diff --git a/.gitignore b/.gitignore index fc756ad..f4ce211 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,9 @@ __pycache__/ # C extensions *.so +# Jupyter +.ipynb_checkpoints + # Distribution / packaging .Python env/ diff --git a/notebooks/utide_real_data_example.ipynb b/notebooks/utide_real_data_example.ipynb index 8859f52..7300beb 100644 --- a/notebooks/utide_real_data_example.ipynb +++ b/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 '" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ExecuteTime": { + "end_time": "2021-04-07T18:56:44.796084Z", + "start_time": "2021-04-07T18:56:44.439315Z" } - ], + }, + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -191,22 +145,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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SibpUVGBCZGTdbsT0FPFkCHZLC5y5Sy/FZxosdrbu3Z0dMr5stwirVwdFL3yz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\nMIgNA2rcm2/i/EznGTMG6Qs0HCL4WzrGejEju1H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al7c4vUeOkc8+c44Ad++OtpkwAWtO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\n2Ov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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ExecuteTime": { + "end_time": "2021-04-07T18:53:18.905236Z", + "start_time": "2021-04-07T18:53:18.550688Z" } - ], + }, + "outputs": [], "source": [ "fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharey=True, sharex=True, figsize=(17, 5))\n", "\n", @@ -222,24 +168,67 @@ } ], "metadata": { + "hide_input": false, "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.8" + "pygments_lexer": "ipython3", + "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, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/tests/test_astron.py b/tests/test_astron.py index 1820bca..a185dca 100644 --- a/tests/test_astron.py +++ b/tests/test_astron.py @@ -9,7 +9,6 @@ octave:11> [a, ad] = t_astron(dns) """ - import numpy as np from utide.astronomy import ut_astron diff --git a/tests/test_normalize.py b/tests/test_normalize.py index 734ed03..92d1f7f 100644 --- a/tests/test_normalize.py +++ b/tests/test_normalize.py @@ -1,8 +1,9 @@ import datetime import numpy as np +import pandas as pd -from utide._time_conversion import _normalize_time +from utide._time_conversion import _date2num, _normalize_time def test_formats(): @@ -19,3 +20,28 @@ def test_formats(): for form in forms[1:]: np.testing.assert_almost_equal(_normalize_time(*form), expected) + + +def test_datenum(): + assert _date2num("1970-01-01") == 0 + assert _date2num("1970-01-01", epoch="0001-01-01") == 719162 + assert _date2num("0001-01-01") == -719162 + assert _date2num("0001-01-01", epoch="0000-12-31") == 1 + assert _date2num(datetime.datetime(1, 1, 1)) == -719162 + assert _date2num(pd.to_datetime("1970-01-01")) == 0 + + +def test_normalize_time_numpy(): + tin = np.arange("0001-01-01", "0001-01-10", dtype="datetime64[D]") # till 09 + texp = np.arange(1, 10) + assert np.array_equal(_normalize_time(tin, epoch=None), texp) + + tin = np.arange("1970-01-01", "1970-01-10", dtype="datetime64[D]") # till 09 + texp = np.arange(719163, 719172) + assert np.array_equal(_normalize_time(tin, epoch=None), texp) + + +def test_normalize_time_pandas(): + tin = pd.date_range("1970-01-01", "1970-01-10") # till 10 + texp = np.arange(719163, 719173) + assert np.array_equal(_normalize_time(tin, epoch=None), texp) diff --git a/tests/test_order_constit.py b/tests/test_order_constit.py index 8c87e45..b836581 100644 --- a/tests/test_order_constit.py +++ b/tests/test_order_constit.py @@ -36,6 +36,7 @@ "method": "ols", "conf_int": "MC", "Rayleigh_min": 0.95, + "epoch": "python", } diff --git a/tests/test_periodogram.py b/tests/test_periodogram.py index 4f82122..b230931 100644 --- a/tests/test_periodogram.py +++ b/tests/test_periodogram.py @@ -1,7 +1,6 @@ """ Tests for periodogram module. """ - import numpy as np import utide.periodogram as pgram diff --git a/tests/test_solve.py b/tests/test_solve.py index cc54bba..d7a1c3d 100644 --- a/tests/test_solve.py +++ b/tests/test_solve.py @@ -2,7 +2,6 @@ Smoke testing--just see if the system runs. """ - # These tests are quick and crude. # TODO: extend the tests by cycling through various combinations # of configuration and data input. @@ -54,6 +53,7 @@ def test_roundtrip(conf_int): "method": "ols", "conf_int": conf_int, "Rayleigh_min": 0.95, + "epoch": "python", } speed_coef = solve(time, time_series, time_series, lat=lat, **opts) @@ -61,7 +61,7 @@ def test_roundtrip(conf_int): amp_err = amp - elev_coef["A"][0] phase_err = phase - elev_coef["g"][0] - ts_recon = reconstruct(time, elev_coef).h + ts_recon = reconstruct(time, elev_coef, epoch="python").h # pure smoke testing of reconstruct vel = reconstruct(time, speed_coef) @@ -99,6 +99,7 @@ def test_masked_input(): "method": "ols", "conf_int": "linear", "Rayleigh_min": 0.95, + "epoch": "python", } t = np.ma.array(time) @@ -152,6 +153,7 @@ def test_robust(): "method": "robust", "conf_int": "linear", "Rayleigh_min": 0.95, + "epoch": "python", } speed_coef = solve(time, noisy, noisy, lat=lat, **opts) @@ -160,7 +162,7 @@ def test_robust(): print(speed_coef.weights, elev_coef.weights) print(speed_coef.rf, elev_coef.rf) - ts_recon = reconstruct(time, elev_coef).h + ts_recon = reconstruct(time, elev_coef, epoch="python").h err = np.std(tide - ts_recon) np.testing.assert_almost_equal(err, 0, decimal=2) @@ -179,6 +181,7 @@ def test_MC(): "conf_int": "MC", "white": False, "Rayleigh_min": 0.95, + "epoch": "python", } speed_coef = solve(time, noisy, noisy, lat=lat, **opts) diff --git a/tests/test_uv.py b/tests/test_uv.py index 88c4671..d0d5ad9 100644 --- a/tests/test_uv.py +++ b/tests/test_uv.py @@ -2,11 +2,9 @@ Full example test. """ - import numpy as np import pytest -from matplotlib.dates import date2num from pandas import date_range from utide import reconstruct, solve @@ -35,8 +33,7 @@ def make_data(): # Signal + some noise. u = _fake_tide(np.arange(N), M2amp=2, M2phase=0) + np.random.randn(N) v = _fake_tide(np.arange(N), M2amp=1, M2phase=np.pi) + np.random.randn(N) - time = date2num(t.to_pydatetime()) - return time, u, v + return t, u, v def test_solve(make_data): @@ -51,6 +48,7 @@ def test_solve(make_data): method="ols", conf_int="linear", Rayleigh_min=0.95, + epoch=None, ) assert isinstance(coef, Bunch) diff --git a/utide/_reconstruct.py b/utide/_reconstruct.py index 716a945..0b3e279 100644 --- a/utide/_reconstruct.py +++ b/utide/_reconstruct.py @@ -8,7 +8,7 @@ def reconstruct( t, coef, - epoch="python", + epoch=None, verbose=True, constit=None, min_SNR=2, @@ -20,15 +20,15 @@ def reconstruct( Parameters ---------- t : array_like - Time in days since ``epoch``. + Time in days since ``epoch``, or array of datetime, np.datetime64, or pd.datetime coef : `Bunch` Data structure returned by `utide.solve`. - epoch : {string, `datetime.date`, `datetime.datetime`}, optional - Valid strings are 'python' (default); 'matlab' if `t` is - an array of Matlab datenums; or an arbitrary date in the - form 'YYYY-MM-DD'. The default corresponds to the Python - standard library `datetime` proleptic Gregorian calendar, - starting with 1 on January 1 of year 1. + epoch : {string, `datetime.date`, `datetime.datetime`}, if datenum is provided in t. + Default `None` if `t` is `datetime`, `np.datetime64`, or `pd.datetime array.` + Optional valid strings are + - 'python' : if `t` is days since '0000-12-31' + - 'matlab' : if `t` is days since '0000-00-00' + Or, an arbitrary date in the form 'YYYY-MM-DD'. verbose : {True, False}, optional True to enable output message (default). False turns off all messages. @@ -54,7 +54,7 @@ def reconstruct( 'min_SNR', and 'min_PE'. The input time array is included as 't_in', and 't_mpl'; the former is the original input time argument, and the - latter is the time as a matplotlib datenum. If 'epoch' + latter is the time as a datenum from '0000-12-31'. If 'epoch' is 'python', these will be identical, and the names will point to the same array. diff --git a/utide/_solve.py b/utide/_solve.py index 20ed0e8..d48f7ff 100644 --- a/utide/_solve.py +++ b/utide/_solve.py @@ -1,7 +1,6 @@ """ Central module for calculating the tidal amplitudes, phases, etc. """ - import numpy as np from ._time_conversion import _normalize_time @@ -28,7 +27,7 @@ "robust_kw": {"weight_function": "cauchy"}, "white": False, "verbose": True, - "epoch": "python", + "epoch": None, } @@ -127,20 +126,19 @@ def solve(t, u, v=None, lat=None, **opts): Parameters ---------- t : array_like - Time in days since `epoch`. + Time in days since `epoch`, or np.datetime64 array, or pandas datetime array. u : array_like Sea-surface height, velocity component, etc. v : {None, array_like}, optional If `u` is a velocity component, `v` is the orthogonal component. lat : float, required Latitude in degrees. - epoch : {string, `datetime.date`, `datetime.datetime`}, optional - Valid strings are 'python' (default); 'matlab' if `t` is - an array of Matlab datenums; or an arbitrary date in the - form 'YYYY-MM-DD'. The default corresponds to the Python - standard library `datetime` proleptic Gregorian calendar, - starting with 1 at 00:00 on January 1 of year 1; this is - the 'datenum' used by Matplotlib. + epoch : {string, `datetime.date`, `datetime.datetime`}, if datenum is provided in t. + Default `None` if `t` is `datetime`, `np.datetime64`, or `pd.datetime array.