Added files completed on 07/23

A24_awparle.py

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+# !/usr/bin/env python3
+
+import numpy as np
+
+## A24_Question 1
+
+# Adam Parler    ENGR 1410-013    March 26, 2014
+#Problem Statement: Create a matrix of minimum values based on a users
+#input.
+
+#Test Case 1
+y = np.array([[-2, 3, 5, 4],[7, 2, -10, 6],[18, 4, -2, 6], [3, 7, 5, -3]])
+# Expected outcome: Min values Row 1: -2 Row 2: -10 Row 3: -2 Row 4: 3
+                   #Max values Row 1: 5 Row 2: 6 Row 3: 18 Row 4: 7
+
+# Test Case 2
+# y=[1; 2; 3; 4]
+#Expected outcome: This matrix does not have more than two columns.
+
+# Variables
+# y=user inputed matrix
+# r=number of rows in y
+# c=number of columns in y
+# p=counter
+# MIN=matrix of numbers in odd numbered columns
+# MAX=matrix of numbers in even numbered columns
+# M=matrix of minimum and maximum values in each row
+# a=number of rows in M
+# b=number of columns in M
+
+#Prompts user to input matrix
+#y=input('Input a matrix with at least two colums:   ');
+[r,c]=y.shape
+
+#checks to make sure it has 2 columns
+if c < 2:
+    print('This matrix does not have more than two columns')
+    sys.exit()
+
+M = np.empty([r,2])
+#loops through each row
+for i in range(r):
+    MIN = []
+    MAX = []
+
+    #loops through the odd columns
+    for j in range(0,c,2):
+        MIN.append(y[i,j])
+
+
+    #loops through the even columns
+    for j in range(1,c,2):
+        MAX.append(y[i,j])
+
+    #finds the minimun and maximum values
+    M[i,0] = min(MIN)
+    M[i,1] = max(MAX)
+
+
+[a,b]=M.shape
+#displays the output information
+print('The smallest value found when comparing the odd-numbered columns:')
+for k in range(a):
+    print(f'Row {k+1:d} = {M[k,0]:.0f}')
+
+print('The largest value found when comparing the even-numbered columns:')
+for h in range(a):
+    print(f'Row {h+1:d} = {M[h,1]:.0f}')
+
+
+#####################################################################################
+
+## A24_Question 2
+#Problem Statement: Allows a user to either select a previously entered
+#material or add a new one.
+
+#Test Case 1
+c = 1
+#Expected outcome: The specific heat of Gold is 0.031 cal/(g deg c)=0.031 BTU/(lb_m deg F)
+
+#Test Case 2
+# c=6
+# p=Uranium
+# Q=2.34
+#Expected outcome: The specific heat of Uranium is 2.340 cal/(g deg c)=2.332 BTU/(lb_m deg F)
+
+#Assumptions
+# 1 joule=9.48x10^-4 BTU
+# 1 joule=.239 cal
+# 1 kg=2.205 lb_m
+# 1 kg=1000 grams
+# change of 1 degree C=change of 1.8 deg F
+#Converts BTU's to calories
+E = 1/.239*(9.48*10**-4)/1; #cal*J/cal*BTU/J [BTU-->J-->cal]
+
+#Converts grams to pound-mass
+M = 1/1000*2.205/1;   #g*kg/g*lb_m/kg [g-->kg-->lb_m]
+
+#Convets change in deg C to change in deg F
+T = 1.8/1; #deg C*(change in deg F)/(change in deg C)  [(deg C)*(deg F)/(deg C)
+
+#Cell array of pre-determined data
+MAT = [['Aluminum', 0.22], ['Calcium', 0.22],['Gold', 0.031], ['Silicon', 0.17], ['Zinc', 0.093]]
+#c=menu('Choose a metal',MAT{:,1},'Enter a new metal');
+
+#Checks to see if the user selected new metal
+if c >= len(MAT):
+    pass
+    #input new metal information
+    #p=input('Enter material name:  ','s');
+    #q=input('Enter specific heat: (cal/(g deg c)   ');
+else:
+    #Pulles previously stored values
+    p = MAT[c][0]
+    q = MAT[c][1]
+
+
+#converts cal/(g deg C) to BTU/(lb_m deg F)
+g=q*E/(M*T);
+
+#prints the results on the screen
+print(f'The specific heat of {p} is {q:.3f} cal/(g deg C) = {g:.3f} BTU/(lb_m deg F).')
+

A25_awparle.py

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+# !/usr/bin/env python3
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Adam Parler    ENGR 1410-013    March 27, 2014
+#Problem Statement: This program classifies plasma and displays the result
+#on a graph.
+
+#Test Case 1
+D = -8;
+T = 2.5;
+# Expected outcome: Phase=Molecular Fluid
+
+#Test Case 2
+# D=-6;
+# T=5;
+# Expected outcome: Phase=Plasma
+
+#Test Case 3
+# D=1.5;
+# T=3;
+# Expected outcome: Phase=Metallic Fluid
+
+#Variables
+# D=Density (g/cc)
+# T=temperature (K)
+# CLASS=phase classification
+# DATA=all division data
+# X=x values for phase division
+# Y=y values for phase division
+# temp=temporary polyfit values
+# Div=values of polyfitted lines
+
+DATA = np.array([[-10, 0, 3.3, 3.9], [0, 2, 3.9, 5.6], [0, 0, 2, 3.9]])
+
+[r,c] = DATA.shape;
+
+Div = np.empty([r-1,2])
+X = np.empty(2)
+Y = np.empty(2)
+for i in range(r-1):
+    X[0] = DATA[i,0]
+    X[1] = DATA[i,1]
+    Y[0] = DATA[i,2]
+    Y[1] = DATA[i,3]
+    temp = np.polyfit(X,Y,1)
+    Div[i,:] = temp
+
+
+# D=input('Enter log of density (g/cc):  ');
+# T=input('Enter log of temperature (K):   ');
+
+if D <= DATA[r-1,0]:
+    if T <= Div[0,0]*D+Div[0,1]:
+        CLASS='Molecular Fluid'
+    else:
+        CLASS='Plasma';
+
+else:
+    if T <= Div[1,0]*D+Div[1,1]:
+        CLASS='Metallic Fluid'
+    else:
+        CLASS='Plasma'
+
+
+
+print(f'For the log density {D:.1f} and log temperature {T:.1f}, the phase is {CLASS}.')
+
+plt.figure()
+plt.axis([-10, 2, 2, 6])
+plt.plot(D,T,'.r', markersize=10)
+plt.text(-6,2.8, 'Molecular Fluid')
+plt.text(-6, 4.5, 'Plasma')
+plt.text(.3,2.3, 'Metallic Fluid')
+plt.text(D+0.125, T-0.05, '<--Your point')
+
+for i in range(r):
+    X[0] = DATA[i,0];
+    X[1] = DATA[i,1];
+    Y[0] = DATA[i,2];
+    Y[1] = DATA[i,3];
+    plt.plot(X,Y,'-k')
+    
+plt.xlabel('Log Density ($\\rho$) [g/cc]')
+plt.ylabel('Log Temperature (T) [K]')
+plt.xticks(range(-10,3,2))
+plt.yticks(range(2,7))
+
+plt.show()
+
+