import glob
import io
import os
from zipfile import ZipFile
import pandas as pd
import requests
import tqdm
import gridstatus
from gridstatus.base import Markets, NotSupported, _interconnection_columns
from gridstatus.caiso import CAISO
from gridstatus.ercot import Ercot
from gridstatus.isone import ISONE
from gridstatus.lmp_config import lmp_config
from gridstatus.miso import MISO
from gridstatus.nyiso import NYISO
from gridstatus.pjm import PJM
from gridstatus.spp import SPP
[docs]GREEN_CHECKMARK_HTML_ENTITY = "✅"
[docs]RED_X_HTML_ENTITY = "❌"
[docs]all_isos = [MISO, CAISO, PJM, Ercot, SPP, NYISO, ISONE]
[docs]def list_isos():
"""List available ISOs"""
isos = [[i.name, i.iso_id, i.__name__] for i in all_isos]
return pd.DataFrame(isos, columns=["Name", "Id", "Class"])
[docs]def get_iso(iso_id):
"""Get an ISO by its id"""
for i in all_isos:
if i.iso_id == iso_id:
return i
raise KeyError
[docs]def make_availability_df():
methods = [
"get_status",
"get_fuel_mix",
"get_load",
"get_load_forecast",
"get_storage",
]
availability = {}
for i in tqdm.tqdm(gridstatus.all_isos):
availability[i.__name__] = {}
for method in methods:
availability[i.__name__][method] = {}
for date in ["latest", "today", "historical"]:
test = date
if date == "historical":
test = pd.Timestamp.now(
tz=i.default_timezone,
).date() - pd.Timedelta(days=3)
if method == "get_load_forecast" and date == "latest":
is_defined = RED_X_HTML_ENTITY
else:
try:
getattr(i(), method)(test)
is_defined = GREEN_CHECKMARK_HTML_ENTITY
except NotSupported:
is_defined = RED_X_HTML_ENTITY
except NotImplementedError:
is_defined = RED_X_HTML_ENTITY
availability[i.__name__][method][date] = is_defined
availability_dfs = {}
for i in all_isos:
availability_dfs[i.__name__] = pd.DataFrame(availability[i.__name__])
return availability_dfs
[docs]def make_availability_table():
dfs = make_availability_df()
markdown = ""
for method, df in sorted(dfs.items()):
markdown += "## " + method + "\n"
# df.index = ["`" + v + "`" for v in df.index.values]
markdown += df.to_markdown() + "\n"
return markdown
def _handle_date(date, tz=None):
if date == "today":
date = pd.Timestamp.now(tz=tz).normalize()
if not isinstance(date, pd.Timestamp):
date = pd.to_datetime(date)
if tz and date.tzinfo is None:
date = date.tz_localize(tz)
return date
[docs]LMP_METHOD_NAMES = ["get_lmp", "get_spp"]
[docs]def make_lmp_availability_df():
availability = {}
DOES_NOT_EXIST_SENTINEL = "dne"
for iso in tqdm.tqdm(gridstatus.all_isos):
availability[iso.__name__] = {"Method": "-"}
matching_method_name = None
for method_name in LMP_METHOD_NAMES:
if (
getattr(iso(), method_name, DOES_NOT_EXIST_SENTINEL)
!= DOES_NOT_EXIST_SENTINEL
):
matching_method_name = method_name
break
if matching_method_name is None:
continue
availability[iso.__name__]["Method"] = f"`{matching_method_name}`"
matching_method = getattr(iso(), matching_method_name)
config = lmp_config.get_support(matching_method)
for market, supported_dates in config.items():
availability[iso.__name__][market.name] = ", ".join(
supported_dates,
)
return pd.DataFrame(availability).fillna("-")
[docs]def convert_bool_to_emoji(value):
"""If value is boolean, convert to Green Checkmark or Red X. Otherwise, leave be."""
if isinstance(value, bool):
if value:
return GREEN_CHECKMARK_HTML_ENTITY
else:
return RED_X_HTML_ENTITY
else:
return value
[docs]def make_lmp_availability_table():
transposed = make_lmp_availability_df().transpose()
transposed = transposed.rename(
columns={
Markets.REAL_TIME_5_MIN: "REAL_TIME_5_MIN",
Markets.REAL_TIME_15_MIN: "REAL_TIME_15_MIN",
Markets.REAL_TIME_HOURLY: "REAL_TIME_HOURLY",
Markets.DAY_AHEAD_HOURLY: "DAY_AHEAD_HOURLY",
},
)
transposed = transposed.sort_index().applymap(convert_bool_to_emoji)
return transposed.to_markdown() + "\n"
[docs]def filter_lmp_locations(df, locations):
"""
Filters DataFrame by locations, which can be a list, "ALL" or None
Arguments:
df (pandas.DataFrame): DataFrame to filter
locations: "ALL" or list of locations to filter "Location" column by
"""
if locations == "ALL" or locations is None:
return df
return df[df["Location"].isin(locations)]
[docs]def get_zip_file(url):
# todo add retry logic
# todo does this need to be a with statement?
r = requests.get(url)
z = ZipFile(io.BytesIO(r.content))
return z.open(z.namelist()[0])
[docs]def is_today(date, tz):
return _handle_date(date, tz=tz).date() == pd.Timestamp.now(tz=tz).date()
[docs]def is_within_last_days(date, days, tz):
"""Returns whether date is within N days"""
now = pd.Timestamp.now(tz=tz).date()
date_value = _handle_date(date, tz=tz).date()
period_start = (now - pd.DateOffset(days=days)).date()
return date_value <= now and date_value >= period_start
[docs]def is_dst_end(date):
return (date.dst() - (date + pd.DateOffset(1)).dst()).seconds == 3600
[docs]def load_folder(path, time_zone=None, verbose=True):
"""Load a single DataFrame for same schema csv files in a folder
Arguments:
path (str): path to folder
time_zone (str): time zone to localize to timestamps.
By default returns as UTC
verbose (bool, optional): print verbose output. Defaults to True.
Returns:
pandas.DataFrame: A DataFrame of all files
"""
all_files = glob.glob(os.path.join(path, "*.csv"))
all_files = sorted(all_files)
dfs = []
for f in tqdm.tqdm(all_files, disable=not verbose):
df = pd.read_csv(f, parse_dates=True)
dfs.append(df)
data = pd.concat(dfs).reset_index(drop=True)
if "Time" in data.columns:
data["Time"] = pd.to_datetime(data["Time"], utc=True)
if time_zone:
data["Time"] = data["Time"].dt.tz_convert(time_zone)
# todo make sure dates get parsed
# todo make sure rows are sorted by time
return data
[docs]def get_interconnection_queues():
"""Get interconnection queue data for all ISOs"""
all_queues = []
for iso in tqdm.tqdm(all_isos):
iso = iso()
# only shared columns
queue = iso.get_interconnection_queue()[_interconnection_columns]
queue.insert(0, "ISO", iso.name)
queue.reset_index(drop=True, inplace=True)
all_queues.append(queue)
pd.concat(all_queues)
all_queues = pd.concat(all_queues).reset_index(drop=True)
return all_queues
[docs]def move_cols_to_front(df, cols_to_move):
"""Move columns to front of DataFrame"""
cols = list(df.columns)
for c in cols_to_move:
cols.remove(c)
return df[cols_to_move + cols]
