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https://github.com/robtillaart/multimap
Arduino library for fast non-linear mapping or interpolation of values
https://github.com/robtillaart/multimap
arduino interpolation linear map multimap non-linear
Last synced: about 1 month ago
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Arduino library for fast non-linear mapping or interpolation of values
- Host: GitHub
- URL: https://github.com/robtillaart/multimap
- Owner: RobTillaart
- License: mit
- Created: 2020-04-09T11:52:37.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-04-13T09:08:23.000Z (9 months ago)
- Last Synced: 2024-08-07T18:27:45.553Z (5 months ago)
- Topics: arduino, interpolation, linear, map, multimap, non-linear
- Language: C++
- Homepage:
- Size: 30.3 KB
- Stars: 28
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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# MultiMap
Arduino library for fast non-linear mapping or interpolation of values.
## Description
In Arduino applications often the 'raw' value of a sensor is mapped upon a more
usable value. E.g. the value of analogRead() 0 .. 1023 is mapped onto 0 .. 5.0 Volt.
This is often done by the map function which does a linear interpolation.
This means in code:
```cpp
output = C1 + input * C2
```
As C1 and C2 are to be determined, Arduino has the **map()** function that calculates the
two variables runtime from two given mapping points (I1, O1) and (I2, O2).
```cpp
output = map(input, I1, I2, O1, O2):
```
In many cases when there is no linear mapping possible as the 'points' are not on a single straight line.
To solve this one needs non-linear math to calculate the output.
The **multiMap()** function simulates this math by approximating the non-linear function with multiple
linear line segments.
Of course this approximation introduces an error.
By increasing the number of points and choose their position strategically the average error will be reduced.
Note: some functions are hard to approximate with multiMap as they go to infinity or have a singularity.
Think of **tan(x)** around x = PI/2 (90°) or **sin(1/x)** around zero.
#### Related
Other mapping libraries
- https://github.com/RobTillaart/FastMap
- https://github.com/RobTillaart/Gamma
- https://github.com/RobTillaart/map2colour
- https://github.com/RobTillaart/moduloMap
- https://github.com/RobTillaart/MultiMap
## Interface
```cpp
#include "MultiMap.h"
```
#### Usage
The basic call for **multiMap()** is:
```cpp
output = Multimap(input, inputArray, outputArray, size);
```
**multiMap()** needs two equally sized arrays representing the reference 'points' named
**inputArray\[\]** and **outputArray\[\]** both of the **datatype**.
**multiMap()** will do a lookup of the input value in the inputArray\[\].
If it cannot find the index of an exact point it will determine a weighted position between two points.
This optional weighted point is used to interpolate a value from data in the output\[\] array.
- The **inputArray\[\]** must have increasing values,
there is no such restriction for the **output\[\]** array.
- The values of the **inputArray\[\]** do not need to have the same distance (non-equidistant).
E.g an array like { 1, 10, 100, 1000 } is valid.
- **multiMap()** automatically constrains the output to the first and last value in the **output\[\]** array.
This is a explicit difference with the **map()** function.
Therefore it is important to extend the range of the arrays to cover all possible values.
## Performance
**multiMap()** does a linear search for the inputValue in the inputArray.
This implies that usage of larger and more precise arrays will take more time.
Furthermore "low" input values will be found faster than "high" values.
As every usage of multiMap() is unique one should always do a performance check to see
if there is a substantial gain in the case at hand. In my experience there often is.
#### MultiMapBS
Experimental 0.1.7 => use with care.
**multiMapBS()** MMBS for short, is a very similar function as **multiMap()**.
The main difference is that MMBS uses binary search instead of linear search.
First performance tests indicate that for array sizes about 10 MMBS is on par
with **multiMap()**. This is expected as both need on average about 5 steps
to find the right interval.
Be sure to do your own tests to see if MMBS improves your performance.
#### MultiMapCache
Experimental 0.1.7 => use with care.
**multiMapCache()** MMC for short, is a very similar function as **multiMap()**.
The main difference is that MMC caches the last input and output value.
The goal is to improve the performance by preventing searching the same
value again and again.
If the input sequence has a lot of repeating values e.g. 2 2 2 2 2 2 5 5 5 5 5 4 4 4 4 2 2 2 2 2 2
MMC will be able to return the value from cache often.
Otherwise keeping cache is overhead.
Be sure to do your own tests to see if MMC improves your performance.
A possible variation is to cache the last interval - lower and upper index.
It would allow a to test that value and improve the linear search.
(to be investigated).
#### MultiMap two types
Experimental 0.2.0 => use with care.
**multiMap()** MMTT for short, is a very similar function as **multiMap()**.
The main difference is that MMTT uses two different types, typical the input
is an integer type and the output is a float or double type.
It is expected that there will be a gain if two different sized integer types are used.
This is not tested.
See the example **multimap_distance_two_types.ino**
```cpp
// for a sharp distance range finder
float sharp2cm2(int val)
{
// out[] holds the distances in cm
float out[] = {150, 140, 130, 120, 110, 100, 90, 80, 70, 60, 50, 40, 30, 20};
// in[] holds the measured analogRead() values for that distance
int in[] = { 90, 97, 105, 113, 124, 134, 147, 164, 185, 218, 255, 317, 408, 506};
float dist = multiMap(val, in, out, 14);
return dist;
}
```
A first test indicate that using the int type for the input in the example
is substantial (~37%) faster per call. Test on UNO, time in micros per call.
| types | time us | call |
|:-------:|:---------:|:-------|
| 1 | 194.93 | ```float dist = multiMap(val, in, out, 14);``` |
| 2 | 121.97 | ```float dist = multiMap(val, in, out, 14);``` |
Furthermore it is obvious that there is less need for RAM if the integer type is smaller
in size than the float type.
Be sure to do your own tests to see if MMTT improves your performance.
## Operation
See examples
Please note the fail example as this shows that in the intern math overflow can happen.
## Future
#### Must
- improve documentation
#### Should
- investigate multiMapCache behaviour
- determine overhead.
- extend unit tests
- multi type versions
#### Could
- Investigate class implementation
- basic call ```out = mm.map(value);```
- runtime adjusting input and output array **begin(in[], out[])**
- performance / footprint
- less parameter passing
- **isInRange(value)**?
- caching last value / position / index (does that help?)
- flag if input value was "IN_MIN" < input < "IN_MAX",
now it is constrained without user being informed.
- Investigate a 2D multiMap e.g. for complex numbers?
- is it possible / feasible?
#### Wont
- should the lookup tables be merged into one array of pairs?
- you cannot reuse e.g. the input array or the output array then.
this would not improve the memory footprint.
## Support
If you appreciate my libraries, you can support the development and maintenance.
Improve the quality of the libraries by providing issues and Pull Requests, or
donate through PayPal or GitHub sponsors.
Thank you,