generated from dopt-python/py311
basic data analysis
This commit is contained in:
parent
f65e1f2981
commit
d5546b7fd0
75
pdm.lock
generated
75
pdm.lock
generated
@ -5,7 +5,7 @@
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groups = ["default", "data", "dev", "lint", "nb", "tests"]
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strategy = ["inherit_metadata"]
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lock_version = "4.5.0"
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content_hash = "sha256:17e3ecabeaf176fccc05c9f1c4d567ce29a09cf0cf905e53f399e4319116285c"
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content_hash = "sha256:c32739e18120fad1a688e3030b22d6e3a8a1d1412f5f16295cab4ea173479591"
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[[metadata.targets]]
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requires_python = ">=3.11"
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@ -2935,6 +2935,79 @@ files = [
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{file = "ruff-0.14.11.tar.gz", hash = "sha256:f6dc463bfa5c07a59b1ff2c3b9767373e541346ea105503b4c0369c520a66958"},
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]
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[[package]]
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name = "scipy"
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version = "1.17.0"
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requires_python = ">=3.11"
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summary = "Fundamental algorithms for scientific computing in Python"
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groups = ["data"]
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dependencies = [
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"numpy<2.7,>=1.26.4",
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]
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files = [
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{file = "scipy-1.17.0-cp314-cp314t-win_arm64.whl", hash = "sha256:031121914e295d9791319a1875444d55079885bbae5bdc9c5e0f2ee5f09d34ff"},
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{file = "scipy-1.17.0.tar.gz", hash = "sha256:2591060c8e648d8b96439e111ac41fd8342fdeff1876be2e19dea3fe8930454e"},
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]
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[[package]]
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name = "seaborn"
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version = "0.13.2"
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@ -2,12 +2,13 @@
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import json
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import pprint
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from collections import Counter
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from datetime import datetime
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from pathlib import Path
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from zoneinfo import ZoneInfo
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import pandas as pd
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import polars as pl
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WRITE_TO_DISK = False
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from scipy import stats
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# %%
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p_data_base = (Path.cwd() / "../data/Datenauszug_20251212").resolve()
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@ -75,7 +76,7 @@ folder_to_types
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# [timestep, process_step, pressure_value, valve_value]
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# valid states are ps = [101, 102, 110]
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schema = {
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schema_read = {
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"DU1260": pl.Float64,
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"V1560": pl.Boolean,
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"ps": pl.UInt32,
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@ -83,37 +84,60 @@ schema = {
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"type_num": pl.UInt8,
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"id": pl.UInt64,
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}
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df = pl.DataFrame(schema=schema)
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schema = {
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"DU1260": pl.Float64,
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"V1560": pl.Boolean,
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"ps": pl.UInt32,
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"ts": pl.Datetime,
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"type_num": pl.UInt8,
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"id": pl.UInt64,
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"ts_delta_step": pl.Duration,
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"ts_delta_cum": pl.Duration,
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}
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df = pl.DataFrame(schema=schema).with_columns(pl.col("ts").dt.replace_time_zone("UTC"))
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count = 0
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for idx, file in enumerate(p_data_base.glob("**/*.json")):
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with open(file, "r") as f:
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data = json.load(f)
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type_num = data["initial"]["dsc_TypeNumber"]["value"]
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df_file = pl.DataFrame(data["rows"], schema_overrides=schema)
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df_file = pl.DataFrame(data["rows"], schema_overrides=schema_read)
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df_file = df_file.with_columns(
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pl.col("ts").str.to_datetime(time_zone="UTC"),
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pl.lit(type_num).alias("type_num").cast(pl.UInt8),
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pl.lit(idx).alias("id").cast(pl.UInt64),
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)
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df_file = df_file.with_columns(
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(pl.col.ts - pl.col.ts.shift(1))
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.alias("ts_delta_step")
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.fill_null(pl.lit(0).cast(pl.Duration))
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)
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df_file = df_file.with_columns(
