def pairwiseRasterToSave(array_input, lower_left_corner, dsc, spatial_ref, pairwise_output_folder, inputrastername1, inputrastername2, calculationtype, score, matrixinput, dictpairwiserastername, sum_of_all_cells, listpairwiserastername, tableOption3IsChecked, somePolygonsIsTrue, numbOfSome, tooloption):
if (numbOfSome != 0):
pairwise_marine_use_array = np.copy(array_input)
pairwise_marine_use_array = pairwise_marine_use_array.astype(int)
pairwise_marine_use_array[pairwise_marine_use_array!=0.] = 1.
pairwiseRaster = arcpy.NumPyArrayToRaster(pairwise_marine_use_array, lower_left_corner, dsc.meanCellWidth, dsc.meanCellHeight, 0)
gf.createNewPath(pairwise_output_folder)
raster_outputname = gf.returnRasterFilename1(inputrastername1, inputrastername2, calculationtype)
pairwiseRaster.save(pairwise_output_folder+"\\"+raster_outputname)
arcpy.DefineProjection_management(pairwise_output_folder+"\\"+raster_outputname, spatial_ref)
# if it is chosen to only add polygons to map with highest and lowest scores:
if ((tableOption3IsChecked == "true") and (somePolygonsIsTrue == "true") and (numbOfSome != "all")):
if sum_of_all_cells in dictpairwiserastername:
dictpairwiserastername[sum_of_all_cells].append(raster_outputname)
if (tooloption == "producescoremap"):
dictpairwiserastername[sum_of_all_cells].append(score)
dictpairwiserastername[sum_of_all_cells].append(matrixinput)
else:
if (tooloption == "producescoremap"):
dictpairwiserastername[sum_of_all_cells] = [raster_outputname, score, matrixinput]
elif (tooloption == "producecountmap"):
dictpairwiserastername[sum_of_all_cells] = [raster_outputname, matrixinput]
# if it is chosen to add all polygons to map:
else:
listpairwiserastername.append(raster_outputname)
if (tooloption == "producescoremap"):
listpairwiserastername.append(score)
listpairwiserastername.append(matrixinput)
return (dictpairwiserastername, listpairwiserastername)
def forTable1getTotalMatrixSumCountPerMarineUse(dicttable1, scoretypeToFocusOn, finalfoldername, table1name, tooloption):
dftable1 = pd.DataFrame.from_dict(dicttable1, orient='index')
if (scoretypeToFocusOn == "all") and (tooloption == "producescoremap"):
columnA = "Sum of inputted synergy scores"
columnB = "Count of inputted synergy scores"
columnC = "Sum of inputted conflict scores"
columnD = "Count of inputted conflict scores"
dftable1.columns = [columnA, columnB, columnC, columnD]
elif (scoretypeToFocusOn == "all") and (tooloption == "producecountmap"):
columnA = "Count of all combinations including empty outputs"
columnB = "Count of inputted synergy scores"
columnC = "Count of inputted conflict scores"
dftable1.columns = [columnA, columnB, columnC]
elif (scoretypeToFocusOn == "positive") and (tooloption == "producescoremap"):
columnA = "Sum of inputted synergy scores"
columnB = "Count of inputted synergy scores"
dftable1.columns = [columnA, columnB]
elif (scoretypeToFocusOn == "positive") and (tooloption == "producecountmap"):
columnA = "Count of inputted synergy scores"
dftable1.columns = [columnA]
elif (scoretypeToFocusOn == "negative") and (tooloption == "producescoremap"):
columnA = "Sum of inputted conflict scores"
columnB = "Count of inputted conflict scores"
dftable1.columns = [columnA, columnB]
elif (scoretypeToFocusOn == "negative") and (tooloption == "producecountmap"):
columnA = "Count of inputted conflict scores"
dftable1.columns = [columnA]
if (scoretypeToFocusOn == "all") or (scoretypeToFocusOn == "positive") or ((scoretypeToFocusOn == "negative") and (tooloption == "producecountmap")):
dftable1 = dftable1.sort_values(by=[columnA], ascending=False)
elif (scoretypeToFocusOn == "negative") and (tooloption == "producescoremap"):
dftable1 = dftable1.sort_values(by=[columnA], ascending=True)
gf.createNewPath(os.path.join(os.path.dirname(__file__),finalfoldername))
dftable1.to_excel(os.path.join(os.path.dirname(__file__),finalfoldername,table1name))
def pairwiseRasterToPolygon(rastername, specific_score, matrixinput, pairwise_output_folder, listpairwisepolygon, listpairwisepolygonname, tooloption):
gf.createNewPath(pairwise_output_folder)
marineuseoutput_shp_excl_folder = rastername[:-4]+".shp"
marineuseoutput_shp_incl_folder = pairwise_output_folder+"\\"+marineuseoutput_shp_excl_folder
arcpy.RasterToPolygon_conversion(arcpy.sa.Raster(pairwise_output_folder+"\\"+rastername), marineuseoutput_shp_incl_folder, "NO_SIMPLIFY","VALUE","MULTIPLE_OUTER_PART")
gf.addField(marineuseoutput_shp_incl_folder, "TEXT", fieldname = "finalvalue", precision = "", decimals = "", field_length = 20)
if (tooloption == "producescoremap"):
gf.calculateField1(marineuseoutput_shp_incl_folder, "finalvalue", str(specific_score), expression_type="PYTHON")
elif (tooloption == "producecountmap"):
gf.calculateField1(marineuseoutput_shp_incl_folder, "finalvalue", str(1), expression_type="PYTHON")
if (type(matrixinput) == float) and (tooloption == "producescoremap"):
matrixinput=''
elif (type(matrixinput) == float) and (tooloption == "producecountmap"):
matrixinput = str(matrixinput)
if (len(matrixinput)%254 == 0) and (tooloption == "producescoremap"):
numberroundsneeded = ((len(matrixinput)/254)+1)
elif (len(matrixinput)%254 == 0) and (tooloption == "producecountmap"):
numberroundsneeded = int((len(matrixinput)/254)+1)
if (tooloption == "producescoremap"):
numberroundsneeded = ((len(matrixinput)/254)+2)
elif (tooloption == "producecountmap"):
numberroundsneeded = int((len(matrixinput)/254)+2)
matrixstart=0
matrixend=254
if len(matrixinput) > 0: #if matrixinput is not an empty string
for fieldcount in range(1,numberroundsneeded):
