from rowers.metrics import axes, axlabels, yaxminima, yaxmaxima, get_yaxminima, get_yaxmaxima from rowers.dataprep import nicepaceformat, niceformat, strfdelta from rowers.datautils import p0, rpetotss from rowers.metrics import rowingmetrics, metricsdicts from scipy.spatial import ConvexHull, Delaunay from scipy.stats import linregress, percentileofscore from pytz import timezone as tz, utc from rowers.models import course_spline, VirtualRaceResult, InStrokeAnalysis, ForceCurveAnalysis from bokeh.palettes import Category20c, Category10 from bokeh.layouts import layout from bokeh.resources import CDN, INLINE from rowers.dataprep import timedeltaconv, rscore_approx from pandas.core.groupby.groupby import DataError import rowers.datautils as datautils from rowers.utils import lbstoN import rowers.c2stuff as c2stuff import rowers.metrics as metrics import rowers.dataprep as dataprep from rowers.dataprep import rdata import rowers.utils as utils from rowers.rower_rules import ispromember from scipy.interpolate import griddata from scipy.signal import savgol_filter from scipy import optimize from django.utils.timezone import activate from django.utils.timezone import get_current_timezone from holoviews import opts import holoviews as hv import pandas as pd import numpy as np import math import datetime from rowers import mytypes from rowers.courses import ( course_coord_center, course_coord_maxmin, polygon_coord_center, course_coord_crewnerd_navigation, ) from django.conf import settings from collections import OrderedDict from bokeh.core.properties import value from rowers.opaque import encoder from bokeh.models import ( OpenURL, TapTool, CrosshairTool, Span, Label, SaveTool, PanTool, BoxZoomTool, WheelZoomTool, ResetTool,) from bokeh.models.glyphs import ImageURL from bokeh.transform import cumsum from bokeh.models import ( LinearAxis, LogAxis, Range1d, DatetimeTickFormatter, HoverTool, Axis, PrintfTickFormatter ) from bokeh.layouts import column as layoutcolumn from bokeh.layouts import row as layoutrow from bokeh.embed import components import colorsys from rowers.models import ( Workout, User, Rower, WorkoutForm, RowerForm, GraphImage, GeoPolygon, GeoCourse, GeoPoint, ) from rowers.tasks import handle_setcp from rowingdata import rower as rrower from rowingdata import main as rmain from rowingdata import cumcpdata, histodata from rowingdata import rowingdata as rrdata from math import pi, log2 from django.utils import timezone from rowingdata import make_cumvalues from bokeh.palettes import Dark2_8 as palette from bokeh.palettes import Set1_4 as palette2 from bokeh.models.glyphs import MultiLine import itertools from bokeh.plotting import figure, ColumnDataSource, curdoc from bokeh.models import CustomJS, Slider, TextInput, BoxAnnotation, Band import arrow from rowers.utils import myqueue, totaltime_sec_to_string import django_rq queue = django_rq.get_queue('default') queuelow = django_rq.get_queue('low') queuehigh = django_rq.get_queue('low') import requests from rowers.serializers import * activate(settings.TIME_ZONE) thetimezone = get_current_timezone() def get_chart(end_point, chart_data, debug=False): if debug: print(chart_data) url = settings.ROWSANDALL_CHARTS_URL+end_point headers = {'authorization':"Bearer {token}".format(token=settings.ROWSANDALL_CHARTS_TOKEN)} try: response = requests.post(url, json=chart_data, headers=headers) except Exception as err: if debug: print("Chart Server Error") print(err) script = '' div = 'Chart Server Error' return script, div if debug: print("Status Code",response.status_code) if response.status_code == 200: script = response.json()['script'] div = response.json()['div'] else: script = '' try: div = response.reason except AttributeError: div = 'The chart server errored' #if not debug: # script = jsmin(script) return script, div # Example for 3D def filmdeaths(): data = pd.read_csv("~/Downloads/filmdeathcounts.csv") chart_data = data.to_dict("records") chart_data_dict = {"data": chart_data} script, div = get_chart("/filmdeaths", chart_data_dict) return script, div # Example for BokehJS def sleep(): data = { 'work': 8, 'eat': 2, 'commute': 2, 'sport': 0, 'tv': 1, 'sleep': 8, } script, div = get_chart("/sleep", data) return script, div def workoutname(id): try: w = Workout.objects.get(id=id) except Workout.DoesNotExist: return '' return str(w) def all_goldmedalstandards(workouts, startdate, enddate): dates = [] testpowers = [] testduration = [] ids = [] for w in workouts: goldmedalstandard, goldmedalseconds = dataprep.workout_goldmedalstandard( w) if goldmedalseconds > 60: dates.append(arrow.get(w.date).datetime) testpowers.append(goldmedalstandard) testduration.append(goldmedalseconds) ids.append(w.id) return dates, testpowers, testduration, ids def errorbar(fig, x, y, source=ColumnDataSource(), xerr=False, yerr=False, color='black', point_kwargs={}, error_kwargs={}): xvalues = source.data[x] yvalues = source.data[y] xerrvalues = source.data['xerror'] yerrvalues = source.data['yerror'] try: colorvalues = source.data['color'] except KeyError: # pragma: no cover colorvalues = ["#%02x%02x%02x" % (255, 0, 0) for x in xvalues] try: if xerr: x_err_x = [] x_err_y = [] err_color = [] for px, py, err, color in zip(xvalues, yvalues, xerrvalues, colorvalues): x_err_x.append((px - err, px + err)) x_err_y.append((py, py)) (r, g, b) = tuple(int(color[i:i+2], 16) for i in (1, 3, 5)) h, s, v = colorsys.rgb_to_hsv(r/255., g/255., b/255.) v = v*0.8 r, g, b = colorsys.hsv_to_rgb(h, s, v) color2 = "#%02x%02x%02x" % ( int(255.*r), int(255.*g), int(255*b)) err_color.append(color2) fig.multi_line(x_err_x, x_err_y, color=err_color, name='xerr', **error_kwargs) except TypeError: # pragma: no cover pass try: if yerr: y_err_x = [] y_err_y = [] err_color = [] for px, py, err, color in zip(xvalues, yvalues, yerrvalues, colorvalues): y_err_x.append((px, px)) y_err_y.append((py - err, py + err)) (r, g, b) = tuple(int(color[i:i+2], 16) for i in (1, 3, 5)) h, s, v = colorsys.rgb_to_hsv(r/255., g/255., b/255.) v = v*0.8 r, g, b = colorsys.hsv_to_rgb(h, s, v) color2 = "#%02x%02x%02x" % ( int(255.*r), int(255.*g), int(255*b)) err_color.append(color2) fig.multi_line(y_err_x, y_err_y, color=err_color, name='yerr', **error_kwargs) except TypeError: # pragma: no cover pass fig.circle(x, y, source=source, name='data', color=color, **point_kwargs) def tailwind(bearing, vwind, winddir): """ Calculates head-on head/tailwind in direction of rowing positive numbers are tail wind """ b = np.radians(bearing) w = np.radians(winddir) vtail = -vwind*np.cos(w-b) return vtail def interactive_hr_piechart(df, rower, title, totalseconds=0): if df.empty: return "", "Not enough data to make a chart" df.sort_values(by='hr', inplace=True) df['timehr'] = df['deltat']*df['hr'] sumtimehr = df['deltat'].sum() if totalseconds == 0: totalseconds = sumtimehr hrzones = rower.hrzones qry = 'hr < {ut2}'.format(ut2=rower.ut2) qrydata = df.query(qry) frac_lut2 = totalseconds*qrydata['deltat'].sum()/sumtimehr qry = '{ut2} <= hr < {ut1}'.format(ut1=rower.ut1, ut2=rower.ut2) frac_ut2 = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr qry = '{ut1} <= hr < {at}'.format(ut1=rower.ut1, at=rower.at) frac_ut1 = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr qry = '{at} <= hr < {tr}'.format(at=rower.at, tr=rower.tr) frac_at = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr qry = '{tr} <= hr < {an}'.format(tr=rower.tr, an=rower.an) frac_tr = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr qry = 'hr >= {an}'.format(an=rower.an) frac_an = totalseconds*df.query(qry)['deltat'].sum()/sumtimehr datadict = { '<{ut2}'.format(ut2=hrzones[1]): frac_lut2, '{ut2}'.format(ut2=hrzones[1]): frac_ut2, '{ut1}'.format(ut1=hrzones[2]): frac_ut1, '{at}'.format(at=hrzones[3]): frac_at, '{tr}'.format(tr=hrzones[4]): frac_tr, '{an}'.format(an=hrzones[5]): frac_an, } colors = ['gray', 'yellow', 'lime', 'blue', 'purple', 'red'] data = pd.Series(datadict).reset_index( name='value').rename(columns={'index': 'zone'}) data['angle'] = data['value']/data['value'].sum() * 2*pi data['color'] = colors data['zone'] = [ '<{ut2}'.format(ut2=hrzones[1]), '{ut2}'.format(ut2=hrzones[1]), '{ut1}'.format(ut1=hrzones[2]), '{at}'.format(at=hrzones[3]), '{tr}'.format(tr=hrzones[4]), '{an}'.format(an=hrzones[5]) ] data['totaltime'] = pd.Series([pretty_timedelta(v) for v in data['value']]) data_dict = data.to_dict("records") chart_data = { 'data': data_dict, 'title': "HR "+ title } script, div = get_chart("/hrpie", chart_data) return script, div def pretty_timedelta(secs): hours, remainder = divmod(secs, 3600) minutes, seconds = divmod(remainder, 60) return '{}:{:02}:{:02}'.format(int(hours), int(minutes), int(seconds)) def mapcolors(x): try: return mytypes.color_map[x] except KeyError: # pragma: no cover return mytypes.colors[-1] def interactive_workouttype_piechart(workouts): if len(workouts) == 0: return "", "Not enough workouts to make a chart" datadict = {} for w in workouts: try: # label = mytypes.workouttypes_ordered[w.workouttype] label = w.workouttype except KeyError: # pragma: no cover label = w.workouttype try: datadict[label] += 60*(60*w.duration.hour + w.duration.minute)+w.duration.second except KeyError: datadict[label] = 60*(60*w.duration.hour + w.duration.minute)+w.duration.second data = pd.Series(datadict).reset_index( name='value').rename(columns={'index': 'type'}) data['angle'] = data['value']/data['value'].sum() * 2*pi data = pd.DataFrame(data) data['color'] = data['type'].apply(lambda x: mapcolors(x)) data['totaltime'] = data['value'].apply(lambda x: pretty_timedelta(x)) try: data['type'] = data['type'].apply( lambda x: mytypes.workouttypes_ordered[x]) except KeyError: # pragma: no cover pass data_dict = data.to_dict("records") chart_data = { "data": data_dict, "title": "Types" } script, div = get_chart("/workouttypepie", chart_data, debug=False) return script, div def interactive_boxchart(datadf, fieldname, extratitle='', spmmin=0, spmmax=0, workmin=0, workmax=0): if datadf.empty: # pragma: no cover return '', 'It looks like there are no data matching your filter' columns = datadf.columns if fieldname not in columns: # pragma: no cover return '', 'It looks like there are no data matching your filter' if 'date' not in columns: # pragma: no cover return '', 'Not enough data' try: datadf.date = datadf.date.apply(lambda x:x.strftime("%Y-%m-%d")) except AttributeError: datadf.date = "2000-01-01" datadf['value'] = datadf[fieldname] data_dict = datadf.to_dict("records") boxplot_data = { "metric": metricsdicts[fieldname]["verbose_name"], "data": data_dict } script, div = get_chart("/boxplot", boxplot_data) return script, div def interactive_planchart(data, startdate, enddate): hv.extension('bokeh') yaxmaximum = data['executed'].max() if data['planned'].max() > yaxmaximum: # pragma: no cover yaxmaximum = data['planned'].max() if yaxmaximum == 0: # pragma: no cover yaxmaximum = 250 yrange1 = Range1d(start=0, end=1.1*yaxmaximum) tidy_df = data.melt(id_vars=['startdate'], value_vars=[ 'executed', 'planned']) bars = hv.Bars(tidy_df, ['startdate', 'variable'], ['value']) bars.opts( opts.Bars(show_legend=True, tools=['tap', 'hover'], legend_position='bottom', show_frame=True)) p = hv.render(bars) p.width = 550 p.height = 350 p.y_range = yrange1 p.toolbar_location = 'above' #p.sizing_mode = 'stretch_both' script, div = components(p) return script, div def interactive_activitychart(workouts, startdate, enddate, stack='type', toolbar_location=None, yaxis='trimp'): dates = [] dates_sorting = [] types = [] rowers = [] durations = [] rscores = [] trimps = [] links = [] rowersinitials = {} seen = ['seen'] idseen = [] startdate = datetime.datetime( year=startdate.year, month=startdate.month, day=startdate.day) enddate = datetime.datetime( year=enddate.year, month=enddate.month, day=enddate.day) duration = enddate-startdate totaldays = duration.total_seconds()/(24*3600) for w in workouts: aantal = 1 initials = w.user.user.first_name[0:aantal] + \ w.user.user.last_name[0:aantal] if w.user.id not in idseen: while initials in seen: # pragma: no cover aantal += 1 initials = w.user.user.first_name[0:aantal] + \ w.user.user.last_name[0:aantal] seen.append(initials) idseen.append(w.user.id) rowersinitials[w.user.id] = initials for w in workouts: dd = w.date.strftime('%m/%d') dd2 = w.date.strftime('%Y/%m/%d') dd3 = w.date.strftime('%Y/%m') du = w.duration.hour*60+w.duration.minute rscore = w.rscore trimp = w.trimp if rscore == 0: # pragma: no cover rscore = w.hrtss if totaldays < 30: dates.append(dd) dates_sorting.append(dd2) else: # pragma: no cover dates.append(dd3) dates_sorting.append(dd3) durations.append(du) rscores.append(rscore) trimps.append(trimp) links.append( "{siteurl}/rowers/workout/{code}/".format( siteurl=settings.SITE_URL, code=encoder.encode_hex(w.id) ) ) types.append(w.workouttype) try: rowers.append(rowersinitials[w.user.id]) except IndexError: # pragma: no cover rowers.append(str(w.user)) try: d = utc.localize(startdate) except (ValueError, AttributeError): # pragma: no cover d = startdate try: enddate = utc.localize(enddate) except (ValueError, AttributeError): # pragma: no cover pass # add dates with no activity while d <= enddate: dd = d.strftime('%d') if totaldays < 30: dates.append(d.strftime('%m/%d')) dates_sorting.append(d.strftime('%Y/%m/%d')) else: # pragma: no cover dates.append(d.strftime('%Y/%m')) dates_sorting.append(d.strftime('%Y/%m')) durations.append(0) rscores.append(0) trimps.append(0) links.append('') try: types.append(types[0]) except IndexError: types.append('rower') try: rowers.append(rowers[0]) except IndexError: try: rowers.append(str(workouts[0].user)) except IndexError: rowers.append(' ') d += datetime.timedelta(days=1) thedict = { 'date': dates, 'date_sorting': dates_sorting, 'duration': durations, 'trimp': trimps, 'rscore': rscores, 