[client/py] - added multi rate calculated channel example

client/py/examples/multi-rate-calculated/calculated_interpolation.py

@@ -0,0 +1,112 @@
+#  Copyright 2024 Synnax Labs, Inc.
+#
+#  Use of this software is governed by the Business Source License included in the file
+#  licenses/BSL.txt.
+#
+#  As of the Change Date specified in that file, in accordance with the Business Source
+#  License, use of this software will be governed by the Apache License, Version 2.0,
+#  included in the file licenses/APL.txt.
+
+import synnax as sy
+
+"""
+This example demonstrates how to calculate the average of two sensor channels that
+are being sampled at different rates, using numpy's interpolation function to correctly
+align the timestamps of the two channels. This example is more complex than the
+'calculate_simple.py' example, and requires more computational resources to run.
+
+This example must be run in conjunction with the 'simulated_daq.py' file in this
+directory. Run the 'simulated_daq.py' file first, and then run this file.
+"""
+
+# We've logged in via the CLI, so there's no need to provide credentials here.
+# See https://docs.synnaxlabs.com/reference/python-client/get-started for more information.
+client = sy.Synnax()
+
+# We create a separate index channel to store the timestamps for the calculated values.
+# These will store the same timestamps as the raw time channel, but will be used to
+# index the calculated values.
+derived_time_ch = client.channels.create(
+    name="derived_time",
+    is_index=True,
+    retrieve_if_name_exists=True
+)
+
+# We'll store the average of "stream_write_example_data_1" and "stream_write_example_data_2"
+# in this channel.
+average_example_data_1 = client.channels.create(
+    name="average_example_data_1",
+    index=derived_time_ch.key,
+    data_type=sy.DataType.FLOAT32,
+    retrieve_if_name_exists=True
+)
+
+current_values = dict()
+
+TO_READ = ["time_ch_1", "time_ch_2", "data_ch_1", "data_ch_2"]
+
+import numpy as np
+
+
+def interpolate(data_ch_1_time, data_ch_1, data_ch_2_time, data_ch_2):
+    # Start off by converting the data to numpy arrays
+    data_ch_1_time = np.array(data_ch_1_time)
+    data_ch_1 = np.array(data_ch_1)
+    data_ch_2_time = np.array(data_ch_2_time)
+    data_ch_2 = np.array(data_ch_2)
+
+    # Check whether any of the timestamps overlap. If not, we can't interpolate.
+    start_time = max(data_ch_1_time[0], data_ch_2_time[0])
+    end_time = min(data_ch_1_time[-1], data_ch_2_time[-1])
+    if start_time > end_time:
+        return np.array([]), np.array([]), np.array([])
+
+    combined_timestamps = np.unique(
+        np.concatenate((data_ch_1_time, data_ch_2_time)))
+    # We only want to interpolate values that are within the range of both channels.
+    avg_timestamps = combined_timestamps[
+        (combined_timestamps >= start_time) & (combined_timestamps <= end_time)
+        ]
+    # Interpolate the values for each channel
+    sensor1_values_interp = np.interp(
+        avg_timestamps,
+        data_ch_1_time,
+        data_ch_1
+    )
+    sensor2_values_interp = np.interp(
+        avg_timestamps,
+        data_ch_2_time,
+        data_ch_2
+    )
+    # Return the interpolated values and the timestamps
+    return sensor1_values_interp, sensor2_values_interp, avg_timestamps
+
+
+with client.open_writer(
+    start=sy.TimeStamp.now(),
+    channels=["derived_time", "average_example_data_1"],
+    enable_auto_commit=True
+) as writer:
+    with client.open_streamer(TO_READ) as s:
+        for fr in s:
+            time = fr["time_ch_1"][-1]
+            for k, v in fr.items():
+                current_values[k] = fr[k]
+            # If we still don't have data yet from all four channels, skip and wait for
+            # the next frame.
+            if len(current_values.items()) < 4:
+                continue
+            # Interpolate the values for each channel, and get the timestamps for the average
+            # channel.
+            sensor_1, sensor_2, time = interpolate(
+                current_values["time_ch_1"],
+                current_values["data_ch_1"],
+                current_values["time_ch_2"],
+                current_values["data_ch_2"]
+            )
+            # This means we have no samples to write
+            if len(sensor_1) == 0:
+                continue
+            # Calculate the average of the two sensors
+            avg = (sensor_1 + sensor_2) / 2
+            writer.write({"derived_time": time, "average_example_data_1": avg, })

