
""" Baseline example that needs to be beaten """

import numpy as np
import matplotlib.pyplot as plt

x, y, yerr = np.loadtxt("data/data.txt", unpack=True)

A = np.vstack((np.ones_like(x), x)).T
C = np.diag(yerr * yerr)
cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A)))
b_ls, m_ls = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y)))

fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=yerr, c="k", fmt="o")
x_range = np.array([min(x), max(x)])
ax.plot(x_range, m_ls * x_range + b_ls, c="#666666", lw=2, zorder=-100)
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("assets/result.png")

print m_ls, b_ls


""" Baseline example that needs to be beaten """

import os
import numpy as np
import matplotlib.pyplot as plt

x, y, yerr = np.loadtxt("data/data.txt", unpack=True)

A = np.vstack((np.ones_like(x), x)).T
C = np.diag(yerr * yerr)
cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A)))
b_ls, m_ls = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y)))

fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=yerr, c="k", fmt="o")
x_range = np.array([min(x), max(x)])
ax.plot(x_range, m_ls * x_range + b_ls, c="#666666", lw=2, zorder=-100)
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("assets/result.png")

print("Results of m, b: ({0:.4f} {1:.4f})".format(m_ls, b_ls))

# Let's store result parameters in environment variables, and we will deal
# with more complex values (e.g., uncertainties, etc) later
os.environ["RESULT_M"] = "{0:.5f}".format(m_ls)
os.environ["RESULT_B"] = "{0:.5f}".format(b_ls)

Add RESULT_M and RESULT_B to environment varaible
Add RESULT_M and RESULT_B to environment varaible [ci skip]

  
  """ Baseline example that needs to be beaten """
  
+ import os
  import numpy as np
  import matplotlib.pyplot as plt
  
  x, y, yerr = np.loadtxt("data/data.txt", unpack=True)
  
  A = np.vstack((np.ones_like(x), x)).T
  C = np.diag(yerr * yerr)
  cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A)))
  b_ls, m_ls = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y)))
  
  fig, ax = plt.subplots()
  ax.errorbar(x, y, yerr=yerr, c="k", fmt="o")
  x_range = np.array([min(x), max(x)])
  ax.plot(x_range, m_ls * x_range + b_ls, c="#666666", lw=2, zorder=-100)
  ax.set_xlabel("x")
  ax.set_ylabel("y")
  fig.savefig("assets/result.png")
  
- print m_ls, b_ls
+ print("Results of m, b: ({0:.4f} {1:.4f})".format(m_ls, b_ls))
  
+ # Let's store result parameters in environment variables, and we will deal
+ # with more complex values (e.g., uncertainties, etc) later
+ os.environ["RESULT_M"] = "{0:.5f}".format(m_ls)
+ os.environ["RESULT_B"] = "{0:.5f}".format(b_ls)
+ 
Add RESULT_M and RESULT_B to environment varaible
## Code Before:

""" Baseline example that needs to be beaten """

import numpy as np
import matplotlib.pyplot as plt

x, y, yerr = np.loadtxt("data/data.txt", unpack=True)

A = np.vstack((np.ones_like(x), x)).T
C = np.diag(yerr * yerr)
cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A)))
b_ls, m_ls = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y)))

fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=yerr, c="k", fmt="o")
x_range = np.array([min(x), max(x)])
ax.plot(x_range, m_ls * x_range + b_ls, c="#666666", lw=2, zorder=-100)
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("assets/result.png")

print m_ls, b_ls

## Instruction:
Add RESULT_M and RESULT_B to environment varaible
## Code After:

""" Baseline example that needs to be beaten """

import os
import numpy as np
import matplotlib.pyplot as plt

x, y, yerr = np.loadtxt("data/data.txt", unpack=True)

A = np.vstack((np.ones_like(x), x)).T
C = np.diag(yerr * yerr)
cov = np.linalg.inv(np.dot(A.T, np.linalg.solve(C, A)))
b_ls, m_ls = np.dot(cov, np.dot(A.T, np.linalg.solve(C, y)))

fig, ax = plt.subplots()
ax.errorbar(x, y, yerr=yerr, c="k", fmt="o")
x_range = np.array([min(x), max(x)])
ax.plot(x_range, m_ls * x_range + b_ls, c="#666666", lw=2, zorder=-100)
ax.set_xlabel("x")
ax.set_ylabel("y")
fig.savefig("assets/result.png")

print("Results of m, b: ({0:.4f} {1:.4f})".format(m_ls, b_ls))

# Let's store result parameters in environment variables, and we will deal
# with more complex values (e.g., uncertainties, etc) later
os.environ["RESULT_M"] = "{0:.5f}".format(m_ls)
os.environ["RESULT_B"] = "{0:.5f}".format(b_ls)