'type': types, 'rower': rowers, 'link': links, } df = pd.DataFrame(thedict) df.sort_values('date_sorting', inplace=True) data_dict = df.to_dict("records") hv.extension('bokeh') if stack == 'type': table = hv.Table(df, [('date', 'Date'), ('type', 'Workout Type')], [('duration', 'Minutes'), ('rscore', 'rScore'), ('trimp', 'TRIMP'), ('link', 'link')]) else: table = hv.Table(df, [('date', 'Date'), ('rower', 'Rower')], [('duration', 'Minutes'), ('rscore', 'rScore'), ('trimp', 'TRIMP'), ('link', 'link')]) bars = table.to.bars(['date', stack], [yaxis]) if stack == 'type': bars.opts( opts.Bars(cmap=mytypes.color_map, show_legend=True, stacked=True, tools=['tap', 'hover'], width=550, xrotation=45, padding=(0, (0, .1)), legend_position='bottom', show_frame=True)) else: bars.opts( opts.Bars(cmap='Category10', show_legend=True, stacked=True, tools=['tap', 'hover'], width=550, xrotation=45, padding=(0, (0, .1)), legend_position='bottom', show_frame=True)) p = hv.render(bars) p.title.text = 'Activity {d1} to {d2}'.format( d1=startdate.strftime("%Y-%m-%d"), d2=enddate.strftime("%Y-%m-%d"), ) p.width = 550 p.height = 350 p.toolbar_location = toolbar_location p.y_range.start = 0 #p.sizing_mode = 'stretch_both' taptool = p.select(type=TapTool) callback = CustomJS(args={'links': df.link}, code=""" var index = cb_data.source.selected['1d'].indices[0]; console.log(links); console.log(index); console.log(links[index]); window.location.href = links[index] """) taptool.js_on_event('tap', callback) script, div = components(p) return script, div def interactive_activitychart2(workouts, startdate, enddate, stack='type', toolbar_location=None, yaxis='duration'): dates = [] dates_sorting = [] types = [] rowers = [] durations = [] rscores = [] trimps = [] links = [] distances = [] rowersinitials = {} seen = ['seen'] idseen = [] startdate = datetime.datetime( year=startdate.year, month=startdate.month, day=startdate.day) enddate = datetime.datetime( year=enddate.year, month=enddate.month, day=enddate.day) duration = enddate-startdate totaldays = duration.total_seconds()/(24*3600) for w in workouts: aantal = 1 initials = w.user.user.first_name[0:aantal] + \ w.user.user.last_name[0:aantal] if w.user.id not in idseen: while initials in seen: # pragma: no cover aantal += 1 initials = w.user.user.first_name[0:aantal] + \ w.user.user.last_name[0:aantal] seen.append(initials) idseen.append(w.user.id) rowersinitials[w.user.id] = initials for w in workouts: dd = w.date.strftime('%m/%d') dd2 = w.date.strftime('%Y/%m/%d') dd3 = w.date.strftime('%Y/%m') du = w.duration.hour*60+w.duration.minute trimp = w.trimp rscore = w.rscore distance = w.distance if rscore == 0: # pragma: no cover rscore = w.hrtss if totaldays <= 30: # pragma: no cover dates.append(dd) dates_sorting.append(dd2) else: dates.append(dd3) dates_sorting.append(dd3) durations.append(du) trimps.append(trimp) rscores.append(rscore) distances.append(distance) links.append( "{siteurl}/rowers/workout/{code}/".format( siteurl=settings.SITE_URL, code=encoder.encode_hex(w.id) ) ) types.append(w.workouttype) try: rowers.append(rowersinitials[w.user.id]) except IndexError: # pragma: no cover rowers.append(str(w.user)) try: d = utc.localize(startdate) except (ValueError, AttributeError): # pragma: no cover d = startdate try: enddate = utc.localize(enddate) except (ValueError, AttributeError): # pragma: no cover pass # add dates with no activity while d <= enddate: dd = d.strftime('%d') dates.append(d.strftime('%Y-%m-%d')) dates_sorting.append(d.strftime('%Y/%m/%d')) durations.append(0) trimps.append(0) rscores.append(0) distances.append(0) links.append('') types.append('rower') try: rowers.append(rowers[0]) except IndexError: # pragma: no cover try: rowers.append(str(workouts[0].user)) except IndexError: rowers.append(' ') d += datetime.timedelta(days=1) thedict = { 'date': dates, 'date_sorting': dates_sorting, 'duration': durations, 'trimp': trimps, 'rscore': rscores, 'type': types, 'rower': rowers, 'distance': distances, 'link': links, } df = pd.DataFrame(thedict) data_dict = df.to_dict("records") if totaldays < 30: datebin = "day" elif totaldays < 50: datebin = "week" else: datebin = "month" stacknames = { 'TRIMP': 'trimp', 'distance': 'distance', 'time': 'duration', 'rScore': 'rscore', 'duration': 'duration', } chart_data = { 'data': data_dict, 'title': 'Activity {d1} to {d2}'.format( d1=startdate.strftime("%Y-%m-%d"), d2=enddate.strftime("%Y-%m-%d"), ), 'datebin': datebin, 'colorby': 'type', 'stackby': stacknames[yaxis], 'doreduce': True, 'dosort': True, 'colors': mytypes.color_map, } script, div = get_chart("/activity_bar", chart_data, debug=True) return script, div if totaldays > 30 and yaxis == 'duration': # pragma: no cover df['duration'] = df['duration']/60 elif yaxis == 'TRIMP': df.drop('duration', inplace=True, axis='columns') df.drop('rscore', inplace=True, axis='columns') df.drop('distance', inplace=True, axis='columns') elif yaxis == 'rScore': # pragma: no cover df.drop('duration', inplace=True, axis='columns') df.drop('trimp', inplace=True, axis='columns') df.drop('distance', inplace=True, axis='columns') elif yaxis == 'distance': # pragma: no cover df.drop('duration', inplace=True, axis='columns') df.drop('trimp', inplace=True, axis='columns') df.drop('rscore', inplace=True, axis='columns') df['color'] = df['type'].apply(lambda x: mapcolors(x)) df.sort_values('date_sorting', inplace=True) hv.extension('bokeh') # table = hv.Table(df,[('date','Date'),('type','Workout Type')], # [('duration','Minutes'),('trimp','TRIMP'),('rscore','rScore'),('link','link')]) types_order = mytypes.workouttypes_ordered # bars=table.to.bars(['date',stack],[yaxis]) bars = hv.Bars(df, kdims=['date', stack]).aggregate( function=np.sum).redim.values(types=types_order) # print(mytypes.color_map) bars.opts( opts.Bars(cmap=mytypes.color_map, show_legend=True, stacked=True, tools=['tap', 'hover'], width=550, xrotation=45, padding=(0, (0, .1)), legend_position='bottom', show_frame=True)) p = hv.render(bars) p.title.text = 'Activity {d1} to {d2}'.format( d1=startdate.strftime("%Y-%m-%d"), d2=enddate.strftime("%Y-%m-%d"), ) p.xaxis.axis_label = 'Period' if yaxis == 'duration': p.yaxis.axis_label = 'Duration (min)' if totaldays > 30: # pragma: no cover p.yaxis.axis_label = 'Duration (h)' elif yaxis == 'TRIMP': p.yaxis.axis_label = 'TRIMP' elif yaxis == 'distance': # pragma: no cover p.yaxis.axis_label = 'Distance (m)' else: # pragma: no cover p.yaxis.axis_label = 'rScore' p.width = 550 p.height = 350 p.toolbar_location = toolbar_location #p.sizing_mode = 'stretch_both' p.y_range.start = 0 taptool = p.select(type=TapTool) callback = CustomJS(args={'links': df['link']}, code=""" var index = cb_data.source.selected['1d'].indices[0]; console.log(links); console.log(index); console.log(links[index]); window.location.href = links[index] """) taptool.js_on_event('tap', callback) script, div = components(p) return script, div def interactive_forcecurve(theworkouts, workstrokesonly=True, plottype='scatter', spm_min=15, spm_max=45, notes='', dist_min=0,dist_max=0, work_min=0,work_max=1500): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' ids = [int(w.id) for w in theworkouts] boattype = theworkouts[0].boattype columns = ['catch', 'slip', 'wash', 'finish', 'averageforce', 'peakforceangle', 'peakforce', 'spm', 'distance', 'workoutstate', 'driveenergy'] rowdata = dataprep.getsmallrowdata_db(columns, ids=ids, workstrokesonly=workstrokesonly) rowdata.dropna(axis=1, how='all', inplace=True) rowdata.dropna(axis=0, how='any', inplace=True) workoutstatesrest = [3] if workstrokesonly: try: rowdata = rowdata[~rowdata['workoutstate'].isin(workoutstatesrest)] except KeyError: # pragma: no cover pass if rowdata.empty: return "", "No Valid Data Available", "", "" try: covariancematrix = np.cov( rowdata['peakforceangle'], y=rowdata['peakforce']) eig_vals, eig_vecs = np.linalg.eig(covariancematrix) a = rowdata['peakforceangle']-rowdata['peakforceangle'].median() F = rowdata['peakforce']-rowdata['peakforce'].median() Rinv = eig_vecs R = np.linalg.inv(Rinv) x = R[0, 0]*a+R[0, 1]*F y = R[1, 0]*a+R[1, 1]*F x05 = x.quantile(q=0.01) x25 = x.quantile(q=0.15) x75 = x.quantile(q=0.85) x95 = x.quantile(q=0.99) y05 = y.quantile(q=0.01) y25 = y.quantile(q=0.15) y75 = y.quantile(q=0.85) y95 = y.quantile(q=0.99) a25 = Rinv[0, 0]*x25 + rowdata['peakforceangle'].median() F25 = Rinv[1, 0]*x25 + rowdata['peakforce'].median() a25b = Rinv[0, 1]*y25 + rowdata['peakforceangle'].median() F25b = Rinv[1, 1]*y25 + rowdata['peakforce'].median() a75 = Rinv[0, 0]*x75 + rowdata['peakforceangle'].median() F75 = Rinv[1, 0]*x75 + rowdata['peakforce'].median() a75b = Rinv[0, 1]*y75 + rowdata['peakforceangle'].median() F75b = Rinv[1, 1]*y75 + rowdata['peakforce'].median() a05 = Rinv[0, 0]*x05 + rowdata['peakforceangle'].median() F05 = Rinv[1, 0]*x05 + rowdata['peakforce'].median() a05b = Rinv[0, 1]*y05 + rowdata['peakforceangle'].median() F05b = Rinv[1, 1]*y05 + rowdata['peakforce'].median() a95 = Rinv[0, 0]*x95 + rowdata['peakforceangle'].median() F95 = Rinv[1, 0]*x95 + rowdata['peakforce'].median() a95b = Rinv[0, 1]*y95 + rowdata['peakforceangle'].median() F95b = Rinv[1, 1]*y95 + rowdata['peakforce'].median() except KeyError: # pragma: no cover a25 = 0 F25 = 0 a25b = 0 F25b = 0 a75 = 0 F75 = 0 a75b = 0 F75b = 0 a05 = 0 F05 = 0 a05b = 0 F05b = 0 a95 = 0 F95 = 0 a95b = 0 F95b = 0 try: catchav = rowdata['catch'].median() catch25 = rowdata['catch'].quantile(q=0.25) catch75 = rowdata['catch'].quantile(q=0.75) catch05 = rowdata['catch'].quantile(q=0.05) catch95 = rowdata['catch'].quantile(q=0.95) except KeyError: # pragma: no cover catchav = 0 catch25 = 0 catch75 = 0 catch05 = 0 catch95 = 0 try: finishav = rowdata['finish'].median() finish25 = rowdata['finish'].quantile(q=0.25) finish75 = rowdata['finish'].quantile(q=0.75) finish05 = rowdata['finish'].quantile(q=0.05) finish95 = rowdata['finish'].quantile(q=0.95) except KeyError: # pragma: no cover finishav = 0 finish25 = 0 finish75 = 0 finish05 = 0 finish95 = 0 try: washav = (rowdata['finish']-rowdata['wash']).median() wash25 = (rowdata['finish']-rowdata['wash']).quantile(q=0.25) wash75 = (rowdata['finish']-rowdata['wash']).quantile(q=0.75) wash05 = (rowdata['finish']-rowdata['wash']).quantile(q=0.05) wash95 = (rowdata['finish']-rowdata['wash']).quantile(q=0.95) except KeyError: # pragma: no cover washav = 0 wash25 = 0 wash75 = 0 wash05 = 0 wash95 = 0 try: slipav = (rowdata['slip']+rowdata['catch']).median() slip25 = (rowdata['slip']+rowdata['catch']).quantile(q=0.25) slip75 = (rowdata['slip']+rowdata['catch']).quantile(q=0.75) slip05 = (rowdata['slip']+rowdata['catch']).quantile(q=0.05) slip95 = (rowdata['slip']+rowdata['catch']).quantile(q=0.95) except KeyError: # pragma: no cover slipav = 0 slip25 = 0 slip75 = 0 slip05 = 0 slip95 = 0 try: peakforceav = rowdata['peakforce'].median() except KeyError: # pragma: no cover peakforceav = 0 try: averageforceav = rowdata['averageforce'].median() except KeyError: # pragma: no cover averageforceav = 0 try: peakforceangleav = rowdata['peakforceangle'].median() except KeyError: # pragma: no cover peakforceangleav = 0 # thresholdforce /= 4.45 # N to lbs thresholdforce = 100 if 'x' in boattype else 200 points2575 = [ (catch25, 0), # 0 (slip25, thresholdforce), # 1 (a75, F75), # 4 (a25b, F25b), # 9 (a25, F25), # 2 (wash75, thresholdforce), # 5 (finish75, 0), # 6 (finish25, 0), # 7 (wash25, thresholdforce), # 8 (a75b, F75b), # 3 (slip75, thresholdforce), # 10 (catch75, 0), # 11 ] points0595 = [ (catch05, 0), # 0 (slip05, thresholdforce), # 1 (a95, F95), # 4 (a05b, F05b), # 9 (a05, F05), # 2 (wash95, thresholdforce), # 5 (finish95, 0), # 6 (finish05, 0), # 7 (wash05, thresholdforce), # 8 (a95b, F95b), # 3 (slip95, thresholdforce), # 10 (catch95, 0), # 11 ] angles2575 = [] forces2575 = [] for x, y in points2575: angles2575.append(x) forces2575.append(y) angles0595 = [] forces0595 = [] for x, y in points0595: angles0595.append(x) forces0595.append(y) x = [catchav, slipav, peakforceangleav, washav, finishav] y = [0, thresholdforce, peakforceav, thresholdforce, 0] source = ColumnDataSource( data=dict( x=x, y=y, )) sourceslipwash = ColumnDataSource( data=dict( xslip=[slipav, washav], yslip=[thresholdforce, thresholdforce] ) ) source2 = ColumnDataSource( rowdata ) if plottype == 'scatter': # pragma: no cover try: sourcepoints = ColumnDataSource( data=dict( peakforceangle=rowdata['peakforceangle'], peakforce=rowdata['peakforce'] ) ) except KeyError: sourcepoints = ColumnDataSource( data=dict( peakforceangle=[], peakforce=[] ) ) else: sourcepoints = ColumnDataSource( data=dict( peakforceangle=[], peakforce=[] )) sourcerange = ColumnDataSource( data=dict( x2575=angles2575, y2575=forces2575, x0595=angles0595, y0595=forces0595, ) ) plot = figure(tools=TOOLS, toolbar_sticky=False, toolbar_location="above", width=800, height=600) #plot.sizing_mode = 'stretch_both' # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkx = 0.99 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} plot.image_url([watermarkurl], watermarkx, watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, x_range_name="watermark", y_range_name="watermark", ) avf = Span(location=averageforceav, dimension='width', line_color='blue', line_dash=[6, 6], line_width=2) plot.patch('x0595', 'y0595', source=sourcerange, color="red", alpha=0.05) plot.patch('x2575', 'y2575', source=sourcerange, color="red", alpha=0.2) plot.line('x', 'y', source=source, color="red") plot.circle('xslip', 'yslip', source=sourceslipwash, color="red") plot.circle('peakforceangle', 'peakforce', source=sourcepoints, color='black', alpha=0.1) if plottype == 'line': multilinedatax = [] multilinedatay = [] for i in range(len(rowdata)): try: x = [ rowdata['catch'].values[i], rowdata['slip'].values[i]+rowdata['catch'].values[i], rowdata['peakforceangle'].values[i], rowdata['finish'].values[i]-rowdata['wash'].values[i], rowdata['finish'].values[i] ] y = [ 0, thresholdforce, rowdata['peakforce'].values[i], thresholdforce, 0] except KeyError: # pragma: no cover x = [0, 0] y = [0, 0] multilinedatax.append(x) multilinedatay.append(y) sourcemultiline = ColumnDataSource(dict( x=multilinedatax, y=multilinedatay, )) sourcemultiline2 = ColumnDataSource(dict( x=multilinedatax, y=multilinedatay, )) glyph = MultiLine(xs='x', ys='y', line_color='black', line_alpha=0.05) plot.add_glyph(sourcemultiline, glyph) else: # pragma: no cover sourcemultiline = ColumnDataSource(dict( x=[], y=[])) sourcemultiline2 = ColumnDataSource(dict( x=[], y=[])) plot.line('x', 'y', source=source, color="red") plot.add_layout(avf) peakflabel = Label(x=760, y=460, x_units='screen', y_units='screen', text="Fpeak: {peakforceav:6.2f}".format( peakforceav=peakforceav), background_fill_alpha=.7, background_fill_color='white', text_color='blue', ) avflabel = Label(x=770, y=430, x_units='screen', y_units='screen', text="Favg: {averageforceav:6.2f}".format( averageforceav=averageforceav), background_fill_alpha=.7, background_fill_color='white', text_color='blue', ) catchlabel = Label(x=765, y=400, x_units='screen', y_units='screen', text="Catch: {catchav:6.2f}".format(catchav=catchav), background_fill_alpha=0.7, background_fill_color='white', text_color='red', ) peakforceanglelabel = Label(x=725, y=370, x_units='screen', y_units='screen', text="Peak angle: {peakforceangleav:6.2f}".format( peakforceangleav=peakforceangleav), background_fill_alpha=0.7, background_fill_color='white', text_color='red', ) finishlabel = Label(x=760, y=340, x_units='screen', y_units='screen', text="Finish: {finishav:6.2f}".format( finishav=finishav), background_fill_alpha=0.7, background_fill_color='white', text_color='red', ) sliplabel = Label(x=775, y=310, x_units='screen', y_units='screen', text="Slip: {slipav:6.2f}".format(slipav=slipav-catchav), background_fill_alpha=0.7, background_fill_color='white', text_color='red', ) washlabel = Label(x=765, y=280, x_units='screen', y_units='screen', text="Wash: {washav:6.2f}".format( washav=finishav-washav), background_fill_alpha=0.7, background_fill_color='white', text_color='red', ) lengthlabel = Label(x=755, y=250, x_units='screen', y_units='screen', text="Length: {length:6.2f}".format( length=finishav-catchav), background_fill_alpha=0.7, background_fill_color='white', text_color='green' ) efflengthlabel = Label(x=690, y=220, x_units='screen', y_units='screen', text="Effective Length: {length:6.2f}".format( length=washav-slipav), background_fill_alpha=0.7, background_fill_color='white', text_color='green' ) annolabel = Label(x=50, y=450, x_units='screen', y_units='screen', text='', background_fill_alpha=0.7, background_fill_color='white', text_color='black', ) sliderlabel = Label(x=10, y=470, x_units='screen', y_units='screen', text='', background_fill_alpha=0.7, background_fill_color='white', text_color='black', text_font_size='10pt', ) plot.add_layout(peakflabel) plot.add_layout(peakforceanglelabel) plot.add_layout(avflabel) plot.add_layout(catchlabel) plot.add_layout(sliplabel) plot.add_layout(washlabel) plot.add_layout(finishlabel) plot.add_layout(annolabel) plot.add_layout(sliderlabel) plot.add_layout(lengthlabel) plot.add_layout(efflengthlabel) plot.xaxis.axis_label = "Angle" plot.yaxis.axis_label = "Force (N)" try: plot.title.text = theworkouts[0].name except ValueError: # pragma: no cover plot.title.text = "" plot.title.text_font_size = "1.0em" yrange1 = Range1d(start=0, end=900) plot.y_range = yrange1 xrange1 = Range1d(start=yaxmaxima['catch'], end=yaxmaxima['finish']) plot.x_range = xrange1 callback = CustomJS(args=dict( source=source, source2=source2, sourceslipwash=sourceslipwash, sourcepoints=sourcepoints, avf=avf, avflabel=avflabel, catchlabel=catchlabel, finishlabel=finishlabel, sliplabel=sliplabel, washlabel=washlabel, peakflabel=peakflabel, peakforceanglelabel=peakforceanglelabel, annolabel=annolabel, sliderlabel=sliderlabel, lengthlabel=lengthlabel, efflengthlabel=efflengthlabel, plottype=plottype, sourcemultiline=sourcemultiline, sourcemultiline2=sourcemultiline2 ), code=""" var data = source.data var data2 = source2.data var dataslipwash = sourceslipwash.data var datapoints = sourcepoints.data var multilines = sourcemultiline.data var multilines2 = sourcemultiline2.data var plottype = plottype var multilinesx = multilines2['x'] var multilinesy = multilines2['y'] var x = data['x'] var y = data['y'] var xslip = dataslipwash['xslip'] var spm1 = data2['spm'] var distance1 = data2['distance'] var driveenergy1 = data2['driveenergy'] var thresholdforce = y[1] var c = source2.data['catch'] var finish = data2['finish'] var slip = data2['slip'] var wash = data2['wash'] var peakforceangle = data2['peakforceangle'] var peakforce = data2['peakforce'] var averageforce = data2['averageforce'] var peakforcepoints = datapoints['peakforce'] var peakforceanglepoints = datapoints['peakforceangle'] var annotation = annotation.value var minspm = minspm.value var maxspm = maxspm.value var mindist = mindist.value var maxdist = maxdist.value var minwork = minwork.value var maxwork = maxwork.value sliderlabel.text = 'SPM: '+minspm.toFixed(0)+'-'+maxspm.toFixed(0) sliderlabel.text += ', Dist: '+mindist.toFixed(0)+'-'+maxdist.toFixed(0) sliderlabel.text += ', WpS: '+minwork.toFixed(0)+'-'+maxwork.toFixed(0) var catchav = 0 var finishav = 0 var slipav = 0 var washav = 0 var peakforceangleav = 0 var averageforceav = 0 var peakforceav = 0 var count = 0 datapoints['peakforceangle'] = [] datapoints['peakforce'] = [] multilines['x'] = [] multilines['y'] = [] for (var i=0; i=minspm && spm1[i]<=maxspm) { if (distance1[i]>=mindist && distance1[i]<=maxdist) { if (driveenergy1[i]>=minwork && driveenergy1[i]<=maxwork) { if (plottype=='scatter') { datapoints['peakforceangle'].push(peakforceangle[i]) datapoints['peakforce'].push(peakforce[i]) } if (plottype=='line') { multilines['x'].push(multilinesx[i]) multilines['y'].push(multilinesy[i]) } catchav += c[i] finishav += finish[i] slipav += slip[i] washav += wash[i] peakforceangleav += peakforceangle[i] averageforceav += averageforce[i] peakforceav += peakforce[i] count += 1 } } } } catchav /= count finishav /= count slipav /= count washav /= count peakforceangleav /= count peakforceav /= count averageforceav /= count data['x'] = [catchav,catchav+slipav,peakforceangleav,finishav-washav,finishav] data['y'] = [0,thresholdforce,peakforceav,thresholdforce,0] dataslipwash['xslip'] = [catchav+slipav,finishav-washav] dataslipwash['yslip'] = [thresholdforce,thresholdforce] var length = finishav-catchav var efflength = length-slipav-washav avf.location = averageforceav avflabel.text = 'Favg: '+averageforceav.toFixed(2) catchlabel.text = 'Catch: '+catchav.toFixed(2) finishlabel.text = 'Finish: '+finishav.toFixed(2) sliplabel.text = 'Slip: '+slipav.toFixed(2) washlabel.text = 'Wash: '+washav.toFixed(2) peakflabel.text = 'Fpeak: '+peakforceav.toFixed(2) peakforceanglelabel.text = 'Peak angle: '+peakforceangleav.toFixed(2) annolabel.text = annotation lengthlabel.text = 'Length: '+length.toFixed(2) efflengthlabel.text = 'Effective Length: '+efflength.toFixed(2) // console.log(count); // console.log(multilines['x'].length); // console.log(multilines['y'].length); // change DOM elements document.getElementById("id_spm_min").value = minspm; document.getElementById("id_spm_max").value = maxspm; document.getElementById("id_dist_min").value = mindist; document.getElementById("id_dist_max").value = maxdist; document.getElementById("id_notes").value = annotation; document.getElementById("id_work_min").value = minwork; document.getElementById("id_work_max").value = maxwork; // source.trigger('change'); source.change.emit(); sourceslipwash.change.emit() sourcepoints.change.emit(); sourcemultiline.change.emit(); """) annotation = TextInput( width=140, title="Type your plot notes here", value="", name="annotation") annotation.js_on_change('value', callback) callback.args["annotation"] = annotation slider_spm_min = Slider(width=140, start=15.0, end=55, value=15, step=.1, title="Min SPM", name="min_spm_slider") slider_spm_min.js_on_change('value', callback) callback.args["minspm"] = slider_spm_min slider_spm_max = Slider(width=140, start=15.0, end=55, value=55, step=.1, title="Max SPM", name="max_spm_slider") slider_spm_max.js_on_change('value', callback) callback.args["maxspm"] = slider_spm_max slider_work_min = Slider(width=140, start=0, end=1500, value=0, step=10, title="Min Work per Stroke", name="min_work_slider") slider_work_min.js_on_change('value', callback) callback.args["minwork"] = slider_work_min slider_work_max = Slider(width=140, start=0, end=1500, value=1500, step=10, title="Max Work per Stroke", name="max_work_slider") slider_work_max.js_on_change('value', callback) callback.args["maxwork"] = slider_work_max distmax = 100+100*int(rowdata['distance'].max()/100.) slider_dist_min = Slider(width=140, start=0, end=distmax, value=0, step=50, title="Min Distance", name="min_dist_slider") slider_dist_min.js_on_change('value', callback) callback.args["mindist"] = slider_dist_min if dist_max == 0: dist_max = distmax slider_dist_max = Slider(width=140, start=0, end=distmax, value=distmax, step=50, title="Max Distance", name="max_dist_slider") slider_dist_max.js_on_change('value', callback) callback.args["maxdist"] = slider_dist_max thesliders = layoutcolumn([annotation, slider_spm_min, slider_spm_max, slider_dist_min, slider_dist_max, slider_work_min, slider_work_max, ] ) mylayout = layoutrow([thesliders, plot]) #mylayout.sizing_mode = 'stretch_both' script, div = components(mylayout) js_resources = INLINE.render_js() css_resources = INLINE.render_css() return [script, div, js_resources, css_resources] def weightfromrecord(row,metricchoice): vv = row[metricchoice] if vv > 0: return vv if metricchoice == 'rscore': # pragma: no cover return rscore_approx(row) return 0 # pragma: no cover def getfatigues( df, fatigues, fitnesses, dates, testpower, testduration, impulses, startdate, enddate, user, metricchoice, kfatigue, kfitness): fatigue = 0 fitness = 0 impulses = [] for f in fatigues: impulses.append(0) lambda_a = 2/(kfatigue+1) lambda_c = 2/(kfitness+1) nrdays = (enddate-startdate).days for i in range(nrdays+1): date = startdate+datetime.timedelta(days=i) datekey = date.strftime('%Y-%m-%d') weight = 0 try: df2 = df.loc[date.date()] if type(df2) == pd.Series: # pragma: no cover weight += weightfromrecord(df2,metricchoice) else: for index, row in df2.iterrows(): weight += weightfromrecord(row,metricchoice) except KeyError: pass impulses.append(weight) fatigue = (1-lambda_a)*fatigue+weight*lambda_a fitness = (1-lambda_c)*fitness+weight*lambda_c fatigues.append(fatigue) fitnesses.append(fitness) dates.append(arrow.get(date).datetime) testpower.append(np.nan) testduration.append(np.nan) return fatigues, fitnesses, dates, testpower, testduration, impulses def goldmedalscorechart(user, startdate=None, enddate=None): # to avoid data mess later on startdate = arrow.get(startdate).datetime.replace( hour=0, minute=0, second=0, microsecond=0) enddate = enddate+datetime.timedelta(days=1) enddate = arrow.get(enddate).datetime.replace( hour=0, minute=0, second=0, microsecond=0) # marker workouts workouts = Workout.objects.filter(user=user.rower, date__gte=startdate, date__lte=enddate, workouttype__in=mytypes.rowtypes, duplicate=False).order_by('date') markerworkouts = workouts.filter(rankingpiece=True) outids = [w.id for w in markerworkouts] dates = [arrow.get(w.date).datetime for w in markerworkouts] testpower = [ w.goldmedalstandard if w.rankingpiece else np.nan for w in markerworkouts] testduration = [ w.goldmedalseconds if w.rankingpiece else 0 for w in markerworkouts] df = pd.DataFrame({ 'id': outids, 'date': dates, 'testpower': testpower, 'testduration': testduration, }) df.sort_values(['date'], inplace=True) mask = df['testpower'].isnull() dates = df.mask(mask)['date'].dropna().values testpower = df.mask(mask)['testpower'].dropna().values ids = df.mask(mask)['id'].dropna().values outids = df.mask(mask)['id'].dropna().unique() # all workouts alldates, alltestpower, allduration, allids = all_goldmedalstandards( workouts, startdate, enddate) nrdays = (enddate-startdate).days td = [] markerscore = [] score = [] markerduration = [] duration = [] workoutid = [] for i in range(len(dates)): id = ids[i] w = Workout.objects.get(id=id) # td.append(arrow.get(dd).datetime) td.append(arrow.get(w.date).datetime) markerscore.append(testpower[i]) markerduration.append(testduration[i]) score.append(testpower[i]) duration.append(testduration[i]) workoutid.append(id) for i in range(len(alldates)): td.append(arrow.get(alldates[i]).datetime) markerscore.append(np.nan) score.append(alltestpower[i]) markerduration.append(np.nan) duration.append(allduration[i]) workoutid.append(allids[i]) for i in range(nrdays+1): td.append(arrow.get(startdate+datetime.timedelta(days=i)).datetime) markerscore.append(np.nan) score.append(np.nan) markerduration.append(np.nan) duration.append(np.nan) workoutid.append(0) df = pd.DataFrame({ 'markerscore': markerscore, 'markerduration': markerduration, 'score': score, 'duration': duration, 'date': td, 'id': workoutid, }) df['url'] = df['id'].apply(lambda x: settings.SITE_URL + '/rowers/workout/{id}/'.format(id=encoder.encode_hex(x))) df['workout'] = df['id'].apply(lambda x: workoutname(x)) df.sort_values(['date'], inplace=True) # find index values where score is max idx = df.groupby(['date'])['score'].transform(max) == df['score'] df = df[idx] df.fillna(value=0, inplace=True) df['dat1'] = df['date'].map(lambda x: x.to_pydatetime(x).strftime("%Y-%m-%d")) df2 = pd.DataFrame({ 'markerscore': df['markerscore'], 'score': df['score'], 'markerduration':df['markerduration'].apply( lambda x: totaltime_sec_to_string(x, shorten=True)), 'duration': df['duration'].apply( lambda x: totaltime_sec_to_string(x, shorten=True)), 'date': df['dat1'], 'url':df['url'], 'workout':df['workout'] }) data_dicts = df2.to_dict("records") chart_data = { 'data': data_dicts } script, div = get_chart("/markerworkouts", chart_data) return script, div, outids def performance_chart(user, startdate=None, enddate=None, kfitness=42, kfatigue=7, metricchoice='trimp', doform=False, dofatigue=False, showtests=False): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' TOOLS2 = 'box_zoom,hover' # to avoid data mess later on startdate = arrow.get(startdate).datetime.replace( hour=0, minute=0, second=0, microsecond=0) enddate = enddate+datetime.timedelta(days=1) enddate = arrow.get(enddate).datetime.replace( hour=0, minute=0, second=0, microsecond=0) modelchoice = 'coggan' p0 = 0 k1 = 1 k2 = 1 dates = [] testpower = [] fatigues = [] fitnesses = [] testduration = [] impulses = [] outids = [] workouts = Workout.objects.filter(user=user.rower, date__gte=startdate-datetime.timedelta(days=90), date__lte=enddate, duplicate=False).order_by('date') # make fast dict for dates / workouts records = [] for w in workouts: dd = { 'date':w.date, 'trimp':w.trimp, 'rscore':w.rscore, 'hrtss':w.hrtss, 'duration':w.duration, 'id':w.id, 'rpe':w.rpe, } records.append(dd) df = pd.DataFrame.from_records(records) if df.empty: # pragma: no cover return ['', 'No Data', 0, 0, 0, outids] df.set_index('date', inplace=True) markerworkouts = Workout.objects.filter( user=user.rower, date__gte=startdate-datetime.timedelta(days=90), date__lte=enddate, duplicate=False, rankingpiece=True, workouttype__in=mytypes.rowtypes).order_by('date') outids = [w.id for w in markerworkouts] dates = [arrow.get(w.date).datetime for w in workouts] testpower = [ w.goldmedalstandard if w.rankingpiece else np.nan for w in workouts] impulses = [np.nan for w in workouts] testduration = [ w.goldmedalseconds if w.rankingpiece else 0 for w in workouts] fitnesses = [np.nan for w in workouts] fatigues = [np.nan for w in workouts] fatigues, fitnesses, dates, testpower, testduration, impulses = getfatigues( df, fatigues, fitnesses, dates, testpower, testduration, impulses, startdate -datetime.timedelta(days=90), enddate, user, metricchoice, kfatigue, kfitness ) df = pd.DataFrame({ 'date': dates, 'testpower': testpower, 'testduration': testduration, 'fatigue': fatigues, 'fitness': fitnesses, 'impulse': impulses, }) endfitness = fitnesses[-2] endfatigue = fatigues[-2] endform = endfitness-endfatigue if modelchoice == 'banister': # pragma: no cover df['fatigue'] = k2*df['fatigue'] df['fitness'] = p0+k1*df['fitness'] df['form'] = df['fitness']-df['fatigue'] df.sort_values(['date'], inplace=True) df = df.groupby(['date']).max() df['date'] = df.index.values mask = df['date'] > np.datetime64(startdate.astimezone( tz=datetime.timezone.utc).replace(tzinfo=None)) df = df.loc[mask] df2 = pd.DataFrame({ "testpower" :df['testpower'], "testduration":df['testduration'].apply( lambda x: totaltime_sec_to_string(x, shorten=True)), "fitness":df['fitness'], "fatigue":df['fatigue'], "form":df['form'], "impulse":df['impulse'], "date": df['date'].map(lambda x: x.strftime('%Y-%m-%d')), }) df2.fillna(value=0, inplace=True) data_dict = df2.to_dict("records") chart_data = { 'data': data_dict, 'title': 'Performance Manager '+user.first_name, 'plotform' : doform, 'plotfatigue': dofatigue, } script, div = get_chart("/performance", chart_data) return [script, div, endfitness, endfatigue, endform, outids] def interactive_histoall(theworkouts, histoparam, includereststrokes, spmmin=0, spmmax=55, extratitle='', workmin=0, workmax=1500): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' ids = [int(w.id) for w in theworkouts] workstrokesonly = not includereststrokes rowdata = dataprep.getsmallrowdata_db( [histoparam], ids=ids, doclean=True, workstrokesonly=workstrokesonly) rowdata.dropna(axis=0, how='any', inplace=True) rowdata = dataprep.filter_df(rowdata, 'spm', spmmin, largerthan=True) rowdata = dataprep.filter_df(rowdata, 'spm', spmmax, largerthan=False) rowdata = dataprep.filter_df( rowdata, 'driveenergy', workmin, largerthan=True) rowdata = dataprep.filter_df( rowdata, 'driveenergy', workmax, largerthan=False) if rowdata.empty: return "", "No Valid Data Available" try: histopwr = rowdata[histoparam].values except KeyError: return "", "No data" if len(histopwr) == 0: # pragma: no cover return "", "No valid data available" # throw out nans histopwr = histopwr[~np.isinf(histopwr)] if histoparam == 'catch': # pragma: no cover histopwr = histopwr[histopwr < yaxminima[histoparam]] histopwr = histopwr[histopwr > yaxmaxima[histoparam]] else: histopwr = histopwr[histopwr > yaxminima[histoparam]] histopwr = histopwr[histopwr < yaxmaxima[histoparam]] data_dict = {"data": histopwr.tolist(), "metric": metricsdicts[histoparam]["verbose_name"]} script, div = get_chart("/histogram", data_dict, debug=False) return script, div def course_map(course): course_dict = GeoCourseSerializer(course).data script, div = get_chart("/map", course_dict) return script, div def leaflet_chart(lat, lon, name="", raceresult=0): try: if lat.empty or lon.empty: # pragma: no cover return [0, "invalid coordinate data"] except AttributeError: if not len(lat) or not len(lon): # pragma: no cover return [0, "invalid coordinate data"] # Throw out 0,0 df = pd.DataFrame({ 'lat': lat, 'lon': lon }) df = df.replace(0, np.nan) df = df.loc[(df != 0).any(axis=1)] df.fillna(method='bfill', axis=0, inplace=True) df.fillna(method='ffill', axis=0, inplace=True) lat = df['lat'] lon = df['lon'] if lat.empty or lon.empty: # pragma: no cover return [0, "invalid coordinate data"] latmean = lat.mean() lonmean = lon.mean() latbegin = lat[lat.index[0]] longbegin = lon[lon.index[0]] latend = lat[lat.index[-1]] longend = lon[lon.index[-1]] coordinates = zip(lat, lon) data = { 'coordinates': [{'latitude': c[0], 'longitude': c[1]} for c in list(coordinates)], 'latmean': latmean, 'lonmean': lonmean, 'latbegin': latbegin, 'latend': latend, 'longbegin': longbegin, 'longend': longend, } if raceresult != 0: record = VirtualRaceResult.objects.get(id=raceresult) course = record.course course_dict = GeoCourseSerializer(course).data data['course'] = course_dict coordinates = zip(lat, lon) script, div = get_chart("/workoutmap", data) return script, div def leaflet_chart_compare(course, workoutids, labeldict={}, startenddict={}): data = [] for id in workoutids: if id != 0 and id is not None: try: w = Workout.objects.get(id=id) rowdata = rdata(w.csvfilename) time = rowdata.df['TimeStamp (sec)'] df = pd.DataFrame({ 'workoutid': id, 'lat': rowdata.df[' latitude'], 'lon': rowdata.df[' longitude'], 'time': time-time[0], }) data.append(df) except (Workout.DoesNotExist, KeyError): # pragma: no cover pass try: df = pd.concat(data, axis=0) except ValueError: # pragma: no cover df = pd.DataFrame() latmean, lonmean, coordinates = course_coord_center(course) course_dict = GeoCourseSerializer(course).data # Throw out 0,0 df = df.replace(0, np.nan) df = df.loc[(df != 0).any(axis=1)] df.fillna(method='bfill', axis=0, inplace=True) df.fillna(method='ffill', axis=0, inplace=True) try: lat = df['lat'] lon = df['lon'] except KeyError: # pragma: no cover return [0, "invalid coordinate data"] if lat.empty or lon.empty: # pragma: no cover return [0, "invalid coordinate data"] colors = itertools.cycle(palette) try: items = itertools.izip(workoutids, colors) except AttributeError: items = zip(workoutids, colors) trajectories = [] for id, color in items: group = df[df['workoutid'] == int(id)].copy() try: startsecond, endsecond = startenddict[id] except KeyError: # pragma: no cover startsecond = 0 endsecond = 0 try: label = labeldict[id] except KeyError: # pragma: no cover label = str(id) group.sort_values(by='time', ascending=True, inplace=True) group.dropna(axis=0, how='any', inplace=True) if endsecond > 0: group['time'] = group['time'] - startsecond mask = group['time'] < 0 group.mask(mask, inplace=True) mask = group['time'] > (endsecond-startsecond) group.mask(mask, inplace=True) lat = group['lat'].dropna() lon = group['lon'].dropna() coordinates = zip(lat, lon) trajectory_dict = { 'coordinates' :[{'latitude':c[0], 'longitude': c[1]} for c in list(coordinates)], 'color': color, 'label': label, } trajectories.append(trajectory_dict) mapdata = { 'course': course_dict, 'latmean': latmean, 'lonmean': lonmean, 'trajectories': trajectories, } script, div = get_chart("/mapcompare", mapdata) return script, div def interactive_agegroupcpchart(age, normalized=False): durations = [1, 4, 30, 60] distances = [100, 500, 1000, 2000, 5000, 6000, 10000, 21097, 42195] fhduration = [] fhpower = [] for distance in distances: worldclasspower = c2stuff.getagegrouprecord( age, sex='female', distance=distance, weightcategory='hwt' ) velo = (worldclasspower/2.8)**(1./3.) try: # pragma: no cover duration = distance/velo fhduration.append(duration) fhpower.append(worldclasspower) except ZeroDivisionError: pass for duration in durations: worldclasspower = c2stuff.getagegrouprecord( age, sex='female', duration=duration, weightcategory='hwt' ) try: velo = (worldclasspower/2.8)**(1./3.) distance = int(60*duration*velo) fhduration.append(60.*duration) fhpower.append(worldclasspower) except ValueError: # pragma: no cover pass flduration = [] flpower = [] for distance in distances: worldclasspower = c2stuff.getagegrouprecord( age, sex='female', distance=distance, weightcategory='lwt' ) velo = (worldclasspower/2.8)**(1./3.) try: # pragma: no cover duration = distance/velo flduration.append(duration) flpower.append(worldclasspower) except ZeroDivisionError: pass for duration in durations: worldclasspower = c2stuff.getagegrouprecord( age, sex='female', duration=duration, weightcategory='lwt' ) try: velo = (worldclasspower/2.8)**(1./3.) distance = int(60*duration*velo) flduration.append(60.*duration) flpower.append(worldclasspower) except ValueError: # pragma: no cover pass mlduration = [] mlpower = [] for distance in distances: worldclasspower = c2stuff.getagegrouprecord( age, sex='male', distance=distance, weightcategory='lwt' ) velo = (worldclasspower/2.8)**(1./3.) try: # pragma: no cover duration = distance/velo mlduration.append(duration) mlpower.append(worldclasspower) except ZeroDivisionError: mlduration.append(duration) mlpower.append(np.nan) for duration in durations: worldclasspower = c2stuff.getagegrouprecord( age, sex='male', duration=duration, weightcategory='lwt' ) try: velo = (worldclasspower/2.8)**(1./3.) distance = int(60*duration*velo) mlduration.append(60.*duration) mlpower.append(worldclasspower) except ValueError: # pragma: no cover mlduration.append(60.*duration) mlpower.append(np.nan) mhduration = [] mhpower = [] for distance in distances: worldclasspower = c2stuff.getagegrouprecord( age, sex='male', distance=distance, weightcategory='hwt' ) velo = (worldclasspower/2.8)**(1./3.) try: # pragma: no cover duration = distance/velo mhduration.append(duration) mhpower.append(worldclasspower) except ZeroDivisionError: mhduration.append(duration) mhpower.append(np.nan) for duration in durations: worldclasspower = c2stuff.getagegrouprecord( age, sex='male', duration=duration, weightcategory='hwt' ) try: velo = (worldclasspower/2.8)**(1./3.) distance = int(60*duration*velo) mhduration.append(60.*duration) mhpower.append(worldclasspower) except ValueError: # pragma: no cover mhduration.append(60.*duration) mhpower.append(np.nan) def fitfunc(pars, x): return pars[0] / (1+(x/pars[2])) + pars[1]/(1+(x/pars[3])) def errfunc(pars, x, y): return fitfunc(pars, x)-y # p0 = [500,350,10,8000] # fitting WC data to three parameter CP model if len(fhduration) >= 4: p1fh, success = optimize.leastsq(errfunc, p0[:], args=(fhduration, fhpower)) else: # pragma: no cover p1fh = None # fitting WC data to three parameter CP model if len(flduration) >= 4: p1fl, success = optimize.leastsq(errfunc, p0[:], args=(flduration, flpower)) else: # pragma: no cover p1fl = None # fitting WC data to three parameter CP model if len(mlduration) >= 4: p1ml, success = optimize.leastsq(errfunc, p0[:], args=(mlduration, mlpower)) else: # pragma: no cover p1ml = None if len(mhduration) >= 4: p1mh, success = optimize.leastsq(errfunc, p0[:], args=(mhduration, mhpower)) else: # pragma: no cover p1mh = None fitt = pd.Series(10**(4*np.arange(100)/100.)) fitpowerfh = fitfunc(p1fh, fitt) fitpowerfl = fitfunc(p1fl, fitt) fitpowerml = fitfunc(p1ml, fitt) fitpowermh = fitfunc(p1mh, fitt) if normalized: facfh = fitfunc(p1fh, 60) facfl = fitfunc(p1fl, 60) facml = fitfunc(p1ml, 60) facmh = fitfunc(p1mh, 60) fitpowerfh /= facfh fitpowerfl /= facfl fitpowermh /= facmh fitpowerml /= facml fhpower /= facfh flpower /= facfl mlpower /= facml mhpower /= facmh sourcemh = ColumnDataSource( data=dict( mhduration=mhduration, mhpower=mhpower, ) ) sourcefl = ColumnDataSource( data=dict( flduration=flduration, flpower=flpower, ) ) sourcefh = ColumnDataSource( data=dict( fhduration=fhduration, fhpower=fhpower, ) ) sourceml = ColumnDataSource( data=dict( mlduration=mlduration, mlpower=mlpower, ) ) sourcefit = ColumnDataSource( data=dict( duration=fitt, fitpowerfh=fitpowerfh, fitpowerfl=fitpowerfl, fitpowerml=fitpowerml, fitpowermh=fitpowermh, ) ) x_axis_type = 'log' TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' plot = figure(width=900, x_axis_type=x_axis_type, tools=TOOLS) #plot.sizing_mode = 'stretch_both' plot.line('duration', 'fitpowerfh', source=sourcefit, legend_label='Female HW', color='blue') plot.line('duration', 'fitpowerfl', source=sourcefit, legend_label='Female LW', color='red') plot.line('duration', 'fitpowerml', source=sourcefit, legend_label='Male LW', color='green') plot.line('duration', 'fitpowermh', source=sourcefit, legend_label='Male HW', color='orange') plot.circle('flduration', 