client/py/examples/multi-rate-calculated/calculated_simple.py

@@ -0,0 +1,65 @@
+#  Copyright 2024 Synnax Labs, Inc.
+#
+#  Use of this software is governed by the Business Source License included in the file
+#  licenses/BSL.txt.
+#
+#  As of the Change Date specified in that file, in accordance with the Business Source
+#  License, use of this software will be governed by the Apache License, Version 2.0,
+#  included in the file licenses/APL.txt.
+
+import synnax as sy
+
+"""
+This example demonstrates how to  calculate the average of two sensor channels that
+are being sampled at different rates. This example uses a naive method that simply grabs
+and uses the latest value from each channel. This approach is simple, computationally
+inexpensive, and works well when both channels are operating at consistent rates.
+Good examples of this are a sensor operating at 100Hz and another at 50Hz.
+"""
+
+# We've logged in via the CLI, so there's no need to provide credentials here.
+# See https://docs.synnaxlabs.com/reference/python-client/get-started for more information.
+client = sy.Synnax()
+
+# We create a separate index channel to store the timestamps for the calculated values.
+# These will store the same timestamps as the raw time channel, but will be used to
+# index the calculated values.
+derived_time_ch = client.channels.create(
+    name="derived_time",
+    is_index=True,
+    retrieve_if_name_exists=True
+)
+
+# We'll store the average of "stream_write_example_data_1" and "stream_write_example_data_2"
+# in this channel.
+average_example_data_1 = client.channels.create(
+    name="average_example_data_1",
+    index=derived_time_ch.key,
+    data_type=sy.DataType.FLOAT32,
+    retrieve_if_name_exists=True
+)
+
+TO_READ = ["time_ch_1", "time_ch_2", "data_ch_1", "data_ch_2"]
+
+# Create a dictionary to store the latest values of each channel.
+current_values = dict()
+
+with client.open_writer(
+    start=sy.TimeStamp.now(),
+    channels=["derived_time", "average_example_data_1"],
+    enable_auto_commit=True
+) as writer:
+    with client.open_streamer(TO_READ) as s:
+        for fr in s:
+            time = fr["time_ch_1"][-1]
+            # Store the latest values in state.
+            for k, v in fr.items():
+                current_values[k] = fr[k][-1]
+
+            # If we don't have values for all channels, skip this iteration.
+            if len(current_values.items()) < 4:
+                continue
+
+            # Caluclate and write the average.
+            avg = (current_values["data_ch_1"] + current_values["data_ch_2"]) / 2
+            writer.write({"derived_time": time, "average_example_data_1": avg})

