From 20e018f225c6cf8886a6b6f4db865be59ad7f215 Mon Sep 17 00:00:00 2001
From: Daniel Sali <daniel.sali@alphacruncher.com>
Date: Mon, 6 Jan 2025 20:05:08 +0100
Subject: [PATCH] Remove wait on .jls file, define DynareError
---
examples/example1pf_bad.mod | 60 ++++++
src/dynare/dynare.py | 366 ++++++++++++++++++------------------
src/dynare/errors.py | 17 ++
test/test_bad_model.py | 10 +
4 files changed, 269 insertions(+), 184 deletions(-)
create mode 100644 examples/example1pf_bad.mod
create mode 100644 src/dynare/errors.py
create mode 100644 test/test_bad_model.py
diff --git a/examples/example1pf_bad.mod b/examples/example1pf_bad.mod
new file mode 100644
index 0000000..cf3a8e6
--- /dev/null
+++ b/examples/example1pf_bad.mod
@@ -0,0 +1,60 @@
+// cyclic reduction algorithm
+var y, c, k, a, h, b;
+varexo e, u;
+
+verbatim;
+% I want these comments included in
+% example1.m 1999q1 1999y
+%
+var = 1;
+end;
+
+parameters beta, rho, alpha, delta, theta, psi, tau;
+
+alpha = 0.36;
+rho = 0.95;
+tau = 0.025;
+beta = 0.99;
+delta = 0.025;
+psi = 0;
+theta = 2.95;
+
+phi = 0.1;
+
+model;
+c*theta*h^(1+psi)=(1-alpha)*y;
+k = beta*(((exp(b)*c)/(exp(b(+1))*c(+1)))
+ *(exp(b(+1))*alpha*y(+1)+(1-delta)*k));
+y = exp(a)*(k(-1)^alpha)*(h^(1-alpha));
+k = exp(b)*(y-c)+(1-delta)*k(-1);
+a = rho*a(-1)+tau*b(-1) + e;
+b = tau*a(-1)+rho*b(-1) + u;
+end;
+
+steady_state_model;
+ K_Y = beta*alpha /(1 - beta*(1 - delta));
+ H_Y = K_Y^(-alpha/(1 - alpha));
+ C_Y = 1 - delta*K_Y;
+ y = (theta*C_Y*H_Y^(1 + psi)/(1 - alpha))^(-1/(1 + psi));
+ c = C_Y*y;
+ k = K_Y*y;
+ h = H_Y*y;
+ a = 0;
+ b = 0;
+end;
+
+spooky;
+
+shocks;
+ var e;
+ periods 1;
+ values 0.01;
+ var u;
+ periods 2;
+ values 0.015;
+end;
+
+perfect_foresight_setup(periods = 100);
+perfect_foresight_solver;
+
+
diff --git a/src/dynare/dynare.py b/src/dynare/dynare.py
index b4888de..d0be355 100644
--- a/src/dynare/dynare.py
+++ b/src/dynare/dynare.py
@@ -1,204 +1,202 @@
import logging
-import time
-import os
from pathlib import Path
-from juliacall import Main as jl
+from juliacall import Main as jl, JuliaError
from .dynare_context import Context
+from .errors import DynareError
logger = logging.getLogger("dynare.dynare")
def dynare(model: str | Path) -> Context:
- if isinstance(model, str):
- model = Path(model)
- jl.seval("using Serialization")
- jl.seval("using Dynare")
- # Double-escape '\' as julia will also interpret the string. Only relevant on Windows
- resolved_path = str(model.resolve()).replace("\\", "\\\\")
- jl.seval(f'@dynare "{resolved_path}"')
-
- jls_path = model.parent / model.stem / "output" / f"{model.stem}.jls"
-
- timeout = int(os.getenv("DYNARE_WAIT", 600))
- start_time = time.time()
-
- while not jls_path.exists():
- if time.time() - start_time > timeout:
- logger.error(
- f"Timeout reached: {timeout} seconds. The file {jls_path.resolve()} was not created."
