This document is a quickstart guide for eptr2 package. It is a Python client for EPIAS Transparency Platform v2.0 API. It is an unofficial package with Apache License 2.0.
You can easily install it from PyPI with the following commmand.
pip install eptr2If you want to the additional features, it is recommended to install it with the extras. Extras currently include pandas and streamlit. You can install the package with the following command.
pip install "eptr2[allextras]"You can simply use EPTR2 class to call services with convenience methods. You need to register with the EPIAS Transparency Platform to get your username (i.e. registration email) and password. The platform also accommodates an English version.
Below is an example of getting Market Clearing Price (MCP) / Piyasa Takas Fiyatı (PTF). All services use the same pattern.
from eptr2 import EPTR2
eptr = EPTR2(
username="YOUR_USERNAME", password="YOUR_PASSWORD"
)
res = eptr.call("mcp", start_date="2024-07-29", end_date="2024-07-29")There are more than 213 calls available. You can search for available calls with eptr.get_available_calls() function. This is almost an exhaustive list of available calls in the platform currently.
Starting from version 1.0.0, eptr2 package includes a live tutorial feature as a Streamlit app (p.s. You need to have Streamlit installed). You can run the following code to start the tutorial. Its functionality is almost the same as eptr2demo app.
from eptr2.tutorials import run_demo_app
run_demo_app(username="YOUR_USERNAME",password="YOUR_PASSWORD")With version 1.0.2 you can get a calculator tutorial to get imbalance and KÜPST cost estimates for any date and hour with custom actual and forecast values.
from eptr2.tutorials import run_calc_app
run_calc_app(username="YOUR_USERNAME",password="YOUR_PASSWORD")More tutorials are expected to be added in the future.
🇬🇧 eptr2 (EPIAS Transparency 2.0) package is a thin wrapper around EPIAS Transparency Platform v2.0 API brought to you by Robokami. It is an unofficial package with Apache License 2.0 (free and permissable use for commercial applications, see details). eptr2 accesses currently more than 213 services with convenience methods.
🇹🇷 eptr2 (EPİAŞ Transparency 2.0) paketi Robokami tarafından EPİAŞ Şeffaflık Platformu 2.0 API'si üzerine geliştirilmiş bir Python paketidir. Apache License 2.0 ile lisanslanmıştır (ücretsiz ve büyük ölçüde serbest kullanım). eptr2 213'ten fazla veri servisine erişim sağlar.
There are default aliases for the calls. For instance, "ptf" is an alias for "mcp". You can use aliases to call services.
res = eptr.call("ptf", start_date="2024-07-29", end_date="2024-07-29")You can also create aliases for your calls. Just prepare an alias dictionary and add it to the EPTR2 object.
custom_aliases = {"market-clearing-price": "mcp", "system-marginal-price": "smp"}
eptr = EPTR2(username="YOUR_USERNAME",password="YOUR_PASSWORD", custom_aliases=custom_aliases)As a warning aliases may overwrite the default keys and default aliases. For instance if your alias is "mcp" pointing to "smp", now default "mcp" call is overwritten with "mcp" alias pointing to "smp".
Library will also have default aliases. You can check aliases with eptr.get_aliases() function. If you want to include custom aliases, you can get them with include_custom_aliases parameter. eptr.get_available_calls() function may also include aliases.
eptr.get_aliases(include_custom_aliases = True)
eptr.get_available_calls(include_aliases = True)New feature from version 1.0.0
Composite functions are combinations of multiple calls under a single table for a purpose. That purpose might be to gather reporting data or training data for forecast models. You can create your own composite functions with eptr2 package or use already available ones.
An side note: We process and manipulate data in composite functions so it is not just the merged data frames of base eptr2 calls.
Our first composite function is get_hourly_consumption_and_forecast_data. It returns a data frame with a combination of Load Plan, UECM and Real Time Consumption.
from eptr2 import EPTR2
from eptr2.composite import get_hourly_consumption_and_forecast_data
eptr = EPTR2(username="YOUR_USERNAME",password="YOUR_PASSWORD")
df = get_hourly_consumption_and_forecast_data(eptr, start_date="2024-07-29", end_date="2024-07-29")
print(df)Our second set of composite functions is about prices and costs.
get_price_and_cost: It returns a data frame with a combination of MCP, SMP, WAP (optional) and associated imbalance and KUPST (optional) costs.get_imbalance_data: It returns a data frame with a combination of Imbalance Prices, Imbalance Volumes and Imbalance Costs (optional).
from eptr2 import EPTR2
from eptr2.composite import get_price_and_cost, get_imbalance_data
eptr = EPTR2(username="YOUR_USERNAME",password="YOUR_PASSWORD")
df_cost = get_price_and_cost(eptr, start_date="2024-07-29", end_date="2024-07-29")
print(df_cost)
df_imbalance = get_imbalance_data(eptr, start_date="2024-07-29", end_date="2024-07-29")
print(df_imbalance)With v1.0.2, you can get production and production plan data as well with composite functions. If you know the necessary ids, you can get the specific production values (omit the parameters to get totals).
There are three composite functions. One for getting actual production data (real time, UEVM), one for getting plan data (KGUP v1, KGUP, KUDUP), and one to get both and merge into a single table. Each column except date/time/hour get their own suffix from the data source (e.g. "wind_uevm").
from eptr2 import EPTR2
from eptr2.composite import get_price_and_cost, get_imbalance_data
eptr = EPTR2(username="YOUR_USERNAME",password="YOUR_PASSWORD")
actual_df = get_hourly_production_data(
eptr=eptr,
start_date="2024-11-01",
end_date="2024-11-01",
rt_pp_id=641, ## ATATÜRK HES
uevm_pp_id=142, ## ATATÜRK HES
verbose=True,
)
plan_df = get_hourly_production_plan_data(
eptr=eptr,
start_date="2024-11-01",
end_date="2024-11-01",
org_id=195, ## EÜAŞ
uevcb_id=3525325, ## ATATÜRK HES
verbose=True,
)
wrap_df = wrapper_hourly_production_plan_and_realized(
eptr=eptr, start_date="2024-11-01", end_date="2024-11-01", verbose=True
)