gf.addField(marineuseoutput_shp_incl_folder, "TEXT", "full_desc"+str(fieldcount), precision = "", decimals = "", field_length = 254)
gf.calculateField1(marineuseoutput_shp_incl_folder, "full_desc"+str(fieldcount), "'"+matrixinput[matrixstart:matrixend]+"'", expression_type="PYTHON")
matrixstart=matrixstart+254
matrixend=matrixend+254
if matrixend > len(matrixinput):
matrixend = len(matrixinput)
arcpy.DeleteField_management(marineuseoutput_shp_incl_folder, "Id")
listpairwisepolygon.append(marineuseoutput_shp_incl_folder)
listpairwisepolygonname.append(marineuseoutput_shp_excl_folder)
return (listpairwisepolygon, listpairwisepolygonname)
def calculateMonteCarloIterationRaster(
dictactivities, matrixexcelfile, inputfolder, iteration_folder,
lower_left_corner, dsc, spatial_ref, iteration_outputname,
oceanrastername_with_path, iterations, starttime, starttimetobeupdated,
countScoreRasterIsTrue, changeScoreInputs, changeRankingMethod):
# preset parameters:
outputrasterlist = []
outputrasternamelist = []
outputextrarasterlist = []
outputextrarasternamelist = []
dfdesc = pd.read_excel(matrixexcelfile)
listOfLongActivityNames = dictactivities.keys()
firstmontecarloraster = True
# iterate through the Monte Carlo rounds:
for iteration in range(iterations):
starttimetobeupdated = gf.resetTime(starttimetobeupdated)
# create random factors for test 2 (change scoring scale):
if changeRankingMethod == "true":
# factor for 2.75:
factor_2_75 = gf.getRandomValueBetween0And1()
# factor for 2.50:
factor_2_50 = gf.getRandomValueBetween0And1()
# factor for 2.00:
factor_2_00 = gf.getRandomValueBetween0And1()
# factor for 1.75:
factor_1_75 = gf.getRandomValueBetween0And1()
# factor for 1.50:
factor_1_50 = gf.getRandomValueBetween0And1()
# factor for 1.25:
factor_1_25 = gf.getRandomValueBetween0And1()
# factor for 1.00:
factor_1_00 = gf.getRandomValueBetween0And1()
firstpairwiserasterinround = True
iterator1 = 0
iterator2 = 1
# get pairwise scores and turn them iteratively into total scores for the specific Monte Carlo round:
for column in listOfLongActivityNames:
inputrastername1 = dictactivities.get(
column) # get marine use raster 1
while iterator2 in range((iterator1 + 1),
len(listOfLongActivityNames)):
alldesc_nparray = dfdesc.loc[
dfdesc['Idcolumn'] == listOfLongActivityNames[iterator2],
column].values
# get scores:
if len(alldesc_nparray) > 0:
alldesc = alldesc_nparray.item(0)
if len(str(alldesc)) >= 4:
if alldesc[:1] == "-":
spec_score = float(alldesc[:5].replace(",", "."))
else:
spec_score = float(alldesc[:4].replace(",", "."))
inputrastername2 = dictactivities.get(
listOfLongActivityNames[iterator2]
) #get marine use raster 2
# create baseline total score raster where the score inputs stay unchanged and create an array that tracks the presence of conflicts/synergies:
if (firstmontecarloraster
== True) and (firstpairwiserasterinround
== True):
score_baseline = scoresToRasters3(
inputfolder, inputrastername1,
inputrastername2, spec_score)
cells_with_score_array = np.copy(score_baseline)
cells_with_score_array[
cells_with_score_array >
0.] = 1 #1 is to count the presence of a conflict or synergy
cells_with_score_array[
cells_with_score_array <
0.] = 1 #1 is to count the presence of a conflict or synergy
cells_with_score_array = cells_with_score_array.astype(
int)
elif (firstmontecarloraster == True):
score_baseline += scoresToRasters3(
inputfolder, inputrastername1,
inputrastername2, spec_score)
cells_with_score_array = gf.updateScoreCellCount(
score_baseline, cells_with_score_array,
"montecarlo")
# test 1: test score category input variability:
if changeScoreInputs == "true":
if spec_score == -3.00:
spec_score = (spec_score +
np.random.choice([0.00, 1.00]))
elif spec_score == -2.00:
spec_score = (
spec_score +
np.random.choice([-1.00, 0.00, 1.00]))
elif spec_score == -1.00:
spec_score = (
spec_score +
np.random.choice([-1.00, 0.00, 2.00]))
elif spec_score == 1.00:
spec_score = (spec_score + np.random.choice(
[-2.00, 0.00, 0.25, 0.50, 0.75, 1.00]))
elif spec_score == 1.25:
spec_score = (spec_score + np.random.choice(
[-2.25, -0.25, 0.00, 0.25, 0.50, 0.75]))
elif spec_score == 1.50:
spec_score = (spec_score + np.random.choice([
-0.50, -0.25, 0.00, 0.25, 0.50, 1.00, 1.25,
1.50
]))
elif spec_score == 1.75:
spec_score = (spec_score + np.random.choice([
-0.75, -0.50, -0.25, 0.00, 0.25, 0.75,
1.00, 1.25
]))
elif spec_score == 2.00:
spec_score = (spec_score + np.random.choice([
-1.00, -0.75, -0.50, -0.25, 0.00, 0.50,
0.75, 1.00
]))
elif spec_score == 2.50:
spec_score = (spec_score + np.random.choice([
-1.50, -1.25, -1.00, -0.75, -0.50, 0.00,
0.25, 0.50
]))
elif spec_score == 2.75:
spec_score = (spec_score + np.random.choice(
[-0.75, -0.25, 0.00, 0.25]))
elif spec_score == 3.00:
spec_score = (spec_score + np.random.choice(
[-1.00, -0.50, -0.25, 0.00]))
# test 2: test relative difference of scores (scoring scale):
if changeRankingMethod == "true":
if (spec_score == 2.75):
spec_score = 3.0 * factor_2_75
elif (spec_score == 2.50):
spec_score = (3.0 * factor_2_75) * factor_2_50
elif (spec_score == 2.00):
spec_score = ((3.0 * factor_2_75) *
factor_2_50) * factor_2_00
elif (spec_score == 1.75):
spec_score = ((
(3.0 * factor_2_75) * factor_2_50) *
factor_2_00) * factor_1_75
elif (spec_score == 1.50):
spec_score = (
(((3.0 * factor_2_75) * factor_2_50) *
factor_2_00) * factor_1_75) * factor_1_50
elif (spec_score == 1.25):
spec_score = ((((
(3.0 * factor_2_75) * factor_2_50) *
factor_2_00) * factor_1_75) *
factor_1_50) * factor_1_25
elif (spec_score == 1.00):
spec_score = (((
(((3.0 * factor_2_75) * factor_2_50) *
factor_2_00) * factor_1_75) * factor_1_50)
