From 61c975f6836bdeba8b8174a41c682017c459f234 Mon Sep 17 00:00:00 2001
From: jakobrunge <jakobrunge@gmail.com>
Date: Sun, 3 Jul 2022 11:36:38 +0200
Subject: [PATCH] further fixes for multiple dataset functionality

---
 setup.py                                          |  2 +-
 .../tigramite_tutorial_multiple_datasets.ipynb    | 15 +++++++++++++--
 2 files changed, 14 insertions(+), 3 deletions(-)

diff --git a/setup.py b/setup.py
index 2805cb8e..0c95fefe 100644
--- a/setup.py
+++ b/setup.py
@@ -63,7 +63,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.1.0.0",
+    version="5.1.0.1",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",
diff --git a/tutorials/tigramite_tutorial_multiple_datasets.ipynb b/tutorials/tigramite_tutorial_multiple_datasets.ipynb
index fcf70b5c..62bc8574 100644
--- a/tutorials/tigramite_tutorial_multiple_datasets.ipynb
+++ b/tutorials/tigramite_tutorial_multiple_datasets.ipynb
@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# PCMCI and PCMCI$^+$ on multiple datasets of time series\n",
+    "# Tigramite methods on multiple datasets of time series\n",
     "\n",
-    "This notebook explains the multiple datasets functionality for [TIGRAMITE](https://github.com/jakobrunge/tigramite), which allows to run PCMCI and PCMCI$^+$ on multiple datasets of time series. We refer to this as Multidata-PCMCI in short. Familiarity with the basic usage of PCMCI or PCMCI$^+$ is assumed."
+    "This notebook explains the multiple datasets functionality for [TIGRAMITE](https://github.com/jakobrunge/tigramite), which allows to run causal discovery methods such as PCMCI and PCMCI$^+$ or also the CausalEffect class tools on multiple datasets of time series. Here we focus on the PCMCI/PCMCI$^+$ functionality and refer to this as Multidata-PCMCI in short. Familiarity with the basic usage of PCMCI or PCMCI$^+$ is assumed."
    ]
   },
   {
@@ -433,6 +433,17 @@
     "For more information on missing values and masking please refer to the [respective tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_missing_masking.ipynb) in the GitHub TIGRAMITE repository."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6. Integration into other Tigramite methods\n",
+    "\n",
+    "As mentioned in the beginning, the highly modular setup of tigramite implies that you can use the new multiple dataset feature also in other methods of tigramite that are based on the DataFrame class, for example the CausalEffect class to estimate causal effects given causal graphs.\n",
+    "\n",
+    "For more information on missing values and masking please refer to the [respective tutorial](https://github.com/jakobrunge/tigramite/blob/master/tutorials/tigramite_tutorial_missing_masking.ipynb) in the GitHub TIGRAMITE repository."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,