fix: remove cameo style solutions

DD-DeCaF · Aug 6, 2017 · 037aed4 · 037aed4
1 parent ae07ebb
commit 037aed4
Showing 1 changed file with 53 additions and 56 deletions.
diff --git a/escher-01.ipynb b/escher-01.ipynb
@@ -45,7 +45,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import escher\n",
@@ -111,7 +113,7 @@
    "source": [
     "model = cobra.io.read_sbml_model('data/e_coli_core.xml.gz')\n",
     "solution = model.optimize()\n",
-    "print('Growth rate: %.2f' % solution.f)"
+    "print('Growth rate: %.2f' % solution.objective_value)"
    ]
   },
   {
@@ -149,46 +151,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Faster map generation in cameo\n",
-    "\n",
-    "Cameo provides shortcuts for generating Escher visualizations. Try it out for the same data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "heading_collapsed": true
    },
-   "outputs": [],
-   "source": [
-    "cam_model = cameo.load_model('e_coli_core')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cam_solution = cameo.fba(cam_model)\n",
-    "cam_solution.objective_value"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cam_solution.display_on_map(map_name='e_coli_core.Core metabolism')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# Escher for experimental data (20min)\n",
     "\n",
@@ -204,7 +169,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -214,7 +182,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Escher expects a dictionary, so let's make a dictionary out of this data and pass it into a Builder as `metabolite_data`."
    ]
@@ -223,7 +193,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "hidden": true
    },
    "outputs": [],
    "source": [
@@ -233,7 +204,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "b = escher.Builder(map_name='e_coli_core.Core metabolism',\n",
@@ -250,7 +224,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "## Gene data & dataset comparison with Escher\n",
     "\n",
@@ -262,7 +238,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "b = escher.Builder(map_name='e_coli_core.Core metabolism',\n",
@@ -273,7 +252,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "You can provide genes by ID (the locus tags show on the map) or by name (you can see these when you hover over a gene).\n",
     "\n",
@@ -283,7 +264,10 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "rnaseq = pd.read_table('data/S6_RNA-seq_aerobic_to_anaerobic.csv', sep=',', header=0, index_col=0)\n",
@@ -293,7 +277,8 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "hidden": true
    },
    "source": [
     "Notice that we have two datasets here. For multiple datasets, Escher expects an array of data dictionaries. E.g.:\n",
@@ -307,7 +292,8 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "hidden": true
    },
    "outputs": [],
    "source": [
@@ -316,15 +302,20 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Let's plot it on the map! Genes are visualized on reactions, so the `reaction_scale` and `reaction_styles` options still work here. We also have new options for `reaction_compare_style` which can be 'fold', 'log2_fold', or 'diff' and define how the two datasets are compared on the map."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true,
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "b = escher.Builder(map_name='e_coli_core.Core metabolism',\n",
@@ -343,7 +334,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Right away, we can see that genes activated in anaerobic conditions (e.g. fermentation pathways) are red, and genes activated in aerobic conditions (e.g. TCA cycle) are green."
    ]
@@ -367,7 +360,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# pass the model to a new builder\n",
@@ -385,7 +380,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "b = escher.Builder(map_json='data/CHANGE_ME.json')\n",
@@ -409,7 +406,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,