async def async_update(self):
"""Fetch new state data for the sensor."""
dry_temp = self.get_dry_temp(self._entity_dry_temp)
rel_hum = self.get_rel_hum(self._entity_rel_hum)
if dry_temp is not None and rel_hum is not None:
import psychrolib
psychrolib.SetUnitSystem(psychrolib.SI)
TDewPoint = psychrolib.GetTDewPointFromRelHum(dry_temp, rel_hum)
self._state = round(TDewPoint, 1)
async def async_update(self):
"""Fetch new state data for the sensor."""
dry_temp = self.get_dry_temp(self._entity_dry_temp)
rel_hum = self.get_rel_hum(self._entity_rel_hum)
pressure = self.get_press(self._entity_press)
if dry_temp is not None and rel_hum is not None and press is not None:
import psychrolib
psychrolib.SetUnitSystem(psychrolib.SI)
MoistAirDensity = psychrolib.GetMoistAirDenisity(dry_temp, rel_hum, press)
self._state = round(MoistAirDensity, 1)
def import_weather_data(self, file_path):
"""Reads weather hourly data from file path. 8760hrs of DB and Dew Point expected"""
psychrolib.SetUnitSystem(psychrolib.IP)
self.tmy3 = pd.read_csv(file_path,
skiprows=list(range(18)),
header=0,
index_col=[0, 1],
usecols=[0, 1, 3, 4, 5])
self.ambient_db = np.array(
self.tmy3['Dry Bulb Temperature {C}']) * 9 / 5 + 32
self.ambient_dp = np.array(
self.tmy3['Dew Point Temperature {C}']) * 9 / 5 + 32
self.ambient_rh = np.array(self.tmy3['Relative Humidity {%}']) / 100
self.air_entering_w = psychrolib.GetHumRatioFromRelHum(
self.ambient_db, self.ambient_rh, self.PRESSURE)
self.air_entering_h = psychrolib.GetMoistAirEnthalpy(
self.ambient_db, self.air_entering_h)
self.air_entering_wb = psychrolib.GetTWetBulbFromTDewPoint(
self.ambient_db, self.ambient_dp, self.PRESSURE)
import psychrolib
psychrolib.SetUnitSystem(psychrolib.SI)
print("")
inval = input ("Please enter the dry-bulb temperature: ")
inv = float(inval)
print("")
inval3 = input ("Please enter the humidity ratio: ")
inv3 = float(inval3)
enth = psychrolib.GetMoistAirEnthalpy(inv, inv3)
ens = str(enth)
enth2 = psychrolib.GetSatAirEnthalpy(inv, inv3)
ensb = str(enth2)
enth3 = psychrolib.GetDryAirEnthalpy(inv)
ensc = str(enth3)
print("")
print ("The moist-air enthalpy is: " + ens)
import regionmask
import seaborn as sns
import warnings
import xarray as xr
from collections import defaultdict
from intake import open_catalog
from matplotlib.colors import Normalize
from matplotlib.lines import Line2D
from pathlib import Path
from scipy import stats
from src import Labour
absolute_zero = -273.15
psl.SetUnitSystem(psl.SI)
plt.style.use("seaborn-colorblind")
# Dictionary for translating month names
month_dict = defaultdict(lambda: -1)
for i in range(1, 13):
month_dict[calendar.month_abbr[i]] = i
month_dict[None] = -1
# Silence warning about dividing by 0 or nan.
np.seterr(divide="ignore", invalid="ignore")
warnings.filterwarnings("once", ".*No gridpoint belongs to any region.*")
warnings.filterwarnings("once", ".*Geometry is in a geographic CRS.*")
warnings.filterwarnings("once", ".*invalid value .*")
warnings.filterwarnings("once", ".*All-NaN slice.*")
warnings.filterwarnings("once", ".*invalid value encountered in.*")
## Memory Optimization
# https://www.kaggle.com/rohanrao/ashrae-divide-and-conquer @vopani
# Original code from https://www.kaggle.com/gemartin/load-data-reduce-memory-usage by @gemartin
import gc
import numpy as np
import pandas as pd
from pandas.api.types import is_datetime64_any_dtype as is_datetime
from pathlib import Path
import zipfile
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
import psychrolib as psychrometric
psychrometric.SetUnitSystem(psychrometric.SI)
DATA_PATH = Path.cwd() / 'data/raw'
assert DATA_PATH.exists(), DATA_PATH
DATA_FEATHER_PATH = Path.cwd() / 'data/feather'
DATA_FEATHER_PATH.mkdir(parents=True, exist_ok=True)
ZIPPED = False
MERGE = True
MERGE_ON_NAN_CLEANING = True
def reduce_mem_usage(df, use_float16=False):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
# Fig 1c
import psychrolib as psy
import numpy as np
# Set the unit system, for example to SI (can be either psychrolib.SI or psychrolib.IP)
psy.SetUnitSystem(psy.SI)
amb_pressure = 86846.203 #pa
Tdb = np.array([[20.5, 19.8, 18.1, 20.2, 21.5], [20.8, 19.9, 17.4, 19.5, 21.4],
[21.2, 20.2, 19.4, 21.2, 21.9]])
Twb = np.array([[10.0, 13.9, 17.2, 19.8, 19.0], [10.3, 13.1, 16.2, 18.6, 18.7],
[10.3, 15.6, 18.8, 21.1, 19.6]])
spec_vol_air = np.zeros([Tdb.shape[0], Tdb.shape[1]])
spec_hum = np.zeros([Tdb.shape[0], Tdb.shape[1]])
for i in range(Tdb.shape[0]):
for j in range(Tdb.shape[1]):
spec_hum[i, j] = psy.GetSpecificHumFromHumRatio(
psy.GetHumRatioFromTWetBulb(Tdb[i, j], Twb[i, j], amb_pressure))
# spec_vol_air[i,j] = psy.GetDryAirVolume(Tdb[i,j], amb_pressure)
spec_vol_air[i, j] = psy.GetMoistAirVolume(
Tdb[i, j],
psy.GetHumRatioFromTWetBulb(Tdb[i, j], Twb[i, j], amb_pressure),
amb_pressure)
print(spec_vol_air)
# fig 1e
print(spec_hum)
# fig 1f
for i in range(3):
def SetUnitSystem_SI():
psychrolib.SetUnitSystem(psychrolib.SI)
psyf.setunitsystem(2)
psyc.SetUnitSystem(2)
def SetUnitSystem_IP():
psychrolib.SetUnitSystem(psychrolib.IP)
psyf.setunitsystem(1)
psyc.SetUnitSystem(1)
