Add arrayutils with linear_map function

Makefile

@@ -31,6 +31,9 @@ $(MODULES_PATH)/emlkmeans.mpy:
 $(MODULES_PATH)/eml_iir_q15.mpy:
 	make -C src/eml_iir_q15/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
 
+$(MODULES_PATH)/emlearn_arrayutils.mpy:
+	make -C src/emlearn_arrayutils/ ARCH=$(ARCH) MPY_DIR=$(MPY_DIR_ABS) V=1 clean dist
+
 emltrees.results: $(MODULES_PATH)/emltrees.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_trees.py
 
@@ -52,6 +55,9 @@ emlkmeans.results: $(MODULES_PATH)/emlkmeans.mpy
 eml_iir_q15.results: $(MODULES_PATH)/eml_iir_q15.mpy
 	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_iir_q15.py
 
+emlearn_arrayutils.results: $(MODULES_PATH)/emlearn_arrayutils.mpy
+	MICROPYPATH=$(MODULES_PATH) $(MICROPYTHON_BIN) tests/test_arrayutils.py
+
 .PHONY: clean
 
 clean:
@@ -68,8 +74,8 @@ release:
 	zip -r $(RELEASE_NAME).zip $(RELEASE_NAME)
 	#cp $(RELEASE_NAME).zip emlearn-micropython-latest.zip
 
-check: emltrees.results emlneighbors.results emliir.results eml_iir_q15.results emlfft.results emlkmeans.results tinymaix_cnn.results
+check: emltrees.results emlneighbors.results emliir.results eml_iir_q15.results emlfft.results emlkmeans.results emlearn_arrayutils.results tinymaix_cnn.results
 
-dist: $(MODULES_PATH)/emltrees.mpy $(MODULES_PATH)/emlneighbors.mpy $(MODULES_PATH)/emliir.mpy $(MODULES_PATH)/eml_iir_q15.mpy $(MODULES_PATH)/emlfft.mpy $(MODULES_PATH)/emlkmeans.mpy $(MODULES_PATH)/tinymaix_cnn.mpy
+dist: $(MODULES_PATH)/emltrees.mpy $(MODULES_PATH)/emlneighbors.mpy $(MODULES_PATH)/emliir.mpy $(MODULES_PATH)/eml_iir_q15.mpy $(MODULES_PATH)/emlfft.mpy $(MODULES_PATH)/emlkmeans.mpy $(MODULES_PATH)/emlearn_arrayutils.mpy $(MODULES_PATH)/tinymaix_cnn.mpy
 
 

README.md

@@ -39,6 +39,7 @@ It can be combined with feature preprocessing, including neural networks to addr
 - [xor_trees](./examples/xor_trees/). A "Hello World", using RandomForest.
 - [mnist_cnn](./examples/mnist_cnn/). Basic image classification, using Convolutional Neural Network.
 - [har_trees](./examples/har_trees/). Accelerometer-based Human Activity Recognition, using Random Forest
+- [soundlevel_iir](./examples/har_trees/). Sound Level Meter, using Infinite Impulse Response (IIR) filters.
 
 ## Prerequisites
 

examples/soundlevel_iir/README.md

@@ -0,0 +1,80 @@
+
+# Sound level meter using Infinite Impulse Response (IIR) filters
+
+This is a sound level meter implemented in MicroPython with
+emlearn-micropython.
+It implements the standard processing typically used in a
+sound level meter used for noise measurements:
+A frequency weighting and Fast (125ms) or Slow (1second)
+time integration.
+It then computes the soundlevel in decibels.
+When using Fast integration, this measurement is known as LAF.
+Or with Slow integration time, known as LAS.
+
+## Hardware requirements
+
+The device must have an `I2S` microphone,
+and support the `machine.I2S` MicroPython module.
+It has been tested on an ESP32 device, namely the LilyGo T-Camera Mic v1.6.
+
+## Notes on measurement correctness
+
+NOTE: In order to have reasonable output values,
+the microphone sensitivity must be correctly specified.
+Ideally you also check/calibrate wrt to a know good sound level meter.
+
+NOTE: There is no compensation for non-linear frequency responses in microphone.
+
+## Install requirements
+
+Make sure to have Python 3.10+ installed.
+
+Make sure to have the Unix port of MicroPython 1.23 setup.
+On Windows you can use Windows Subsystem for Linux (WSL), or Docker.
+
+Install the dependencies of this example:
+```console
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Running on host
+
+```console
+curl -o emliir.mpy https://github.com/emlearn/emlearn-micropython/raw/refs/heads/gh-pages/builds/master/x64_6.3/emliir.mpy
+curl -o emlearn_arrayutils.mpy https://github.com/emlearn/emlearn-micropython/raw/refs/heads/gh-pages/builds/master/x64_6.3/emlearn_arrayutils.mpy
+
+micropython soundlevel_test.py
+```
+
+## Running on device
+
+!Make sure you have it running successfully on host first.
+
+Flash your device with a standard MicroPython firmware,
+from the MicroPython.org downloads page.
+
+Download native modules.
+```console
+
+```
+
+```console
+mpremote cp device/emliir.mpy :
+mpremote cp device/emlearn_arrayutils.mpy :
+mpremote cp soundlevel.py :
+mpremote run soundlevel_run.py
+```
+
+## Running with live camera input
+
+This example requires hardware with SSD1306 screen,
+in addition to an I2S microphone.
+It has been tested on Lilygo T-Camera Mic v1.6.
+By adapting the pins, it will probably also work on Lilygo T-Camera S3 v1.6.
+
+```
+mpremote run soundlevel_screen.py
+```
+

