Saravanan666/pypi-org huggingface dataset:
Package: pollev-history-compiler
PollEvHistoryCompilerHave you ever wanted to compile your Poll Everywhere history into a more usable form?
This project allows you to use a script to load it into almost any form you want!
You can filter by presenter, and use different types of encodings so that you can load polls with almost any data in them.
Right now most of the support is forMultiple choicetype polls.InstallationThere are two approaches to the installation of this package/script.
Clone the repository, and within the root folder run:pip install .Install the package from PyPI:pip install pollev-history-compilerUsageIt works as follows as a command-line script.pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)> --output-file <OUTPUT PATH (dir)> <OPTIONS>If no output path is supplied, the script will use theGood options include presenter name:pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --presenter <NAME> <OPTIONS>Remove hidden questions from the result:pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --remove_hiddenRemove image-related questions from the result:pollev-compiler <INPUT CSV> <TRANSFORM (json, yaml, toml, etc.)>  --output-file <OUTPUT PATH (dir)> --remove_imagesMarkdown format is now supported!pollev-compiler <INPUT CSV> markdownThis process usesmarkdownifyto turn the pre-existing HTML functionality into a markdown file.You can now use the--quiz_modeoption in thehtmltransform to turn the output to a interactable quiz that highlights your guesses,
and your score on the quiz (correct over total). You can do this like:pollev-compiler <INPUT CSV> html --quiz_modeThe output consists of a JavaScript file, CSS file, and HTML file. Without quiz mode, the JavaScript file isn't added.Documentation
Package: pollination-adaptive-comfort-map
Adaptive Comfort MapAdaptive thermal comfort map recipe for Pollination.Compute spatially-resolved operative temperature and adaptive thermal comfort from
a Honeybee model and EPW. Raw results are written into aresults/folder and
include CSV matrices of hourly operative temperatures and thermal conditions. Processed
metrics of Thermal Comfort Percent (TCP) can be found in themetrics/folder.MethodsThis recipe uses EnergyPlus to obtain longwave radiant temperatures and indoor air
temperatures. The outdoor air temperature and air speed are taken directly from
the EPW. All outdoor points are assumed to be at one half of the EPW meteorological
wind speed (effectively representing wind speed at ground/human height).Longwave radiant temperatures are achieved by computing spherical view factors
from each sensor to the surfaces of the model using Radiance. These view factors
are then multiplied by the surface temperatures output by EnergyPlus to yield
longwave MRT at each sensor. All indoor shades (eg. those representing furniture)
are assumed to be at the room-average MRT. For outdoor sensors, the EnergyPlus
outdoor surface temperatures are used and each sensor's sky view is multiplied by
the EPW sky temperature to account for longwave radiant exchange with the sky.
All outdoor context shades and the ground are assumed to be at the EPW air
temperature unless they have been modeled as Honeybee rooms.A Radiance-based enhanced 2-phase method is used for all shortwave MRT calculations,
which precisely represents direct sun by tracing a ray from each sensor to the
solar position. The energy properties of the model geometry are what determine
the reflectance and transmittance of the Radiance materials in this shortwave
calculation.To determine Thermal Comfort Percent (TCP), the occupancy schedules of the energy
model are used. Any hour of the occupancy schedule that is 0.1 or greater will be
considered occupied. For outdoor sensors, all hours are considered occupied.
