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use{
crate::*,
average::WeightedMean,
num_traits::AsPrimitive,
std::borrow::Borrow
};
/// # Heatmap with mean of y-axis
/// * stores heatmap in row-major order: the rows of the heatmap are contiguous,
/// and the columns are strided
/// * enables you to quickly create a heatmap
/// * you can create gnuplot scripts to plot the heatmap
/// * for each x-axis bin, the y-axis mean is calculated
/// * …
///
/// # Difference to `HeatmapF64`
/// * [`HeatmapF64`](crate::heatmap::HeatmapF64) does not contain the averages for th y-axis,
/// but can be transposed and also used for Y-Histograms which take types which do not
/// implement AsPrimitive<f64>
pub struct HeatmapF64Mean<HistX, HistY>
{
pub(crate) heatmap: HeatmapF64<HistX, HistY>,
pub(crate) mean: Vec<WeightedMean>
}
impl<HistX, HistY> HeatmapF64Mean<HistX, HistY>
{
/// Internal [`HeatmapF64`](crate::heatmap::HeatmapF64)
pub fn heatmap(&self) -> &HeatmapF64<HistX, HistY>
{
&self.heatmap
}
}
impl<HistX, HistY> HeatmapF64Mean<HistX, HistY>
where HistX: Histogram,
HistY: Histogram,
{
/// # Create a heatmap
/// * creates new instance
/// * `hist_x` defines the bins along the x-axis
/// * `hist_y` defines the bins along the y-axis
pub fn new(hist_x: HistX, hist_y: HistY) -> Self
{
let heatmap = HeatmapF64::new(hist_x, hist_y);
let x_bins = heatmap.hist_width.bin_count();
let mean = (0..x_bins)
.map(|_| WeightedMean::new())
.collect();
Self{
heatmap,
mean
}
}
/// # Update Heatmap
/// * similar to [`count` of `HeatmapF64`](crate::heatmap::HeatmapF64::count)
///
/// This time, however, any value that is out of bounds will be ignored for
/// the calculation of the mean of the y-axis, meaning also values which correspond
/// to a valid x-bin will be ignored, if their y-value is not inside the Y Histogram.
/// The mean respects the `weight`
pub fn count_inside_heatmap<X, Y, A, B>(
&mut self,
x_val: A,
y_val: B,
weight: f64
) -> Result<(usize, usize), HeatmapError>
where HistX: HistogramVal<X>,
HistY: HistogramVal<Y>,
A: Borrow<X>,
B: Borrow<Y>,
Y: AsPrimitive<f64>
{
let x = x_val.borrow();
let y = y_val.borrow();
let res = self.heatmap.count(x, y, weight);
if let Ok((x, _)) = res {
let y_f64 = y.as_();
if y_f64.is_finite(){
self.mean[x].add(y_f64, weight);
}
}
res
}
/// # Update heatmap
/// * Corresponds to [`count` of `HeatmapU`](crate::heatmap::HeatmapU::count)
///
/// The difference is, that the mean of the y-axis is updated as long as `y_val` is finite
/// and `x_val` is in bounds (because the mean is calculated for each bin in the x direction
/// separately). The calculated average respects the `weight`
pub fn count<X, Y, A, B>(&mut self, x_val: A, y_val: B, weight: f64) -> Result<(usize, usize), HeatmapError>
where HistX: HistogramVal<X>,
HistY: HistogramVal<Y>,
A: Borrow<X>,
B: Borrow<Y>,
Y: AsPrimitive<f64>
{
let x = x_val.borrow();
let y = y_val.borrow();
let res = self.count_inside_heatmap(x, y, weight);
match res
{
Ok(_) => {},
Err(_) => {
let y_f64 = y.as_();
if y_f64.is_finite() {
if let Ok(x_bin) = self
.heatmap
.hist_width
.get_bin_index(x)
{
self.mean[x_bin].add(y_f64, weight);
}
}
}
}
res
}
/// # Internal slice for mean
/// * The mean is calculated from this slice
/// * The mean corresponds to the bins of the x-axis
pub fn mean_slice(&self) -> &[WeightedMean]
{
&self.mean
}
/// # Iterate over the calculated mean
/// * iterates over the means
/// * The mean corresponds to the bins of the x-axis
/// * if a bin on the x-axis has no entries, the corresponding
/// mean will be `f64::NAN`
pub fn mean_iter(&'_ self) -> impl Iterator<Item=f64> + '_
{
self.mean
.iter()
.map(
|v|
{
if v.is_empty(){
f64::NAN
} else {
v.mean()
}
}
)
}