* factor_1_25) * factor_1_00
elif (spec_score == -2.00):
spec_score = ((-3.0 * factor_2_75) *
factor_2_50) * factor_2_00
elif (spec_score == -1.00):
spec_score = (((
(((-3.0 * factor_2_75) * factor_2_50) *
factor_2_00) * factor_1_75) * factor_1_50)
* factor_1_25) * factor_1_00
# create/update total score raster (score_outras_array) where the score inputs have changed based on the selected test/tests:
if firstpairwiserasterinround == True:
score_outras_array = scoresToRasters3(
inputfolder, inputrastername1,
inputrastername2, spec_score)
firstpairwiserasterinround = False
else:
score_outras_array += scoresToRasters3(
inputfolder, inputrastername1,
inputrastername2, spec_score)
iterator2 += 1 #iterate through all rasters that are after raster1 (to iteratively be raster2)
iterator1 += 1 #new raster1
iterator2 = iterator1 + 1 #start again by iterating through rasters to be raster2 (now the number of following rasters has decreased by 1)
# a Monte Carlo array is created if not existing already:
if firstmontecarloraster == True:
montecarlo_array = np.zeros_like(score_outras_array)
montecarlo_array = montecarlo_array.astype(float)
firstmontecarloraster = False
# the total score raster where the inputs have changed is updated to track the presence of conflicts (value=-1) and the presence of synergies (value=1) in the specific Monte Carlo round:
score_outras_array[score_outras_array > 0.] = 1
score_outras_array[score_outras_array < 0.] = -1
# The Monte Carlo array is counting the number of times in each Monte Carlo round that a raster returns with respectively synergies (value=1), conflicts (value=-1), or not any of the two (value=0):
montecarlo_array = montecarlo_array + score_outras_array
(printline,
starttimetobeupdated) = gf.printTime(starttime, starttimetobeupdated)
arcpy.AddMessage("iteration {} out of {} iterations, {}\n".format(
iteration + 1, iterations, printline))
# Some statistics numpy arrays are produced to adjust the final Monte Carlo raster:
ocean_array = arcpy.RasterToNumPyArray(
oceanrastername_with_path) # read ocean raster
ocean_array = ocean_array.astype(float) # convert ocean raster to float
if_land_in_ocean_array = ocean_array == 0 # get all zero values corresponding to land in the ocean raster
if_no_score_exist_array = cells_with_score_array == 0 # get all raster cells that have no pairwise scores
if_positive_baseline = score_baseline > 0.
if_negative_baseline = score_baseline < 0.
if_neutral_baseline = score_baseline == 0.
if_mostly_positive_iterations = montecarlo_array > 0.
if_average_neutral_iterations = montecarlo_array == 0.
if_mostly_negative_iterations = montecarlo_array < 0.
# Output raster 1: Make Monte Carlo main output raster counting the number of times each raster cell returns positive minus negative:
np.place(
montecarlo_array, if_land_in_ocean_array & if_no_score_exist_array,
np.nan
) # replace all zero values that are both land and zero in the total binary output of all iterations with NoData
scoreRasterOption1A = arcpy.NumPyArrayToRaster(montecarlo_array,
lower_left_corner,
dsc.meanCellWidth,
dsc.meanCellHeight)
(outputrasterlist, outputrasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_montecarlo_all",
scoreRasterOption1A, spatial_ref, outputrasterlist,
outputrasternamelist)
# Output raster 2 (optional): Make basis score raster where inputs are unchanged
if (countScoreRasterIsTrue == "true"):
np.place(score_baseline,
if_land_in_ocean_array & if_no_score_exist_array, np.nan)
basisScoreRaster = arcpy.NumPyArrayToRaster(score_baseline,
lower_left_corner,
dsc.meanCellWidth,
dsc.meanCellHeight)
gf.createNewPath(iteration_folder)
(outputextrarasterlist, outputextrarasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_basis_score",
basisScoreRaster, spatial_ref, outputextrarasterlist,
outputextrarasternamelist)
# Output raster 3: Make Monte Carlo main output raster with robust ("correct") positive/negative/neutral trends:
option1B_array = np.copy(montecarlo_array)
np.place(option1B_array,
((if_mostly_positive_iterations & if_negative_baseline) |
(if_mostly_negative_iterations & if_positive_baseline) |
(if_average_neutral_iterations & if_positive_baseline) |
(if_average_neutral_iterations & if_negative_baseline) |
(if_mostly_positive_iterations & if_neutral_baseline) |
(if_mostly_negative_iterations & if_neutral_baseline)),
np.nan) # set wrong values to NoData
scoreRasterOption1B = arcpy.NumPyArrayToRaster(option1B_array,
lower_left_corner,
dsc.meanCellWidth,
dsc.meanCellHeight)
(outputrasterlist, outputrasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_montecarlo_correct",
scoreRasterOption1B, spatial_ref, outputrasterlist,
outputrasternamelist)
# Output raster 4 (optional): Make basis score raster with robust ("correct") positive/negative/neutral trends:
if (countScoreRasterIsTrue == "true"):
option1Bscore_array = np.copy(score_baseline)
np.place(option1Bscore_array,
((if_mostly_positive_iterations & if_negative_baseline) |
(if_mostly_negative_iterations & if_positive_baseline) |
(if_average_neutral_iterations & if_positive_baseline) |
(if_average_neutral_iterations & if_negative_baseline) |
(if_mostly_positive_iterations & if_neutral_baseline) |
(if_mostly_negative_iterations & if_neutral_baseline)),
np.nan) # set wrong values to NoData
scoreRasterOption1Bscore = arcpy.NumPyArrayToRaster(
option1Bscore_array, lower_left_corner, dsc.meanCellWidth,
dsc.meanCellHeight)
(outputextrarasterlist, outputextrarasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_score_correct",
scoreRasterOption1Bscore, spatial_ref, outputextrarasterlist,
outputextrarasternamelist)
