Merge pull request #333 from digitraceslab/development

Update some URLs

README.md

@@ -100,7 +100,7 @@ features = nilo.extract_features(
 
 ## Documentation
 
-Niimpy documentation is hosted at [readthedocs]https://niimpy.readthedocs.io/en/latest/.
+Niimpy documentation is hosted at [readthedocs]https://digitraceslab.github.io/niimpy/.
 
 ## Development
 

docs/quick-start.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Quick start\n",
     "\n",
-    "We will guide you through the main features of `niimpy`. This guide assumes that you have basic knowledge of Python. Also, please refers to the [installation](https://niimpy.readthedocs.io/en/latest/installation/) page for installing `niimpy`.\n",
+    "We will guide you through the main features of `niimpy`. This guide assumes that you have basic knowledge of Python. Also, please refers to the [installation](https://digitraceslab.github.io/niimpy/about/installation/) page for installing `niimpy`.\n",
     "\n",
     "This guide provides an example of reading and handling Aware battery data. The tutorial will guide you through 4 basic steps of a data analysis pipeline:\n",
     "\n",
@@ -429,7 +429,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For more information, refer to the [Exploration](https://niimpy.readthedocs.io/en/latest/api/niimpy.exploration/) section."
+    "For more information, refer to the [Exploration](https://digitraceslab.github.io/niimpy/user_guide/Exploration) section."
    ]
   }
  ],

docs/user_guide/Exploration.ipynb

@@ -19,12 +19,12 @@
     "\n",
     "This notebook introduces the Niimpy toolbox exploration module, which seeks to address the aforementioned issues. The module has functionalities for exploratory data analysis (EDA) of digital behavioral data. The module aims to produce a summary of the data characteristics, inspecting the structures underlying the data, to detecting patterns and changes in the patterns, and to assess the data quality (e.g., missing data, outliers). This information is highly essential for assessing data validity, data filtering and selection, and for data preprocessing. The module includes functions for [plotting catogorical data](#section_1), [data counts](#section_2), [timeseries lineplots](#section_3), [punchcards](#section_4) and [visualizing missing data.](#section_5)\n",
     "\n",
-    "Exploration module functions are supposed to run after [data preprocessing](https://niimpy.readthedocs.io/en/latest/preprocessing/), but they can be run also on the raw observations.\n",
+    "Exploration module functions are supposed to run after [data preprocessing](https://digitraceslab.github.io/niimpy/user_guide/preprocessing/), but they can be run also on the raw observations.\n",
     "All the functions are implemented by using [Plotly Python Open sourde Library](https://plotly.com/python/). Plotly enables interactive visualizations which in turn makers it easier to explore different aspects of the data (e.g.,specific timerange and summary statistics).\n",
     "\n",
     "This notebook uses several sample dataframes for module demonstration. The sample data is already preprocessed, or will be preprocessed in notebook sections before visualizations. When the sample data is loaded, some of the key characteristics of the data are displayed. \n",
     "\n",
-    "All eploration module functions require the data to follow [data schema.](https://niimpy.readthedocs.io/en/latest/schema/) defined in the Niimpy toolbox [documentation](https://niimpy.readthedocs.io/en/latest/). The user must ensure that the input data follows the specified schema.\n"
+    "All eploration module functions require the data to follow [data schema.](https://digitraceslab.github.io/niimpy/schema/) defined in the Niimpy toolbox [documentation](https://digitraceslab.github.io/niimpy/). The user must ensure that the input data follows the specified schema.\n"
    ]
   },
   {