diff --git a/mdata/ANALYSIS.py b/mdata/ANALYSIS.py
index 739a712..4668b44 100644
--- a/mdata/ANALYSIS.py
+++ b/mdata/ANALYSIS.py
@@ -9,14 +9,27 @@ import matplotlib as mpl
 import matplotlib.pyplot as plt
 import seaborn as sns
 
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from .. import mjpeg
+from ..mjpeg import base
+from ..msteg.steganography import LSB, F3, F4, F5
+
 np.random.seed(sum(map(ord, "whoami")))
-plt.ticklabel_format(style='sci', axis='both', scilimits=(1, 4))
+
+sample_key = [46812L, 20559L, 31360L, 16681L, 27536L, 39553L, 5427L, 63029L, 56572L, 36476L, 25695L,
+              61908L, 63014L, 5908L, 59816L, 56765L]
+
+# plt.ticklabel_format(style='sci', axis='both', scilimits=(0, 0))
+plt.ticklabel_format(style='sci', axis='both')
 
 package_dir = os.path.dirname(os.path.abspath(__file__))
 
 
 def anal_ILSVRC():
-    df_ILS = pd.read_csv('../res/file-tag.tsv', names=['hash', 'width', 'height', 'size', 'quality'], sep='\t')
+    df_ILS = pd.read_csv('../res/file-tag.tsv',
+                         names=['hash', 'width', 'height', 'size', 'quality'], sep='\t')
     print df_ILS[df_ILS.size < 2000000]
     print df_ILS.describe()
     # df_ILS.boxplot(column='size')
@@ -68,7 +81,8 @@ def anal_ILSVRC():
 
 
 def anal_ILSVRC_Test():
-    df_ILS_T = pd.read_csv('../res/file-tag-test.tsv', names=['hash', 'width', 'height', 'size', 'quality','class'], sep='\t')
+    df_ILS_T = pd.read_csv('../res/file-tag-test.tsv',
+                           names=['hash', 'width', 'height', 'size', 'quality', 'class'], sep='\t')
     print df_ILS_T
     print df_ILS_T.size.describe()
 
@@ -80,23 +94,35 @@ def anal_ILSVRC_Test():
     df_ILS_T['class2'] = np.zeros(length, np.int32)
     df_ILS_T.to_csv('../res/file-tag-test.tsv', header=False, index=False, sep='\t')
 
+
 def anal_0000():
-    df_ILS = pd.read_csv('../res/file-tag-test.tsv', names=['hash', 'width', 'height', 'size', 'quality','chosen','class'], sep='\t')
+    df_ILS = pd.read_csv(os.path.join(package_dir, '../res/file-tag-test.tsv'),
+                         names=['hash', 'width', 'height', 'size', 'quality', 'chosen', 'class'],
+                         sep='\t')
     length = df_ILS.shape[0]
     print df_ILS.size.describe()
     print df_ILS.size.order().iloc[map(lambda x: x * length, [1.0 / 3, 2.0 / 3, 0.9999])]
 
+    print df_ILS.size[df_ILS.size == 166500].count() / 4592.0
+    print df_ILS.size[df_ILS.size == 187500].count() / 4592.0
+    print df_ILS.size[df_ILS.size == 250000].count() / 4592.0
+
     print df_ILS.size[df_ILS.size <= 166500].count()
     print df_ILS.size[(df_ILS['size'] > 166500) & (df_ILS['size'] <= 187500)].count()
     print df_ILS.size[df_ILS.size > 187500].count()
 
-    df_ILS.hist(column='size',bins=100)
+    plt.ticklabel_format(style='sci', axis='both')
+    df_ILS.hist(column='size', bins=100)
+    plt.title('')
+    plt.xlabel("Image size")
+    plt.ylabel("Frequency")
     plt.show()
 
 
-
 def pre_crop():
-    df_ILS = pd.read_csv('../res/file-tag-test.tsv', names=['hash', 'width', 'height', 'size', 'quality','chosen','class'], sep='\t')
+    df_ILS = pd.read_csv(os.path.join(package_dir, '../res/file-tag-test.tsv'),
+                         names=['hash', 'width', 'height', 'size', 'quality', 'chosen', 'class'],
+                         sep='\t')
     print df_ILS.shape
     print df_ILS[(df_ILS['width'] >= 300) & (df_ILS['height'] >= 300)].shape
 