# Output raster 5: Make Monte Carlo main output raster with sensitive ("wrong") positive/negative/neutral trends:
option1C_array = np.copy(montecarlo_array)
np.place(option1C_array,
((if_mostly_positive_iterations & if_positive_baseline) |
(if_mostly_negative_iterations & if_negative_baseline) |
(if_average_neutral_iterations & if_neutral_baseline)),
np.nan) # set correct values to NoData
scoreRasterOption1C = arcpy.NumPyArrayToRaster(option1C_array,
lower_left_corner,
dsc.meanCellWidth,
dsc.meanCellHeight)
(outputrasterlist, outputrasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_montecarlo_wrong",
scoreRasterOption1C, spatial_ref, outputrasterlist,
outputrasternamelist)
# Output raster 6(optional): Make basis score raster with sensitive ("wrong") positive/negative/neutral trends:
if (countScoreRasterIsTrue == "true"):
option1Cscore_array = np.copy(score_baseline)
np.place(option1Cscore_array,
((if_mostly_positive_iterations & if_positive_baseline) |
(if_mostly_negative_iterations & if_negative_baseline) |
(if_average_neutral_iterations & if_neutral_baseline)),
np.nan) # set correct values to NoData
scoreRasterOption1Cscore = arcpy.NumPyArrayToRaster(
option1Cscore_array, lower_left_corner, dsc.meanCellWidth,
dsc.meanCellHeight)
(outputextrarasterlist, outputextrarasternamelist) = gf.createRaster(
iteration_folder, iteration_outputname, "_score_wrong",
scoreRasterOption1Cscore, spatial_ref, outputextrarasterlist,
outputextrarasternamelist)
return (outputrasterlist, outputrasternamelist, outputextrarasterlist,
outputextrarasternamelist)
def forTable3getTotalMapSumPerPairwiseCombination(finalfoldername, dftable3, table3name):
gf.createNewPath(os.path.join(os.path.dirname(__file__),finalfoldername))
dftable3.to_excel(os.path.join(os.path.dirname(__file__),finalfoldername,table3name), index=False)
def forTable2getTotalMapSumCountAndAveragePerMarineUse(listOfLongMarineUseNames, dicttable2, finalfoldername, scoretypeToFocusOn, table2name, countChoice, tooloption):
for longname in listOfLongMarineUseNames:
if (tooloption == "producescoremap"):
# calculate average score per pairwise overlap in map
if dicttable2[longname][1] != 0.:
dicttable2[longname][2] = float(dicttable2[longname][0])/float(dicttable2[longname][1])
else:
dicttable2[longname][2] = 0
# get sum of boolean array to get contributing cells
dicttable2[longname][3] = np.sum(dicttable2[longname][3])
# calculate average score per contributing raster cell
if dicttable2[longname][3] == 0.:
dicttable2[longname][4] = 0
else:
dicttable2[longname][4] = float(dicttable2[longname][0])/float(dicttable2[longname][3])
# calculate percent score contribution area
if dicttable2[longname][5] == 0.:
dicttable2[longname][6] = 0
else:
dicttable2[longname][6] = float(dicttable2[longname][3])/float(dicttable2[longname][5])*100
elif (tooloption == "producecountmap"):
# get count of contributing marine use raster cells:
dicttable2[longname][5] = np.sum(dicttable2[longname][5])
df = pd.DataFrame.from_dict(dicttable2, orient='index')
if (tooloption == "producescoremap"):
columnF = "Count of individual marine use raster cells for each marine use"
elif (tooloption == "producecountmap"):
columnA = "Count of individual marine use raster cells for each marine use"
columnB = "Count of ocean raster cells"
columnC = "Ocean coverage percentage of data"
columnF = "Count of each marine use raster cells that contain at least one overlap (overlap as defined by the count setting)"
if (countChoice == "count all combinations with a specific marine use") or (countChoice == "count all"):
columnD = "Total count of spatial pairwise combinations in map for each marine use (including the combinations that do not have a synergy-conflict score)"
columnE = "Average count of spatial pairwise combinations per raster cell in map for each marine use (including the combinations that do not have a synergy-conflict score)"
columnG = "Average count of spatial pairwise combinations per raster cell with overlaps in map for each marine use (including the combinations that do not have a synergy-conflict score)"
if (scoretypeToFocusOn == "all") and (tooloption == "producescoremap"):
columnA = "Total score in map for each marine use"
columnB = "Total count of spatial pairwise conflict-synergy combinations in map for each marine use"
columnC = "Average score per pairwise conflict-synergy combination in map for each marine use"
columnD = "Total count of raster cells that each marine use contribute conflict-synergy scores to in map"
columnE = "Average score per raster cell that each marine use contribute conflict-synergy scores to in map"
columnG = "Percent area with conflict-synergy scores out of the area for each marine use"
elif (scoretypeToFocusOn == "all") and (tooloption == "producecountmap"):
columnD = "Total count of spatial pairwise combinations in map for each marine use (only the combinations that have a synergy-conflict score)"
columnE = "Average count of spatial pairwise combinations per raster cell in map for each marine use (only the combinations that have a synergy-conflict score)"
columnG = "Average count of spatial pairwise combinations per raster cell with conflict-synergy overlaps in map for each marine use (only the combinations that have a synergy-conflict score)"
elif (scoretypeToFocusOn == "positive") and (tooloption == "producescoremap"):
columnA = "Total positive score in map for each marine use"
columnB = "Total count of spatial positive pairwise conflict-synergy combinations in map for each marine use"
columnC = "Average score per positive pairwise conflict-synergy combination in map for each marine use"
