diff --git a/bletl/growth.py b/bletl/growth.py
index 1a0a96a..8f818e5 100644
--- a/bletl/growth.py
+++ b/bletl/growth.py
@@ -352,10 +352,10 @@ def fit_mu_t(
"segment": numpy.arange(TS),
}
with pm.Model(coords=coords) as pmodel:
- pm.ConstantData("known_switchpoints", t_switchpoints_known)
- pm.ConstantData("t_data", t_data, dims="timepoint")
- pm.ConstantData("t_segments", t_segments, dims="segment")
- dt = pm.ConstantData("dt", numpy.diff(t_data), dims="segment")
+ pm.Data("known_switchpoints", t_switchpoints_known)
+ pm.Data("t_data", t_data, dims="timepoint")
+ pm.Data("t_segments", t_segments, dims="segment")
+ dt = pm.Data("dt", numpy.diff(t_data), dims="segment")
# The init dist for the random walk is where each segment starts.
# Here we center it on the user-provided mu_prior,
@@ -428,7 +428,7 @@ def fit_mu_t(
)
calibration_model.loglikelihood(
x=Xt,
- y=pm.ConstantData("backscatter", y, dims=("timepoint",)),
+ y=pm.Data("backscatter", y, dims=("timepoint",)),
name=f"{replicate_id}_{calibration_model.dependent_key}",
dims="timepoint",
)
diff --git a/notebooks/Quickstart_04_Bayesian Analysis of time-Variable Growth Rate.ipynb b/notebooks/Quickstart_04_Bayesian Analysis of time-Variable Growth Rate.ipynb
index 8c3cf53..448050c 100644
--- a/notebooks/Quickstart_04_Bayesian Analysis of time-Variable Growth Rate.ipynb
+++ b/notebooks/Quickstart_04_Bayesian Analysis of time-Variable Growth Rate.ipynb
@@ -303,7 +303,7 @@
"outputs": [
{
"data": {
- "image/svg+xml": "\n\n\n\n\n",
+ "image/svg+xml": "\n\n\n\n\n",
"text/plain": [
""
]
diff --git a/pyproject.toml b/pyproject.toml
index 81462c3..3a4cd51 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "bletl"
-version = "1.6.0"
+version = "1.6.1"
description = "Package for parsing and transforming BioLector raw data."
readme = "README.md"
requires-python = ">=3.8"