diff --git a/README.md b/README.md
index bea09be..a0bddc7 100644
--- a/README.md
+++ b/README.md
@@ -50,7 +50,10 @@ environment.
Installing
----------
----------
-Just type ```make python``` after creating and activating the virtual environment.
+
+For a standalone ```facs``` commandline tool, type: ```make```.
+
+To compile the python bindings: ```make python```, after creating and activating the virtual environment.
Citation
diff --git a/facs/query.c b/facs/query.c
index 0ff19f6..69246b2 100644
--- a/facs/query.c
+++ b/facs/query.c
@@ -396,6 +396,7 @@ void read_process (bloom * bl, Queue * info, Queue * tail, F_set * File_head, fl
int result = 0;
next_job = check_fmt (info, tail, start_point, fmt_type);
// make sure it can handle DOS and Unix format ('\r\n' and '\n')
+ // XXX: what about OSX sinle '\n' ('a0' in hexa)?
if (next_job == NULL)
return;
while (start_point != next_job)
diff --git a/facs/tool.c b/facs/tool.c
index 9781b1d..d6fe538 100644
--- a/facs/tool.c
+++ b/facs/tool.c
@@ -340,7 +340,7 @@ char *check_fmt (Queue *info, Queue *tail, char *start_point, char type)
next_job = info->next->location;
}
else
- {
+ { // XXX: Does this account for OSX-style newlines? next_job is always NULL on OSX fastq files.
next_job = strchr (start_point, '\0');
if (next_job[-1] == '\n' && next_job[-2] == '\n')
next_job -= 1;
diff --git a/facs/utils/benchmarks_facs.ipynb b/facs/utils/benchmarks_facs.ipynb
index f7e3d01..5c12524 100644
--- a/facs/utils/benchmarks_facs.ipynb
+++ b/facs/utils/benchmarks_facs.ipynb
@@ -1,6 +1,6 @@
{
"metadata": {
- "name": "benchmarks_facs"
+ "name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -11,6 +11,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
+ "%pylab inline\n",
"import os\n",
"import matplotlib\n",
"import json\n",
@@ -19,8 +20,16 @@
],
"language": "python",
"metadata": {},
- "outputs": [],
- "prompt_number": 76
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ }
+ ],
+ "prompt_number": 23
},
{
"cell_type": "code",
@@ -35,39 +44,39 @@
"output_type": "stream",
"stream": "stdout",
"text": [
- "[2014-01-21 23:26] INFO: FACS: Establishing connection with database http://facs2.nopcode.org:5984/\n"
+ "[2014-04-18 14:33] INFO: FACS: Establishing connection with database http://facs2.nopcode.org:5984/\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "[2014-01-21 23:26] INFO: FACS: Fetching all documents from database facs\n"
+ "[2014-04-18 14:33] INFO: FACS: Fetching all documents from database facs\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "[2014-01-21 23:27] INFO: FACS: Fetched 351 documents\n"
+ "[2014-04-18 14:34] INFO: FACS: Fetched 252 documents\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "[2014-01-21 23:27] INFO: FACS: Fetching all documents from database fastq_screen\n"
+ "[2014-04-18 14:34] INFO: FACS: Fetching all documents from database fastq_screen\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "[2014-01-21 23:27] INFO: FACS: Fetched 351 documents\n"
+ "[2014-04-18 14:34] INFO: FACS: Fetched 126 documents\n"
]
}
],
- "prompt_number": 77
+ "prompt_number": 24
},
{
"cell_type": "code",
@@ -78,7 +87,7 @@
"language": "python",
"metadata": {},
"outputs": [],
- "prompt_number": 78
+ "prompt_number": 25
},
{
"cell_type": "code",
@@ -93,11 +102,11 @@
"output_type": "stream",
"stream": "stdout",
"text": [
- "{u'threads_fqscr': u'16', u'threads_facs': 16, u'filter_facs': u'/pica/h1/roman/dev/facs/tests/data/bloom/phiX.bloom', u'sample_fqscr': u'simngs_eschColi_K12_1000000.fastq', u'sample_facs': u'/pica/h1/roman/dev/facs/tests/data/synthetic_fastq/simngs_eschColi_K12_1000000.fastq', u'contam_fqscr': 0.0, u'contam_facs': 0.0, u'delta': 3.02251, u'delta_facs': 0.219, u'filter_fqscr': u'phiX'}\n"
+ "{u'threads_fqscr': u'1', u'threads_facs': 16, u'filter_facs': u'/pica/h1/roman/dev/facs/tests/data/bloom/dm3.bloom', u'sample_fqscr': u'simngs_phiX_10000000.fastq', u'sample_facs': u'/pica/h1/roman/dev/facs/tests/data/synthetic_fastq/simngs_phiX_10000000.fastq', u'contam_fqscr': 0.0001, u'contam_facs': 7.1e-05, u'delta': 995.512687, u'delta_facs': 10.71, u'filter_fqscr': u'dm3'}\n"
]
}
],
- "prompt_number": 79
+ "prompt_number": 26
},
{
"cell_type": "code",
@@ -108,7 +117,7 @@
"language": "python",
"metadata": {},
"outputs": [],
- "prompt_number": 80
+ "prompt_number": 27
},
{
"cell_type": "code",
@@ -125,22 +134,18 @@
"frame['filter_facs'] = [filt[0] for filt in frame.filter_facs.map(os.path.splitext)]\n",
"\n",
"# Individual column with read counts\n",
- "frame['reads'] = [os.path.splitext(reads.split('_')[-1])[0] for reads in frame.sample_facs]\n",
- "\n",
- "# Normalize fastq_screen contamination rates\n",
- "# XXX: to be removed soon since test_fastqscreen.py already normalizes this in new code\n",
- "# frame['contam_fqscr'] = [ contam/100 for contam in frame.contam_fqscr ]"
+ "frame['reads'] = [os.path.splitext(reads.split('_')[-1])[0] for reads in frame.sample_facs]"
],
"language": "python",
"metadata": {},
"outputs": [],
- "prompt_number": 81
+ "prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "frame.sort('reads').loc[:,['reads', 'contam_fqscr', 'contam_facs']][:5]"
+ "frame.sort('reads')[:10]"
],
"language": "python",
"metadata": {},
@@ -152,191 +157,210 @@
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
- " <th>reads</th>\n",
- " <th>contam_fqscr</th>\n",
" <th>contam_facs</th>\n",
+ " <th>contam_fqscr</th>\n",
+ " <th>delta_fqscr</th>\n",
+ " <th>delta_facs</th>\n",
+ " <th>filter_facs</th>\n",
+ " <th>filter_fqscr</th>\n",
+ " <th>sample_facs</th>\n",
+ " <th>sample_fqscr</th>\n",
+ " <th>threads_facs</th>\n",
+ " <th>threads_fqscr</th>\n",
+ " <th>reads</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>8 </th>\n",
+ " <th>108</th>\n",
+ " <td> 0.92</td>\n",
+ " <td> 0.91</td>\n",
+ " <td> 0.628752</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
" <td> 100</td>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>9 </th>\n",
+ " <th>102</th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.217739</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
" <td> 100</td>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>46</th>\n",
+ " <th>120</th>\n",
+ " <td> 1.00</td>\n",
+ " <td> 1.00</td>\n",
+ " <td> 0.208827</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
" <td> 100</td>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>47</th>\n",
+ " <th>106</th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.231580</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
" <td> 100</td>\n",
- " <td> 1</td>\n",
- " <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>49</th>\n",
+ " <th>31 </th>\n",
+ " <td> 1.00</td>\n",
+ " <td> 1.00</td>\n",
+ " <td> 0.220209</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
" <td> 100</td>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "output_type": "pyout",
- "prompt_number": 82,
- "text": [
- " reads contam_fqscr contam_facs\n",
- "8 100 0 0\n",
- "9 100 0 0\n",
- "46 100 0 0\n",
- "47 100 1 1\n",
- "49 100 0 0"
- ]
- }
- ],
- "prompt_number": 82
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "frame.sort('reads')[:5]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": [
- {
- "html": [
- "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>contam_facs</th>\n",
- " <th>contam_fqscr</th>\n",
- " <th>delta_fqscr</th>\n",
- " <th>delta_facs</th>\n",
- " <th>filter_facs</th>\n",
- " <th>filter_fqscr</th>\n",
- " <th>sample_facs</th>\n",
- " <th>sample_fqscr</th>\n",
- " <th>threads_facs</th>\n",
- " <th>threads_fqscr</th>\n",
- " <th>reads</th>\n",
" </tr>\n",
- " </thead>\n",
- " <tbody>\n",
" <tr>\n",
- " <th>8 </th>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
- " <td> 0.212956</td>\n",
- " <td> 0.173</td>\n",
+ " <th>32 </th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.674059</td>\n",
+ " <td> 0.921</td>\n",
" <td> dm3</td>\n",
" <td> dm3</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
" <td> 16</td>\n",
" <td> 16</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>9 </th>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
- " <td> 0.121729</td>\n",
- " <td> 0.003</td>\n",
+ " <th>33 </th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.210950</td>\n",
+ " <td> 0.539</td>\n",
" <td> eschColi_K12</td>\n",
" <td> eschColi_K12</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
" <td> 16</td>\n",
" <td> 16</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>46</th>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
- " <td> 0.153477</td>\n",
- " <td> 0.006</td>\n",
- " <td> eschColi_K12</td>\n",
- " <td> eschColi_K12</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
+ " <th>34 </th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.227650</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
" <td> 16</td>\n",
" <td> 16</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>47</th>\n",
- " <td> 1</td>\n",
- " <td> 1</td>\n",
- " <td> 0.150855</td>\n",
- " <td> 0.000</td>\n",
+ " <th>119</th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.209239</td>\n",
+ " <td> 0.017</td>\n",
" <td> phiX</td>\n",
" <td> phiX</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> 16</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
" <td> 16</td>\n",
+ " <td> 8</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>49</th>\n",
- " <td> 0</td>\n",
- " <td> 0</td>\n",
- " <td> 0.244115</td>\n",
- " <td> 0.273</td>\n",
+ " <th>17 </th>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.00</td>\n",
+ " <td> 0.681134</td>\n",
+ " <td> 1.017</td>\n",
" <td> dm3</td>\n",
" <td> dm3</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
- " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
" <td> 16</td>\n",
" <td> 16</td>\n",
" <td> 100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>10 rows \u00d7 11 columns</p>\n",
"</div>"
],
+ "metadata": {},
"output_type": "pyout",
- "prompt_number": 83,
+ "prompt_number": 29,
"text": [
- " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
- "8 0 0 0.212956 0.173 dm3 \n",
- "9 0 0 0.121729 0.003 eschColi_K12 \n",
- "46 0 0 0.153477 0.006 eschColi_K12 \n",
- "47 1 1 0.150855 0.000 phiX \n",
- "49 0 0 0.244115 0.273 dm3 \n",
+ " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
+ "108 0.92 0.91 0.628752 0.774 dm3 \n",
+ "102 0.00 0.00 0.217739 0.017 phiX \n",
+ "120 1.00 1.00 0.208827 0.021 eschColi_K12 \n",
+ "106 0.00 0.00 0.231580 0.016 phiX \n",
+ "31 1.00 1.00 0.220209 0.021 eschColi_K12 \n",
+ "32 0.00 0.00 0.674059 0.921 dm3 \n",
+ "33 0.00 0.00 0.210950 0.539 eschColi_K12 \n",
+ "34 0.00 0.00 0.227650 0.016 phiX \n",
+ "119 0.00 0.00 0.209239 0.017 phiX \n",
+ "17 0.00 0.00 0.681134 1.017 dm3 \n",
+ "\n",
+ " filter_fqscr sample_facs \\\n",
+ "108 dm3 simngs_dm3_100.fastq \n",
+ "102 phiX simngs_eschColi_K12_100.fastq \n",
+ "120 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "106 phiX simngs_dm3_100.fastq \n",
+ "31 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "32 dm3 simngs_eschColi_K12_100.fastq \n",
+ "33 eschColi_K12 simngs_dm3_100.fastq \n",
+ "34 phiX simngs_dm3_100.fastq \n",
+ "119 phiX simngs_eschColi_K12_100.fastq \n",
+ "17 dm3 simngs_phiX_100.fastq \n",
"\n",
- " filter_fqscr sample_facs sample_fqscr threads_facs \\\n",
- "8 dm3 simngs_phiX_100.fastq simngs_phiX_100.fastq 16 \n",
- "9 eschColi_K12 simngs_phiX_100.fastq simngs_phiX_100.fastq 16 \n",
- "46 eschColi_K12 simngs_phiX_100.fastq simngs_phiX_100.fastq 16 \n",
- "47 phiX simngs_phiX_100.fastq simngs_phiX_100.fastq 16 \n",
- "49 dm3 simngs_phiX_100.fastq simngs_phiX_100.fastq 16 \n",
+ " sample_fqscr threads_facs threads_fqscr reads \n",
+ "108 simngs_dm3_100.fastq 16 8 100 \n",
+ "102 simngs_eschColi_K12_100.fastq 16 16 100 \n",
+ "120 simngs_eschColi_K12_100.fastq 16 8 100 \n",
+ "106 simngs_dm3_100.fastq 16 8 100 \n",
+ "31 simngs_eschColi_K12_100.fastq 16 16 100 \n",
+ "32 simngs_eschColi_K12_100.fastq 16 16 100 \n",
+ "33 simngs_dm3_100.fastq 16 16 100 \n",
+ "34 simngs_dm3_100.fastq 16 16 100 \n",
+ "119 simngs_eschColi_K12_100.fastq 16 8 100 \n",
+ "17 simngs_phiX_100.fastq 16 16 100 \n",
"\n",
- " threads_fqscr reads \n",
- "8 16 100 \n",
- "9 16 100 \n",
- "46 16 100 \n",
- "47 16 100 \n",
- "49 16 100 "
+ "[10 rows x 11 columns]"
]
}
],
- "prompt_number": 83
+ "prompt_number": 29
},
{
"cell_type": "code",
@@ -364,109 +388,117 @@
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
- " <td> 351.000000</td>\n",
- " <td> 351.000000</td>\n",
- " <td> 351.000000</td>\n",
- " <td> 351.000000</td>\n",
- " <td> 351.000000</td>\n",
+ " <td> 126.000000</td>\n",
+ " <td> 126.000000</td>\n",
+ " <td> 126.000000</td>\n",
+ " <td> 126.000000</td>\n",
+ " <td> 126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
- " <td> 0.323096</td>\n",
- " <td> 0.323178</td>\n",
- " <td> 23.442771</td>\n",
- " <td> 5.636427</td>\n",
- " <td> 8.333333</td>\n",
+ " <td> 0.322119</td>\n",
+ " <td> 0.321750</td>\n",
+ " <td> 100.674748</td>\n",
+ " <td> 6.908905</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
- " <td> 0.456670</td>\n",
- " <td> 0.456916</td>\n",
- " <td> 59.944428</td>\n",
- " <td> 23.227286</td>\n",
- " <td> 6.137007</td>\n",
+ " <td> 0.444938</td>\n",
+ " <td> 0.444609</td>\n",
+ " <td> 265.897376</td>\n",
+ " <td> 17.113048</td>\n",
+ " <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
- " <td> 0.108737</td>\n",
- " <td> 0.000000</td>\n",
- " <td> 1.000000</td>\n",
+ " <td> 0.207451</td>\n",
+ " <td> 0.013000</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 0.000000</td>\n",
" <td> 0.000000</td>\n",
- " <td> 0.148589</td>\n",
- " <td> 0.006000</td>\n",
- " <td> 1.000000</td>\n",
+ " <td> 0.228632</td>\n",
