Psychrochart

A python 3 library to make psychrometric charts and overlay information on them.

It implements a useful collection of psychrometric equations for moisture and humid air calculations, and the generation of beautiful and high customizable psychrometric charts in SVG with matplotlib.

Psychrometric calculations to make the chart data are done with PsychroLib (summary paper in https://doi.org/10.21105/joss.01137).

Install

Get it from pypi or clone it if you want to run the tests.

pip install psychrochart

Features

SI units (with temperatures in celsius for better readability), with partial compatibility with IP system (imperial units)
Easy style customization based on pydantic models and config presets for full customization of chart limits, included lines and labels, colors, line styles, line widths, etc..
Psychrometric charts within temperature and humidity ratio ranges, for any pressure*, with:
- Saturation line
- Constant RH lines
- Constant enthalpy lines
- Constant wet-bulb temperature lines
- Constant specific volume lines
- Constant dry-bulb temperature lines (internal orthogonal grid, vertical)
- Constant humidity ratio lines (internal orthogonal grid, horizontal)
Plot legend for each family of lines, labeled zones and annotations
Specify labels for each family of lines
Overlay points, arrows, data-series (numpy arrays or pandas series), and convex hulls around points
Define multiple kinds of zones limited by psychrometric values:
- 'dbt-rh' for areas between dry-bulb temperature and relative humidity values,
- 'enthalpy-rh' for areas between constant enthalpy and relative humidity values
- 'volume-rh' for areas between constant volume and relative humidity values
- 'dbt-wmax' for an area between dry-bulb temperature and water vapor content values (:= a rectangle cut by the saturation line),
- 'xy-points' to define arbitrary closed paths in plot coordinates (dbt, abs humidity)
Export as SVG, PNG files, or generate dynamic SVGs with extra CSS and with chart.make_svg(...)

NOTE: The ranges of temperature, humidity and pressure where this library should provide good results are within the normal environments for people to live in.

Don't expect right results if doing other type of thermodynamic calculations.

⚠️ Over saturated water vapor states are not implemented. This library is intended for HVAC applications only ⚠️

Usage

from psychrochart import PsychroChart

# Load default style:
chart_default = PsychroChart.create()
# customize anything
chart_default.config.limits.range_temp_c = (15.0, 35.0)
chart_default.config.limits.range_humidity_g_kg = (5, 25)
chart_default.config.saturation.linewidth = 1
chart_default.config.constant_wet_temp.color = "darkblue"
# plot
axes = chart_default.plot()
axes.get_figure()
# or store on disk
chart_default.save("my-custom-chart.svg")

Called from the terminal (python psychrochart), it plots and shows the default chart using the default matplotlib backend, equivalent to this python script:

from psychrochart import PsychroChart
import matplotlib.pyplot as plt

PsychroChart.create().plot(ax=plt.gca())
plt.show()

Chart customization

The default styling for charts is defined in JSON files that you can change, or you can pass a path of a file in JSON, or a dict, when you create the psychrometric chart object. Included styles are: default, ashrae, ashrae_ip (adjusted for IP units), interior, and minimal.

from pathlib import Path
from psychrochart import load_config, PsychroChart

# Load preconfigured styles:
chart_ashrae_style = PsychroChart.create('ashrae')
chart_ashrae_style.plot()

chart_minimal = PsychroChart.create('minimal')
chart_minimal.plot()

# Get a preconfigured style model and customize it
chart_config = load_config('interior')
chart_config.limits.range_temp_c = (18.0, 32.0)
chart_config.limits.range_humidity_g_kg = (1.0, 40.0)
chart_config.limits.altitude_m = 3000

custom_chart = PsychroChart.create(chart_config)
custom_chart.save("custom-chart.svg")

# serialize the config for future uses
assert chart_config.json() == custom_chart.config.json()
Path('path/to/chart_config_file.json').write_text(chart_config.json())
custom_chart_bis = PsychroChart.create('path/to/chart_config_file.json')
# or even the full psychrochart
Path('path/to/chart_file.json').write_text(custom_chart.json())
custom_chart_bis_2 = PsychroChart.parse_file('path/to/chart_file.json')

# Specify the styles JSON file:
chart_custom = PsychroChart.create('/path/to/json_file.json')
chart_custom.plot()

# Pass a dict with the changes wanted:
custom_style = {
    "figure": {
        "figsize": [12, 8],
        "base_fontsize": 12,
        "title": "My chart",
        "x_label": None,
        "y_label": None,
        "partial_axis": False
    },
    "limits": {
        "range_temp_c": [15, 30],
        "range_humidity_g_kg": [0, 25],
        "altitude_m": 900,
        "step_temp": .5
    },
    "saturation": {"color": [0, .3, 1.], "linewidth": 2},
    "constant_rh": {"color": [0.0, 0.498, 1.0, .7], "linewidth": 2.5,
                    "linestyle": ":"},
    "chart_params": {
        "with_constant_rh": True,
        "constant_rh_curves": [25, 50, 75],
        "constant_rh_labels": [25, 50, 75],
        "with_constant_v": False,
        "with_constant_h": False,
        "with_constant_wet_temp": False,
        "with_zones": False
    },
    "constant_v_annotation": {
        "color":[0.2, 0.2, 0.2],
        "fontsize":7,
        "bbox": dict(boxstyle="square,pad=-0.2", color=[1, 1, 1, 0.9], lw=0.5)
    },
    "constant_h_annotation": {
        "color":[0.2, 0.2, 0.2],
        "fontsize":6,
        "bbox": dict(boxstyle="square,pad=-0.1", color=[1, 1, 1, 0.9], lw=0.5)
    },
    "constant_wet_temp_annotation": {
        "color":[0.2, 0.2, 0.2],
        "fontsize":7,
        "bbox": dict(boxstyle="square,pad=0", color=[1, 1, 1, 0.9], lw=0.5)
    },
    "constant_rh_annotation": {
        "color":[0.2, 0.2, 0.2],
        "fontsize":7,
        "bbox": dict(boxstyle="square,pad=0", color=[1, 1, 1, 0.9], lw=0.5)
    }
}

chart_custom_2 = PsychroChart.create(custom_style)
chart_custom_2.plot()

The custom configuration does not need to include all fields, but only the fields you want to change.

To play with it and see the results, look at this notebook with usage examples.

Development and testing

To run the tests, clone the repository, poetry install it, and run poetry run pytest.

Run poetry run pre-commit run --all-files to apply linters for changes in the code 😜.

License

MIT license, so do with it as you like ;-)

Included styling examples

Default style:

ASHRAE Handbook black and white style: (preset: ashrae)

ASHRAE Handbook black and white style (IP units): (preset: ashrae_ip)

Minimal style: (preset: minimal)

azogue/psychrochart