setup updates

Updated setup to include pytables
gmiaslab · Jul 29, 2019 · 524e5ff · 524e5ff
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Showing 3 changed files with 36 additions and 9 deletions.
diff --git a/docs/examples/.ipynb_checkpoints/pyiomica_examples-checkpoint.ipynb b/docs/examples/.ipynb_checkpoints/pyiomica_examples-checkpoint.ipynb
@@ -258,7 +258,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Time Series Classification"
+    "# Time Series Classification\n",
+    "\n",
+    "In the example below we use data in the 'results' directory to illustrate automatic time series classification. Two time series sets of gene expression intensities are available as '.csv' files:\n",
+    "\n",
+    "- Hourly1TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
+    "- Hourly2TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
+    "\n",
+    "The corresponding times of the sampling are provided in the file \"TimesHourly.csv\" (hourly sampling).\n",
+    "\n",
+    "The custom function processDataTempFunction is executed 3 times, once for each time series, and once for a computation of the 'delta' temporal trends (which considers the paired differences between the different time signals for matching identifiers across the two main sets being compared). The temporal trends, classification and results are output in the corresponding results directories."
    ]
   },
   {
@@ -1157,7 +1166,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Visibility Graph"
+    "# Visibility Graph\n",
+    "\n",
+    "In this example we calculate the visibility graph of the mean signal of a clustered group. Both a circular as well as a horizontal layout are illustrated. Missing data are imputed to the mean of the time series."
    ]
   },
   {
@@ -1273,7 +1284,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test Quantile transform"
+    "# Quantile transform\n",
+    "\n",
+    "In this example we calculate the quantile normalized values of a given dataframe (column-wise)."
    ]
   },
   {
@@ -1384,7 +1397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

diff --git a/docs/examples/pyiomica_examples.ipynb b/docs/examples/pyiomica_examples.ipynb
@@ -258,7 +258,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Time Series Classification"
+    "# Time Series Classification\n",
+    "\n",
+    "In the example below we use data in the 'results' directory to illustrate automatic time series classification. Two time series sets of gene expression intensities are available as '.csv' files:\n",
+    "\n",
+    "- Hourly1TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
+    "- Hourly2TimeSeries_SLV_KallistoNormedGeneGencodeGC.csv\n",
+    "\n",
+    "The corresponding times of the sampling are provided in the file \"TimesHourly.csv\" (hourly sampling).\n",
+    "\n",
+    "The custom function processDataTempFunction is executed 3 times, once for each time series, and once for a computation of the 'delta' temporal trends (which considers the paired differences between the different time signals for matching identifiers across the two main sets being compared). The temporal trends, classification and results are output in the corresponding results directories."
    ]
   },
   {
@@ -1157,7 +1166,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Visibility Graph"
+    "# Visibility Graph\n",
+    "\n",
+    "In this example we calculate the visibility graph of the mean signal of a clustered group. Both a circular as well as a horizontal layout are illustrated. Missing data are imputed to the mean of the time series."
    ]
   },
   {
@@ -1273,7 +1284,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test Quantile transform"
+    "# Quantile transform\n",
+    "\n",
+    "In this example we calculate the quantile normalized values of a given dataframe (column-wise)."
    ]
   },
   {
@@ -1384,7 +1397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.7.3"
   }
  },
  "nbformat": 4,

diff --git a/setup.py b/setup.py
@@ -20,7 +20,7 @@
     author_email='gmiaslab@gmail.com',
     license='MIT',
     url='https://github.com/gmiaslab/pyiomica',
-    download_url='https://github.com/gmiaslab/pyiomica/archive/1.0.0.tar.gz',
+    download_url='https://github.com/gmiaslab/pyiomica/archive/1.0.1.tar.gz',
     keywords=['omics', 'longitudinal','bioinformatics'],
     classifiers=[
         'License :: OSI Approved :: MIT License',
@@ -45,6 +45,7 @@
         'openpyxl>=2.6.2',
         'pandas>=0.24.2',
         'pymysql>=0.9.3',
+        'pytables>=3.5.2',
         'requests>=2.22.0',
         'scikit-learn>=0.21.2',
         'scipy>=1.2.1',