iir: Rename to emlearn_iir

Author: jonnor

Makefile

@@ -16,8 +16,8 @@ $(MODULES_PATH)/emlearn_trees.mpy:
 $(MODULES_PATH)/emlearn_neighbors.mpy:
 	make -C src/emlearn_neighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
-$(MODULES_PATH)/emliir.mpy:
-	make -C src/emliir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+$(MODULES_PATH)/emlearn_iir.mpy:
+	make -C src/emlearn_iir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
 $(MODULES_PATH)/emlearn_fft.mpy:
 	make -C src/emlearn_fft/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
@@ -40,7 +40,7 @@ emlearn_trees.results: $(MODULES_PATH)/emlearn_trees.mpy
 emlearn_neighbors.results: $(MODULES_PATH)/emlearn_neighbors.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_neighbors.py
 
-emliir.results: $(MODULES_PATH)/emliir.mpy
+emlearn_iir.results: $(MODULES_PATH)/emlearn_iir.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_iir.py
 
 emlearn_fft.results: $(MODULES_PATH)/emlearn_fft.mpy
@@ -63,7 +63,7 @@ emlearn_arrayutils.results: $(MODULES_PATH)/emlearn_arrayutils.mpy
 clean:
 	make -C src/emlearn_trees/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
 	make -C src/emlearn_neighbors/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
-	make -C src/emliir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
+	make -C src/emlearn_iir/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean
 	rm -rf ./dist
 
 RELEASE_NAME = emlearn-micropython-$(VERSION)
@@ -74,8 +74,8 @@ release:
 	zip -r $(RELEASE_NAME).zip $(RELEASE_NAME)
 	#cp $(RELEASE_NAME).zip emlearn-micropython-latest.zip
 
-check: emlearn_trees.results emlearn_neighbors.results emliir.results emlearn_iir_q15.results emlearn_fft.results emlearn_kmeans.results emlearn_arrayutils.results emlearn_cnn.results
+check: emlearn_trees.results emlearn_neighbors.results emlearn_iir.results emlearn_iir_q15.results emlearn_fft.results emlearn_kmeans.results emlearn_arrayutils.results emlearn_cnn.results
 
-dist: $(MODULES_PATH)/emlearn_trees.mpy $(MODULES_PATH)/emlearn_neighbors.mpy $(MODULES_PATH)/emliir.mpy $(MODULES_PATH)/emlearn_iir_q15.mpy $(MODULES_PATH)/emlearn_fft.mpy $(MODULES_PATH)/emlearn_kmeans.mpy $(MODULES_PATH)/emlearn_arrayutils.mpy $(MODULES_PATH)/emlearn_cnn.mpy
+dist: $(MODULES_PATH)/emlearn_trees.mpy $(MODULES_PATH)/emlearn_neighbors.mpy $(MODULES_PATH)/emlearn_iir.mpy $(MODULES_PATH)/emlearn_iir_q15.mpy $(MODULES_PATH)/emlearn_fft.mpy $(MODULES_PATH)/emlearn_kmeans.mpy $(MODULES_PATH)/emlearn_arrayutils.mpy $(MODULES_PATH)/emlearn_cnn.mpy
 
 

benchmarks/iir/iir_benchmark.py

@@ -14,9 +14,9 @@
     print(e)
     pass
 
-emliir = None
+emlearn_iir = None
 try:
-    import emliir
+    import emlearn_iir
 except ImportError as e:
     print(e)
     pass
@@ -70,9 +70,9 @@ def main():
         print('ulab', t)
 
     # emlearn
-    if emliir:
+    if emlearn_iir:
         start = time.ticks_us()
-        iir = emliir.new(coeff)
+        iir = emlearn_iir.new(coeff)
         for r in range(repeats):
             iir.run(a)
         t = (time.ticks_diff(time.ticks_us(), start) / repeats ) / 1000.0 # ms

benchmarks/iir/iir_run.py

@@ -27,8 +27,8 @@ def iir_process_file(inp, out, filters, chunksize):
         if len(reader.shape) != 1:
             raise ValueError("Input must be 1d")
         if reader.typecode == 'f':
-            import emliir
-            filter = emliir.new(coefficients)
+            import emlearn_iir
+            filter = emlearn_iir.new(coefficients)
         elif reader.typecode == 'h':
             import emlearn_iir_q15
             filter = emlearn_iir_q15.new(coefficients)

doc/micropython-tinyml-status.md

@@ -167,7 +167,7 @@ FIR filters.
 