'flpower', source=sourcefl, fill_color='red', size=15) plot.circle('fhduration', 'fhpower', source=sourcefh, fill_color='blue', size=15) plot.circle('mlduration', 'mlpower', source=sourceml, fill_color='green', size=15) plot.circle('mhduration', 'mhpower', source=sourcemh, fill_color='orange', size=15) plot.title.text = 'age '+str(age) plot.xaxis.axis_label = "Duration (seconds)" if normalized: plot.yaxis.axis_label = "Power (normalized)" else: plot.yaxis.axis_label = "Power (W)" script, div = components(plot) return script, div def interactive_otwcpchart(powerdf, promember=0, rowername="", r=None, cpfit='data', title='', type='water', wcpower=[], wcdurations=[], cpoverlay=False): powerdf2 = powerdf[~(powerdf == 0).any(axis=1)].copy() # plot tools if (promember == 1): # pragma: no cover TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' else: TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' x_axis_type = 'log' deltas = powerdf2['Delta'].apply(lambda x: timedeltaconv(x)) powerdf2['ftime'] = deltas.apply(lambda x: strfdelta(x)) powerdf2['Deltaminutes'] = powerdf2['Delta']/60. source = ColumnDataSource( data=powerdf2 ) # there is no Paul's law for OTW thesecs = powerdf2['Delta'] theavpower = powerdf2['CP'] p1, fitt, fitpower, ratio = datautils.cpfit(powerdf2) if cpfit == 'automatic' and r is not None: if type == 'water': p1 = [r.p0, r.p1, r.p2, r.p3] ratio = r.cpratio elif type == 'erg': # pragma: no cover p1 = [r.ep0, r.ep1, r.ep2, r.ep3] ratio = r.ecpratio def fitfunc(pars, x): return abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3]))) fitpower = fitfunc(p1, fitt) message = "" # if len(fitpower[fitpower<0]) > 0: # message = "CP model fit didn't give correct results" deltas = fitt.apply(lambda x: timedeltaconv(x)) ftime = niceformat(deltas) # add world class wcpower = pd.Series(wcpower, dtype='float') wcdurations = pd.Series(wcdurations, dtype='float') # fitting WC data to three parameter CP model if len(wcdurations) >= 4: # pragma: no cover def fitfunc(pars, x): return pars[0] / (1+(x/pars[2])) + pars[1]/(1+(x/pars[3])) def errfunc(pars, x, y): return fitfunc(pars, x)-y p1wc, success = optimize.leastsq(errfunc, p0[:], args=(wcdurations, wcpower)) else: p1wc = None if p1wc is not None and cpoverlay: # pragma: no cover fitpowerwc = fitfunc(p1wc, fitt) fitpowerexcellent = 0.7*fitfunc(p1wc, fitt) fitpowergood = 0.6*fitfunc(p1wc, fitt) fitpowerfair = 0.5*fitfunc(p1wc, fitt) fitpoweraverage = 0.4*fitfunc(p1wc, fitt) else: fitpowerwc = 0*fitpower fitpowerexcellent = 0*fitpower fitpowergood = 0*fitpower fitpowerfair = 0*fitpower fitpoweraverage = 0*fitpower fit_data = pd.DataFrame(dict( CP=fitpower, CPmax=ratio*fitpower, duration=fitt/60., ftime=ftime, # workout = workouts, fitpowerwc=fitpowerwc, fitpowerexcellent=fitpowerexcellent, fitpowergood=fitpowergood, fitpowerfair=fitpowerfair, fitpoweraverage=fitpoweraverage, # url = urls, )) if not title: title = "Critical Power for "+rowername chart_dict = { 'data': powerdf2.to_dict("records"), 'fitdata': fit_data.to_dict("records"), 'title': title, } script, div = get_chart("/cp", chart_dict) return [script, div, p1, ratio, message] def interactive_agegroup_plot(df, distance=2000, duration=None, sex='male', weightcategory='hwt'): if df.empty: return '', '' age = df['age'] power = df['power'] name = df['name'] season = df['season'] if duration: # pragma: no cover duration2 = int(duration/60.) plottitle = sex+' '+weightcategory+' %s min' % duration2 else: plottitle = sex+' '+weightcategory+' %s m' % distance # poly_coefficients = np.polyfit(age,power,6) age2 = np.linspace(11, 95) # poly_vals = np.polyval(poly_coefficients,age2) # poly_vals = 0.5*(np.abs(poly_vals)+poly_vals) def fitfunc(pars, x): return np.abs(pars[0])*(1-x/max(120, pars[1])) \ - np.abs(pars[2])*np.exp(-x/np.abs(pars[3]))+np.abs(pars[4])*(np.sin(np.pi*x/max(50, pars[5]))) def errfunc(pars, x, y): return fitfunc(pars, x)-y p0age = [700, 120, 700, 10, 100, 100] p1, success = optimize.leastsq(errfunc, p0age[:], args=(age, power)) expo_vals = fitfunc(p1, age2) expo_vals = 0.5*(np.abs(expo_vals)+expo_vals) source = ColumnDataSource( data=dict( age=age, power=power, season=season, name=name, ) ) sourcefit = ColumnDataSource( data=dict( age2=age2, expo_vals=expo_vals, ) ) TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' plot = figure(tools=TOOLS, width=900) #plot.sizing_mode = 'stretch_both' plot.circle('age', 'power', source=source, fill_color='red', size=15, legend_label='World Record') plot.line('age2', 'expo_vals', source=sourcefit) plot.xaxis.axis_label = "Age" plot.yaxis.axis_label = "Concept2 power" plot.title.text = plottitle hover = plot.select(dict(type=HoverTool)) hover.tooltips = OrderedDict([ ('Name ', '@name'), ('Season ', '@season'), ]) hover.mode = 'mouse' script, div = components(plot) return script, div def interactive_cpchart(rower, thedistances, thesecs, theavpower, theworkouts, promember=0, wcpower=[], wcdurations=[]): message = 0 # plot tools if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' else: # pragma: no cover TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' x_axis_type = 'log' thesecs = pd.Series(thesecs) velo = thedistances/thesecs p = pd.Series(500./velo) p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) source = ColumnDataSource( data=dict( dist=thedistances, duration=thesecs, spm=0*theavpower, tim=niceformat( thesecs.fillna(method='ffill').apply( lambda x: timedeltaconv(x)) ), power=theavpower, fpace=nicepaceformat(p2), ) ) # fitting the data to Paul if len(thedistances) >= 2: paulslope, paulintercept, r, p, stderr = linregress( np.log10(thedistances), p) else: # pragma: no cover paulslope = 5.0/np.log10(2.0) paulintercept = p[0]-paulslope*np.log10(thedistances[0]) fitx = pd.Series(np.arange(100)*2*max(np.log10(thedistances))/100.) fitp = paulslope*fitx+paulintercept fitvelo = 500./fitp fitpower = 2.8*(fitvelo**3) fitt = 10**fitx/fitvelo fitp2 = fitp.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) sourcepaul = ColumnDataSource( data=dict( dist=10**fitx, duration=fitt, power=fitpower, spm=0*fitpower, tim=niceformat( fitt.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) ), fpace=nicepaceformat(fitp2), ) ) def fitfunc(pars, x): return pars[0] / (1+(x/pars[2])) + pars[1]/(1+(x/pars[3])) def errfunc(pars, x, y): return fitfunc(pars, x)-y # p0 = [500,350,10,8000] wcpower = pd.Series(wcpower, dtype='float') wcdurations = pd.Series(wcdurations, dtype='float') # fitting WC data to three parameter CP model if len(wcdurations) >= 4: p1wc, success = optimize.leastsq(errfunc, p0[:], args=(wcdurations, wcpower)) else: # pragma: no cover p1wc = None # fitting the data to three parameter CP model success = 0 p1 = p0 if len(thesecs) >= 4: try: p1, success = optimize.leastsq( errfunc, p0[:], args=(thesecs, theavpower)) except (RuntimeError, RuntimeWarning): # pragma: no cover factor = fitfunc(p0, thesecs.mean())/theavpower.mean() p1 = [p0[0]/factor, p0[1]/factor, p0[2], p0[3]] success = 0 else: # pragma: no cover factor = fitfunc(p0, thesecs.mean())/theavpower.mean() p1 = [p0[0]/factor, p0[1]/factor, p0[2], p0[3]] success = 0 # Get stayer score if success == 1: # pragma: no cover power4min = fitfunc(p1, 240.) power1h = fitfunc(p1, 3600.) power10sec = fitfunc(p1, 10.) r10sec4min = 100.*power10sec/power4min r1h4min = 100.*power1h/power4min combined = r1h4min-0.2*(r10sec4min-100) dataset = pd.read_csv('static/stats/combined_set.csv') stayerscore = int(percentileofscore(dataset['combined'], combined)) else: stayerscore = None fitt = pd.Series(10**(4*np.arange(100)/100.)) fitpower = fitfunc(p1, fitt) if p1wc is not None: fitpowerwc = 0.95*fitfunc(p1wc, fitt) fitpowerexcellent = 0.7*fitfunc(p1wc, fitt) fitpowergood = 0.6*fitfunc(p1wc, fitt) fitpowerfair = 0.5*fitfunc(p1wc, fitt) fitpoweraverage = 0.4*fitfunc(p1wc, fitt) else: # pragma: no cover fitpowerwc = 0*fitpower fitpowerexcellent = 0*fitpower fitpowergood = 0*fitpower fitpowerfair = 0*fitpower fitpoweraverage = 0*fitpower message = "" if len(fitpower[fitpower < 0]) > 0: # pragma: no cover message = "CP model fit didn't give correct results" fitvelo = (fitpower/2.8)**(1./3.) fitdist = fitt*fitvelo fitp = 500./fitvelo fitp2 = fitp.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) sourcecomplex = ColumnDataSource( data=dict( dist=fitdist, duration=fitt, tim=niceformat( fitt.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) ), spm=0*fitpower, power=fitpower, fitpowerwc=fitpowerwc, fitpowerexcellent=fitpowerexcellent, fitpowergood=fitpowergood, fitpowerfair=fitpowerfair, fitpoweraverage=fitpoweraverage, fpace=nicepaceformat(fitp2), ) ) # making the plot plot = figure(tools=TOOLS, x_axis_type=x_axis_type, width=900, toolbar_location="above", toolbar_sticky=False) # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} #plot.sizing_mode = 'scale_both' plot.image_url([watermarkurl], 1.8*max(thesecs), watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, y_range_name="watermark", ) plot.circle('duration', 'power', source=source, fill_color='red', size=15, legend_label='Power') plot.xaxis.axis_label = "Duration (seconds)" plot.yaxis.axis_label = "Power (W)" if stayerscore is not None: # pragma: no cover plot.add_layout( Label(x=100, y=100, x_units='screen', y_units='screen', text='Stayer Score '+str(stayerscore)+'%', background_fill_alpha=0.7, background_fill_color='white', text_color='black') ) # plot.add_layout( # Label(x=100,y=120,x_units='screen',y_units='screen', # text='Stayer Score (6min) '+str(stayerscore2)+'%', # background_fill_alpha=0.7, # background_fill_color='white', # text_color='black') # ) cpdata = dataprep.fetchcperg(rower, theworkouts) if cpdata.empty: # pragma: no cover message = 'Calculations are running in the background. Please refresh this page to see updated results' return ['', '', paulslope, paulintercept, p1, message, p1wc] velo = cpdata['distance']/cpdata['delta'] p = 500./velo p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x)) source2 = ColumnDataSource( data=dict( duration=cpdata['delta'], power=cpdata['cp'], tim=niceformat( cpdata['delta'].fillna(method='ffill').apply( lambda x: timedeltaconv(x)) ), dist=cpdata['distance'], pace=nicepaceformat(p2), ) ) plot.circle('duration', 'power', source=source2, fill_color='blue', size=3, legend_label='Power from segments') hover = plot.select(dict(type=HoverTool)) hover.tooltips = OrderedDict([ ('Duration ', '@tim'), ('Power (W)', '@power{int}'), ('Distance (m)', '@dist{int}'), ('Pace (/500m)', '@fpace'), ]) hover.mode = 'mouse' plot.y_range = Range1d(0, 1.5*max(theavpower)) plot.x_range = Range1d(1, 2*max(thesecs)) plot.legend.orientation = "vertical" plot.line('duration', 'power', source=sourcepaul, legend_label="Paul's Law") plot.line('duration', 'power', source=sourcecomplex, legend_label="CP Model", color='green') if p1wc is not None: plot.line('duration', 'fitpowerwc', source=sourcecomplex, legend_label="World Class", color='Maroon', line_dash='dotted') plot.line('duration', 'fitpowerexcellent', source=sourcecomplex, legend_label="90% percentile", color='Purple', line_dash='dotted') plot.line('duration', 'fitpowergood', source=sourcecomplex, legend_label="75% percentile", color='Olive', line_dash='dotted') plot.line('duration', 'fitpowerfair', source=sourcecomplex, legend_label="50% percentile", color='Gray', line_dash='dotted') plot.line('duration', 'fitpoweraverage', source=sourcecomplex, legend_label="25% percentile", color='SkyBlue', line_dash='dotted') script, div = components(plot) return [script, div, paulslope, paulintercept, p1, message, p1wc] def interactive_windchart(id=0, promember=0): # check if valid ID exists (workout exists) row = Workout.objects.get(id=id) # g = GraphImage.objects.filter(workout=row).order_by("-creationdatetime") f1 = row.csvfilename # create interactive plot plot = figure(width=400, height=300) # get user # u = User.objects.get(id=row.user.id) r = row.user rr = rrower(hrmax=r.max, hrut2=r.ut2, hrut1=r.ut1, hrat=r.at, hrtr=r.tr, hran=r.an, ftp=r.ftp) rowdata = rdata(f1, rower=rr) if rowdata == 0: # pragma: no cover return 0 try: dist = rowdata.df.loc[:, 'cum_dist'] except KeyError: return ['', 'No Data Found'] try: # pragma: no cover vwind = rowdata.df.loc[:, 'vwind'] winddirection = rowdata.df.loc[:, 'winddirection'] bearing = rowdata.df.loc[:, 'bearing'] except KeyError: rowdata.add_wind(0, 0) rowdata.add_bearing() vwind = rowdata.df.loc[:, 'vwind'] winddirection = rowdata.df.loc[:, 'winddirection'] bearing = rowdata.df.loc[:, 'winddirection'] rowdata.write_csv(f1, gzip=True) dataprep.update_strokedata(id, rowdata.df) winddirection = winddirection % 360 winddirection = (winddirection + 360) % 360 tw = tailwind(bearing, vwind, 1.0*winddirection) source = ColumnDataSource( data=dict( dist=dist, vwind=vwind, tw=tw, winddirection=winddirection, ) ) # plot tools if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' else: # pragma: no cover TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair' # making the plot plot = figure(tools=TOOLS, width=400, height=500, # toolbar_location="below", toolbar_sticky=False, ) plot.line('dist', 'vwind', source=source, legend_label="Wind Speed (m/s)") plot.line('dist', 'tw', source=source, legend_label="Tail (+)/Head (-) Wind (m/s)", color='black') try: plot.title.text = row.name except ValueError: # pragma: no cover plot.title.text = "" # plot.title.text_font_size="1.0em" plot.title.text_font = "1.0em" plot.xaxis.axis_label = "Distance (m)" plot.yaxis.axis_label = "Wind Speed (m/s)" plot.y_range = Range1d(-7, 7) #plot.sizing_mode = 'stretch_both' plot.extra_y_ranges = {"winddirection": Range1d(start=0, end=360)} plot.line('dist', 'winddirection', source=source, legend_label='Wind Direction', color="red", y_range_name="winddirection") plot.add_layout(LinearAxis(y_range_name="winddirection", axis_label="Wind Direction (degree)"), 