![goldencheetah average power chart goldencheetah average power chart](https://4p4d3b.media.zestyio.com/Typical-House-Updated_0.jpg)
Whilst in the activity view the chart is refreshed when an activity is selected or, again, if the compare pane for intervals is updated. In trend view the chart is refreshed when a date range or season is selected, or if the compare pane is updated. We highly recommend installing this following modules since they are assumed to be available when developing charts:
#GOLDENCHEETAH AVERAGE POWER CHART INSTALL#
We recommend using `pip' to install and manage python modules and expect the pip module to have been installed. Note for v3.6 on Windows: to use your own Python installation with GoldenCheetah v3.6, instead of the included one, it is necessary to remove/rename python37._pth file parallel to GoldenCheetah.ese, this file is part of Python embeddable distribution and blocks access to other Python installations when present. The path you need to configure Python in GoldenCheetah can be obtained running the Python interpreter and executing the following commands:Īfter you configure Python in GoldenCheetah, restart GoldenCheetah and check Python is recognized in Help > About > Versions. If Python is not installed in (OS dependent) standard locations or present in PATH, the location needs to be configured in Preferences before to enable Python integration. 3.6) must match the one reported in Help > About > Versions, if you are experiencing problems we recommend to install exactly the same version informed there. Since Python is embedded the runtime must match the versions supported by each release of GoldenCheetah, currently at least major and minor Python version (s.t. We build binaries using Python v3.x and they are only compatible with this release, we do not support embedded use of Python 2.7. Python chart is introduced with v3.5 of GoldenCheetah and it can be used in activity view and trend view. in decreasing order of complexity and flexibility: R Charts, Metrics Trends Charts and User Data/Custom Metrics with formulas, and configurable standard charts. There are lots of freely available resources to educate yourself on these topics. Even to use Python charts developed by others, the minimum is to be able to install Python, configure for embedding in GoldenCheetah and manage Python packages using the standard Python package manager (PIP) at the command line in OS. Python embedding is the most powerful and flexible way to extend GoldenCheetah, but that flexibility comes with a price to use it you need to know or be willing to learn about Python. For advanced users, willing to install additional packages, a separate Python installation is recommended, as documented below. To use it check Enable Python and left Python Home empty in options, restart GC after Python config change. New in v3.6: GoldenCheetah v3.6 builds include Python 3.7, with required and recommended modules pre-installed, to allow easier access to curated Python charts on CloudDB for non-technical users. GC.annotate(.) to add an annotation to the chart.GC.setAxis(.) to control axis settings.GC.addCurve(.) to add a curve to the chart.GC.seasonMeasures(all=False, group="Body") to get Daily measures (Body and Hrv available for v3.5).GC.seasonIntervals(type="", compare=False) to get metrics for all intervals.
![goldencheetah average power chart goldencheetah average power chart](https://4.bp.blogspot.com/-Y4yZrkBuSQM/ULevPi8qAoI/AAAAAAAAAD8/99TlChHB6Sw/s1600/20121129_BoonenFest_1.jpg)
#GOLDENCHEETAH AVERAGE POWER CHART SERIES#
GC.seasonPeaks(all=False, filter="", compare=False, series, duration) to get activity peaks for a given series and duration.GC.seasonMeanmax(all=False, filter="", compare=False) to get best mean maximals for a season.GC.seasonMetrics(all=False, filter="", compare=False) to get season metrics.GC.season(all=False, compare=False) to get season details.GC.activityIntervals(type="", activity=None) to get information about activity intervals.GC.activityMeanmax(compare=False) to get mean maximals for all activity data.GC.activityMetrics(compare=False) to get the activity metrics and metadata.GC.xdata(name, series, join="repeat", activity=None) to get interpolated activity xdata series.GC.xdataSeries(name, series, activity=None) to get activity xdata series in its own sampling interval.GC.xdataNames(name="", activity=None) to get activity xdata series names.GC.activityWbal(activity=None) to get wbal series data.GC.series(type, activity=None) to get an individual series data.GC.activity(activity=None) to get the activity data.GC.activities(filter="") to get a list of activities (as dates).GC.athleteZones(date=0, sport="") to get zone config.GC.athlete() to get the athlete details.GC.webpage(filename) to set the webpage.Below you will find details of each of the GC functions available to use within the Python chart.