diff --git a/.gitignore b/.gitignore index a68448e..84f6682 100644 --- a/.gitignore +++ b/.gitignore @@ -94,3 +94,6 @@ ENV/ # MacOS .DS_Store + +#PyCharm +.idea diff --git a/CHANGES.rst b/CHANGES.rst new file mode 100644 index 0000000..7661b3d --- /dev/null +++ b/CHANGES.rst @@ -0,0 +1,39 @@ +Changelog +========= + +0.X.X (unreleased) +------------------ + +Breaking changes: + +- *add item here* + +New features: + +- Stress in zone + +Bug fixes: + +- *add item here* + + +0.1.1 (in progress) +------------------ + +Breaking changes: + +- *add item here* + +New features: + +- Merged vmpy functionality as is: + - io/strava.py + - algorithms/streams.py + - algorithms/metrics.py + - utils.py + +Bug fixes: + +- *add item here* + + diff --git a/requirements.txt b/requirements.txt index aa2371f..a57adf6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,4 @@ isort==4.2.15 lmfit==0.9.7 pandas==0.20.3 pytest==3.2.5 +requests==2.18.4 diff --git a/sweat/algorithms/metrics.py b/sweat/algorithms/metrics.py new file mode 100644 index 0000000..c0275d9 --- /dev/null +++ b/sweat/algorithms/metrics.py @@ -0,0 +1,171 @@ +"""Calculation of performance metrics that change the shape of stream""" + +import numpy as np +import pandas as pd +from sweat.algorithms.streams import rolling_mean, mask_fill, compute_zones +from sweat.utils import cast_array_to_original_type + +import logging +logger = logging.getLogger(__name__) + + +def power_duration_curve(arg, mask=None, value=0.0, **kwargs): + """Power-Duration Curve + + Compute power duration curve from the power stream. Mask-filter options can be + added using the keyword arguments. + + Parameters + ---------- + arg : array-like + Power stream + mask: array-like, optional + Replacement mask (the default is None, which implies no masking) + value: number, optional + Value to use as a replacement (the default is 0.0) + + Returns + ------- + rv : type of input argument + Power-Duration Curve + """ + + y = mask_fill(arg, mask=mask, value=value) + y = pd.Series(y) + + # Compute the accumulated energy from the power data + energy = y.cumsum() + + # Compute the maximum sustainable power using the difference in energy + # This method is x4 faster than using rolling mean + y = np.array([]) + for t in np.arange(1, len(energy)): + y = np.append(y, energy.diff(t).max()/(t)) + y = cast_array_to_original_type(y, type(arg)) + + return y + + +def best_interval(arg, window, mask=None, value=0.0, **kwargs): + """Compute best interval of the stream + + Masking with replacement is controlled by keyword arguments + + Parameters + ---------- + arg: array-like + window : int + Duration of the interval in seconds + mask : array-like of bool, optional + default=None, which means no masking + value : number, optional + Value to use for replacement, default=0.0 + + Returns + ------- + float + """ + + y = rolling_mean(arg, window=window, mask=mask, value=value, **kwargs) + + rv = np.max(y) + + return rv + + +def time_in_zones(arg, **kwargs): + """Time in zones + + Calculate time [sec] spent in each zone + + Parameters + ---------- + arg : array-like, power or heartrate + kwargs : see zones + + Returns + ------- + array-like, the same type as arg + """ + type_arg = type(arg) + z = pd.Series(compute_zones(arg, **kwargs)) + tiz = z.groupby(z).count() + rv = cast_array_to_original_type(tiz, type_arg) + + return rv + + +def normalized_power(arg, mask=None, value=0.0, **kwargs): + """Normalized power + + Parameters + ---------- + arg : array-like + Power stream + mask: array-like of bool, optional + default=None, which means no masking + value : number, optional + Value to use for replacement, default=0.0 + type : {"xPower", "NP} + Determines calculation method to use, default='xPower' + + Returns + ------- + number + """ + + if kwargs.get('type', 'NP') == 'xPower': + _rolling_mean = rolling_mean(arg, window=25, mask=mask, value=value, type='emwa') + else: + _rolling_mean = rolling_mean(arg, window=30, mask=mask, value=value) + + if type(_rolling_mean) == list: + _rolling_mean = np.asarray(_rolling_mean, dtype=np.float) + + rv = np.mean(np.power(_rolling_mean, 4)) ** (1/4) + + return rv + + +def relative_intensity(norm_power, threshold_power): + """Relative intensity + + Parameters + ---------- + norm_power : number + NP or xPower + threshold_power : number + FTP or CP + + Returns + ------- + float + IF or RI + """ + + rv = norm_power/threshold_power + + return rv + + +def stress_score(norm_power, threshold_power, duration): + """Stress Score + + Parameters + ---------- + norm_power : number + NP or xPower + threshold_power : number + FTP or CP + duration : int + Duration in seconds + + Returns + ------- + ss: + TSS or BikeScore + """ + + ss = (duration/3600) * (norm_power/threshold_power)**2 * 100 + + return ss diff --git a/sweat/algorithms/streams.py b/sweat/algorithms/streams.py new file mode 100644 index 0000000..1671be9 --- /dev/null +++ b/sweat/algorithms/streams.py @@ -0,0 +1,203 @@ +"""Operation on Streams that leave the shape of the stream unchanged""" + +import numpy as np +import pandas as pd +from sweat.utils import cast_array_to_original_type + + +# FTP based 7-zones with left bind edge set to -0.001 +POWER_ZONES_THRESHOLD = [-0.001, 0.55, 0.75, 0.9, 1.05, 1.2, 1.5, 10.0] +POWER_ZONES_THRESHOLD_DESC = ["Active Recovery", "Endurance", "Tempo", + "Threshold", "VO2Max", "Anaerobic", "Neuromuscular",] +POWER_ZONES_THRESHOLD_ZNAME = ["Z1", "Z2", "Z3", "Z4", "Z5", "Z6", "Z7"] + +# LTHR based 5-zones with left bind edge set to -0.001 +HEART_RATE_ZONES = [-0.001, 0.68, 0.83, 0.94, 1.05, 10.0] +HEART_RATE_ZONES_DESC = ["Active recovery", "Endurance", "Tempo", "Threshold", "VO2Max",] +HEART_RATE_ZONES_ZNAME = ["Z1", "Z2", "Z3", "Z4", "Z5"] + + +def compute_zones(arg, **kwargs): + """Convert stream into respective zones stream + + Watts streams can be converted either into ftp based 7-zones or into custom zones + HR streams can be converted either in lthr based 5-zones or into custom zones + One of three *ftp*, *lthr* or *zone* keyword parameters must be provided + + Parameters + ---------- + arg : array-like + ftp : number, optional + Value for FTP, will be used for 7-zones calculation + lthr: number, optional + Value for LTHR, will be used for 5-zones calculation + zones: list, optional + List of custom defined zones with left edge set to -1 and right edge to 10000 + + Returns + ------- + array-like of int, the same type as arg + """ + + arg_s = pd.Series(arg) + + if kwargs.get('zones', None): + abs_zones = kwargs.get('zones') + + elif kwargs.get('ftp', None): + abs_zones = np.asarray(POWER_ZONES_THRESHOLD) * kwargs.get('ftp') + + elif kwargs.get('lthr', None): + abs_zones = np.asarray(HEART_RATE_ZONES) * kwargs.get('lthr') + + else: + raise ValueError + + labels = kwargs.get('labels', list(range(1, len(abs_zones)))) + assert len(abs_zones) == (len(labels) + 1) + + y = pd.cut(arg_s, bins=abs_zones, labels=labels) + y = cast_array_to_original_type(y, type(arg)) + + return y + + + +def wpk(power, weight): + """Watts per kilo + + Parameters + ---------- + power : list, ndarray, series + weight : number + + Returns + ------- + array-like + """ + + rv = pd.Series(power, dtype=float)/ weight + rv = cast_array_to_original_type(rv, type(power)) + + return rv + + +def mask_fill(arg, mask=None, value=0.0, **kwargs): + """Replace masked values + + Parameters + ---------- + arg : array-like + mask : array-like of bools, optional + Default value is None, which means no masking will be applied + value : number, optional + Value to use for replacement, default=0.0 + + Returns + ------- + y: type of input argument + + + In case the arg is an ndarray all operations will be performed on the original array. + To preserve original array pass a copy to the function + """ + + if mask is None: + return arg + + y = np.array(arg) + + mask = np.array(mask, dtype=bool) + y[~mask] = value + + rv = cast_array_to_original_type(y, type(arg)) + + return rv + + +def median_filter(arg, window=31, threshold=1, value=None, **kwargs): + """Outlier replacement using median filter + + Detect outliers using median filter and replace with rolling median or specified value + + Parameters + ---------- + arg : array-like + window : int, optional + Size of window (including the sample; default=31 is equal to 15 on either side of value) + threshold : number, optional + default=3 and corresponds to 2xSigma + value : float, optional + Value to be used for replacement, default=None, which means replacement by rolling median value + + Returns + ------- + y: type of input argument + + In case the arg is an ndarray all operations will be performed on the original array. + To preserve original array pass a copy to the function + """ + y = pd.Series(arg) + + rolling_median = y.rolling(window, min_periods=1).median() + + difference = np.abs(y - rolling_median) + + median_abs_deviation = difference.rolling(window, min_periods=1).median() + + outlier_idx = difference > 1.4826 * threshold * median_abs_deviation + """ The factor 1.4826 makes the MAD scale estimate + an unbiased estimate of the standard deviation for Gaussian data. + """ + + if value: + y[outlier_idx] = value + else: + y[outlier_idx] = rolling_median[outlier_idx] + + y = y.as_matrix() + + y = cast_array_to_original_type(y, type(arg)) + + return y + + +def rolling_mean(arg, window=10, mask=None, value=0.0, **kwargs): + """Compute rolling mean + + Compute *uniform* or *ewma* rolling mean of the stream. In-process