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tidied error msgs
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@liborty
liborty committed Jun 10, 2024
1 parent 24b39bd commit d171e6e
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Showing 4 changed files with 53 additions and 61 deletions.
95 changes: 44 additions & 51 deletions src/stats.rs
View file
Original file line number Diff line number Diff line change
@@ -1,18 +1,22 @@
use crate::*; //{RError, nodata_error, sumn, fromop, Params, MutVecg, Stats, Vecg, RE};
use indxvec::Vecops;
use medians::{Medianf64,Median};
use crate::*;
use core::cmp::Ordering::*;
use indxvec::Vecops;
use medians::{Median, Medianf64};

impl<T> Stats for &[T]
where
T: Clone + PartialOrd + Into<f64>
T: Clone + PartialOrd + Into<f64>,
{
/// Vector magnitude
fn vmag(self) -> f64 {
match self.len() {
0 => 0_f64,
1 => self[0].clone().into(),
_ => self.iter().map(|x| x.clone().into().powi(2)).sum::<f64>().sqrt(),
_ => self
.iter()
.map(|x| x.clone().into().powi(2))
.sum::<f64>()
.sqrt(),
}
}

Expand All @@ -36,7 +40,7 @@ where
if c.is_normal() {
Ok(1.0 / c)
} else {
arith_error(format!("vreciprocal: bad component {c}"))
arith_error("vreciprocal: bad component {c}")
}
})
.collect::<Result<Vec<f64>, RE>>()
Expand All @@ -58,9 +62,10 @@ where
// Negated vector (all components swap sign)
fn negv(self) -> Result<Vec<f64>, RE> {
if self.is_empty() {
return nodata_error("negv: empty self vec");
};
Ok(self.iter().map(|x| (-x.clone().into())).collect())
nodata_error("negv: empty self vec")
} else {
Ok(self.iter().map(|x| (-x.clone().into())).collect())
}
}

/// Unit vector
Expand Down Expand Up @@ -116,9 +121,11 @@ where

/// Median and Mad packed into in Params struct
fn medmad(self) -> Result<Params, RE> {
let median = self.qmedian_by(&mut |a,b| a
.partial_cmp(b).unwrap_or(Equal), fromop)?;
Ok(Params{centre:median,spread:self.mad(median, fromop)})
let median = self.qmedian_by(&mut |a, b| a.partial_cmp(b).unwrap_or(Equal), fromop)?;
Ok(Params {
centre: median,
spread: self.mad(median, fromop),
})
}

/// Arithmetic mean and (population) standard deviation
Expand All @@ -133,7 +140,7 @@ where
fn ameanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("ameanstd: empty self vec");
};
let nf = n as f64;
let mut sx2 = 0_f64;
Expand Down Expand Up @@ -165,7 +172,7 @@ where
fn awmean(self) -> Result<f64, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("awmean: empty self vec");
};
let mut iw = 0_f64; // descending linear weights
Ok(self
Expand All @@ -192,7 +199,7 @@ where
fn awmeanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("awmeanstd: empty self vec");
};
let mut sx2 = 0_f64;
let mut w = 0_f64; // descending linear weights
Expand Down Expand Up @@ -224,13 +231,13 @@ where
fn hmean(self) -> Result<f64, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("hmean: empty self vec");
};
let mut sum = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError("attempt to divide by zero".to_owned()));
return arith_error("hmean: attempt to divide by zero");
};
sum += 1.0 / fx
}
Expand All @@ -250,15 +257,15 @@ where
fn hmeanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("hmeanstd: empty self vec");
};
let nf = n as f64;
let mut sx2 = 0_f64;
let mut sx = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError("attempt to divide by zero".to_owned()));
return arith_error("hmeanstd: attempt to divide by zero");
};
let rx = 1_f64 / fx; // work with reciprocals
sx2 += rx * rx;
Expand All @@ -282,14 +289,14 @@ where
fn hwmean(self) -> Result<f64, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("hwmean: empty self vec");
};
let mut sum = 0_f64;
let mut w = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError("attempt to divide by zero".to_owned()));
return arith_error("hwmean: attempt to divide by zero");
};
w += 1_f64;
sum += w / fx;
Expand All @@ -310,7 +317,7 @@ where
fn hwmeanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("hwmeanstd: empty self vec");
};
let nf = sumn(n) as f64;
let mut sx2 = 0_f64;
Expand All @@ -320,7 +327,7 @@ where
w += 1_f64;
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError("attempt to divide by zero".to_owned()));
return arith_error("hwmeanstd: attempt to divide by zero");
};
sx += w / fx; // work with reciprocals
sx2 += w / (fx * fx);
Expand All @@ -346,15 +353,13 @@ where
fn gmean(self) -> Result<f64, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("gmean: empty self vec");
};
let mut sum = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError(
"gmean attempt to take ln of zero".to_owned(),
));
return arith_error("gmean: attempt to take ln of zero");
};
sum += fx.ln()
}
Expand All @@ -375,16 +380,14 @@ where
fn gmeanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("gmeanstd: empty self vec");
};
let mut sum = 0_f64;
let mut sx2 = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError(
"gmeanstd attempt to take ln of zero".to_owned(),
));
return arith_error("gmeanstd: attempt to take ln of zero");
};
let lx = fx.ln();
sum += lx;
Expand Down Expand Up @@ -413,16 +416,14 @@ where
fn gwmean(self) -> Result<f64, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("gwmean: empty self vec");
};
let mut w = 0_f64; // ascending weights
let mut sum = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError(
"gwmean attempt to take ln of zero".to_owned(),
));
return arith_error("gwmean: attempt to take ln of zero");
};
w += 1_f64;
sum += w * fx.ln();
Expand All @@ -442,17 +443,15 @@ where
fn gwmeanstd(self) -> Result<Params, RE> {
let n = self.len();
if n == 0 {
return Err(RError::NoDataError("empty self vec".to_owned()));
return nodata_error("gwmeanstd: empty self vec");
};
let mut w = 0_f64; // ascending weights
let mut sum = 0_f64;
let mut sx2 = 0_f64;
for x in self {
let fx: f64 = x.clone().into();
if !fx.is_normal() {
return Err(RError::ArithError(
"gwmeanstd attempt to take ln of zero".to_owned(),
));
return arith_error("gwmeanstd: attempt to take ln of zero");
};
let lnx = fx.ln();
w += 1_f64;
Expand Down Expand Up @@ -505,9 +504,7 @@ where
let (mut sx, mut sy, mut sxy, mut sx2, mut sy2) = (0_f64, 0_f64, 0_f64, 0_f64, 0_f64);
let n = self.len();
if n < 2 {
return Err(RError::NoDataError(
"autocorr needs a Vec of at least two items".to_owned(),
));
return nodata_error("autocorr needs a Vec of at least two items");
};
let mut x: f64 = self[0].clone().into();
self.iter().skip(1).for_each(|si| {
Expand All @@ -527,11 +524,9 @@ where
fn lintrans(self) -> Result<Vec<f64>, RE> {
let mm = self.minmax();
let min = mm.min.into();
let range = mm.max.into() - min;
let range = mm.max.into() - min;
if range == 0_f64 {
return Err(RError::ArithError(
"lintrans self has zero range".to_owned(),
));
return arith_error("lintrans: self has zero range");
};
Ok(self
.iter()
Expand All @@ -545,13 +540,11 @@ where
fn dfdt(self, centre: f64) -> Result<f64, RE> {
let len = self.len();
if len < 2 {
return Err(RError::NoDataError(format!(
"dfdt time series too short: {len}"
)));
return data_error("dfdt: time series too short: {len}");
};
let mut weight = 0_f64;
let mut weight = 0_f64;
let mut sumwx = 0_f64;
for x in self.iter() {
for x in self.iter() {
weight += 1_f64;
sumwx += weight * x.clone().into();
}
Expand Down
6 changes: 2 additions & 4 deletions src/vecu8.rs
View file
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
use crate::{RError, Vecu8, RE};
use crate::{data_error, Vecu8, RE};

