df = pd.read_excel(inputexcelsheet)
list_use1_options = list(df.columns)[1:]
columnindex = list_use1_options.index(use1)
list_use2_options = list(df.ix[:, 0])
rowindex = list_use2_options.index(use2)
if rowindex >= columnindex:
desc = df.ix[rowindex, columnindex + 1]
else:
desc = df.ix[columnindex, rowindex + 1]
arcpy.AddMessage("\n")
starttimetobeupdated = gf.resetTime(starttimetobeupdated)
try:
if np.isnan(desc):
(printline,
starttimetobeupdated) = gf.printTime(starttime, starttimetobeupdated)
arcpy.AddMessage("Matrix content is empty, {}\n".format(printline))
except:
if rowindex >= columnindex:
arcpy.AddMessage("Matrix content is:")
arcpy.AddMessage("For use1: {} and use2: {}:\n".format(use2, use1))
arcpy.AddMessage("{} \n".format(desc))
(printline,
starttimetobeupdated) = gf.printTime(starttime, starttimetobeupdated)
arcpy.AddMessage("Calculated {}".format(printline))
else:
arcpy.AddMessage("Matrix content is:")
arcpy.AddMessage("For use1: {} and use2: {}:\n".format(use1, use2))
arcpy.AddMessage("{} \n".format(desc))
(printline,
starttimetobeupdated) = gf.printTime(starttime, starttimetobeupdated)
# main code:
arcpy.AddMessage("\n")
starttimetobeupdated = gf.resetTime(starttimetobeupdated)
(output_tif_incl_folder, listpairwisepolygon, listpairwisepolygonname,
synergies_and_or_conflicts_array) = f1.calculatePairwiseAndTotalRasters(
dictMarineUses, inputexcelsheet, scoretypeToFocusOn,
inputfoldernamewithpath, pairwise_output_foldername, 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, "noCountChoice",
"no_chosen_category", "no_chosen_category_score",
"no_attribute_excelsheet", "producescoremap")
(printline, starttimetobeupdated) = gf.printTime(starttime,
starttimetobeupdated)
arcpy.AddMessage(
"Pairwise score rasters and total score raster have been calculated, {}\n".
format(printline))
# make synergy-and-conflict area polygon - part 5:
if includeSynergyAndConflictAtOnceAreaIsTrue == "true":
starttimetobeupdated = gf.resetTime(starttimetobeupdated)
(output_shp_incl_folder,
output_shp_excl_folder) = gf.synergyAndConflictAtOnceRasterToPolygon(
synergies_and_or_conflicts_array, lower_left_corner, dsc, spatial_ref,
finalfoldername, conflict_and_synergy_shapefile)
(printline, starttimetobeupdated) = gf.printTime(starttime,
starttimetobeupdated)
arcpy.AddMessage(
"Synergy-and-conflict-at-once-raster has been calculated, {}\n".format(
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)