columnD = "Total count of raster cells that each marine use contribute synergy scores to in map"
columnE = "Average score per raster cell that each marine use contribute synergy scores to in map"
columnG = "Percent area with synergy scores out of the area for each marine use"
elif (scoretypeToFocusOn == "positive") and (tooloption == "producecountmap"):
columnD = "Total count of spatial positive pairwise combinations in map for each marine use"
columnE = "Average count of spatial positive pairwise combinations per raster cell in map for each marine use"
columnG = "Average count of spatial positive pairwise combinations per raster cell with synergy overlaps in map for each marine use"
elif (scoretypeToFocusOn == "negative") and (tooloption == "producescoremap"):
columnA = "Total negative score in map for each marine use"
columnB = "Total count of spatial negative pairwise conflict-synergy combinations in map for each marine use"
columnC = "Average score per negative pairwise conflict-synergy combination in map for each marine use"
columnD = "Total count of raster cells that each marine use contribute conflict scores to in map"
columnE = "Average score per raster cell that each marine use contribute conflict scores to in map"
columnG = "Percent area with conflict scores out of the area for each marine use"
elif (scoretypeToFocusOn == "negative") and (tooloption == "producecountmap"):
columnD = "Total count of spatial negative pairwise combinations in map for each marine use"
columnE = "Average count of spatial negative pairwise combinations per raster cell in map for each marine use"
columnG = "Average count of spatial negative pairwise combinations per raster cell with conflict overlaps in map for each marine use"
df.columns = [columnA, columnB, columnC, columnD, columnE, columnF, columnG]
if (scoretypeToFocusOn == "all") or (scoretypeToFocusOn == "positive"):
if (tooloption == "producescoremap"):
df = df.sort_values(by=[columnA], ascending=False)
elif (tooloption == "producecountmap"):
df = df.sort_values(by=[columnD], ascending=False)
elif (scoretypeToFocusOn == "negative"):
if (tooloption == "producescoremap"):
df = df.sort_values(by=[columnA], ascending=True)
elif (tooloption == "producecountmap"):
df = df.sort_values(by=[columnD], ascending=True)
if (tooloption == "producecountmap"):
# count coverage percentage:
df[columnC] = df[columnA]/df[columnB]*100
# count average count per marine use raster cell:
df[columnE] = df[columnD]/df[columnA]
# count average count per contributing marine use raster cell:
df[columnG] = df[columnD]/df[columnF]
gf.createNewPath(os.path.join(os.path.dirname(__file__),finalfoldername))
df.to_excel(os.path.join(os.path.dirname(__file__),finalfoldername,table2name))
def calculatePairwiseAndTotalRasters(dictMarineUses, matrixexcelfile, scoretypeToFocusOn, inputfoldernamewithpath, pairwise_output_folder, finalfoldername, tableOption1IsChecked, table1name, tableOption2IsChecked, table2name, tableOption3IsChecked, table3name, somePolygonsIsTrue, numbOfSome, onlyOneMarineUseInFocusIsTrue, marineUseInFocus, includeSynergyAndConflictAtOnceAreaIsTrue, lower_left_corner, dsc, spatial_ref, finaloutputname_only_covering_ocean, oceanrastername_with_path, calculationtype, countChoice, chosen_category, chosen_category_score, attribute_excelsheet, tooloption):
# import ocean array:
ocean_array = arcpy.RasterToNumPyArray(oceanrastername_with_path)
ocean_array = ocean_array.astype(float)
# table 2 inputs:
if (tooloption == "producecountmap") and (tableOption2IsChecked == "true"):
ocean_cell_number = np.count_nonzero(ocean_array)
# preset parameters:
dfdesc = pd.read_excel(matrixexcelfile)
listOfLongMarineUseNames = dictMarineUses.keys()
listpairwiserastername = []
listpairwisepolygon = []
listpairwisepolygonname = []
dictpairwiserastername = {}
dicttable1 = {}
dicttable2 = {}
dftable3 = "false" # false as default but might change
if (tooloption == "producecountmap"):
synergies_and_or_conflicts_array = "false" # false as default but might change
for longname_marine_use in listOfLongMarineUseNames:
# table 1 inputs:
if tableOption1IsChecked == "true":
if (scoretypeToFocusOn == "all"):
if (tooloption == "producescoremap"):
dicttable1[longname_marine_use] = [0]*4
elif (tooloption == "producecountmap"):
dicttable1[longname_marine_use] = [0]*3
elif scoretypeToFocusOn == "positive":
if (tooloption == "producescoremap"):
dicttable1[longname_marine_use] = [0]*2
elif (tooloption == "producecountmap"):
dicttable1[longname_marine_use] = [0]
elif scoretypeToFocusOn == "negative":
if (tooloption == "producescoremap"):
dicttable1[longname_marine_use] = [0]*2
elif (tooloption == "producecountmap"):
dicttable1[longname_marine_use] = [0]
# table 2 inputs:
if tableOption2IsChecked == "true":
dicttable2[longname_marine_use]=[0]*7
if (tooloption == "producescoremap"):
dicttable2[longname_marine_use][3]=np.zeros_like(ocean_array, dtype=bool)
elif (tooloption == "producecountmap"):
dicttable2[longname_marine_use][1] = ocean_cell_number
dicttable2[longname_marine_use][5]=np.zeros_like(ocean_array, dtype=bool)
# table 3 inputs:
if tableOption3IsChecked == "true":
dftable3 = pd.DataFrame(columns=listOfLongMarineUseNames,index=listOfLongMarineUseNames)
dftable3.index.name = 'Idcolumn'
dftable3 = dftable3.reset_index()
# preset parameters:
firstraster = True
firstsynergyandconflictarray = True
firstbasicarray = True
iterator1 = 0
iterator2 = 1
# main code: create total scores or total counts:
for column in listOfLongMarineUseNames:
inputrastername1 = dictMarineUses.get(column)