 IIR filters.
 `scipy.signal.sosfilt` available in [ulab](https://github.com/v923z/micropython-ulab).
-`emliir` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
+`emlearn_iir` available in [emlearn-micropython](https://github.com/emlearn/emlearn-micropython).
 
 ### Fast Fourier Transform (FFT)
 Key part of computing frequency spectrum, or time-frequency representations (spectrogram).

examples/soundlevel_iir/README.md

@@ -25,7 +25,7 @@ sound level meter used for noise measurements:
 
 The following emlearn functionality are used in the implementation:
 
-- `emliir`. IIR filters to implement the A weighting filter
+- `emlearn_iir`. IIR filters to implement the A weighting filter
 - `emlearn_arrayutils.linear_map()`. To convert array values between `float` and `int16`
 
 
@@ -83,7 +83,7 @@ pip install -r requirements.txt
 Install the emlearn modules
 ```console
 micropython -m mip install https://emlearn.github.io/emlearn-micropython/builds/master/x64_6.3/emlearn_arrayutils.mpy
-micropython -m mip install https://emlearn.github.io/emlearn-micropython/builds/master/x64_6.3/emliir.mpy
+micropython -m mip install https://emlearn.github.io/emlearn-micropython/builds/master/x64_6.3/emlearn_iir.mpy
 ```
 
 Compute soundlevels for a file
@@ -103,7 +103,7 @@ Ensure you have an I2S microphone, and that the pinout is correct in `soundlevel
 Install the emlearn modules *for your architecture*. ESP32=xtensawin
 ```console
 mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_arrayutils.mpy
-mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emliir.mpy
+mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_iir.mpy
 ```
 
 Copy example code to the device
@@ -191,7 +191,7 @@ WIFI_PASSWORD = 'MY WIFI PASSWORD'
 Install the emlearn modules *for your architecture*. ESP32=xtensawin
 ```console
 mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_arrayutils.mpy
-mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emliir.mpy
+mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_iir.mpy
 ```
 
 Copy example to the device
@@ -220,7 +220,7 @@ So the example can be adapted to other displays supported by that framework.
 Install the emlearn modules *for your architecture*. ESP32=xtensawin
 ```console
 mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_arrayutils.mpy
-mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emliir.mpy
+mpremote mip install https://emlearn.github.io/emlearn-micropython/builds/master/xtensawin_6.3/emlearn_iir.mpy
 ```
 
 Install UI framework and screen driver

examples/soundlevel_iir/soundlevel.py

@@ -4,7 +4,7 @@
 import array
 from collections import deque
 
-import emliir
+import emlearn_iir
 from emlearn_arrayutils import linear_map
 #import emlearn_iir_q15
 #from iir_python import IIRFilter
@@ -13,7 +13,7 @@ class IIRFilterEmlearn:
 
     def __init__(self, coefficients):
         c = array.array('f', coefficients)
-        self.iir = emliir.new(c)
+        self.iir = emlearn_iir.new(c)
     def process(self, samples):
         self.iir.run(samples)
 

src/emliir/Makefile renamed to src/emlearn_iir/Makefile

@@ -14,7 +14,7 @@ EMLEARN_DIR := $(shell python3 -c "import emlearn; print(emlearn.includedir)")
 DIST_DIR := ../../dist/$(ARCH)_$(MPY_ABI_VERSION)
 
 # Name of module
-MOD = emliir
+MOD = emlearn_iir
 
 # Source files (.c or .py)
 SRC = iir_filter.c

src/emliir/iir_filter.c renamed to src/emlearn_iir/iir_filter.c

@@ -7,7 +7,7 @@ def convert_coefficients(c, scale=(2**13)-1):
     assert (len(c) % 6 == 0), len(c)
     n_stages = len(c) // 6
 
-    # Transposed Direct Form II / sosfilt / emliir
+    # Transposed Direct Form II / sosfilt / emlearn_iir
     # [ b0, b1, b2, 1.0, -a1, -a2 ]
     # Direct Form I / emlearn_iir_q15 
     # [ b0, 0, b1, b2, a1, a2 ]

src/emlearn_iir_q15/iir_filter.py

@@ -1,5 +1,5 @@
 
-import emliir
+import emlearn_iir
 
 import array
 import gc
@@ -26,7 +26,7 @@ def test_iir_del():
         -1.999111423794296,
         0.9991118187443988
     ])
-    model = emliir.new(coefficients)
+    model = emlearn_iir.new(coefficients)
     after_new = gc.mem_alloc()
     added = after_new - before_new