+ " <td> 0.038000</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
- " <td> 0.000067</td>\n",
+ " <td> 0.000156</td>\n",
" <td> 0.000100</td>\n",
- " <td> 0.470986</td>\n",
- " <td> 0.246000</td>\n",
- " <td> 8.000000</td>\n",
+ " <td> 1.094079</td>\n",
+ " <td> 1.202500</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
- " <td> 0.918067</td>\n",
- " <td> 0.915400</td>\n",
- " <td> 15.466919</td>\n",
- " <td> 2.172500</td>\n",
- " <td> 16.000000</td>\n",
+ " <td> 0.917631</td>\n",
+ " <td> 0.914900</td>\n",
+ " <td> 68.664951</td>\n",
+ " <td> 5.319000</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
- " <td> 445.017706</td>\n",
- " <td> 270.101000</td>\n",
- " <td> 16.000000</td>\n",
+ " <td> 1747.636472</td>\n",
+ " <td> 101.781000</td>\n",
+ " <td> 16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
+ "<p>8 rows \u00d7 5 columns</p>\n",
"</div>"
],
+ "metadata": {},
"output_type": "pyout",
- "prompt_number": 84,
+ "prompt_number": 30,
"text": [
" contam_facs contam_fqscr delta_fqscr delta_facs threads_facs\n",
- "count 351.000000 351.000000 351.000000 351.000000 351.000000\n",
- "mean 0.323096 0.323178 23.442771 5.636427 8.333333\n",
- "std 0.456670 0.456916 59.944428 23.227286 6.137007\n",
- "min 0.000000 0.000000 0.108737 0.000000 1.000000\n",
- "25% 0.000000 0.000000 0.148589 0.006000 1.000000\n",
- "50% 0.000067 0.000100 0.470986 0.246000 8.000000\n",
- "75% 0.918067 0.915400 15.466919 2.172500 16.000000\n",
- "max 1.000000 1.000000 445.017706 270.101000 16.000000"
+ "count 126.000000 126.000000 126.000000 126.000000 126\n",
+ "mean 0.322119 0.321750 100.674748 6.908905 16\n",
+ "std 0.444938 0.444609 265.897376 17.113048 0\n",
+ "min 0.000000 0.000000 0.207451 0.013000 16\n",
+ "25% 0.000000 0.000000 0.228632 0.038000 16\n",
+ "50% 0.000156 0.000100 1.094079 1.202500 16\n",
+ "75% 0.917631 0.914900 68.664951 5.319000 16\n",
+ "max 1.000000 1.000000 1747.636472 101.781000 16\n",
+ "\n",
+ "[8 rows x 5 columns]"
]
}
],
- "prompt_number": 84
+ "prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "# Selects specific datasets built with a predefined amount of simulated reads from different combinations of organisms\n",
- "# and quantity of reads\n",
+ "# Slicing main dataframe into individual organism dataframes\n",
+ "\n",
+ "dm3 = frame['sample_fqscr'].map(lambda x: 'dm3' in x)\n",
+ "eschColi = frame['sample_fqscr'].map(lambda x: 'eschColi_K12' in x)\n",
+ "phiX = frame['sample_fqscr'].map(lambda x: 'phiX' in x)\n",
"\n",
- "accuracy1 = frame[ frame.sample_facs.str.contains('100vs400') ]\n",
- "accuracy2 = frame[ frame.sample_facs.str.contains('3000vs6000') ]"
+ "dm3 = frame[dm3]\n",
+ "eschColi = frame[eschColi]\n",
+ "phiX = frame[phiX]"
],
"language": "python",
"metadata": {},
"outputs": [],
- "prompt_number": 86
+ "prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "accuracy2"
+ "eschColi.sort('reads')"
],
"language": "python",
"metadata": {},
@@ -493,304 +525,4372 @@
" </thead>\n",
" <tbody>\n",
" <tr>\n",
- " <th>4 </th>\n",
- " <td> 0.032462</td>\n",
- " <td> 0.0322</td>\n",
- " <td> 0.838452</td>\n",
- " <td> 0.151</td>\n",
+ " <th>102</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.217739</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.674059</td>\n",
+ " <td> 0.921</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>121</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.442168</td>\n",
+ " <td> 0.921</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>119</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.209239</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>120</th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.208827</td>\n",
+ " <td> 0.021</td>\n",
" <td> eschColi_K12</td>\n",
" <td> eschColi_K12</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
" <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>64 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 1.041629</td>\n",
+ " <td> 0.921</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
" <td> 16</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>5 </th>\n",
+ " <th>63 </th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.210353</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>62 </th>\n",
" <td> 0.000000</td>\n",
" <td> 0.0000</td>\n",
- " <td> 0.988345</td>\n",
- " <td> 0.080</td>\n",
+ " <td> 0.210363</td>\n",
+ " <td> 0.017</td>\n",
" <td> phiX</td>\n",
" <td> phiX</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31 </th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.220209</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
" <td> 16</td>\n",
" <td> 16</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 100</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>10 </th>\n",
- " <td> 0.032462</td>\n",
- " <td> 0.0322</td>\n",
- " <td> 0.809962</td>\n",
- " <td> 0.154</td>\n",
+ " <th>53 </th>\n",
+ " <td> 0.999000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.218013</td>\n",
+ " <td> 0.035</td>\n",
" <td> eschColi_K12</td>\n",
" <td> eschColi_K12</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> 8</td>\n",
- " <td> 8</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>11 </th>\n",
+ " <th>21 </th>\n",
" <td> 0.000000</td>\n",
" <td> 0.0000</td>\n",
- " <td> 0.688489</td>\n",
- " <td> 0.099</td>\n",
+ " <td> 0.220144</td>\n",
+ " <td> 0.030</td>\n",
" <td> phiX</td>\n",
" <td> phiX</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> 8</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 8</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>13 </th>\n",
- " <td> 0.888462</td>\n",
- " <td> 0.8861</td>\n",
- " <td> 2.676200</td>\n",
- " <td> 1.416</td>\n",
- " <td> dm3</td>\n",
- " <td> dm3</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> 8</td>\n",
+ " <th>22 </th>\n",
+ " <td> 0.999000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.218054</td>\n",
+ " <td> 0.035</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 8</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>60 </th>\n",
- " <td> 0.888462</td>\n",
- " <td> 0.8861</td>\n",
- " <td> 4.258616</td>\n",
- " <td> 1.030</td>\n",
+ " <th>25 </th>\n",
+ " <td> 0.002000</td>\n",
+ " <td> 0.0020</td>\n",
+ " <td> 0.838629</td>\n",
+ " <td> 1.092</td>\n",
" <td> dm3</td>\n",
" <td> dm3</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
" <td> 16</td>\n",
- " <td> 16</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>242</th>\n",
- " <td> 0.032462</td>\n",
- " <td> 0.0322</td>\n",
- " <td> 4.522326</td>\n",
- " <td> 0.784</td>\n",
+ " <th>98 </th>\n",
+ " <td> 0.999000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.421589</td>\n",
+ " <td> 0.035</td>\n",
" <td> eschColi_K12</td>\n",
" <td> eschColi_K12</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> 1</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 1</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>243</th>\n",
+ " <th>97 </th>\n",
" <td> 0.000000</td>\n",
" <td> 0.0000</td>\n",
- " <td> 1.595111</td>\n",
- " <td> 0.393</td>\n",
+ " <td> 0.207451</td>\n",
+ " <td> 0.030</td>\n",
" <td> phiX</td>\n",
" <td> phiX</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>95 </th>\n",
+ " <td> 0.002000</td>\n",
+ " <td> 0.0020</td>\n",
+ " <td> 0.631262</td>\n",
+ " <td> 1.092</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 1</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1000</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>245</th>\n",
- " <td> 0.888462</td>\n",
- " <td> 0.8861</td>\n",
- " <td> 13.889459</td>\n",
- " <td> 7.139</td>\n",
+ " <th>81 </th>\n",
+ " <td> 0.002000</td>\n",
+ " <td> 0.0020</td>\n",
+ " <td> 0.658294</td>\n",
+ " <td> 1.092</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.223678</td>\n",
+ " <td> 0.030</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45 </th>\n",
+ " <td> 0.003942</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 11.681972</td>\n",
+ " <td> 3.251</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10 </th>\n",
+ " <td> 0.003942</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 98.408732</td>\n",
+ " <td> 3.251</td>\n",
" <td> dm3</td>\n",
" <td> dm3</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
- " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 10.272867</td>\n",
+ " <td> 1.828</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46 </th>\n",
+ " <td> 0.995472</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 86.200919</td>\n",
+ " <td> 5.319</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>79 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 16.837692</td>\n",
+ " <td> 1.828</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
" <td> 1</td>\n",
- " <td> 3000vs6000</td>\n",
+ " <td> 1000000</td>\n",
" </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "</div>"
- ],
- "output_type": "pyout",
- "prompt_number": 87,
- "text": [
- " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
- "4 0.032462 0.0322 0.838452 0.151 eschColi_K12 \n",
- "5 0.000000 0.0000 0.988345 0.080 phiX \n",
- "10 0.032462 0.0322 0.809962 0.154 eschColi_K12 \n",
- "11 0.000000 0.0000 0.688489 0.099 phiX \n",
- "13 0.888462 0.8861 2.676200 1.416 dm3 \n",
- "60 0.888462 0.8861 4.258616 1.030 dm3 \n",
- "242 0.032462 0.0322 4.522326 0.784 eschColi_K12 \n",
- "243 0.000000 0.0000 1.595111 0.393 phiX \n",
- "245 0.888462 0.8861 13.889459 7.139 dm3 \n",
- "\n",
- " filter_fqscr sample_facs \\\n",
- "4 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "5 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "10 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "11 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "13 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "60 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "242 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "243 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "245 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
- "\n",
- " sample_fqscr threads_facs \\\n",
- "4 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
- "5 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
- "10 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 8 \n",
- "11 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 8 \n",
- "13 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 8 \n",
- "60 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
- "242 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 1 \n",
- "243 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 1 \n",
- "245 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 1 \n",
+ " <tr>\n",
+ " <th>107</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 6.285596</td>\n",
+ " <td> 1.828</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>109</th>\n",
+ " <td> 0.003942</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 15.347968</td>\n",
+ " <td> 3.251</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>110</th>\n",
+ " <td> 0.995472</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 24.531883</td>\n",
+ " <td> 5.319</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>113</th>\n",
+ " <td> 0.995472</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 105.642662</td>\n",
+ " <td> 5.319</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>70 </th>\n",
+ " <td> 0.995490</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 230.793156</td>\n",
+ " <td> 41.880</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>71 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 60.088921</td>\n",
+ " <td> 10.476</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52 </th>\n",
+ " <td> 0.995490</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 848.533965</td>\n",
+ " <td> 41.880</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51 </th>\n",
+ " <td> 0.003922</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 114.119080</td>\n",
+ " <td> 12.478</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 162.226401</td>\n",
+ " <td> 10.476</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>99 </th>\n",
+ " <td> 0.995490</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 1091.302946</td>\n",
+ " <td> 41.880</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>100</th>\n",
+ " <td> 0.003922</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 975.072816</td>\n",
+ " <td> 12.478</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 97.366328</td>\n",
+ " <td> 10.476</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>69 </th>\n",
+ " <td> 0.003922</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 148.931867</td>\n",
+ " <td> 12.478</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48 </th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.835371</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.434019</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.237581</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80 </th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 1.733639</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.444665</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83 </th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.461494</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>84 </th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 1.091974</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>86 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.223083</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 0.845511</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>116</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.855492</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61 </th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 14.623352</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>68 </th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 4.827130</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 2.106148</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101</th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 9.079349</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>118</th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 3.006585</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20 </th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 1.096185</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 1.127904</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>117</th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 0.851774</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>54 rows \u00d7 11 columns</p>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 14,
