add initial commit

README.md

@@ -1 +1 @@
-# aquavis
+# This program takes a csv file and displays the data in a useful way. 

data_loaders/csv_loader.py

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+from abc import ABC, abstractmethod
+import pandas as pd
+from data_loaders.data_loader import DataLoader
+
+
+class CsvLoader(DataLoader):
+	def __init__(self, name, delimiter):
+		"""
+		Reads the given csv file
+
+		@type name: str
+		@param name: the name of the csv file
+		@type delimiter: str
+		@param delimiter: the delimiter separating the column names in the csv file
+		"""
+
+		self.name1 = name
+		self.delim = delimiter
+
+	def load(self):
+		"""
+		Reads the csv file into a dataframe
+
+		@rtype: Pandas DataFrame
+		@returns: the pandas dataframe containing the read data from the csv file
+		"""
+
+		return (pd.read_csv(self.name1, self.delim))

data_loaders/data_loader.py

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+#Data Loader
+from abc import ABC, abstractmethod
+
+
+class DataLoader(ABC):
+	def load(self):
+		pass

data_loaders/ecomapper_loader.py

@@ -0,0 +1,9 @@
+from abc import ABC, abstractmethod
+import pandas as pd
+from data_loaders.data_loader import DataLoader 
+class EcomapperLoader(DataLoader):
+	def __init__(self, name, delimiter):
+		self.nm = name#str
+		self.dl = delimiter#str
+	def load(self):
+		return (pd.read_csv(self.nm, self.dl))

data_loaders/json_loader.py

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+from abc import ABC 
+from data_loaders.data_loader import DataLoader
+import json
+
+class JsonLoader(DataLoader):
+	def __init__(self, file_name):
+		self.file = file_name
+	def load(self):
+		with open(self.file) as f:
+  			data = json.load(f)
+		r = []
+		for key, value in data.items():
+			r.append([key, value])
+		return r
+
+

