prepare newest and largest dataset to date

prototypes/01-2_dataset_transform_whole.py

@@ -0,0 +1,168 @@
+# %%
+import datetime
+import json
+import pprint
+import re
+from collections import Counter
+from pathlib import Path
+
+import polars as pl
+
+# %%
+p_data_base = (Path.cwd() / "../data/Datenauszug_20260128").resolve()
+assert p_data_base.exists()
+
+print("Total number of JSON files")
+len(tuple(p_data_base.glob("**/*.json")))
+
+# %%
+# // check size contents of folder
+folders = tuple(p_data_base.glob("*"))
+folder_items = {}
+
+for folder in folders:
+    num_elements = len(tuple(folder.glob("*.json")))
+    if num_elements == 0:
+        continue
+    folder_items[folder] = num_elements
+    print(f"Folder {folder} contains: {num_elements} items")
+# %%
+max_item_folder = Path()
+max_val = float("-inf")
+
+for k, v in folder_items.items():
+    if v > max_val:
+        max_item_folder = k
+        max_val = v
+
+print(f"Maximum number of items : {max_val}\nFolder: {max_item_folder}")
+# %%
+pprint.pprint(folder_items)
+
+# %%
+# analyse type numbers for each folder
+folder_to_types = []
+for idx, folder in enumerate(folders):
+    folder_types = []
+    for file in folder.glob("*.json"):
+        with open(file, "r") as f:
+            data = json.load(f)
+
+        type_num = data["initial"]["dsc_TypeNumber"]["value"]
+        folder_types.append(type_num)
+
+    type_num_count = Counter(folder_types)
+    folder_to_types.append((folder.name, type_num_count))
+
+typenum_counter = Counter()
+for idx in range(len(folder_to_types)):
+    typenum_counter.update(folder_to_types[idx][1])
+
+max_type_num, type_num_count = typenum_counter.most_common(1)[0]
+print(f"Max type number is: {max_type_num}")
+print(f"Number of occurrences: {type_num_count}")
+# %%
+typenum_counter
+# %%
+folder_to_types
+# %%
+# ** one file is one curve
+# concatenate all files in one table
+# %%
+# one time-series entry is defined by its schema:
+# [ts, ps, pressure, valve]
+# [timestep, process_step, pressure_value, valve_value]
+# valid states are ps = [101, 102, 110]
+
+BREAK_EARLY = False
+
+schema_read = {
+    "DU1210": pl.Float64,
+    "DU1260": pl.Float64,
+    "DU1270": pl.Float64,
+    "PRV1270": pl.Int64,
+    "V1210": pl.Boolean,
+    "V1250": pl.Boolean,
+    "V1264": pl.Boolean,
+    "V1270": pl.Boolean,
+    "V1510": pl.Boolean,
+    "V1560": pl.Boolean,
+    "VZ1210": pl.Float64,
+    "ps": pl.UInt32,
+    "ts": pl.String,
+    "type_num": pl.UInt8,
+    "id": pl.UInt64,
+}
+schema = schema_read.copy()
+schema.update(
+    {
+        "ts": pl.Datetime,
+        "id": pl.UInt64,
+        "ts_delta_step": pl.Duration,
+        "ts_delta_cum": pl.Duration,
+    }
+)
+print(schema)
+df = pl.DataFrame(schema=schema).with_columns(pl.col("ts").dt.replace_time_zone("UTC"))
+count = 0
+for idx, file in enumerate(p_data_base.glob("**/*.json"), start=1):
+    with open(file, "r") as f:
+        data = json.load(f)
+
+    type_num = data["initial"]["dsc_TypeNumber"]["value"]
+    df_file = pl.DataFrame(data["rows"], schema_overrides=schema_read)
+    df_file = df_file.with_columns(
+        pl.col("ts").str.to_datetime(time_zone="UTC"),
+        pl.lit(type_num).alias("type_num").cast(pl.UInt8),
+        pl.lit(idx).alias("id").cast(pl.UInt64),
+    )
+    df_file = df_file.with_columns(
+        (pl.col.ts - pl.col.ts.shift(1))
+        .alias("ts_delta_step")
+        .fill_null(pl.lit(0).cast(pl.Duration))
+    )
+    df_file = df_file.with_columns(
+        pl.col("ts_delta_step").cum_sum().alias("ts_delta_cum"),
+    )
+    df = pl.concat((df, df_file))
+    count += 1
+
+    if BREAK_EARLY and idx == 3:
+        break
+
+# df = df.with_columns(pl.col("ts").str.to_datetime(time_zone="UTC"))
+# df = df.select(
+#     ["id", "type_num", "ts", "ts_delta_step", "ts_delta_cum", "ps", "DU1260", "V1560"]
+# )
+df.head()
+# %%
+t = df.columns[0]
+
+sensor_feats: list[str] = []
+pattern = re.compile(r"^([A-Z]+[0-9]+)")
+for feat in df.columns:
+    matches = pattern.match(feat)
+    if matches is None:
+        continue
+
+    sensor_feats.append(matches.group(1))
+
+sensor_feats.sort()
+
+feats_sorted = ["id", "type_num", "ts", "ts_delta_step", "ts_delta_cum", "ps"] + sensor_feats
+
+df = df.select(feats_sorted)
+df.head()
+# %%
+print(f"Files processed: {count}")
+print(f"Length of obtained data: {df.height}")
+# %%
+WRITE_TO_DISK = False
+
+concat_data = p_data_base / "all_data.parquet"
+if WRITE_TO_DISK:
+    df.write_parquet(concat_data)
+else:
+    df = pl.read_parquet(concat_data)
+
+# %%