@@ -105,23 +131,250 @@ def pre_crop():
     # 400x400 932 0.202
 
 
+def plot_hist():
+    dat_performance = np.array([
+        [100, 0.583396, 30.847788, 57.884814, 89.315998, 1.471087, 29.364628, 9.114235, 10.585322,
+         39.94995, 2.235697366],
+        [200, 1.147411, 62.815709, 118.217859, 182.180979, 3.008692, 37.920278, 19.589578, 22.59827,
+         60.518548, 3.010332948],
+        [500, 2.763806, 162.806317, 299.778606, 465.348729, 6.81705, 88.291989, 73.446282,
+         80.263332, 168.555321, 2.760807112],
+        [1000, 6.372794, 329.023151, 600.438977, 935.834922, 15.644418, 159.951099, 186.335413,
+         201.979831, 361.93093, 2.585672692],
+        [2000, 14.960961, 679.357936, 1256.341536, 1950.660433, 31.699596, 313.154748, 387.063702,
+         418.763298, 731.918046, 2.665135043],
+        [5000, 39.880657, 1652.537536, 3067.98039, 4760.398583, 73.070203, 694.454719, 898.458633,
+         971.528836, 1665.983555, 2.857410308]])
+
+    dat_performance = np.transpose(dat_performance)
+    data_size, serial_tot, spark_io, spark_proc, spark_tot = dat_performance[0], dat_performance[4], \
+                                                             dat_performance[8], dat_performance[6], \
+                                                             dat_performance[9]
+
+    data_size = data_size.astype(int)
+    A = [spark_io, spark_proc]
+    E = np.arange(len(data_size))
+    bar_width = 0.5
+    # plt.bar(E, spark_io, width=bar_width)
+    # plt.bar(E, spark_proc, color='#e74c3c', width=bar_width, bottom=spark_io)
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.xticks(E + bar_width / 2, data_size)
+    # # plt.xticks(range(len(data_size)), data_size, size='small')
+    # # plt.ylim(ymax=300000)
+    # plt.show()
+
+    # mpl.rcParams.update({'font.size': 5})
 
+    fig, ax = plt.subplots()
+    rects1 = ax.bar(E, spark_io, bar_width)
+    rects2 = ax.bar(E, spark_proc, bar_width, color='#e74c3c', bottom=spark_io)
 
-if __name__ == '__main__':
-    # anal_ILSVRC()
-    # anal_ILSVRC_Test()
-    # anal_0000()
-    # print timeit.timeit("anal_ILSVRC()", setup="from __main__ import anal_ILSVRC", number=1)
+    # add some text for labels, title and axes ticks
+    plt.xlabel("Data size")
+    ax.set_ylabel('Time(s)')
+    # ax.set_title('IO ratio')
+    ax.set_xticks(E + bar_width / 2)
+    ax.set_xticklabels(data_size)
 
+    ax.legend((rects1[0], rects2[0]), ('IO', 'CPU'), loc=2)
 
