Loading README.md +29 −3 Original line number Diff line number Diff line Loading @@ -5,16 +5,35 @@ Python package/script for downloading trends from Building Logix Data Exchange ( ## Installation ``` pip install git+https://git.isis.vanderbilt.edu/SmartBuildings/bdx@v0.1.0 pip install git+https://git.isis.vanderbilt.edu/SmartBuildings/bdx@v0.2.0 ``` ## Usage ### Command line ``` python -m bdx user password trend start end file ```bash usage: bdx [-h] [--file FILE] [-p] user password trend start end Download data from Building Logix Data Exchange (BDX) and save it to csv. positional arguments: user Username for BDX password Password for BDX trend ID of trend to download start Start time in ISO 8601 format. end Start time in ISO 8601 format. optional arguments: -h, --help show this help message and exit --file FILE Filenname to save CSV. If not provided, printed to console -p, --preprocess Convert to SI units by guessing field names. Date/time formats can be - 2020-02-03T15:30:00Z-6 (i.e. Feb 3 2020 3:30 PM UTC-6 timezone) - 20200203T153000Z-6 # for example: python -m bdx harrypotter wing@rd1umLeviosa 2422 2020-02-03T15:00Z-6 2020-02-04T16:00Z-6 ~/data.csv ``` Loading @@ -39,6 +58,13 @@ dataframe = get_trend(trend_id='2422', password='wing@rd1umLeviosa', start=start, end=end) # Optionally, to convert fields to SI units from bdx import apply_heuristics dataframe, left = apply_heuristiccs(dataframe) print('The following fields did not match and were note converted:', left) ``` ## Requirements Loading bdx/__init__.py +1 −0 Original line number Diff line number Diff line from .data import get_trend from .preprocess import apply_heuristics bdx/__main__.py +19 −7 Original line number Diff line number Diff line Loading @@ -3,6 +3,7 @@ from argparse import ArgumentParser, RawDescriptionHelpFormatter from dateutil.parser import parse from .data import get_trend from .preprocess import apply_heuristics parser = ArgumentParser( prog='bdx', Loading @@ -13,13 +14,16 @@ parser = ArgumentParser( ' - 2020-02-03T15:30:00Z-6 (i.e. Feb 3 2020 3:30 PM UTC-6 timezone)\n' ' - 20200203T153000Z-6\n'), formatter_class=RawDescriptionHelpFormatter) parser.add_argument('user', help='Username for BDX', type=str) parser.add_argument('password', help='Password for BDX', type=str) parser.add_argument('trend', help='ID of trend to download', type=str) parser.add_argument('start', help='Start time in ISO 8601 format.', type=parse) parser.add_argument('end', help='Start time in ISO 8601 format.', type=parse) parser.add_argument('--file', help=('Filenname to save CSV. If not provided, ' 'printed to console'), type=str, default=sys.stdout) parser.add_argument('user', type=str, help='Username for BDX') parser.add_argument('password', type=str, help='Password for BDX') parser.add_argument('trend', type=str, help='ID of trend to download') parser.add_argument('start', type=parse, help='Start time in ISO 8601 format.') parser.add_argument('end', type=parse, help='Start time in ISO 8601 format.') parser.add_argument('--file',type=str, default=sys.stdout, help=('Filenname to save CSV. If not provided, ' 'printed to console')) parser.add_argument('-p', '--preprocess', action='store_true', default=False, help='Convert to SI units by guessing field names.') args = parser.parse_args() Loading @@ -29,4 +33,12 @@ trend = get_trend(trend_id=args.trend, start=args.start, end=args.end) if args.preprocess: trend, left = apply_heuristics(trend) if len(left) > 0: print('The following fields did not match and were not converted:', file=sys.stderr) for field in left: print(field, file=sys.stderr) trend.to_csv(args.file) bdx/preprocess.py 0 → 100644 +61 −0 Original line number Diff line number Diff line """ Rudimentary