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pl.col("ts_delta_step").cum_sum().alias("ts_delta_cum"),
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)
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df = pl.concat((df, df_file))
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count += 1
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df = df.with_columns(pl.col("ts").str.to_datetime(time_zone="UTC"))
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df = df.select(["id", "type_num", "ts", "ps", "DU1260", "V1560"])
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# df = df.with_columns(pl.col("ts").str.to_datetime(time_zone="UTC"))
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df = df.select(
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["id", "type_num", "ts", "ts_delta_step", "ts_delta_cum", "ps", "DU1260", "V1560"]
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)
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df.head()
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# %%
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print(f"Files processed: {count}")
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print(f"Length of obtained data: {len(df)}")
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# %%
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WRITE_TO_DISK = False
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concat_data = p_data_base / "all_data.parquet"
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if WRITE_TO_DISK:
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df.write_parquet(concat_data)
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else:
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df = pl.read_parquet(concat_data)
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# %%
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df.head()
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print(f"Number of entries in data: {len(df)}")
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print(f"Number of curves in data: {len(df.select('id').unique())}")
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df.head()
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# %%
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# valid ps = 101, 102, 110
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# filter all entries which contain invalid error states
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@ -121,8 +145,106 @@ invalid_ids = df.filter(~pl.col("ps").is_in((101, 102, 110))).select("id").uniqu
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print(f"Number of invalid IDs: {len(invalid_ids)}")
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df = df.filter(~pl.col("id").is_in(invalid_ids["id"].implode()))
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print(f"Number of curves in data after cleansing: {len(df.select('id').unique())}")
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# sort chronologically
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df = df.sort(by=["id", "ts"], descending=[False, False])
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# %%
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df.select(["ts", "DU1260"])
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# filter for relevant type number with maximum number of entries
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TARGET_TYPE_NUM = 2
|
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df = df.filter(pl.col.type_num == TARGET_TYPE_NUM)
|
||||
print(f"Number of entries for type num {TARGET_TYPE_NUM}: {len(df)}")
|
||||
print(f"Number of curves in data: {len(df.select('id').unique())}")
|
||||
# %%
|
||||
df.plot.line(x="ts", y="DU1260")
|
||||
current_time = datetime.now(tz=ZoneInfo("UTC"))
|
||||
df_reconst = df.with_columns(
|
||||
(pl.col.ts_delta_cum + pl.lit(current_time)).alias("reconstructed")
|
||||
)
|
||||
# %%
|
||||
df_reconst
|
||||
# %%
|
||||
collection = df_reconst.select(pl.col.id).unique().sort(by="id")["id"][:10]
|
||||
# %%
|
||||
series = df_reconst.filter(pl.col.id.is_in(collection))
|
||||
series
|
||||
# %%
|
||||
series.select(pl.exclude("ts_delta_step", "ts_delta_cum")).plot.line(
|
||||
x="reconstructed", y="DU1260"
|
||||
)
|
||||
|
||||
# %%
|
||||
series.group_by("id").agg(pl.col("ts_delta_cum").max())
|
||||
# %%
|
||||
series.group_by("id").agg(pl.len())
|
||||
|
||||
# ** simple stats
|
||||
# try to separate anomalies by time/duration
|
||||
# // "Duration Anomalies"
|
||||
# IQR
|
||||
durations = df_reconst.group_by("id").agg(pl.col("ts_delta_cum").max())
|
||||
durations = durations.with_columns(pl.col.ts_delta_cum.dt.total_microseconds())
|
||||
durations.head()
|
||||
|
||||
FACTOR = 1.5
|
||||
iqr = stats.iqr(durations["ts_delta_cum"])
|
||||
quantiles = stats.quantile(durations["ts_delta_cum"], [0.25, 0.75])
|
||||
print(f"Quantiles (0.25, 0.75): {quantiles}")
|
||||
print(f"IQR: {iqr}")
|
||||
iqr_lb = max(iqr - FACTOR * quantiles[0], 0)
|
||||
iqr_ub = iqr + FACTOR * quantiles[1]
|
||||
print(f"Lower bound: {iqr_lb}")
|
||||
print(f"Upper bound: {iqr_ub}")
|
||||
durations.describe()
|
||||
# %%
|
||||
df_reconst.filter(pl.col.ps == 102).filter(
|
||||
pl.col.ts_delta_cum > pl.duration(microseconds=iqr_ub)
|
||||
)
|
||||
# %%
|
||||
filter_out_time = (
|
||||
df_reconst.filter(pl.col.ts_delta_cum > pl.duration(microseconds=iqr_ub))
|
||||
.select("id")
|
||||
.unique()
|
||||
)
|
||||
df_out_time = df_reconst.filter(pl.col.id.is_in(filter_out_time["id"].implode()))
|
||||
df_out_time
|
||||
# TODO calculate duration for each phase
|
||||
ids_out = df_out_time["id"].unique().implode()
|
||||
df_remain = df_reconst.filter(~pl.col.id.is_in(ids_out))
|
||||
df_remain
|
||||
# %%
|
||||
df_analyse = (
|
||||
df_remain.group_by("id")
|
||||
.agg(pl.len().alias("count"), pl.col("ts_delta_cum").max())
|
||||
.with_columns(
|
||||
(pl.col.count / pl.col.ts_delta_cum.dt.total_microseconds()).alias(
|
||||
"mean_sampling_rate"
|
||||
)
|
||||
)
|
||||
)
|
||||
# %%
|
||||
df_analyse.describe()
|
||||
# %%
|
||||
df_analyse2 = (
|
||||
df_reconst.group_by("id")
|
||||
.agg(pl.len().alias("count"), pl.col("ts_delta_cum").max())
|
||||
.with_columns(
|
||||
(pl.col.count / pl.col.ts_delta_cum.dt.total_microseconds()).alias(
|
||||
"mean_sampling_rate"
|
||||
)
|
||||
)
|
||||
)
|
||||
df_analyse2.describe()
|
||||
# %%
|
||||
df2
|
||||
# %%
|
||||
series
|
||||
# %%
|
||||
# %%
|
||||
|
||||
series.head()
|
||||
# %%
|
||||
temp = df.filter(pl.col.id.is_in(collection))
|
||||
temp
|
||||
# %%
|
||||
temp = temp.with_columns((pl.col.ts_delta + pl.lit(current_time)).alias("reconstructed"))
|
||||
# %%
|
||||
temp
|
||||
# %%
|
||||
|
||||
@ -153,4 +153,5 @@ data = [
|
||||
"polars>=1.37.1",
|
||||
"seaborn>=0.13.2",
|
||||
"altair>=6.0.0",
|
||||
"scipy>=1.17.0",
|
||||
]
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user