imxp_current.py

from profiled import profiled
from ixmp import TimeSeries, Platform
import pandas as pd
import cProfile
import io
import pstats
import contextlib


@contextlib.contextmanager
def profiled():
    pr = cProfile.Profile()
    pr.enable()
    yield
    pr.disable()
    s = io.StringIO()
    ps = pstats.Stats(pr, stream=s).sort_stats("cumulative")
    ps.print_stats()
    # uncomment this to see who's calling what
    # ps.print_callers()
    print(s.getvalue())

ndps = 120_000

mp = Platform("local")
ts = TimeSeries(mp, "Model", "Scen", version="new")


def make_dp(i):
    return ("region", "variable", "unit", i, 1.23)

dps = [make_dp(i) for i in range(ndps)]

df = pd.DataFrame(dps, columns=["region", "variable", "unit", "year", "value"])

print(df)
with profiled():
    ts.add_timeseries(df)
    ts.commit("test")


with profiled():
    df2 = ts.timeseries()
print(df2)

ixmp_next.py

from ixmp.db.models import *
from ixmp.core.platform import Platform
from ixmp.core.scenario import Scenario
from ixmp.core.models import DataPoint
from datetime import datetime
from profiled import profiled
import pandas as pd
import cProfile
import io
import pstats
import contextlib


@contextlib.contextmanager
def profiled():
    pr = cProfile.Profile()
    pr.enable()
    yield
    pr.disable()
    s = io.StringIO()
    ps = pstats.Stats(pr, stream=s).sort_stats("cumulative")
    ps.print_stats()
    # uncomment this to see who's calling what
    # ps.print_callers()
    print(s.getvalue())

ndps = 300_000

mp = Platform("test2")
s = Scenario(mp, "Model 100", "Scenario 100", version="new")

now = datetime.utcnow().isoformat()


def make_dp():
    return (
        "Region 1",
        "Quantity 0",
        "Unit 0",
        DataPoint.Type.BASE.value,
        0.0,
        1999,
        now,
        "category",
    )


df = pd.DataFrame(
    [make_dp() for _ in range(ndps)],
    columns=[
        "region",
        "quantity",
        "unit",
        "type",
        "value",
        "step_year",
        "step_datetime",
        "step_category",
    ],
)
print(df)
with profiled():
    df = s.add_datapoints(df)

with profiled():
    df = s.tabulate_datapoints()
print(df)

plot.py

import matplotlib.pyplot as plt

def plot_create(ax):
    ixmp_cur = ([
        10,
        50,
        100,
        120
    ], [0.504, 21.020, 81.933, 114.865])
    ixmp_next = ([
        10,
        50,
        100,
        200,
        300,
        500
    ], [0.342, 1.957, 3.593, 7.628, 12.313, 20.262])
    ax.plot(*ixmp_next,'go--', *ixmp_cur, 'bo--')
    ax.set(ylabel='secs')
    ax.set_title("Add")

def plot_read(ax):
    ixmp_cur = ([
        10,
        50,
        100,
        120
    ], [0.239, 1.078, 2.060, 2.340])
    ixmp_next = ([
        10,
        50,
        100,
        200,
        300,
        500
    ], [0.037, 0.389, 0.617, 1.017, 1.686, 2.835])

    ax.plot(*ixmp_next,'g*--', *ixmp_cur, 'b*--')
    ax.set(xlabel='1000x data points', ylabel='secs')
    ax.set_title("Tabulate")

fig, (ax1, ax2) = plt.subplots(2)
plot_create(ax1)
plot_read(ax2)
plt.show()