+ "text": [
+ " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
+ "102 0.000000 0.0000 0.217739 0.017 phiX \n",
+ "32 0.000000 0.0000 0.674059 0.921 dm3 \n",
+ "121 0.000000 0.0000 0.442168 0.921 dm3 \n",
+ "119 0.000000 0.0000 0.209239 0.017 phiX \n",
+ "120 1.000000 1.0000 0.208827 0.021 eschColi_K12 \n",
+ "64 0.000000 0.0000 1.041629 0.921 dm3 \n",
+ "63 1.000000 1.0000 0.210353 0.021 eschColi_K12 \n",
+ "62 0.000000 0.0000 0.210363 0.017 phiX \n",
+ "31 1.000000 1.0000 0.220209 0.021 eschColi_K12 \n",
+ "53 0.999000 0.9990 0.218013 0.035 eschColi_K12 \n",
+ "21 0.000000 0.0000 0.220144 0.030 phiX \n",
+ "22 0.999000 0.9990 0.218054 0.035 eschColi_K12 \n",
+ "25 0.002000 0.0020 0.838629 1.092 dm3 \n",
+ "98 0.999000 0.9990 0.421589 0.035 eschColi_K12 \n",
+ "97 0.000000 0.0000 0.207451 0.030 phiX \n",
+ "95 0.002000 0.0020 0.631262 1.092 dm3 \n",
+ "81 0.002000 0.0020 0.658294 1.092 dm3 \n",
+ "54 0.000000 0.0000 0.223678 0.030 phiX \n",
+ "45 0.003942 0.0032 11.681972 3.251 dm3 \n",
+ "10 0.003942 0.0032 98.408732 3.251 dm3 \n",
+ "43 0.000000 0.0000 10.272867 1.828 phiX \n",
+ "46 0.995472 0.9959 86.200919 5.319 eschColi_K12 \n",
+ "79 0.000000 0.0000 16.837692 1.828 phiX \n",
+ "107 0.000000 0.0000 6.285596 1.828 phiX \n",
+ "109 0.003942 0.0032 15.347968 3.251 dm3 \n",
+ "110 0.995472 0.9959 24.531883 5.319 eschColi_K12 \n",
+ "113 0.995472 0.9959 105.642662 5.319 eschColi_K12 \n",
+ "70 0.995490 0.9959 230.793156 41.880 eschColi_K12 \n",
+ "71 0.000000 0.0000 60.088921 10.476 phiX \n",
+ "52 0.995490 0.9959 848.533965 41.880 eschColi_K12 \n",
+ "51 0.003922 0.0032 114.119080 12.478 dm3 \n",
+ "4 0.000000 0.0000 162.226401 10.476 phiX \n",
+ "99 0.995490 0.9959 1091.302946 41.880 eschColi_K12 \n",
+ "100 0.003922 0.0032 975.072816 12.478 dm3 \n",
+ "57 0.000000 0.0000 97.366328 10.476 phiX \n",
+ "69 0.003922 0.0032 148.931867 12.478 dm3 \n",
+ "48 0.333000 0.3325 0.835371 0.070 eschColi_K12 \n",
+ "104 0.333000 0.3325 0.434019 0.070 eschColi_K12 \n",
+ "28 0.000000 0.0000 0.237581 0.020 phiX \n",
+ "80 0.613556 0.6113 1.733639 1.421 dm3 \n",
+ "12 0.000000 0.0000 0.444665 0.020 phiX \n",
+ "83 0.333000 0.3325 0.461494 0.070 eschColi_K12 \n",
+ "84 0.613556 0.6113 1.091974 1.421 dm3 \n",
+ "86 0.000000 0.0000 0.223083 0.020 phiX \n",
+ "103 0.613556 0.6113 0.845511 1.421 dm3 \n",
+ "116 0.000000 0.0000 0.855492 0.456 phiX \n",
+ "61 0.888462 0.8861 14.623352 1.809 dm3 \n",
+ "68 0.032462 0.0322 4.827130 0.576 eschColi_K12 \n",
+ "67 0.000000 0.0000 2.106148 0.456 phiX \n",
+ "101 0.888462 0.8861 9.079349 1.809 dm3 \n",
+ "118 0.888462 0.8861 3.006585 1.809 dm3 \n",
+ "20 0.032462 0.0322 1.096185 0.576 eschColi_K12 \n",
+ "13 0.000000 0.0000 1.127904 0.456 phiX \n",
+ "117 0.032462 0.0322 0.851774 0.576 eschColi_K12 \n",
"\n",
- " threads_fqscr reads \n",
- "4 16 3000vs6000 \n",
- "5 16 3000vs6000 \n",
- "10 8 3000vs6000 \n",
- "11 8 3000vs6000 \n",
- "13 8 3000vs6000 \n",
- "60 16 3000vs6000 \n",
- "242 1 3000vs6000 \n",
- "243 1 3000vs6000 \n",
- "245 1 3000vs6000 "
+ " filter_fqscr sample_facs \\\n",
+ "102 phiX simngs_eschColi_K12_100.fastq \n",
+ "32 dm3 simngs_eschColi_K12_100.fastq \n",
+ "121 dm3 simngs_eschColi_K12_100.fastq \n",
+ "119 phiX simngs_eschColi_K12_100.fastq \n",
+ "120 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "64 dm3 simngs_eschColi_K12_100.fastq \n",
+ "63 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "62 phiX simngs_eschColi_K12_100.fastq \n",
+ "31 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "53 eschColi_K12 simngs_eschColi_K12_1000.fastq \n",
+ "21 phiX simngs_eschColi_K12_1000.fastq \n",
+ "22 eschColi_K12 simngs_eschColi_K12_1000.fastq \n",
+ "25 dm3 simngs_eschColi_K12_1000.fastq \n",
+ "98 eschColi_K12 simngs_eschColi_K12_1000.fastq \n",
+ "97 phiX simngs_eschColi_K12_1000.fastq \n",
+ "95 dm3 simngs_eschColi_K12_1000.fastq \n",
+ "81 dm3 simngs_eschColi_K12_1000.fastq \n",
+ "54 phiX simngs_eschColi_K12_1000.fastq \n",
+ "45 dm3 simngs_eschColi_K12_1000000.fastq \n",
+ "10 dm3 simngs_eschColi_K12_1000000.fastq \n",
+ "43 phiX simngs_eschColi_K12_1000000.fastq \n",
+ "46 eschColi_K12 simngs_eschColi_K12_1000000.fastq \n",
+ "79 phiX simngs_eschColi_K12_1000000.fastq \n",
+ "107 phiX simngs_eschColi_K12_1000000.fastq \n",
+ "109 dm3 simngs_eschColi_K12_1000000.fastq \n",
+ "110 eschColi_K12 simngs_eschColi_K12_1000000.fastq \n",
+ "113 eschColi_K12 simngs_eschColi_K12_1000000.fastq \n",
+ "70 eschColi_K12 simngs_eschColi_K12_10000000.fastq \n",
+ "71 phiX simngs_eschColi_K12_10000000.fastq \n",
+ "52 eschColi_K12 simngs_eschColi_K12_10000000.fastq \n",
+ "51 dm3 simngs_eschColi_K12_10000000.fastq \n",
+ "4 phiX simngs_eschColi_K12_10000000.fastq \n",
+ "99 eschColi_K12 simngs_eschColi_K12_10000000.fastq \n",
+ "100 dm3 simngs_eschColi_K12_10000000.fastq \n",
+ "57 phiX simngs_eschColi_K12_10000000.fastq \n",
+ "69 dm3 simngs_eschColi_K12_10000000.fastq \n",
+ "48 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "104 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "28 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "80 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "12 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "83 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "84 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "86 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "103 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "116 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "61 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "68 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "67 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "101 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "118 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "20 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "13 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "117 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "\n",
+ " sample_fqscr threads_facs \\\n",
+ "102 simngs_eschColi_K12_100.fastq 16 \n",
+ "32 simngs_eschColi_K12_100.fastq 16 \n",
+ "121 simngs_eschColi_K12_100.fastq 16 \n",
+ "119 simngs_eschColi_K12_100.fastq 16 \n",
+ "120 simngs_eschColi_K12_100.fastq 16 \n",
+ "64 simngs_eschColi_K12_100.fastq 16 \n",
+ "63 simngs_eschColi_K12_100.fastq 16 \n",
+ "62 simngs_eschColi_K12_100.fastq 16 \n",
+ "31 simngs_eschColi_K12_100.fastq 16 \n",
+ "53 simngs_eschColi_K12_1000.fastq 16 \n",
+ "21 simngs_eschColi_K12_1000.fastq 16 \n",
+ "22 simngs_eschColi_K12_1000.fastq 16 \n",
+ "25 simngs_eschColi_K12_1000.fastq 16 \n",
+ "98 simngs_eschColi_K12_1000.fastq 16 \n",
+ "97 simngs_eschColi_K12_1000.fastq 16 \n",
+ "95 simngs_eschColi_K12_1000.fastq 16 \n",
+ "81 simngs_eschColi_K12_1000.fastq 16 \n",
+ "54 simngs_eschColi_K12_1000.fastq 16 \n",
+ "45 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "10 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "43 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "46 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "79 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "107 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "109 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "110 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "113 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "70 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "71 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "52 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "51 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "4 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "99 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "100 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "57 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "69 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "48 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "104 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "28 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "80 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "83 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "84 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "86 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "103 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "116 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "61 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "68 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "67 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "101 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "118 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "20 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "13 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "117 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "\n",
+ " threads_fqscr reads \n",
+ "102 16 100 \n",
+ "32 16 100 \n",
+ "121 8 100 \n",
+ "119 8 100 \n",
+ "120 8 100 \n",
+ "64 1 100 \n",
+ "63 1 100 \n",
+ "62 1 100 \n",
+ "31 16 100 \n",
+ "53 16 1000 \n",
+ "21 8 1000 \n",
+ "22 8 1000 \n",
+ "25 8 1000 \n",
+ "98 1 1000 \n",
+ "97 1 1000 \n",
+ "95 1 1000 \n",
+ "81 16 1000 \n",
+ "54 16 1000 \n",
+ "45 16 1000000 \n",
+ "10 1 1000000 \n",
+ "43 16 1000000 \n",
+ "46 16 1000000 \n",
+ "79 1 1000000 \n",
+ "107 8 1000000 \n",
+ "109 8 1000000 \n",
+ "110 8 1000000 \n",
+ "113 1 1000000 \n",
+ "70 8 10000000 \n",
+ "71 8 10000000 \n",
+ "52 16 10000000 \n",
+ "51 16 10000000 \n",
+ "4 1 10000000 \n",
+ "99 1 10000000 \n",
+ "100 1 10000000 \n",
+ "57 16 10000000 \n",
+ "69 8 10000000 \n",
+ "48 1 3000vs6000 \n",
+ "104 8 3000vs6000 \n",
+ "28 8 3000vs6000 \n",
+ "80 1 3000vs6000 \n",
+ "12 1 3000vs6000 \n",
+ "83 16 3000vs6000 \n",
+ "84 16 3000vs6000 \n",
+ "86 16 3000vs6000 \n",
+ "103 8 3000vs6000 \n",
+ "116 8 3000vs93000 \n",
+ "61 1 3000vs93000 \n",
+ "68 1 3000vs93000 \n",
+ "67 1 3000vs93000 \n",
+ "101 16 3000vs93000 \n",
+ "118 8 3000vs93000 \n",
+ "20 16 3000vs93000 \n",
+ "13 16 3000vs93000 \n",
+ "117 8 3000vs93000 \n",
+ "\n",
+ "[54 rows x 11 columns]"
]
}
],
- "prompt_number": 87
+ "prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "accuracy1 = accuracy1.loc[:,['contam_facs', 'contam_fqscr']]\n",
- "accuracy1.pivot_table(values=['contam_facs', 'contam_fqscr'], rows=['contam_fqscr'])\n",
- "accuracy1.plot(title='Contamination detection accuracy for simNGS-generated dm3 sample (~100% dm3)', kind='barh', logy=True, logx=True, figsize=(8,6))\n",
- "\n",
- "accuracy2 = accuracy2.loc[:,['contam_facs', 'contam_fqscr']]\n",
- "accuracy2.pivot_table(values=['contam_facs', 'contam_fqscr'], rows=['contam_fqscr'])\n",
- "accuracy2.plot(title='Contamination detection accuracy for simNGS-generated ecoli/dm3 sample (3000vs6000 simNGS reads)', kind='barh', logy=True, logx=True, figsize=(8,6))"
+ "phiX.sort('reads')"
],
"language": "python",
"metadata": {},
"outputs": [
{
- "output_type": "pyout",
- "prompt_number": 88,
- "text": [
- "<matplotlib.axes.AxesSubplot at 0xc4002ec>"
- ]
- },
- {
- "output_type": "display_data",
- "png": 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o2rUr1qxZI0YorDnSWjoAI8A51g/OM2Oi0vndE2q1GuPGjcO6desgk8mg0WhQ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\nSk9P19q1a02UAwAA+sDI9EQgEFAgENDUqVMlScX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evTsOHjwIAGjevDkSEhLQpEkTWCwWnDp1Ck8//TT+8Y9/oEePHvDy8kKHDh2Q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\niAgcFBIREREROCgkIiIiIgD/A6DksocUSke9AAAAAElFTkSuQmCC\n"
- }
- ],
- "prompt_number": 88
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For samples that screen against other organisms than dm3, there is no detection neither from FACS nor Fastq_screen. On the other hand, when dm3 is present, both screeners detect about the same amount.\n",
- "\n",
- "The fact that it is not 100% might be explained by simNGS mechanics: some reads (~5% in the graph above) might have mutated too much to be recognizable for any of the filters."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "# Remove accuracy-only datasets so that they don't disturb in performance plots\n",
- "# (operator ~ prepending the dataframe condition acts as \"not\" containing string)\n",
- "\n",
- "frame = frame[ ~frame.sample_facs.str.contains('100vs400') ]\n",
- "frame = frame[ ~frame.sample_facs.str.contains('3000vs6000') ]"
- ],
- "language": "python",
- "metadata": {},
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>contam_facs</th>\n",
+ " <th>contam_fqscr</th>\n",
+ " <th>delta_fqscr</th>\n",
+ " <th>delta_facs</th>\n",
+ " <th>filter_facs</th>\n",
+ " <th>filter_fqscr</th>\n",
+ " <th>sample_facs</th>\n",
+ " <th>sample_fqscr</th>\n",