/// # Get a mean vector
/// * The entries are the means corresponds to the bins of the x-axis
/// * if a bin on the x-axis has no entries, the corresponding
/// mean will be `f64::NAN`
///
/// # Note
/// * If you want to iterate over the mean values, use
/// [`mean_iter`](Self::mean_iter) instead
pub fn mean(&self) -> Vec<f64>
{
let mut mean = Vec::with_capacity(self.mean.len());
mean.extend(self.mean_iter());
mean
}
/// # Create a gnuplot script to plot your heatmap
/// * `writer`: The gnuplot script will be written to this
/// * `gnuplot_output_name`: how shall the file, created by executing gnuplot,
/// be called? Ending of file will be set automatically
/// # Note
/// * This is the same as calling [`gnuplot`](Self::gnuplot) with default
/// [`GnuplotSettings`](crate::heatmap::GnuplotSettings) and default
/// [`GnuplotPointSettings`](crate::heatmap::GnuplotPointSettings)
/// * The default axis are the bin indices, which, e.g, means they always
/// begin at 0. You have to set the axis via the [GnuplotSettings](crate::heatmap::GnuplotSettings)
pub fn gnuplot_quick<W>(
&self,
writer: W
) -> std::io::Result<()>
where
W: std::io::Write
{
self.gnuplot(
writer,
GnuplotSettings::default(),
GnuplotPointSettings::default()
)
}
/// # Create a gnuplot script to plot your heatmap
/// This function writes a file, that can be plotted in the terminal via [gnuplot](http://www.gnuplot.info/)
/// ```bash
/// gnuplot gnuplot_file
/// ```
/// ## Parameter:
/// * `writer`: writer gnuplot script will be written to
/// * `gnuplot_output_name`: how shall the file, created by executing gnuplot, be called? File suffix (ending) will be set automatically
/// * `settings`: Here you can set the axis, choose between terminals and more.
/// I recommend that you take a look at [GnuplotSettings](crate::heatmap::GnuplotSettings)
/// * `point_color`: the mean (in y-direction) will be plotted as points in the heatmap.
/// Here you can choose the point color
/// ## Notes
/// The default axis are the bin indices, which, e.g, means they always
/// begin at 0. You have to set the axis via the [GnuplotSettings](crate::heatmap::GnuplotSettings)
pub fn gnuplot<W, P, GS>(
&self,
mut writer: W,
settings: GS,
points: P
) -> std::io::Result<()>
where
W: std::io::Write,
P: Borrow<GnuplotPointSettings>,
GS: Borrow<GnuplotSettings>
{
let settings: &GnuplotSettings = settings.borrow();
let point: &GnuplotPointSettings = points.borrow();
let x_len = self.heatmap.width;
let y_len = self.heatmap.height;
settings.write_heatmap_helper1(
&mut writer,
x_len,
y_len
)?;
writeln!(writer, "$mean_data << EOD")?;
for (index, value) in self.mean_iter().enumerate()
{
writeln!(writer, "{} {:e}", index, value)?;
}
writeln!(writer, "EOD")?;
writeln!(writer, "$data << EOD")?;
self.heatmap.write_to(&mut writer)?;
writeln!(writer, "EOD")?;
write!(writer, "splot $data matrix with image t \"{}\" ", settings.get_title())?;
writeln!(writer, ",\\")?;
if point.frame
{
write!(writer, "$mean_data u 1:2:(1) pointtype 7 lc \"")?;
point.frame_color.write_hex(&mut writer)?;
writeln!(writer, "\" pointsize {} notitle,\\", point.frame_size())?;
}
write!(writer, "$mean_data u 1:2:(1) pt 7 lc \"")?;
point.color.write_hex(&mut writer)?;
writeln!(writer, "\" ps {} t \"{}\"", point.get_size(), point.get_legend())?;
settings.terminal.finish(writer)
}
}
#[cfg(test)]
mod tests{
use super::*;
use crate::HistUsizeFast;
#[test]
fn average_test()
{
let hist_x = HistUsizeFast::new_inclusive(0, 10)
.unwrap();
let hist_y = hist_x.clone();
let mut heatmap_mean = HeatmapF64Mean::new(hist_x, hist_y);
for x in 0..=10 {
for y in 0..=10{
heatmap_mean.count_inside_heatmap(x, y, 1.0).unwrap();
}
}
for i in heatmap_mean.mean_iter() {
assert_eq!(i, 5.0);
}
}
}