Working out ideas on graph representation of batches.

notebooks_examples/batch/batch-music.py

@@ -0,0 +1,84 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     custom_cell_magics: kql
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.11.2
+#   kernelspec:
+#     display_name: process-improve
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import os
+import pathlib
+import sys
+
+import numpy as np
+import pandas as pd
+
+# Some basic checks, and logging set up.
+while not str(pathlib.Path.cwd().name).lower().startswith("process-improve"):
+    os.chdir(pathlib.Path.cwd().parents[0])
+basecwd = pathlib.Path.cwd()
+sys.path.insert(0, str(basecwd))
+assert basecwd.exists()
+
+import process_improve.datasets.batch as batch_ds
+from process_improve.batch.data_input import melted_to_dict
+from process_improve.batch.plotting import plot_all_batches_per_tag
+
+pd.options.plotting.backend = "plotly"
+pd.options.display.max_columns = 20
+pd.options.display.width = 200
+
+
+# %%
+# Import the data: a dictionary of dataframes
+
+dryer_raw = pd.read_csv(pathlib.Path(batch_ds.__path__._recalculate()[0]) / "dryer.csv")
+df_dict = melted_to_dict(dryer_raw, batch_id_col="batch_id")
+
+# %%
+n_levels = 180
+percentile_low, percentile_high = 0.05, 0.95  # we create a range of n_levels between this low and high value
+
+# Ideally, use more than 1 tag to align on. These columns must exist in all data frames for all batches.
+columns_to_import = ["AgitatorPower", "AgitatorTorque", "JacketTemperature", "DryerTemp"]
+tag_to_plot = columns_to_import[2]
+
+# %%
+# Plot some data, to get an idea of what is present
+plot_all_batches_per_tag(df_dict=df_dict, tag=tag_to_plot, time_column="ClockTime", x_axis_label="Time [hours]")
+
+
+# %%
+for column in columns_to_import:
+    series = dryer_raw[column]
+    percentiles = series.quantile([percentile_low, percentile_high])
+
+    # Create a range of n_levels between the low and high percentile. then map the data to these levels,
+    # but don't clip the values; rather allow them to be outside the range, even if negative.
+
+    bins, step_size = np.linspace(percentiles[percentile_low], percentiles[percentile_high], n_levels + 1, retstep=True)
+    # Extend the bins to the left and right to allow for values outside the range, in the same step size
+    all_bins = np.concatenate(
+        [
+            np.flip(np.arange(start=bins[0], stop=series.min() - step_size, step=-step_size))[:-1],
+            bins,
+            np.arange(start=bins[-1], stop=series.max() + step_size, step=+step_size)[1:],
+        ]
+    )
+    levels = pd.cut(series, bins=all_bins, labels=range(len(all_bins) - 1), include_lowest=True)
+
+
+# %%
+# Next steps: graph storage of the nodes, and then create a graph of the transitions between the nodes.
+# Handle pulses.
+
+# %%

requirements.txt

@@ -14,7 +14,9 @@ tqdm
 
 # Notebook imports
 ipywidgets
+nbformat
 
+# Dev environment
 flake8
 tox
 coverage