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Anthrobotanica Investigations
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Quadruped
Plant Bio-signal Data Exploration & Feature Classification¶
Intro¶
This is an exploration of the bio-signals produced by a baby rubberplant (Peperomia obtusifolia). The houseplant has a robotic prosthesis that moves based on the plant's bio-signals. Four surface electrodes are each attached to a leaf, the signals are amplified 100x and fed to a script that controls the prosthesis motors.
In [1]:
from glob import glob
from os import listdir
from os.path import join
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import random
from scipy import stats
# Enable interactive matplotlib plots
%matplotlib notebook
Data Acquisition¶
The baby rubberplant has surface electrodes on four of its leaves which are connected to the prosthesis computer by insulated coaxial cables.
Sensor data is collected as comma separated files (.csv) in the folder /datasets grouped by stimuli and electrode channel (i.e. the leaf that is stimulated). Each dataset are approximately 10 minutes recording of 4 single-ended channels at 140 Hz in 16-bit resolution. Each channel is amplified 100x by LM358 op-amps and read by ADS1115 16-bit analog-to-digital converter.
Under is the code used for data gathering. The whole script, ads1115-to-csv.py, is in the current folder.
f = open("log.csv", "w", newline="")
c = csv.writer(f)
c.writerow(["Datetime", "chan0", "chan1", "chan2", "chan3"]) # List of fieldnames
for i in range(SAMPLES):
c.writerow([datetime.now().isoformat(sep=' ', timespec='milliseconds'),
"{:8.6f}".format(chan0.voltage),
"{:8.6f}".format(chan1.voltage),
"{:8.6f}".format(chan2.voltage),
"{:8.6f}".format(chan3.voltage)])
f.close()
The biosignals are grouped in the following folders:
/datasets/baseline
No light source, approximately 1 week since watering.
../chan_0_light, chan_1_light, chan_2_light, chan_3_light
The denoted channel has a lightbulb with artificial sunlight approx. 10 centimeter from the leaf, while the three other channels are covered with a dark blanket.
../drought
Approx. 10 minutes recording with dry soil (1 month since watering) plus soaked soil.
../chan_0_pain, chan_1_pain, chan_2_pain, chan_3_pain
The leaf corresponding to the channel has been cut after approx. 10 minutes baseline recording. The cut is 5 millimeter long from the outside of the leaf blade in proximity to the surface sensor.
In [2]:
# Import datasets and set 'Datetime' column as dataset index
datasets = glob('datasets/*.csv')
dfs = {}
for filename in datasets:
df = pd.read_csv(filename)
df['Datetime'] = pd.to_datetime(df['Datetime'], format='%Y-%m-%d %H:%M:%S.%f')
df = df.set_index('Datetime')
dfs[str(filename)[9:-4]] = df
Data Analysis¶
In [3]:
dfs['baseline'].describe()
Out[3]:
Previews of the different sensor data:¶
In [4]:
alpha_val = 0.7 # Signal smoothing by exponential moving averages. Small weighting factor results in high degree of smoothing, larger value provides quicker response to recent changes.
fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(8,5))
dfs['baseline'].ewm(alpha=alpha_val).mean().plot(ax=axes[0,0], legend=None, xlabel=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[0,0].set_title("Baseline")
axes[0,0].set_xlabel('')
dfs['drought'].ewm(alpha=alpha_val).mean().plot(ax=axes[0,1], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[0,1].set_title("Drought")
axes[0,1].set_xlabel('')
axes[0,2].set_visible(False)
axes[0,3].set_visible(False)
dfs['chan_0_light'].ewm(alpha=alpha_val).mean().plot(ax=axes[1,0], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[1,0].set_title("Chn 0 light")
axes[1,0].set_xlabel('')
axes[1,0].set_ylabel('Signal Voltage')
dfs['chan_1_light'].ewm(alpha=alpha_val).mean().plot(ax=axes[1,1], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[1,1].set_title("Chn 1 light")
axes[1,1].set_xlabel('')
dfs['chan_2_light'].ewm(alpha=alpha_val).mean().plot(ax=axes[1,2], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[1,2].set_title("Chn 2 light")
axes[1,2].set_xlabel('')
dfs['chan_3_light'].ewm(alpha=alpha_val).mean().plot(ax=axes[1,3], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[1,3].set_title("Chn 3 light")
axes[1,3].set_xlabel('')
dfs['chan_0_pain'].ewm(alpha=alpha_val).mean().plot(ax=axes[2,0], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[2,0].set_title("Chn 0 pain")
axes[2,0].set_xlabel('')
dfs['chan_1_pain'].ewm(alpha=alpha_val).mean().plot(ax=axes[2,1], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[2,1].set_title("Chn 1 pain")
axes[2,1].set_xlabel('Time Axis')
axes[2,1].xaxis.set_label_coords(1.05, -0.05)
dfs['chan_2_pain'].ewm(alpha=alpha_val).mean().plot(ax=axes[2,2], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[2,2].set_title("Chn 2 pain")
axes[2,2].set_xlabel('')
dfs['chan_3_pain'].ewm(alpha=alpha_val).mean().plot(ax=axes[2,3], legend=None).tick_params(top=False, bottom=False, left=False, right=False, labelleft=False, labelbottom=False)
axes[2,3].set_title("Chn 3 pain")
axes[2,3].set_xlabel('')
plt.tight_layout()
plt.show()
");
if (!fig.cell_info) {
console.error('Failed to find cell for figure', id, fig);
return;
}
fig.cell_info[0].output_area.element.one(
'cleared',
{ fig: fig },
fig._remove_fig_handler
);
};
mpl.figure.prototype.handle_close = function (fig, msg) {
var width = fig.canvas.width / fig.ratio;
fig.cell_info[0].output_area.element.off(
'cleared',
fig._remove_fig_handler
);
// Update the output cell to use the data from the current canvas.
fig.push_to_output();
var dataURL = fig.canvas.toDataURL();
// Re-enable the keyboard manager in IPython - without this line, in FF,
// the notebook keyboard shortcuts fail.
IPython.keyboard_manager.enable();
fig.parent_element.innerHTML =
'
';
fig.close_ws(fig, msg);
};
mpl.figure.prototype.close_ws = function (fig, msg) {
fig.send_message('closing', msg);
// fig.ws.close()
};
mpl.figure.prototype.push_to_output = function (_remove_interactive) {
// Turn the data on the canvas into data in the output cell.
var width = this.canvas.width / this.ratio;
var dataURL = this.canvas.toDataURL();
this.cell_info[1]['text/html'] =
'';
};
mpl.figure.prototype.updated_canvas_event = function () {
// Tell IPython that the notebook contents must change.
IPython.notebook.set_dirty(true);
this.send_message('ack', {});
var fig = this;
// Wait a second, then push the new image to the DOM so
// that it is saved nicely (might be nice to debounce this).
setTimeout(function () {
fig.push_to_output();
}, 1000);
};
mpl.figure.prototype._init_toolbar = function () {
var fig = this;
var toolbar = document.createElement('div');
toolbar.classList = 'btn-toolbar';
this.root.appendChild(toolbar);
function on_click_closure(name) {
return function (_event) {
return fig.toolbar_button_onclick(name);
};
}
function on_mouseover_closure(tooltip) {
return function (event) {
if (!event.currentTarget.disabled) {
return fig.toolbar_button_onmouseover(tooltip);
}
};
}
fig.buttons = {};
var buttonGroup = document.createElement('div');
buttonGroup.classList = 'btn-group';
var button;
for (var toolbar_ind in mpl.toolbar_items) {
var name = mpl.toolbar_items[toolbar_ind][0];
var tooltip = mpl.toolbar_items[toolbar_ind][1];
var image = mpl.toolbar_items[toolbar_ind][2];
var method_name = mpl.toolbar_items[toolbar_ind][3];
if (!name) {
/* Instead of a spacer, we start a new button group. */
if (buttonGroup.hasChildNodes()) {
toolbar.appendChild(buttonGroup);
}
buttonGroup = document.createElement('div');
buttonGroup.classList = 'btn-group';
continue;
}
button = fig.buttons[name] = document.createElement('button');
button.classList = 'btn btn-default';
button.href = '#';
button.title = name;
button.innerHTML = '';
button.addEventListener('click', on_click_closure(method_name));
button.addEventListener('mouseover', on_mouseover_closure(tooltip));
buttonGroup.appendChild(button);
}
if (buttonGroup.hasChildNodes()) {
toolbar.appendChild(buttonGroup);
}
// Add the status bar.
