,
Fabian Hertner [aut],
Baptiste Schmid [ctb],
Damiano Preatoni [ctb],
Johannes De Groeve [ctb],
Felix Liechti [ctb]| Title: | Migration Traffic Rate Calculation Package for 'Birdscan MR1' Radars |
|---|---|
| Description: | Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) <doi:10.5281/zenodo.5734960>). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) <doi:10.1111/ecog.04025>. |
| Authors: | Birgen Haest [aut, cre] ,
Fabian Hertner [aut],
Baptiste Schmid [ctb],
Damiano Preatoni [ctb],
Johannes De Groeve [ctb],
Felix Liechti [ctb] |
| Maintainer: | Birgen Haest <[email protected]> |
| License: | GPL-3 |
| Version: | 0.3.0.9005 |
| Built: | 2025-03-04 05:50:26 UTC |
| Source: | https://github.com/birdscancommunity/birdscanr |
The function ‘addDayNightInfoPerEcho’ adds three columns ‘dayOrNight’, ”dayOrCrepOrNight' and ‘dateSunset’ to the echo data. This allows the user to filter echo data easily by “day” and “night”, or "day", "crepuscular", and "night".
addDayNightInfoPerEcho( echoData, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar" )addDayNightInfoPerEcho( echoData, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar" )
echoData |
dataframe with the echo data from the data list created by the function ‘extractDBData’ |
sunriseSunset |
dataframe with sunrise/sunset and civil twilight times created by the function ‘twilight’ |
sunOrCivil |
optional character variable, Set to “sun” to use sunrise/sunset times or to “civil” to use civil twilight times to group echoes into day/night. Default is "civil". |
crepuscule |
optional character variable, Set to “nauticalSolar” to use the time between nautical dusk/dawn and sunrise/sunset times to define the crepuscular period, or to "nauticalCivil" to use the time between nautical and civil dusk/dawn to define the crepuscular period, or to "civilSolar" to use the time between civil dusk/dawn and sunrise/sunset times to define the crepuscular period. Default is "nauticalSolar". |
data frame with thre columns added, i.e. 'dayOrNight', 'dayOrCrepOrNight', and 'dateSunset'.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") # Get sunrise/sunset information # =========================================================================== sunrisesunset = twilight(timeRange = c("2021-01-15 00:00", "2021-01-31 00:00"), latLon = siteLocation, timeZone = targetTimeZone) # Add day/night info to echo data # =========================================================================== echoData = addDayNightInfoPerEcho(echoData = dbData$echoData, sunriseSunset = pulseLengthSelection, sunOrCivil = "civil") ## End(Not run)## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") # Get sunrise/sunset information # =========================================================================== sunrisesunset = twilight(timeRange = c("2021-01-15 00:00", "2021-01-31 00:00"), latLon = siteLocation, timeZone = targetTimeZone) # Add day/night info to echo data # =========================================================================== echoData = addDayNightInfoPerEcho(echoData = dbData$echoData, sunriseSunset = pulseLengthSelection, sunOrCivil = "civil") ## End(Not run)
Table to allow for easy abbreviations of the standard classes of the Birdscan MR1.
data(classAbbreviations)data(classAbbreviations)
An object of class "data.frame".
data(classAbbreviations)data(classAbbreviations)
The function compileData creates a standardized Birdscan
MR1 data product, including metadata, that can be used to publish the
dataset in a standardized manner. This results in improved accessibility
across datasets and owners. The function uses the output from
extractDbData and allows for specific filtering of the dataset,
e.g., by time range, pulse type, and class.
compileData( echoData = NULL, protocolData = NULL, blindTimesData = NULL, sunriseSunsetData = NULL, radarSiteData = NULL, dbName = NULL, pulseTypeSelection = NULL, rotationSelection = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, echoValidator = FALSE, filePath = NULL, tagOutputFile = c(NULL, NULL), saveCSV = FALSE )compileData( echoData = NULL, protocolData = NULL, blindTimesData = NULL, sunriseSunsetData = NULL, radarSiteData = NULL, dbName = NULL, pulseTypeSelection = NULL, rotationSelection = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, echoValidator = FALSE, filePath = NULL, tagOutputFile = c(NULL, NULL), saveCSV = FALSE )
echoData |
dataframe with the echo data from the data list created by
the function |
protocolData |
dataframe with the protocol data from the data list created by
the function |
blindTimesData |
dataframe with the manual blind times created by
the function |
radarSiteData |
dataframe/vector with the database site table |
dbName |
Name of the database. Can be a useful meta data. |
pulseTypeSelection |
character vector with the pulse types which should be included in the subset. Options: “S”, “M”, “L”, i.e. short-, medium-, long-pulse, respectively. Default is NULL: no filtering applied based on pulseType. |
rotationSelection |
numeric vector to select the operation modes with and/or without antenna rotation. Options: 0, 1. (0 = no rotation, 1 = rotation). Default is NULL: no filtering applied based on rotation mode. |
timeRangeTargetTZ |
Character vector of length 2, with start and end of time range, formatted as "%Y-%m-%d %H:%M". Echoes outside the time range will be excluded. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
classSelection |
character string vector with the classes that should be included. |
classProbCutOff |
numeric cutoff value for class probabilities. Echoes with a lower class probability will be excluded. |
altitudeRange_AGL |
numeric vector of length 2 with start and end of the altitude range. Echoes outside the altitude range will be excluded. |
echoValidator |
logical, If set to FALSE - default -, no additional filters is applied; if set to TRUE, echoes labelled by the echo validator as “non-bio scatterer” will be excluded. |
filePath |
If given, the data-list is saved as RDS. |
tagOutputFile |
Vector of two elements for prefix & suffix to file name, given 'filePath' is not NULL. |
saveCSV |
if true, save tables as CSV in a folder nested in 'filePath'. |
Returns filtered data table - echo, protocol, blindTimes, sunriseSunset,
radarSite - and necessary parameters as input for computeMTR.
Baptiste Schmid, [email protected]; Birgen Haest, [email protected];
This function will estimate the density (expressed as #objects / km3) based on the observations in your database. Note that this function only works properly on Birdscan MR1 database versions >= 1.7.0.4 as the variable feature37.speed is required for the density calculation.
computeDensity( dbName, echoes, classSelection, altitudeRange, altitudeBinSize, timeRange, timeBinDuration_sec, timeZone, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar", protocolData, visibilityData, manualBlindTimes = NULL, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE, computeAltitudeDistribution = TRUE )computeDensity( dbName, echoes, classSelection, altitudeRange, altitudeBinSize, timeRange, timeBinDuration_sec, timeZone, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar", protocolData, visibilityData, manualBlindTimes = NULL, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE, computeAltitudeDistribution = TRUE )
dbName |
Character string, containing the name of the database you are processing |
echoes |
dataframe with the echo data from the data list created by the function ‘extractDBData’ or a subset of it created by the function ‘filterEchoData’. |
classSelection |
character string vector with all classes which should be used to calculate the density. The density and number of Echoes will be calculated for each class as well as for all classes together. |
altitudeRange |
numeric vector of length 2 with the start and end of the altitude range in meter a.g.l. |
altitudeBinSize |
numeric, size of the altitude bins in meter. |
timeRange |
Character vector of length 2, with start and end of time range, formatted as "%Y-%m-%d %H:%M" |
timeBinDuration_sec |
duration of timeBins in seconds (numeric). for values <= 0 a duration of 1 hour will be set |
timeZone |
time zone in which the time bins should be created as string, e.g. "Etc/GMT0" |
sunriseSunset |
dataframe with sunrise/sunset, and civil and nautical dawn/dusk. Computed with the function 'twilight'. |
sunOrCivil |
sunrise/sunset or civil dawn/dusk used to split day and night. Supported values: "sun" or "civil". Default: "civil" |
crepuscule |
optional character variable, Set to “nauticalSolar” to use the time between nautical dusk/dawn and sunrise/sunset times to define the crepuscular period, or to "nauticalCivil" to use the time between nautical and civil dusk/dawn to define the crepuscular period, or to "civilSolar" to use the time between civil dusk/dawn and sunrise/sunset times to define the crepuscular period. Default is "nauticalSolar". |
protocolData |
dataframe with the protocol data from the data list
created by the function |
visibilityData |
dataframe with the visibility data from the data list created by the function ‘extractDBData’. |
manualBlindTimes |
dataframe with the manual blind times created by the
function |
saveBlindTimes |
Logical, determines whether to save the blind times to a file. Default: False. |
blindTimesOutputDir |
Character string containing the path to save the blind times to. Default: 'your-working-directory' |
blindTimeAsMtrZero |
character string vector with the blind time types which should be treated as observation time with MTR zero. |
propObsTimeCutoff |
numeric between 0 and 1. If the density is computed per day and night, time bins with a proportional observation time smaller than propObsTimeCutoff are ignored when combining the time bins. If the density is computed for each time bin, the parameter is ignored. |
computePerDayNight |
logical, TRUE: density is computed per day and night. The time bins of each day and night will be combined and the mean density is computed for each day and night. The spread (first and third Quartile) for each day and night are also computed. The spread is dependent on the chosen time bin duration/amount of time bins; When FALSE: density is computed for each time bin. This option computes the density for each time bin defined in the time bin dataframe. The time bins that were split due to sunrise/sunset during the time bin will be combined to one bin. |
computePerDayCrepusculeNight |
logical, TRUE: density is computed per crepusculeMorning, day, crepusculeEvening, and night. The time bins of each of these diel phases will be combined and the mean density is computed for each phase. The spread (first and third Quartile) for each phase is also computed. The spread is dependent on the chosen time bin duration/amount of time bins; When FALSE: density is computed for each time bin. This option computes the density for each time bin defined in the time bin dataframe. The time bins that were split due to sunrise/sunset during the time bin will be combined to one bin. Default = FALSE. |
computeAltitudeDistribution |
logical, TRUE: compute the mean height and altitude distribution of density for the pre-defined quantiles 0.05, 0.25, 0.5, 0.75, 0.95 |
Density
Birgen Haest, [email protected]; Fabian Hertner, [email protected]; Baptiste Schmid, [email protected];
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" crepuscule = "nauticalSolar" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = crepuscule) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.density = c("insect") densityData = computeDensity(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.density, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", crepuscule = crepuscule, protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE computeAltitudeDistribution = TRUE) ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" crepuscule = "nauticalSolar" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = crepuscule) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.density = c("insect") densityData = computeDensity(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.density, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", crepuscule = crepuscule, protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE computeAltitudeDistribution = TRUE) ## End(Not run)
This function will estimate the Activity / Migration Traffic Rates (MTR, expressed as #objects / km / hour) based on the observations in your database.
computeMTR( dbName, echoes, classSelection, altitudeRange, altitudeBinSize, timeRange, timeBinDuration_sec, timeZone, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar", protocolData, visibilityData, manualBlindTimes = NULL, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE, computeAltitudeDistribution = TRUE )computeMTR( dbName, echoes, classSelection, altitudeRange, altitudeBinSize, timeRange, timeBinDuration_sec, timeZone, sunriseSunset, sunOrCivil = "civil", crepuscule = "nauticalSolar", protocolData, visibilityData, manualBlindTimes = NULL, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE, computeAltitudeDistribution = TRUE )
dbName |
Character string, containing the name of the database you are processing |
echoes |
dataframe with the echo data from the data list created by the function ‘extractDBData’ or a subset of it created by the function ‘filterEchoData’. |
classSelection |
character string vector with all classes which should be used to calculate the MTR. The MTR and number of Echoes will be calculated for each class as well as for all classes together. |
altitudeRange |
numeric vector of length 2 with the start and end of the altitude range in meter a.g.l. |
altitudeBinSize |
numeric, size of the altitude bins in meter. |
timeRange |
Character vector of length 2, with start and end of time range, formatted as "%Y-%m-%d %H:%M" |
timeBinDuration_sec |
duration of timeBins in seconds (numeric). for values <= 0 a duration of 1 hour will be set |
timeZone |
time zone in which the time bins should be created as string, e.g. "Etc/GMT0" |
sunriseSunset |
dataframe with sunrise/sunset, and civil and nautical dawn/dusk. Computed with the function 'twilight'. |
sunOrCivil |
sunrise/sunset or civil dawn/dusk used to split day and night. Supported values: "sun" or "civil". Default: "civil" |
crepuscule |
optional character variable, Set to “nauticalSolar” to use the time between nautical dusk/dawn and sunrise/sunset times to define the crepuscular period, or to "nauticalCivil" to use the time between nautical and civil dusk/dawn to define the crepuscular period, or to "civilSolar" to use the time between civil dusk/dawn and sunrise/sunset times to define the crepuscular period. Default is "nauticalSolar". |
protocolData |
dataframe with the protocol data from the data list
created by the function |
visibilityData |
dataframe with the visibility data from the data list created by the function ‘extractDBData’. |
manualBlindTimes |
dataframe with the manual blind times created by the
function |
saveBlindTimes |
Logical, determines whether to save the blind times to a file. Default: False. |
blindTimesOutputDir |
Character string containing the path to save the blind times to. Default: 'your-working-directory' |
blindTimeAsMtrZero |
character string vector with the blind time types which should be treated as observation time with MTR zero. |
propObsTimeCutoff |
numeric between 0 and 1. If the MTR is computed per day and night, time bins with a proportional observation time smaller than propObsTimeCutoff are ignored when combining the time bins. If the MTR is computed for each time bin, the parameter is ignored. |
computePerDayNight |
logical, TRUE: MTR is computed per day and night. The time bins of each day and night will be combined and the mean MTR is computed for each day and night. The spread (first and third Quartile) for each day and night are also computed. The spread is dependent on the chosen time bin duration/amount of time bins; When FALSE: MTR is computed for each time bin. This option computes the MTR for each time bin defined in the time bin dataframe. The time bins that were split due to sunrise/sunset during the time bin will be combined to one bin. |
computePerDayCrepusculeNight |
logical, TRUE: MTR is computed per crepusculeMorning, day, crepusculeEvening, and night. The time bins of each of these diel phases will be combined and the mean MTR is computed for each phase. The spread (first and third Quartile) for each phase is also computed. The spread is dependent on the chosen time bin duration/amount of time bins; When FALSE: MTR is computed for each time bin. This option computes the MTR for each time bin defined in the time bin dataframe. The time bins that were split due to sunrise/sunset during the time bin will be combined to one bin. Default = FALSE. |
computeAltitudeDistribution |
logical, TRUE: compute the mean height and altitude distribution of MTR for the pre-defined quantiles 0.05, 0.25, 0.5, 0.75, 0.95 |
Migration Traffic Rates
Fabian Hertner, [email protected]; Baptiste Schmid, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" crepuscule = "nauticalSolar" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = crepuscule) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", crepuscule = crepuscule, protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE computeAltitudeDistribution = TRUE) ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" crepuscule = "nauticalSolar" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = crepuscule) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", crepuscule = crepuscule, protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computePerDayCrepusculeNight = FALSE computeAltitudeDistribution = TRUE) ## End(Not run)
Compute blind times and observation times during time bins based on protocol data and blind times
computeObservationTime( timeBins, protocolData, blindTimes, blindTimeAsMtrZero = NULL )computeObservationTime( timeBins, protocolData, blindTimes, blindTimeAsMtrZero = NULL )
timeBins |
dataframe with the time bins created by the function
|
protocolData |
dataframe with the protocol data from the data list
created by the function |
blindTimes |
dataframe containing the blind times created by the
function |
blindTimeAsMtrZero |
character string vector with the blind time types which should be treated as observation time with MTR zero. |
returns a dataframe with the time bins completed with the observation times of each time bin.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
Converts timestamps from radar time zone to an user-defined time zone
convertTimeZone( data = NULL, colNames = "", originTZ = "Etc/GMT0", targetTZ = "Etc/GMT0" )convertTimeZone( data = NULL, colNames = "", originTZ = "Etc/GMT0", targetTZ = "Etc/GMT0" )
data |
a data frame containing BirdScan data |
colNames |
a character vector containing valid column names, as present
in |
originTZ |
character, the time zone name of data to be converted (default is "etc/GMT0") |
targetTZ |
character, the time zone name to convert data into (default is "etc/GMT0") |
a data frame identical to data, any columns declared in colNames
will have their name changed with a suffix (_originTZ or _targetTZ) added.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Add day/night info to echo data # =========================================================================== echoData = convertTimeZone(data = dbData$echoData, colNames = c("time_stamp"), originTZ = "Etc/GMT0", targetTZ = "Etc/GMT-2") ## End(Not run)## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Add day/night info to echo data # =========================================================================== echoData = convertTimeZone(data = dbData$echoData, colNames = c("time_stamp"), originTZ = "Etc/GMT0", targetTZ = "Etc/GMT-2") ## End(Not run)
Create time bins with a given duration. Time bins expanding over a day/night change will be split in two time bins.
createTimeBins( timeRange, timeBinDuration_sec, timeZone, sunriseSunset, dnBins = TRUE, crepBins = FALSE, sunOrCivil = "civil", crepuscule = "nauticalSolar" )createTimeBins( timeRange, timeBinDuration_sec, timeZone, sunriseSunset, dnBins = TRUE, crepBins = FALSE, sunOrCivil = "civil", crepuscule = "nauticalSolar" )
timeRange |
vector of length 2, with start and end of time range as POSIXct |
timeBinDuration_sec |
duration of timeBins in seconds (numeric). for values <= 0 a duration of 1 hour will be set |
timeZone |
time zone in which the time bins should be created as string, e.g. "Etc/GMT0" |
sunriseSunset |
dataframe with sunrise/sunset, civil dawn/dusk. computed with function 'twilight' |
dnBins |
Logical. Default TRUE. Determines whether timebins based on day/night values (determined by the parameter 'sunOrCivil') are created. |
crepBins |
Logical. Default FALSE. Determines whether timebins with crepuscular time phases are created (determined by the parameter 'crepuscule'). |
sunOrCivil |
sunrise/sunset or civil dawn/dusk used to split day and night. Supported values: "sun" or "civil", default: "civil" |
crepuscule |
Used to split into crepusculeMorning, day, crepusculeEvening, and night. Set to “nauticalSolar” to use the time between nautical dusk/dawn and sunrise/sunset times to define the crepuscular period, or to "nauticalCivil" to use the time between nautical and civil dusk/dawn to define the crepuscular period, or to "civilSolar" to use the time between civil dusk/dawn and sunrise/sunset times to define the crepuscular period. Default is "nauticalSolar". |
returns a dataframe with the time bins information
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
Load the data from the database or file and save it to file
extractDbData( dbDriverChar = "SQL Server", dbServer = NULL, dbName = NULL, dbUser = NULL, dbPwd = NULL, saveDbToFile = FALSE, dbDataDir = NULL, radarTimeZone = NULL, targetTimeZone = "Etc/GMT0", listOfRfFeaturesToExtract = NULL, siteLocation = NULL, sunOrCivil = "civil", crepuscule = "nauticalSolar" )extractDbData( dbDriverChar = "SQL Server", dbServer = NULL, dbName = NULL, dbUser = NULL, dbPwd = NULL, saveDbToFile = FALSE, dbDataDir = NULL, radarTimeZone = NULL, targetTimeZone = "Etc/GMT0", listOfRfFeaturesToExtract = NULL, siteLocation = NULL, sunOrCivil = "civil", crepuscule = "nauticalSolar" )
dbDriverChar |
'SQL Server' The name of the driver. Should be either 'SQL Server' or 'PostgreSQL'. If 'PostgreSQL', it connects to cloud.birdradar.com |
dbServer |
NULL The name of the Server |
dbName |
NULL The name of the Database |
dbUser |
NULL The USER name of the Server |
dbPwd |
NULL The password for the user name |
saveDbToFile |
FALSE Set to TRUE if you want to save the extracted database data to an rds file. The output filename is automatically set to dbName_DataExtract.rds |
dbDataDir |
NULL The path to the output directory where to store the extracted dataset. If the directory does not exist, it will be created. |
radarTimeZone |
NULL String specifying the radar time zone. Default is NULL: extract the time zone from the site table of the 'SQL' database. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
listOfRfFeaturesToExtract |
NULL or a list of feature to extract |
siteLocation |
Geographic location of the radar measurements in decimal format: c(Latitude, Longitude) |
sunOrCivil |
optional character variable, Set to “sun” to use sunrise/sunset times or to “civil” to use civil twilight times to group echoes into day/night. Default is "civil". |
crepuscule |
optional character variable, Set to “nauticalSolar” to use the time between nautical dusk/dawn and sunrise/sunset times to define the crepuscular period, or to "nauticalCivil" to use the time between nautical and civil dusk/dawn to define the crepuscular period, or to "civilSolar" to use the time between civil dusk/dawn and sunrise/sunset times to define the crepuscular period. Default is "nauticalSolar". |
a list of R objects with data extracted from the Database: 'echoData', 'protocolData', 'siteData', 'visibilityData', 'timeBinData', 'rfFeatures', 'availableClasses', 'availableBatClasses', 'classProbabilitiesAndMtrFactors', 'batProbabilitiesAndMtrFactors'
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") ## End(Not run)
With the function filterData both the echo and protocol data can be filtered by several parameters. The function returns the filtered echo and protocol data.
filterData( echoData = NULL, protocolData = NULL, pulseTypeSelection = NULL, rotationSelection = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, manualBlindTimes = NULL, echoValidator = FALSE )filterData( echoData = NULL, protocolData = NULL, pulseTypeSelection = NULL, rotationSelection = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, manualBlindTimes = NULL, echoValidator = FALSE )
echoData |
dataframe with the echo data from the data list created by the function |
protocolData |
dataframe with the protocol data from the data list created by the function |
pulseTypeSelection |
character vector with the pulse types which should be included in the subset. Options: “S”, “M”, “L” (short-, medium-, long-pulse). Default is NULL: no filtering applied based on pulseType. |
rotationSelection |
numeric vector to select the operation modes with and/or without antenna rotation. Options: 0, 1. (0 = no rotation, 1 = rotation). Default is NULL: no filtering applied based on rotation mode. |
timeRangeTargetTZ |
Character vector of length 2, with start and end of time range, formatted as "%Y-%m-%d %H:%M". Echoes outside the time range will be excluded. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
classSelection |
character string vector with the classes that should be included. |
classProbCutOff |
numeric cutoff value for class probabilities. Echoes with a lower class probability will be excluded. |
altitudeRange_AGL |
numeric vector of length 2 with start and end of the altitude range. Echoes outside the altitude range will be excluded. |
manualBlindTimes |
dataframe with the manual blind times created by the
function |
echoValidator |
logical, if set to TRUE, echoes labelled by the echo validator as “non-bio scatterer” will be excluded. If set to FALSE, all echoes are included. |
returns the filtered echo and protocol data in the same format as
provided in the parameters echoData and protocolData.
Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== pulseLengthSelection = "S" rotationSelection = 1 timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") classSelection = c("passerine_type", "wader_type", "swift_type", "large_bird", "unid_bird", "bird_flock") classProbCutoff = NULL altitudeRange = c(50, 1000) data(manualBlindTimes) cManualBlindTimes = manualBlindTimes useEchoValidator = FALSE # Filter the data # =========================================================================== filteredData = filterData(echoData = dbData$echoData, protocolData = dbData$protocolData, pulseTypeSelection = pulseLengthSelection, rotationSelection = rotationSelection, timeRangeTargetTZ = timeRangeData, targetTimeZone = targetTimeZone, classSelection = classSelection, classProbCutOff = classProbCutoff, altitudeRange_AGL = altitudeRange, manualBlindTimes = cManualBlindTimes, echoValidator = useEchoValidator) ## End(Not run)## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== pulseLengthSelection = "S" rotationSelection = 1 timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") classSelection = c("passerine_type", "wader_type", "swift_type", "large_bird", "unid_bird", "bird_flock") classProbCutoff = NULL altitudeRange = c(50, 1000) data(manualBlindTimes) cManualBlindTimes = manualBlindTimes useEchoValidator = FALSE # Filter the data # =========================================================================== filteredData = filterData(echoData = dbData$echoData, protocolData = dbData$protocolData, pulseTypeSelection = pulseLengthSelection, rotationSelection = rotationSelection, timeRangeTargetTZ = timeRangeData, targetTimeZone = targetTimeZone, classSelection = classSelection, classProbCutOff = classProbCutoff, altitudeRange_AGL = altitudeRange, manualBlindTimes = cManualBlindTimes, echoValidator = useEchoValidator) ## End(Not run)
With the function filterEchoData the echo data can be
filtered by several parameters. The function returns the filtered echo data.
filterEchoData( echoData = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", protocolData = NULL, classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, manualBlindTimes = NULL, echoValidator = FALSE )filterEchoData( echoData = NULL, timeRangeTargetTZ = NULL, targetTimeZone = "Etc/GMT0", protocolData = NULL, classSelection = NULL, classProbCutOff = NULL, altitudeRange_AGL = NULL, manualBlindTimes = NULL, echoValidator = FALSE )
echoData |
dataframe with the echo data from the data list created by
the function |
timeRangeTargetTZ |
Character vector of length 2, with start and end of time range, formatted as "%Y-%m-%d %H:%M". Echoes outside the time range will be excluded. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
protocolData |
dataframe with the protocol data from the data list
created by the function |
classSelection |
character string vector with the classes that should be included. |
classProbCutOff |
numeric cutoff value for class probabilities. Echoes with a lower class probability will be excluded. |
altitudeRange_AGL |
numeric vector of length 2 with start and end of the altitude range. Echoes outside the altitude range will be excluded. |
manualBlindTimes |
dataframe with the manual blind times created by the
function |
echoValidator |
logical, if set to TRUE, echoes labelled by the echo validator as “non-bio scatterer” will be excluded. If set to FALSE, all echoes are included. |
returns the filtered echo data in the same format as provided in the
parameter echoData.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") classSelection = c("passerine_type", "wader_type", "swift_type", "large_bird", "unid_bird", "bird_flock") classProbCutoff = NULL altitudeRange = c(50, 1000) data(manualBlindTimes) cManualBlindTimes = manualBlindTimes useEchoValidator = FALSE # Filter the echo data # =========================================================================== filteredEchoData = filterEchoData(echoData = dbData$echoData, timeRangeTargetTZ = timeRangeData, targetTimeZone = targetTimeZone, protocolData - dbData$protocolData, classSelection = classSelection, classProbCutOff = classProbCutoff, altitudeRange_AGL = altitudeRange, manualBlindTimes = cManualBlindTimes, echoValidator = useEchoValidator) ## End(Not run)## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") classSelection = c("passerine_type", "wader_type", "swift_type", "large_bird", "unid_bird", "bird_flock") classProbCutoff = NULL altitudeRange = c(50, 1000) data(manualBlindTimes) cManualBlindTimes = manualBlindTimes useEchoValidator = FALSE # Filter the echo data # =========================================================================== filteredEchoData = filterEchoData(echoData = dbData$echoData, timeRangeTargetTZ = timeRangeData, targetTimeZone = targetTimeZone, protocolData - dbData$protocolData, classSelection = classSelection, classProbCutOff = classProbCutoff, altitudeRange_AGL = altitudeRange, manualBlindTimes = cManualBlindTimes, echoValidator = useEchoValidator) ## End(Not run)
With the function filterProtocolData the protocol data
can be filtered by the operation mode (pulse-type and antenna rotation). The
function returns the filtered subset of the protocol data which can later be
used to filter the echoes based on the operation mode/protocol
filterProtocolData( protocolData = NULL, pulseTypeSelection = NULL, rotationSelection = NULL )filterProtocolData( protocolData = NULL, pulseTypeSelection = NULL, rotationSelection = NULL )
protocolData |
dataframe with the protocol data from the data list
created by the function |
pulseTypeSelection |
character vector with the pulse types which should be included in the subset. Options: “S”, “M”, “L” (short-, medium-, long-pulse). Default is NULL: no filtering applied based on pulseType. |
rotationSelection |
numeric vector to select the operation modes with and/or without antenna rotation. Options: 0, 1. (0 = no rotation, 1 = rotation). Default is NULL: no filtering applied based on rotation mode. |
returns the filtered protocol data in the same format as provided in
the parameter protocolData.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== pulseLengthSelection = "S" rotationSelection = 1 # Filter the echo data # =========================================================================== filteredProtocolData = filterProtocolData(protocolData = dbData$protocolData, pulseTypeSelection = pulseLengthSelection, rotationSelection = rotationSelection) ## End(Not run)## Not run: # Set server, database, and other input settings for data extraction # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Set input settings for filtering of the data # =========================================================================== pulseLengthSelection = "S" rotationSelection = 1 # Filter the echo data # =========================================================================== filteredProtocolData = filterProtocolData(protocolData = dbData$protocolData, pulseTypeSelection = pulseLengthSelection, rotationSelection = rotationSelection) ## End(Not run)
gets the 'rfClasses' table from a 'Birdscan MR1' 'SQL' database
getBatClassification(dbConnection, dbDriverChar)getBatClassification(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
dbDriverChar 'SQL Server' The name of the driver. Should be either 'SQL Server' or 'PostgreSQL'. If 'PostgreSQL', it connects to cloud.birdradar.com |
A list containing three variables: (1) batClassificationTable: The 'batClassification' database table; (2) classProbabilitiesAndMtrFactors: A dataframe containing the classification probabilities for all classes for each object; and (3) availableClasses: the classes used for the classification of the objects.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) rfClassification = getBatClassification(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) rfClassification = getBatClassification(dbConnection, dbDriverChar) ## End(Not run)
load collection from 'Birdscan MR1' 'SQL' database
getCollectionTable(dbConnection, dbDriverChar, timeInterval)getCollectionTable(dbConnection, dbDriverChar, timeInterval)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
timeInterval |
An optional vector of timestamps (either as |
A dataframe with the collection table
Fabian Hertner, [email protected]; Birgen Haest, [email protected]; Bart Kranstauber, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) collectionTable = getCollectionTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) collectionTable = getCollectionTable(dbConnection, dbDriverChar) ## End(Not run)
load echo rffeature map from 'Birdscan MR1' 'SQL' database
getEchoFeatures(dbConnection, dbDriverChar, listOfRfFeaturesToExtract)getEchoFeatures(dbConnection, dbDriverChar, listOfRfFeaturesToExtract)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
listOfRfFeaturesToExtract |
a list of feature to extract |
A list of the features extracted
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) # Set list of Rf features you also want to extract # Vector with RF features to extract. Feature IDs can be found in the # 'rfFeatures' table in the sql database. # Example: Get wing beat frequency and credibility: c(167, 168) # Set to NULL to not extract any. # =========================================================================== listOfRfFeaturesToExtract = c(167, 168) echoFeatures = getEchoFeatures(dbConnection, dbDriverChar, listOfRfFeaturesToExtract) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) # Set list of Rf features you also want to extract # Vector with RF features to extract. Feature IDs can be found in the # 'rfFeatures' table in the sql database. # Example: Get wing beat frequency and credibility: c(167, 168) # Set to NULL to not extract any. # =========================================================================== listOfRfFeaturesToExtract = c(167, 168) echoFeatures = getEchoFeatures(dbConnection, dbDriverChar, listOfRfFeaturesToExtract) ## End(Not run)
gets the echoValidationTable from an already connected database
getEchoValidationTable(dbConnection, dbDriverChar)getEchoValidationTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
dbDriverChar 'SQL Server' The name of the driver. Should be either 'SQL Server' or 'PostgreSQL'. If 'PostgreSQL', it connects to cloud.birdradar.com |
A dataframe called echovalidationTable
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) echovalidationTable = getEchoValidationTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) echovalidationTable = getEchoValidationTable(dbConnection, dbDriverChar) ## End(Not run)
load visibility table from an already connected 'Birdscan MR1' 'SQL' database
getManualVisibilityTable(dbConnection, dbDriverChar)getManualVisibilityTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
A dataframe with the manual visibility table
Baptiste Schmid [email protected]; Birgen Haest [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) manualVisibilityTable = getManualVisibilityTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) manualVisibilityTable = getManualVisibilityTable(dbConnection, dbDriverChar) ## End(Not run)
load protocol table from an already connected 'Birdscan MR1' 'SQL' database
getProtocolTable(dbConnection, dbDriverChar)getProtocolTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
A dataframe with the protocol table
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) protocolTable = getProtocolTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) protocolTable = getProtocolTable(dbConnection, dbDriverChar) ## End(Not run)
get the Radar table from an already connected DB and rename the columns appropriately
getRadarTable(dbConnection, dbDriverChar)getRadarTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. |
the radar table as a data frame
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) radarTable = getRadarTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) radarTable = getRadarTable(dbConnection, dbDriverChar) ## End(Not run)
gets the 'rfClasses' table from a 'Birdscan MR1' 'SQL' database
getRfClassification(dbConnection, dbDriverChar)getRfClassification(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
dbDriverChar 'SQL Server' The name of the driver. Should be either 'SQL Server' or 'PostgreSQL'. If 'PostgreSQL', it connects to cloud.birdradar.com |
A list containing three variables: (1) rfclassificationTable: The 'rfClassification' database table; (2) classProbabilitiesAndMtrFactors: A dataframe containing the classification probabilities for all classes for each object; and (3) availableClasses: the classes used for the classification of the objects.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) rfClassification = getRfClassification(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) rfClassification = getRfClassification(dbConnection, dbDriverChar) ## End(Not run)
load site table from an already connected 'Birdscan MR1' 'SQL' database
getSiteTable(dbConnection, dbDriverChar)getSiteTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
A dataframe with the site table
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) siteTable = getSiteTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) siteTable = getSiteTable(dbConnection, dbDriverChar) ## End(Not run)
load time bins table from an already connected 'Birdscan MR1' 'SQL' database
getTimeBinsTable(dbConnection, dbDriverChar)getTimeBinsTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
A dataframe with the time bins table
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) timeBinsTable = getTimeBinsTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) timeBinsTable = getTimeBinsTable(dbConnection, dbDriverChar) ## End(Not run)
load visibility table from an already connected 'Birdscan MR1' 'SQL' database
getVisibilityTable(dbConnection, dbDriverChar)getVisibilityTable(dbConnection, dbDriverChar)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver. If different from 'PostgreSQL' it connects to cloud.birdradar.com |
A dataframe with the visibility table
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) visibilityTable = getVisibilityTable(dbConnection, dbDriverChar) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) visibilityTable = getVisibilityTable(dbConnection, dbDriverChar) ## End(Not run)
Load manual blind times from csv file.
For the MTR computation the times when the radar was blind have to be known.
The radar itself can be blind in case of a protocol change (block time at the
beginning of each protocol, usually 60s) or due to rain/snow or clutter
(nearby objects, leaves or similar on radome, etc.). These times are stored
in the visibility table or in the time_bins table in relation to the time
bins duration (5min). To be flexible and not fixed to the 5 min time bins
created by the radar, the visibility table is used in this script. In
addition to the radar blind times, manual blind times can be defined. Manual
blind times have to be defined in a csv file and are loaded with the function
‘loadManualBlindTimes’. A example dataset is available by running:
data(manualBlindTimes)
write.csv(manualBlindTimes, file = 'the output file destination', row.names = F)
The file path is defined as a global variable ‘manualBlindTimesFile’. A
custom file and filepath can be used instead. The manual blind times have to
be entered with 3 columns:
start time 'yyyy-mm-dd hh:mm:ss', stop time 'yyyy-MM-dd hh:mm:ss', type.
Example: 2021-01-16 04:15:00,2021-01-16 05:42:00,rain 2021-01-17 16:33:00,2021-01-17 18:04:00,clutter Manual blind time types can be chosen freely. When computing observation times, it can be decided if some of the defined manual blind time types should be treated as observed time with MTR zero or as blind time (e.g. rain). If no file is present or the file is empty, no manual blind times will be computed.
loadManualBlindTimes(filePath, blindTimesTZ, targetTZ)loadManualBlindTimes(filePath, blindTimesTZ, targetTZ)
filePath |
character string, absolute filepath of the manual blind time file |
blindTimesTZ |
time zone of the blind times |
targetTZ |
target time zone of the blind times |
A dataframe with the manual blind times
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # load manual blind time example data from birdscanR package data(manualBlindTimes) # Save example manual blind times to a file write.csv(manualBlindTimes, file = "manualBlindTimes.csv", row.names = F) # Read the manual blind times from file manualBlindTimes.new = loadManualBlindTimes(filePath = "./manualBlindTimes.csv", blindTimesTZ = "ETC/GMT", targetTZ = "ETC/GMT") ## End(Not run)## Not run: # load manual blind time example data from birdscanR package data(manualBlindTimes) # Save example manual blind times to a file write.csv(manualBlindTimes, file = "manualBlindTimes.csv", row.names = F) # Read the manual blind times from file manualBlindTimes.new = loadManualBlindTimes(filePath = "./manualBlindTimes.csv", blindTimesTZ = "ETC/GMT", targetTZ = "ETC/GMT") ## End(Not run)
To create your own manual blind times file, just copy this file, and adjust.
data(manualBlindTimes)data(manualBlindTimes)
An object of class "data.frame".
data(manualBlindTimes)data(manualBlindTimes)
Function to merge manual blind times with blind times from visibility table. For further processing the radar (visibility) and manual blind times have to be merged with the function ‘mergeVisibilityAndManualBlindTimes’. This function will add a blind time type to the radar/visibility blind times. Blind times during the block time (usually 60s) at the beginning of each protocol are given the type 'protocolChange', the rest of the radar blind times are given the type “visibility”. After that the visibility and manual blind times will be merged. In case manual blind times and radar blind times are overlapping, radar blind times with type “visibility” will be overwritten, but not radar blind times with type “protocolChange”.
mergeVisibilityAndManualBlindTimes( visibilityData, manualBlindTimes = NULL, protocolData )mergeVisibilityAndManualBlindTimes( visibilityData, manualBlindTimes = NULL, protocolData )
visibilityData |
dataframe with the visibility data from the data list created by the function ‘extractDBData’. |
manualBlindTimes |
dataframe with the manual blind times created by the function ‘loadManualBlindTimes’. |
protocolData |
dataframe with the protocol data from the data list created by the function ‘extractDBData’ or a subset of it created by the function ‘filterProtocolData’. |
dataframe with overall blind times
Fabian Hertner, [email protected]; Birgen Haest, [email protected] Baptiste Schmid, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) # Get visibility table # =========================================================================== visibilityTable = getVisibilityTable(dbConnection, dbDriverChar) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Merge manual and automatic blind times # =========================================================================== blindTimes = mergeVisibilityAndManualBlindTimes(visibilityData = visibilityTable, manualBlindTimes = cManualBlindTimes, protocolData = protocolData) ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) # Get visibility table # =========================================================================== visibilityTable = getVisibilityTable(dbConnection, dbDriverChar) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Merge manual and automatic blind times # =========================================================================== blindTimes = mergeVisibilityAndManualBlindTimes(visibilityData = visibilityTable, manualBlindTimes = cManualBlindTimes, protocolData = protocolData) ## End(Not run)
This function creates a time series plot showing all of the observed echoes at their respective altitudes. These plots are helpful to roughly visually explore your data (and for example spot oddities).
plotExploration( echoData = NULL, timeRange = NULL, targetTimeZone = "Etc/GMT0", manualBlindTimes = NULL, visibilityData = NULL, protocolData = NULL, sunriseSunset = NULL, maxAltitude = NULL, filePath = NULL )plotExploration( echoData = NULL, timeRange = NULL, targetTimeZone = "Etc/GMT0", manualBlindTimes = NULL, visibilityData = NULL, protocolData = NULL, sunriseSunset = NULL, maxAltitude = NULL, filePath = NULL )
echoData |
dataframe with the echo data from the data list created by the function ‘extractDBData’ or a subset of it created by the function ‘filterEchoData’ |
timeRange |
optional list of string vectors length 2, start and end time of the time ranges that should be plotted. The date/time format is “yyyy-MM-dd hh:mm”. If not set, all echo data is plotted in one plot. Note: Too long time-ranges may produce an error if the created image is too large and the function can’t allocate the file. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
manualBlindTimes |
optional dataframe with the manual blind times created by the function ‘loadManualBlindTimes’. If not set, manual blind times are not shown in the plot. |
visibilityData |
optional dataframe with the visibility data created by the function ‘extractDBData’. If not set, visibility data are not shown in the plot. |
protocolData |
optional dataframe with the protocol data used to filter the echoes, created by the function ‘extractDBData’ or a subset of it created by the function ‘filterProtocolData’. If not set, periods without a protocol are not shown in the plot. |
sunriseSunset |
optional dataframe with sunrise/sunset, civil, and nautical twilight times created by the function ‘twilight’. If not set, day/night times are not shown in the plot. |
maxAltitude |
optional numeric, fixes the maximum value of the y-Scale of the plot to the given value. If negative or not set, the y-Scale is auto-scaled. |
filePath |
character string, path of the directory where the plot should be saved. The function ‘savePlotToFile’ is used to save the plots as png files with an auto-generated filename. |
png files stored in the directory specified in 'filePath'
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: #' # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get manual blindtimes # =========================================================================== data("manualBlindTimes") cManualBlindTimes = manualBlindTimes # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) plotExploration(echoData = dbData$echoData, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", manualBlindTimes = cManualBlindTimes, visibilityData = dbData$visibilityData, protocolData = dbData$protocolData, sunriseSunset = dbData$sunriseSunset, maxAltitude = -1, filePath = "./") ## End(Not run)## Not run: #' # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get manual blindtimes # =========================================================================== data("manualBlindTimes") cManualBlindTimes = manualBlindTimes # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) plotExploration(echoData = dbData$echoData, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", manualBlindTimes = cManualBlindTimes, visibilityData = dbData$visibilityData, protocolData = dbData$protocolData, sunriseSunset = dbData$sunriseSunset, maxAltitude = -1, filePath = "./") ## End(Not run)
Plots a time series of MTR values as a bar plot. For each bar the spread (first and third Quartile) is shown as error bars as well as the numbers of echoes. Periods with no observation are indicated with grey, negative bars.
plotLongitudinalMTR( mtr, maxMTR, timeRange = NULL, targetTimeZone = "Etc/GMT0", plotClass = "allClasses", propObsTimeCutoff = 0.2, plotSpread = TRUE, filePath = NULL )plotLongitudinalMTR( mtr, maxMTR, timeRange = NULL, targetTimeZone = "Etc/GMT0", plotClass = "allClasses", propObsTimeCutoff = 0.2, plotSpread = TRUE, filePath = NULL )
mtr |
data frame with MTR values created by the function ‘computeMTR’. |
maxMTR |
optional numeric variable, fixes the maximum value of the y-Scale of the plot to the given value. If negative or not set, the y-Scale is auto-scaled. |
timeRange |
optional list of string vectors length 2, start and end time of the time ranges that should be plotted. The date/time format is “yyyy-MM-dd hh:mm”. |
targetTimeZone |
"Etc/GMT0" String specifying the target time zone. Default is "Etc/GMT0". |
plotClass |
character string with the class of which the MTR data should be plotted. If not set or set to “allClasses”, MTR of all classes will be plotted. |
propObsTimeCutoff |
numeric between 0 and 1. If the MTR is computed per day and night, time bins with a proportional observation time smaller than propObsTimeCutoff are ignored when combining the time bins. If the MTR is computed for each time bin, the parameter is ignored. |
plotSpread |
logical, choose if the spread (first and third quartile) should be plotted. |
filePath |
character string, path of the directory where the plot should be saved. The function ‘savePlotToFile’ is used to save the plots as png files with an auto-generated filename. |
png files stored in the directory specified with 'filePath'
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computeAltitudeDistribution = TRUE) # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) plotExplorationplotLongitudinalMTR(mtr = mtrData, maxMTR = -1, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", plotClass = "allClasses", propObsTimeCutoff = 0.2, plotSpread = TRUE, filePath = "./") ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computeAltitudeDistribution = TRUE) # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) plotExplorationplotLongitudinalMTR(mtr = mtrData, maxMTR = -1, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", plotClass = "allClasses", propObsTimeCutoff = 0.2, plotSpread = TRUE, filePath = "./") ## End(Not run)
Run an 'SQL' query on an already connected database
QUERY(dbConnection, dbDriverChar, query, as.is = FALSE)QUERY(dbConnection, dbDriverChar, query, as.is = FALSE)
dbConnection |
a valid database connection |
dbDriverChar |
the name of the driver |
query |
an 'SQL' string with your query |
as.is |
If TRUE, leaves data as it is |
the result of the query
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) QUERY(dbConnection = dbConnection, dbDriverChar = dbDriverChar, query = "Select * From collection order by row asc") ## End(Not run)## Not run: # Set server and database settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" # Open the connection with the database # =========================================================================== dsn = paste0("driver=", dbDriverChar, ";server=", dbServer, ";database=", dbName, ";uid=", rstudioapi::askForPassword("Database user"), ";pwd=", rstudioapi::askForPassword("Database password")) dbConnection = RODBC::odbcDriverConnect(dsn) QUERY(dbConnection = dbConnection, dbDriverChar = dbDriverChar, query = "Select * From collection order by row asc") ## End(Not run)
reclassifies echoes based on bat classification
reclassToBats( echoData = NULL, batClassProbabilitiesAndMtrFactors = NULL, reclassToBatCutoff = -1 )reclassToBats( echoData = NULL, batClassProbabilitiesAndMtrFactors = NULL, reclassToBatCutoff = -1 )
echoData |
echodata dataframe, output from extractDbData |
batClassProbabilitiesAndMtrFactors |
probabilities of bat classification, output from extractDbData' |
reclassToBatCutoff |
Threshold (0..1), classification of echoes with bat probability higher than reclassToBatCutoff will be set to 'bat' |
echoData dataframe
Fabian Hertner, [email protected]
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") #' # Reclass To Bats # =========================================================================== dbData$echoData = reclassToBats(echoData = dbData$echoData, batClassProbabilitiesAndMtrFactors = dbData$batClassProbabilitiesAndMtrFactors, reclassToBatCutoff = 0.5) ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil, crepuscule = "nauticalSolar") #' # Reclass To Bats # =========================================================================== dbData$echoData = reclassToBats(echoData = dbData$echoData, batClassProbabilitiesAndMtrFactors = dbData$batClassProbabilitiesAndMtrFactors, reclassToBatCutoff = 0.5) ## End(Not run)
saves MTR data to a .rds file in the directory filepath.
If the directory is not existing it will be created if possible.
saveMTR( mtr, filepath, fileName = NULL, fileNamePrefix = NULL, dbName = NULL, rotSelection = NULL, pulseTypeSelection = NULL, classAbbreviations = NULL )saveMTR( mtr, filepath, fileName = NULL, fileNamePrefix = NULL, dbName = NULL, rotSelection = NULL, pulseTypeSelection = NULL, classAbbreviations = NULL )
mtr |
dataframe with MTR values created by the function |
filepath |
character string, path of the directory. If the directory does not exist it will be created if possible. |
fileName |
Filename (string) for the file. If not set, the filename will be built using the input of the variables 'filenamePrefix', 'dbName', 'classAbbreviations', and other info in the 'mtr' data. If set, overrides the automatic filename creation. |
fileNamePrefix |
prefix of the filename (string). If not set, "mtr" is used. Different information about the MTR data will be appended to the filename. |
dbName |
character string, name of the database. Used to create the filename, if 'fileName' is not provided. |
rotSelection |
numeric vector, rotation selection which was used to filter protocols. Used to create the filename, if 'fileName' is not provided. If not set, the rotation selection will not be appended to the filename. |
pulseTypeSelection |
character vector, pulse type selection which was used to filter protocols. Used to create the filename, if 'fileName' is not provided. If not set, the pulse type selection will not be appended to the filename. |
classAbbreviations |
Two-column dataframe with character first column named 'class' and character second 'abbr', containing the full names of the classes and their abbreviations to use in the output filename. Default = NULL, meaning the abbreviations will be used that are stored in the package; See data(classAbbreviations). Used to create the filename, if 'fileName' is not provided. |
No return value, used to save MTR to file.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computeAltitudeDistribution = TRUE) saveMTR(mtr = mtrData, filepath = getwd()) ## End(Not run)## Not run: # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" timeRangeData = c("2021-01-15 00:00", "2021-01-31 00:00") # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get sunrise/sunset # =========================================================================== sunriseSunset = twilight(timeRange = timeRangeData, latLon = c(47.494427, 8.716432), timeZone = targetTimeZone) # Get manual blind times # =========================================================================== data(manualBlindTimes) cManualBlindTimes = manualBlindTimes # Compute migration traffic rate # =========================================================================== classSelection.mtr = c("insect") mtrData = computeMTR(dbName = dbName, echoes = dbData$echoData, classSelection = classSelection.mtr, altitudeRange = c(25, 1025), altitudeBinSize = 50, timeRange = timeRangeData, timeBinDuration_sec = 1800, timeZone = targetTimeZone, sunriseSunset = sunriseSunset, sunOrCivil = "civil", protocolData = dbData$protocolData, visibilityData = dbData$visibilityData, manualBlindTimes = cManualBlindTimes, saveBlindTimes = FALSE, blindTimesOutputDir = getwd(), blindTimeAsMtrZero = NULL, propObsTimeCutoff = 0, computePerDayNight = FALSE, computeAltitudeDistribution = TRUE) saveMTR(mtr = mtrData, filepath = getwd()) ## End(Not run)
saves created plots as .png.
savePlotToFile( plot = NULL, filePath = NULL, plotType = NULL, plotWidth_mm = NULL, plotHeight_mm = NULL, timeRange = NULL, classSelection = NULL, altitudeRange = NULL, classAbbreviations = NULL )savePlotToFile( plot = NULL, filePath = NULL, plotType = NULL, plotWidth_mm = NULL, plotHeight_mm = NULL, timeRange = NULL, classSelection = NULL, altitudeRange = NULL, classAbbreviations = NULL )
plot |
'ggplot' plot to be saved |
filePath |
character string, path of the directory, e.g. "your-project-directory/Data/MTR". If the directory does not exist it will be created if possible. |
plotType |
character string, name/description of the plot, used to create the filename. If not set, the pulse type selection will not be appended to the filename |
plotWidth_mm |
numeric, width of the plot in mm. If not set, the size of the png will be set automatically. |
plotHeight_mm |
numeric, height of the plot in mm. If not set, the size of the png will be set automatically. |
timeRange |
POSIXct vector of size 2, time range of the plot, used to create the filename. If not set, the pulse type selection will not be appended to the filename |
classSelection |
character string vector, classes that were used to create the plot, used to create the filename. If not set, the pulse type selection will not be appended to the filename |
altitudeRange |
numeric vector of size 2, altitude range used to create the plot, used to create the filename. If not set, the pulse type selection will not be appended to the filename |
classAbbreviations |
Two-column dataframe with character first column named 'class' and character second 'abbr', containing the full names of the classes and their abbreviations to use in the output filename. Default = NULL, meaning the abbreviations will be used that are stored in the package; See data(classAbbreviations). |
No return value, used to save plots to file.
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: #' # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get manual blindtimes # =========================================================================== data("manualBlindTimes") cManualBlindTimes = manualBlindTimes # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) cPlot = plotExploration(echoData = dbData$echoData, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", manualBlindTimes = cManualBlindTimes, visibilityData = dbData$visibilityData, protocolData = dbData$protocolData, sunriseSunset = dbData$sunriseSunset, maxAltitude = -1, filePath = "./") # Save plot # =========================================================================== savePlotToFile(plot = cPlot, filePath = "./", plotType = "S", plotWidth_mm = 400, plotHeight_mm = 200) ## End(Not run)## Not run: #' # Set server, database, and other input settings # =========================================================================== dbServer = "MACHINE\\SERVERNAME" # Set the name of your SQL server dbName = "db_Name" # Set the name of your database dbDriverChar = "SQL Server" # Set either "SQL Server" or "PostgreSQL" mainOutputDir = file.path(".", "results") radarTimeZone = "Etc/GMT0" targetTimeZone = "Etc/GMT0" listOfRfFeaturesToExtract = c(167, 168) siteLocation = c(47.494427, 8.716432) sunOrCivil = "civil" # Get data # =========================================================================== dbData = extractDbData(dbDriverChar = dbDriverChar, dbServer = dbServer, dbName = dbName, saveDbToFile = TRUE, dbDataDir = mainOutputDir, radarTimeZone = radarTimeZone, targetTimeZone = targetTimeZone, listOfRfFeaturesToExtract = listOfRfFeaturesToExtract, siteLocation = siteLocation, sunOrCivil = sunOrCivil) # Get manual blindtimes # =========================================================================== data("manualBlindTimes") cManualBlindTimes = manualBlindTimes # Make Plot # =========================================================================== timeRangePlot = list(c("2021-01-15 00:00", "2021-01-22 00:00"), c("2021-01-23 00:00", "2021-01-31 00:00")) cPlot = plotExploration(echoData = dbData$echoData, timeRange = timeRangePlot, targetTimeZone = "Etc/GMT0", manualBlindTimes = cManualBlindTimes, visibilityData = dbData$visibilityData, protocolData = dbData$protocolData, sunriseSunset = dbData$sunriseSunset, maxAltitude = -1, filePath = "./") # Save plot # =========================================================================== savePlotToFile(plot = cPlot, filePath = "./", plotType = "S", plotWidth_mm = 400, plotHeight_mm = 200) ## End(Not run)
Get the time of nautical (sun at 12 degrees below horizon), civil (sun at 6 degrees below horizon) and solar (sun at 0 degrees below horizon) dawn and dusk for each day over a given time range.
twilight(timeRange, latLon, crs_datum = "WGS84", timeZone)twilight(timeRange, latLon, crs_datum = "WGS84", timeZone)
timeRange |
A two-element character vector with elements of the form %Y-%m-%d defining the start and end of the timerange for which you want to get the twilight information. |
latLon |
A list of X, Y coordinates |
crs_datum |
The coordinate reference system and datum of the X, Y coordinates. Default = "WGS84. |
timeZone |
The time zone of the area of interest |
A data frame with the results
Fabian Hertner, [email protected]; Birgen Haest, [email protected]
## Not run: sunrisesunset = twilight(timeRange = c("2021-01-15 00:00", "2021-01-31 00:00"), latLon = c(47.494427, 8.716432), timeZone = "Etc/GMT0") ## End(Not run)## Not run: sunrisesunset = twilight(timeRange = c("2021-01-15 00:00", "2021-01-31 00:00"), latLon = c(47.494427, 8.716432), timeZone = "Etc/GMT0") ## End(Not run)