preprocessing for data by guessing column names: - Temperatures (F to K) - Power (kW, BTU to W) - Flow (Gallons/min to m^3/s) - Pressure (PSI to Pascals) """ import re from typing import Tuple, List, Callable import pandas as pd from .thermo import f2k, gpm2m3s, psi2pa HEURISTICS = ( ('_Temp|Temp$|_Humidity|Wet_Bulb', f2k), ('Kw$', lambda kw: 1000 * kw), ('Power$', lambda p: p), ('Percent$', lambda pct: 0.01 * pct), ('Flow$', gpm2m3s), ('Press$', psi2pa), ) def apply_heuristics(df: pd.DataFrame, heuristics: Tuple[Tuple[str, Callable]] = HEURISTICS) \ -> Tuple[pd.DataFrame, List[str]]: """ Apply functions to columns based on matching field names. Parameters ---------- df : pd.DataFrame The dataframe which is processed. heuristics : Tuple[Tuple[str, Callable]], optional A tuple of tuples, where each tuple containes a regular expression to match field names followed by a function that takes a column and returns a converted column, by default bdx.preprocess.HEURISTICS Returns ------- Tuple[pd.DataFrame, List[str]] The converted dataframe, and a list of column names which were not matched. """ left = [] for column in df: matched = False for pattern, func in heuristics: if re.search(pattern, column) is not None: matched = True df[column] = func(df[column]) if not matched: left.append(column) return df, left bdx/thermo.py 0 → 100644 +201 −0 Original line number Diff line number Diff line """ Contains formulae for thermodynamics operations. Adapted from https://git.isis.vanderbilt.edu/SmartBuildings/EngineeringScienceBuilding/blob/0ffc3c8234ecca50606cfd9ed1e1d464f998b832/src/thermo.py """ from collections import namedtuple from numbers import Number import numpy as np from scipy.optimize import newton # physics constants _Constants = namedtuple('Constants', field_names = ( 'cv', # specific volumetric heat capacity of water (J / m^3 . K) 'cm', # specific mass heat capacity of water (J / kg .K) )) CONSTANTS = _Constants(cv=4.1796e6, cm=4.1813e3) # Constants used by vaporpressure() and dewpoint() _ABConstants = namedtuple('ArdenBuckConstants', 'a b c d') ABC = _ABConstants(a=0.61121, b=18.678, c=257.14, d=234.5) # Temperature conversion functions # f: Farenheit # c: Celsius # k: Kelvin def f2c(f: float): return (f - 32) / 1.8 def f2k(f: float): return c2k(f2c(f)) def c2f(c: float): return (c * 1.8) + 32 def c2k(c: float): return c + 273.15 def k2c(k: float): return k - 273.15 def k2f(k: float): return c2f(k2c(k)) # Energy conversion functions # btu: British Thermal Unit # j: Joule def btu2j(btu: float): return btu * 1055.056 def j2btu(j: float): return j / 1055.056 # Power conversion functions # btuhr: BTU / hour # w: Watt # ton: Ton def btuhr2w(btu: float): return btu2j(btu) / 3600 def btuhr2ton(btuhr: float): return btuhr / 12000 def ton2btuhr(ton: float): return ton * 12000 def ton2w(ton: float): return btuhr2w(ton2btuhr(ton)) def w2btuhr(w: float): return j2btu(w * 3600) def w2ton(w: float): return btuhr2ton(w2btuhr(w)) # Flow rate / volume conversion functions # gph: Gallons Per Hour # gpm: Gallons Per Minute # m3s: Cubic Metres per second def gph2m3s(gph: float): return gph * 3.7854e-3 / 3600 def gpm2m3s(gpm: float): return gpm * 3.7854e-3 / 60 # Pressure conversion def psi2pa(psi: float): return psi * 6894.75728 def vaporpressure(t: float) -> float: """ Uses Arden Buck equation to find saturation vapor pressure from ambient temperature in Kelvin. Parameters ---------- t: float Temperature in Kelvin. Returns ------- float The vapor pressure in Pascals. """ t = k2c(t) arg = (ABC.b - t / ABC.d) * (t / (ABC.c + t)) return ABC.a * np.exp(arg) * 1000 def dewpoint(t: float, rh: float) -> float: """ Uses Magnus formula enhanced w/ Arden Buck constants to calculate dew point