examples/soundlevel_iir/color_setup.py

@@ -0,0 +1,13 @@
+
+import gc
+from machine import I2C, Pin
+
+from drivers.ssd1306.ssd1306 import SSD1306_I2C as SSD
+
+# LiLyGo T-Camera Mic
+i2c = I2C(scl=Pin(22), sda=Pin(21))
+
+oled_width = 128
+oled_height = 64
+gc.collect()  # Precaution before instantiating framebuf
+ssd = SSD(oled_width, oled_height, i2c)

examples/soundlevel_iir/iot_blynk.py

@@ -0,0 +1,95 @@
+
+
+import time
+import requests
+
+class BlynkClient():
+    """
+    Ref:
+    https://docs.blynk.io/en/blynk.cloud/device-https-api/upload-set-of-data-with-timestamps-api
+
+    """
+
+    def __init__(self,
+            token,
+            hostname='blynk.cloud',
+            protocol='https',
+        ):
+
+        self._telemetry_url = protocol + '://' + hostname + '/external/api/batch/update?token=' + token
+
+
+    def post_telemetry(self, values : list[dict[str, float]]):
+        """
+        Send multiple telemetry values.
+        The 'time' key should be in Unix milliseconds.
+        """
+        stream_values = {}
+
+        # Blynk HTTP API currently cannot send multiple timestamped values on multiple streams
+        # so we shuffle the data into being organized per-stream
+        for datapoint in values:
+            if 'time' in datapoint.keys():
+                t = datapoint['time']
+                for key, value in datapoint.items():
+                    if key == 'time':
+                        continue
+                    if key not in stream_values.keys():
+                        stream_values[key] = []
+                    stream_values[key].append((t, value))
+            else:
+                print('WARNING: ignored datapoint without time')
+
+        # NOTE: if no timestamps are provided (and there are multiple values)
+        # then regular batch update would be better, since it could be done in 1 request
+        # https://docs.blynk.io/en/blynk.cloud/device-https-api/update-multiple-datastreams-api
+
+        for key, datapoints in stream_values.items():
+            self.post_timestamped_values(key, datapoints)
+
+    def post_timestamped_values(self, pin : str, values : list[tuple[int, float]]):
+        """
+        Post multiple values from different times, for 1 stream
+        Each entry in values must be a tuple with (timestamp, value)
+        """
+
+        payload = values
+        url = self._telemetry_url+f'&pin={pin}'
+        r = requests.post(url, json=payload)
+        assert r.status_code == 200, (r.status_code, r.content)
+
+def unix_time_seconds():
+    timestamp = time.time()
+    epoch_year = time.gmtime(0)[0]
+
+    if epoch_year == 2020:
+        # seconds between 2000 (MicroPython epoch) and 1970 (Unix epoch)
+        epoch_difference = 946684800
+        timestamp = timestamp + epoch_difference
+    elif epoch_year == 1970:
+        pass
+    else:
+        raise ValueError('Unknown epoch year')
+
+    return float(timestamp)
+
+def main():
+    # bc3ab311-4e92-11ef-b45a-8f71ad378839
+    BLYNK_AUTH_TOKEN = 'Cxvp01Mvo2-A8er9mGRWLfHnPcTNvaTP'
+    api = BlynkClient(token=BLYNK_AUTH_TOKEN)
+
+    t = int(unix_time_seconds() * 1000)
+
+    values = []
+    for s in range(0, 60, 10):
+        v = {'time': t-(s*1000), 'V1': 78.0+s}
+        values.append(v)
+
+    api.post_telemetry(values)
+    print(values)
+    print('Posted telemetry')
+
+if __name__ == '__main__':
+    main()
+
+

examples/soundlevel_iir/iot_thingsboard.py

@@ -0,0 +1,71 @@
+
+import time
+import requests
+
+class ThingsBoard():
+    """
+    Ref:
+    https://thingsboard.io/docs/reference/http-api/
+    """
+
+    def __init__(self,
+            token,
+            hostname='thingsboard.cloud',
+            protocol='https',
+        ):
+
+
+        self._telemetry_url = protocol + '://' + hostname + '/api/v1/' + token + '/telemetry'
+
+    def post_telemetry(self, values : list[dict]):
+        """
+        Send multiple telemetry values.
+        The 'time' key should be in Unix milliseconds.
+        """
+
+        def encode_one(d):
+            o = { 'values': {} }
+            if 'time' in d:
+                o['ts'] = d['time']
+            for k, v in d.items():
+                if k == 'time':
+                    continue
+                o['values'][k] = v
+            return o
+
+        payload = [ encode_one(v) for v in values ]
+
+
+        r = requests.post(self._telemetry_url, json=payload)
+        assert r.status_code == 200, (r.status_code, r.content)
+
+def unix_time_seconds():
+    timestamp = time.time()
+    epoch_year = time.gmtime(0)[0]
+
+    if epoch_year == 2020:
+        # seconds between 2000 (MicroPython epoch) and 1970 (Unix epoch)
+        epoch_difference = 946684800
+        timestamp = timestamp + epoch_difference
+    elif epoch_year == 1970:
+        pass
+    else:
+        raise ValueError('Unknown epoch year')
+
+    return float(timestamp)
+
+# bc3ab311-4e92-11ef-b45a-8f71ad378839
+ACCESS_TOKEN = '7AiV0dXRPWKrxrLcI4wO'
+api = ThingsBoard(token=ACCESS_TOKEN)
+
+t = int(unix_time_seconds() * 1000)
+
+values = []
+for s in range(0, 60, 10):
+    v = {'time': t-(s*1000), 'db2': 78.0+s, 'hex': 'ABCEDFE123122312452231DFABCEDF'}
+    values.append(v)
+
+api.post_telemetry(values)
+print(values)
+print('Posted telemetry')
+