shimeri 0.1.3

A Python package for calculating psychrometric properties of moist air and plotting psychrometric charts.

shimeri

A Python package for calculating psychrometric properties of moist air and plotting psychrometric charts based on plotly.

shimeri means "moisture" or "humidity" in Japanese.

Installation

pip install shimeri

Quick Usage

Usage example below.

import shimeri as sh
import numpy as np
import plotly.graph_objects as go
import pandas as pd

if __name__ == "__main__":
    # instantiate psychrometric calculator
    pc = sh.PsychrometricCalculator()

    # calculate psychrometric properties at 25degC dry-bulb temperature and 50% relative humidity
    db, wb, rh, hr, en = pc.get_all(db=25, rh=50)
    print(
        f"DB={db:.1f}degC, WB={wb:.1f}degC, RH={rh:.1f}%, HR={hr:.1f}g/kg, EN={en:.1f}kJ/kg"
    )

    # initialize a psychrometric chart
    fig = sh.PsychrometricChart()

    # plot random points
    rng = np.random.default_rng()
    dbs = rng.normal(25, 5, 30)
    rhs = rng.normal(50, 5, 30)
    dbs, wbs, rhs, hrs, ens = pc.get_all(db=dbs, rh=rhs)
    fig.add_points(
        en=ens,
        hr=hrs,
        name="random points",
        mode="markers",
    )

    # density plot
    df = pd.read_csv("db_rh_tokyo_2023.csv", parse_dates=True, index_col=0)
    dbs = df.loc["2023-07":"2023-08", "db"].to_numpy()
    rhs = df.loc["2023-07":"2023-08", "rh"].to_numpy()
    # hrs = pc.get_hr_from_db_rh(dbs, rhs)
    # ens = pc.get_en_from_db_hr(dbs, hrs)
    dbs, wbs, rhs, hrs, ens = pc.get_all(db=dbs, rh=rhs)
    fig.add_trace(
        go.Histogram2dContour(
            x=dbs,
            y=hrs,
            name="tokyo summer 2023",
            colorscale=[[0, "rgba(255,255,255,0)"], [1, "rgba(255,0,0,255)"]],
            contours_showlines=False,
            showscale=False,
        )
    )
    dbs = df.loc["2023-01":"2023-02", "db"].to_numpy()
    rhs = df.loc["2023-01":"2023-02", "rh"].to_numpy()
    hrs = pc.get_hr_from_db_rh(dbs, rhs)
    ens = pc.get_en_from_db_hr(dbs, hrs)
    fig.add_trace(
        go.Histogram2dContour(
            x=dbs,
            y=hrs,
            name="tokyo winter 2023",
            colorscale=[[0, "rgba(255,255,255,0)"], [1, "rgba(0,0,255,255)"]],
            contours_showlines=False,
            showscale=False,
        )
    )

    # add a line from points
    dbs = np.array([26.0, 35.0])
    rhs = np.array([50.0, 60.0])
    hrs = pc.get_hr_from_db_rh(dbs, rhs)
    ens = pc.get_en_from_db_hr(dbs, hrs)
    fig.add_points(
        en=ens,
        hr=hrs,
        name="a line",
        mode="lines",
    )

    # draw constant humidity ratio line from half-mixed point to rh=90%
    hr_mixed = (hrs[0] + hrs[1]) * 0.5
    db_mixed = (dbs[0] + dbs[1]) * 0.5

    db_90, wb_90, rh_90, hr_90, en_90 = pc.get_all(hr=hr_mixed, rh=90)

    fig.draw_iso_hr_line(
        hr=hr_mixed,
        db_range=np.array([db_mixed, db_90]),
        mode="lines+markers",
    )

    # draw a line of constant relative humidity
    fig.draw_iso_rh_line(
        rh=90,
        db_range=np.array([db_90, 15.0]),
        mode="lines",
    )

    fig.show()

Sample Result:

Attention

The PsychrometricCalculator.get_all() method uses convergence calculations. Especially when calculating from wet-bulb temperature and enthalpy, convergence can be poor, potentially leading to inaccurate results. (The poor convergence can be understood from the fact that the slopes of wet-bulb temperature and enthalpy lines are similar on the psychrometric chart.