+ " <th>threads_facs</th>\n",
+ " <th>threads_fqscr</th>\n",
+ " <th>reads</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>19 </th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.213116</td>\n",
+ " <td> 0.038</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.439895</td>\n",
+ " <td> 0.013</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.638649</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.420386</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40 </th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.431265</td>\n",
+ " <td> 0.038</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>111</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.211178</td>\n",
+ " <td> 0.013</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>112</th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.211833</td>\n",
+ " <td> 0.038</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.212676</td>\n",
+ " <td> 0.013</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.681134</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>72 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.632531</td>\n",
+ " <td> 1.313</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>74 </th>\n",
+ " <td> 0.998000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.220445</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 2.863820</td>\n",
+ " <td> 1.313</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.225909</td>\n",
+ " <td> 0.058</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7 </th>\n",
+ " <td> 0.998000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.452245</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.217786</td>\n",
+ " <td> 0.058</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56 </th>\n",
+ " <td> 0.998000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.217264</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>58 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.854444</td>\n",
+ " <td> 1.313</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>73 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.213699</td>\n",
+ " <td> 0.058</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>66 </th>\n",
+ " <td> 0.000066</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 112.148124</td>\n",
+ " <td> 2.012</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>60 </th>\n",
+ " <td> 0.995194</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 69.023493</td>\n",
+ " <td> 4.066</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>125</th>\n",
+ " <td> 0.000066</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 14.296514</td>\n",
+ " <td> 2.012</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>65 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 48.823824</td>\n",
+ " <td> 1.846</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>124</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 7.339768</td>\n",
+ " <td> 1.846</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16 </th>\n",
+ " <td> 0.995194</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 84.579529</td>\n",
+ " <td> 4.066</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41 </th>\n",
+ " <td> 0.995194</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 23.491512</td>\n",
+ " <td> 4.066</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 7.200159</td>\n",
+ " <td> 1.846</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14 </th>\n",
+ " <td> 0.000066</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 13.093603</td>\n",
+ " <td> 2.012</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 67.589326</td>\n",
+ " <td> 13.787</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 480.008990</td>\n",
+ " <td> 13.787</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2 </th>\n",
+ " <td> 0.995249</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 710.577691</td>\n",
+ " <td> 32.584</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36 </th>\n",
+ " <td> 0.995249</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 920.436917</td>\n",
+ " <td> 32.584</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49 </th>\n",
+ " <td> 0.000071</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 112.764168</td>\n",
+ " <td> 10.710</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0 </th>\n",
+ " <td> 0.000071</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 995.512687</td>\n",
+ " <td> 10.710</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75 </th>\n",
+ " <td> 0.000071</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 143.908266</td>\n",
+ " <td> 10.710</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>76 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 71.387078</td>\n",
+ " <td> 13.787</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>77 </th>\n",
+ " <td> 0.995249</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 294.349582</td>\n",
+ " <td> 32.584</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>36 rows \u00d7 11 columns</p>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 15,
+ "text": [
+ " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
+ "19 1.000000 1.0000 0.213116 0.038 phiX \n",
+ "18 0.000000 0.0000 0.439895 0.013 eschColi_K12 \n",
+ "59 0.000000 0.0000 0.638649 1.017 dm3 \n",
+ "42 0.000000 0.0000 0.420386 1.017 dm3 \n",
+ "40 1.000000 1.0000 0.431265 0.038 phiX \n",
+ "111 0.000000 0.0000 0.211178 0.013 eschColi_K12 \n",
+ "112 1.000000 1.0000 0.211833 0.038 phiX \n",
+ "39 0.000000 0.0000 0.212676 0.013 eschColi_K12 \n",
+ "17 0.000000 0.0000 0.681134 1.017 dm3 \n",
+ "72 0.000000 0.0000 0.632531 1.313 dm3 \n",
+ "74 0.998000 0.9990 0.220445 0.017 phiX \n",
+ "5 0.000000 0.0000 2.863820 1.313 dm3 \n",
+ "6 0.000000 0.0000 0.225909 0.058 eschColi_K12 \n",
+ "7 0.998000 0.9990 0.452245 0.017 phiX \n",
+ "55 0.000000 0.0000 0.217786 0.058 eschColi_K12 \n",
+ "56 0.998000 0.9990 0.217264 0.017 phiX \n",
+ "58 0.000000 0.0000 0.854444 1.313 dm3 \n",
+ "73 0.000000 0.0000 0.213699 0.058 eschColi_K12 \n",
+ "66 0.000066 0.0001 112.148124 2.012 dm3 \n",
+ "60 0.995194 0.9958 69.023493 4.066 phiX \n",
+ "125 0.000066 0.0001 14.296514 2.012 dm3 \n",
+ "65 0.000000 0.0000 48.823824 1.846 eschColi_K12 \n",
+ "124 0.000000 0.0000 7.339768 1.846 eschColi_K12 \n",
+ "16 0.995194 0.9958 84.579529 4.066 phiX \n",
+ "41 0.995194 0.9958 23.491512 4.066 phiX \n",
+ "15 0.000000 0.0000 7.200159 1.846 eschColi_K12 \n",
+ "14 0.000066 0.0001 13.093603 2.012 dm3 \n",
+ "50 0.000000 0.0000 67.589326 13.787 eschColi_K12 \n",
+ "1 0.000000 0.0000 480.008990 13.787 eschColi_K12 \n",
+ "2 0.995249 0.9958 710.577691 32.584 phiX \n",
+ "36 0.995249 0.9958 920.436917 32.584 phiX \n",
+ "49 0.000071 0.0001 112.764168 10.710 dm3 \n",
+ "0 0.000071 0.0001 995.512687 10.710 dm3 \n",
+ "75 0.000071 0.0001 143.908266 10.710 dm3 \n",
+ "76 0.000000 0.0000 71.387078 13.787 eschColi_K12 \n",
+ "77 0.995249 0.9958 294.349582 32.584 phiX \n",
+ "\n",
+ " filter_fqscr sample_facs sample_fqscr \\\n",
+ "19 phiX simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "18 eschColi_K12 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "59 dm3 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "42 dm3 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "40 phiX simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "111 eschColi_K12 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "112 phiX simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "39 eschColi_K12 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "17 dm3 simngs_phiX_100.fastq simngs_phiX_100.fastq \n",
+ "72 dm3 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "74 phiX simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "5 dm3 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "6 eschColi_K12 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "7 phiX simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "55 eschColi_K12 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "56 phiX simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "58 dm3 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "73 eschColi_K12 simngs_phiX_1000.fastq simngs_phiX_1000.fastq \n",
+ "66 dm3 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "60 phiX simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "125 dm3 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "65 eschColi_K12 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "124 eschColi_K12 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "16 phiX simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "41 phiX simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "15 eschColi_K12 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "14 dm3 simngs_phiX_1000000.fastq simngs_phiX_1000000.fastq \n",
+ "50 eschColi_K12 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "1 eschColi_K12 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "2 phiX simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "36 phiX simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "49 dm3 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "0 dm3 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "75 dm3 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "76 eschColi_K12 simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "77 phiX simngs_phiX_10000000.fastq simngs_phiX_10000000.fastq \n",
+ "\n",
+ " threads_facs threads_fqscr reads \n",
+ "19 16 16 100 \n",
+ "18 16 16 100 \n",
+ "59 16 1 100 \n",
+ "42 16 8 100 \n",
+ "40 16 8 100 \n",
+ "111 16 1 100 \n",
+ "112 16 1 100 \n",
+ "39 16 8 100 \n",
+ "17 16 16 100 \n",
+ "72 16 8 1000 \n",
+ "74 16 8 1000 \n",
+ "5 16 1 1000 \n",
+ "6 16 1 1000 \n",
+ "7 16 1 1000 \n",
+ "55 16 16 1000 \n",
+ "56 16 16 1000 \n",
+ "58 16 16 1000 \n",
+ "73 16 8 1000 \n",
+ "66 16 1 1000000 \n",
+ "60 16 1 1000000 \n",
+ "125 16 8 1000000 \n",
+ "65 16 1 1000000 \n",
+ "124 16 8 1000000 \n",
+ "16 16 16 1000000 \n",
+ "41 16 8 1000000 \n",
+ "15 16 16 1000000 \n",
+ "14 16 16 1000000 \n",
+ "50 16 16 10000000 \n",
+ "1 16 1 10000000 \n",
+ "2 16 1 10000000 \n",
+ "36 16 16 10000000 \n",
+ "49 16 16 10000000 \n",
+ "0 16 1 10000000 \n",
+ "75 16 8 10000000 \n",
+ "76 16 8 10000000 \n",
+ "77 16 8 10000000 \n",
+ "\n",
+ "[36 rows x 11 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 15
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "dm3.sort('reads')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>contam_facs</th>\n",
+ " <th>contam_fqscr</th>\n",
+ " <th>delta_fqscr</th>\n",
+ " <th>delta_facs</th>\n",
+ " <th>filter_facs</th>\n",
+ " <th>filter_fqscr</th>\n",
+ " <th>sample_facs</th>\n",
+ " <th>sample_fqscr</th>\n",
+ " <th>threads_facs</th>\n",
+ " <th>threads_fqscr</th>\n",
+ " <th>reads</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>44 </th>\n",
+ " <td> 0.920000</td>\n",
+ " <td> 0.9100</td>\n",
+ " <td> 0.673775</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td> 0.920000</td>\n",
+ " <td> 0.9100</td>\n",
+ " <td> 0.628752</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.231580</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.226763</td>\n",
+ " <td> 0.539</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47 </th>\n",
+ " <td> 0.920000</td>\n",
+ " <td> 0.9100</td>\n",
+ " <td> 0.628286</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.210950</td>\n",
+ " <td> 0.539</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.227650</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.211287</td>\n",
+ " <td> 0.539</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>114</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.225330</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>92 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.212446</td>\n",
+ " <td> 0.027</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>91 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.210926</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>85 </th>\n",
+ " <td> 0.917000</td>\n",
+ " <td> 0.9210</td>\n",
+ " <td> 0.658177</td>\n",
+ " <td> 0.703</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>87 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.212351</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.210869</td>\n",
+ " <td> 0.027</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27 </th>\n",
+ " <td> 0.917000</td>\n",
+ " <td> 0.9210</td>\n",
+ " <td> 0.632960</td>\n",
+ " <td> 0.703</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.227470</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.215520</td>\n",
+ " <td> 0.027</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>115</th>\n",
+ " <td> 0.917000</td>\n",
+ " <td> 0.9210</td>\n",
+ " <td> 0.631765</td>\n",
+ " <td> 0.703</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>82 </th>\n",
+ " <td> 0.000006</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 10.207863</td>\n",
+ " <td> 1.789</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24 </th>\n",
+ " <td> 0.917631</td>\n",
+ " <td> 0.9149</td>\n",
+ " <td> 40.650027</td>\n",
+ " <td> 9.936</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23 </th>\n",
+ " <td> 0.000261</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 7.338175</td>\n",
+ " <td> 2.138</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89 </th>\n",
+ " <td> 0.000261</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 6.463202</td>\n",
+ " <td> 2.138</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96 </th>\n",
+ " <td> 0.000006</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 16.781235</td>\n",
