I am a rich billionaire. I have kidnapped you budding bioinformaticians studying Part III Systems Biology (with the help of your course organisers), because my mad doctor has run away before telling me my diagnosis.
I have locked this room! Until you can determine my diagnosis using the clues the mad doctor has left behind, and can tell me how to treat it, I will not let you out!
If I die before you can figure it out unfortunately you will be trapped in this room forever….
The mad doctor sent me an email after his escape, stating that the skills you acquired in the Introduction to Python course should help you follow the clues he provided in the instructions below and in the encrypted files.
I was advised to get you to compete in three to four teams. A different person must type in each of the tasks with others only being allowed to talk - not type. There will be extra reward clues on offer for the most elegant solutions.
Level:
First install the cryptography module.
Then import the decryptor module provided in decryptor.py to your python script.
Only then will you be able to investigate the secret files.
#decryptor.py should be in your working directory (the same folder as the Jupyterlab notebook where you are running the code)
import cryptography
import os
print(os.getcwd())
#os.chdir("../../materials")
os.chdir("../course_files/encrypted_data_escape_room")
import decryptor
from decryptor import decrypt/Users/kavishah/Documents/Cambridge_PhD/1_PhD_Project_Harnessing_evolution/10_Teaching_Course-Design/Python-Intro/materials[84, 104, 101, 32, 121, 101, 97, 114, 32, 116, 104, 97, 116, 32, 68, 78, 65, 32, 119, 97, 115, 32, 100, 105, 115, 99, 111, 118, 101, 114, 101, 100, 32, 105, 110]Use the password and the decrypt() function to open the file secret_file_1.txt.enc. This will decrypt the secret file and return a decrypted copy in your working directory. Use the help() function to investigate use of the module and function.
secret_file_1.txt, load this sequence and pull the characters at the fibonacci numbers.The password to decrypt secret_file_2.tsv.enc is all lowercase: (third-word-in-clue) + (first word in the clue) + (6th letter in the 5th word) + (the last word backwards)
secret_file_2.csv. Multiply the animal_weight by 4, and log transform the madness_index using the natural log. Plot the output as a scatter plot.The password to decrypt secret_file_3.txt.enc is the first 5 letters of the cell type represented - all in Upper case
decrypt('secret_file_2.csv.enc', 'fatalillnessoraeppa', 'secret_file_2.csv')
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Step 1: Import the CSV file
# Replace 'your_file.csv' with the actual file name/path
secret2_df = pd.read_csv('secret_file_2.csv')
print(secret2_df.head())
#secret2_df = pd.read_csv('secret_file_2.csv', header=1 , names=['animal_weight', 'madness_index'])
#print(secret2_df.head())
secret2_df.dropna(inplace=True) # This will remove any row with NaN values
#secret2_df = secret2_df.drop([0], axis=1)
print(secret2_df.head())
secret2_df = secret2_df.apply(pd.to_numeric, errors='coerce').dropna()
print('df-floats', secret2_df.head())
print(secret2_df.dtypes)
#secret2_df['animal_weight'] = secret2_df['animal_weight'].astype(float)
# Step 2: Multiply animal_weight by 4
secret2_df['animal_weight'] = secret2_df['animal_weight'] * 4
print('df-floats-animal-weights*4', secret2_df.head())
secret2_df = secret2_df[(secret2_df != 0).all(axis=1)]
# Step 3: Log transform the madness_index using the natural log
secret2_df = secret2_df[secret2_df['madness_index'] > 0] # Remove rows with negative madness_index
secret2_df['madness_index'] = np.log(secret2_df['madness_index'])
# Step 4: Plot the output as a scatter plot
plt.figure(figsize=(4, 4))
plt.scatter(secret2_df['animal_weight'], secret2_df['madness_index'], color='blue')
plt.title('Scatter Plot of Animal Weight vs. Log Transformed Madness Index')
plt.xlabel('Animal Weight (multiplied by 4)')
plt.ylabel('Log Transformed Madness Index')
plt.grid(True)
plt.show()
secret2_df = secret2_df[secret2_df['animal_weight'] < 1000] # Remove outlier
plt.figure(figsize=(4, 4))
plt.scatter(secret2_df['animal_weight'], secret2_df['madness_index'], color='blue', s=0.01)
plt.title('Scatter Plot of Animal Weight vs. Log Transformed Madness Index')
plt.xlabel('Animal Weight (multiplied by 4)')
plt.ylabel('Log Transformed Madness Index')
plt.grid(True)
plt.show()Unlocked file created: unlocked_data
animal_weight madness_index
0 115.0 1.6516362549940018e+38
1 115.25 1.6516362549940018e+38
2 114.75 4.4896128191743455e+38
3 115.0 4.4896128191743455e+38
4 115.25 4.4896128191743455e+38
animal_weight madness_index
0 115.0 1.6516362549940018e+38
1 115.25 1.6516362549940018e+38
2 114.75 4.4896128191743455e+38
3 115.0 4.4896128191743455e+38
4 115.25 4.4896128191743455e+38
df-floats animal_weight madness_index
0 115.00 1.651636e+38
1 115.25 1.651636e+38
2 114.75 4.489613e+38
3 115.00 4.489613e+38
4 115.25 4.489613e+38
animal_weight float64
madness_index float64
dtype: object
df-floats-animal-weights*4 animal_weight madness_index
0 460.0 1.651636e+38
1 461.0 1.651636e+38
2 459.0 4.489613e+38
3 460.0 4.489613e+38
4 461.0 4.489613e+38
Password = “NEURO” Cell type is neurone
secret_file_3.txt.This will show you the clue. The password to decrypt secret_file_4.tsv.enc is an animal all upper case.