while iterator2 in range((iterator1+1),len(listOfLongMarineUseNames)):
inputrastername2 = dictMarineUses.get(listOfLongMarineUseNames[iterator2]) #get raster for marine use 2
# make pairwise rasters: set score=0 if score is never needed and get matrixinput:
if (tooloption == "producecountmap") and (scoretypeToFocusOn == "all") and ((countChoice == "count all") or (countChoice == "count all combinations with a specific marine use")) and (tableOption1IsChecked == "false") and (includeSynergyAndConflictAtOnceAreaIsTrue == "false"):
specific_score = 0
# set matrixinput = '' if no pairwise polygons should be created and added:
if ((somePolygonsIsTrue == "true") and (numbOfSome == 0)):
matrixinput = ''
else:
# get matrixinput if pairwise polygons should be created and added:
try:
matrixinput_nparray = dfdesc.loc[dfdesc['Idcolumn'] == listOfLongMarineUseNames[iterator2], column].values
matrixinput = str(matrixinput_nparray.item(0))
# set matrixinput = '' if no description can be found in matrix:
except:
matrixinput = 'nan'
# make pairwise rasters: get score if score is needed and get matrixinput:
else:
# get matrixinput:
matrixinput_nparray = dfdesc.loc[dfdesc['Idcolumn'] == listOfLongMarineUseNames[iterator2], column].values
if len(matrixinput_nparray) > 0:
matrixinput = matrixinput_nparray.item(0)
# get score if it exists:
if (len(str(matrixinput)) >= 4):
if str(matrixinput)[:1] == "-":
specific_score = float(matrixinput[:5].replace(",",".")) #get the score
else:
specific_score = float(matrixinput[:4].replace(",",".")) #get the score
# if score do not exist, score is set to 0:
else:
specific_score = 0
else:
specific_score = 0
# make pairwise rasters: get spatial and temporal attributes if that is needed for the count type:
if (tooloption == "producecountmap"):
if ((countChoice == "count vertically different overlaps") or (countChoice == "count vertically same overlaps") or (countChoice == "count pairwise overlaps where both marine uses are mobile")):
if attribute_excelsheet == "false":
quit()
else:
try:
dfattributes = pd.read_excel(attribute_excelsheet)
except:
arcpy.AddMessage("ERROR: Problems regarding reading excelfile with spatial and temporal attributes")
quit()
raster1long = dictMarineUses.keys()[dictMarineUses.values().index(inputrastername1)]
raster2long = dictMarineUses.keys()[dictMarineUses.values().index(inputrastername2)]
if ((countChoice == "count vertically different overlaps") or (countChoice == "count vertically same overlaps")):
act1_vertical = ((dfattributes[dfattributes["Idcolumn"] == raster1long]['Vertical information']).to_string(index=False)) #gather vertical attribute of pairwise marine use 1
act2_vertical = ((dfattributes[dfattributes["Idcolumn"] == raster2long]['Vertical information']).to_string(index=False)) #gather vertical attribute of pairwise marine use 2
if (act1_vertical == "BENTHIC" or act2_vertical == "BENTHIC") and (act1_vertical == "SURFACE" or act2_vertical == "SURFACE"):
notActualOverlapping = "true"
elif (act1_vertical == "BENTHIC" and act2_vertical == "BENTHIC") or (act1_vertical == "BENTHIC" and act2_vertical == "WHOLE WATER COLUMN") or (act1_vertical == "WHOLE WATER COLUMN" and act2_vertical == "BENTHIC") or (act1_vertical == "SURFACE" and act2_vertical == "SURFACE") or (act1_vertical == "SURFACE" and act2_vertical == "WHOLE WATER COLUMN") or (act1_vertical == "WHOLE WATER COLUMN" and act2_vertical == "SURFACE"):
notActualOverlapping = "false"
else:
notActualOverlapping = ""
else:
notActualOverlapping = ""
if (countChoice == "count pairwise overlaps where both marine uses are mobile"):
act1_temporal = ((dfattributes[dfattributes["Idcolumn"] == raster1long]['Temporal information']).to_string(index=False)) # gather temporal attribute of pairwise marine use 1
act2_temporal = ((dfattributes[dfattributes["Idcolumn"] == raster2long]['Temporal information']).to_string(index=False)) # gather temporal attribute of pairwise marine use 2
if (act1_temporal == "MOBILE" and act2_temporal == "MOBILE"):
bothTemporal = "true"
else:
bothTemporal = ""
else:
bothTemporal = ""
else:
notActualOverlapping = ""
bothTemporal = ""
# make marine use pairwise rasters:
if ((tooloption == "producescoremap") and (len(str(matrixinput)) >= 4)) or (tooloption == "producecountmap"):
inputmarineuselongname1 = column
inputmarineuselongname2 = listOfLongMarineUseNames[iterator2]
# make pairwise score rasters for all, synergies (positive), or conflicts (negative) --->:
if (scoretypeToFocusOn == "all") or ((scoretypeToFocusOn == "positive") and (specific_score > 0)) or ((scoretypeToFocusOn == "negative") and (specific_score < 0)):
# ---> for specified conditions:
if ((onlyOneMarineUseInFocusIsTrue == "false") and (tooloption == "producescoremap")) or (
(onlyOneMarineUseInFocusIsTrue == "true") and (marineUseInFocus == "false") and (tooloption == "producescoremap")) or (
(onlyOneMarineUseInFocusIsTrue == "true") and ((column == marineUseInFocus) or (listOfLongMarineUseNames[iterator2] == marineUseInFocus)) and (tooloption == "producescoremap")) or (
(countChoice == "count all") and (tooloption == "producecountmap")) or (
(countChoice == "count all combinations with a specific marine use") and ((marineUseInFocus == listOfLongMarineUseNames[iterator2]) or (marineUseInFocus == column)) and (tooloption == "producecountmap")) or (
(countChoice == "count combinations with a specific marine use where synergy-conflict inputs exist") and ((specific_score > 0) or (specific_score < 0)) and ((marineUseInFocus == listOfLongMarineUseNames[iterator2]) or (marineUseInFocus == column)) and (tooloption == "producecountmap")) or (