+ try:
+ if isinstance(model, str):
+ model = Path(model)
+ jl.seval("using Serialization")
+ jl.seval("using Dynare")
+ # Double-escape '\' as julia will also interpret the string. Only relevant on Windows
+ resolved_path = str(model.resolve()).replace("\\", "\\\\")
+
+ jl.seval(
+ "using DataFrames, Tables, PythonCall, Dynare, LinearRationalExpectations"
+ )
+
+ # Convert the Julia AxisArrayTable fields of a Context to a Pandas DataFrame with PythonCall.pytable
+ jl.seval(
+ """
+ using Dynare: EstimationResults, EstimatedParameters, SymbolTable, ModFileInfo
+
+ struct PyWork
+ analytical_steadystate_variables::Vector{Int}
+ data::Py
+ datafile::String
+ params::Vector{Float64}
+ residuals::Vector{Float64}
+ dynamic_variables::Vector{Float64}
+ exogenous_variables::Vector{Float64}
+ observed_variables::Vector{String}
+ Sigma_m::Matrix{Float64}
+ jacobian::Matrix{Float64}
+ qr_jacobian::Matrix{Float64}
+ model_has_trend::Vector{Bool}
+ histval::Matrix{Union{Float64,Missing}}
+ homotopy_setup::Vector{NamedTuple{(:name, :type, :index, :endvalue, :startvalue), Tuple{Symbol, SymbolType, Int64, Float64, Union{Float64, Missing}}}}
+ initval_endogenous::Matrix{Union{Float64,Missing}}
+ initval_exogenous::Matrix{Union{Float64,Missing}}
+ initval_exogenous_deterministic::Matrix{Union{Float64,Missing}}
+ endval_endogenous::Matrix{Union{Float64,Missing}}
+ endval_exogenous::Matrix{Union{Float64,Missing}}
+ endval_exogenous_deterministic::Matrix{Union{Float64,Missing}}
+ scenario::Dict{PeriodsSinceEpoch, Dict{PeriodsSinceEpoch, Dict{Symbol, Pair{Float64, Symbol}}}}
+ shocks::Vector{Float64}
+ perfect_foresight_setup::Dict{String,Any}
+ estimated_parameters::EstimatedParameters
+ end
+
+ function convert_to_pywork(work::Work)::PyWork
+ # Convert the AxisArrayTable data to a Pandas DataFrame using pytable
+ py_data = pytable(work.data)
+
+ return PyWork(
+ work.analytical_steadystate_variables,
+ py_data,
+ work.datafile,
+ work.params,
+ work.residuals,
+ work.dynamic_variables,
+ work.exogenous_variables,
+ work.observed_variables,
+ work.Sigma_m,
+ work.jacobian,
+ work.qr_jacobian,
+ work.model_has_trend,
+ work.histval,
+ work.homotopy_setup,
+ work.initval_endogenous,
+ work.initval_exogenous,
+ work.initval_exogenous_deterministic,
+ work.endval_endogenous,
+ work.endval_exogenous,
+ work.endval_exogenous_deterministic,
+ work.scenario,
+ work.shocks,
+ work.perfect_foresight_setup,
+ work.estimated_parameters
)
- raise TimeoutError(
- f"Timeout reached: {timeout} seconds. The file {jls_path.resolve()} was not created."
+ end
+
+ struct PySimulation
+ firstperiod::PeriodsSinceEpoch
+ lastperiod::PeriodsSinceEpoch
+ name::String
+ statement::String
+ data::Py
+ end
+
+ function convert_to_pysimulation(simulation::Simulation)::PySimulation
+ # Convert the AxisArrayTable data to a Pandas DataFrame using pytable
+ py_data = pytable(simulation.data)
+
+ return PySimulation(
+ simulation.firstperiod,
+ simulation.lastperiod,
+ simulation.name,
+ simulation.statement,
+ py_data
)
- logger.debug(f"Waiting for the file {jls_path.resolve()} to be created")
- time.sleep(1)
+ end
- jl.seval("using DataFrames, Tables, PythonCall, Dynare, LinearRationalExpectations")
+
+ # Define the PyModelResult structure with Pandas DataFrame fields
+ mutable struct PyModelResult
+ irfs::Dict{Symbol, Py}
+ trends::Trends
+ stationary_variables::Vector{Bool}
+ estimation::EstimationResults
+ filter::Py # Pandas DataFrame
+ forecast::Vector{Py} # Vector of Pandas DataFrames
+ initial_smoother::Py # Pandas DataFrame
+ linearrationalexpectations::LinearRationalExpectationsResults
+ simulations::Vector{PySimulation}
+ smoother::Py # Pandas DataFrame
+ solution_derivatives::Vector{Matrix{Float64}}
+ # sparsegrids::SparsegridsResults
+ end
- # Convert the Julia AxisArrayTable fields of a Context to a Pandas DataFrame with PythonCall.pytable
- jl.seval(
- """
- using Dynare: EstimationResults, EstimatedParameters, SymbolTable, ModFileInfo
