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Revert "Correction of the autocorrelation estimate" #4

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Revert "Correction of the autocorrelation estimate" #4

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46 changes: 27 additions & 19 deletions TOATS.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -134,7 +134,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# Date format test\n",
Expand Down Expand Up @@ -224,7 +226,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def decimal_month(years, months, days):\n",
Expand Down Expand Up @@ -275,7 +279,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# calculate and display monthly and annual statistics\n",
Expand Down Expand Up @@ -451,7 +457,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# create series of dates adjusted to start at 0 for use in the regression model \n",
Expand Down Expand Up @@ -481,7 +489,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# calculate and plot trend \n",
Expand Down Expand Up @@ -516,21 +526,17 @@
"outputs": [],
"source": [
"TDTi_dict = {} # time of detection\n",
"\n",
"# autocorrelation at lag 1 of the time series noise\n",
"for k, v in wls_model_dict.items():\n",
" model = v['model']\n",
" ts_mean = var_trends[k]['ts_mean']\n",
" decimal_year_zero = v['decimal_year_zero']\n",
" ts_variable_deseasoned = v['ts_variable_deseasoned']\n",
" \n",
" # Detrended monthly anomalies\n",
" trend_to_remove_TDT = model.predict(decimal_year_zero)\n",
" detrended_TDT = [ts_variable_deseasoned[i]-trend_to_remove_TDT[i] for i in range(0, len(ts_mean[k]['datetime_mean']))]\n",
" \n",
" # autocorrelation at lag 1 of the time series noise (i.e. detrended monthly anomalies) \n",
" autocorr = sm.tsa.stattools.acf(detrended_TDT,fft=False,nlags=1)[1:]\n",
" decimal_year_zero = v['decimal_year_zero']\n",
" autocorr = sm.tsa.stattools.acf(ts_variable_deseasoned,fft=False,nlags=1)[1:]\n",
" ts_mean = var_trends[k]['ts_mean']\n",
"\n",
" # standard deviation of detrended monthly anomalies\n",
" model = v['model']\n",
" trend_to_remove_TDT = model.predict(decimal_year_zero)\n",
" detrended_TDT = [ts_variable_deseasoned[i]-trend_to_remove_TDT[i] for i in range(0, len(ts_mean[k]['datetime_mean']))]\n",
" std_dev = np.std(detrended_TDT)\n",
"\n",
" # time of detection \n",
Expand Down Expand Up @@ -611,7 +617,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# summary of OLS Regression Results\n",
Expand Down Expand Up @@ -759,9 +767,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.5"
"version": "3.10.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
"nbformat_minor": 2
}