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@@ -28,6 +28,7 @@ TiPlot is an open-source visualisation tool for flight logs. With TiPlot, you ca
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- 3D viewer for displaying the paths of your UAVs.
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- Add multiple UAVs to compare flights.
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- Sync the 3D view with a specific timestamp by hovering over it.
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- Perform customized operations on data currently being processed.
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- Ability to send a data dictionary and entities to be displayed in the 3D view over TCP port `5555` from a Python script or a Jupyter notebook.
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- Layouts are automatically saved after every change.
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- Drag and drop plots to reposition them.
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### Prebuilt AppImage
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To install TiPlot using the prebuilt AppImage, follow these steps:
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To run TiPlot using the prebuilt AppImage, follow these steps:
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1. Download the latest AppImage from the [releases page](https://github.com/tilak-io/tiplot/releases/latest).
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2. Make the AppImage executable by running:
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For more info check out the Jupyter notebooks in the [templates](https://github.com/tilak-io/tiplot/blob/main/templates) folder.
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## Running Sequences and Custom Operations
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In this guide, we will walk you through the process of creating and executing sequences, which are Python functions containing a set of operations to manipulate log data. These sequences can be customized to suit your specific needs. Follow these steps to get started:
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1.**Set Preferred Editor**: Ensure that you have configured your preferred code editor in the tool settings by navigating to `Tools > Settings > General > External Editor`. If you intend to use a terminal-based editor such as Vim or Nano, make sure to specify the appropriate command to launch the terminal. (i.e: `kitty nvim` for kitty, `gnome-terminal -- nvim` for ubuntu's default terminal, `xterm -e nvim` for xterm...)
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2.**Create a New Sequence**: To begin, create a new sequence by navigating to `Tools > Sequences > Add New Sequence`.
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3.**Provide a Descriptive Name**: Give your sequence a meaningful and descriptive name that reflects its purpose.
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4.**Edit the Sequence**: Upon creating a new sequence, your preferred code editor will open with a template function. You can now define the operations you want to perform on the log data.
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Here's an example of a sequence that transforms log data from the NED (North-East-Down) representation to the ENU (East-North-Up) representation for a topic called `vehicle_local_position_enu`:
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```python
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defhandle_data(datadict):
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import numpy as np
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new = datadict
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# Coordinate transformation matrix from NED to ENU
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NED_to_ENU= np.array([
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[0, 1, 0], # East (ENU X) corresponds to North (NED Y)
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[1, 0, 0], # North (ENU Y) corresponds to East (NED X)
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[0, 0, -1] # Up (ENU Z) corresponds to -Down (NED Z)
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])
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# Apply the coordinate transformation to the 'x', 'y', and 'z' columns
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