:
-        ratio_mean[j] = ratio_mean[j] / ratio_count[j]
-        ratio2_mean[j] = ratio2_mean[j] / ratio_count[j]
-    else:
-        ratio_mean[j] = 0
-        ratio2_mean[j] = 0
-
-
-ratio_sigma = numpy.sqrt(ratio2_mean - ratio_mean * ratio_mean)
-
-file = open("non_parametric_100_z0p0.dat",'w')
-file.write("# M_500 mean sigma count\n")
-for j in range(numpy.size(massBins)-1):
-    file.write("%e %f %f %i\n"%(10**(0.5*(numpy.log10(massBins[j])+numpy.log10(massBins[j+1]))), ratio_mean[j], ratio_sigma[j], ratio_count[j]))
-file.close()
diff --git a/profile.py b/profile.py
deleted file mode 100644
index 16785fe8c8aebb5b57586550911ee13fcd72dac7..0000000000000000000000000000000000000000
--- a/profile.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy
-import eagle
-import sys
-import os
-
-
-sim='/cosma5/data/Eagle/ScienceRuns/Planck1/L0100N1504/PE/Z0p10_W1p00_E_3p0_0p3_ALPHA1p0e6_rhogas1_reposlim3p0soft_100mgas_cosma/data/'
-tag='028_z000p000'
-
-
-R_500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_R_Crit500", numThreads=64)
-R_2500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_R_Crit2500", numThreads=64)
-M_500 = eagle.readArray("SUBFIND_GROUP", sim, tag, "FOF/Group_M_Crit500", numThreads=64) * 1e10
-
-ratio = R_500 / R_2500
-
-massBins = numpy.logspace(8.1, 15.9, 40)
-ratio_count = numpy.zeros(numpy.size(massBins))
-ratio_mean = numpy.zeros(numpy.size(massBins))
-ratio2_mean = numpy.zeros(numpy.size(massBins))
-
-for i in range(numpy.size(R_500)):
-    if M_500[i] > 1e6 and R_500[i] > 0.001 and R_2500[i] > 0.001:
-        bin = -1
-        for j in range(numpy.size(massBins)-1):
-            if M_500[i] > massBins[j] and M_500[i] < massBins[j+1]:
-                bin = j
-                break
-
-        if bin != -1:
-            ratio_count[bin] = ratio_count[bin] + 1
-            ratio_mean[bin] = ratio_mean[bin] + ratio[i]
-            ratio2_mean[bin] = ratio2_mean[bin] + ratio[i]*ratio[i]
-
-
-
-for j in range(numpy.size(massBins)):
-    if ratio_count[j] != 0:
-        ratio_mean[j] = ratio_mean[j] / ratio_count[j]
-        ratio2_mean[j] = ratio2_mean[j] / ratio_count[j]
-    else:
-        ratio_mean[j] = 0
-        ratio2_mean[j] = 0
-
-
-ratio_sigma = numpy.sqrt(ratio2_mean - ratio_mean * ratio_mean)
-
-file = open("non_parametric_100_z0p0.dat",'w')
-file.write("# M_500 mean sigma count\n")
-for j in range(numpy.size(massBins)-1):
-    file.write("%e %f %f %i\n"%(10**(0.5*(numpy.log10(massBins[j])+numpy.log10(massBins[j+1]))), ratio_mean[j], ratio_sigma[j], ratio_count[j]))
-file.close()