Package: pollination-annual-daylight
annual-daylightAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses a 2-phase method for daylight simulation. If the accuracy of the direct sunlight is important, you can use theannual-daylight-enhancedrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylightannual daylight metricsannual-daylightannual-daylight.vizannual daylight metricsvisualizationannual-daylight.vizannual-daylight.fullannual daylight metricsvisualizationresults folderannual-daylight.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-daylight-enhanced
annual-daylight-enhancedAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses an enhanced 2-phase method for daylight simulation with accurate direct sunlight calculation. If the accuracy of the direct sunlight is not important, you can use theannual-daylightrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylight-enhancedannual daylight metricsannual-daylight-enhancedannual-daylight-enhanced.vizannual daylight metricsvisualizationannual-daylight-enhanced.vizannual-daylight-enhanced.fullannual daylight metricsvisualizationresults folderannual-daylight-enhanced.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-daylight-enhanced.full
annual-daylight-enhancedAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses an enhanced 2-phase method for daylight simulation with accurate direct sunlight calculation. If the accuracy of the direct sunlight is not important, you can use theannual-daylightrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylight-enhancedannual daylight metricsannual-daylight-enhancedannual-daylight-enhanced.vizannual daylight metricsvisualizationannual-daylight-enhanced.vizannual-daylight-enhanced.fullannual daylight metricsvisualizationresults folderannual-daylight-enhanced.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-daylight-enhanced.viz
annual-daylight-enhancedAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses an enhanced 2-phase method for daylight simulation with accurate direct sunlight calculation. If the accuracy of the direct sunlight is not important, you can use theannual-daylightrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylight-enhancedannual daylight metricsannual-daylight-enhancedannual-daylight-enhanced.vizannual daylight metricsvisualizationannual-daylight-enhanced.vizannual-daylight-enhanced.fullannual daylight metricsvisualizationresults folderannual-daylight-enhanced.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-daylight.full
annual-daylightAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses a 2-phase method for daylight simulation. If the accuracy of the direct sunlight is important, you can use theannual-daylight-enhancedrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylightannual daylight metricsannual-daylightannual-daylight.vizannual daylight metricsvisualizationannual-daylight.vizannual-daylight.fullannual daylight metricsvisualizationresults folderannual-daylight.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-daylight.viz
annual-daylightAnnual daylight recipe for Pollination.Use this recipe to calculate annual daylight for a HBJSON model.This recipe uses a 2-phase method for daylight simulation. If the accuracy of the direct sunlight is important, you can use theannual-daylight-enhancedrecipe.VersionsThere are three different versions of the recipe. All three versions use the same simulation methodology and workflow, although their outputs vary.VersionOutputsPyPIannual-daylightannual daylight metricsannual-daylightannual-daylight.vizannual daylight metricsvisualizationannual-daylight.vizannual-daylight.fullannual daylight metricsvisualizationresults folderannual-daylight.fullFor more informationsee the recipe on Pollination.
Package: pollination-annual-energy-use
annual-energy-useCalculate annual energy use intensity (EUI) for a Honeybee model and optionally
get several HTML reports to visualize the energy use.The recipe outputs a file calledeui.jsonin the format below:{
  "eui": 306.852,
  "total_floor_area": 66129.6,
  "conditioned_floor_area": 66129.6,
  "total_energy": 20292008.333,
  "end_uses": {
    "heating": 30.924,
    "cooling": 84.342,
    "interior_lighting": 27.451,
    "interior_equipment": 164.115,
    "pumps": 0.019
  }
}
Package: pollination-annual-irradiance
Annual IrradianceCalculate solar irradiance (W/m2) and radiation (kWh/m2) at each time step provided by a wea file.The fundamental calculation of this recipe is the same as that ofAnnual Daylight Enhancedin that a detailed
accounting of direct sun is computed at each time step. However, the recipe computes
broadband solar irradiance in W/m2 instead of visible illuminance in lux.The detailed matrices of W/m2 at each time step are stored underresults/total.
Detailed matrices of just the direct component (W/m2) are output toresults/direct.
The following values are also recorded for each sensor point under themetricsfolder:average_irradiance: The average irradiance in W/m2 over the Wea time periodpeak_irradiance: The highest irradiance value in W/m2 during the Wea time periodcumulative_radiation: The cumulative radiation in kWh/m2 over the Wea time periodThepeak_irradiancevalue is suitable for assessing the worst-case solar load
on cooling design days or the highest radiant temperatures that occupants might
experience in over the time period of the Wea.
Package: pollination-annual-radiation
Annual RadiationAnnual radiation recipe for PollinationThis recipe calculates the annual radiation for each time step provided by a wea file.
The outputs are stored underresults/directandresults/total. Nighttime hours are
filtered before running the simulation. To match the results for each of the hours,
see the list of hours in sun-up-hours.txt.MethodsThis recipe calculates the total amount of Radiation by calculating the direct sunlight
radiation from sun disks and adding them to the contribution from indirect sky radiation.Total radiation = direct sun radiation + indirect sky radiationThe number of bounces for direct radiation is set to 0 and cannot be modified by the user.
You can adjust the number of bounces for indirect sky radiation.LimitationsThis recipe is limited to calculating up to9999number of suns at a time. This means
you might need to break down the annual studies with smaller time steps into smaller
runs. This limitation is a result of how Radiance sets a limit on number of modifiers
in a single run.See here.
We can address this limitation by either building our own version of Radiance
or breaking down the modifier list and looping over them and merging back the results.
Package: pollination-annual-sky-radiation
Annual Sky RadiationAnnual Sky Radiation recipe for PollinationThis recipe calculate the annual sky radiation for each timestep provided in the wea
file. Nigh-time hours are filtered before running the simulation. To match the results
for each hours see the list of hours in sun-up-hours.txt.LimitationsThis recipe uses Radiance'sgendaymtxto generate the sky. "Gendaymtx takes a weather
tape as input and produces a matrix of sky patch values using the Perez all weather
model". "If there is a sun in the description, gendaymtx will include its contribution
in the four nearest sky patches, distributing energy according to centroid proximity".
This means that the value of direct sun is diffused between these patches. This
approximation is fine for studies such as cumulative radiation or for outdoor studies
where the amount of exact direct radiation is not critical. For accurate direct radiation
seeannual_radiationrecipe.
Package: pollination-appendix-g-performance
Appendix G PerformanceThis recipe generates a baseline Honeybee Model from the input Model, which is
consistent with ASHRAE 90.1 Appendix G 2016 (and later), This includes adjusting
the geometry, constructions, lighting, HVAC, SHW, and removing any clearly-defined
energy conservation measures like daylight controls.Note that all schedules are essentially unchanged in the baseline model, meaning
that additional post-processing of setpoints may be necessary to account for
energy conservation strategies like expanded comfort ranges, ceiling fans, and
personal thermal comfort devices. It may also be necessary to adjust hot water
loads loads in cases where low-flow fixtures are implemented.After the creation of the baseline model, this recipe will simulate it in
EnergyPlus, performing 4 separate simulations in parallel for each of the 4
cardinal directions per the Appendix G specification. Alongside these baseline
simulations, the input Model will be simulated to get the energy performance
of the proposed building. At the end, all energy use results will be
post-processed along with the energy costs inputs to estimate the Appendix G
PCI. An additional computation will also be run to estimate the number of
LEED "Optimize Energy Performance" points for LEED v4.The recipe outputs a file calledappendix_g_summary.json, which contains the PCI
improvement for the latest versions of ASHRAE 90.1 in the format below:{"proposed_eui":112.866,"proposed_energy":3517144.444,"proposed_cost":703428.89,"baseline_eui":235.3,"baseline_energy":7332474.306,"baseline_cost":1214797.19,"pci_t_2016":0.666,"pci_t_2019":0.591,"pci_t_2022":0.574,"pci":0.579,"pci_improvement_2016":13.055,"pci_improvement_2019":2.0219,"pci_improvement_2022":-0.880}The recipe also outputs a file calledleed_summary.json, which contains the
ASHRAE 90.1-2016 PCI for both cost and carbon (GHG) emissions in the format below:{"proposed_eui":112.866,"proposed_cost":703428.89,"proposed_carbon":464263.067,"baseline_eui":235.3,"baseline_cost":1214797.19,"baseline_carbon":1577657.766,"pci":0.579,"pci_target":0.666,"pci_improvement":13.055,"carbon":0.294,"carbon_target":0.633,"carbon_improvement":53.511,"leed_points":9}
Package: pollination-apps
Pollination AppsA python library to setup and deploy Apps to Pollination!Installation> pip install pollination-appsDocumentationYou can access the full docs for this package and its CLIhere.Local DevelopmentClone this repo locallygit clone git@github.com:ladybug-tools/pollination-appsorgit clone https://github.com/ladybug-tools/pollination-appsInstall dependencies:cd pollination-appspip install -r dev-requirements.txtpip install -r requirements.txtRun Tests:python -m pytest tests/Generate Documentation:sphinx-apidoc-f-e-d4-o./docs/modules./pollination-appssphinx-build-bhtml./docs./docs/_buildPreview Documentation:python -m http.server --directory ./docs/_build/Now you can see the documentation preview athttp://localhost:8000
Package: pollination-cumulative-radiation
Cumulative RadiationCumulative Radiation recipe for PollinationThis recipe calculates average irradiance (W/m2) and cumulative radiation (kWh/m2)
over the time period of a specified Wea.LimitationsThis recipe uses Radiance'sgendaymtxto generate the sky instead of directly
tracing the line of sight from sensors to the solar position.Gendaymtx takes a weather tape as input and produces a matrix of sky patch valuesusing the Perez all weather model. If there is a sun in the description, gendaymtxwill include its contribution in the four nearest sky patches, distributing energyaccording to centroid proximity.This means that the direct sun is diffused between several sky patches and so the
precise line between shadow and sun for each hour is blurred. This approximation
is fine for studies where the timestep-by-timestep irradiance values do not need
to be exact. For accurate modeling of direct irradiance on a timestep-by-timestep
basis, see theannual_irradiancerecipe.
Package: pollination-direct-sun-hours
direct-sun-hoursDirect sun hours recipe calculates the number of hours of direct sun received by grids of sensors during
the time period of a specified Wea. The recipe generates 2 sub-folders of results:direct_sun_hours: Contains matrices of zero/one values indicating whether
each sensor is exposed to the sun at a given time step of the input Wea.cumulative: The cumulative number of hours that each sensor can see the
sun. Each value is always in hours.Using the -viz version of this recipe will produce a VTKjs file for visualizing
the cumulative results.
Package: pollination-direct-sun-hours.express
direct-sun-hoursDirect sun hours recipe calculates the number of hours of direct sun received by grids of sensors during
the time period of a specified Wea. The recipe generates 2 sub-folders of results:direct_sun_hours: Contains matrices of zero/one values indicating whether
each sensor is exposed to the sun at a given time step of the input Wea.cumulative: The cumulative number of hours that each sensor can see the
sun. Each value is always in hours.Using the -viz version of this recipe will produce a VTKjs file for visualizing
the cumulative results.
Package: pollination-direct-sun-hours.viz
direct-sun-hoursDirect sun hours recipe calculates the number of hours of direct sun received by grids of sensors during
the time period of a specified Wea. The recipe generates 2 sub-folders of results:direct_sun_hours: Contains matrices of zero/one values indicating whether
each sensor is exposed to the sun at a given time step of the input Wea.cumulative: The cumulative number of hours that each sensor can see the
sun. Each value is always in hours.Using the -viz version of this recipe will produce a VTKjs file for visualizing
the cumulative results.
Package: pollination-honeybee-energy
Pollination Honeybee Energy pluginA plugin adds energy simulation functions to Pollination.SourcesThe following packages and engines are included with this plugin:honeybee-energyPython package.PyPI,source.honeybee-energy-standardsPython package.PyPI,source.honeybee-openstudio-gemRuby gem.Rubygems,source.IronbugIronbug, including OpenStudio .NET bindings.source.OpenStudio, including OpenStudio CLI.sourceEnergyPlus.source.
Package: pollination-honeybee-radiance
Pollination Honeybee RadiancePollination honeybee-radiance adds daylight simulation functions to Pollination.SourcesThe following software components are included with this plugin:Python 3.10honeybee-radiancePython package.PyPI,sourcehoneybee-radiance-commandPython package.PyPI,sourcehoneybee-radiance-folderPython package.PyPI,sourceRadiancephysics-based rendering engine.docs,sourceFunctionsYou can see the list of available functions in honeybee-radiance pluginhereon Pollination.ExamplesFor real-world examples see any of these Ladybug Tools recipes for Pollination:Annual Daylight recipeAnnual Irradiance recipePoint-in-Time Grid recipePoint-in-Time View recipeDaylight Factor recipeSky View recipe
Package: pollination-imageless-annual-glare
imageless-annual-glareRun an annual glare study for a Honeybee model to compute hourly Daylight Glare
Probability (DGP) for each sensor in a model's sensor grids.This recipe uses the image-less glare method developed by Nathaniel Jones to
estimate glare at each sensor.More information on this method can be found here.The resulting DGP is used to compute Glare Autonomy (GA), which is the percentage
of occupied time that a view is free of glare.