+ " <td> 1.789</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94 </th>\n",
+ " <td> 0.917631</td>\n",
+ " <td> 0.9149</td>\n",
+ " <td> 152.819053</td>\n",
+ " <td> 9.936</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93 </th>\n",
+ " <td> 0.000261</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 47.703680</td>\n",
+ " <td> 2.138</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>90 </th>\n",
+ " <td> 0.917631</td>\n",
+ " <td> 0.9149</td>\n",
+ " <td> 95.274927</td>\n",
+ " <td> 9.936</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26 </th>\n",
+ " <td> 0.000006</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 6.060514</td>\n",
+ " <td> 1.789</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>123</th>\n",
+ " <td> 0.000240</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 70.471625</td>\n",
+ " <td> 13.373</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8 </th>\n",
+ " <td> 0.000240</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 494.496125</td>\n",
+ " <td> 13.373</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9 </th>\n",
+ " <td> 0.000004</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 168.785631</td>\n",
+ " <td> 9.916</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35 </th>\n",
+ " <td> 0.917971</td>\n",
+ " <td> 0.9153</td>\n",
+ " <td> 940.853700</td>\n",
+ " <td> 101.781</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37 </th>\n",
+ " <td> 0.000004</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 101.717528</td>\n",
+ " <td> 9.916</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38 </th>\n",
+ " <td> 0.000240</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 71.551788</td>\n",
+ " <td> 13.373</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78 </th>\n",
+ " <td> 0.917971</td>\n",
+ " <td> 0.9153</td>\n",
+ " <td> 301.252240</td>\n",
+ " <td> 101.781</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>122</th>\n",
+ " <td> 0.000004</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 57.989411</td>\n",
+ " <td> 9.916</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3 </th>\n",
+ " <td> 0.917971</td>\n",
+ " <td> 0.9153</td>\n",
+ " <td> 1747.636472</td>\n",
+ " <td> 101.781</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>84 </th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 1.091974</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80 </th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 1.733639</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td> 0.613556</td>\n",
+ " <td> 0.6113</td>\n",
+ " <td> 0.845511</td>\n",
+ " <td> 1.421</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.434019</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48 </th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.835371</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.237581</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.444665</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>86 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.223083</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83 </th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.461494</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>116</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.855492</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20 </th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 1.096185</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>68 </th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 4.827130</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 2.106148</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61 </th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 14.623352</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101</th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 9.079349</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 1.127904</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>118</th>\n",
+ " <td> 0.888462</td>\n",
+ " <td> 0.8861</td>\n",
+ " <td> 3.006585</td>\n",
+ " <td> 1.809</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>117</th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 0.851774</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>54 rows \u00d7 11 columns</p>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 16,
+ "text": [
+ " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
+ "44 0.920000 0.9100 0.673775 0.774 dm3 \n",
+ "108 0.920000 0.9100 0.628752 0.774 dm3 \n",
+ "106 0.000000 0.0000 0.231580 0.016 phiX \n",
+ "105 0.000000 0.0000 0.226763 0.539 eschColi_K12 \n",
+ "47 0.920000 0.9100 0.628286 0.774 dm3 \n",
+ "33 0.000000 0.0000 0.210950 0.539 eschColi_K12 \n",
+ "34 0.000000 0.0000 0.227650 0.016 phiX \n",
+ "11 0.000000 0.0000 0.211287 0.539 eschColi_K12 \n",
+ "114 0.000000 0.0000 0.225330 0.016 phiX \n",
+ "92 0.000000 0.0000 0.212446 0.027 eschColi_K12 \n",
+ "91 0.000000 0.0000 0.210926 0.021 phiX \n",
+ "85 0.917000 0.9210 0.658177 0.703 dm3 \n",
+ "87 0.000000 0.0000 0.212351 0.021 phiX \n",
+ "88 0.000000 0.0000 0.210869 0.027 eschColi_K12 \n",
+ "27 0.917000 0.9210 0.632960 0.703 dm3 \n",
+ "29 0.000000 0.0000 0.227470 0.021 phiX \n",
+ "30 0.000000 0.0000 0.215520 0.027 eschColi_K12 \n",
+ "115 0.917000 0.9210 0.631765 0.703 dm3 \n",
+ "82 0.000006 0.0000 10.207863 1.789 phiX \n",
+ "24 0.917631 0.9149 40.650027 9.936 dm3 \n",
+ "23 0.000261 0.0001 7.338175 2.138 eschColi_K12 \n",
+ "89 0.000261 0.0001 6.463202 2.138 eschColi_K12 \n",
+ "96 0.000006 0.0000 16.781235 1.789 phiX \n",
+ "94 0.917631 0.9149 152.819053 9.936 dm3 \n",
+ "93 0.000261 0.0001 47.703680 2.138 eschColi_K12 \n",
+ "90 0.917631 0.9149 95.274927 9.936 dm3 \n",
+ "26 0.000006 0.0000 6.060514 1.789 phiX \n",
+ "123 0.000240 0.0001 70.471625 13.373 eschColi_K12 \n",
+ "8 0.000240 0.0001 494.496125 13.373 eschColi_K12 \n",
+ "9 0.000004 0.0000 168.785631 9.916 phiX \n",
+ "35 0.917971 0.9153 940.853700 101.781 dm3 \n",
+ "37 0.000004 0.0000 101.717528 9.916 phiX \n",
+ "38 0.000240 0.0001 71.551788 13.373 eschColi_K12 \n",
+ "78 0.917971 0.9153 301.252240 101.781 dm3 \n",
+ "122 0.000004 0.0000 57.989411 9.916 phiX \n",
+ "3 0.917971 0.9153 1747.636472 101.781 dm3 \n",
+ "84 0.613556 0.6113 1.091974 1.421 dm3 \n",
+ "80 0.613556 0.6113 1.733639 1.421 dm3 \n",
+ "103 0.613556 0.6113 0.845511 1.421 dm3 \n",
+ "104 0.333000 0.3325 0.434019 0.070 eschColi_K12 \n",
+ "48 0.333000 0.3325 0.835371 0.070 eschColi_K12 \n",
+ "28 0.000000 0.0000 0.237581 0.020 phiX \n",
+ "12 0.000000 0.0000 0.444665 0.020 phiX \n",
+ "86 0.000000 0.0000 0.223083 0.020 phiX \n",
+ "83 0.333000 0.3325 0.461494 0.070 eschColi_K12 \n",
+ "116 0.000000 0.0000 0.855492 0.456 phiX \n",
+ "20 0.032462 0.0322 1.096185 0.576 eschColi_K12 \n",
+ "68 0.032462 0.0322 4.827130 0.576 eschColi_K12 \n",
+ "67 0.000000 0.0000 2.106148 0.456 phiX \n",
+ "61 0.888462 0.8861 14.623352 1.809 dm3 \n",
+ "101 0.888462 0.8861 9.079349 1.809 dm3 \n",
+ "13 0.000000 0.0000 1.127904 0.456 phiX \n",
+ "118 0.888462 0.8861 3.006585 1.809 dm3 \n",
+ "117 0.032462 0.0322 0.851774 0.576 eschColi_K12 \n",
+ "\n",
+ " filter_fqscr sample_facs \\\n",
+ "44 dm3 simngs_dm3_100.fastq \n",
+ "108 dm3 simngs_dm3_100.fastq \n",
+ "106 phiX simngs_dm3_100.fastq \n",
+ "105 eschColi_K12 simngs_dm3_100.fastq \n",
+ "47 dm3 simngs_dm3_100.fastq \n",
+ "33 eschColi_K12 simngs_dm3_100.fastq \n",
+ "34 phiX simngs_dm3_100.fastq \n",
+ "11 eschColi_K12 simngs_dm3_100.fastq \n",
+ "114 phiX simngs_dm3_100.fastq \n",
+ "92 eschColi_K12 simngs_dm3_1000.fastq \n",
+ "91 phiX simngs_dm3_1000.fastq \n",
+ "85 dm3 simngs_dm3_1000.fastq \n",
+ "87 phiX simngs_dm3_1000.fastq \n",
+ "88 eschColi_K12 simngs_dm3_1000.fastq \n",
+ "27 dm3 simngs_dm3_1000.fastq \n",
+ "29 phiX simngs_dm3_1000.fastq \n",
+ "30 eschColi_K12 simngs_dm3_1000.fastq \n",
+ "115 dm3 simngs_dm3_1000.fastq \n",
+ "82 phiX simngs_dm3_1000000.fastq \n",
+ "24 dm3 simngs_dm3_1000000.fastq \n",
+ "23 eschColi_K12 simngs_dm3_1000000.fastq \n",
+ "89 eschColi_K12 simngs_dm3_1000000.fastq \n",
+ "96 phiX simngs_dm3_1000000.fastq \n",
+ "94 dm3 simngs_dm3_1000000.fastq \n",
+ "93 eschColi_K12 simngs_dm3_1000000.fastq \n",
+ "90 dm3 simngs_dm3_1000000.fastq \n",
+ "26 phiX simngs_dm3_1000000.fastq \n",
+ "123 eschColi_K12 simngs_dm3_10000000.fastq \n",
+ "8 eschColi_K12 simngs_dm3_10000000.fastq \n",
+ "9 phiX simngs_dm3_10000000.fastq \n",
+ "35 dm3 simngs_dm3_10000000.fastq \n",
+ "37 phiX simngs_dm3_10000000.fastq \n",
+ "38 eschColi_K12 simngs_dm3_10000000.fastq \n",
+ "78 dm3 simngs_dm3_10000000.fastq \n",
+ "122 phiX simngs_dm3_10000000.fastq \n",
+ "3 dm3 simngs_dm3_10000000.fastq \n",
+ "84 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "80 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "103 dm3 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "104 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "48 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "28 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "12 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "86 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "83 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "116 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "20 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "68 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "67 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "61 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "101 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "13 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "118 dm3 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "117 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "\n",
+ " sample_fqscr threads_facs \\\n",
+ "44 simngs_dm3_100.fastq 16 \n",
+ "108 simngs_dm3_100.fastq 16 \n",
+ "106 simngs_dm3_100.fastq 16 \n",
+ "105 simngs_dm3_100.fastq 16 \n",
+ "47 simngs_dm3_100.fastq 16 \n",
+ "33 simngs_dm3_100.fastq 16 \n",
+ "34 simngs_dm3_100.fastq 16 \n",
+ "11 simngs_dm3_100.fastq 16 \n",
+ "114 simngs_dm3_100.fastq 16 \n",
+ "92 simngs_dm3_1000.fastq 16 \n",
+ "91 simngs_dm3_1000.fastq 16 \n",
+ "85 simngs_dm3_1000.fastq 16 \n",
+ "87 simngs_dm3_1000.fastq 16 \n",
+ "88 simngs_dm3_1000.fastq 16 \n",
+ "27 simngs_dm3_1000.fastq 16 \n",
+ "29 simngs_dm3_1000.fastq 16 \n",
+ "30 simngs_dm3_1000.fastq 16 \n",
+ "115 simngs_dm3_1000.fastq 16 \n",
+ "82 simngs_dm3_1000000.fastq 16 \n",
+ "24 simngs_dm3_1000000.fastq 16 \n",
+ "23 simngs_dm3_1000000.fastq 16 \n",
+ "89 simngs_dm3_1000000.fastq 16 \n",
+ "96 simngs_dm3_1000000.fastq 16 \n",
+ "94 simngs_dm3_1000000.fastq 16 \n",
+ "93 simngs_dm3_1000000.fastq 16 \n",
+ "90 simngs_dm3_1000000.fastq 16 \n",
+ "26 simngs_dm3_1000000.fastq 16 \n",
+ "123 simngs_dm3_10000000.fastq 16 \n",
+ "8 simngs_dm3_10000000.fastq 16 \n",
+ "9 simngs_dm3_10000000.fastq 16 \n",
+ "35 simngs_dm3_10000000.fastq 16 \n",
+ "37 simngs_dm3_10000000.fastq 16 \n",
+ "38 simngs_dm3_10000000.fastq 16 \n",
+ "78 simngs_dm3_10000000.fastq 16 \n",
+ "122 simngs_dm3_10000000.fastq 16 \n",
+ "3 simngs_dm3_10000000.fastq 16 \n",
+ "84 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "80 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "103 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "104 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "48 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "28 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "86 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "83 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "116 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "20 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "68 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "67 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "61 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "101 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "13 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "118 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "117 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "\n",
+ " threads_fqscr reads \n",
+ "44 16 100 \n",
+ "108 8 100 \n",
+ "106 8 100 \n",
+ "105 8 100 \n",
+ "47 1 100 \n",
+ "33 16 100 \n",
+ "34 16 100 \n",
+ "11 1 100 \n",
+ "114 1 100 \n",
+ "92 1 1000 \n",
+ "91 1 1000 \n",
+ "85 16 1000 \n",
+ "87 16 1000 \n",
+ "88 16 1000 \n",
+ "27 8 1000 \n",
+ "29 8 1000 \n",
+ "30 8 1000 \n",
+ "115 1 1000 \n",
+ "82 16 1000000 \n",
+ "24 8 1000000 \n",
+ "23 8 1000000 \n",
+ "89 16 1000000 \n",
+ "96 1 1000000 \n",
+ "94 1 1000000 \n",
+ "93 1 1000000 \n",
+ "90 16 1000000 \n",
+ "26 8 1000000 \n",
+ "123 8 10000000 \n",
+ "8 1 10000000 \n",
+ "9 1 10000000 \n",
+ "35 16 10000000 \n",
+ "37 16 10000000 \n",
+ "38 16 10000000 \n",
+ "78 8 10000000 \n",
+ "122 8 10000000 \n",
+ "3 1 10000000 \n",
+ "84 16 3000vs6000 \n",
+ "80 1 3000vs6000 \n",
+ "103 8 3000vs6000 \n",
+ "104 8 3000vs6000 \n",
+ "48 1 3000vs6000 \n",
+ "28 8 3000vs6000 \n",
+ "12 1 3000vs6000 \n",
+ "86 16 3000vs6000 \n",
+ "83 16 3000vs6000 \n",
+ "116 8 3000vs93000 \n",
+ "20 16 3000vs93000 \n",
+ "68 1 3000vs93000 \n",
+ "67 1 3000vs93000 \n",
+ "61 1 3000vs93000 \n",
+ "101 16 3000vs93000 \n",
+ "13 16 3000vs93000 \n",
+ "118 8 3000vs93000 \n",
+ "117 8 3000vs93000 \n",
+ "\n",
+ "[54 rows x 11 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 16
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "runtimes_dm3 = dm3.loc[:,['reads', 'delta_fqscr', 'delta_facs']]"
+ ],
+ "language": "python",
+ "metadata": {},
"outputs": [],
- "prompt_number": 89
+ "prompt_number": 17