impl Vecu8 for &[u8] {
/// Probability density function of bytes data
Expand Down Expand Up @@ -31,9 +31,7 @@ impl Vecu8 for &[u8] {
fn jointpdfu8(self, v: &[u8]) -> Result<Vec<Vec<u32>>, RE> {
let n = self.len();
if v.len() != n {
return Err(RError::DataError(
"jointpdfu8 argument vectors must be of equal length!".to_owned(),
));
return data_error("jointpdfu8: argument vectors must be of equal length!");
}
let mut res: Vec<Vec<u32>> = vec![vec![0_u32; 256]; 256];
self.iter()
Expand Down
9 changes: 5 additions & 4 deletions src/vecvec.rs
View file
Original file line number Diff line number Diff line change
Expand Up @@ -555,13 +555,14 @@ where
.par_iter()
.fold(
|| vec![0_f64; (d+1)*d/2],
| mut cov: Vec<f64>, p | {
| mut cov: Vec<f64>, selfp | {
let mut covsub = 0_usize; // subscript into the flattened array cov
let vm = p.vsub(mid); // zero mean vector
vm.iter().enumerate().for_each(|(i,thisc)|
let vm = selfp.vsub(mid); // zero mean vector
vm.iter().enumerate().for_each(|(i,diag)|
// its products up to and including the diagonal (itself)
// the number of elements to take is always the subscript + 1!
vm.iter().take(i+1).for_each(|vmi| {
cov[covsub] += thisc*vmi;
cov[covsub] += diag*vmi;
covsub += 1;
}));
cov
Expand Down
4 changes: 2 additions & 2 deletions src/vecvecg.rs
View file
Original file line number Diff line number Diff line change
Expand Up @@ -349,7 +349,7 @@ impl<T,U> VecVecg<T,U> for &[Vec<T>]
/// Like `gmedian` but returns also the sum of unit vecs and the sum of reciprocals.
fn wgmparts(self, ws:&[U], eps: f64) -> Result<(Vec<f64>,f64),RE> {
if self.len() != ws.len() {
return Err(RError::DataError("wgmparts and ws lengths mismatch".to_owned())); };
return data_error("wgmparts: ws length mismatch"); };
let mut g = self.wacentroid(ws); // start iterating from the weighted centre
let mut recsum = 0f64;
loop { // vector iteration till accuracy eps is exceeded
Expand Down Expand Up @@ -387,7 +387,7 @@ impl<T,U> VecVecg<T,U> for &[Vec<T>]
if n != mid.len() {
return data_error("wcovar self and m dimensions mismatch"); };
if self.len() != ws.len() {
return data_error("wcovar self and ws lengths mismatch"); };
return data_error("wcovar self and weights lengths mismatch"); };
let (mut covsum,wsum) = self
.par_iter().zip(ws)
.fold(
Expand Down

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