Add the option to download 'takeout scripts' #504
Annotations
2 errors
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Test:
gui/test/app/Scripting/makePyRuntime.test.ts#L201
AssertionError: expected 'TITLE="my title"\nimport argparse\nim…' to deeply equal 'TITLE="my title"\nimport argparse\nim…'
- Expected
+ Received
TITLE="my title"
import argparse
import json
import os
import cmdstanpy
HERE = os.path.dirname(os.path.abspath(__file__))
argparser = argparse.ArgumentParser(prog=f"Stan-Playground: {TITLE}")
argparser.add_argument("--install-cmdstan", action="store_true", help="Install cmdstan if it is missing")
args, _ = argparser.parse_known_args()
+ data = ""
_option_names_map = {
"init_radius": "inits",
"num_warmup": "iter_warmup",
"num_samples": "iter_sampling",
"num_chains": "chains",
}
if os.path.isfile(os.path.join(HERE, "sampling_opts.json")):
print("loading sampling_opts.json")
with open(os.path.join(HERE, "sampling_opts.json")) as f:
s = json.load(f)
sampling_opts = {_option_names_map.get(k, k): v for k, v in s.items()}
else:
sampling_opts = {}
try:
cmdstanpy.cmdstan_path()
except Exception:
if args.install_cmdstan:
cmdstanpy.install_cmdstan()
else:
raise ValueError("cmdstan not found, use --install-cmdstan to install")
print("compiling model")
model = cmdstanpy.CmdStanModel(stan_file=os.path.join(HERE, "main.stan"))
print("sampling")
fit = model.sample(data=data, **sampling_opts)
print(fit.summary())
# Used in pyodideWorker for running analysis.py
from typing import TYPE_CHECKING, List, TypedDict
import numpy as np
import pandas as pd
import stanio
# We don't import this unconditionaly because
# we only install it when the user's script needs it
if TYPE_CHECKING:
from arviz import InferenceData
class SpData(TypedDict):
draws: List[List[float]]
paramNames: List[str]
numChains: int
class DrawsObject:
def __init__(self, sp_data: SpData):
self._all_parameter_names: List[str] = sp_data["paramNames"]
self._params = stanio.parse_header(",".join(self._all_parameter_names))
self._num_chains: int = sp_data["numChains"]
# draws come in as num_params by (num_chains * num_draws)
self._draws = (
np.array(sp_data["draws"])
.transpose()
.reshape(self._num_chains, -1, len(self._all_parameter_names))
)
def __repr__(self) -> str:
return f"""SpDraws with {self._num_chains} chains, {self._draws.shape[1]} draws, and {self._draws.shape[2]} parameters.
Methods:
- as_dataframe(): return a pandas DataFrame of the draws.
- as_numpy(): return a numpy array indexed by (chain, draw, parameter)
- as_arviz(): return an arviz InferenceData object
- get(pname: str): return a numpy array of the parameter values for the given parameter name"""
def as_dataframe(self) -> pd.DataFrame:
# The first column is the chain id
# The second column is the draw number
# The remaining columns are the parameter values
(num_chains, num_draws, num_params) = self._draws.shape
flattened = self._draws.reshape(-1, num_params)
chain_ids = np.repeat(np.arange(1, num_chains + 1), num_draws)
draw_numbers = np.tile(np.arange(1, num_draws + 1), num_chains)
data = np.column_stack((chain_ids, draw_numbers, flattened))
df = pd.DataFrame(data, columns=["chain", "draw"] + self._all_parameter_names)
return df
def as_numpy(self) -> np.ndarray:
return np.array(self._draws)
def get(self, pname: str) -> np.ndarray:
if pname not in self._params:
raise ValueError(f"Parameter {pname} not found")
return self._params[pname].extract_reshape(self._draws)
def to_arviz(self) -> "InferenceData":
import arviz as az
return az.from_dict(
posterior={pname: self.get(pname) for pname in self.parameter_names},
)
@Property
def parameter_names(self) -> List[str]:
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Test
Process completed with exit code 1.
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