-    pre_crop()
-    pass
+    height1 = [rect.get_height() for rect in rects1]
+    height2 = [rect.get_height() for rect in rects2]
+    for i in range(len(rects1)):
+        height = rects1[i].get_height() + rects2[i].get_height()
+        ax.text(rects1[i].get_x() + rects1[i].get_width() / 2, 1.005 * height, '%d%%' %
+                int((100 * 1.0*height1[i]/height)),
+                ha='center', va='bottom')
+
+    # height1 = [rect.get_height() for rect in rects1]
+    # height2 = [rect.get_height() for rect in rects2]
+    # for i in range(len(rects1)):
+    #     ax.text(rects1[i].get_x() + rects1[i].get_width() / 2, 0.5 * height1[i], '%f' % (0.1 *
+    #                                                                                      height1[
+    #                                                                                          i] /
+    #                                                                                      height2[
+    #                                                                                          i]),
+    #             ha='center', va='bottom')
+
+
+    plt.show()
+
+
+def plot_line_performance():
+    # performance
+    dat_performance = np.array([
+        [100, 0.583396, 30.847788, 57.884814, 89.315998, 1.471087, 29.364628, 9.114235, 10.585322,
+         39.94995, 2.235697366],
+        [200, 1.147411, 62.815709, 118.217859, 182.180979, 3.008692, 37.920278, 19.589578, 22.59827,
+         60.518548, 3.010332948],
+        [500, 2.763806, 162.806317, 299.778606, 465.348729, 6.81705, 88.291989, 73.446282,
+         80.263332, 168.555321, 2.760807112],
+        [1000, 6.372794, 329.023151, 600.438977, 935.834922, 15.644418, 159.951099, 186.335413,
+         201.979831, 361.93093, 2.585672692],
+        [2000, 14.960961, 679.357936, 1256.341536, 1950.660433, 31.699596, 313.154748, 387.063702,
+         418.763298, 731.918046, 2.665135043],
+        [5000, 39.880657, 1652.537536, 3067.98039, 4760.398583, 73.070203, 694.454719, 898.458633,
+         971.528836, 1665.983555, 2.857410308]])
+
+    dat_performance = np.transpose(dat_performance)
+    data_size, serial_tot, spark_io, spark_proc, spark_tot = dat_performance[0], dat_performance[4], \
+                                                             dat_performance[8], dat_performance[6], \
+                                                             dat_performance[9]
+
+    # sns.set_style("white")
+    # data_size = data_size.astype(int)
+    # plt.plot(range(len(data_size)), serial_tot, marker='o', label='serial total')
+    # plt.plot(range(len(data_size)), spark_tot, marker='o', linestyle='--', label='spark total')
+    # plt.plot(range(len(data_size)), spark_io, marker='o', linestyle=':', label='spark io')
+    # plt.plot(range(len(data_size)), spark_proc, marker='o', linestyle='-.', label='spark proc')
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.xticks(range(len(data_size)), data_size, size='small')
+    # plt.legend(loc=2)
+    # plt.show()
+
+    plt.plot(data_size, serial_tot, marker='o', label='serial total')
+    plt.plot(data_size, spark_tot, marker='o', linestyle='--', label='spark total')
+    plt.plot(data_size, spark_io, marker='o', linestyle=':', label='spark io')
+    plt.plot(data_size, spark_proc, marker='o', linestyle='-.', label='spark proc')
+    plt.xlabel("Data size")
+    plt.ylabel("Time(s)")
+    plt.legend(loc=2)
+    plt.show()
 
 
+def plot_line_io():
+    # io
+    dat_io = np.array([
+        [100, 10.585322, 29.364628, 39.94995, 10.286684, 27.079774, 37.366458, 49.995647,
+         55.280739],
+        [200, 22.59827, 37.920278, 60.518548, 22.731275, 38.491461, 61.222736, 76.258928,
+         83.836657],
+        [500, 80.263332, 88.291989, 168.555321, 64.610839, 88.241193, 152.852032, 177.039349,
+         143.524813],
+        [1000, 201.979831, 159.951099, 361.93093, 172.359455, 158.694248, 331.053703, 467.126756,
+         315.578952],
+        [2000, 418.763298, 313.154748, 731.918046, 390.990209, 313.085707, 704.075916, 802.138669,
+         734.133909],
+        [5000, 971.528836, 694.454719, 1665.983555, 898.468232, 717.603061, 1616.071293,
+         1860.610954, 1677.044038]])
+
+    dat_io = np.transpose(dat_io)
+    data_size, happybase_uncomp_io, happybase_uncomp_cpu, happybase_uncomp_tot, happybase_comp_io, happybase_comp_cpu, happybase_comp_tot, dist_uncomp, dist_comp = dat_io
+    # data_size = data_size.astype(int)
+    # plt.plot(range(len(data_size)), dist_uncomp, marker='o', label='dist-uncompressed total')
+    # plt.plot(range(len(data_size)), dist_comp, marker='o', label='dist-compressed total')
+    # plt.plot(range(len(data_size)), happybase_uncomp_tot, marker='o', label='happybase-uncompressed total')
+    # plt.plot(range(len(data_size)), happybase_comp_tot, marker='o', label='happybase-compressed total')
+    #
+    # plt.plot(range(len(data_size)), happybase_uncomp_io, marker='o', linestyle='--',
+    #          label='happybase-uncompressed io')
+    # plt.plot(range(len(data_size)), happybase_comp_io, marker='o', linestyle='--',
+    #          label='happybase-compressed io')
+    # plt.plot(range(len(data_size)), happybase_uncomp_cpu, marker='o', linestyle='--',
+    #          label='happybase-uncompressed cpu')
+    # plt.plot(range(len(data_size)), happybase_comp_cpu, marker='o', linestyle='--',
+    #          label='happybase-compressed cpu')
+    #
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time")
+    # plt.xticks(range(len(data_size)), data_size, size='small')
+    # plt.legend(loc=2)
+    # plt.show()
 