masking with replacement is + controlled by optional keyword parameters + + Parameters + ---------- + arg : array-like + window : int + Size of the moving window in sec, default=10 + mask : array-like of boolean, optional + Default value is None, which means no masking will be applied + value : number, optional + Value to use for replacement, default=0.0 + type : {"uniform", "emwa"}, optional + Type of averaging, default="uniform" + + Returns + ------- + y: type of input argument + + The moving array will indicate which samples to set to zero before + applying rolling mean. + """ + if mask is not None: + arg = mask_fill(arg, mask, value, **kwargs) + + y = pd.Series(arg) + + if kwargs.get('type', 'uniform') == 'ewma': + y = y.ewm(span=window, min_periods=1).mean().values + else: + y = y.rolling(window, min_periods=1).mean().values + + y = cast_array_to_original_type(y, type(arg)) + + return y + diff --git a/sweat/io/__init__.py b/sweat/io/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/sweat/io/strava.py b/sweat/io/strava.py new file mode 100644 index 0000000..db2b611 --- /dev/null +++ b/sweat/io/strava.py @@ -0,0 +1,215 @@ +"""Very thin wrapper around the Strava API v3 + + STRAVA_ACCESS_TOKEN must be explicitly provided as an input + + ALL returned values are python objects e.g. dict or list +""" +import requests +import json +import logging + +logger = logging.getLogger(__name__) + + +def retrieve_athlete(access_token): + """Retrieve current(authenticated) athlete + + API V3: https://strava.github.io/api/v3/athlete/#get-details + + Parameters + ---------- + access_token : str + Settings/My API Applications/Your Access Token + + Returns + ------- + dict + """ + + endpoint_url = "https://www.strava.com/api/v3/athlete" + + r = requests.get(endpoint_url, + headers=authorization_header(access_token)) + + if r.ok: + + athlete = json.loads(r.text) + + else: + + logger.error('Retrieve Athlete Failed with a reason {}'.format(r.reason)) + athlete = None + + return athlete + + +def retrieve_zones(access_token, **kwargs): + """Retrieve Power and Heartrate zones + + API V3: https://strava.github.io/api/v3/athlete/#zones + + Parameters + ---------- + access_token : str + Settings/My API Applications/Your Access Token + + Returns + ------- + dict + """ + + endpoint_url = "https://www.strava.com/api/v3/athlete/zones" + + r = requests.get(endpoint_url, + headers=authorization_header(access_token)) + + if r.ok: + + zones = json.loads(r.text) + + else: + + logger.error('Retrieve Zones Failed with a reason {}'.format(r.reason)) + zones = None + + return zones + + +def retrieve_activity(activity_id, access_token): + """Retrieve a detailed representation of activity + + API V3: https://strava.github.io/api/v3/activities/#get-details + + Parameters + ---------- + activity_id: int + access_token : str + Settings/My API Applications/Your Access Token + + Returns + ------- + dict + """ + + endpoint_url = "https://www.strava.com/api/v3/activities/{}".format(activity_id) + + r = requests.get(endpoint_url, + headers=authorization_header(access_token)) + + if r.ok: + + activity = json.loads(r.text) + + else: + + logger.error('Retrieve Activity Failed with a reason {}'.format(r.reason)) + activity = None + + return activity + + +def retrieve_streams(activity_id, access_token, **kwargs): + """Retrieve activity streams + + API V3: https://strava.github.io/api/v3/streams/#activity + + Parameters + ---------- + activity_id : int + access_token : str + Settings/My API Applications/Your Access Token + type: {None, 'original'} + Returns serialized original API response if set to 'original' + + Returns + ------- + streams : list + Streams are the list of dicts + """ + + types = kwargs.get("types", + "time,latlng,distance,altitude,velocity_smooth,heartrate,cadence,watts,temp,moving,grade_smooth") + + endpoint_url = "https://www.strava.com/api/v3/activities/{}/streams/{}".format(activity_id, types) + + r = requests.get(endpoint_url, + headers=authorization_header(access_token)) + + if r.ok: + + streams = json.loads(r.text) + + else: + + logger.error('Retrieve Streams Failed with a reason {}'.format(r.reason)) + streams = None + + if streams and not kwargs.get('type', None): + + streams = stream2dict(streams) + + return streams + + +def stream2dict(stream_list): + """Convert stream list into stream dict + + Parameters + ---------- + stream_list : list + Stream in list form (list of dicts), as returned by Strava API v3 + + Returns + ------- + stream_dict : dict + Stream in dict form, with key set to *stream name* and value set to the actual stream list. + In this form, the stream is ready to be consumed by pandas + """ + + stream_dict = {} + + for s in stream_list: + + stream_dict.update({s['type']: s['data']}) + + return stream_dict + + +def zones2list(zones, type="power"): + """Convert zones Strava response into a list + + Parameters + ---------- + zones : dict + Strava API zones response + type : {"power", "heart_rate"} + + Returns + ------- + y : list + Zones boundaries with left edge set to -1 and right to 10000 + """ + + y = list(map(lambda x: x['min'], zones[type]["zones"])) + y[0] = -1 + y.append(10000) + + return y + + +def authorization_header(access_token): + """Authorization header dict to be used with requests.get() + + Parameters + ---------- + access_token : str + Settings/My API Applications/Your Access Token + + Returns + ------- + header : dict + """ + + rv = {'Authorization': 'Bearer {}'.format(access_token)} + + return rv diff --git a/sweat/utils.py b/sweat/utils.py new file mode 100644 index 0000000..10dc517 --- /dev/null +++ b/sweat/utils.py @@ -0,0 +1,31 @@ +import numpy as np +import pandas as pd +import logging + +logger = logging.getLogger(__name__) + + +def cast_array_to_original_type(arg, arg_type): + """Cast array to another array-like type + + Parameters + ---------- + arg: array-like {list, ndarray, pd.Series} + arg_type: type + + Returns + ------- + casted : arg_type array-like + """ + + if arg_type == list: + return list(arg) + + elif arg_type == np.ndarray: + return np.array(arg) + + elif arg_type == pd.Series: + return pd.Series(arg) + + else: + raise ValueError("arg_type must be list, ndarray or pd.Series") diff --git a/tests/algorithms/test_metrics.py b/tests/algorithms/test_metrics.py new file mode 100644 index 0000000..8b8adea --- /dev/null +++ b/tests/algorithms/test_metrics.py @@ -0,0 +1,103 @@ +import numpy as np +import pandas as pd +from unittest import mock +from sweat.algorithms import metrics + + +@mock.patch('sweat.algorithms.metrics.rolling_mean') +def test_best_interval(test_rolling_mean): + + stream = [1,1,1,1,1] + test_rolling_mean.return_value = stream + + assert metrics.best_interval(stream, 5) == 1 + + +@mock.patch('sweat.algorithms.streams.compute_zones') +def test_time_in_zones(zones): + + power = [0.55, 0.75, 0.9, 1.05, 1.2, 1.5, 10.0] + zones.return_value = [1,2,3,4,5,6,7] + + rv = metrics.time_in_zones(power, ftp=1.0) + expected = [1, 1, 1, 1, 1, 1, 1] + + assert type(rv) == list + assert rv == expected + + +def test_normalized_power(): + + stream = np.ones(30) + moving = np.ones(30, dtype=bool) + + assert metrics.normalized_power(stream, moving) == 1 + +def test_normalized_power_xpower(): + + stream = np.ones(30) + moving = np.ones(30, dtype=bool) + + assert metrics.normalized_power(stream, moving, type='xPower') == 1 + + +def test_relative_intensity(): + + norm_power = 300.0 + threshold_power = 300.0 + + assert metrics.relative_intensity(norm_power, threshold_power) == 1.0 + + +def test_stress_score(): + + norm_power = 300.0 + threshold_power = 300.0 + duration = 3600 + + assert metrics.stress_score(norm_power, threshold_power, duration) == 100.0 + + + + +def test_power_duration_curve(): + + power = np.arange(101) + rv = metrics.power_duration_curve(power) + + assert type(power) == np.ndarray + assert rv[0] == 100.0 + assert rv[49] == 75.5 + assert rv[-1] == 50.5 + +def test_power_duration_curve_list(): + + stream = [0, 0, 0] + expected = [0, 0] + + rv = metrics.power_duration_curve(stream) + + assert type(rv) == list + assert rv == expected + + +def test_power_duration_curve_ndarray(): + + stream = np.zeros(3) + expected = np.zeros(2) + + rv = metrics.power_duration_curve(stream) + + assert type(rv) == np.ndarray + assert (rv == expected).all() + + +def test_power_duration_curve_series(): + + stream = pd.Series(np.zeros(3)) + expected = pd.Series(np.zeros(2)) + + rv = metrics.power_duration_curve(stream) + + assert type(rv) == pd.Series + assert (rv == expected).all() \ No newline at end of file diff --git a/tests/algorithms/test_streams.py b/tests/algorithms/test_streams.py new file mode 100644 index 0000000..bb8e071 --- /dev/null +++ b/tests/algorithms/test_streams.py @@ -0,0 +1,270 @@ +import numpy as np +import pandas as pd +from sweat.algorithms import streams + + +def test_mask_filter_mask_none(): + + stream = [1, 2, 3] + mask = None + + expected = [1, 2, 3] + + rv = streams.mask_fill(stream, mask) + + assert type(rv) == list + assert rv == expected + + +def test_mask_filter_list(): + + stream = [1, 2, 3, 4, 5] + mask = [True, True, False, True, True] + + expected = [1, 2, 0.0, 4, 5] + + rv = streams.mask_fill(stream, mask) + + assert type(rv) == list + assert rv == expected + + +def test_mask_filter_ndarray(): + + stream = np.asarray([1, 2, 3, 4, 5]) + mask = np.asarray([True, True, False, True, True], dtype=bool) + + expected = np.asarray([1, 2, 0.0, 4, 5]) + rv = streams.mask_fill(stream, mask) + + assert type(rv) == np.ndarray + assert (rv == expected).all() + + +def test_mask_filter_series(): + + stream = pd.Series([1, 2, 3, 4, 5]) + mask = pd.Series([True, True, False, True, True]) + + expected = pd.Series([1, 2, 0.0, 4, 5]) + rv = streams.mask_fill(stream, mask) + + assert type(rv) == pd.Series + assert (rv == expected).all() + + +def test_mask_filter_list_with_replacement(): + + stream = [1, 2, 3, 4, 5] + mask = [True, True, False, True, True] + + expected = [1, 2, 10.0, 4, 5] + + rv = streams.mask_fill(stream, mask, value=10.0) + + assert type(rv) == list + assert rv == expected + + +def test_rolling_mean_ndarray(): + + stream = np.asarray([1, 2, 3, 4, 5]) + expected = np.asarray([1, 1.5, 2.5, 3.5, 4.5]) + + rv = streams.rolling_mean(stream, 2) + + assert type(rv) == np.ndarray + assert (rv == expected).all() + + +def test_rolling_mean_list(): + + stream = [1, 2, 3, 4, 5] + expected = [1, 1.5, 2.5, 3.5, 4.5] + rv = streams.rolling_mean(stream, 2) + + assert type(rv) == list + assert rv == expected + + +def test_rolling_mean_list_with_mask(): + + stream = [1, 2, 3, 4, 5] + mask = [True, True, False, True, True] + expected = [1, 1.5, 1.0, 2.0, 4.5] + rv = streams.rolling_mean(stream, window=2, mask=mask) + + assert type(rv) == list + assert rv == expected + + +def test_rolling_mean_with_mask_ndarray(): + + stream = np.asarray([1, 2, 3, 4, 5]) + mask = np.asarray([True, True, False, True, True], dtype=bool) + expected = np.asarray([1, 1.5, 1.0, 2.0, 4.5]) + rv = streams.rolling_mean(stream, window=2, mask=mask) + + assert type(rv) == np.ndarray + assert (rv == expected).all() + + +def test_rolling_mean_list_emwa(): + + stream = list(np.ones(30)) + expected = list(np.ones(30)) + rv = streams.rolling_mean(stream, 2, type='ewma') + + assert type(rv) == list + assert rv == expected + + +def test_rolling_mean_real_data(test_stream): + + rv = streams.rolling_mean(test_stream['watts'], + mask=test_stream['moving'], + window=1) + + assert type(rv) == list + assert rv == test_stream['watts'] + + +def test_hampel_filter(): + + stream = np.ones(60) + stream[-1] = 2 + + rv = streams.median_filter(stream) + assert type(rv) == np.ndarray + assert (rv == np.ones(60)).all() + + +def test_hampel_filter_list(): + + stream = np.ones(60) + stream[-1] = 2 + stream = stream.tolist() + + expected = np.ones(60) + expected = expected.tolist() + + rv = streams.median_filter(stream) + + assert type(rv) == list + assert rv == expected + + +def test_hampel_filter_with_replacement(): + + stream = np.ones(60) + stream[-1] = 2 + + expected = np.ones(60) + expected[-1] = 10 + + rv = streams.median_filter(stream, value=10) + + assert type(rv) == np.ndarray + assert (rv == expected).all() + + + +def test_wpk(): + + power = [1,2,3] + weight = 2 + + rv = streams.wpk(power, weight) + expected = [0.5,1.0,1.5] + assert type(rv) == list + assert rv == expected + + rv = streams.wpk(np.array(power), weight) + expected = np.array([0.5, 1.0, 1.5]) + assert type(rv) == np.ndarray + assert (rv == expected).all() + + rv = streams.wpk(pd.Series(power), weight) + expected = pd.Series([0.5, 1.0, 1.5]) + assert type(rv) == pd.Series + assert (rv == expected).all() + + + +def test_zones_power_ftp_list(): + + stream = [0.55, 0.75, 0.9, 1.05, 1.2, 1.5, 10.0] + expected = [1, 2, 3, 4, 5, 6, 7] + + rv = streams.compute_zones(stream, ftp=1.0) + + assert type(rv) == list + assert rv == expected + + +def test_zones_heart_rate_lthr_list(): + + stream = [0.6, 0.8, 0.9, 1.0, 1.1] + expected = [1, 2, 3, 4, 5] + + rv = streams.compute_zones(stream, lthr=1.0) + + assert type(rv) == list + assert rv == expected + + +def test_zones_power_explicit_zones_list(): + + stream = [1, 150, 210, 250, 300, 350, 450] + expected = [1, 2, 3, 4, 5, 6, 7] + + rv = streams.compute_zones(stream, zones=[-1, 144, 196, 235, 274, 313, 391, 10000]) + + assert type(rv) == list + assert rv == expected + + +def test_zones_heart_rate_explicit_zones_list(): + + stream = [60, 120, 150, 160, 170, 180] + expected = [1, 1, 2, 3, 4, 5] + + rv = streams.compute_zones(stream, zones=[-1, 142, 155, 162, 174, 10000]) + + assert type(rv) == list + assert rv == expected + + +def test_zones_power_ftp_list_of_int(): + + stream = [1, 2,] + ftp=1.0 + expected = [4, 7,] + + rv = streams.compute_zones(stream, ftp=ftp) + + assert type(rv) == list + assert rv == expected + + +def test_zones_power_ftp_unordered_list(): + + stream = [2, 1, 3] + ftp=1.0 + expected = [7, 4, 7,] + + rv = streams.compute_zones(stream, ftp=ftp) + + assert type(rv) == list + assert rv == expected + + +def test_zones_power_ftp_ndarray(): + + stream = np.asarray([0.55, 0.75, 0.9, 1.05, 1.2, 1.5, 10.0]) + expected = np.asarray(list(range(1,8))) + + rv = streams.compute_zones(stream, ftp=1.0) + + assert type(rv) == np.ndarray + assert (rv == expected).all() diff --git a/tests/conftest.py b/tests/conftest.py index 15d8bb6..9102f93 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,39 @@ import pandas as pd import pytest +import os +from sweat.io import strava +import json @pytest.fixture def power(): return pd.Series(range(100)) + + +@pytest.fixture +def test_stream(): + + current_dir = os.path.dirname(os.path.abspath(__file__)) + + f_name = os.path.normpath(os.path.join(current_dir, + 'example_files/streams_1202065_1354978421.json')) + with open(f_name) as f: + + _stream = json.load(f) + + return strava.stream2dict(_stream) + + +@pytest.fixture +def test_stream_with_nans(): + + current_dir = os.path.dirname(os.path.abspath(__file__)) + + f_name = os.path.normpath(os.path.join(current_dir, + 'example_files/streams_1202065_1299011495.json')) + + with open(f_name) as f: + + _stream = json.load(f) + + return strava.stream2dict(_stream) diff --git a/tests/example_files/activity_1202065_1299011495.json b/tests/example_files/activity_1202065_1299011495.json new file mode 100644 index 0000000..b460aab --- /dev/null +++ b/tests/example_files/activity_1202065_1299011495.json @@ -0,0 +1,2256 @@ +{ + "start_date": "2017-12-02T12:18:39Z", + "laps": [ + { + "start_date": "2017-12-02T12:18:39Z", + "distance": 5948.98, + "start_index": 0, + "average_cadence": 86.9, + "elapsed_time": 976, + "start_date_local": "2017-12-02T13:18:39Z", + "activity": { + "id": 1299011495, + "resource_state": 1 + }, + "max_speed": 9.8, + "resource_state": 2, + "id": 4064097796, + "average_heartrate": 118.9, + "average_speed": 6.1, + 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3544.5, 3550.6, 3556.6, 3562.3, 3567.5, 3572.6, 3577.4, 3582.5, 3588.4, 3595.2, 3602.6, 3609.9, 3617.2, 3624.6, 3631.8, 3638.8, 3645.5, 3652.1, 3658.6, 3665.1, 3671.7, 3678.3, 3685.1, 3691.8, 3698.6, 3705.5, 3712.3, 3719.2, 3726.1, 3733.1, 3740.0, 3747.1, 3753.9, 3761.0, 3767.4, 3773.8, 3780.2, 3786.8, 3793.2, 3799.7, 3806.3, 3813.3, 3820.4, 3827.4, 3834.5, 3841.6, 3848.9, 3856.2, 3863.6, 3871.2, 3878.7, 3886.2, 3893.8, 3901.3, 3908.8, 3916.2, 3923.6, 3930.9, 3938.2, 3945.4, 3952.6, 3960.0, 3967.5, 3975.0, 3982.3, 3989.7, 3997.0, 4004.4, 4011.7, 4018.9, 4026.2, 4033.6, 4041.0, 4048.3, 4055.6, 4062.9, 4070.2, 4077.3, 4084.4, 4091.4, 4098.7, 4106.1, 4113.4, 4120.8, 4128.2, 4135.6, 4143.0, 4150.4, 4157.8, 4165.3, 4172.9, 4180.1, 4187.7, 4195.3, 4202.7, 4210.3, 4217.9, 4225.5, 4232.9, 4240.5, 4248.1, 4255.8, 4263.4, 4271.1, 4278.9, 4286.5, 4294.0, 4301.6, 4309.0, 4316.4, 4323.8, 4331.1, 4338.4, 4345.7, 4352.9, 4359.9, 4366.8, 4373.7, 4380.3, 4386.0, 4390.7, 4394.4, 4397.3, 4399.9, 4402.5, 4404.9, 4406.8, 4408.8, 4410.9, 4412.5, 4413.2, 4414.0, 4415.5, 4417.6, 4420.2, 4423.5, 4427.7, 4432.5, 4438.5, 4445.5, 4453.2, 4461.1, 4469.2, 4477.6, 4486.1, 4494.8, 4503.5, 4512.2, 4521.0, 4529.9, 4538.6, 4547.4, 4556.1, 4564.5, 4572.9, 4581.3, 4590.0, 4598.8, 4607.8, 4616.8, 4626.0, 4635.0, 4644.0, 4652.9, 4661.7, 4670.3, 4678.8, 4687.2, 4695.4, 4703.4, 4711.1, 4718.7, 4725.8, 4732.3, 4738.7, 4746.0, 4753.7, 4761.7, 4769.9, 4778.1, 4786.5, 4794.7, 4803.0, 4811.4, 4819.6, 4827.9, 4836.0, 4843.6, 4851.3, 4859.2, 4867.2, 4875.1, 4883.0, 4891.0, 4899.0, 4906.9, 4915.0, 4922.5, 4930.6, 4938.6, 4946.5, 4954.5, 4962.5, 4970.5, 4978.6, 4986.6, 4995.3, 5003.6, 5011.9, 5020.3, 5028.8, 5037.4, 5045.7, 5054.2, 5062.6, 5071.1, 5079.4, 5087.6, 5095.8, 5103.6, 5111.5, 5119.4, 5127.4, 5135.6, 5143.7, 5151.7, 5159.9, 5168.0, 5176.3, 5184.5, 5192.5, 5200.6, 5208.8, 5216.9, 5225.0, 5233.1, 5241.1, 5249.1, 5257.2, 5265.1, 5273.1, 5281.0, 5288.9, 5296.8, 5304.7, 5312.4, 5320.1, 5327.5, 5334.7, 5341.8, 5348.9, 5356.4, 5363.7, 5371.1, 5378.7, 5386.2, 5393.5, 5401.4, 5409.0, 5416.4, 5423.7, 5431.1, 5438.4, 5445.3, 5452.4, 5459.5, 5466.7, 5473.5, 5480.5, 5487.5, 5494.5, 5501.6, 5508.8, 5515.9, 5523.2, 5530.4, 5537.5, 5544.5, 5551.0, 5556.8, 5561.8, 5565.8, 5569.1, 5571.5, 5573.4, 5574.7, 5576.0, 5577.6, 5579.4, 5581.9, 5585.1, 5588.9, 5593.2, 5598.1, 5603.5, 5609.2, 5615.3, 5621.8, 5628.5, 5635.4, 5642.4, 5649.4, 5656.4, 5663.5, 5671.0, 5678.2, 5685.6, 5693.2, 5701.1, 5709.3, 5717.6, 5725.7, 5734.3, 5742.9, 5751.7, 5760.7, 5769.9, 5779.2, 5788.7, 5798.4, 5808.2, 5818.1, 5827.9, 5837.6, 5846.8, 5855.2, 5862.9, 5869.9, 5876.9, 5884.3, 5891.8, 5899.4, 5906.7, 5913.7, 5920.8, 5927.7, 5934.7, 5941.8, 5949.0, 5956.2, 5963.4, 5970.6, 5977.8, 5985.0, 5992.7, 6000.4, 6008.1, 6015.8, 6023.5, 6031.2, 6039.0, 6046.8, 6054.5, 6062.3, 6070.1, 6077.9, 6085.6, 6093.5, 6101.4, 6109.4, 6117.5, 6125.6, 6133.9, 6142.3, 6150.8, 6159.5, 6168.4, 6177.3, 6186.3, 6195.3, 6203.9, 6213.1, 6222.4, 6231.9, 6241.4, 6250.6, 6259.7, 