###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.