Update docs

Author: sitic

README.md

@@ -14,7 +14,7 @@
 
 ## Installation
 
-`optimap` is available for macOS, Windows and Linux, see [Installing optimap](https://optimap.readthedocs.io/en/latest/chapters/getting_started/#installing-optimap) for more detailed information.
+`optimap` is available for macOS, Windows and Linux, see the [Getting Started](https://optimap.readthedocs.io/en/latest/chapters/getting_started/) guide for more detailed information.
 
 ### Installing pre-built binaries (macOS, Windows, Linux)
 
@@ -36,13 +36,9 @@ To update optimap to the latest version run
 pip install --upgrade opticalmapping[all]
 ```
 
-## Getting Started
-
-See the [Getting Started](https://optimap.readthedocs.io/en/latest/chapters/getting_started/) guide and the [Tutorials](https://optimap.readthedocs.io/en/latest/tutorials/) for an introduction to optimap and installation instructions.
-
 ## About optimap
 
-`optimap` is a script-based software, which means that you run Python-based analysis scripts rather than using a graphical user interface. We provide several example scripts which explain the usage of `optimap`, see [Tutorials](https://optimap.readthedocs.io/en/latest/tutorials/). The example scripts can also be downloaded directly by clicking on the right link in the green box at the top of each tutorial page. `optimap` is developed by members of the [Cardiac Vision Laboratory](https://cardiacvision.ucsf.edu) at the [University of California, San Franicsco](https://www.ucsf.edu). `optimap` was created for cardiovascular scientists in particular, but might also be useful for scientists in other fields, for instance, when performing calcium imaging or physiological research with moving cells or tissues. `optimap` is open-source, freely available, and relies on open-source packages such as NumPy, SciPy, matplotlib and OpenCV.
+`optimap` is a script-based software, which means that you run Python-based analysis scripts rather than using a graphical user interface. We provide several example scripts which explain the usage of `optimap`, see [Tutorials](https://optimap.readthedocs.io/en/latest/tutorials/). The example scripts can also be downloaded directly by clicking on the right link in the green box at the top of each tutorial page. `optimap` is developed by members of the [Cardiac Vision Laboratory](https://cardiacvision.ucsf.edu) at the [University of California, San Franicsco](https://www.ucsf.edu). `optimap` was created for cardiovascular scientists in particular, but might also be useful for scientists in other fields, for instance, when performing calcium imaging or physiological research with moving cells or tissues. `optimap` is open-source, freely available, and relies on open-source packages such as NumPy, SciPy, Matplotlib and OpenCV.
 
 ## Links
 

docs/tutorials/basics.ipynb

@@ -120,7 +120,7 @@
     "* .npy (numpy array)\n",
     "* .mat (MATLAB), loads the first field in the file\n",
     "\n",
-    "We can use the {func}`optimap.load_video` function to load a video file, see also [Tutorial 13](io.ipynb). The code below will automatically download an example file from our website [cardiacvision.ucsf.edu](https://cardiacvision.ucsf.edu) and load it into our workspace as a video. Alternatively, you could load your own file by replacing `filepath` with the filename of your video which you have stored somewhere on your computer. The example file shows a fibrillating, weakly contracting rabbit heart stained with voltage-sensitive dye (Di-4-ANEPPS) imaged using a Basler acA720-520um camera at 500fps. Due to the staining, the action potential wave is inverted, i.e. an upstroke is observed as a negative deflection. The data is from {cite}`Chowdhary2023` and we extracted a short part of the original recording and saved the otherwise unprocessed raw video data as a numpy file (`.npy`).  Experimenters: Jan Lebert, Shrey Chowdhary & Jan Christoph (University of California, San Francisco, USA), 2023."
+    "We can use the {func}`optimap.load_video` function to load a video file, see also [Tutorial 13: Import / Export (IO)](io.ipynb). The code below will automatically download an example file from our website [cardiacvision.ucsf.edu](https://cardiacvision.ucsf.edu) and load it into our workspace as a video. Alternatively, you could load your own file by replacing `filepath` with the filename of your video which you have stored somewhere on your computer. The example file shows a fibrillating, weakly contracting rabbit heart stained with voltage-sensitive dye (Di-4-ANEPPS) imaged using a Basler acA720-520um camera at 500fps. Due to the staining, the action potential wave is inverted, i.e. an upstroke is observed as a negative deflection. The data is from {cite}`Chowdhary2023` and we extracted a short part of the original recording and saved the otherwise unprocessed raw video data as a numpy file (`.npy`).  Experimenters: Jan Lebert, Shrey Chowdhary & Jan Christoph (University of California, San Francisco, USA), 2023."
    ]
   },
   {
@@ -141,7 +141,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`optimap` imports video data as three-dimensional NumPy array,  where the first dimension is time and the other two dimensions are the x- and y-dimensions, respectively. This convention is used throughout the library. The function `optimap.print_properties()` displays the dimensions and maximal and minimal intensity values of a video. Our example file has 1040 video frames. See {func}`load_video` for additional arguments, e.g. to load only a subset of the frames or to use memory mapping to reduce memory usage.\n",
+    "`optimap` imports video data as three-dimensional NumPy array, where the first dimension is time and the other two dimensions are the x- and y-dimensions, respectively. This convention is used throughout the library. The function {func}`print_properties()` displays the dimensions and maximal and minimal intensity values of a video. Our example file has 1040 video frames. See {func}`load_video` for additional arguments, e.g. to load only a subset of the frames or to use memory mapping to reduce memory usage.\n",
     "\n",
     "```python\n",
     "video = om.load_video('Example.dat', start_frame=100, frames=1000, step=2, use_mmap=True)\n",