rvignesh2102

[rvignesh2102]

import pandas as pd

import numpy as np

import os

import seaborn

import matplotlib.pyplot as plt

#default directory

dir(os)

os.chdir ("/Users/rvignesh/Documents/Python Code")

data = pd.read_csv('mars_carter.csv', low_memory="false") #low memory false to avoid error

#print (data)

print(len(data)) #Number of rows

print(len(data.columns)) #Number of columns

#average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)

print ('average elevation of each of the manually determined N points along the crater rim in KM')

C2= data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase')

P2=data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True")

print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)")

print(C2)

print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)")

print (P2)

#the maximum number of cohesive layers in any azimuthal direction that could be reliably identified

print ('the maximum number of cohesive layers in any azimuthal direction that could be reliably identified')

C3= data["NUMBER_LAYERS"].value_counts(sort='Flase')

P3=data["NUMBER_LAYERS"].value_counts(sort='False', normalize="True")

print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified")

print(C3)

print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified")

print (P3)

#Interested in crater layer and rim floor depth. Hence, do not require data in which no layer formation occured or rimdepth is 0

Depth_rimfloor = data[(data['DEPTH_RIMFLOOR_TOPOG']>0)]

D1 = Depth_rimfloor.copy()

Layer_number = data[(data["NUMBER_LAYERS"]>0)]

D2 = Layer_number.copy()

#No Such Data is missing, Excluding 0 depth and layers by giving them NAN

D1['DEPTH_RIMFLOOR_TOPOG']=D1['DEPTH_RIMFLOOR_TOPOG'].replace(0,np.nan)

D2['NUMBER_LAYERS']=D1['NUMBER_LAYERS'].replace(0,np.nan)

#Depth excluding 0

print ('Depth from rim floor excluding 0')

C4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase')

P4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True")

print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)")

print(C4)

print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)")

print (P4)

#Layers excluding 0

print ('Number of layers excluding 0')

C5= D2["NUMBER_LAYERS"].value_counts(sort='Flase')

P5= data["NUMBER_LAYERS"].value_counts(sort='False', normalize="True")

print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified")

print(C5)

print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified")

print (P5)

#Product of depth and no of layers. Performing for practice only

#D1["Product of depth and no of layers"]=D1['DEPTH_RIMFLOOR_TOPOG']*D2['NUMBER_LAYERS']

#print(D1)

D1["DEPTH_RIMFLOOR_TOPOG"]=D1["DEPTH_RIMFLOOR_TOPOG"].astype('category')

E1=D1.groupby("DEPTH_RIMFLOOR_TOPOG").size()

print(E1)

E2=D1.groupby("DEPTH_RIMFLOOR_TOPOG").size()*100/len(data)

print(E2)

E4=D1.groupby("NUMBER_LAYERS").size()

print(E4)

E5=D1.groupby("NUMBER_LAYERS").size()*100/len(data)

print(E5)

seaborn.countplot(x="DEPTH_RIMFLOOR_TOPOG",data=D1)

plt.xlabel("Depth of rimfloor")

plt.title("depth of rimfloor from the top")

plt.show()

#secondary variable

D2['NUMBER_LAYERS']=D1['NUMBER_LAYERS']/20

E6=D2.groupby("NUMBER_LAYERS").size()

print(E6)

seaborn.displot(x="NUMBER_LAYERS",data=D1)

plt.xlabel("number of layer")

plt.title("nmaximum number of cohesive layers in any azimuthal direction that could be reliably identified")

plt.show()

scatterplot = seaborn.regplot(x="NUMBER_LAYERS", y="DEPTH_RIMFLOOR_TOPOG", fit_reg=False, data=D1)

plt.xlabel("Number of layers")

plt.ylabel("Depth of rimfloor")

plt.title("relationship between no of layer and depth of rimfloor")

plt.show()

There doesn't seem to be any relationship between the depth of rimfloor and no of layer in crater.However, the data shows that it's more likely to have a cater with 0 depth. The number of layers might be dependent on the velocity and angle of impact and the type of soil and not the depth of crater

[rvignesh2102]

import pandas as pd import numpy as np import os

default directory

dir(os) os.chdir ("/Users/rvignesh/Documents/Python Code") data = pd.read_csv('mars_carter.csv', low_memory="false") #low memory false to avoid error

print (data)

print(len(data)) #Number of rows print(len(data.columns)) #Number of columns

average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)

print ('average elevation of each of the manually determined N points along the crater rim in KM') C2= data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase') P2=data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True") print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print(C2) print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print (P2)

the maximum number of cohesive layers in any azimuthal direction that could be reliably identified

print ('the maximum number of cohesive layers in any azimuthal direction that could be reliably identified') C3= data["NUMBER_LAYERS"].value_counts(sort='Flase') P3=data["NUMBER_LAYERS"].value_counts(sort='False', normalize="True") print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print(C3) print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print (P3)