+    plt.plot(data_size, dist_uncomp, marker='o', label='dist-uncompressed total')
+    plt.plot(data_size, dist_comp, marker='D', label='dist-compressed total')
+    plt.plot(data_size, happybase_uncomp_tot, marker='o', label='happybase-uncompressed total')
+    plt.plot(data_size, happybase_comp_tot, marker='D', label='happybase-compressed total')
+
+    plt.plot(data_size, happybase_uncomp_io, marker='o', linestyle='--',
+             label='happybase-uncompressed io')
+    plt.plot(data_size, happybase_comp_io, marker='D', linestyle='--',
+             label='happybase-compressed io')
+    plt.plot(data_size, happybase_uncomp_cpu, marker='o', linestyle='--',
+             label='happybase-uncompressed cpu')
+    plt.plot(data_size, happybase_comp_cpu, marker='D', linestyle='--',
+             label='happybase-compressed cpu')
+
+    plt.xlabel("Data size")
+    plt.ylabel("Time")
+    plt.legend(loc=2)
+    plt.show()
 
 
+    # plt.subplot(2, 2, 1)
+    # plt.plot(data_size, dist_uncomp, marker='o', label='dist-uncompressed total')
+    # plt.plot(data_size, dist_comp, marker='o', label='dist-compressed total')
+    # # plt.title('Performance with(out) Compression')
+    # plt.ylabel("Time")
+    # plt.legend(loc=2)
+    #
+    # plt.subplot(2, 2, 2)
+    # plt.plot(data_size, happybase_uncomp_tot, marker='o', label='happybase-uncompressed total')
+    # plt.plot(data_size, happybase_comp_tot, marker='o', label='happybase-compressed total')
+    # plt.legend(loc=2)
+    #
+    # plt.subplot(2, 2, 3)
+    # plt.plot(data_size, happybase_uncomp_io, marker='o', linestyle='--',
+    #          label='happybase-uncompressed io')
+    # plt.plot(data_size, happybase_comp_io, marker='o', linestyle='--',
+    #          label='happybase-compressed io')
+    # plt.ylabel("Time")
+    # plt.xlabel("Data size")
+    # plt.legend(loc=2)
+    #
+    # plt.subplot(2, 2, 4)
+    # plt.plot(data_size, happybase_uncomp_cpu, marker='o', linestyle='--',
+    #          label='happybase-uncompressed cpu')
+    # plt.plot(data_size, happybase_comp_cpu, marker='o', linestyle='--',
+    #          label='happybase-compressed cpu')
+    # plt.xlabel("Data size")
+    # plt.legend(loc=2)
+    # plt.show()
+
+    # plt.plot(data_size, dist_uncomp, marker='o', label='dist-uncompressed total')
+    # plt.plot(data_size, dist_comp, marker='D', linestyle='--',label='dist-compressed total')
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.legend(loc=2)
+    # plt.show()
+    #
+    # plt.plot(data_size, happybase_uncomp_tot, marker='o', label='happybase-uncompressed total')
+    # plt.plot(data_size, happybase_comp_tot, marker='D', linestyle='--',label='happybase-compressed total')
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.legend(loc=2)
+    # plt.show()
+    #
+    # plt.plot(data_size, happybase_uncomp_io, marker='o',
+    #          label='happybase-uncompressed io')
+    # plt.plot(data_size, happybase_comp_io, marker='D', linestyle='--',
+    #          label='happybase-compressed io')
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.legend(loc=2)
+    # plt.show()
+    #
+    # plt.plot(data_size, happybase_uncomp_cpu, marker='o',
+    #          label='happybase-uncompressed cpu')
+    # plt.plot(data_size, happybase_comp_cpu, marker='D', linestyle='--',
+    #          label='happybase-compressed cpu')
+    #
+    # plt.xlabel("Data size")
+    # plt.ylabel("Time(s)")
+    # plt.legend(loc=2)
+    # plt.show()
 
 
+if __name__ == '__main__':
+    # anal_ILSVRC()
+    # anal_ILSVRC_Test()
+    # anal_0000()
+    # print timeit.timeit("anal_ILSVRC()", setup="from __main__ import anal_ILSVRC", number=1)
 
 
+    # pre_crop()
+    # plot_line()
+    anal_0000()
+    pass
diff --git a/msteg/__init__.py b/msteg/__init__.py
index 77cd137..e184b09 100644
--- a/msteg/__init__.py
+++ b/msteg/__init__.py
@@ -7,7 +7,8 @@ from ..common import *
 