6268.6, 6277.5, 6286.3, 6295.1, 6303.9, 6312.7, 6321.5, 6330.1, 6338.6, 6347.0, 6355.4, 6363.9, 6372.3, 6380.7, 6389.2, 6397.6, 6406.0, 6414.4, 6422.9, 6431.3, 6439.7, 6448.1, 6456.3, 6464.6, 6472.8, 6481.0, 6489.3, 6497.5, 6505.6, 6513.9, 6522.1, 6530.3, 6538.5, 6546.7, 6555.0, 6563.3, 6571.7, 6580.1, 6588.6, 6597.0, 6605.5, 6614.0, 6622.6, 6631.1, 6639.7, 6648.3, 6656.9, 6665.6, 6674.3, 6683.0, 6691.7, 6700.3, 6709.0, 6717.7, 6726.3, 6734.9, 6743.5, 6752.1, 6760.7, 6769.3, 6777.9, 6786.7, 6795.5, 6804.3, 6813.1, 6821.9, 6830.6, 6839.2, 6847.8, 6856.2, 6864.8, 6873.2, 6881.7, 6890.4, 6899.0, 6907.7, 6916.2, 6924.6, 6933.0, 6941.4, 6949.8, 6958.2, 6966.7, 6975.3, 6983.9, 6992.5, 7001.1, 7009.7, 7018.3, 7027.0, 7035.6, 7044.2, 7053.0, 7061.8, 7070.6, 7079.5, 7088.4, 7097.4, 7106.5, 7115.6, 7124.8, 7134.0, 7143.2, 7152.4, 7161.5, 7170.6, 7180.0, 7189.5, 7198.9, 7208.4, 7217.6, 7226.9, 7236.4, 7245.9, 7255.4, 7264.9, 7274.3, 7283.8, 7293.2, 7302.8, 7312.4, 7321.9, 7331.5, 7341.1, 7350.7, 7360.4, 7370.2, 7380.0, 7389.9, 7399.8, 7409.6, 7419.5, 7429.4, 7439.4, 7449.3, 7459.1, 7468.8, 7478.3, 7487.7, 7497.0, 7506.2, 7515.2, 7524.1, 7532.9, 7541.6, 7550.3, 7559.0, 7567.8, 7576.5, 7585.3, 7594.0, 7602.8, 7611.6, 7620.3, 7629.0, 7637.7, 7646.3, 7655.0, 7663.5, 7672.0, 7680.5, 7689.2, 7697.9, 7706.7, 7715.6, 7724.6, 7733.8, 7743.1, 7752.5, 7762.0, 7771.5, 7780.9, 7790.2, 7798.9, 7806.9, 7815.5, 7824.3, 7833.1, 7841.9, 7850.6, 7859.2, 7867.8, 7876.3, 7884.8, 7893.3, 7901.7, 7910.1, 7918.5, 7926.9, 7935.2, 7943.6, 7952.0, 7960.5, 7969.0, 7977.6, 7986.2, 7994.8, 8003.5, 8012.2, 8020.9, 8029.5, 8038.2, 8046.8, 8055.4, 8063.9, 8072.4, 8080.7, 8089.0, 8097.4, 8105.6, 8113.8, 8122.0, 8130.2, 8138.3, 8146.5, 8154.5, 8162.7, 8170.8, 8178.9, 8187.0, 8195.0, 8202.9, 8210.8, 8218.7, 8226.7, 8234.6, 8242.6, 8250.6, 8258.7, 8266.7, 8274.6, 8282.5, 8290.3, 8298.0, 8305.9, 8313.7, 8321.5, 8329.3, 8337.0, 8345.0, 8352.8, 8360.7, 8368.4, 8376.0, 8383.7, 8391.3, 8398.5, 8405.9, 8413.2, 8420.5, 8427.9, 8435.3, 8442.6, 8449.9, 8457.2, 8464.4, 8471.3, 8477.9, 8484.6, 8491.5, 8498.1, 8504.7, 8511.0, 8517.4, 8523.9, 8530.8, 8538.4, 8545.8, 8553.3, 8560.2, 8566.7, 8572.4, 8578.3, 8584.6, 8590.9, 8597.6, 8604.7, 8612.0, 8619.4, 8627.0, 8634.8, 8642.7, 8650.7, 8658.8, 8666.8, 8674.9, 8682.9, 8690.9, 8698.8, 8706.6, 8714.3, 8721.9, 8729.6, 8737.2, 8744.9, 8752.6, 8760.0, 8766.9, 8773.5, 8779.7, 8785.8, 8792.2, 8799.3, 8806.8, 8814.5, 8822.3, 8830.1, 8838.0, 8846.0, 8854.1, 8862.4, 8870.7, 8879.1, 8887.6, 8896.1, 8904.6, 8913.2, 8921.9, 8930.6, 8939.4, 8948.3, 8957.3, 8966.3, 8975.5, 8984.7, 8993.9, 9003.1, 9012.4, 9021.8, 9031.2, 9040.6, 9050.0, 9059.4, 9068.8, 9078.3, 9087.8, 9097.4, 9107.0, 9116.5, 9125.8, 9134.9, 9143.5, 9151.4, 9158.1, 9164.2, 9169.4, 9173.8, 9177.6, 9181.4, 9185.7, 9190.5, 9195.7, 9201.5, 9207.8, 9214.8, 9222.5, 9230.7, 9239.0, 9247.7, 9256.5, 9265.6, 9274.7, 9283.9, 9293.2, 9302.6, 9312.2, 9321.8, 9331.6, 9341.5, 9351.7, 9362.1, 9372.8, 9383.7, 9394.5, 9405.3, 9416.0, 9426.6, 9437.0, 9447.4, 9457.8, 9468.1, 9478.4, 9488.6, 9498.7, 9508.7, 9518.5, 9528.3, 9538.0, 9547.5, 9556.9, 9566.3, 9575.5, 9584.8, 9594.0, 9603.3, 9612.7, 9622.0, 9631.3, 9640.6, 9649.8, 9659.0, 9668.2, 9677.3, 9686.4, 9695.5, 9704.4, 9713.4, 9722.2, 9731.0, 9739.7, 9748.4, 9757.0, 9765.7, 9774.3, 9782.8, 9791.3, 9799.9, 9808.5, 9816.9, 9825.3, 9833.7, 9842.2, 9850.7, 9859.1, 9867.6, 9876.1, 9884.6, 9893.2, 9901.7, 9910.4, 9919.1, 9927.8, 9936.6, 9945.5, 9954.6, 9963.6, 9972.8, 9982.1, 9991.5, 10000.9, 10010.5, 10020.1, 10029.7, 10039.4, 10049.1, 10058.9, 10068.7, 10078.5, 10088.4, 10098.3, 10108.1, 10117.8, 10127.5, 10137.1, 10146.7, 10156.2, 10165.8, 10175.3, 10184.8, 10194.2, 10203.6, 10212.9, 10222.1, 10231.1, 10240.1, 10249.0, 10258.0, 10267.0, 10276.0, 10285.0, 10294.1, 10303.0, 10312.0, 10321.0, 10329.9, 10338.7, 10347.5, 10356.2, 10364.9, 10373.6, 10382.3, 10390.9, 10399.4, 10408.0, 10416.5, 10425.0, 10433.5, 10441.9, 10450.2, 10458.5, 10466.8, 10475.1, 10483.3, 10491.5, 10499.9, 10508.3, 10516.8, 10525.4, 10534.0, 10542.8, 10551.6, 10560.5, 10569.5, 10578.6, 10587.8, 10597.2, 10606.6, 10616.0, 10625.4, 10634.9, 10644.4, 10654.0, 10663.6, 10673.2, 10682.8, 10692.3, 10701.6, 10710.7, 10720.0, 10729.3, 10738.7, 10748.0, 10757.1, 10766.3, 10775.4, 10784.5, 10793.7, 10802.8, 10811.9, 10820.9, 10830.0, 10839.0, 10847.9, 10856.9, 10865.9, 10874.9, 10883.9, 10893.0, 10902.2, 10911.4, 10920.6, 10929.8, 10939.0, 10948.4, 10957.7, 10967.1, 10976.3, 10985.6, 10995.0, 11004.4, 11013.7, 11023.0, 11032.3, 11041.6, 11050.8, 11060.0, 11069.0, 11078.2, 11087.3, 11096.4, 11105.6, 11114.8, 11123.9, 11133.1, 11142.3, 11151.4, 11160.6, 11169.8, 11179.0, 11188.1, 11197.3, 11206.4, 11215.6, 11224.8, 11233.9, 11243.0, 