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "runtimes_dm3.sort('reads')"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>reads</th>\n",
+ " <th>delta_fqscr</th>\n",
+ " <th>delta_facs</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>44 </th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.673775</td>\n",
+ " <td> 0.774</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>108</th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.628752</td>\n",
+ " <td> 0.774</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>106</th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.231580</td>\n",
+ " <td> 0.016</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>105</th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.226763</td>\n",
+ " <td> 0.539</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47 </th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.628286</td>\n",
+ " <td> 0.774</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33 </th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.210950</td>\n",
+ " <td> 0.539</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34 </th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.227650</td>\n",
+ " <td> 0.016</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11 </th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.211287</td>\n",
+ " <td> 0.539</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>114</th>\n",
+ " <td> 100</td>\n",
+ " <td> 0.225330</td>\n",
+ " <td> 0.016</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>92 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.212446</td>\n",
+ " <td> 0.027</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>91 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.210926</td>\n",
+ " <td> 0.021</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>85 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.658177</td>\n",
+ " <td> 0.703</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>87 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.212351</td>\n",
+ " <td> 0.021</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>88 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.210869</td>\n",
+ " <td> 0.027</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.632960</td>\n",
+ " <td> 0.703</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.227470</td>\n",
+ " <td> 0.021</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30 </th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.215520</td>\n",
+ " <td> 0.027</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>115</th>\n",
+ " <td> 1000</td>\n",
+ " <td> 0.631765</td>\n",
+ " <td> 0.703</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>82 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 10.207863</td>\n",
+ " <td> 1.789</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 40.650027</td>\n",
+ " <td> 9.936</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 7.338175</td>\n",
+ " <td> 2.138</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>89 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 6.463202</td>\n",
+ " <td> 2.138</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 16.781235</td>\n",
+ " <td> 1.789</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>94 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 152.819053</td>\n",
+ " <td> 9.936</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 47.703680</td>\n",
+ " <td> 2.138</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>90 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 95.274927</td>\n",
+ " <td> 9.936</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26 </th>\n",
+ " <td> 1000000</td>\n",
+ " <td> 6.060514</td>\n",
+ " <td> 1.789</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>123</th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 70.471625</td>\n",
+ " <td> 13.373</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 494.496125</td>\n",
+ " <td> 13.373</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 168.785631</td>\n",
+ " <td> 9.916</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 940.853700</td>\n",
+ " <td> 101.781</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 101.717528</td>\n",
+ " <td> 9.916</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 71.551788</td>\n",
+ " <td> 13.373</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>78 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 301.252240</td>\n",
+ " <td> 101.781</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>122</th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 57.989411</td>\n",
+ " <td> 9.916</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3 </th>\n",
+ " <td> 10000000</td>\n",
+ " <td> 1747.636472</td>\n",
+ " <td> 101.781</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>84 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 1.091974</td>\n",
+ " <td> 1.421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>80 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 1.733639</td>\n",
+ " <td> 1.421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>103</th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.845511</td>\n",
+ " <td> 1.421</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>104</th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.434019</td>\n",
+ " <td> 0.070</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.835371</td>\n",
+ " <td> 0.070</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.237581</td>\n",
+ " <td> 0.020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.444665</td>\n",
+ " <td> 0.020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>86 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.223083</td>\n",
+ " <td> 0.020</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>83 </th>\n",
+ " <td> 3000vs6000</td>\n",
+ " <td> 0.461494</td>\n",
+ " <td> 0.070</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>116</th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 0.855492</td>\n",
+ " <td> 0.456</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20 </th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 1.096185</td>\n",
+ " <td> 0.576</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>68 </th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 4.827130</td>\n",
+ " <td> 0.576</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>67 </th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 2.106148</td>\n",
+ " <td> 0.456</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>61 </th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 14.623352</td>\n",
+ " <td> 1.809</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>101</th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 9.079349</td>\n",
+ " <td> 1.809</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13 </th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 1.127904</td>\n",
+ " <td> 0.456</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>118</th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 3.006585</td>\n",
+ " <td> 1.809</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>117</th>\n",
+ " <td> 3000vs93000</td>\n",
+ " <td> 0.851774</td>\n",
+ " <td> 0.576</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>54 rows \u00d7 3 columns</p>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 18,
+ "text": [
+ " reads delta_fqscr delta_facs\n",
+ "44 100 0.673775 0.774\n",
+ "108 100 0.628752 0.774\n",
+ "106 100 0.231580 0.016\n",
+ "105 100 0.226763 0.539\n",
+ "47 100 0.628286 0.774\n",
+ "33 100 0.210950 0.539\n",
+ "34 100 0.227650 0.016\n",
+ "11 100 0.211287 0.539\n",
+ "114 100 0.225330 0.016\n",
+ "92 1000 0.212446 0.027\n",
+ "91 1000 0.210926 0.021\n",
+ "85 1000 0.658177 0.703\n",
+ "87 1000 0.212351 0.021\n",
+ "88 1000 0.210869 0.027\n",
+ "27 1000 0.632960 0.703\n",
+ "29 1000 0.227470 0.021\n",
+ "30 1000 0.215520 0.027\n",
+ "115 1000 0.631765 0.703\n",
+ "82 1000000 10.207863 1.789\n",
+ "24 1000000 40.650027 9.936\n",
+ "23 1000000 7.338175 2.138\n",
+ "89 1000000 6.463202 2.138\n",
+ "96 1000000 16.781235 1.789\n",
+ "94 1000000 152.819053 9.936\n",
+ "93 1000000 47.703680 2.138\n",
+ "90 1000000 95.274927 9.936\n",
+ "26 1000000 6.060514 1.789\n",
+ "123 10000000 70.471625 13.373\n",
+ "8 10000000 494.496125 13.373\n",
+ "9 10000000 168.785631 9.916\n",
+ "35 10000000 940.853700 101.781\n",
+ "37 10000000 101.717528 9.916\n",
+ "38 10000000 71.551788 13.373\n",
+ "78 10000000 301.252240 101.781\n",
+ "122 10000000 57.989411 9.916\n",
+ "3 10000000 1747.636472 101.781\n",
+ "84 3000vs6000 1.091974 1.421\n",
+ "80 3000vs6000 1.733639 1.421\n",
+ "103 3000vs6000 0.845511 1.421\n",
+ "104 3000vs6000 0.434019 0.070\n",
+ "48 3000vs6000 0.835371 0.070\n",
+ "28 3000vs6000 0.237581 0.020\n",
+ "12 3000vs6000 0.444665 0.020\n",
+ "86 3000vs6000 0.223083 0.020\n",
+ "83 3000vs6000 0.461494 0.070\n",
+ "116 3000vs93000 0.855492 0.456\n",
+ "20 3000vs93000 1.096185 0.576\n",
+ "68 3000vs93000 4.827130 0.576\n",
+ "67 3000vs93000 2.106148 0.456\n",
+ "61 3000vs93000 14.623352 1.809\n",
+ "101 3000vs93000 9.079349 1.809\n",
+ "13 3000vs93000 1.127904 0.456\n",
+ "118 3000vs93000 3.006585 1.809\n",
+ "117 3000vs93000 0.851774 0.576\n",
+ "\n",
+ "[54 rows x 3 columns]"
+ ]
+ }
+ ],
+ "prompt_number": 18
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "runtimes_dm3.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads']).plot(title='eschColi_K12 FACS vs Fastq_screen runtimes (log scale)', kind='barh', logy=True, logx=True)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 19,
+ "text": [
+ "<matplotlib.axes.AxesSubplot at 0x10b791a10>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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9pq2srADUvReuX78uLLv3/5o0lcnGxgbp6emN7p+VlYVXX30VX3zxBQoLC1FUVAQ3NzeN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\nAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x10b791fd0>"
+ ]
+ }
+ ],
+ "prompt_number": 19
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "runtimes_eschColi = eschColi.loc[:,['reads', 'delta_fqscr', 'delta_facs']]\n",
+ "runtimes_eschColi.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads']).plot(title='eschColi_K12 FACS vs Fastq_screen runtimes (log scale)', kind='barh', logy=True, logx=True)\n",
+ "\n",
+ "runtimes_phiX = phiX.loc[:,['reads', 'delta_fqscr', 'delta_facs']]\n",
+ "runtimes_phiX.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads']).plot(title ='phiX FACS vs Fastq_screen runtimes (log scale)', kind='barh', logy=True, logx=True)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 20,
+ "text": [
+ "<matplotlib.axes.AxesSubplot at 0x10b79db10>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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gX79+mDJlCsaOHYuxY8cCAJYsWYIFCxbA29sbHh4e8Pb2Fr516Orq1ssZGxvb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\nLzFjxgzcu3cPI0eOxIgRI6R8WIwxxv6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+ "text": [
+ "<matplotlib.figure.Figure at 0x10b5f8410>"
+ ]
+ },
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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49957sW7duhrbJyIiK7s59dI8YWFh4ubmJg4ODuLh4SHLli2T3NxcCQkJEV9f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\npGksVEREpGksVEREpGksVEREpGn/D+IpW/NYnHlTAAAAAElFTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x10b59cc50>"
+ ]
+ }
+ ],
+ "prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "# Slicing main dataframe into individual organism dataframes\n",
- "\n",
- "dm3 = frame['sample_fqscr'].map(lambda x: 'dm3' in x)\n",
- "eschColi = frame['sample_fqscr'].map(lambda x: 'eschColi_K12' in x)\n",
- "phiX = frame['sample_fqscr'].map(lambda x: 'phiX' in x)\n",
+ "#frame.mem_facs[0], frame.mem_fqscr[0]\n",
+ "#frame.loc[:,['mem_facs', 'mem_fqscr']]\n",
+ "#[avg for avg in frame['mem_facs']]\n",
"\n",
- "dm3 = frame[dm3]\n",
- "eschColi = frame[eschColi]\n",
- "phiX = frame[phiX]"
+ "#mem_usage = DataFrame(data = {'mem_facs': [[1,2], [8,9]],\n",
+ "#'mem_fqscr': [[3,4], [10,12]]})\n",
+ "#mem_usage_mean = mem_usage.applymap(lambda x: sum(x)/len(x))\n",
+ "#mem_usage_mean.plot()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'DataFrame' object has no attribute 'mem_facs'",
+ "output_type": "pyerr",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-21-2b2679d7b8ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmem_facs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmem_fqscr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#frame.loc[:,['mem_facs', 'mem_fqscr']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#[avg for avg in frame['mem_facs']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#mem_usage = DataFrame(data = {'mem_facs': [[1,2], [8,9]],\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m/Users/roman/.virtualenvs/facs/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1600\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1601\u001b[0m raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[0;32m-> 1602\u001b[0;31m (type(self).__name__, name))\n\u001b[0m\u001b[1;32m 1603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1604\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'mem_facs'"
+ ]
+ }
+ ],
+ "prompt_number": 21
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "frame"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>contam_facs</th>\n",
+ " <th>contam_fqscr</th>\n",
+ " <th>delta_fqscr</th>\n",
+ " <th>delta_facs</th>\n",
+ " <th>filter_facs</th>\n",
+ " <th>filter_fqscr</th>\n",
+ " <th>sample_facs</th>\n",
+ " <th>sample_fqscr</th>\n",
