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humanPlotPrepPVals.py
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humanPlotPrepPVals.py
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import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib as mpl
from matplotlib.pyplot import figure
import os
generateMonths = False
lessThanYear = False
predictorToCategory = {
"LINKNO":"morphology",
"quality":"data_quality",
"strmOrder":"size",
"Magnitude":"size",
"strmDrop":"morphology",
"WSNO":"other",
"length_km":"size",
"area_sqkm":"size",
"drain_den":"morphology",
"gelev_m":"morphology",
"garea_sqkm":"size",
"gord":"size",
"PathLength":"size",
"TotalLength":"size",
"yr10data":"data_quality",
"yr15data":"data_quality",
"MeanTemp01":"climate",
"MeanTemp02":"climate",
"MeanTemp03":"climate",
"MeanTemp04":"climate",
"MeanTemp05":"climate",
"MeanTemp06":"climate",
"MeanTemp07":"climate",
"MeanTemp08":"climate",
"MeanTemp09":"climate",
"MeanTemp10":"climate",
"MeanTemp11":"climate",
"MeanTemp12":"climate",
"MeanTempAnn":"climate",
"MeanPrec01":"climate",
"MeanPrec02":"climate",
"MeanPrec03":"climate",
"MeanPrec04":"climate",
"MeanPrec05":"climate",
"MeanPrec06":"climate",
"MeanPrec07":"climate",
"MeanPrec08":"climate",
"MeanPrec09":"climate",
"MeanPrec10":"climate",
"MeanPrec11":"climate",
"MeanPrec12":"climate",
"MeanPrecAnn":"climate",
"CumPrec01":"size",
"CumPrec02":"size",
"CumPrec03":"size",
"CumPrec04":"size",
"CumPrec05":"size",
"CumPrec06":"size",
"CumPrec07":"size",
"CumPrec08":"size",
"CumPrec09":"size",
"CumPrec10":"size",
"CumPrec11":"size",
"CumPrec12":"size",
"CumPrecTotal":"size",
"bio1":"climate",
"bio2":"climate",
"bio3":"climate",
"bio4":"climate",
"bio5":"climate",
"bio6":"climate",
"bio7":"climate",
"bio8":"climate",
"bio9":"climate",
"bio10":"climate",
"bio11":"climate",
"bio12":"climate",
"bio13":"climate",
"bio14":"climate",
"bio15":"climate",
"bio16":"climate",
"bio17":"climate",
"bio18":"climate",
"bio19":"climate",
"FEOW_ID":"other",
"ID":"other",
"Ecoregion_Name":"region",
"cls1":"land_cover",
"cls2":"land_cover",
"cls3":"land_cover",
"cls4":"land_cover",
"cls5":"land_cover",
"cls6":"land_cover",
"cls7":"human",
"cls8":"land_cover",
"cls9":"human",
"cls10":"land_cover",
"cls11":"land_cover",
"cls12":"land_cover",
"cls1":"land_cover",
"Dam_SurfaceArea":"human",
"Dam_Count":"human",
"HydroLakes_Area_sqkm":"size",
"MeanPopden_2000":"human",
"MeanPopden_2005":"human",
"MeanPopden_2010":"human",
"MeanPopden_2015":"human",
"MeanHumanFootprint":"human",
"meanPercentDC_Imperfectly":"data_quality",
"meanPercentDC_ModeratelyWell":"data_quality",
"meanPercentDC_ModeratelyWell":"data_quality",
"meanPercentDC_Poor":"data_quality",
"meanPercentDC_SomewhatExcessive":"data_quality",
"meanPercentDC_VeryPoor":"data_quality",
"meanPercentDC_Well":"data_quality",
"Continent":"region",
"BIOME":"region",
"ECO_NAME":"region",
"tempcv":"climate",
"precipcv":"climate",
"forest":"land_cover",
"otherveg":"land_cover",
"human":"human",
"water":"size",#"water",
"damareaN":"human",
"damcountN":"human",
"strmsize":"size"
}
numFound = 0
for dfile in os.listdir():
if dfile.startswith("ml_r_squared"):
print(dfile)
df = pd.read_csv(dfile)
if numFound == 0:
bigDf = df
if numFound > 0:
bigDf = bigDf.append(df)
numFound += 1
#if numFound >= 1:
# break
#print(dfile)
print(bigDf)
print("numFound: ", numFound)
#quit()
#category = []
timePeriod = []
for index, row in bigDf.iterrows():
#category.append(predictorToCategory[row["feature"]])
if row["period"] < 40:
#timePeriod.append("1: < 1 month")
timePeriod.append("1")
elif row["period"] < 180:
#timePeriod.append("2: < several months")
timePeriod.append("2")
elif row["period"] < 365:
#timePeriod.append("3: < 1 year")
timePeriod.append("3")
elif row["period"] < 1095:
#timePeriod.append("4: < 3 years")
timePeriod.append("4")
elif row["period"] < 2190:
#timePeriod.append("5: < 6 years")
timePeriod.append("5")
elif row["period"] < 10000:
#timePeriod.append("6: < 10 years")
timePeriod.append("6")
else:
print("error")
print(row["period"])
#bigDf["cateogry"] = category
bigDf["time_period"] = timePeriod
bigDf = bigDf[bigDf["model_type"] != "DTR"]
print(bigDf)
sns.violinplot(data=bigDf, x="time_period", y="r_squared", hue="model_type", split=True)
plt.title("Model R-Squared Values for Predicting Frequency Decompositions, n=" + str(len(bigDf["model_type"])), wrap=True)
plt.xlabel("time period")
plt.ylabel("r-squared")
plt.tight_layout()
plt.savefig("frequency_from_catchment_rsquared.png")
plt.show()
#bigDf.to_csv("ml_importances_with_categories.csv", index=False)
#print(bigDf)