tornadopy 0.1.61

A Python library for tornado chart generation and analysis

TornadoPy

A Python library for fast tornado, distribution, and correlation plots from uncertainty-analysis results exported from SLB Petrel.

TornadoPy uses Polars for data handling and Matplotlib for publication-quality charts.

Installation

pip install tornadopy

Quick start

from tornadopy import TornadoProcessor, tornado_plot, distribution_plot, correlation_plot

# 1. Load the Excel workbook into a dataset
ds = TornadoProcessor("uncertainty_results.xlsx")

# 2. (Optional) define reusable filter presets
#    A filter is a spatial selection only — never include 'property' here.
ds.set_filter("north", {"zone": ["north_main", "north_flank"]})

# 3. Plot — the plot function decides which parameter (sheet) and property to use
fig, ax, _ = tornado_plot(
    ds, property="stoiip", filters="north", title="STOIIP sensitivity", unit="MM bbl"
)
fig, ax, _ = distribution_plot(
    ds, parameter="NetPay", property="stoiip", filters="north"
)
fig, ax, _ = correlation_plot(
    ds, parameter="Full_Uncertainty", filters="north",
    variables=["NetPay", "Porosity", "NTG"],
)

API mental model

TornadoProcessor                   the dataset
  └─ holds: data + filter presets + introspection
  └─ no opinions on which property or sheet to plot

tornado_plot / distribution_plot / correlation_plot
  └─ accept the dataset
  └─ accept property and (where relevant) parameter/sheet
  └─ accept filters as either a stored preset name or an inline dict

Inspecting the dataset

ds.parameters()                       # ['Full_Uncertainty', 'NetPay', ...]
ds.properties("Full_Uncertainty")     # ['stoiip', 'giip', ...]
ds.unique_values("zone", "Full_Uncertainty")

ds.show_filters("Full_Uncertainty")
# {'zone': ['north_main', 'north_flank', ...], 'contact_segment': [...]}

ds.show_parameters()
# {
#   'Full_Uncertainty': {
#       'n_cases': 1854,
#       'properties': ['stoiip', 'giip'],
#       'filters': {'zone': [...], 'contact_segment': [...]},
#       'is_base_case': False,
#   },
#   'NetPay': {...},
# }

ds.describe()                         # Pretty-printed overview + usage examples

Filters

A filter is a dict of dynamic-field selections. The spatial fields (zones, segments, boundaries) come from your Excel header rows. The property key is not allowed — pass property to the plot or compute call instead.

# Inline filter
tornado_plot(ds, property="stoiip", filters={"zone": "north_main"})

# Multiple values aggregate
distribution_plot(
    ds, parameter="NetPay", property="stoiip",
    filters={"zone": ["north_main", "north_flank"]},
)

# Stored presets — reuse by name
ds.set_filters({
    "north": {"zone": ["north_main", "north_flank"]},
    "south": {"zone": ["south_main", "south_flank"]},
})
ds.list_filters()        # ['north', 'south']
ds.get_filter("north")   # {'zone': [...]}

Default parameter

distribution_plot and correlation_plot need a parameter (sheet). If you omit it, the first sheet is used and a warning is printed listing all available parameters. tornado_plot does not take parameter — a tornado chart is inherently across all sheets.

Base / reference cases

ds.base_case("stoiip")
ds.base_case("stoiip", filters="north")
ds.ref_case("stoiip", filters="north")

The base / reference sheet is set at construction time (base_case="Base_case" by default). Sheet 0 = base; sheet 1 = reference.

Statistics (raw)

For numerical work without plotting, use compute and compute_batch directly. Same rule: property is a kwarg, not a filter key.

ds.compute("p90p10", parameter="NetPay", property="stoiip", filters="north")
ds.compute_batch("p90p10", property="stoiip", filters="north")  # all sheets

Available stats: p90p10, minmax, p1p99, p25p75, mean, median, std, cv, sum, count, variance, range, percentile (options={"p": 75}), distribution.

Case selection

Find representative cases that best match statistical targets:

fig, ax, _ = tornado_plot(
    ds, property="stoiip", filters="north",
    case_selection=True,
    selection_criteria={"stoiip": 0.6, "giip": 0.4},
)

selection_criteria keys can be:

• a property name → uses the call's main filter
• a stored-filter name → uses that filter's spatial fields plus its name as the property (the 'property' ban applies; if you need different properties per zone set, use the explicit combinations form)

ds.set_filter("north", {"zone": ["north_main", "north_flank"]})
ds.set_filter("south", {"zone": ["south_main"]})

tornado_plot(
    ds, property="stoiip", filters="north",
    case_selection=True,
    selection_criteria={
        "combinations": [
            {"filters": "north", "properties": {"stoiip": 0.5, "giip": 0.2}},
            {"filters": "south", "properties": {"stoiip": 0.3}},
        ]
    },
)

Excel layout

Each parameter is one sheet:

Metadata rows (optional):
    Key: Value

Header block (one or more rows, combined automatically):
    Zone     Segment   Property
    north    main      stoiip   north  flank  stoiip   south  main  stoiip

Case marker:
    Case     Case      Case     ...

Data rows:
    Case1    123.4     456.7    ...
    Case2    125.1     458.2    ...

Rules:

1. The "Case" row's first column is the literal string Case.
2. Headers above it define columns; multiple header rows are combined.
3. The data block follows the Case row; one row per uncertainty case.
4. Each parameter is a separate sheet.
5. Base-case sheet (default "Base_case"): row 0 = base, row 1 = reference.

Plot styling

Each plot function accepts a settings dict to override defaults — colors, fonts, gridlines, etc. See the docstrings for keys.

tornado_plot(
    ds, property="stoiip",
    settings={
        "figsize": (12, 8),
        "pos_dark": "#2E5BFF",
        "neg_dark": "#E74C3C",
        "show_percentage_diff": True,
    },
)

Requirements

• Python ≥ 3.9
• numpy ≥ 1.20
• polars ≥ 0.18
• fastexcel ≥ 0.9
• matplotlib ≥ 3.5

License

MIT — see LICENSE.

Issues / contributions

https://github.com/kkollsga/tornadopy