+ " <th>threads_facs</th>\n",
+ " <th>threads_fqscr</th>\n",
+ " <th>reads</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0 </th>\n",
+ " <td> 0.000071</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 995.512687</td>\n",
+ " <td> 10.710</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 480.008990</td>\n",
+ " <td> 13.787</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2 </th>\n",
+ " <td> 0.995249</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 710.577691</td>\n",
+ " <td> 32.584</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3 </th>\n",
+ " <td> 0.917971</td>\n",
+ " <td> 0.9153</td>\n",
+ " <td> 1747.636472</td>\n",
+ " <td> 101.781</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 162.226401</td>\n",
+ " <td> 10.476</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 2.863820</td>\n",
+ " <td> 1.313</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6 </th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.225909</td>\n",
+ " <td> 0.058</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7 </th>\n",
+ " <td> 0.998000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.452245</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8 </th>\n",
+ " <td> 0.000240</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 494.496125</td>\n",
+ " <td> 13.373</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9 </th>\n",
+ " <td> 0.000004</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 168.785631</td>\n",
+ " <td> 9.916</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td> 0.003942</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 98.408732</td>\n",
+ " <td> 3.251</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.211287</td>\n",
+ " <td> 0.539</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.444665</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 1.127904</td>\n",
+ " <td> 0.456</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td> 0.000066</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 13.093603</td>\n",
+ " <td> 2.012</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 7.200159</td>\n",
+ " <td> 1.846</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td> 0.995194</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 84.579529</td>\n",
+ " <td> 4.066</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.681134</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.439895</td>\n",
+ " <td> 0.013</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.213116</td>\n",
+ " <td> 0.038</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td> 0.032462</td>\n",
+ " <td> 0.0322</td>\n",
+ " <td> 1.096185</td>\n",
+ " <td> 0.576</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 3000vs93000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.220144</td>\n",
+ " <td> 0.030</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td> 0.999000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.218054</td>\n",
+ " <td> 0.035</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td> 0.000261</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 7.338175</td>\n",
+ " <td> 2.138</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td> 0.917631</td>\n",
+ " <td> 0.9149</td>\n",
+ " <td> 40.650027</td>\n",
+ " <td> 9.936</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td> 0.002000</td>\n",
+ " <td> 0.0020</td>\n",
+ " <td> 0.838629</td>\n",
+ " <td> 1.092</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td> 0.000006</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 6.060514</td>\n",
+ " <td> 1.789</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> simngs_dm3_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td> 0.917000</td>\n",
+ " <td> 0.9210</td>\n",
+ " <td> 0.632960</td>\n",
+ " <td> 0.703</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.237581</td>\n",
+ " <td> 0.020</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.227470</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.215520</td>\n",
+ " <td> 0.027</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> simngs_dm3_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.220209</td>\n",
+ " <td> 0.021</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.674059</td>\n",
+ " <td> 0.921</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> simngs_eschColi_K12_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.210950</td>\n",
+ " <td> 0.539</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.227650</td>\n",
+ " <td> 0.016</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td> 0.917971</td>\n",
+ " <td> 0.9153</td>\n",
+ " <td> 940.853700</td>\n",
+ " <td> 101.781</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td> 0.995249</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 920.436917</td>\n",
+ " <td> 32.584</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td> 0.000004</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 101.717528</td>\n",
+ " <td> 9.916</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td> 0.000240</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 71.551788</td>\n",
+ " <td> 13.373</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> simngs_dm3_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.212676</td>\n",
+ " <td> 0.013</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td> 1.000000</td>\n",
+ " <td> 1.0000</td>\n",
+ " <td> 0.431265</td>\n",
+ " <td> 0.038</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td> 0.995194</td>\n",
+ " <td> 0.9958</td>\n",
+ " <td> 23.491512</td>\n",
+ " <td> 4.066</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> simngs_phiX_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.420386</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 8</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 10.272867</td>\n",
+ " <td> 1.828</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td> 0.920000</td>\n",
+ " <td> 0.9100</td>\n",
+ " <td> 0.673775</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td> 0.003942</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 11.681972</td>\n",
+ " <td> 3.251</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td> 0.995472</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 86.200919</td>\n",
+ " <td> 5.319</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>47</th>\n",
+ " <td> 0.920000</td>\n",
+ " <td> 0.9100</td>\n",
+ " <td> 0.628286</td>\n",
+ " <td> 0.774</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> simngs_dm3_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>48</th>\n",
+ " <td> 0.333000</td>\n",
+ " <td> 0.3325</td>\n",
+ " <td> 0.835371</td>\n",
+ " <td> 0.070</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 3000vs6000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>49</th>\n",
+ " <td> 0.000071</td>\n",
+ " <td> 0.0001</td>\n",
+ " <td> 112.764168</td>\n",
+ " <td> 10.710</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 67.589326</td>\n",
+ " <td> 13.787</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> simngs_phiX_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>51</th>\n",
+ " <td> 0.003922</td>\n",
+ " <td> 0.0032</td>\n",
+ " <td> 114.119080</td>\n",
+ " <td> 12.478</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52</th>\n",
+ " <td> 0.995490</td>\n",
+ " <td> 0.9959</td>\n",
+ " <td> 848.533965</td>\n",
+ " <td> 41.880</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>53</th>\n",
+ " <td> 0.999000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.218013</td>\n",
+ " <td> 0.035</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>54</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.223678</td>\n",
+ " <td> 0.030</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>55</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.217786</td>\n",
+ " <td> 0.058</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> eschColi_K12</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>56</th>\n",
+ " <td> 0.998000</td>\n",
+ " <td> 0.9990</td>\n",
+ " <td> 0.217264</td>\n",
+ " <td> 0.017</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>57</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 97.366328</td>\n",
+ " <td> 10.476</td>\n",
+ " <td> phiX</td>\n",
+ " <td> phiX</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> simngs_eschColi_K12_10000000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 10000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>58</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.854444</td>\n",
+ " <td> 1.313</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> simngs_phiX_1000.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>59</th>\n",
+ " <td> 0.000000</td>\n",
+ " <td> 0.0000</td>\n",
+ " <td> 0.638649</td>\n",
+ " <td> 1.017</td>\n",
+ " <td> dm3</td>\n",
+ " <td> dm3</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> simngs_phiX_100.fastq</td>\n",
+ " <td> 16</td>\n",
+ " <td> 1</td>\n",
+ " <td> 100</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th></th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>126 rows \u00d7 11 columns</p>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 22,
+ "text": [
+ " contam_facs contam_fqscr delta_fqscr delta_facs filter_facs \\\n",
+ "0 0.000071 0.0001 995.512687 10.710 dm3 \n",
+ "1 0.000000 0.0000 480.008990 13.787 eschColi_K12 \n",
+ "2 0.995249 0.9958 710.577691 32.584 phiX \n",
+ "3 0.917971 0.9153 1747.636472 101.781 dm3 \n",
+ "4 0.000000 0.0000 162.226401 10.476 phiX \n",
+ "5 0.000000 0.0000 2.863820 1.313 dm3 \n",
+ "6 0.000000 0.0000 0.225909 0.058 eschColi_K12 \n",
+ "7 0.998000 0.9990 0.452245 0.017 phiX \n",
+ "8 0.000240 0.0001 494.496125 13.373 eschColi_K12 \n",
+ "9 0.000004 0.0000 168.785631 9.916 phiX \n",
+ "10 0.003942 0.0032 98.408732 3.251 dm3 \n",
+ "11 0.000000 0.0000 0.211287 0.539 eschColi_K12 \n",
+ "12 0.000000 0.0000 0.444665 0.020 phiX \n",
+ "13 0.000000 0.0000 1.127904 0.456 phiX \n",
+ "14 0.000066 0.0001 13.093603 2.012 dm3 \n",
+ "15 0.000000 0.0000 7.200159 1.846 eschColi_K12 \n",
+ "16 0.995194 0.9958 84.579529 4.066 phiX \n",
+ "17 0.000000 0.0000 0.681134 1.017 dm3 \n",
+ "18 0.000000 0.0000 0.439895 0.013 eschColi_K12 \n",
+ "19 1.000000 1.0000 0.213116 0.038 phiX \n",
+ "20 0.032462 0.0322 1.096185 0.576 eschColi_K12 \n",
+ "21 0.000000 0.0000 0.220144 0.030 phiX \n",
+ "22 0.999000 0.9990 0.218054 0.035 eschColi_K12 \n",
+ "23 0.000261 0.0001 7.338175 2.138 eschColi_K12 \n",
+ "24 0.917631 0.9149 40.650027 9.936 dm3 \n",
+ "25 0.002000 0.0020 0.838629 1.092 dm3 \n",
+ "26 0.000006 0.0000 6.060514 1.789 phiX \n",
+ "27 0.917000 0.9210 0.632960 0.703 dm3 \n",
+ "28 0.000000 0.0000 0.237581 0.020 phiX \n",
+ "29 0.000000 0.0000 0.227470 0.021 phiX \n",
+ "30 0.000000 0.0000 0.215520 0.027 eschColi_K12 \n",
+ "31 1.000000 1.0000 0.220209 0.021 eschColi_K12 \n",
+ "32 0.000000 0.0000 0.674059 0.921 dm3 \n",
+ "33 0.000000 0.0000 0.210950 0.539 eschColi_K12 \n",
+ "34 0.000000 0.0000 0.227650 0.016 phiX \n",
+ "35 0.917971 0.9153 940.853700 101.781 dm3 \n",
+ "36 0.995249 0.9958 920.436917 32.584 phiX \n",
+ "37 0.000004 0.0000 101.717528 9.916 phiX \n",
+ "38 0.000240 0.0001 71.551788 13.373 eschColi_K12 \n",
+ "39 0.000000 0.0000 0.212676 0.013 eschColi_K12 \n",
+ "40 1.000000 1.0000 0.431265 0.038 phiX \n",
+ "41 0.995194 0.9958 23.491512 4.066 phiX \n",
+ "42 0.000000 0.0000 0.420386 1.017 dm3 \n",
+ "43 0.000000 0.0000 10.272867 1.828 phiX \n",
+ "44 0.920000 0.9100 0.673775 0.774 dm3 \n",
+ "45 0.003942 0.0032 11.681972 3.251 dm3 \n",
+ "46 0.995472 0.9959 86.200919 5.319 eschColi_K12 \n",
+ "47 0.920000 0.9100 0.628286 0.774 dm3 \n",
+ "48 0.333000 0.3325 0.835371 0.070 eschColi_K12 \n",
+ "49 0.000071 0.0001 112.764168 10.710 dm3 \n",
+ "50 0.000000 0.0000 67.589326 13.787 eschColi_K12 \n",
+ "51 0.003922 0.0032 114.119080 12.478 dm3 \n",
+ "52 0.995490 0.9959 848.533965 41.880 eschColi_K12 \n",
+ "53 0.999000 0.9990 0.218013 0.035 eschColi_K12 \n",
+ "54 0.000000 0.0000 0.223678 0.030 phiX \n",
+ "55 0.000000 0.0000 0.217786 0.058 eschColi_K12 \n",
+ "56 0.998000 0.9990 0.217264 0.017 phiX \n",
+ "57 0.000000 0.0000 97.366328 10.476 phiX \n",
+ "58 0.000000 0.0000 0.854444 1.313 dm3 \n",
+ "59 0.000000 0.0000 0.638649 1.017 dm3 \n",
+ " ... ... ... ... ... \n",
+ "\n",
+ " filter_fqscr sample_facs \\\n",
+ "0 dm3 simngs_phiX_10000000.fastq \n",
+ "1 eschColi_K12 simngs_phiX_10000000.fastq \n",
+ "2 phiX simngs_phiX_10000000.fastq \n",
+ "3 dm3 simngs_dm3_10000000.fastq \n",
+ "4 phiX simngs_eschColi_K12_10000000.fastq \n",
+ "5 dm3 simngs_phiX_1000.fastq \n",
+ "6 eschColi_K12 simngs_phiX_1000.fastq \n",
+ "7 phiX simngs_phiX_1000.fastq \n",
+ "8 eschColi_K12 simngs_dm3_10000000.fastq \n",
+ "9 phiX simngs_dm3_10000000.fastq \n",
+ "10 dm3 simngs_eschColi_K12_1000000.fastq \n",
+ "11 eschColi_K12 simngs_dm3_100.fastq \n",
+ "12 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "13 phiX simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "14 dm3 simngs_phiX_1000000.fastq \n",
+ "15 eschColi_K12 simngs_phiX_1000000.fastq \n",
+ "16 phiX simngs_phiX_1000000.fastq \n",
+ "17 dm3 simngs_phiX_100.fastq \n",
+ "18 eschColi_K12 simngs_phiX_100.fastq \n",
+ "19 phiX simngs_phiX_100.fastq \n",
+ "20 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq \n",
+ "21 phiX simngs_eschColi_K12_1000.fastq \n",
+ "22 eschColi_K12 simngs_eschColi_K12_1000.fastq \n",
+ "23 eschColi_K12 simngs_dm3_1000000.fastq \n",
+ "24 dm3 simngs_dm3_1000000.fastq \n",
+ "25 dm3 simngs_eschColi_K12_1000.fastq \n",
+ "26 phiX simngs_dm3_1000000.fastq \n",
+ "27 dm3 simngs_dm3_1000.fastq \n",
