edited parameter description

demos/notebooks/demo_pipeline.ipynb

@@ -28,7 +28,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Getting help\n",
+    "### Getting more help\n",
     "More detailed background information can be found in the [original CNMF paper](https://pubmed.ncbi.nlm.nih.gov/26774160/) and [the Caiman paper](https://pubmed.ncbi.nlm.nih.gov/30652683/). If you have questions about this demo, or the underlying algorithms, you can ask questions at our [gitter channel](https://app.gitter.im/#/room/#agiovann_Constrained_NMF:gitter.im). If you find a bug, or there are features you would like to see added, feel free to [open an issue](https://github.com/flatironinstitute/CaImAn/issues)."
    ]
   },
@@ -187,11 +187,9 @@
    "metadata": {},
    "source": [
     "## Setting up parameters\n",
-    "While there are many parameters used in CaImAn for the different steps in the pipeline, you only have to think about a few of them in each step, which we will discuss below. \n",
+    "Parameters are defined in a standalone parameters object. We create this object by passing parameters as a single dictionary to the `CNMFParams` class below. The parameters *not* explicitly defined in the dictionary will assume default values. A paramter object (let's call it `parameters`) is effectively a collection of dictionaries that each contains parameters relevant to different settings. Some related to the dataset in general (`parameters.data`), while most are relaed to particular aspects of the workflow such as motion correction (`parameters.motion`), component extraction (`parameters.spatial` and `parameters.temporal`), quality evaluation (`parameters.quality`), and others. \n",
     "\n",
-    "Parameters are defined in a standalone parameters object. We create this object by passing parameters as a single dictionary to the `CNMFParams` class below. The parameters *not* explicitly defined in the dictionary will assume default values. A paramter object (let's call it `parameters`) is effectively a collection of dictionaries that each contains parameters relevant to different settings. Some are generic, related to the dataset in general (`parameters.data`), while most are specific to particular aspects of the workflow such as motion correction (`parameters.motion`), component extraction (`parameters.spatial` and `parameters.temporal`), quality evaluation (`parameters.quality`), and others. \n",
-    "\n",
-    "First, let's define some parameters to be put in a dictionary. We will not discuss the parameters in detail in this section, but will when we reach the relevant stages of the pipeline."
+    "First, let's define some parameters to be put in a dictionary. We will not discuss the parameters in detail in this section, but will go over them when we reach the relevant stages of the pipeline."
    ]
   },
   {
@@ -235,8 +233,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Note that our sample dataset `Sue_2x_3000_40_-46.tif` has been spatially downsampled by a factor of 2, so has a lower resolution (2um/pixel) than the original. As a result several parameters (`gSig`, `strides`, `max_shifts`, `rf`, `stride_cnmf`) have lower values (half that compared to a dataset with a spatial resolution 1um/pixel).\n",
-    " \n",
     "We place the above values in a dictionary, which we then pass to `CNMFParams` class defining our parameters object:"
    ]
   },