-
- struct PyWork
- analytical_steadystate_variables::Vector{Int}
- data::Py
- datafile::String
- params::Vector{Float64}
- residuals::Vector{Float64}
- dynamic_variables::Vector{Float64}
- exogenous_variables::Vector{Float64}
- observed_variables::Vector{String}
- Sigma_m::Matrix{Float64}
- jacobian::Matrix{Float64}
- qr_jacobian::Matrix{Float64}
- model_has_trend::Vector{Bool}
- histval::Matrix{Union{Float64,Missing}}
- homotopy_setup::Vector{NamedTuple{(:name, :type, :index, :endvalue, :startvalue), Tuple{Symbol, SymbolType, Int64, Float64, Union{Float64, Missing}}}}
- initval_endogenous::Matrix{Union{Float64,Missing}}
- initval_exogenous::Matrix{Union{Float64,Missing}}
- initval_exogenous_deterministic::Matrix{Union{Float64,Missing}}
- endval_endogenous::Matrix{Union{Float64,Missing}}
- endval_exogenous::Matrix{Union{Float64,Missing}}
- endval_exogenous_deterministic::Matrix{Union{Float64,Missing}}
- scenario::Dict{PeriodsSinceEpoch, Dict{PeriodsSinceEpoch, Dict{Symbol, Pair{Float64, Symbol}}}}
- shocks::Vector{Float64}
- perfect_foresight_setup::Dict{String,Any}
- estimated_parameters::EstimatedParameters
- end
-
- function convert_to_pywork(work::Work)::PyWork
- # Convert the AxisArrayTable data to a Pandas DataFrame using pytable
- py_data = pytable(work.data)
-
- return PyWork(
- work.analytical_steadystate_variables,
- py_data,
- work.datafile,
- work.params,
- work.residuals,
- work.dynamic_variables,
- work.exogenous_variables,
- work.observed_variables,
- work.Sigma_m,
- work.jacobian,
- work.qr_jacobian,
- work.model_has_trend,
- work.histval,
- work.homotopy_setup,
- work.initval_endogenous,
- work.initval_exogenous,
- work.initval_exogenous_deterministic,
- work.endval_endogenous,
- work.endval_exogenous,
- work.endval_exogenous_deterministic,
- work.scenario,
- work.shocks,
- work.perfect_foresight_setup,
- work.estimated_parameters
- )
- end
-
- struct PySimulation
- firstperiod::PeriodsSinceEpoch
- lastperiod::PeriodsSinceEpoch
- name::String
- statement::String
- data::Py
- end
-
- function convert_to_pysimulation(simulation::Simulation)::PySimulation
- # Convert the AxisArrayTable data to a Pandas DataFrame using pytable
- py_data = pytable(simulation.data)
-
- return PySimulation(
- simulation.firstperiod,
- simulation.lastperiod,
- simulation.name,
- simulation.statement,
- py_data
- )
- end
+ function convert_to_pymodelresult(model_result::ModelResults)::PyModelResult
+ py_irfs = Dict{Symbol, Py}()
+ for (key, axis_array_table) in model_result.irfs
+ py_irfs[key] = pytable(axis_array_table)
+ end
+ py_forecast = [pytable(forecast) for forecast in model_result.forecast]
- # Define the PyModelResult structure with Pandas DataFrame fields
- mutable struct PyModelResult
- irfs::Dict{Symbol, Py}
- trends::Trends
- stationary_variables::Vector{Bool}
- estimation::EstimationResults
- filter::Py # Pandas DataFrame
- forecast::Vector{Py} # Vector of Pandas DataFrames
- initial_smoother::Py # Pandas DataFrame
- linearrationalexpectations::LinearRationalExpectationsResults
- simulations::Vector{PySimulation}
- smoother::Py # Pandas DataFrame
- solution_derivatives::Vector{Matrix{Float64}}
- # sparsegrids::SparsegridsResults
- end
-
- function convert_to_pymodelresult(model_result::ModelResults)::PyModelResult
- py_irfs = Dict{Symbol, Py}()
- for (key, axis_array_table) in model_result.irfs
- py_irfs[key] = pytable(axis_array_table)
+ return PyModelResult(
+ py_irfs,
+ model_result.trends,
+ model_result.stationary_variables,
+ model_result.estimation,
+ pytable(model_result.filter),
+ py_forecast,
+ pytable(model_result.initial_smoother),
+ model_result.linearrationalexpectations,
+ [convert_to_pysimulation(simulation) for simulation in model_result.simulations],
+ pytable(model_result.smoother),
+ model_result.solution_derivatives,
+ # model_result.sparsegrids
+ )
+ end
+
+ struct PyResults
+ model_results::Vector{PyModelResult}
end
+