+ "28 phiX simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "29 phiX simngs_dm3_1000.fastq \n",
+ "30 eschColi_K12 simngs_dm3_1000.fastq \n",
+ "31 eschColi_K12 simngs_eschColi_K12_100.fastq \n",
+ "32 dm3 simngs_eschColi_K12_100.fastq \n",
+ "33 eschColi_K12 simngs_dm3_100.fastq \n",
+ "34 phiX simngs_dm3_100.fastq \n",
+ "35 dm3 simngs_dm3_10000000.fastq \n",
+ "36 phiX simngs_phiX_10000000.fastq \n",
+ "37 phiX simngs_dm3_10000000.fastq \n",
+ "38 eschColi_K12 simngs_dm3_10000000.fastq \n",
+ "39 eschColi_K12 simngs_phiX_100.fastq \n",
+ "40 phiX simngs_phiX_100.fastq \n",
+ "41 phiX simngs_phiX_1000000.fastq \n",
+ "42 dm3 simngs_phiX_100.fastq \n",
+ "43 phiX simngs_eschColi_K12_1000000.fastq \n",
+ "44 dm3 simngs_dm3_100.fastq \n",
+ "45 dm3 simngs_eschColi_K12_1000000.fastq \n",
+ "46 eschColi_K12 simngs_eschColi_K12_1000000.fastq \n",
+ "47 dm3 simngs_dm3_100.fastq \n",
+ "48 eschColi_K12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq \n",
+ "49 dm3 simngs_phiX_10000000.fastq \n",
+ "50 eschColi_K12 simngs_phiX_10000000.fastq \n",
+ "51 dm3 simngs_eschColi_K12_10000000.fastq \n",
+ "52 eschColi_K12 simngs_eschColi_K12_10000000.fastq \n",
+ "53 eschColi_K12 simngs_eschColi_K12_1000.fastq \n",
+ "54 phiX simngs_eschColi_K12_1000.fastq \n",
+ "55 eschColi_K12 simngs_phiX_1000.fastq \n",
+ "56 phiX simngs_phiX_1000.fastq \n",
+ "57 phiX simngs_eschColi_K12_10000000.fastq \n",
+ "58 dm3 simngs_phiX_1000.fastq \n",
+ "59 dm3 simngs_phiX_100.fastq \n",
+ " ... ... \n",
+ "\n",
+ " sample_fqscr threads_facs \\\n",
+ "0 simngs_phiX_10000000.fastq 16 \n",
+ "1 simngs_phiX_10000000.fastq 16 \n",
+ "2 simngs_phiX_10000000.fastq 16 \n",
+ "3 simngs_dm3_10000000.fastq 16 \n",
+ "4 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "5 simngs_phiX_1000.fastq 16 \n",
+ "6 simngs_phiX_1000.fastq 16 \n",
+ "7 simngs_phiX_1000.fastq 16 \n",
+ "8 simngs_dm3_10000000.fastq 16 \n",
+ "9 simngs_dm3_10000000.fastq 16 \n",
+ "10 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "11 simngs_dm3_100.fastq 16 \n",
+ "12 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "13 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "14 simngs_phiX_1000000.fastq 16 \n",
+ "15 simngs_phiX_1000000.fastq 16 \n",
+ "16 simngs_phiX_1000000.fastq 16 \n",
+ "17 simngs_phiX_100.fastq 16 \n",
+ "18 simngs_phiX_100.fastq 16 \n",
+ "19 simngs_phiX_100.fastq 16 \n",
+ "20 simngs.mixed_eschColi_K12_dm3_3000vs93000.fastq 16 \n",
+ "21 simngs_eschColi_K12_1000.fastq 16 \n",
+ "22 simngs_eschColi_K12_1000.fastq 16 \n",
+ "23 simngs_dm3_1000000.fastq 16 \n",
+ "24 simngs_dm3_1000000.fastq 16 \n",
+ "25 simngs_eschColi_K12_1000.fastq 16 \n",
+ "26 simngs_dm3_1000000.fastq 16 \n",
+ "27 simngs_dm3_1000.fastq 16 \n",
+ "28 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "29 simngs_dm3_1000.fastq 16 \n",
+ "30 simngs_dm3_1000.fastq 16 \n",
+ "31 simngs_eschColi_K12_100.fastq 16 \n",
+ "32 simngs_eschColi_K12_100.fastq 16 \n",
+ "33 simngs_dm3_100.fastq 16 \n",
+ "34 simngs_dm3_100.fastq 16 \n",
+ "35 simngs_dm3_10000000.fastq 16 \n",
+ "36 simngs_phiX_10000000.fastq 16 \n",
+ "37 simngs_dm3_10000000.fastq 16 \n",
+ "38 simngs_dm3_10000000.fastq 16 \n",
+ "39 simngs_phiX_100.fastq 16 \n",
+ "40 simngs_phiX_100.fastq 16 \n",
+ "41 simngs_phiX_1000000.fastq 16 \n",
+ "42 simngs_phiX_100.fastq 16 \n",
+ "43 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "44 simngs_dm3_100.fastq 16 \n",
+ "45 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "46 simngs_eschColi_K12_1000000.fastq 16 \n",
+ "47 simngs_dm3_100.fastq 16 \n",
+ "48 simngs.mixed_eschColi_K12_dm3_3000vs6000.fastq 16 \n",
+ "49 simngs_phiX_10000000.fastq 16 \n",
+ "50 simngs_phiX_10000000.fastq 16 \n",
+ "51 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "52 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "53 simngs_eschColi_K12_1000.fastq 16 \n",
+ "54 simngs_eschColi_K12_1000.fastq 16 \n",
+ "55 simngs_phiX_1000.fastq 16 \n",
+ "56 simngs_phiX_1000.fastq 16 \n",
+ "57 simngs_eschColi_K12_10000000.fastq 16 \n",
+ "58 simngs_phiX_1000.fastq 16 \n",
+ "59 simngs_phiX_100.fastq 16 \n",
+ " ... ... \n",
+ "\n",
+ " threads_fqscr reads \n",
+ "0 1 10000000 \n",
+ "1 1 10000000 \n",
+ "2 1 10000000 \n",
+ "3 1 10000000 \n",
+ "4 1 10000000 \n",
+ "5 1 1000 \n",
+ "6 1 1000 \n",
+ "7 1 1000 \n",
+ "8 1 10000000 \n",
+ "9 1 10000000 \n",
+ "10 1 1000000 \n",
+ "11 1 100 \n",
+ "12 1 3000vs6000 \n",
+ "13 16 3000vs93000 \n",
+ "14 16 1000000 \n",
+ "15 16 1000000 \n",
+ "16 16 1000000 \n",
+ "17 16 100 \n",
+ "18 16 100 \n",
+ "19 16 100 \n",
+ "20 16 3000vs93000 \n",
+ "21 8 1000 \n",
+ "22 8 1000 \n",
+ "23 8 1000000 \n",
+ "24 8 1000000 \n",
+ "25 8 1000 \n",
+ "26 8 1000000 \n",
+ "27 8 1000 \n",
+ "28 8 3000vs6000 \n",
+ "29 8 1000 \n",
+ "30 8 1000 \n",
+ "31 16 100 \n",
+ "32 16 100 \n",
+ "33 16 100 \n",
+ "34 16 100 \n",
+ "35 16 10000000 \n",
+ "36 16 10000000 \n",
+ "37 16 10000000 \n",
+ "38 16 10000000 \n",
+ "39 8 100 \n",
+ "40 8 100 \n",
+ "41 8 1000000 \n",
+ "42 8 100 \n",
+ "43 16 1000000 \n",
+ "44 16 100 \n",
+ "45 16 1000000 \n",
+ "46 16 1000000 \n",
+ "47 1 100 \n",
+ "48 1 3000vs6000 \n",
+ "49 16 10000000 \n",
+ "50 16 10000000 \n",
+ "51 16 10000000 \n",
+ "52 16 10000000 \n",
+ "53 16 1000 \n",
+ "54 16 1000 \n",
+ "55 16 1000 \n",
+ "56 16 1000 \n",
+ "57 16 10000000 \n",
+ "58 16 1000 \n",
+ "59 1 100 \n",
+ " ... ... \n",
+ "\n",
+ "[126 rows x 11 columns]"
+ ]
+ }
],
- "language": "python",
- "metadata": {},
- "outputs": [],
- "prompt_number": 90
+ "prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
- "input": [
- "# Ready to plot\n",
- "\n",
- "runtimes_eschColi = eschColi.loc[:,['reads', 'delta_fqscr', 'delta_facs']]\n",
- "ecoli_table = runtimes_eschColi.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads'])\n",
- "ecoli_table.plot(title='eschColi_K12 FACS vs Fastq_screen runtimes (log scale)', kind='bar', logy=True, logx=True)\n",
- "\n",
- "runtimes_phiX = phiX.loc[:,['reads', 'delta_fqscr', 'delta_facs']]\n",
- "phiX_table = runtimes_phiX.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads'])\n",
- "phiX_table.plot(title ='phiX FACS vs Fastq_screen runtimes (log scale)', kind='bar', logy=True, logx=True)\n",
- "\n",
- "runtimes_dm3 = dm3.loc[:,['reads', 'delta_fqscr', 'delta_facs']]\n",
- "dm3_table = runtimes_dm3.pivot_table(values=['delta_fqscr', 'delta_facs'], rows=['reads'])\n",
- "dm3_table.plot(title='eschColi_K12 FACS vs Fastq_screen runtimes (log scale)', kind='bar', logy=True, logx=True)"
- ],
+ "input": [],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "output_type": "pyout",
- "prompt_number": 91,
- "text": [
- "<matplotlib.axes.AxesSubplot at 0xc14168c>"
- ]
- },
- {
- "output_type": "display_data",
- "png": 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obndGLtw589O3fGddQ6/P5f8t+/v7m1Q85rQsOXP7357Ns3w/8Wu1WsTGxgKA\nVC91qbclsnHjRuzcuROrVq0CAKxbtw7JycnYs2cP9u7dCwcHB+Tm5iIgIIAtEQvHx4zMQYtuibi5\nuSE5ORk3b96EEAK7du2Cu7s7xowZg7i4OABAXFwcQkJCGi1QshwcS2w45o50qbclMmDAAEydOhXe\n3t5o1aoVBg4ciJkzZ6K4uBihoaFYvXo11Go1EhISmiteIqIWi3M6UoPwMSNz0KJbIkREZDpYsPXg\njDOGYx/WcMwd6WIyBdtGYdO0U4QpbAyKizPOEJGpqPdDx+ZUfKW4SXtPxVGG/zpdS5pxpqKiotF+\nZ4Tf1DMcc0e6mMwZtqkw9Rlndu7cCTc3NygUCrzwwgvw8/PD6tWrAQAZGRnw8/ODQqFA586dMXHi\nRGl/x44dg0ajgb29PRwcHKQv60RFReHpp59GWFgYbG1tpeGaRGR6WLCrMfUZZy5duoRx48ZhyZIl\nuHz5Mnr37o2ff/5ZasUsWrQII0eORFFREXJycvDiiy8CqPqVv+HDh2PUqFHIzc1FRkYGAgMDpWNt\n3boV48ePx5UrVzB58uRGyyf7sIZj7kgXFuxqTH3GmR9++AEPPfQQnnrqKbRu3Rrz5s2rMXNMmzZt\nkJmZiZycHLRp0wZDhgwBUPXOoFu3bpg/fz7atGmDjh07wsfHR7rdkCFDEBwcDABo27btPWSMiJoT\nC3Y1pj7jzPnz59G9e/ca65ycnKT///Of/4QQAj4+PnjooYewZs0aAMC5c+fQq1evOvd79z4bC/uw\nhmPuSBcW7GpMfcaZbt264dy5c9KyEKLGskqlwhdffIGcnBz8+9//xqxZs3Dq1Ck4Ozvj9OnTOvfZ\n0FEwRGR8LNjVmPqMM6NHj8axY8fw3Xffoby8HDExMcjLy5O2b9q0CdnZ2QCqPqiUyWRo3bo1nnji\nCeTm5uKjjz7CrVu3UFxcjJSUFABNOwKGfVjDMXeki8kM65Pbyu9r6F1D9q/PnRlnZsyYgddffx2j\nRo3SOePMyZMn0a5dOwwdOlTnW9c7P0Nrb2+PXr164eDBg9KMM7du3cKYMWMMmnHG3t4emzZtwosv\nvohp06YhLCwMjz76qLT94MGDmD9/Pq5cuQKVSoWYmBjppxp37tyJuXPn4s0338QDDzyA+fPnw8fH\nh2fYRGaEvyVi5gICAhAWFobp06c36XH4mJE54G+JkMljISVqGViwjWzJkiWQy+W1LqNHj27wPky1\npcE+rOGoMb+UAAAbnklEQVSYO9LFZHrYLdXChQuxcOFCg2+/d+/eRoyGiEwZe9jUIHzMyBy0+B72\nn3/+CS8vL+lia2uLmJgYFBQUQKPRwNXVFUFBQSgqKmq0YImIqDa9Bbtv3744fPgwDh8+jN9++w3t\n27fH2LFjER0dDY1Gg/T0dAQGBiI6Oro54iUzwj6s4Zg70uWeeti7du2Ci4sLnJycsHXrVuzbtw8A\nEB4eDn9//wYXbaVSabIflJFu/C1uIuO7p4IdHx+PSZMmAQDy8/OhUqkAVH0lOj8/v8H7KSgouJfD\nkpni72EYjrkjXRpcsEtLS/Gf//wHy5Ytq7Wtvm/LRURESN+2UygU8PT0lJ6Md972cZnLXOZyYy1L\nztz+t2fzLN9P/FqtFrGxsQAg1UtdGjxKZMuWLfj888+xfft2AICbmxu0Wi0cHByQm5uLgIAApKWl\n1dy5BYws0Gq1UoLp3jB3hmPuDNPiR4ncsWHDBqkdAgDBwcHS7CRxcXEICQlphDCJiKguDTrDvn79\nOnr06IEzZ85ALq/6EaWCggKEhobi7NmzUKvVSEhIgEKhqLlzCzjDJiLzYeln2Eb54gwRUVOw9ILN\n3xLRo9YHGdRgzJ3hmDvShQWbiMhMsCVCRBaDLREiIjIJLNh6sJdoOObOcMwd6cKCTURkJtjDJiKL\nwR42ERGZBBZsPdhLNBxzZzjmjnRhwSYiMhPsYRORxWAPm4iITAILth7sJRqOuTMcc0e6sGATEZkJ\n9rCJyGKwh01ERCahQQW7qKgITz/9NPr16wd3d3ccOHAABQUF0Gg0cHV1RVBQEIqKipo6VqNgL9Fw\nzJ3hmDvSpUEFe+7cuRg1ahROnDiB33//HW5uboiOjoZGo0F6ejoCAwMRHR3d1LESEbVoenvYV65c\ngZeXF06fPl1jvZubG/bt2weVSoW8vDz4+/tb5KzpRGQ+WnwP+8yZM+jcuTOmTZuGgQMHYsaMGbh+\n/Try8/OhUqkAACqVCvn5+Y0WLBER1Wal7wrl5eU4dOgQPvnkEzz88MOYN29erfaHTCar+sumQ0RE\nBNRqNQBAoVDA09MT/v7+AP7XpzPl5dTUVMybN89k4jGn5eXLl5vd420qy9V72KYQjzktS87c/rdn\n8yzf7+MdGxsLAFK91EVvSyQvLw++vr44c6Yqup9++glLly7F6dOnsXfvXjg4OCA3NxcBAQEW2RLR\narVSguneMHeGY+4M0+JbIg4ODnByckJ6ejoAYNeuXXjwwQcxZswYxMXFAQDi4uIQEhLSaMGaEr5o\nDMfcGY65I10a9MWZI0eO4Nlnn0VpaSl69+6NNWvWoKKiAqGhoTh79izUajUSEhKgUChq7twCzrCJ\nyHxY+hk2v+moB9+aGo65MxxzZxhLL9j8piMRkZngGTYRWQyeYRMRkUlgwdaj1thOajDmznDMHenC\ngk1EZCbYwyYii8EeNhERmQQWbD3YSzQcc2c45o50YcEmIjIT7GETkcVgD5uIiEwCC7Ye7CUajrkz\nHHNHurBgExGZCfawichisIdNREQmoUEFW61Wo3///vDy8oKPjw8AoKCgABqNBq6urggKCkJRUVGT\nBmos7CUajrkzHHNHujSoYMtkMmi1Whw+fBgpKSkAgOjoaGg0GqSnpyMwMLDWxLxERNS4GtTD7tmz\nJw4ePAh7e3tpnZubG/bt2weVSoW8vDz4+/tb5CS8RGQ+2MO+fePhw4fD29sbK1euBADk5+dDpVIB\nAFQqFfLz8xstWCIiqq1BBfu///0vDh8+jMTERHz66afYv39/je0ymazqL5sFYi/RcMyd4Zg70sWq\nIVfq2rUrAKBz584YO3YsUlJSpFaIg4MDcnNz0aVLF523jYiIgFqtBgAoFAp4enpKk4veeVKa8nJq\naqpJxWNOy6mpqSYVD5dbxrLkzO1/ezbP8v3Er9VqERsbCwBSvdRFbw/7xo0bqKiogFwux/Xr1xEU\nFITFixdj165dsLe3x4IFCxAdHY2ioqJaHzyyh01EzcnSe9h6z7Dz8/MxduxYAEB5eTmmTJmCoKAg\neHt7IzQ0FKtXr4ZarUZCQkKjBUtERLXxm456aLVa6S0M3RvmznDMnWEs/Qyb33QkIjITPMMmIovB\nM2wiIjIJLNh61BoqRA3G3BmOuSNdWLCJiMwEe9hEZDHYwyYiIpPAgq0He4mGY+4Mx9yRLizYRERm\ngj1sIrIY7GETEZFJYMHWg71EwzF3hmPuSBcWbCIiM8EeNhFZDPawiYjIJLBg68FeouGYO8Mxd6RL\ngwp2RUUFvLy8MGbMGABAQUEBNBoNXF1dERQUhKKioiYNkoiIGliwP/roI7i7u0szo0dHR0Oj0SA9\nPR2BgYG15nK0JJz1w3DMneGYO9JFb8HOzs7GDz/8gGeffVZqgm/duhXh4eEAgPDwcGzevLlpoyQi\nIv0Fe/78+XjvvffQqtX/rpqfnw+VSgUAUKlUyM/Pb7oIjYy9RMMxd4Zj7kiXemdN37ZtG7p06QIv\nL686n0AymUxqlegSEREBtVoNAFAoFPD09JTe7t3Zpykvp6ammlQ85rScmppqUvFwuWUsS87c/rdn\n8yzfT/xarRaxsbEAINVLXeodh71w4UKsW7cOVlZWKCkpwdWrV/HUU0/h119/hVarhYODA3JzcxEQ\nEIC0tLTaO+c4bCJqRi16HPaSJUtw7tw5nDlzBvHx8Xjsscewbt06BAcHIy4uDgAQFxeHkJCQRguU\niIh0u6dx2HdaH5GRkdi5cydcXV2xZ88eREZGNklwpqDW2yxqMObOcMwd6VJvD7s6Pz8/+Pn5AQDs\n7Oywa9euJguKiIhq42+JEJHFaNE9bCIiMh0s2Hqwl2g45s5wzB3pwoJNRGQm2MMmIovBHjYREZkE\nFmw92Es0HHNnOOaOdGHBJiIyE+xhE5HFYA+biIhMAgu2HuwlGo65MxxzR7qwYBMRmQn2sInIYrCH\nTUREJoEFWw/2Eg3H3BmOuSNdWLCJiMxEvT3skpIS+Pn54datWygtLcWTTz6JpUuXoqCgABMmTEBW\nVhbUajUSEhKgUChq75w9bCJqRi26h922bVvs3bsXqamp+P3337F371789NNPiI6OhkajQXp6OgID\nAxEdHd1ogRIRkW56WyLt27cHAJSWlqKiogJKpRJbt25FeHg4ACA8PBybN29u2iiNiL1EwzF3hmPu\nSBe9BbuyshKenp5QqVQICAjAgw8+iPz8fKhUKgCASqVCfn5+kwdKRNTS6Z2Et1WrVkhNTcWVK1cw\nYsQI7N27t8Z2mUwmzaauS0REBNRqNQBAoVDA09MT/v7+AP53FmHqy3eYSjzmsnxnnanEY07L/v7+\nJhWPOS1Lztz+t2fzLN9P/FqtFrGxsQAg1Utd7umLM2+//TbatWuHVatWQavVwsHBAbm5uQgICEBa\nWlrtnfNDRyJqRi36Q8dLly6hqKgIAHDz5k3s3LkTXl5eCA4ORlxcHAAgLi4OISEhjRaoqan1V5sa\njLkzHHNHutTbEsnNzUV4eDgqKytRWVmJsLAwBAYGwsvLC6GhoVi9erU0rI+IiJoWf0uEiCxGi26J\nEBGR6WDB1oO9RMMxd4Zj7kg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VafvmzZuRmZkJoPyDSplMhiZNmuCRRx5BdnY2VqxYgTt37qCgoACpqakA6ncE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\nPXpI67RaLcLCwuDj44Pw8HD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JiKzE/wf1OT0p5kwU2wAAAABJRU5ErkJggg==\n"
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xyvpOnjwJf39/ODg4wNHREQsXLgRQ+tfDqFGjEBoaChsbG8TExNxH9oioLrGQ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\n"
- }
- ],
- "prompt_number": 91
+ "outputs": []
}
],
"metadata": {}
diff --git a/facs/utils/performance.py b/facs/utils/performance.py
index 8c763cc..e1bf580 100644
--- a/facs/utils/performance.py
+++ b/facs/utils/performance.py
@@ -108,7 +108,8 @@ def facs_vs_fastq_screen():
facs_filt_name, _ = os.path.splitext(os.path.basename(fcs['bloom_filter']))
if facs_filt_name == fqscr['fastq_screen_index']:
# Do not assume the test run went well
- if len(fqscr['organisms']) > 0:
+ if len(fqscr['organisms']) > 0 and fqscr.get('memory_usage', None) and \
+ fcs.get('memory_usage', None):
if fqscr.get('begin_timestamp') and fcs.get('begin_timestamp'):
# Fetch timing info for each program
begin = fqscr['begin_timestamp']
@@ -138,6 +139,10 @@ def facs_vs_fastq_screen():
contam_fcs = fcs.get('contamination_rate')
contam_fqscr = fqscr.get('contamination_rate')
+ # Memory usage
+ mem_facs = fcs.get('memory_usage')
+ mem_fqscr = fqscr.get('memory_usage')
+
results[run] = dict(delta = delta.total_seconds(),
contam_fqscr = contam_fqscr,
delta_facs = delta_fcs.total_seconds(),
@@ -147,7 +152,9 @@ def facs_vs_fastq_screen():
filter_fqscr = fqscr.get('fastq_screen_index'),
filter_facs = fcs.get('bloom_filter'),
threads_facs = fcs.get('threads'),
- threads_fqscr = fqscr.get('threads'))
+ threads_fqscr = fqscr.get('threads'),
+ mem_facs = mem_facs,
+ mem_fqscr = mem_fqscr)
return json.dumps(results)