temperature. Parameters ---------- t: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The dew point temperature in Kelvin. """ t = k2c(t) arg = (ABC.b - t / ABC.d) * (t / (ABC.c + t)) gamma = np.log(rh * 100 * np.exp(arg) / 100) return c2k((ABC.c * gamma) / (ABC.b - gamma)) def wetbulb(t: float, rh: float) -> float: """ Uses Roland Stull's formula to calculate wet bulb temperature from ambient temperature and relative humidity. Parameters ---------- t: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The wet bulb temperature in Kelvin. """ t = k2c(t) rh *= 100 # convert from fraction to percentage return c2k( t * np.arctan(0.151977 * np.sqrt(rh + 8.313659)) + np.arctan(t + rh) - np.arctan(rh - 1.676331) + 0.00391838 * np.power(rh, 1.5) * np.arctan(0.023101 * rh) - 4.686035) def _stull_eq(t, rh, tw): """ Sets up the stull equation in the form: `f(t, rh) = tw => f(t, rh) - tw = 0` The new form can be represented as: `g(t) = f(t, rh) - tw` The root of `g(t)` is the ambient temperature. Used by `tambient()`. All temperatues in the equation are Celsius. """ rh *= 100 # convert from fraction to percentage return - tw + (t * np.arctan(0.151977 * np.sqrt(rh + 8.313659)) + np.arctan(t + rh) - np.arctan(rh - 1.676331) + 0.00391838 * np.power(rh, 1.5) * np.arctan(0.023101 * rh) - 4.686035) def ambient(tw: float, rh: float) -> float: """ Uses Roland Stull's formula and Newton's method to calculate ambient temperature from wet bulb temperature and relative humidity. Parameters ---------- tw: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The ambient temperature in Kelvin. """ tw = k2c(tw) if isinstance(tw, Number) and isinstance(rh, Number): return c2k(newton(_stull_eq, x0=tw, args=(rh, tw))) else: t = np.zeros(len(tw)) for i, (tw_, rh_) in enumerate(zip(tw, rh)): t[i] = newton(_stull_eq, x0=tw_, args=(rh_, tw_)) return c2k(t) Loading
README.md +29 −3 Original line number Diff line number Diff line Loading @@ -5,16 +5,35 @@ Python package/script for downloading trends from Building Logix Data Exchange ( ## Installation ``` pip install git+https://git.isis.vanderbilt.edu/SmartBuildings/bdx@v0.1.0 pip install git+https://git.isis.vanderbilt.edu/SmartBuildings/bdx@v0.2.0 ``` ## Usage ### Command line ``` python -m bdx user password trend start end file ```bash usage: bdx [-h] [--file FILE] [-p] user password trend start end Download data from Building Logix Data Exchange (BDX) and save it to csv. positional arguments: user Username for BDX password Password for BDX trend ID of trend to download start Start time in ISO 8601 format. end Start time in ISO 8601 format. optional arguments: -h, --help show this help message and exit --file FILE Filenname to save CSV. If not provided, printed to console -p, --preprocess Convert to SI units by guessing field names. Date/time formats can be - 2020-02-03T15:30:00Z-6 (i.e. Feb 3 2020 3:30 PM UTC-6 timezone) - 20200203T153000Z-6 # for example: python -m bdx harrypotter wing@rd1umLeviosa 2422 2020-02-03T15:00Z-6 2020-02-04T16:00Z-6 ~/data.csv ``` Loading @@ -39,6 +58,13 @@ dataframe = get_trend(trend_id='2422', password='wing@rd1umLeviosa', start=start, end=end) # Optionally, to convert fields to SI units from bdx import apply_heuristics dataframe, left = apply_heuristiccs(dataframe) print('The following fields did not match and were note converted:', left) ``` ## Requirements Loading
bdx/__init__.py +1 −0 Original line number Diff line number Diff line from .data import get_trend from .preprocess import apply_heuristics