(countChoice == "count combinations where synergy-conflict inputs exist") and ((specific_score > 0) or (specific_score < 0)) and (tooloption == "producecountmap")) or (
(countChoice == "count combinations for a specific conflict-synergy category") and ((specific_score > 0) or (specific_score < 0)) and (specific_score == float(chosen_category_score)) and (tooloption == "producecountmap")) or (
(countChoice == "count multiuse-options mentioned in the MUSES project") and ('MUSES' in str(matrixinput)) and ((specific_score > 0) or (specific_score < 0)) and (tooloption == "producecountmap")) or (
(countChoice == "count vertically different overlaps") and (notActualOverlapping == "true") and ((specific_score > 0) or (specific_score < 0)) and (tooloption == "producecountmap")) or (
(countChoice == "count vertically same overlaps") and (notActualOverlapping == "false") and ((specific_score > 0) or (specific_score < 0)) and (tooloption == "producecountmap")) or (
(countChoice == "count pairwise overlaps where both marine uses are mobile") and (bothTemporal == "true") and ((specific_score > 0) or (specific_score < 0)) and (tooloption == "producecountmap")):
(outras_array, array_pairwise_score, dicttable2, dftable3, dictpairwiserastername, listpairwiserastername) = scoresToRasters1(inputfoldernamewithpath, inputrastername1, inputrastername2, specific_score, lower_left_corner, dsc, matrixinput, pairwise_output_folder, dictpairwiserastername, calculationtype, tableOption2IsChecked, dicttable2, inputmarineuselongname1, inputmarineuselongname2, tableOption3IsChecked, dftable3, "true", listpairwiserastername, somePolygonsIsTrue, numbOfSome, spatial_ref, includeSynergyAndConflictAtOnceAreaIsTrue, tooloption)
if firstraster == True:
cummulated_score_outras_array = outras_array # set total score raster = first pairwise score raster to start accumulating scores
firstraster = False
# make synergy-and-conflict-at-once area polygon - part 1:
if (includeSynergyAndConflictAtOnceAreaIsTrue == "true") and (firstsynergyandconflictarray == True):
if (tooloption == "producescoremap"):
synergies_and_or_conflicts_array = np.copy(outras_array)
elif (tooloption == "producecountmap"):
synergies_and_or_conflicts_array = np.copy(array_pairwise_score)
synergies_and_or_conflicts_array[synergies_and_or_conflicts_array>0.] = 2 #2 is code for synergies
synergies_and_or_conflicts_array[synergies_and_or_conflicts_array<0.] = 1 #1 is code for conflicts
synergies_and_or_conflicts_array = synergies_and_or_conflicts_array.astype(int)
firstsynergyandconflictarray = False
# make basic raster with values=1 for conflict-synergy extent - part 1:
elif (firstbasicarray == True):
if (tooloption == "producescoremap"):
cells_with_score_array = np.copy(outras_array)
cells_with_score_array[cells_with_score_array>0.] = 1 #1 is to count cells with scores
cells_with_score_array[cells_with_score_array<0.] = 1 #1 is to count cells with scores
elif (tooloption == "producecountmap"):
cells_with_score_array = np.copy(outras_array)
cells_with_score_array = cells_with_score_array.astype(int)
firstbasicarray = False
else:
cummulated_score_outras_array += outras_array # update total score raster iteratively as the pairwise score rasters are created
# make synergy-and-conflict-at-once area polygon - part 2:
if (includeSynergyAndConflictAtOnceAreaIsTrue == "true"):
if (tooloption == "producescoremap"):
synergies_and_or_conflicts_array = gf.updateSynergyAndConflictInCellKnowledge(outras_array, synergies_and_or_conflicts_array, specific_score)
elif (tooloption == "producecountmap"):
synergies_and_or_conflicts_array = gf.updateSynergyAndConflictInCellKnowledge(array_pairwise_score, synergies_and_or_conflicts_array, specific_score)
# make basic raster with values=1 for conflict-synergy extent - part 2:
else:
cells_with_score_array = gf.updateScoreCellCount(outras_array, cells_with_score_array, tooloption)
# table 1 inputs:
if (tableOption1IsChecked == "true"):
if ((scoretypeToFocusOn == "all") and (specific_score > 0) and (tooloption == "producescoremap")) or (
(scoretypeToFocusOn == "positive") and (specific_score > 0) and (tooloption == "producescoremap")) or (
(scoretypeToFocusOn == "negative") and (specific_score < 0) and (tooloption == "producescoremap")):
addScoreToDictPerMarineUseInMatrix(0, column, dicttable1, specific_score, listOfLongMarineUseNames, iterator2)
addScoreToDictPerMarineUseInMatrix(1, column, dicttable1, 1, listOfLongMarineUseNames, iterator2)
elif ((scoretypeToFocusOn == "all") and (specific_score < 0) and (tooloption == "producescoremap")):
addScoreToDictPerMarineUseInMatrix(2, column, dicttable1, specific_score, listOfLongMarineUseNames, iterator2)
addScoreToDictPerMarineUseInMatrix(3, column, dicttable1, 1, listOfLongMarineUseNames, iterator2)
elif (tooloption == "producecountmap"):
addScoreToDictPerMarineUseInMatrix(0, column, dicttable1, 1, listOfLongMarineUseNames, iterator2)
if (scoretypeToFocusOn == "all") and (specific_score > 0):
addScoreToDictPerMarineUseInMatrix(1, column, dicttable1, 1, listOfLongMarineUseNames, iterator2)
elif (scoretypeToFocusOn == "all") and (specific_score < 0):
addScoreToDictPerMarineUseInMatrix(2, column, dicttable1, 1, listOfLongMarineUseNames, iterator2)
if ((specific_score > 0) and (scoretypeToFocusOn == "negative")) or ((specific_score < 0) and (scoretypeToFocusOn == "positive")):