` + Optional valid strings are + - 'python' : if `t` is days since '0000-12-31' + - 'matlab' : if `t` is days since '0000-00-00' + Or, an arbitrary date in the form 'YYYY-MM-DD'. constit : {'auto', sequence}, optional List of strings with standard letter abbreviations of tidal constituents; or 'auto' to let the list be determined diff --git a/utide/_time_conversion.py b/utide/_time_conversion.py index 15efd4c..0cb3cd0 100644 --- a/utide/_time_conversion.py +++ b/utide/_time_conversion.py @@ -2,44 +2,83 @@ Utility for allowing flexible time input. """ -import warnings +import numpy as np -from datetime import date, datetime +# to be added to get 1 on 1st January of year 1 from unix epoch '1970-01-01' +_DAY_TO_GREGORIAN_EPOCH = 719163 -try: - from datetime import timezone +# milisecond in a day +_MS_PER_DAY = 1000 * 86400 - have_tz = True -except ImportError: - have_tz = False +def _date2num(date, epoch="1970-01-01 00:00:00.000"): + """ + Numpy based date to datenum calculator. + + `date` and `epoch` can be anything parsable by np.datetime64 - string, datetime, datetime64, + pandas datetime. + + Default `epoch` is the unix epoch 1970-01-01 00:00:00. + """ + date = np.asarray(date) -def _normalize_time(t, epoch): - if epoch == "python": - return t - if epoch == "matlab": - return t - 366 try: - epoch = datetime.strptime(epoch, "%Y-%m-%d") - except (TypeError, ValueError): - pass - if isinstance(epoch, date): - if not hasattr(epoch, "time"): - return t + epoch.toordinal() - # It must be a datetime, which is also an instance of date. - if epoch.tzinfo is not None: - if have_tz: - epoch = epoch.astimezone(timezone.utc) - else: - warnings.warn( - "Timezone info in epoch is being ignored;" " UTC is assumed.", - ) - ofs = ( - epoch.toordinal() - + epoch.hour / 24 - + epoch.minute / 1440 - + epoch.second / 86400 + date = date.astype("datetime64[ms]") + except ValueError: + raise ValueError( + f"Cannot convert date argument ({date}) to scalar or array of numpy datetime64 dtype.", + ) + + try: + epoch = np.datetime64(epoch, "ms") + except ValueError: + raise ValueError( + f"Cannot convert epoch argument ({epoch}) to numpy datetime64 dtype.", ) - return t + ofs - raise ValueError("Cannot parse epoch as string or date or datetime") + + # datenum calculation + datenum = (date - epoch).astype(float) / _MS_PER_DAY + return datenum + + +def _python_gregorian_datenum(date): + """ + Number of days since 0000-12-31. + + Python gregorian time is 1 on 1st day of 1st year. Essentially, it means, + the epoch for python gregorian time is 0000-12-31. With _date2num() defined + above, this amounts to 719163 days from the unix-epoch 1970-01-01 00:00:00. + To avoid repetative calculation, this is defined as _DAY_TO_GREGORIAN_EPOCH. + """ + return _date2num(date) + _DAY_TO_GREGORIAN_EPOCH + + +def _normalize_time(t, epoch=None): + """ + Convert datetime or datenum array to proper input datenum array with an + epoch from '0000-12-31' - 1st Jan of 1st year is 1. + + `t` input time or datenum array + `epoch` either 'python', 'matlab', or np.datetime64 compatible value + """ + t = np.asarray(t) + + if epoch is None: + # default datetime, datetime64, or datetime array + return _python_gregorian_datenum(t) + + if t.dtype.kind in ("if"): + if epoch == "python": + return t + elif epoch == "matlab": + return t - 366 + else: + try: + ofs = _python_gregorian_datenum(epoch) + except ValueError: + raise ValueError("Cannot parse epoch as string or date or datetime") + else: + return t + ofs + else: + raise ValueError("Can not process time array as timestamp or datenum.") diff --git a/utide/utilities.py b/utide/utilities.py index 3220e85..42864db 100644 --- a/utide/utilities.py +++ b/utide/utilities.py @@ -1,3 +1,5 @@ +import warnings + import numpy as np from scipy.io import loadmat @@ -167,6 +169,14 @@ def _check_strict(self, strict, kw): ek.sort() raise KeyError(f"Update keys {bk} don't match existing keys {ek}") + @property + def t_mpl(self): + warnings.warn( + "t_mpl is being depreciated. Please directly input a time array and use variable t_in for plots.", + category=FutureWarning, + ) + return self["t_mpl"] + # The following functions ending with loadbunch() and showmatbunch() # are taken from the repo