3 files changed, 44 insertions, 0 deletions

diff --git a/binary_data.py b/binary_data.py
new file mode 100644
index 0000000..e5b47aa
--- /dev/null
+++ b/binary_data.py
@@ -0,0 +1,29 @@
+import numpy
+
+from util import load_binary_file
+from stats import iqr_remove_outliers, iqr_cutoff
+
+def compact_file(fname, scale=None, extend=1.5):
+    data = load_binary_file(fname)
+
+    if not scale is None:
+        data *= scale
+
+    data.sort()
+
+    iqr_min, iqr_max = iqr_cutoff(data, extend)
+
+    min_idx, max_idx = numpy.searchsorted(data, [iqr_min, iqr_max])
+
+    samples = data[min_idx:max_idx]
+
+    filtered = len(data) - len(samples)
+    max = samples[-1]
+    min = samples[0]
+    med = numpy.median(samples)
+    avg = numpy.mean(samples)
+
+    std = numpy.std(samples)
+    var = numpy.var(samples)
+
+    return [len(samples), filtered, max, avg, min, med, std, var]
diff --git a/stats.py b/stats.py
index f6c4401..cede673 100644
--- a/stats.py
+++ b/stats.py
@@ -13,6 +13,12 @@ def iqr(vect):
 def cutoff_max(vect, percentile=99):
     return s.scoreatpercentile(vect, percentile)
 
+def iqr_cutoff(vect, extend):
+    (spread, low, high) = iqr(vect)
+    min_val = low  - extend * spread
+    max_val = high + extend * spread
+    return min_val, max_val
+
 def iqr_is_not_outlier(table, col=1, extend=1.5):
     "create a filter function that flags outliers"
     (spread, low, high) = iqr(table[:,col])
diff --git a/util.py b/util.py
index 7d81d4f..b44cc6c 100644
--- a/util.py
+++ b/util.py
@@ -8,6 +8,15 @@ def load_csv_file(fname, *args, **kargs):
     f.close() # don't leak file handles
     return data
 
+def load_csv_file_fast(fname):
+    data = np.loadtxt(fname, delimiter=",")
+    return data
+
+def load_binary_file(fname, dtype='float32', modify=False):
+    data = np.memmap(fname, dtype=dtype,
+                     mode='r+' if modify else 'c')
+    return data
+
 def write_csv_file(fname, rows, header=None, width=None,
                    break_col=None):
     if fname is None: