###Datafile: ######1. ctd_extract_good.csv (ctd_extract_TF.py):
- TF, get good ctd data, If TF==True, good; If False, bad.
######2. ctd_good.csv (nearestIndexInMod.py):
- TF, get good ctd data, If TF==True, good; If False, bad.
- modNearestIndex, return the index of nearest point in model.
- modDepthLayer, return whcih layer in model observation belongs to.
######3. ctdWithModTempByDepth.csv (ctdWithModTempByDepth.py):
- TF, get good ctd data, If TF==True, good; If False, bad.
- modNearestIndex, return the index of nearest point in model.
- modDepthLayer, return whcih layer in model observation belongs to.
- modTempByDepth, return the temp in model calculated by depth rather than layer.
###Module: ######1. turtleModule.py
- mon_alpha2num Return num from name of month
- np_datetime Return a datetime from ctd observation "END_DATE"
- bottom_value Return the bottom temp from obs "TEMP_VALS" str
- index_by_depth Return a list with 2 part divided by 'depth'
- str2list Convert a str to list
- str2ndlist Convert a str to multidimensional arrays(especially for new column added to datafile)
- angle_conversion
- dist Calculate the dist from longitude and latitude
- closet_num Return the index of the closet number in list
- draw_basemap Draw basemap
- intersection Calculate point of intersection of 2 lines
######2. watertempModule.py Note: Using module named jata
- This is a module of classes we might use.
###Code: ######1. ctd_extract_TF.py
- Create new data file "ctd_extract_good.csv" with new column TF.(For every ctd position, if it has at least one gps position within 3km and 3h, it's good.)
######2. nearestIndexInMod.py
- Create new data file "ctd_good.csv" with new column TF, modNearestIndex, modDepthLayer
######3. ctdWithModTempByDepth.py
- Create new data file "ctdWithModTempByDepth.csv" with new column TF, modNearestIndex, modDepthLayer, modTempByDepth
######4. dataMap.py:
- Draw data map of "raw_ctd", "good_ctd", "raw_gps", "good_gps" and so on.
######5. errorMapLayer.py
- errorMapLayer4In1.png Plot 4 maps in 1 fig to show which layer has the most errors
- errorMapLayerBar.png Error bar
- errorMapLayerDepthBar.png Error depth bar
######6. errorMapDepth.py
- errorMapDepth4In1.png Plot 4 maps in 1 fig to show which depth has the most errors
- errorMapDepthErrorBar.png Error bar
- errorMapDepthRatioOfError.png
######7. obsVSmodel_bottomtemp.py
- Draw the correlation of the deepest observation(we assume it's the bottom of ocean) and appropriate model data.
######8. obsVSmodel_deepestbottom.py
- If the deepest observation depth is “>50m”(or “<50m”, or “all”), draw the correlation of this observation and appropriate model data.
######9. obsVSmodel_deepshallow.py
- Draw the correlation of observation and model between deep and shallow(50m)
######10. obsVSmodel_shore.py
- Draw the correlation of observation and model between onshore and offshore(50m)
######11. deepestDepth.py
- Return ratio of the deepest depth
######12. timeSeries.py
- Draw temp change of one specific turtle data and model data.
######13. gridOfError.py
- Divide the whole area into drifferent girds, and plot the number of observation and error in each grid.