+ struct PyContext
+ symboltable::SymbolTable
+ models::Vector{Model}
+ modfileinfo::ModFileInfo
+ results::PyResults # Now holds PyModelResult instead of ModelResult
+ work::PyWork
+ workspaces::Dict
+ end
+
+ function convert_to_pycontext(ctx::Context)::PyContext
+ # Convert each ModelResults in the Context to PyModelResult
+ py_model_results = [convert_to_pymodelresult(model_result) for model_result in ctx.results.model_results]
+
+ # Create a PyResults structure with the converted PyModelResults
+ py_results = PyResults(py_model_results)
+
+ # Convert the Work structure
+ py_work = convert_to_pywork(ctx.work)
- py_forecast = [pytable(forecast) for forecast in model_result.forecast]
-
- return PyModelResult(
- py_irfs,
- model_result.trends,
- model_result.stationary_variables,
- model_result.estimation,
- pytable(model_result.filter),
- py_forecast,
- pytable(model_result.initial_smoother),
- model_result.linearrationalexpectations,
- [convert_to_pysimulation(simulation) for simulation in model_result.simulations],
- pytable(model_result.smoother),
- model_result.solution_derivatives,
- # model_result.sparsegrids
+ # Return a new PyContext with PyResults and PyWork
+ return PyContext(
+ ctx.symboltable,
+ ctx.models,
+ ctx.modfileinfo,
+ py_results, # PyResults instead of Results
+ py_work,
+ ctx.workspaces
+ )
+ end
+ """
)
- end
+ jl.seval(f'@dynare "{resolved_path}"')
- struct PyResults
- model_results::Vector{PyModelResult}
- end
-
- struct PyContext
- symboltable::SymbolTable
- models::Vector{Model}
- modfileinfo::ModFileInfo
- results::PyResults # Now holds PyModelResult instead of ModelResult
- work::PyWork
- workspaces::Dict
- end
-
- function convert_to_pycontext(ctx::Context)::PyContext
- # Convert each ModelResults in the Context to PyModelResult
- py_model_results = [convert_to_pymodelresult(model_result) for model_result in ctx.results.model_results]
-
- # Create a PyResults structure with the converted PyModelResults
- py_results = PyResults(py_model_results)
-
- # Convert the Work structure
- py_work = convert_to_pywork(ctx.work)
-
- # Return a new PyContext with PyResults and PyWork
- return PyContext(
- ctx.symboltable,
- ctx.models,
- ctx.modfileinfo,
- py_results, # PyResults instead of Results
- py_work,
- ctx.workspaces
+ jls_path = model.parent / model.stem / "output" / f"{model.stem}.jls"
+
+ if not jls_path.exists():
+ raise DynareError(
+ f"Model evaluation failed. No JLS file found at {jls_path}"
+ )
+
+ context = jl.seval(
+ f"""ctx = Serialization.deserialize("{jls_path.resolve()}");
+ convert_to_pycontext(ctx)"""
)
- end
- """
- )
- context = jl.seval(
- f"""ctx = Serialization.deserialize("{jls_path.resolve()}");
- convert_to_pycontext(ctx)"""
- )
- return Context.from_julia(context)
+ return Context.from_julia(context)
+ except JuliaError as e:
+ raise DynareError.from_julia_error(e)
+ except Exception as e:
+ raise DynareError(f"An unexpected error occurred: {e}") from e
diff --git a/src/dynare/errors.py b/src/dynare/errors.py
new file mode 100644
index 0000000..2dfb6a2
--- /dev/null
+++ b/src/dynare/errors.py
@@ -0,0 +1,17 @@
+from typing import Self
+
+
+class DynareError(Exception):
+ """Exception raised for errors occurring during the execution of the dynare Julia command."""
+
+ def __init__(self, message):
+ self.message = message
+ super().__init__(self.message)
+
+ def __str__(self):
+ return f"DynareError: {self.message}"
+
+ @classmethod
+ def from_julia_error(cls, julia_error) -> Self:
+ message = f"JuliaError:\n{str(julia_error)}"
+ return cls(message)
diff --git a/test/test_bad_model.py b/test/test_bad_model.py
new file mode 100644
index 0000000..8f8836a
--- /dev/null
+++ b/test/test_bad_model.py
@@ -0,0 +1,10 @@
+from pathlib import Path
+from dynare import dynare
+
+
+def test_dynare():
+ print(dynare(Path(__file__).parent.parent / "examples" / "example1pf_bad.mod"))
+
+
+if __name__ == "__main__":
+ test_dynare()
--
GitLab