Merge pull request #98 from jamal919/master

:enh: Updates time input handling

.gitignore

@@ -5,6 +5,9 @@ __pycache__/
 # C extensions
 *.so
 
+# Jupyter
+.ipynb_checkpoints
+
 # Distribution / packaging
 .Python
 env/

notebooks/utide_real_data_example.ipynb

@@ -3,7 +3,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:48:45.015791Z",
+ "start_time": "2021-04-07T18:48:44.379460Z"
+ }
+ },
  "outputs": [],
  "source": [
  "%matplotlib notebook\n",
@@ -12,7 +17,6 @@
  "\n",
  "import numpy as np\n",
  "import matplotlib.pyplot as plt\n",
- "import matplotlib.dates as mdates\n",
  "\n",
  "import pandas as pd\n",
  "\n",
@@ -31,7 +35,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:48:51.738343Z",
+ "start_time": "2021-04-07T18:48:51.721594Z"
+ }
+ },
  "outputs": [],
  "source": [
  "with open('can1998.dtf') as f:\n",
@@ -49,7 +58,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:48:59.807296Z",
+ "start_time": "2021-04-07T18:48:59.691677Z"
+ }
+ },
  "outputs": [],
  "source": [
  "\n",
@@ -88,7 +102,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:49:10.424925Z",
+ "start_time": "2021-04-07T18:49:10.394298Z"
+ }
+ },
  "outputs": [],
  "source": [
  "bad = obs['flag'] == 2\n",
@@ -105,18 +124,23 @@
  "cell_type": "markdown",
  "metadata": {},
  "source": [
- "The utide package works with ordinary numpy arrays, not with Pandas Series or Dataframes, so we need to make a `time` variable in floating point days since a given epoch, and use the `values` attribute of the elevation anomaly (a Pandas Series) to extract the underlying numpy ndarray."
+ "From **utide v2.7** pandas `datetime` index can be passed directly as the time variable. For elevation or current, utide only works with ordinary numpy arrays, so we use the `to_numpy()` method of the elevation anomaly (a Pandas Series) to extract the underlying numpy ndarray.\n",
+ "\n",
+ "Note: The utide package before 2.7 worked only with a `time` array in floating point days since `0000-12-31`."
  ]
  },
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:50:30.514193Z",
+ "start_time": "2021-04-07T18:50:29.975228Z"
+ }
+ },
  "outputs": [],
  "source": [
- "time = mdates.date2num(obs.index.to_pydatetime())\n",
- "\n",
- "coef = utide.solve(time, obs['anomaly'].values,\n",
+ "coef = utide.solve(obs.index, obs['anomaly'].to_numpy(),\n",
  " lat=-25,\n",
  " method='ols',\n",
  " conf_int='MC')"
@@ -132,7 +156,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:50:36.744260Z",
+ "start_time": "2021-04-07T18:50:36.734396Z"
+ }
+ },
  "outputs": [],
  "source": [
  "print(coef.keys())"
@@ -141,10 +170,15 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:50:56.895599Z",
+ "start_time": "2021-04-07T18:50:56.504970Z"
+ }
+ },
  "outputs": [],
  "source": [
- "tide = utide.reconstruct(time, coef)"
+ "tide = utide.reconstruct(obs.index, coef)"
  ]
  },
  {
@@ -157,7 +191,12 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:50:59.473572Z",
+ "start_time": "2021-04-07T18:50:59.461815Z"
+ }
+ },
  "outputs": [],
  "source": [
  "print(tide.keys())"
@@ -166,13 +205,16 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {},
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-04-07T18:51:02.163453Z",
+ "start_time": "2021-04-07T18:51:02.035621Z"
+ }
+ },
  "outputs": [],
  "source": [
- "#t = obs.index.values # dtype is '<M8[ns]' (numpy datetime64)\n",
- "# It is more efficient to supply the time directly as matplotlib\n",
- "# datenum floats:\n",
- "t = tide.t_mpl\n",
+ "# t = obs.index.values # dtype is '<M8[ns]' (numpy datetime64)\n",
+ "t = tide.t_in\n",
  "\n",
  "fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharey=True, sharex=True)\n",
  "\n",
@@ -183,21 +225,13 @@
  "fig.legend(ncol=3, loc='upper center')\n",
  "fig.autofmt_xdate()"
  ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
  }
  ],
  "metadata": {
  "anaconda-cloud": {},
+ "hide_input": false,
  "kernelspec": {
- "display_name": "Python [default]",
+ "display_name": "Python 3",
  "language": "python",
  "name": "python3"
  },
@@ -211,7 +245,49 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.7.10"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
  }
  },
  "nbformat": 4,