Interested in crater layer and rim floor depth. Hence, do not require data in which no layer formation occured or rimdepth is 0

Depth_rimfloor = data[(data['DEPTH_RIMFLOOR_TOPOG']>0)] D1 = Depth_rimfloor.copy() Layer_number = data[(data["NUMBER_LAYERS"]>0)] D2 = Layer_number.copy()

No Such Data is missing, Excluding 0 depth and layers by giving them NAN

D1['DEPTH_RIMFLOOR_TOPOG']=D1['DEPTH_RIMFLOOR_TOPOG'].replace(0,np.nan) D2['NUMBER_LAYERS']=D1['NUMBER_LAYERS'].replace(0,np.nan)

Depth excluding 0

print ('Depth from rim floor excluding 0') C4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase') P4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True") print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print(C4) print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print (P4)

Layers excluding 0

print ('Number of layers excluding 0') C5= D2["NUMBER_LAYERS"].value_counts(sort='Flase') P5= data["NUMBER_LAYERS"].value_counts(sort='False', normalize="True") print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print(C5) print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print (P5)

Product of depth and no of layers. Performing for practice only

D1["Product of depth and no of layers"]=D1['DEPTH_RIMFLOOR_TOPOG']*D2['NUMBER_LAYERS'] print(D1)

[rvignesh2102]

Week 2

import pandas as pd import numpy as np import os

dir(os) os.chdir ("/Users/rvignesh/Documents/Python Code") data = pd.read_csv('mars_carter.csv', low_memory="false") #low memory false to avoid error

print(len(data)) print(len(data.columns)) print ('average elevation of each of the manually determined N points along the crater rim in KM') C2= data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase') P2=data["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True") print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print(C2) print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print (P2) print ('the maximum number of cohesive layers in any azimuthal direction that could be reliably identified') C3= data["NUMBER_LAYERS"].value_counts(sort='Flase') P3=data["NUMBER_LAYERS"].value_counts(sort='False', normalize="True") print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print(C3) print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print (P3) Depth_rimfloor = data[(data['DEPTH_RIMFLOOR_TOPOG']>0)] D1 = Depth_rimfloor.copy() Layer_number = data[(data["NUMBER_LAYERS"]>0)] D2 = Layer_number.copy() print ('Depth from rim floor excluding 0') C4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='Flase') P4= D1["DEPTH_RIMFLOOR_TOPOG"].value_counts(sort='False', normalize="True") print ("The count of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print(C4) print ("The aggregate of average elevation of each of the manually determined N points along (or inside) the crater rim(units are km)") print (P4) print ('Number of layers excluding 0') C5= D2["NUMBER_LAYERS"].value_counts(sort='Flase') P5= D2["NUMBER_LAYERS"].value_counts(sort='False', normalize="True") print ("The count of the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print(C5) print ("The aggregate the maximum number of cohesive layers in any azimuthal direction that could be reliably identified") print (P5)

[rvignesh2102]

Week 1

After reviewing the codebook for Martian craters, I am interested in understanding the factors contributing to surface layer formation on Mars. I am particularly intrigued to find out what affects the formation of layers in the Mars craters in turn may be able to understand its impact in geological formation.

From my research and literature review, I've learned that impact velocity is influenced by the object’s mass(O'Keefe, J. D., & Ahrens, T. J. (1977). Meteorite impact ejecta: Dependence of mass and energy lost on planetary escape velocity. Science, 198(4323), 1249-1251) which as lo depends on the planets esacpe velocity, as air resistance is negligible for large meteors (Fisher, W. J. (1934). Mass and Velocity of Meteorites and the Air Density Along Their Luminous Paths. Harvard College Observatory Circular, vol. 385, pp. 1-16, 385, 1-16). Additionally, studies indicate that the depth of a crater depends on several factors, including impact velocity (Ahrens, T. J., Xia, K., & Coker, D. (2002, July). Depth of cracking beneath impact craters: New constraint for impact velocity. In AIP Conference Proceedings (Vol. 620, No. 1, pp. 1393-1396). American Institute of Physics), surface hardness of the soil, and the presence of ice layers on Mars (Senft, L. E., & Stewart, S. T. (2008). Impact crater formation in icy layered terrains on Mars. Meteoritics & Planetary Science, 43(12), 1993-2013).

After examining the data, I have come up with a hypothesis that the depth of the crater on Mars is likely to have a correlation with the number of layers present. This proposes that the impact velocity and other factors could affect both the depth of the crater and the formation of layers on the Martian surface. Further investigation into these relationships can provide valuable insights into the geological processes that shape the landscape of Mars.