decrypt('secret_file_3.txt.enc', 'NEURO', 'secret_file_3.txt')
with open('secret_file_3.txt', 'r') as file:
encoded_clue = file.read() # Read the entire content of the file
print(encoded_clue)Unlocked file created: unlocked_data
The animal encoded in the equations below has been associated with a type of this illness. The animal all lowercase is also the clue for the next secret file
( x^2 = y^3 - 2y + 2 ), between ( y = -2 ) and ( y = 1 )
( y = x^2 ), between ( x = -2.5 ) and ( x = 2.5 )
( (x - 0.7)^2 + (y + 0.4)^2 = 0.05 )
( (x + 0.7)^2 + (y + 0.4)^2 = 0.05 )
( y = -0.5x^2 - 3 ), between ( x = -1.82 ) and ( x = 1.82 )
( y = -(0.4x - 2)^2 - 3 ), between ( x = 0 ) and ( x = 7 )
( y = -(0.4x + 2)^2 - 3 ), between ( x = -7 ) and ( x = 0 )
( y = (0.4x - 3)^2 + 2 ), between ( x = 2 ) and ( x = 12.2 )
( y = (0.4x + 3)^2 + 2 ), between ( x = -12.2 ) and ( x = -2 )
( x = (0.5y + 3)^2 + 5 ), between ( y = -3.5 ) and ( y = 1 )
( -x = (0.5y + 3)^2 + 5 ), between ( y = -3.5 ) and ( y = 1 )
( y = (0.3x - 6)^2 ), between ( x = 12 ) and ( x = 17 )
( y = (0.3x + 6)^2 ), between ( x = -17 ) and ( x = -12 )
Then plot the equations:
import numpy as np
import matplotlib.pyplot as plt
# Create a figure and axis
fig, ax = plt.subplots(figsize=(10, 10))
# 1. Plot: x^2 = y^3 - 2y + 2, between y = -2 and y = 1
y1 = np.linspace(-2, 1, 400)
x1 = np.sqrt(y1**3 - 2*y1 + 2)
ax.plot(x1, y1, color='black')
ax.plot(-x1, y1, color='black')
# 2. Plot: y = x^2, between x = -2.5 and x = 2.5
x2 = np.linspace(-2.5, 2.5, 400)
y2 = x2**2
ax.plot(x2, y2, color='black')
# 3. Plot: (x - 0.7)^2 + (y + 0.4)^2 = 0.05
theta = np.linspace(0, 2*np.pi, 400)
x3 = 0.7 + np.sqrt(0.05) * np.cos(theta)
y3 = -0.4 + np.sqrt(0.05) * np.sin(theta)
ax.plot(x3, y3, color='black')
# 4. Plot: (x + 0.7)^2 + (y + 0.4)^2 = 0.05
x4 = -0.7 + np.sqrt(0.05) * np.cos(theta)
y4 = -0.4 + np.sqrt(0.05) * np.sin(theta)
ax.plot(x4, y4, color='black')
# 5. Plot: y = -0.5x^2 - 3, between x = -1.82 and x = 1.82
x5 = np.linspace(-1.82, 1.82, 400)
y5 = -0.5 * x5**2 - 3
ax.plot(x5, y5, color='black')
# 6. Plot: y = -(0.4x - 2)^2 - 3, between x = 0 and x = 7
x6 = np.linspace(0, 7, 400)
y6 = -(0.4*x6 - 2)**2 - 3
ax.plot(x6, y6, color='black')
# 7. Plot: y = -(0.4x + 2)^2 - 3, between x = -7 and x = 0
x7 = np.linspace(-7, 0, 400)
y7 = -(0.4*x7 + 2)**2 - 3
ax.plot(x7, y7, color='black')
# 8. Plot: y = (0.4x - 3)^2 + 2, between x = 2 and x = 12.2
x8 = np.linspace(2, 12.2, 400)
y8 = (0.4*x8 - 3)**2 + 2
ax.plot(x8, y8, color='black')
# 9. Plot: y = (0.4x + 3)^2 + 2, between x = -12.2 and x = -2
x9 = np.linspace(-12.2, -2, 400)
y9 = (0.4*x9 + 3)**2 + 2
ax.plot(x9, y9, color='black')
# 10. Plot: x = (0.5y + 3)^2 + 5, between y = -3.5 and y = 1
y10 = np.linspace(-3.5, 1, 400)
x10 = (0.5*y10 + 3)**2 + 5
ax.plot(x10, y10, color='black')
# 11. Plot: -x = (0.5y + 3)^2 + 5, between y = -3.5 and y = 1
y11 = np.linspace(-3.5, 1, 400)
x11 = -(0.5*y10 + 3)**2 - 5
ax.plot(x11, y10, color='black')
# 12. Plot: y = (0.3x - 6)^2, between x = 12 and 17
x12 = np.linspace(12, 17, 400)
y12 = (0.3*x12 - 6)**2
ax.plot(x12, y12, color='black')
# 13. Plot: y = (0.3x + 6)^2, between x = -17 and -12
x13 = np.linspace(-17, -12, 400)
y13 = (0.3*x13 + 6)**2
ax.plot(x13, y13, color='black')
# Remove labels, legend, and keep the grid
ax.set_xlabel('')
ax.set_ylabel('')