 __all__ = ['StegBase', 'sample_key']
 
-sample_key = [46812L, 20559L, 31360L, 16681L, 27536L, 39553L, 5427L, 63029L, 56572L, 36476L, 25695L, 61908L, 63014L,
+sample_key = [46812L, 20559L, 31360L, 16681L, 27536L, 39553L, 5427L, 63029L, 56572L, 36476L, 25695L,
+              61908L, 63014L,
               5908L, 59816L, 56765L]
 
 
@@ -38,7 +39,6 @@ class StegBase(object):
         self.capacity = np.sum(self.cov_data != 0) - np.size(self.cov_data) / 64
         return self.cov_data
 
-
     def _get_hid_data(self, src_hidden, frommem=False):
         """
         Returnsthe secret data as byte sequence.
@@ -55,19 +55,20 @@ class StegBase(object):
         self.hid_data = np.array(raw, dtype=np.uint8)
 
         if np.size(self.hid_data) * 8 > self.capacity:
-            raise Exception("Cover image is too small to embed data.Cannot fit %d bits in %d NZ-DCT coefficients" % (
-                np.size(self.hid_data) * 8, self.capacity))
+            raise Exception(
+                "Cover image is too small to embed data.Cannot fit %d bits in %d NZ-DCT coefficients" % (
+                    np.size(self.hid_data) * 8, self.capacity))
         return self.hid_data
 
-
-    def _post_embed_actions(self, src_cover, src_hidden, tgt_stego):
+    def _post_embed_actions(self, src_cover, src_hidden, tgt_stego, frommem=False):
         """
         This function isn't named very accurately. It actually calls the
         _raw_embed function in inherited classes.
         """
         try:
             cov_data = self._get_cov_data(src_cover)
-            hid_data = self._get_hid_data(src_hidden)
+            # hid_data = self._get_hid_data(src_hidden)
+            hid_data = self._get_hid_data(src_hidden, frommem)
             # print hid_data.dtype,type(hid_data),hid_data.tolist()
             cov_data, bits_cnt = self._raw_embed(cov_data, hid_data)
 
@@ -130,7 +131,6 @@ class StegBase(object):
             # print hid_data.dtype,type(hid_data),hid_data.tolist()
             hid_data[4:].tofile(tgt_hidden)
 
-
             self._display_rate('extract', self.capacity, bits_cnt)
 
             # self._display_stats("extracted", emb_size,
@@ -140,7 +140,6 @@ class StegBase(object):
             print "Exception when extracting!"
             raise
 
-
     def _looks_like_jpeg(self, path):
         try:
             with open(path, 'r') as f:
diff --git a/msteg/steganography/F3.py b/msteg/steganography/F3.py
index dd3711d..8ea0bc1 100644
--- a/msteg/steganography/F3.py
+++ b/msteg/steganography/F3.py
@@ -1,6 +1,5 @@
 __author__ = 'chunk'
 
-
 import math
 import numpy as np
 from .. import *
@@ -18,7 +17,7 @@ class F3(StegBase):
         """
         StegBase.__init__(self)
 
-    def embed_raw_data(self, src_cover, src_hidden, tgt_stego):
+    def embed_raw_data(self, src_cover, src_hidden, tgt_stego, frommem=False):
         """ This method embeds arbitrary data into a cover image.
         The cover image must be a JPEG.
 
@@ -33,7 +32,7 @@ class F3(StegBase):
         lossy compression.
         """
         self.t0 = time.time()
-        self._post_embed_actions(src_cover, src_hidden, tgt_stego)
+        self._post_embed_actions(src_cover, src_hidden, tgt_stego, frommem)
 
     def extract_raw_data(self, src_steg, tgt_hidden):
         """ This method extracts secret data from a stego image. It is
@@ -65,7 +64,7 @@ class F3(StegBase):
             i += 1
             if i == hid_data.size: break
 
-        return cov_data,i
+        return cov_data, i
 
     def _raw_extract(self, steg_data, num_bits):
         """
@@ -81,7 +80,7 @@ class F3(StegBase):
             hid_data[j] = x & 1
             j = j + 1
 