client/py/examples/multi-rate-calculated/simulated_daq.py

@@ -0,0 +1,104 @@
+#  Copyright 2024 Synnax Labs, Inc.
+#
+#  Use of this software is governed by the Business Source License included in the file
+#  licenses/BSL.txt.
+#
+#  As of the Change Date specified in that file, in accordance with the Business Source
+#  License, use of this software will be governed by the Apache License, Version 2.0,
+#  included in the file licenses/APL.txt.
+
+import time
+import numpy as np
+import synnax as sy
+
+"""
+This example sets up a simulated data acquisition system that writes data to two
+channels at different rates (along with their indexes). This example should be run
+in conjunction with the 'calculated_interpolation.py' example or the
+'calculated_simple.py' example to demonstrate how to calculate derived values from
+these channels with different rates.
+"""
+
+# We've logged in via the CLI, so there's no need to provide credentials here.
+# See https://docs.synnaxlabs.com/reference/python-client/get-started for more information.
+client = sy.Synnax()
+
+# Create an index channel that will be used to store our timestamps for the first
+# channel, operating at rate 1.
+time_ch_1 = client.channels.create(
+    name="time_ch_1",
+    is_index=True,
+    data_type=sy.DataType.TIMESTAMP,
+    retrieve_if_name_exists=True,
+)
+
+# Create a second index channel that will be used to store our timestamps for the second
+# channel, operating at rate 2.
+time_ch_2 = client.channels.create(
+    name="time_ch_2",
+    is_index=True,
+    data_type=sy.DataType.TIMESTAMP,
+    retrieve_if_name_exists=True,
+)
+
+# Data for the first channel, operating at rate 1.
+data_ch_1 = client.channels.create(
+    name="data_ch_1",
+    index=time_ch_1.key,
+    data_type=sy.DataType.FLOAT32,
+    retrieve_if_name_exists=True,
+)
+
+# Data for the second channel, operating at rate 2.
+data_ch_2 = client.channels.create(
+    name="data_ch_2",
+    index=time_ch_2.key,
+    data_type=sy.DataType.FLOAT32,
+    retrieve_if_name_exists=True,
+)
+
+# We'll start our write at the current time. This timestamp should be the same as or
+# just before the first timestamp we write.
+start = sy.TimeStamp.now()
+
+# Set a rough data rate of 20 Hz. This won't be exact because we're sleeping for a
+# fixed amount of time, but it's close enough for demonstration purposes.
+rough_rate = sy.Loop(sy.Rate.HZ * 30)
+
+
+# Open the writer as a context manager. This will make sure the writer is properly
+# closed when we're done writing. We'll write to both the time and data channels. In
+# this example, we provide the keys of the channels we want to write to, but you can
+# also provide the names and write that way.
+start = sy.TimeStamp.now()
+with client.open_writer(
+    start, [time_ch_1.key, time_ch_2.key, data_ch_1.key, data_ch_2.key],
+    enable_auto_commit=True
+) as writer:
+    i = 0
+    while rough_rate.wait():
+        time = sy.TimeStamp.now()
+        time_2 = time + sy.TimeSpan.MILLISECOND * 3
+        # Generate data to write to the first channel.
+        data_to_write = {
+            time_ch_1.key: [np.int64(time), np.int64(time_2)],
+            data_ch_1.key: [
+                np.float32(np.sin(i / 10)),
+                np.float32(np.sin((i + 1) / 10))
+            ],
+        }
+
+        # Only write to the second channel every third iteration, so its rate is 10Hz,
+        # instead of 30Hz.
+        if i % 3 == 0:
+            # Generate timestamps at a different time to introduce intentional
+            # misalignment.
+            time = sy.TimeStamp.now()
+            time_2 = time + sy.TimeSpan.MILLISECOND
+            data_to_write[time_ch_2.key] = [np.int64(time), np.int64(time_2)]
+            data_to_write[data_ch_2.key] = [np.sin(i / 100), np.sin((i + 1) / 100)]
+
+        writer.write(data_to_write)
+        i += 1
+
+print(sy.TimeSpan.since(start))

client/py/examples/stream_write.py

@@ -51,9 +51,7 @@
 
 # Set a rough data rate of 20 Hz. This won't be exact because we're sleeping for a
 # fixed amount of time, but it's close enough for demonstration purposes.
-rough_rate = sy.Rate.HZ * 50
-
-total = 5000
+loop = sy.Loop(sy.Rate.HZ * 50)
 
 # Open the writer as a context manager. This will make sure the writer is properly
 # closed when we're done writing. We'll write to both the time and data channels. In
@@ -64,7 +62,7 @@
     start, [time_ch.key, data_ch_1.key, data_ch_2.key], enable_auto_commit=True
 ) as writer:
     i = 0
-    while i < total:
+    while loop.wait():
         # Write the data to the writer
         writer.write(
             {