Package: pollination-incident-irradiance
Incident IrradianceIncident irradiance recipe for PollinationThis recipe calculates the incident irradiance for each time step provided by a wea file.
The outputs are stored underresults/directandresults/total. Nighttime hours are
filtered before running the simulation. To match the results for each of the hours,
see the list of hours in sun-up-hours.txt.MethodsThis recipe calculates the total amount of Radiation by calculating the direct sunlight
radiation from sun disks and adding them to the contribution from indirect sky radiation.incident_radiation = direct_sun_radiation + indirect_sky_radiationThe recipe is structured in a manner that ambient bounces are not included.
Package: pollination-io
Pollination IOAPollinationextension to facilitate working with Pollination SDK and apps.Installationpython -m pip install -U pollination-ioLocal DevelopmentClone this repo locallygit clone git@github.com:pollination/pollination-io#orgit clone https://github.com/pollination/pollination-ioInstall dependencies:cd pollination-iopip install -r dev-requirements.txtpip install -r requirements.txtRun Tests:python -m pytest tests/
Package: pollination-irradiance
IrradianceCalculate solar irradiance (W/m2) and radiation (kWh/m2) at each time step provided by a wea file.The fundamental calculation of this recipe is the same as that ofAnnual Daylight Enhancedin that a detailed accounting of direct sun is computed at each time step.
However, the recipe computes broadband solar irradiance in W/m2 instead of
visible illuminance in lux.The detailed matrices of W/m2 at each time step are stored underresults/total.
Detailed matrices of just the direct component (W/m2) are output toresults/direct.
The following values are also recorded for each sensor point under themetricsfolder:average_irradiance: The average irradiance in W/m2 over the Wea time periodpeak_irradiance: The highest irradiance value in W/m2 during the Wea time periodcumulative_radiation: The cumulative radiation in kWh/m2 over the Wea time periodThepeak_irradiancevalue is suitable for assessing the worst-case solar load
on cooling design days or the highest radiant temperatures that occupants might
experience in over the time period of the Wea.
Package: pollination-irradiance.express
Irradiance ExpressIrradiance Express recipe for PollinationThis recipe calculate the solar irradiance (W/m2) for each timestep provided in the wea
file. Nigh-time hours are filtered before running the simulation. To match the results
for each hours see the list of hours in sun-up-hours.txt.LimitationsThis recipe uses Radiance'sgendaymtxto generate the sky. "Gendaymtx takes a weather
tape as input and produces a matrix of sky patch values using the Perez all weather
model". "If there is a sun in the description, gendaymtx will include its contribution
in the four nearest sky patches, distributing energy according to centroid proximity".
This means that the value of direct sun is diffused between these patches. This
approximation is fine for studies such as cumulative radiation or for outdoor studies
where the amount of exact direct radiation is not critical. For accurate accounting
of direct sun see theIrradiancerecipe.
Package: pollination-irradiance.viz
IrradianceCalculate solar irradiance (W/m2) and radiation (kWh/m2) at each time step provided by a wea file.The fundamental calculation of this recipe is the same as that ofAnnual Daylight Enhancedin that a detailed accounting of direct sun is computed at each time step.
However, the recipe computes broadband solar irradiance in W/m2 instead of
visible illuminance in lux.The detailed matrices of W/m2 at each time step are stored underresults/total.
Detailed matrices of just the direct component (W/m2) are output toresults/direct.
The following values are also recorded for each sensor point under themetricsfolder:average_irradiance: The average irradiance in W/m2 over the Wea time periodpeak_irradiance: The highest irradiance value in W/m2 during the Wea time periodcumulative_radiation: The cumulative radiation in kWh/m2 over the Wea time periodThepeak_irradiancevalue is suitable for assessing the worst-case solar load
on cooling design days or the highest radiant temperatures that occupants might
experience in over the time period of the Wea.