Source code for ogstools.meshplotlib.levels
# Copyright (c) 2012-2024, OpenGeoSys Community (http://www.opengeosys.org)
# Distributed under a Modified BSD License.
# See accompanying file LICENSE.txt or
# http://www.opengeosys.org/project/license
#
"""Utilities to create nicely spaced levels."""
from math import nextafter
import numpy as np
[docs]
def nice_num(val: float) -> float:
"""
Return the closest number of the form 10**x * {1,2,4,5}.
Fractions containing only these number are ensured to have
terminating decimal representations.
"""
pow10 = 10 ** np.floor(np.log10(val))
vals = np.array([1.0, 2.0, 4.0, 5.0, 10.0])
return pow10 * vals[np.argmin(np.abs(val / pow10 - vals))]
[docs]
def nice_range(lower: float, upper: float, n_ticks: float) -> np.ndarray:
"""
Return an array in the interval (lower, upper) with terminating decimals.
The length of the arrays will be close to n_ticks.
"""
base = nice_num(upper - lower)
tick_spacing = nice_num(base / (n_ticks - 1))
nice_lower = np.ceil(lower / tick_spacing) * tick_spacing
nice_upper = np.ceil(upper / tick_spacing) * tick_spacing
res = np.arange(nice_lower, nice_upper, tick_spacing)
return res[(res > lower) & (res < upper)]
[docs]
def adaptive_rounding(vals: np.ndarray, precision: int) -> np.ndarray:
"""
Return the given values rounded to significant digits.
The significant digits are based of the median decimal exponent and the
given precision.
"""
if vals.size == 0:
return vals
median_exp = median_exponent(vals)
rounded_vals = np.stack([np.round(v, precision - median_exp) for v in vals])
if len(set(rounded_vals)) > 1:
return rounded_vals
return np.stack([np.round(v, 12 - median_exp) for v in vals])
[docs]
def compute_levels(lower: float, upper: float, n_ticks: int) -> np.ndarray:
"""
Return an array in the interval [lower, upper] with terminating decimals.
The length of the arrays will be close to n_ticks.
At the boundaries the tickspacing may differ from the remaining array.
"""
if lower == upper:
return np.asarray([lower, nextafter(lower, np.inf)])
result = nice_range(lower, upper, n_ticks)
return np.unique(
adaptive_rounding(
np.append(np.append(lower, result), upper), precision=3
)
)
