"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "airpass = pd.read_csv(\n",
+ " \"../tests/statespace/_data/airpass.csv\",\n",
+ " parse_dates=True,\n",
+ " date_format=\"%Y %b\",\n",
+ " index_col=0,\n",
+ " dtype={\"value\": float},\n",
+ ").rename(columns={\"value\": \"passengers\"})\n",
+ "airpass.index.freq = airpass.index.inferred_freq\n",
+ "airpass.apply(np.log).plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "f20d1146",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " Model Requirements \n",
+ " \n",
+ " Variable Shape Constraints Dimensions \n",
+ " ────────────────────────────────────────────────────────────────────────────────── \n",
+ " initial_level_trend (2,) ('state_level_trend',) \n",
+ " sigma_level_trend (2,) Positive ('shock_level_trend',) \n",
+ " params_annual (11,) ('state_annual',) \n",
+ " sigma_annual () Positive None \n",
+ " sigma_error () Positive None \n",
+ " P0 (14, 14) Positive semi-definite ('state', 'state_aux') \n",
+ " \n",
+ "These parameters should be assigned priors inside a PyMC model block before calling \n",
+ " the build_statespace_graph method. \n",
+ "\n"
+ ],
+ "text/plain": [
+ "\u001b[3m Model Requirements \u001b[0m\n",
+ " \n",
+ " \u001b[1m \u001b[0m\u001b[1mVariable \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mShape \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1mConstraints \u001b[0m\u001b[1m \u001b[0m \u001b[1m \u001b[0m\u001b[1m Dimensions\u001b[0m\u001b[1m \u001b[0m \n",
+ " ────────────────────────────────────────────────────────────────────────────────── \n",
+ " initial_level_trend \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'state_level_trend'\u001b[0m,\u001b[1m)\u001b[0m \n",
+ " sigma_level_trend \u001b[1m(\u001b[0m\u001b[1;36m2\u001b[0m,\u001b[1m)\u001b[0m Positive \u001b[1m(\u001b[0m\u001b[32m'shock_level_trend'\u001b[0m,\u001b[1m)\u001b[0m \n",
+ " params_annual \u001b[1m(\u001b[0m\u001b[1;36m11\u001b[0m,\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[32m'state_annual'\u001b[0m,\u001b[1m)\u001b[0m \n",
+ " sigma_annual \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
+ " sigma_error \u001b[1m(\u001b[0m\u001b[1m)\u001b[0m Positive \u001b[3;35mNone\u001b[0m \n",
+ " P0 \u001b[1m(\u001b[0m\u001b[1;36m14\u001b[0m, \u001b[1;36m14\u001b[0m\u001b[1m)\u001b[0m Positive semi-definite \u001b[1m(\u001b[0m\u001b[32m'state'\u001b[0m, \u001b[32m'state_aux'\u001b[0m\u001b[1m)\u001b[0m \n",
+ " \n",
+ "\u001b[2;3mThese parameters should be assigned priors inside a PyMC model block before calling \u001b[0m\n",
+ "\u001b[2;3m the build_statespace_graph method. \u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from pymc_extras.statespace import structural as st\n",
+ "\n",
+ "ll = st.LevelTrend(order=2)\n",
+ "se = st.FrequencySeasonality(season_length=12, name=\"annual\")\n",
+ "me = st.MeasurementError(name=\"error\")\n",
+ "mod = ll + se + me\n",
+ "ss_mod = mod.build(mode=\"NUMBA\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "id": "a55aed59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "initial_trend_dims, sigma_trend_dims, annual_dims, P0_dims = ss_mod.param_dims.values()\n",
+ "coords = ss_mod.coords"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "2a0c39b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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