ax.grid(True)
# Set equal aspect ratio
ax.set_aspect('equal', adjustable='datalim')
# Set axis limits to range from -20 to +20
ax.set_xlim([-20, 20])
ax.set_ylim([-20, 20])
# Show the plot
plt.show()/var/folders/bd/9_jp6g0s1cv178k5425cr_740000gn/T/ipykernel_49801/3161528567.py:9: RuntimeWarning: invalid value encountered in sqrt
x1 = np.sqrt(y1**3 - 2*y1 + 2)
Look at the plots for the animal Password = “BAT”
secret_file_4.tsvThe password for secret_file_5.txt.enc is in the clue. The password is all lower case.
secret_file_5.txt. Put the amino acid sequence contents into a numpy array. Each letter should be in a new element in the array and each line forms a new row. Order the amino acids in alphabetical order. Convert the array to numeric encoding, with A being represented by ‘0’ and the last amino acid by ‘19’. Plot a heatmap of your matrix.The family name of the animal species in the heatmap (all lower case) is the clue to decrypt the next file, secret_file_6.txt.enc
decrypt('secret_file_5.txt.enc', 'virus', 'secret_file_5.txt')
import numpy as np
import matplotlib.pyplot as plt
# Define the 20 amino acids in alphabetical order
amino_acids = "ACDEFGHIKLMNPQRSTVWY"
# Create a dictionary to map amino acid letters to numeric values
# A is mapped to 0, C to 1, ..., Y to 19
amino_acid_to_value = {amino_acid: index for index, amino_acid in enumerate(amino_acids)}
def text_file_to_numpy_array(file_path):
# Read the text file and convert it into a numpy array
with open(file_path, 'r') as file:
lines = file.readlines()
# Remove newline characters and convert to numpy array of letters
amino_acid_rows = [list(line.strip()) for line in lines]
#print(amino_acid_rows[0])
#print(amino_acid_rows[1])
#print(amino_acid_rows[2])
# Create a numpy array from the list of rows
amino_acid_array = np.array(amino_acid_rows)
# Convert the amino acid letters to numbers using the dictionary
number_array = np.vectorize(amino_acid_to_value.get)(amino_acid_array)
return number_array
def plot_heatmap(number_array):
# Plot the heatmap using matplotlib
plt.imshow(number_array, cmap='hot', interpolation='nearest')
plt.colorbar(label='Amino Acid Intensity (A = 0 to Y = 19)')
plt.title('Amino Acid Heatmap')
plt.show()
# Example usage
file_path = 'secret_file_5.txt' # Replace with your text file path
amino_acid_array = text_file_to_numpy_array(file_path)
plot_heatmap(amino_acid_array)Unlocked file created: unlocked_data
Password = “canidae” heatmap shows a dog
The message clue in secret_file_6.txt is encoded as triangular numbers where ‘a’ = 1 and therefore the first triangular number and ‘z’ = 26, and therefore the 26th triangular number.
Write code to retrieve the message
The last two words without gaps (all lower case) is the password for the final clue in secret_file_7.txt.enc.
For the last clue, load the dictionary in secret file secret_file_7.txt. The next word in the clue can be retrieved by using the “key” which equals the current word’s “value”. The first word in the clue is “if”
Congratulations if you successfully figured out my diagnosis! You may now leave the room. I must hurry and get treated before symptoms start to occur!
Hopefully that was a fun exercise! Please see the key takeaways from this course
for loops and while loops enable one to build more complex programs