var status_bar = document.createElement('span');
status_bar.classList = 'mpl-message pull-right';
toolbar.appendChild(status_bar);
this.message = status_bar;
// Add the close button to the window.
var buttongrp = document.createElement('div');
buttongrp.classList = 'btn-group inline pull-right';
button = document.createElement('button');
button.classList = 'btn btn-mini btn-primary';
button.href = '#';
button.title = 'Stop Interaction';
button.innerHTML = '';
button.addEventListener('click', function (_evt) {
fig.handle_close(fig, {});
});
button.addEventListener(
'mouseover',
on_mouseover_closure('Stop Interaction')
);
buttongrp.appendChild(button);
var titlebar = this.root.querySelector('.ui-dialog-titlebar');
titlebar.insertBefore(buttongrp, titlebar.firstChild);
};
mpl.figure.prototype._remove_fig_handler = function (event) {
var fig = event.data.fig;
fig.close_ws(fig, {});
};
mpl.figure.prototype._root_extra_style = function (el) {
el.style.boxSizing = 'content-box'; // override notebook setting of border-box.
};
mpl.figure.prototype._canvas_extra_style = function (el) {
// this is important to make the div 'focusable
el.setAttribute('tabindex', 0);
// reach out to IPython and tell the keyboard manager to turn it's self
// off when our div gets focus
// location in version 3
if (IPython.notebook.keyboard_manager) {
IPython.notebook.keyboard_manager.register_events(el);
} else {
// location in version 2
IPython.keyboard_manager.register_events(el);
}
};
mpl.figure.prototype._key_event_extra = function (event, _name) {
var manager = IPython.notebook.keyboard_manager;
if (!manager) {
manager = IPython.keyboard_manager;
}
// Check for shift+enter
if (event.shiftKey && event.which === 13) {
this.canvas_div.blur();
// select the cell after this one
var index = IPython.notebook.find_cell_index(this.cell_info[0]);
IPython.notebook.select(index + 1);
}
};
mpl.figure.prototype.handle_save = function (fig, _msg) {
fig.ondownload(fig, null);
};
mpl.find_output_cell = function (html_output) {
// Return the cell and output element which can be found *uniquely* in the notebook.
// Note - this is a bit hacky, but it is done because the "notebook_saving.Notebook"
// IPython event is triggered only after the cells have been serialised, which for
// our purposes (turning an active figure into a static one), is too late.
var cells = IPython.notebook.get_cells();
var ncells = cells.length;
for (var i = 0; i < ncells; i++) {
var cell = cells[i];
if (cell.cell_type === 'code') {
for (var j = 0; j < cell.output_area.outputs.length; j++) {
var data = cell.output_area.outputs[j];
if (data.data) {
// IPython >= 3 moved mimebundle to data attribute of output
data = data.data;
}
if (data['text/html'] === html_output) {
return [cell, data, j];
}
}
}
}
};
// Register the function which deals with the matplotlib target/channel.
// The kernel may be null if the page has been refreshed.
if (IPython.notebook.kernel !== null) {
IPython.notebook.kernel.comm_manager.register_target(
'matplotlib',
mpl.mpl_figure_comm
);
}