(outras_array, array_pairwise_score, dicttable2, dftable3, dictpairwiserastername, listpairwiserastername) = scoresToRasters1(inputfoldernamewithpath, inputrastername1, inputrastername2, specific_score, lower_left_corner, dsc, matrixinput, pairwise_output_folder, dictpairwiserastername, calculationtype, tableOption2IsChecked, dicttable2, inputmarineuselongname1, inputmarineuselongname2, tableOption3IsChecked, dftable3, "false", listpairwiserastername, somePolygonsIsTrue, numbOfSome, spatial_ref, includeSynergyAndConflictAtOnceAreaIsTrue, tooloption)
# make synergy-and-conflict-at-once area polygon - part 3:
if (includeSynergyAndConflictAtOnceAreaIsTrue == "true"):
if (firstsynergyandconflictarray == True):
if (tooloption == "producescoremap"):
synergies_and_or_conflicts_array = np.copy(outras_array)
elif (tooloption == "producecountmap"):
synergies_and_or_conflicts_array = np.copy(array_pairwise_score)
if ((specific_score > 0) and (scoretypeToFocusOn == "negative")):
synergies_and_or_conflicts_array[synergies_and_or_conflicts_array>0.] = 2 #2 is code for synergies
elif ((specific_score < 0) and (scoretypeToFocusOn == "positive")):
synergies_and_or_conflicts_array[synergies_and_or_conflicts_array<0.] = 1 #1 is code for conflicts
synergies_and_or_conflicts_array = synergies_and_or_conflicts_array.astype(int)
firstsynergyandconflictarray = False
else:
if (tooloption == "producescoremap"):
synergies_and_or_conflicts_array = gf.updateSynergyAndConflictInCellKnowledge(outras_array, synergies_and_or_conflicts_array, specific_score)
elif (tooloption == "producecountmap"):
synergies_and_or_conflicts_array = gf.updateSynergyAndConflictInCellKnowledge(array_pairwise_score, synergies_and_or_conflicts_array, specific_score)
iterator2 += 1 #iterate through all rasters that are after raster1 (to iteratively be raster2)
iterator1 += 1 #new raster1
iterator2 = iterator1+1 #start again by iterating through rasters to be raster2 (now the number of following rasters has decreased by 1)
# make synergy-and-conflict-at-once area polygon - part 4:
if (includeSynergyAndConflictAtOnceAreaIsTrue == "true"):
cells_with_no_output = synergies_and_or_conflicts_array == 0
# make basic raster with values=1 for conflict-synergy extent - part 4:
else:
cells_with_no_output = cells_with_score_array == 0
# convert total score numpy array to total score raster with terrestrial/land area as NoData:
ocean_array_land = ocean_array == 0
np.place(cummulated_score_outras_array, ocean_array_land & cells_with_no_output, np.nan)
scoreRaster = arcpy.NumPyArrayToRaster(cummulated_score_outras_array, lower_left_corner, dsc.meanCellWidth, dsc.meanCellHeight)
gf.createNewPath(finalfoldername)
output_tif_incl_folder = finalfoldername+"\\"+finaloutputname_only_covering_ocean
scoreRaster.save(output_tif_incl_folder)
arcpy.DefineProjection_management(output_tif_incl_folder, spatial_ref)
# table 1 inputs:
if tableOption1IsChecked == "true":
forTable1getTotalMatrixSumCountPerMarineUse(dicttable1, scoretypeToFocusOn, finalfoldername, table1name, tooloption)
# table 2 inputs:
if tableOption2IsChecked == "true":
forTable2getTotalMapSumCountAndAveragePerMarineUse(listOfLongMarineUseNames, dicttable2, finalfoldername, scoretypeToFocusOn, table2name, countChoice, tooloption)
# table 3 inputs:
if tableOption3IsChecked == "true":
forTable3getTotalMapSumPerPairwiseCombination(finalfoldername, dftable3, table3name)
# if is chosen to only add pairwise polygons to map with highest and lowest scores:
if (tableOption3IsChecked == "true") and (somePolygonsIsTrue == "true") and (numbOfSome != "all") and (numbOfSome != 0) and (tooloption == "producescoremap"):
# add numbOfSome lowest negative pairwise score polygons - conflicst first:
if (scoretypeToFocusOn == "all") or (scoretypeToFocusOn == "negative"):
dictpairwiserastername_new = OrderedDict(sorted(dictpairwiserastername.items()))
# add numbOfSome highest positive pairwise score polygons - synergies first:
if (scoretypeToFocusOn == "all") or (scoretypeToFocusOn == "positive"):
dictpairwiserastername_new = OrderedDict(sorted(dictpairwiserastername.items(), reverse=True))
(listpairwisepolygon, listpairwisepolygonname) = gf.getNumbOfSomePolygonElementsFromOrderedDict(dictpairwiserastername_new, numbOfSome, listpairwisepolygon, listpairwisepolygonname, pairwise_output_folder, 3, tooloption)
if (tableOption3IsChecked == "true") and (somePolygonsIsTrue == "true") and (numbOfSome != "all") and (tooloption == "producecountmap"):
dictpairwiserastername_new = OrderedDict(sorted(dictpairwiserastername.items(), reverse=True))
(listpairwisepolygon, listpairwisepolygonname) = gf.getNumbOfSomePolygonElementsFromOrderedDict(dictpairwiserastername_new, numbOfSome, listpairwisepolygon, listpairwisepolygonname, pairwise_output_folder, 2, tooloption)
# add all pairwise score polygons to map (default setting unless numbOfSome is set)
elif (numbOfSome != 0) and (tooloption == "producescoremap"):
(listpairwisepolygon, listpairwisepolygonname) = gf.getNumbOfAllPolygonElementsFromOrderedList(listpairwiserastername, listpairwisepolygon, listpairwisepolygonname, pairwise_output_folder, 3, tooloption)
elif (numbOfSome != 0) and (tooloption == "producecountmap"):
(listpairwisepolygon, listpairwisepolygonname) = gf.getNumbOfAllPolygonElementsFromOrderedList(listpairwiserastername, listpairwisepolygon, listpairwisepolygonname, pairwise_output_folder, 2, tooloption)
# output total score raster, pairwise polygons to be added to the map and their names, and synergies-and-conflicts-at-once-array if requested:
if (includeSynergyAndConflictAtOnceAreaIsTrue == "true"):
return (output_tif_incl_folder, listpairwisepolygon, listpairwisepolygonname, synergies_and_or_conflicts_array)
else:
return (output_tif_incl_folder, listpairwisepolygon, listpairwisepolygonname, "redundant")