-        return hid_data,j
+        return hid_data, j
 
     def __str__(self):
         return 'F3'
diff --git a/msteg/steganography/F4.py b/msteg/steganography/F4.py
index 9ad5c5b..0153233 100644
--- a/msteg/steganography/F4.py
+++ b/msteg/steganography/F4.py
@@ -13,7 +13,7 @@ class F4(StegBase):
     with the F3 algorithm and <i>extract_raw_data</i> to extract data
     which was embedded previously. """
 
-    def __init__(self, key=sample_key):
+    def __init__(self, key=None):
         """
         Constructor of the F3 class.
         """
@@ -27,15 +27,21 @@ class F4(StegBase):
 
         cov_data = self.cov_jpeg.getsignal(channel='Y')
         self.cov_data = np.array(cov_data, dtype=np.int16)
+        self.capacity = np.sum(self.cov_data != 0) - self.cov_data.size / 64
+
+        # signals = self.cov_jpeg.getsignal(channel='Y')
+        # self.signals = np.array(signals, dtype=np.int16)
+        # self.capacity = np.sum(self.signals != 0)
+
         return self.cov_data
 
-    def embed_raw_data(self, src_cover, src_hidden, tgt_stego):
+    def embed_raw_data(self, src_cover, src_hidden, tgt_stego, frommem=False):
 
         self.t0 = time.time()
 
         try:
             cov_data = self._get_cov_data(src_cover)
-            hid_data = self._get_hid_data(src_hidden)
+            hid_data = self._get_hid_data(src_hidden, frommem)
             # print hid_data.dtype,type(hid_data),hid_data.tolist()
 
             cov_data, bits_cnt = self._raw_embed(cov_data, hid_data)
@@ -47,8 +53,8 @@ class F4(StegBase):
             self.cov_jpeg.setsignal(cov_data, channel='Y')
             self.cov_jpeg.Jwrite(tgt_stego)
 
-            cov_bits = np.sum(cov_data != 0) - cov_data.size / 64
-            self._display_rate(cov_bits, bits_cnt)
+            # cov_bits = np.sum(cov_data != 0) - cov_data.size / 64
+            self._display_rate('embed', self.capacity, bits_cnt)
 
             # # size_cov = os.path.getsize(tgt_stego)
             # size_cov = np.size(cov_data) / 8
diff --git a/msteg/steganography/LSB.py b/msteg/steganography/LSB.py
index e5fd118..4f5f40e 100644
--- a/msteg/steganography/LSB.py
+++ b/msteg/steganography/LSB.py
@@ -12,13 +12,13 @@ class LSB(StegBase):
     with the F3 algorithm and <i>extract_raw_data</i> to extract data
     which was embedded previously. """
 
-    def __init__(self):
+    def __init__(self, key=None):
         """
         Constructor of the F3 class.
         """
-        StegBase.__init__(self)
+        StegBase.__init__(self, key)
 
-    def embed_raw_data(self, src_cover, src_hidden, tgt_stego):
+    def embed_raw_data(self, src_cover, src_hidden, tgt_stego, frommem=False):
         """ This method embeds arbitrary data into a cover image.
         The cover image must be a JPEG.
 
@@ -33,7 +33,7 @@ class LSB(StegBase):
         lossy compression.
         """
         self.t0 = time.time()
-        StegBase._post_embed_actions(self, src_cover, src_hidden, tgt_stego)
+        StegBase._post_embed_actions(self, src_cover, src_hidden, tgt_stego, frommem)
 
     def extract_raw_data(self, src_steg, tgt_hidden):
         """ This method extracts secret data from a stego image. It is
@@ -59,11 +59,12 @@ class LSB(StegBase):
             if x == 0 or x == 1 or cnt % 64 == 0: continue
 
             m = (hid_data[i] & 1)
-            x[...] = (x & 0xfffffffe) | m  # '0xfffe' is enough, for elements are expected to be short integers.
+            x[...] = (
+                         x & 0xfffffffe) | m  # '0xfffe' is enough, for elements are expected to be short integers.
             i += 1
             if i == hid_data.size: break
 