bdx/__main__.py +19 −7 Original line number Diff line number Diff line Loading @@ -3,6 +3,7 @@ from argparse import ArgumentParser, RawDescriptionHelpFormatter from dateutil.parser import parse from .data import get_trend from .preprocess import apply_heuristics parser = ArgumentParser( prog='bdx', Loading @@ -13,13 +14,16 @@ parser = ArgumentParser( ' - 2020-02-03T15:30:00Z-6 (i.e. Feb 3 2020 3:30 PM UTC-6 timezone)\n' ' - 20200203T153000Z-6\n'), formatter_class=RawDescriptionHelpFormatter) parser.add_argument('user', help='Username for BDX', type=str) parser.add_argument('password', help='Password for BDX', type=str) parser.add_argument('trend', help='ID of trend to download', type=str) parser.add_argument('start', help='Start time in ISO 8601 format.', type=parse) parser.add_argument('end', help='Start time in ISO 8601 format.', type=parse) parser.add_argument('--file', help=('Filenname to save CSV. If not provided, ' 'printed to console'), type=str, default=sys.stdout) parser.add_argument('user', type=str, help='Username for BDX') parser.add_argument('password', type=str, help='Password for BDX') parser.add_argument('trend', type=str, help='ID of trend to download') parser.add_argument('start', type=parse, help='Start time in ISO 8601 format.') parser.add_argument('end', type=parse, help='Start time in ISO 8601 format.') parser.add_argument('--file',type=str, default=sys.stdout, help=('Filenname to save CSV. If not provided, ' 'printed to console')) parser.add_argument('-p', '--preprocess', action='store_true', default=False, help='Convert to SI units by guessing field names.') args = parser.parse_args() Loading @@ -29,4 +33,12 @@ trend = get_trend(trend_id=args.trend, start=args.start, end=args.end) if args.preprocess: trend, left = apply_heuristics(trend) if len(left) > 0: print('The following fields did not match and were not converted:', file=sys.stderr) for field in left: print(field, file=sys.stderr) trend.to_csv(args.file)
bdx/preprocess.py 0 → 100644 +61 −0 Original line number Diff line number Diff line """ Rudimentary preprocessing for data by guessing column names: - Temperatures (F to K) - Power (kW, BTU to W) - Flow (Gallons/min to m^3/s) - Pressure (PSI to Pascals) """ import re from typing import Tuple, List, Callable import pandas as pd from .thermo import f2k, gpm2m3s, psi2pa HEURISTICS = ( ('_Temp|Temp$|_Humidity|Wet_Bulb', f2k), ('Kw$', lambda kw: 1000 * kw), ('Power$', lambda p: p), ('Percent$', lambda pct: 0.01 * pct), ('Flow$', gpm2m3s), ('Press$', psi2pa), ) def apply_heuristics(df: pd.DataFrame, heuristics: Tuple[Tuple[str, Callable]] = HEURISTICS) \ -> Tuple[pd.DataFrame, List[str]]: """ Apply functions to columns based on matching field names. Parameters ---------- df : pd.DataFrame The dataframe which is processed. heuristics : Tuple[Tuple[str, Callable]], optional A tuple of tuples, where each tuple containes a regular expression to match field names followed by a function that takes a column and returns a converted column, by default bdx.preprocess.HEURISTICS Returns ------- Tuple[pd.DataFrame, List[str]] The converted dataframe, and a list of column names which were not matched. """ left = [] for column in df: matched = False for pattern, func in heuristics: if re.search(pattern, column) is not None: matched = True df[column] = func(df[column]) if not matched: left.append(column) return df, left
bdx/thermo.py 0 → 100644 +201 −0 Original line number Diff line number Diff line """ Contains formulae for thermodynamics operations. Adapted from https://git.isis.vanderbilt.edu/SmartBuildings/EngineeringScienceBuilding/blob/0ffc3c8234ecca50606cfd9ed1e1d464f998b832/src/thermo.py """ from collections import namedtuple from numbers import Number import numpy as np from scipy.optimize import newton # physics constants _Constants = namedtuple('Constants', field_names = ( 'cv', # specific volumetric heat capacity of water (J / m^3 . K) 'cm', # specific mass heat