'right') script, div = components(plot) return [script, div] def interactive_streamchart(id=0, promember=0): # check if valid ID exists (workout exists) row = Workout.objects.get(id=id) # g = GraphImage.objects.filter(workout=row).order_by("-creationdatetime") f1 = row.csvfilename # create interactive plot plot = figure(width=400, ) # get user # u = User.objects.get(id=row.user.id) r = row.user rr = rrower(hrmax=r.max, hrut2=r.ut2, hrut1=r.ut1, hrat=r.at, hrtr=r.tr, hran=r.an, ftp=r.ftp) rowdata = rdata(f1, rower=rr) if rowdata == 0: # pragma: no cover return "", "No Valid Data Available" try: dist = rowdata.df.loc[:, 'cum_dist'] except KeyError: return ['', 'No Data found'] try: vstream = rowdata.df.loc[:, 'vstream'] except KeyError: rowdata.add_stream(0) vstream = rowdata.df.loc[:, 'vstream'] rowdata.write_csv(f1, gzip=True) dataprep.update_strokedata(id, rowdata.df) # plot tools if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' else: # pragma: no cover TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair' # making the plot plot = figure(tools=TOOLS, width=400, height=500, # toolbar_location="below", toolbar_sticky=False, ) plot.line(dist, vstream, legend_label="River Stream Velocity (m/s)") try: plot.title.text = row.name except ValueError: # pragma: no cover plot.title.text = "" plot.title.text_font_size = "1.0em" plot.xaxis.axis_label = "Distance (m)" plot.yaxis.axis_label = "River Current (m/s)" plot.y_range = Range1d(-2, 2) #plot.sizing_mode = 'stretch_both' script, div = components(plot) return [script, div] def forcecurve_multi_interactive_chart(selected): # pragma: no cover df_plot = pd.DataFrame() ids = [analysis.id for analysis in selected] columns = ['catch', 'slip', 'wash', 'finish', 'averageforce', 'peakforceangle', 'peakforce', 'spm', 'distance', 'workoutstate', 'driveenergy'] for analysis in selected: workstrokesonly = not analysis.include_rest_strokes spm_min = analysis.spm_min spm_max = analysis.spm_max dist_min = analysis.dist_min dist_max = analysis.dist_max work_min = analysis.work_min work_max = analysis.work_max rowdata = dataprep.getsmallrowdata_db(columns, ids=[analysis.workout.id], workstrokesonly=workstrokesonly) rowdata = rowdata[rowdata['spm']>spm_min] rowdata = rowdata[rowdata['spm']work_min] rowdata = rowdata[rowdata['driveenergy']dist_min] catchav = rowdata['catch'].median() finishav = rowdata['finish'].median() washav = (rowdata['finish']-rowdata['wash']).median() slipav = (rowdata['slip']+rowdata['catch']).median() peakforceav = rowdata['peakforce'].median() peakforceangleav = rowdata['peakforceangle'].median() thresholdforce = 100 if 'x' in analysis.workout.boattype else 200 x = [catchav, slipav, peakforceangleav, washav, finishav] y = [0, thresholdforce, peakforceav, thresholdforce, 0] xname = 'x_'+str(analysis.id) yname = 'y_'+str(analysis.id) df_plot[xname] = x df_plot[yname] = y source = ColumnDataSource( df_plot ) TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' plot = figure(width=920,tools=TOOLS, toolbar_location='above', toolbar_sticky=False) #plot.sizing_mode = 'stretch_both' # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkx = 0.99 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} plot.image_url([watermarkurl], watermarkx, watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, x_range_name="watermark", y_range_name="watermark", ) colors = itertools.cycle(palette) try: items = itertools.izip(ids, colors) except AttributeError: items = zip(ids, colors) for id, color in items: xname = 'x_'+str(id) yname = 'y_'+str(id) analysis = ForceCurveAnalysis.objects.get(id=id) legendlabel = '{name}'.format( name = analysis.name, ) if analysis.notes: legendlabel = '{name} - {notes}'.format( name = analysis.name, notes = analysis.notes ) plot.line(xname,yname,source=source,legend_label=legendlabel, line_width=2, color=color) plot.legend.location = "top_left" plot.xaxis.axis_label = "Angle" plot.yaxis.axis_label = "Force (N)" script, div = components(plot) return (script, div) def instroke_multi_interactive_chart(selected, *args, **kwargs): # pragma: no cover df_plot = pd.DataFrame() ids = [analysis.id for analysis in selected] metrics = list(set([analysis.metric for analysis in selected])) maximum_values = {} for metric in metrics: maximum_values[metric] = 0 for analysis in selected: #start_second, end_second, spm_min, spm_max, name activeminutesmin = int(analysis.start_second/60.) activeminutesmax = int(analysis.end_second/60.) rowdata = rrdata(csvfile=analysis.workout.csvfilename) data = rowdata.get_instroke_data( analysis.metric, spm_min=analysis.spm_min, spm_max=analysis.spm_max, activeminutesmin=activeminutesmin, activeminutesmax=activeminutesmax, ) mean_vals = data.mean() if analysis.metric == 'boat accelerator curve': mean_vals[0] = (mean_vals[1]+ mean_vals[len(mean_vals)-1])/2. if len(metrics) > 1: if mean_vals.max() > maximum_values[analysis.metric]: maximum_values[analysis.metric] = mean_vals.max() xvals = np.arange(len(mean_vals)) xname = 'x_'+str(analysis.id) yname = 'y_'+str(analysis.id) df_plot[xname] = pd.Series(xvals) df_plot[yname] = pd.Series(mean_vals) if len(metrics) > 1: for analysis in selected: yname = 'y_'+str(analysis.id) df_plot[yname] = df_plot[yname] / maximum_values[analysis.metric] source = ColumnDataSource( df_plot ) TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' plot = figure(width=920,tools=TOOLS, toolbar_location='above', toolbar_sticky=False) #plot.sizing_mode = 'stretch_both' # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkx = 0.99 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} if len(metrics)>1: plot.yaxis.axis_label = 'Scaled' else: plot.yaxis.axis_label = metrics[0] plot.image_url([watermarkurl], watermarkx, watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, x_range_name="watermark", y_range_name="watermark", ) colors = itertools.cycle(palette) try: items = itertools.izip(ids, colors) except AttributeError: items = zip(ids, colors) for id, color in items: xname = 'x_'+str(id) yname = 'y_'+str(id) analysis = InStrokeAnalysis.objects.get(id=id) legendlabel = '{name} - {metric} - {workout}'.format( name = analysis.name, metric = analysis.metric, date = analysis.date, workout = str(analysis.workout) ) plot.line(xname,yname,source=source,legend_label=legendlabel, line_width=2, color=color) script, div = components(plot) return (script, div) def instroke_interactive_chart(df,metric, workout, spm_min, spm_max, activeminutesmin, activeminutesmax, individual_curves, name='',notes=''): # pragma: no cover df_pos = (df+abs(df))/2. df_min = -(-df+abs(-df))/2. if df.empty: return "", "No data in selection" mean_vals = df.median().replace(0, np.nan) q75 = df_pos.quantile(q=0.75).replace(0,np.nan) q25 = df_pos.quantile(q=0.25).replace(0,np.nan) q75min = df_min.quantile(q=0.75).replace(0,np.nan) q25min = df_min.quantile(q=0.25).replace(0,np.nan) mean_vals = mean_vals.interpolate() xvals = np.arange(len(mean_vals)) df_plot = pd.DataFrame({ 'x':xvals, 'median':mean_vals, 'high':q75, 'low':q75min, 'high 2':q25min, 'low 2': q25, }) df_plot['high'].update(df_plot.pop('high 2')) df_plot['low'].update(df_plot.pop('low 2')) try: df_plot.interpolate(axis=1,inplace=True) except TypeError: pass TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' plot = figure(width=920,tools=TOOLS, toolbar_location='above', toolbar_sticky=False) #plot.sizing_mode = 'stretch_both' plot.title.text = str(workout) + ' - ' + metric # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkx = 0.99 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} plot.image_url([watermarkurl], watermarkx, watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, x_range_name="watermark", y_range_name="watermark", ) source = ColumnDataSource( df_plot ) TIPS = OrderedDict([ ('x','@x'), ('median','@median'), ('high','@high'), ('low','@low') ]) hover = plot.select(type=HoverTool) hover.tooltips = TIPS s = 'SPM: {spm_min} - {spm_max}'.format( spm_min = spm_min, spm_max = spm_max, ) label = Label(x=50, y=450, x_units='screen',y_units='screen', text=s, background_fill_alpha=0.7, background_fill_color='white', text_color='black', ) s2 = 'Time: {activeminutesmin} - {activeminutesmax}'.format( activeminutesmin=datetime.timedelta(seconds=60*activeminutesmin), activeminutesmax=datetime.timedelta(seconds=60*activeminutesmax) ) label2 = Label(x=50,y=400, x_units='screen', y_units='screen', text=s2, background_fill_alpha=0.7, background_fill_color='white', text_color='black', ) plot.add_layout(label) plot.add_layout(label2) if name: namelabel = Label(x=50, y=480, x_units='screen', y_units='screen', text=name, background_fill_alpha=0.7, background_fill_color='white', text_color='black', ) plot.add_layout(namelabel) if notes: noteslabel = Label(x=50, y=50, x_units='screen', y_units='screen', text=notes, background_fill_alpha=0.7, background_fill_color='white', text_color='black', ) plot.add_layout(noteslabel) if individual_curves: for index,row in df.iterrows(): plot.line(xvals,row,color='lightgray',line_width=1) else: plot.varea('x', y1='high', y2='low',source=source,fill_color="lightgray",alpha=0.5) plot.line('x','median',source=source,legend_label='median',color="black", line_width=3) medrange = mean_vals.max()-mean_vals.min() yrange = Range1d(start=mean_vals.min()-0.2*medrange, end=mean_vals.max()+0.2*medrange,) plot.y_range = yrange plot.add_tools(HoverTool(tooltips=TIPS)) if metric == 'boat accelerator curve': plot.yaxis.axis_label = "Boat acceleration (m/s^2)" elif metric == 'instroke boat speed': plot.yaxis.axis_label = "Boat Speed (m/s)" vavg = mean_vals.median() elif metric == 'oar angle velocity curve': plot.yaxis.axis_label = "Oar Angular Velocity (degree/s)" elif metric == 'seat curve': plot.yaxis.axis_label = "Seat Speed (m/s)" plot.xaxis.axis_label = 'Time (%)' try: script, div = components(plot) except ValueError: script = "" div = "Something went wrong with the chart" return (script, div) def interactive_chart(id=0, promember=0, intervaldata={}): # Add hover to this comma-separated string and see what changes if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' else: TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' columns = ['time', 'pace', 'hr', 'fpace', 'ftime', 'spm'] datadf = dataprep.getsmallrowdata_db(columns, ids=[id]) datadf.dropna(axis=0, how='any', inplace=True) row = Workout.objects.get(id=id) if datadf.empty: return "", "No Valid Data Available" try: _ = datadf['spm'] except KeyError: # pragma: no cover datadf['spm'] = 0 try: _ = datadf['pace'] except KeyError: # pragma: no cover datadf['pace'] = 0 data_dict = datadf.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] intervals = [] # add shaded bar chart areas if intervaldata != {}: intervaldf = pd.DataFrame(intervaldata) intervaldf['itime'] = intervaldf['itime']*1.e3 intervaldf['time'] = intervaldf['itime'].cumsum() intervaldf['time'] = intervaldf['time'].shift(1) intervaldf.loc[0, 'time'] = 0 intervaldf['time_r'] = intervaldf['time'] + intervaldf['itime'] intervaldf['value'] = 100 mask = intervaldf['itype'] == 3 intervaldf.loc[mask, 'value'] = 0 intervaldf['bottom'] = 0 intervals = intervaldf.to_dict("records") chart_data = { 'title': row.name, 'x': "time", 'y1': "pace", 'y2': "spm", 'data': data_dict, 'metrics': metrics_list, 'intervals': intervals, } script, div = get_chart("/interactive", chart_data) return script, div def interactive_chart_video(videodata): try: spm = videodata['spm'] except KeyError: # pragma: no cover return "", "No SPM data" time = range(len(spm)) data = zip(time, spm) data2 = [] for t, s in data: data2.append( {'x': t, 'y': s}) markerpoint = { 'x': time[0], 'y': spm[0], 'r': 10, } chart_data = { 'data': data2, 'markerpoint': markerpoint, } script, div = get_chart("/videochart", chart_data) return [script, div] def interactive_multiflex(datadf, xparam, yparam, groupby, extratitle='', ploterrorbars=False, title=None, binsize=1, colorlegend=[], spmmin=0, spmmax=0, workmin=0, workmax=0): if datadf.empty: # pragma: no cover return ['', '

No non-zero data in selection

'] if xparam == 'workoutid': # pragma: no cover xparamname = 'Workout' else: xparamname = axlabels[xparam] if yparam == 'workoutid': # pragma: no cover yparamname = 'Workout' else: yparamname = axlabels[yparam] if groupby == 'workoutid': # pragma: no cover groupname = 'Workout' elif groupby == 'date': # pragma: no cover groupname = 'Date' else: groupname = axlabels[groupby] if title is None: title = '{y} vs {x} grouped by {gr}'.format( x=xparamname, y=yparamname, gr=groupname, ) if extratitle is not None: title = title+' '+extratitle if xparam == 'cumdist': # pragma: no cover res = make_cumvalues(datadf[xparam]) datadf[xparam] = res[0] if xparam == 'distance': # pragma: no cover xaxmax = datadf[xparam].max() xaxmin = datadf[xparam].min() elif xparam == 'time': # pragma: no cover tseconds = datadf.loc[:, 'time'] xaxmax = tseconds.max() xaxmin = 0 elif xparam == 'workoutid': # pragma: no cover xaxmax = datadf[xparam].max()-5 xaxmin = datadf[xparam].min()+5 else: xaxmax = yaxmaxima[xparam] xaxmin = yaxminima[xparam] if yparam == 'distance': # pragma: no cover yaxmax = datadf[yparam].max() yaxmin = datadf[yparam].min() elif yparam == 'time': # pragma: no cover tseconds = datadf.loc[:, 'time'] yaxmax = tseconds.max() yaxmin = 0 elif yparam == 'workoutid': # pragma: no cover yaxmax = datadf[yparam].max()-5 yaxmin = datadf[yparam].min()+5 else: yaxmax = yaxmaxima[yparam] yaxmin = yaxminima[yparam] data_dict = datadf.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] chart_data = { 'title': title, 'x': xparam, 'y': yparam, 'data': data_dict, 'metrics': metrics_list, 'errorbars':ploterrorbars, 'groupname': groupname, } script, div = get_chart("/trendflex", chart_data) return script, div def interactive_cum_flex_chart2(theworkouts, promember=0, xparam='spm', yparam1='power', yparam2='spm', workstrokesonly=False, extratitle='', trendline=False): ids = [int(w.id) for w in theworkouts] columns = [name for name, d in metrics.rowingmetrics] columns_basic = [name for name, d in metrics.rowingmetrics if d['group'] == 'basic'] columns = columns + ['spm', 'driveenergy', 'distance'] columns_basic = columns_basic + ['spm', 'driveenergy', 'distance'] datadf = pd.DataFrame() if promember: datadf = dataprep.getsmallrowdata_db(columns, ids=ids, doclean=True, workstrokesonly=workstrokesonly, for_chart=True) else: datadf = dataprep.getsmallrowdata_db(columns_basic, ids=ids, doclean=True, workstrokesonly=workstrokesonly, for_chart=True) try: _ = datadf[yparam2] except KeyError: # pragma: no cover yparam2 = 'None' try: _ = datadf[yparam1] except KeyError: yparam1 = 'None' datadf.dropna(axis=1, how='all', inplace=True) datadf.dropna(axis=0, how='any', inplace=True) # test if we have drive energy try: # pragma: no cover _ = datadf['driveenergy'].mean() except KeyError: # pragma: no cover datadf['driveenergy'] = 500. # test if we have power try: # pragma: no cover _ = datadf['power'].mean() except KeyError: # pragma: no cover datadf['power'] = 50. yparamname1 = axlabels[yparam1] if yparam2 != 'None': yparamname2 = axlabels[yparam2] # check if dataframe not empty if datadf.empty: # pragma: no cover return ['', '

No non-zero data in selection

', '', ''] try: datadf['x1'] = datadf.loc[:, xparam] except KeyError: # pragma: no cover try: datadf['x1'] = datadf['distance'] except KeyError: try: datadf['x1'] = datadf['time'] except KeyError: # pragma: no cover return ['', '

No non-zero data in selection

', '', ''] try: datadf['y1'] = datadf.loc[:, yparam1] except KeyError: try: datadf['y1'] = datadf['pace'] except KeyError: # pragma: no cover return ['', '

No non-zero data in selection

', '', ''] if yparam2 != 'None': try: datadf['y2'] = datadf.loc[:, yparam2] except KeyError: # pragma: no cover datadf['y2'] = datadf['y1'] else: # pragma: no cover datadf['y2'] = datadf['y1'] datadf['xname'] = axlabels[xparam] datadf['yname1'] = axlabels[yparam1] if yparam2 != 'None': datadf['yname2'] = axlabels[yparam2] else: # pragma: no cover datadf['yname2'] = axlabels[yparam1] def func(x, a, b): return a*x+b x1 = datadf['x1'] y1 = datadf['y1'] popt, pcov = optimize.curve_fit(func, x1, y1) ytrend = func(x1, popt[0], popt[1]) datadf['ytrend'] = ytrend data_dict = datadf.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] chart_data = { 'title': extratitle, 'x': xparam, 'y1': yparam1, 'y2': yparam2, 'data': data_dict, 'metrics': metrics_list, 'trendline': trendline, } script, div = get_chart("/dots", chart_data) return script, div def interactive_flexchart_stacked(id, r, xparam='time', yparam1='pace', yparam2='power', yparam3='hr', yparam4='spm', mode='erg'): columns = [name for name, d in metrics.rowingmetrics] columns_basic = [name for name, d in metrics.rowingmetrics if d['group'] == 'basic'] columns = columns + ['spm', 'driveenergy', 'distance'] columns_basic = columns_basic + ['spm', 'driveenergy', 'distance'] rowdata = pd.DataFrame() row = Workout.objects.get(id=id) if ispromember(r.user): rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=True, workstrokesonly=False, for_chart=True) else: rowdata = dataprep.getsmallrowdata_db(columns_basic, ids=[id], doclean=True, workstrokesonly=False, for_chart=True) if r.usersmooth > 1: # pragma: no cover for column in columns: try: if metricsdicts[column]['maysmooth']: nrsteps = int(log2(r.usersmooth)) for i in range(nrsteps): rowdata[column] = utils.ewmovingaverage( rowdata[column], 5) except KeyError: pass if len(rowdata) < 2: if ispromember(r.user): rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=False, workstrokesonly=False, for_chart=True) else: rowdata = dataprep.getsmallrowdata_db(columns_basic, ids=[id], doclean=False, workstrokesonly=False, for_chart=True) if rowdata.empty: return "", "No valid data" try: tseconds = rowdata.loc[:, 'time'] except KeyError: # pragma: no cover return '', 'No time data - cannot make flex plot' try: rowdata['x1'] = rowdata.loc[:, xparam] except KeyError: # pragma: no cover rowdata['x1'] = 0*rowdata.loc[:, 'time'] try: rowdata['y1'] = rowdata.loc[:, yparam1] except KeyError: # pragma: no cover rowdata['y1'] = 0*rowdata.loc[:, 'time'] rowdata[yparam1] = rowdata['y1'] try: # pragma: no cover rowdata['y2'] = rowdata.loc[:, yparam2] except KeyError: rowdata['y2'] = 0*rowdata.loc[:, 'time'] rowdata[yparam2] = rowdata['y2'] try: rowdata['y3'] = rowdata.loc[:, yparam3] except KeyError: # pragma: no cover rowdata['y3'] = 0*rowdata.loc[:, 'time'] rowdata[yparam3] = rowdata['y3'] try: rowdata['y4'] = rowdata.loc[:, yparam4] except KeyError: # pragma: no cover rowdata['y4'] = 0*rowdata.loc[:, 'time'] rowdata[yparam4] = rowdata['y4'] # replace nans rowdata.fillna(value=0, inplace=True) data_dict = rowdata.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] chart_data = { 'title': row.name, 'x': xparam, 'y1': yparam1, 'y2': yparam2, 'y3': yparam3, 'y4': yparam4, 'data': data_dict, 'metrics': metrics_list, } script, div = get_chart("/stacked", chart_data) return script, div def interactive_flex_chart2(id, r, promember=0, xparam='time', yparam1='pace', yparam2='hr', plottype='line', workstrokesonly=False, trendline=False, mode='rower'): columns = [name for name, d in metrics.rowingmetrics] columns_basic = [name for name, d in metrics.rowingmetrics if d['group'] == 'basic'] columns = columns + ['spm', 'driveenergy', 'distance'] columns_basic = columns_basic + ['spm', 'driveenergy', 'distance'] datadf = pd.DataFrame() if promember: rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=True, workstrokesonly=workstrokesonly, for_chart=True) else: rowdata = dataprep.getsmallrowdata_db(columns_basic, ids=[id], doclean=True, workstrokesonly=workstrokesonly, for_chart=True) if r.usersmooth > 1: # pragma: no cover for column in columns: try: if metricsdicts[column]['maysmooth']: nrsteps = int(log2(r.usersmooth)) for i in range(nrsteps): rowdata[column] = utils.ewmovingaverage( rowdata[column], 5) except KeyError: pass try: if len(rowdata) < 2: if promember: rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=False, workstrokesonly=False, for_chart=True) else: rowdata = dataprep.getsmallrowdata_db(columns_basic, ids=[id], doclean=False, workstrokesonly=False, for_chart=True) workstrokesonly = False except (KeyError, TypeError): # pragma: no cover workstrokesonly = False try: _ = rowdata[yparam2] except (KeyError, TypeError): # pragma: no cover yparam2 = 'None' try: _ = rowdata[yparam1] except (TypeError, KeyError): # pragma: no cover yparam1 = 'None' # test if we have drive energy try: _ = rowdata['driveenergy'].mean() except (KeyError, TypeError): rowdata['driveenergy'] = 500. # test if we have power try: _ = rowdata['power'].mean() except (KeyError, TypeError): rowdata['power'] = 50. # replace nans rowdata.fillna(value=0, inplace=True) row = Workout.objects.get(id=id) if rowdata.empty: return "", "No valid data", workstrokesonly workoutstatesrest = [3] if workstrokesonly: # pragma: no cover try: rowdata = rowdata[~rowdata['workoutstate'].isin(workoutstatesrest)] except KeyError: pass try: tseconds = rowdata.loc[:, 'time'] except KeyError: # pragma: no cover return '', 'No time data - cannot make flex plot', workstrokesonly try: rowdata['x1'] = rowdata.loc[:, xparam] except KeyError: # pragma: no cover rowdata['x1'] = 0*rowdata.loc[:, 'time'] try: rowdata['y1'] = rowdata.loc[:, yparam1] except KeyError: # pragma: no cover rowdata['y1'] = 0*rowdata.loc[:, 'time'] rowdata[yparam1] = rowdata['y1'] if yparam2 != 'None': try: rowdata['y2'] = rowdata.loc[:, yparam2] except KeyError: # pragma: no cover rowdata['y2'] = 0*rowdata.loc[:, 'time'] rowdata[yparam2] = rowdata['y2'] else: # pragma: no cover rowdata['y2'] = rowdata['y1'] if xparam == 'time': xaxmax = tseconds.max() xaxmin = tseconds.min() elif xparam == 'distance' or xparam == 'cumdist': xaxmax = rowdata['x1'].max() xaxmin = rowdata['x1'].min() else: # pragma: no cover try: xaxmax = get_yaxmaxima(r, xparam, mode) xaxmin = get_yaxminima(r, xparam, mode) except KeyError: xaxmax = rowdata['x1'].max() xaxmin = rowdata['x1'].min() # average values if xparam != 'time': try: x1mean = rowdata['x1'].mean() except TypeError: # pragma: no cover x1mean = 0 else: # pragma: no cover x1mean = 0 y1mean = rowdata['y1'].mean() y2mean = rowdata['y2'].mean() try: rowdata['xname'] = axlabels[xparam] except KeyError: # pragma: no cover rowdata['xname'] = xparam try: rowdata['yname1'] = axlabels[yparam1] except KeyError: # pragma: no cover rowdata['yname1'] = yparam1 if yparam2 != 'None': try: rowdata['yname2'] = axlabels[yparam2] except KeyError: # pragma: no cover rowdata['yname2'] = yparam2 else: # pragma: no cover rowdata['yname2'] = rowdata['yname1'] def func(x, a, b): return a*x+b x1 = rowdata['x1'] y1 = rowdata['y1'] try: popt, pcov = optimize.curve_fit(func, x1, y1) ytrend = func(x1, popt[0], popt[1]) rowdata['ytrend'] = ytrend except TypeError: # pragma: no cover rowdata['ytrend'] = y1 data_dict = rowdata.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] chart_data = { 'title': row.name, 'x': xparam, 'y1': yparam1, 'y2': yparam2, 'data': data_dict, 'metrics': metrics_list, 'trendline': trendline, 'plottype': plottype, } script, div = get_chart("/flex", chart_data) return script, div, workstrokesonly def thumbnails_set(r, id, favorites): charts = [] columns = [f.xparam for f in favorites] columns += [f.yparam1 for f in favorites] columns += [f.yparam2 for f in favorites] columns += ['time'] rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=True) try: rowdata.dropna(axis=1, how='all', inplace=True) except TypeError: # pragma: no cover return [ {'script': "", 'div': "", 'notes': "" }] if rowdata.empty: try: rowdata = dataprep.getsmallrowdata_db(columns, ids=[id], doclean=False, workstrokesonly=False) except: # pragma: no cover return [ {'script': "", 'div': "", 'notes': "" }] if rowdata.empty: return [ {'script': "", 'div': "", 'notes': "" }] lengte = len(rowdata) maxlength = 50 if lengte > maxlength: try: bins = np.linspace(rowdata['time'].min(), rowdata['time'].max(), maxlength) groups = rowdata.groupby(np.digitize(rowdata['time'], bins)) rowdata = groups.mean() except (KeyError, TypeError): # pragma: no cover pass for f in favorites: script, div = thumbnail_flex_chart( rowdata, id=id, xparam=f.xparam, yparam1=f.yparam1, yparam2=f.yparam2, plottype=f.plottype, ) charts.append({ 'script': script, 'div': div, 'notes': f.notes}) return charts def thumbnail_flex_chart(rowdata, id=0, promember=0, xparam='time', yparam1='pace', yparam2='hr', plottype='line', workstrokesonly=False): try: _ = rowdata[yparam2] except KeyError: yparam2 = 'None' try: _ = rowdata[yparam1] except KeyError: yparam1 = 'None' try: tseconds = rowdata.loc[:, 'time'] except KeyError: # pragma: no cover return '', 'No time data - cannot make flex plot' try: rowdata['x1'] = rowdata.loc[:, xparam] except KeyError: # pragma: no cover rowdata['x1'] = 0*rowdata.loc[:, 'time'] try: rowdata['y1'] = rowdata.loc[:, yparam1] except KeyError: # pragma: no cover rowdata['y1'] = 0*rowdata.loc[:, 'time'] if yparam2 != 'None': try: rowdata['y2'] = rowdata.loc[:, yparam2] except KeyError: # pragma: no cover rowdata['y2'] = 0*rowdata.loc[:, 'time'] else: rowdata['y2'] = rowdata['y1'] if xparam == 'time': xaxmax = tseconds.max() xaxmin = tseconds.min() elif xparam == 'distance' or xparam == 'cumdist': # pragma: no cover xaxmax = rowdata['x1'].max() xaxmin = rowdata['x1'].min() else: xaxmax = yaxmaxima[xparam] xaxmin = yaxminima[xparam] x_axis_type = 'linear' y_axis_type = 'linear' if xparam == 'time': x_axis_type = 'datetime' if yparam1 == 'pace': # pragma: no cover y_axis_type = 'datetime' rowdata['xname'] = axlabels[xparam] try: rowdata['yname1'] = axlabels[yparam1] except KeyError: # pragma: no cover rowdata['yname1'] = axlabels[xparam] if yparam2 != 'None': rowdata['yname2'] = axlabels[yparam2] else: rowdata['yname2'] = axlabels[yparam1] # prepare data source = ColumnDataSource( rowdata ) plot = figure(x_axis_type=x_axis_type, y_axis_type=y_axis_type, width=200, height=150, ) # plot.sizing_mode = 'stretch_both' plot.sizing_mode = 'fixed' plot.toolbar.logo = None plot.toolbar_location = None plot.xaxis.axis_label_text_font_size = "7pt" plot.yaxis.axis_label_text_font_size = "7pt" plot.xaxis.major_label_text_font_size = "7pt" plot.yaxis.major_label_text_font_size = "7pt" if plottype == 'line': plot.line('x1', 'y1', source=source) elif plottype == 'scatter': plot.scatter('x1', 'y1', source=source, fill_alpha=0.4, line_color=None) try: plot.xaxis.axis_label = axlabels[xparam] except KeyError: # pragma: no cover plot.xaxis.axis_label = 'X' try: plot.yaxis.axis_label = axlabels[yparam1] except KeyError: # pragma: no cover plot.yaxis.axis_label = 'Y' try: yrange1 = Range1d(start=yaxminima[yparam1], end=yaxmaxima[yparam1]) except KeyError: # pragma: no cover yrange1 = Range1d(start=yparam1.min(), end=yparam1.max()) plot.y_range = yrange1 if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'): xrange1 = Range1d(start=yaxminima[xparam], end=yaxmaxima[xparam]) plot.x_range = xrange1 if xparam == 'time': xrange1 = Range1d(start=xaxmin, end=xaxmax) plot.x_range = xrange1 plot.xaxis[0].formatter = DatetimeTickFormatter( hours=["%H"], minutes=["%M"], seconds=["%S"], days=["0"], months=[""], years=[""] ) if yparam1 == 'pace': # pragma: no cover plot.yaxis[0].formatter = DatetimeTickFormatter( seconds=["%S"], minutes=["%M"] ) if yparam2 != 'None': yrange2 = Range1d(start=yaxminima[yparam2], end=yaxmaxima[yparam2]) plot.extra_y_ranges["yax2"] = yrange2 # = {"yax2": yrange2} if plottype == 'line': plot.line('x1', 'y2', color="red", y_range_name="yax2", source=source) elif plottype == 'scatter': # pragma: no cover plot.scatter('x1', 'y2', source=source, fill_alpha=0.4, line_color=None, color="red", y_range_name="yax2") plot.add_layout(LinearAxis(y_range_name="yax2", axis_label=axlabels[yparam2], major_label_text_font_size="7pt", axis_label_text_font_size="7pt", ), 'right', ) script, div = components(plot) return [script, div] def interactive_multiple_compare_chart(ids, xparam, yparam, plottype='line', promember=0, workstrokesonly=True, labeldict=None, startenddict={}): message = '' errormessage = '' columns = [name for name, d in metrics.rowingmetrics] columns_basic = [name for name, d in metrics.rowingmetrics if d['group'] == 'basic'] add_columns = [ 'ftime', 'distance', 'fpace', 'power', 'hr', 'spm', 'time', 'pace', 'workoutstate', 'workoutid' ] columns = columns + add_columns columns_basic = columns_basic + add_columns compute = False doclean = False if workstrokesonly: compute = True doclean = True datadf = pd.DataFrame() if promember: datadf = dataprep.getsmallrowdata_db(columns, ids=ids, doclean=doclean, compute=compute, workstrokesonly=workstrokesonly, for_chart=True) else: datadf = dataprep.getsmallrowdata_db(columns_basic, ids=ids, doclean=doclean, compute=compute, workstrokesonly=workstrokesonly, for_chart=True) # check if dataframe not empty if datadf.empty: # pragma: no cover return ['