-        return cov_data
+        return cov_data, i
 
     def _raw_extract(self, steg_data, num_bits):
         """
diff --git a/res/high/pic1.jpg b/res/high/pic1.jpg
new file mode 100644
index 0000000..12c06e5
Binary files /dev/null and b/res/high/pic1.jpg differ
diff --git a/res/high/pic2.jpg b/res/high/pic2.jpg
new file mode 100644
index 0000000..0b89aee
Binary files /dev/null and b/res/high/pic2.jpg differ
diff --git a/res/high/pic3.jpg b/res/high/pic3.jpg
new file mode 100644
index 0000000..82d9205
Binary files /dev/null and b/res/high/pic3.jpg differ
diff --git a/res/high/pic3_dest.jpg b/res/high/pic3_dest.jpg
new file mode 100644
index 0000000..c4ea6a4
Binary files /dev/null and b/res/high/pic3_dest.jpg differ
diff --git a/res/high/pic3_f5_1_0.8.jpg b/res/high/pic3_f5_1_0.8.jpg
new file mode 100644
index 0000000..2a692a7
Binary files /dev/null and b/res/high/pic3_f5_1_0.8.jpg differ
diff --git a/res/high/pic3_orig.jpg b/res/high/pic3_orig.jpg
new file mode 100644
index 0000000..82d9205
Binary files /dev/null and b/res/high/pic3_orig.jpg differ
diff --git a/test/test_jpeg.py b/test/test_jpeg.py
index 777cc72..6273832 100644
--- a/test/test_jpeg.py
+++ b/test/test_jpeg.py
@@ -1,8 +1,11 @@
 __author__ = 'chunk'
 
 import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
 from .. import mjpeg
 from ..mjpeg import base
+from ..msteg.steganography import LSB, F3, F4, F5
 
 from ..common import *
 
@@ -17,9 +20,14 @@ sample = [[7, 12, 14, -12, 1, 0, -1, 0],
           [0, 0, 0, 0, 0, 0, 0, 0],
           [0, 0, 0, 0, 0, 0, 0, 0]]
 
-sample_key = [46812L, 20559L, 31360L, 16681L, 27536L, 39553L, 5427L, 63029L, 56572L, 36476L, 25695L, 61908L, 63014L,
+sample_key = [46812L, 20559L, 31360L, 16681L, 27536L, 39553L, 5427L, 63029L, 56572L, 36476L, 25695L,
+              61908L, 63014L,
               5908L, 59816L, 56765L]
 
+# plt.ticklabel_format(style='sci', axis='both', scilimits=(1, 4))
+
+plt.ticklabel_format(style='sci', axis='both')
+
 package_dir = os.path.dirname(os.path.abspath(__file__))
 
 
@@ -33,7 +41,7 @@ def test_setblocks():
 
     imb.setCoefMatrix(np.array([[0] * 800 for i in range(600)]), channel='Y')
 
-    block = imb.getCoefBlock(channel='Y', loc=(-1, 2))
+    block = imb.getCoefBlock(channe99l='Y', loc=(-1, 2))
     print block
 
     imb.Jwrite("res/test4.jpg")
@@ -165,7 +173,37 @@ def test_jpeg():
     """
 
 
+def test_hist():
+    ima = mjpeg.Jpeg(os.path.join(package_dir, "../res/high/pic3_orig.jpg"), key=sample_key)
+    print ima.getQuality()
+    print ima.getCapacity('All')
+
+    capacity = ima.getCapacity()
+    print capacity
+    rate = 0.65
+    hidden = np.random.bytes(int(int(capacity) * rate) / 8)
+    steger = F5.F5(sample_key, 1)
+    steger2 = F4.F4(key=None)
+    steger3 = LSB.LSB(key=None)
+    embed_rate = steger3.embed_raw_data(os.path.join(package_dir, "../res/high/pic3_orig.jpg"),
+                                       hidden,
+                                       os.path.join(package_dir, "../res/high/pic3_dest.jpg"),
+                                       frommem=True)
+    #
+    print embed_rate
+
+    imb = mjpeg.Jpeg(os.path.join(package_dir, "../res/high/pic3_dest.jpg"), key=sample_key)
+    print imb.getQuality()
+    print imb.getCapacity('All')
 
+    A = imb.rawsignal().tolist()
+    E = [i for i in range(-8, 10)]
+    plt.hist(A, E, histtype='bar', rwidth=0.8, align='left')
+    plt.xlabel("JPEG coefficients after quantisation")
+    plt.ylabel("Frequency")
+    plt.xticks([i for i in range(-8, 9)])
+    plt.ylim(ymax=300000)
+    plt.show()
 
 
 if __name__ == '__main__':
@@ -214,14 +252,3 @@ if __name__ == '__main__':
     # sample = np.array(sample)[mmask]
     # print np.hstack([[],sample])
     pass
-
-
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--
libgit2 0.21.2