11252.1, 11261.2, 11270.3, 11279.3, 11288.2, 11297.0, 11305.7, 11314.0, 11322.2, 11330.1, 11337.5, 11344.5, 11351.6, 11359.3, 11367.5, 11375.9, 11384.6, 11393.5, 11402.6, 11411.8, 11420.9, 11430.1, 11439.3, 11448.5, 11457.7, 11466.9, 11476.1, 11485.3, 11494.6, 11503.8, 11513.1, 11522.3, 11531.4, 11540.6, 11549.7, 11558.7, 11567.8, 11576.8, 11585.8, 11594.8, 11603.7, 11612.7, 11621.8, 11631.0, 11640.2, 11649.5, 11658.9, 11668.5, 11678.3, 11688.4, 11698.6, 11708.9, 11719.3, 11729.7, 11740.2, 11750.8, 11761.6, 11772.4, 11783.3, 11794.2, 11805.2, 11816.2, 11827.2, 11838.0, 11848.5, 11858.8, 11869.0, 11879.4, 11889.9, 11900.4, 11910.8, 11921.3, 11931.7, 11942.1, 11952.3, 11962.3, 11972.0, 11981.6, 11991.4, 12001.1, 12010.5, 12019.8, 12029.0, 12037.9, 12046.7, 12055.4, 12064.1, 12073.4, 12082.1, 12090.7, 12100.0, 12108.6, 12117.1, 12125.5, 12133.9, 12142.3, 12150.6, 12158.7, 12166.8, 12174.9, 12183.0, 12190.9, 12198.7, 12206.7, 12215.0, 12223.4, 12232.3, 12241.5, 12250.8, 12260.6, 12270.0, 12279.3, 12288.5, 12297.6, 12306.7, 12315.8, 12324.9, 12334.2, 12343.5, 12352.8, 12361.9, 12371.1, 12380.5, 12389.9, 12399.3, 12408.6, 12418.0, 12427.3, 12436.6, 12446.1, 12455.3, 12464.5, 12473.8, 12483.0, 12492.2, 12501.3, 12510.3, 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mode 100644 index 0000000..5fbf45e --- /dev/null +++ b/tests/io/test_strava.py @@ -0,0 +1,68 @@ +import pytest +from sweat.io.strava import authorization_header +from sweat.io.strava import stream2dict +from sweat.io.strava import zones2list + + +@pytest.fixture +def test_zones(): + + rv = { + 'heart_rate': {'custom_zones': True, + 'zones': [{'max': 142, 'min': 0}, + {'max': 155, 'min': 142}, + {'max': 162, 'min': 155}, + {'max': 174, 'min': 162}, + {'max': -1, 'min': 174}]}, + 'power': {'zones': [{'max': 143, 'min': 0}, + {'max': 195, 'min': 144}, + {'max': 234, 'min': 196}, + {'max': 273, 'min': 235}, + {'max': 312, 'min': 274}, + {'max': 390, 'min': 313}, + {'max': -1, 'min': 391}]}} + + return rv + + +def test_authorization_header(): + + access_token = 'abc123' + expected = {'Authorization': 'Bearer abc123'} + + assert authorization_header(access_token)==expected + + +def test_stream2dict(): + + stream_list = [ + { + "data": [1,2,3], + "type": "numbers" + }, + { + "data": ['a', 'b', 'c'], + "type": "letters" + } + ] + + expected = { + "numbers": [1, 2, 3], + "letters": ["a", "b", "c"] + } + + assert stream2dict(stream_list) == expected + + +def test_zones2list_power(test_zones): + + rv = zones2list(test_zones, type="power") + expected = [-1, 144, 196, 235, 274, 313, 391, 10000] + assert rv == expected + + +def test_zones2list_heart_rate(test_zones): + + rv = zones2list(test_zones, type="heart_rate") + expected = [-1, 142, 155, 162, 174, 10000] + assert rv == expected diff --git a/tests/test_models.py b/tests/test_models.py index d4671f6..b3f1ffd 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -3,6 +3,7 @@ import numpy as np import pandas as pd import pytest +import os from sweat import exceptions, models @@ -22,9 +23,11 @@ def wdf(): @pytest.fixture def wdf_small(): + athlete = models.Athlete(cp=200, w_prime=20000) + current_dir = os.path.dirname(os.path.abspath(__file__)) wdf = models.WorkoutDataFrame( - pd.read_csv('tests/example_files/workout_1_short.csv') + pd.read_csv(os.path.join(current_dir, 'example_files/workout_1_short.csv')) ) wdf = wdf.set_index('time') wdf.athlete = athlete @@ -34,8 +37,9 @@ def wdf_small(): @pytest.fixture def wdf_big(): athlete = models.Athlete(cp=200, w_prime=20000, weight=80) + current_dir = os.path.dirname(os.path.abspath(__file__)) wdf = models.WorkoutDataFrame( - pd.read_csv('tests/example_files/workout_1.csv') + pd.read_csv(os.path.join(current_dir, 'example_files/workout_1.csv')) ) wdf = wdf.set_index('time') wdf.athlete = athlete diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000..9123ada --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,30 @@ +import numpy as np +import pandas as pd +from sweat.utils import cast_array_to_original_type + + +def test_cast_array_to_original_type(): + + arg = [0, 0, 0] + + rv = cast_array_to_original_type(np.array(arg), type(arg)) + assert type(rv) == type(arg) + + rv = cast_array_to_original_type(pd.Series(arg), type(arg)) + assert type(rv) == type(arg) + + arg = np.array([0, 0, 0]) + + rv = cast_array_to_original_type(list(arg), type(arg)) + assert type(rv) == type(arg) + + rv = cast_array_to_original_type(pd.Series(arg), type(arg)) + assert type(rv) == type(arg) + + arg = pd.Series([0, 0, 0]) + + rv = cast_array_to_original_type(list(arg), type(arg)) + assert type(rv) == type(arg) + + rv = cast_array_to_original_type(np.array(arg), type(arg)) + assert type(rv) == type(arg) \ No newline at end of file