No non-zero data in selection

', ''] datadf['workoutid'] = datadf['workoutid'].astype(int) datadf.dropna(axis=1, how='all', inplace=True) datadf.dropna(axis=0, how='all', inplace=True) nrworkouts = len(ids) try: tseconds = datadf.loc[:, 'time'] except KeyError: # pragma: no cover try: tseconds = datadf.loc[:, xparam] except: return ['

A chart data error occurred

', ''] # check if dataframe not empty if datadf.empty: # pragma: no cover return ['

No non-zero data in selection

', ''] if (xparam == 'time'): datadf[xparam] = datadf[xparam] - datadf[xparam].iloc[0] datadf = datadf.fillna(0) data_dict = datadf.to_dict("records") metrics_list = [{'name': name, 'rowingmetrics':d } for name, d in metrics.rowingmetrics] workoutsdict = [{'id': id, 'label': labeldict[id]} for id in ids] chart_data = { 'title': '', 'x': xparam, 'y': yparam, 'data': data_dict, 'metrics': metrics_list, 'plottype': plottype, 'workouts': workoutsdict, } script, div = get_chart("/compare", chart_data) return script, div, message, errormessage if xparam != 'distance' and xparam != 'time' and xparam != 'cumdist': # pragma: no cover xaxmax = yaxmaxima[xparam] xaxmin = yaxminima[xparam] elif xparam == 'time' and not startenddict: xaxmax = tseconds.max() xaxmin = tseconds.min() elif xparam == 'time' and startenddict: # pragma: no cover deltas = [pair[1]-pair[0] for key, pair in startenddict.items()] xaxmin = 0 xaxmax = pd.Series(deltas).max()*1000. if xaxmax == 0: xaxmax = tseconds.max() else: xaxmax = datadf['distance'].max() xaxmin = datadf['distance'].min() if yparam == 'distance': # pragma: no cover yaxmin = datadf['distance'].min() yaxmax = datadf['distance'].max() elif yparam == 'cumdist': # pragma: no cover yaxmin = datadf['cumdist'].min() yaxmax = datadf['cumdist'].max() else: yaxmin = yaxminima[yparam] yaxmax = yaxmaxima[yparam] x_axis_type = 'linear' y_axis_type = 'linear' # Add hover to this comma-separated string and see what changes if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,crosshair' else: # pragma: no cover TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,crosshair' if yparam == 'pace': y_axis_type = 'datetime' yaxmax = 90.*1e3 yaxmin = 150.*1e3 if xparam == 'time': x_axis_type = 'datetime' plot = figure(x_axis_type=x_axis_type, y_axis_type=y_axis_type, tools=TOOLS, toolbar_location="above", width=920, height=500, toolbar_sticky=False) # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkw = 184 watermarkh = 35 plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} #plot.sizing_mode = 'stretch_both' plot.image_url([watermarkurl], 0.05, 0.9, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor='top_left', dilate=True, x_range_name="watermark", y_range_name="watermark", ) colors = itertools.cycle(palette) cntr = 0 l1 = [] try: items = itertools.izip(ids, colors) except AttributeError: items = zip(ids, colors) for id, color in items: group = datadf[datadf['workoutid'] == int(id)].copy() try: startsecond, endsecond = startenddict[id] except KeyError: startsecond = 0 endsecond = 0 group.sort_values(by='time', ascending=True, inplace=True) if endsecond > 0: group['time'] = group['time'] - 1.e3*startsecond mask = group['time'] < 0 group.mask(mask, inplace=True) mask = group['time'] > 1.e3*(endsecond-startsecond) group.mask(mask, inplace=True) if xparam == 'cumdist': group['cumdist'] = group['cumdist'] - group['cumdist'].min() res = make_cumvalues(group[xparam]) group[xparam] = res[0] elif xparam == 'distance': group['distance'] = group['distance'] - group['distance'].min() try: group['x'] = group[xparam] except KeyError: # pragma: no cover group['x'] = group['time'] errormessage = xparam+' has no values. Plot invalid' try: group['y'] = group[yparam] except KeyError: group['y'] = 0.0*group['x'] ymean = group['y'].mean() f = group['time'].diff().mean() if f != 0 and not np.isnan(f): windowsize = 2 * (int(20000./(f))) + 1 else: windowsize = 1 if windowsize > 3 and windowsize < len(group['y']): try: group['y'] = savgol_filter(group['y'], windowsize, 3) except ValueError: # pragma: no cover pass ylabel = Label(x=100, y=60+nrworkouts*20-20*cntr, x_units='screen', y_units='screen', text=axlabels[yparam] + ": {ymean:6.2f}".format(ymean=ymean), background_fill_alpha=.7, background_fill_color='white', text_color=color, ) if yparam != 'time' and yparam != 'pace': plot.add_layout(ylabel) source = ColumnDataSource( group ) TIPS = OrderedDict([ ('time', '@ftime'), ('pace', '@fpace'), ('hr', '@hr'), ('spm', '@spm{1.1}'), ('distance', '@distance{5}'), ]) hover = plot.select(type=HoverTool) hover.tooltips = TIPS if labeldict: try: legend_label = labeldict[id] except KeyError: # pragma: no cover legend_label = str(id) else: # pragma: no cover legend_label = str(id) if plottype == 'line': l1.append(plot.line('x', 'y', source=source, color=color, legend_label=legend_label, line_width=2)) else: l1.append(plot.scatter('x', 'y', source=source, color=color, legend_label=legend_label, fill_alpha=0.4, line_color=None)) plot.add_tools(HoverTool(renderers=[l1[cntr]], tooltips=TIPS)) cntr += 1 plot.legend.location = 'top_right' plot.xaxis.axis_label = axlabels[xparam] plot.yaxis.axis_label = axlabels[yparam] if (xparam != 'time') and (xparam != 'distance') and (xparam != 'cumdist'): # pragma: no cover xrange1 = Range1d(start=yaxminima[xparam], end=yaxmaxima[xparam]) plot.x_range = xrange1 yrange1 = Range1d(start=yaxmin, end=yaxmax) plot.y_range = yrange1 if xparam == 'time': xrange1 = Range1d(start=xaxmin, end=xaxmax) plot.x_range = xrange1 plot.xaxis[0].formatter = DatetimeTickFormatter( hours=["%H"], minutes=["%M"], seconds=["%S"], days=["0"], months=[""], years=[""] ) if yparam == 'pace': plot.yaxis[0].formatter = DatetimeTickFormatter( seconds=["%S"], minutes=["%M"] ) script, div = components(plot) return [script, div, message, errormessage] def interactive_otw_advanced_pace_chart(id=0, promember=0): # check if valid ID exists (workout exists) rowdata, row = dataprep.getrowdata_db(id=id) rowdata.dropna(axis=1, how='all', inplace=True) rowdata.dropna(axis=0, how='any', inplace=True) if rowdata.empty: return "", "No Valid Data Available" # Add hover to this comma-separated string and see what changes if (promember == 1): TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' else: # pragma: no cover TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair' source = ColumnDataSource( rowdata ) plot = figure(x_axis_type="datetime", y_axis_type="datetime", tools=TOOLS, width=920, toolbar_sticky=False) # add watermark watermarkurl = "/static/img/logo7.png" watermarkrange = Range1d(start=0, end=1) watermarkalpha = 0.6 watermarkx = 0.99 watermarky = 0.01 watermarkw = 184 watermarkh = 35 watermarkanchor = 'bottom_right' plot.extra_y_ranges = {"watermark": watermarkrange} plot.extra_x_ranges = {"watermark": watermarkrange} #plot.sizing_mode = 'scale_both' plot.image_url([watermarkurl], watermarkx, watermarky, watermarkw, watermarkh, global_alpha=watermarkalpha, w_units='screen', h_units='screen', anchor=watermarkanchor, dilate=True, x_range_name="watermark", y_range_name="watermark", ) try: plot.title.text = row.name except ValueError: # pragma: no cover plot.title.text = "" #plot.title.text_font_size = value("1.2em") plot.xaxis.axis_label = "Time" plot.yaxis.axis_label = "Pace (/500m)" plot.xaxis[0].formatter = DatetimeTickFormatter( hours=["%H"], minutes=["%M"], seconds=["%S"], days=["0"], months=[""], years=[""] ) plot.yaxis[0].formatter = DatetimeTickFormatter( seconds=["%S"], minutes=["%M"] ) ymax = 1.0e3*90 ymin = 1.0e3*210 plot.y_range = Range1d(ymin, ymax) hover = plot.select(dict(type=HoverTool)) plot.line('time', 'pace', source=source, legend_label="Pace", color="black") plot.line('time', 'nowindpace', source=source, legend_label="Corrected Pace", color="red") hover.tooltips = OrderedDict([ ('Time', '@ftime'), ('Pace', '@fpace'), ('Corrected Pace', '@fnowindpace'), ('HR', '@hr{int}'), ('SPM', '@spm{1.1}'), ]) hover.mode = 'mouse' try: script, div = components(plot) except: # pragma: no cover script = '' div = '' return [script, div] def get_zones_report(rower, startdate, enddate, trainingzones='hr', date_agg='week', yaxis='time'): dates = [] dates_sorting = [] minutes = [] hours = [] zones = [] enddate = enddate + datetime.timedelta(days=1) workouts = Workout.objects.filter( user=rower, startdatetime__gte=startdate, startdatetime__lte=enddate, duplicate=False, ).order_by("-startdatetime") ids = [w.id for w in workouts] columns = ['workoutid', 'hr', 'power', 'time'] df = dataprep.getsmallrowdata_db(columns, ids=ids) try: df['deltat'] = df['time'].diff().clip(lower=0).clip(upper=20*1e3) except KeyError: # pragma: no cover pass df = dataprep.clean_df_stats(df, workstrokesonly=False, ignoreadvanced=True, ignorehr=False) hrzones = rower.hrzones powerzones = rower.powerzones for w in workouts: dd3 = w.date.strftime('%Y/%m') dd4 = '{year}/{week:02d}'.format( week=arrow.get(w.date).isocalendar()[1], year=w.date.strftime('%y') ) dd4 = (w.date - datetime.timedelta(days=w.date.weekday()) ).strftime('%y/%m/%d') # print(w.date,arrow.get(w.date),arrow.get(w.date).isocalendar()) iswater = w.workouttype in mytypes.otwtypes qryw = 'workoutid == {workoutid}'.format(workoutid=w.id) qry = 'hr < {ut2}'.format(ut2=rower.ut2) if trainingzones == 'power': qry = 'power < {ut2}'.format(ut2=rower.pw_ut2) timeinzone = df.query(qry).query(qryw)['deltat'].sum()/(60*1e3) if date_agg == 'week': dates.append(dd4) dates_sorting.append(dd4) else: # pragma: no cover dates.append(dd3) dates_sorting.append(dd3) minutes.append(timeinzone) hours.append(timeinzone/60.) if trainingzones == 'hr': zones.append('<{ut2}'.format(ut2=hrzones[1])) else: zones.append('<{ut2}'.format(ut2=powerzones[1])) # print(w,dd,timeinzone,'= enddate: # pragma: no cover st = startdate startdate = enddate enddate = st hrzones = rower.hrzones powerzones = rower.powerzones color_map = { '<{ut2}'.format(ut2=hrzones[1]): 'green', hrzones[1]: 'lime', hrzones[2]: 'yellow', hrzones[3]: 'blue', hrzones[4]: 'purple', hrzones[5]: 'red', } if trainingzones == 'power': color_map = { '<{ut2}'.format(ut2=powerzones[1]): 'green', powerzones[1]: 'lime', powerzones[2]: 'yellow', powerzones[3]: 'blue', powerzones[4]: 'purple', powerzones[5]: 'red', } zones_order = [ '<{ut2}'.format(ut2=hrzones[1]), hrzones[1], hrzones[2], hrzones[3], hrzones[4], hrzones[5] ] if trainingzones == 'power': zones_order = [ '<{ut2}'.format(ut2=powerzones[1]), powerzones[1], powerzones[2], powerzones[3], powerzones[4], powerzones[5] ] df = pd.DataFrame(data) df2 = pd.DataFrame(data) df.drop('minutes', inplace=True, axis='columns') df.sort_values('date_sorting', inplace=True) df.drop('date_sorting', inplace=True, axis='columns') df['totaltime'] = 0 if df.empty: # pragma: no cover return '', 'No Data Found' if yaxis == 'percentage': dates = list(set(df['date'].values)) for date in dates: qry = 'date == "{d}"'.format(d=date) totaltime = df.query(qry)['hours'].sum() mask = df['date'] == date df.loc[mask, 'totaltime'] = totaltime df['percentage'] = 100.*df['hours']/df['totaltime'] df.drop('hours', inplace=True, axis='columns') df.drop('totaltime', inplace=True, axis='columns') hv.extension('bokeh') xrotation = 0 nrdates = len(list(set(df['date'].values))) if nrdates > 10: xrotation = 45 bars = hv.Bars(df, kdims=['date', 'zones']).aggregate( function=np.sum).redim.values(zones=zones_order) bars.opts( opts.Bars(cmap=color_map, show_legend=True, stacked=True, tools=['tap', 'hover'], width=550, padding=(0, (0, .1)), legend_position='bottom', xrotation=xrotation, show_frame=False) ) p = hv.render(bars) p.title.text = 'Activity {d1} to {d2} for {r}'.format( d1=startdate.strftime("%Y-%m-%d"), d2=enddate.strftime("%Y-%m-%d"), r=str(rower), ) if date_agg == 'week': p.xaxis.axis_label = 'Week' else: # pragma: no cover p.xaxis.axis_label = 'Month' if yaxis == 'percentage': p.yaxis.axis_label = 'Percentage' p.width = 550 p.height = 350 p.toolbar_location = 'right' p.y_range.start = 0 #p.sizing_mode = 'stretch_both' if yaxis == 'percentage': tidy_df = df2.groupby(['date']).sum() source2 = ColumnDataSource(tidy_df) y2rangemax = tidy_df.loc[:, 'hours'].max()*1.1 p.extra_y_ranges["yax2"] = Range1d(start=0, end=y2rangemax) p.line('date', 'hours', source=source2, y_range_name="yax2", color="black", width=5) p.circle('date', 'hours', source=source2, y_range_name="yax2", color="black", size=10) # p.circle('date', 'hours', source=source2, y_range_name="yax2", color="black", size=10, # legend_label='Hours') p.add_layout(LinearAxis(y_range_name="yax2", axis_label='Hours'), 'right') script, div = components(p) return script, div