How to use the eemeter.caltrack.usage_per_day.CalTRACKUsagePerDayCandidateModel function in eemeter

def test_candidate_model_json_with_warning():
    eemeter_warning = EEMeterWarning(
        qualified_name="qualified_name", description="description", data={}
    )
    candidate_model = CalTRACKUsagePerDayCandidateModel(
        model_type="model_type",
        formula="formula",
        status="status",
        warnings=[eemeter_warning],
    )
    assert candidate_model.json() == {
        "formula": "formula",
        "model_params": {},
        "model_type": "model_type",
        "r_squared_adj": None,
        "status": "status",
        "warnings": [
            {
                "data": {},
                "description": "description",
                "qualified_name": "qualified_name",

def test_plot_caltrack_candidate_with_range():
    candidate_model = CalTRACKUsagePerDayCandidateModel(
        model_type="intercept_only",
        formula="formula",
        status="QUALIFIED",
        model_params={"intercept": 1},
    )
    ax = candidate_model.plot(temp_range=(10, 20))
    data = ax.lines[0].get_xydata()
    assert data.shape == (10, 2)

def test_model_results_json_with_nan_r_squared_adj():
    candidate_model = CalTRACKUsagePerDayCandidateModel(
        model_type="model_type",
        formula="formula",
        status="status",
        r_squared_adj=np.nan,
    )
    model_results = CalTRACKUsagePerDayModelResults(
        status="status",
        method_name="method_name",
        model=candidate_model,
        r_squared_adj=np.nan,
    )
    assert model_results.json() == {
        "candidates": None,
        "interval": None,
        "metadata": {},
        "method_name": "method_name",

def candidate_model_cdd_only():
    return CalTRACKUsagePerDayCandidateModel(
        model_type="cdd_only",
        formula="formula",
        status="QUALIFIED",
        model_params={"intercept": 1, "beta_cdd": 1, "cooling_balance_point": 65},
    )

def test_model_results_json_with_model_metrics():
    candidate_model = CalTRACKUsagePerDayCandidateModel(
        model_type="model_type", formula="formula", status="status", r_squared_adj=0.5
    )
    model_results = CalTRACKUsagePerDayModelResults(
        status="status",
        method_name="method_name",
        model=candidate_model,
        r_squared_adj=np.nan,
    )
    model_metrics = ModelMetrics(
        observed_input=pd.Series([0, 1, 2]), predicted_input=pd.Series([1, 0, 2])
    )
    json_result = model_results.json()
    json.dumps(json_result)  # just make sure it's valid json

    assert "totals_metrics" in json_result
    assert "avgs_metrics" in json_result

def test_plot_caltrack_candidate_error():
    candidate_model = CalTRACKUsagePerDayCandidateModel(
        model_type="intercept_only",
        formula="formula",
        status="ERROR",
        model_params={"intercept": 1},
    )
    ax = candidate_model.plot()
    assert ax is None

model_type,
            balance_point,
            "cdd",
            data[cdd_column],
            period_days,
            minimum_total_cdd,
        )
    )
    degree_day_warnings.extend(
        get_too_few_non_zero_degree_day_warning(
            model_type, balance_point, "cdd", data[cdd_column], minimum_non_zero_cdd
        )
    )

    if len(degree_day_warnings) > 0:
        return CalTRACKUsagePerDayCandidateModel(
            model_type=model_type,
            formula=formula,
            status="NOT ATTEMPTED",
            warnings=degree_day_warnings,
        )

    try:
        model = smf.wls(formula=formula, data=data, weights=weights)
    except Exception as e:
        return get_fit_failed_candidate_model(model_type, formula)

    result = model.fit()
    r_squared_adj = result.rsquared_adj
    beta_cdd_p_value = result.pvalues[cdd_column]

    # CalTrack 3.3.1.3

period_days,
            minimum_total_hdd,
        )
    )
    degree_day_warnings.extend(
        get_too_few_non_zero_degree_day_warning(
            model_type,
            heating_balance_point,
            "hdd",
            data[hdd_column],
            minimum_non_zero_hdd,
        )
    )

    if len(degree_day_warnings) > 0:
        return CalTRACKUsagePerDayCandidateModel(
            model_type, formula, "NOT ATTEMPTED", warnings=degree_day_warnings
        )

    try:
        model = smf.wls(formula=formula, data=data, weights=weights)
    except Exception as e:
        return get_fit_failed_candidate_model(model_type, formula)

    result = model.fit()
    r_squared_adj = result.rsquared_adj
    beta_cdd_p_value = result.pvalues[cdd_column]
    beta_hdd_p_value = result.pvalues[hdd_column]

    # CalTrack 3.3.1.3
    model_params = {
        "intercept": result.params["Intercept"],

Returns
    -------
    candidate_model : :any:`eemeter.CalTRACKUsagePerDayCandidateModel`
        Candidate model instance with status ``'ERROR'``, and warning with
        traceback.
    """
    warnings = [
        EEMeterWarning(
            qualified_name="eemeter.caltrack_daily.{}.model_results".format(model_type),
            description=(
                "Error encountered in statsmodels.formula.api.ols method. (Empty data?)"
            ),
            data={"traceback": traceback.format_exc()},
        )
    ]
    return CalTRACKUsagePerDayCandidateModel(
        model_type=model_type, formula=formula, status="ERROR", warnings=warnings
    )

model_warnings.extend(
        get_parameter_p_value_too_high_warning(
            model_type,
            model_params,
            parameter,
            beta_hdd_p_value,
            beta_hdd_maximum_p_value,
        )
    )

    if len(model_warnings) > 0:
        status = "DISQUALIFIED"
    else:
        status = "QUALIFIED"

    return CalTRACKUsagePerDayCandidateModel(
        model_type=model_type,
        formula=formula,
        status=status,
        warnings=model_warnings,
        model_params=model_params,
        model=model,
        result=result,
        r_squared_adj=r_squared_adj,
    )