capacity of water (J / kg .K) )) CONSTANTS = _Constants(cv=4.1796e6, cm=4.1813e3) # Constants used by vaporpressure() and dewpoint() _ABConstants = namedtuple('ArdenBuckConstants', 'a b c d') ABC = _ABConstants(a=0.61121, b=18.678, c=257.14, d=234.5) # Temperature conversion functions # f: Farenheit # c: Celsius # k: Kelvin def f2c(f: float): return (f - 32) / 1.8 def f2k(f: float): return c2k(f2c(f)) def c2f(c: float): return (c * 1.8) + 32 def c2k(c: float): return c + 273.15 def k2c(k: float): return k - 273.15 def k2f(k: float): return c2f(k2c(k)) # Energy conversion functions # btu: British Thermal Unit # j: Joule def btu2j(btu: float): return btu * 1055.056 def j2btu(j: float): return j / 1055.056 # Power conversion functions # btuhr: BTU / hour # w: Watt # ton: Ton def btuhr2w(btu: float): return btu2j(btu) / 3600 def btuhr2ton(btuhr: float): return btuhr / 12000 def ton2btuhr(ton: float): return ton * 12000 def ton2w(ton: float): return btuhr2w(ton2btuhr(ton)) def w2btuhr(w: float): return j2btu(w * 3600) def w2ton(w: float): return btuhr2ton(w2btuhr(w)) # Flow rate / volume conversion functions # gph: Gallons Per Hour # gpm: Gallons Per Minute # m3s: Cubic Metres per second def gph2m3s(gph: float): return gph * 3.7854e-3 / 3600 def gpm2m3s(gpm: float): return gpm * 3.7854e-3 / 60 # Pressure conversion def psi2pa(psi: float): return psi * 6894.75728 def vaporpressure(t: float) -> float: """ Uses Arden Buck equation to find saturation vapor pressure from ambient temperature in Kelvin. Parameters ---------- t: float Temperature in Kelvin. Returns ------- float The vapor pressure in Pascals. """ t = k2c(t) arg = (ABC.b - t / ABC.d) * (t / (ABC.c + t)) return ABC.a * np.exp(arg) * 1000 def dewpoint(t: float, rh: float) -> float: """ Uses Magnus formula enhanced w/ Arden Buck constants to calculate dew point temperature. Parameters ---------- t: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The dew point temperature in Kelvin. """ t = k2c(t) arg = (ABC.b - t / ABC.d) * (t / (ABC.c + t)) gamma = np.log(rh * 100 * np.exp(arg) / 100) return c2k((ABC.c * gamma) / (ABC.b - gamma)) def wetbulb(t: float, rh: float) -> float: """ Uses Roland Stull's formula to calculate wet bulb temperature from ambient temperature and relative humidity. Parameters ---------- t: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The wet bulb temperature in Kelvin. """ t = k2c(t) rh *= 100 # convert from fraction to percentage return c2k( t * np.arctan(0.151977 * np.sqrt(rh + 8.313659)) + np.arctan(t + rh) - np.arctan(rh - 1.676331) + 0.00391838 * np.power(rh, 1.5) * np.arctan(0.023101 * rh) - 4.686035) def _stull_eq(t, rh, tw): """ Sets up the stull equation in the form: `f(t, rh) = tw => f(t, rh) - tw = 0` The new form can be represented as: `g(t) = f(t, rh) - tw` The root of `g(t)` is the ambient temperature. Used by `tambient()`. All temperatues in the equation are Celsius. """ rh *= 100 # convert from fraction to percentage return - tw + (t * np.arctan(0.151977 * np.sqrt(rh + 8.313659)) + np.arctan(t + rh) - np.arctan(rh - 1.676331) + 0.00391838 * np.power(rh, 1.5) * np.arctan(0.023101 * rh) - 4.686035) def ambient(tw: float, rh: float) -> float: """ Uses Roland Stull's formula and Newton's method to calculate ambient temperature from wet bulb temperature and relative humidity. Parameters ---------- tw: float Temperature in Kelvin. rh: float Relative humidity [0-1]. Returns ------- float The ambient temperature in Kelvin. """ tw = k2c(tw) if isinstance(tw, Number) and isinstance(rh, Number): return c2k(newton(_stull_eq, x0=tw, args=(rh, tw))) else: t = np.zeros(len(tw)) for i, (tw_, rh_) in enumerate(zip(tw, rh)): t[i] = newton(_stull_eq, x0=tw_, args=(rh_, tw_)) return c2k(t)