driver.py

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+from abc import ABC, abstractmethod
+from processors.drop_close import DropClose
+from processors.outlier_removal import OutlierRemoval
+from processors.distance_calculator import DistanceCalculator
+from processors.step_range import StepRange
+from processors.processor import Processor
+from data_loaders.csv_loader import CsvLoader
+from data_loaders.data_loader import DataLoader  
+from models.gpr import GPR
+from models.svr import SV
+from models.model_manager import ModelManager
+from models.model import Model
+from visualizers.visualization import Visualization
+from visualizers.two_d_vis import TwoDVis
+from visualizers.three_d_vis import ThreeDVis
+import matplotlib as mpl 
+from matplotlib import pyplot as plt
+import argparse
+import json
+
+
+parser = argparse.ArgumentParser(description = "This program takes a csv file and displays the data in a useful way.")
+parser.add_argument('--json', '-j', help = "name of json file (default = no file)")
+parser.add_argument('--file', '-f', required = True, help = "name of csv file")
+parser.add_argument('--extra_data', '-e', required = True, help = "name of column of user-selected data")
+parser.add_argument('--delimiter', '-d', help = "delimiter separating the column names in the file (make this first in json file) (default = ',')")
+parser.add_argument('--latitude', '-l', help = "name of latitude column (default = 'Latitude')")
+parser.add_argument('--longitude', '-lo', help = "name of longitude column (default = 'Longitude')")
+parser.add_argument('--depth', '-de', help = "name of depth column (default = 'Depth')")
+parser.add_argument('--current_step', '-c', help = "name of current step column (default = 'Current Step')")
+parser.add_argument('--dist_tol', '-dt', help = "tolerance for points being too close together on chart (default = 0.0001)")
+parser.add_argument('--outlier_tol', '-o', help = "tolerance for outliers (in standard deviations) (default = 3)")
+parser.add_argument('--first', '-fi', help = "first step in range being examined (default = 0)")
+parser.add_argument('--last', '-la', help = "last step in range being examined (default = 1)")
+parser.add_argument('--predictor', '-p', help = "whether or not to use a predictor model to interpolate (default = no)", action = 'store_true')
+parser.add_argument('--mse_tol', '-t', help = "tolerance for MSE (default = 0.01)")
+parser.add_argument('--model', '-m', help = "type of model to preferably use(1(GPR)/2(SVR)) (default = 1)")
+parser.add_argument('--size', '-s', help = "number of points per row/column in the square/cubic grid that represents the model (default = 20)")
+parser.add_argument('--dimension', '-di', help = "dimensions of model (default = 2)")
+parser.add_argument('--plot', '-pl', help = "dimensions of hard data plot (default = 2)")
+
+
+args = parser.parse_args()
+file_name = args.file 
+user = args.extra_data
+
+delimiter = ","
+lat = "Latitude"
+lon = "Longitude"
+dep = "Depth"
+range_name = "Current Step"
+tol = 0.0001
+outlier_tol = 3
+range_min = 0
+range_max = 1
+ans = 'n'
+tol_mse = 0.01
+ans1 = 1
+length = 20
+ans2 = 2
+ans3 = 2
+
+if args.json != None:
+	with open(arg.json) as f:
+  		data = json.load(f)
+	json_file = []
+	for key, value in data.items():
+		json_file.append([key, value])
+	for i in range(len(json_file)):
+		if jason_file[i][0] == "delimiter":
+			delimiter = json_file[i][1]
+		elif jason_file[i][0] == "latitude":
+			lat = json_file[i][1]
+		elif jason_file[i][0] == "longitude":
+			lon = json_file[i][1]
+		elif jason_file[i][0] == "depth":
+			dep = json_file[i][1]
+		elif jason_file[i][0] == "current_step":
+			range_name = json_file[i][1]
+		elif jason_file[i][0] == "dist_tol":
+			tol = json_file[i][1]
+		elif jason_file[i][0] == "outlier_tol":
+			outlier_tol = json_file[i][1]
+		elif jason_file[i][0] == "first":
+			range_min = json_file[i][1]
+		elif jason_file[i][0] == "last":
+			range_max = json_file[i][1]
+		elif jason_file[i][0] == "predictor":
+			if jason_file[i][1] == "True":
+				ans = 'y'
+		elif jason_file[i][0] == "mse_tol":
+			tol_mse = json_file[i][1]
+		elif jason_file[i][0] == "model":
+			ans1 = json_file[i][1]
+		elif jason_file[i][0] == "size":
+			length = json_file[i][1]
+		elif jason_file[i][0] == "dimension":
+			ans2 = json_file[i][1]
+		else:
+			ans3 = json_file[i][1]
+
+if args.delimiter != None:
+	delimiter = args.delimiter
+if args.latitude != None:
+	lat = args.latitude
+if args.depth != None:
+	dep = args.depth
+if args.current_step != None:
+	range_name = args.current_step
+if args.dist_tol != None:
+	tol = args.dist_tol
+if args.outlier_tol != None:
+	outlier_tol = args.outlier_tol
+if args.first != None:
+	range_min = args.first
+if args.last != None:
+	range_max = args.last
+if args.predictor == True:
+	ans = 'y'
+if args.mse_tol != None:
+	tol_mse = args.mse_tol
+if args.model != None:
+	ans1 = args.model
+if args.size != None:
+	length = args.size
+if args.dimension != None:
+	ans2 = args.dimension
+if args.plot != None:
+	ans3 = args.plot
+
+
+file = CsvLoader(name = file_name, delimiter = delimiter)
+read_file = file.load()
+
+distance = DistanceCalculator(lat_name = lat, long_name = lon, file = read_file)
+distance_arr = distance.process()
+
+file_ranger = StepRange(step_name = range_name, step_min = range_min, step_max = range_max, file = read_file)
+file_ranged = file_ranger.process()
+
+close_dropper = DropClose(dep_name = dep, dist_arr = distance_arr, tolerance = tol, file = file_ranged)
+close_dropped = close_dropper.process()
+
+outlier_remover = OutlierRemoval(tolerance = outlier_tol, user_name = user, file = close_dropped)
+outlier_removed = outlier_remover.process()
+
+distance = DistanceCalculator(lat_name = lat, long_name = lon, file = outlier_removed)
+distance_arr = distance.process()
+
+if ans == "y":
+
+	Gauss = GPR(dist_array = distance_arr, user_name = user, dep_name = dep, length = length, file = outlier_removed, lat_n = lat, lon_n = lon, dims = ans2)
+	svr_mod = SV(dist_arr = distance_arr, user_name = user, dep_name = dep, length = length, file = outlier_removed, lat_n = lat, lon_n = lon, dims = ans2)
+	if ans1 == 1:
+		model_manager = ModelManager(primary = Gauss, secondary = svr_mod, tolerance = tol_mse, dimension = ans2)
+		x = model_manager.fit()
+	else:
+		model_manager = ModelManager(primary = svr_mod, secondary = Gauss, tolerance = tol_mse, dimension = ans2)
+		x = model_manager.fit()
+	if ans2 == 2:
+		gridpoints = model_manager.predict()
+		plt.figure()
+		cmap = mpl.cm.jet;
+		plt.xlabel("Distance (m)")
+		plt.ylabel(dep)
+		cb = plt.colorbar(plt.scatter(gridpoints[0], gridpoints[1], c = gridpoints[2], cmap = cmap))
+		cb.set_label(user)
+		if len(gridpoints) == 4:
+			plt.figure()
+			plt.xlabel("Distance (m)")
+			plt.ylabel(dep)
+			cb1 = plt.colorbar(plt.scatter(gridpoints[0], gridpoints[1], c = gridpoints[3], cmap = cmap))
+			cb1.set_label("Variance (" + str(user) + ")")
+		plt.show()
+	else:
+		cmap = mpl.cm.jet
+		gridpoints = model_manager.predict()
+		fig = plt.figure()
+		ax = fig.add_subplot(111, projection = '3d')
+		ax.set_xlabel(lat)
+		ax.set_ylabel(lon)
+		ax.set_zlabel(dep)
+		cb = plt.colorbar(ax.scatter(gridpoints[0], gridpoints[1], gridpoints[2], c = gridpoints[3], cmap = cmap))
+		cb.set_label(user)
+		if len(gridpoints) == 5:
+			fig1 = plt.figure()
+			axis = fig1.add_subplot(111, projection = '3d')
+			axis.set_xlabel(lat)
+			axis.set_ylabel(lon)
+			axis.set_zlabel(dep)
+			cb1 = plt.colorbar(axis.scatter(gridpoints[0], gridpoints[1], gridpoints[2], c = gridpoints[4], cmap = cmap))
+			cb1.set_label("Variance (" + str(user) + ")")
+		plt.show()
+if ans3 == 2:
+	graph = TwoDVis(dep_name = dep, dist_arr = distance_arr, user_name = user, file = outlier_removed)
+	graph.plot()
+else:
+	graph = ThreeDVis(dep_name = dep, dist_arr = distance_arr, user_name = user, lat_name = lat, lon_name = lon, file = outlier_removed, )
+	graph.plot()