import numpy as np
import skfuzzy as fuzz
import matplotlib.pyplot as plt

# Generate universe variables
#   * Quality and service on subjective ranges [0, 10]
#   * Tip has a range of [0, 25] in units of percentage points
goals = np.arange(0, 35, 1)
xGPerMatch = np.arange(0, 1, 0.01)
shotsPerMatch=np.arange(0, 5, 0.01)
onTargetPerMatch  = np.arange(0, 5, 0.01)
ocena=np.arange(0, 101, 1)

# Generate fuzzy membership functions
goals_low = fuzz.trapmf(goals, [0, 0, 10,15])
goals_md = fuzz.trimf(goals, [10, 15, 20])
goals_hi = fuzz.trapmf(goals, [15, 20, 35, 100])
xGPerMatch_lo = fuzz.trapmf(xGPerMatch, [0,0, 0.2, 0.5])
xGPerMatch_md = fuzz.trimf(xGPerMatch, [0.2, 0.5, 0.7])
xGPerMatch_hi = fuzz.trapmf(xGPerMatch, [0.5, 0.7, 2, 10])
shotsPerMatch_lo = fuzz.trapmf(shotsPerMatch, [0,0, 1, 2])
shotsPerMatch_md = fuzz.trapmf(shotsPerMatch, [1, 2, 3, 4])
shotsPerMatch_hi = fuzz.trapmf(shotsPerMatch, [3, 4, 4.5, 10])
onTargetPerMatch_lo = fuzz.trapmf(onTargetPerMatch, [0, 0, 0.5, 1])
onTargetPerMatch_md = fuzz.trimf(onTargetPerMatch, [0.5, 1, 1.5])
onTargetPerMatch_hi = fuzz.trimf(onTargetPerMatch, [1, 1.5, 5])
ocena_lo = fuzz.trapmf(ocena, [0, 0, 40,50])
ocena_md = fuzz.trapmf(ocena, [40, 50, 60, 80])
ocena_hi = fuzz.trapmf(ocena, [70, 80, 100, 100])


# Visualize these universes and membership functions
fig, (ax0, ax1, ax2, ax3, ax4) = plt.subplots(nrows=5, figsize=(8, 9))

ax0.plot(goals, goals_low, 'b', linewidth=1.5, label='Mało')
ax0.plot(goals, goals_md, 'g', linewidth=1.5, label='Średnio')
ax0.plot(goals, goals_hi, 'r', linewidth=1.5, label='Dużo')
ax0.set_title('Liczba bramek')
ax0.legend()

ax1.plot(xGPerMatch, xGPerMatch_lo, 'b', linewidth=1.5, label='Mało')
ax1.plot(xGPerMatch, xGPerMatch_md, 'g', linewidth=1.5, label='Średno')
ax1.plot(xGPerMatch, xGPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
ax1.set_title('Service quality')
ax1.legend()

ax2.plot(shotsPerMatch, shotsPerMatch_lo, 'b', linewidth=1.5, label='Mało')
ax2.plot(shotsPerMatch, shotsPerMatch_md, 'g', linewidth=1.5, label='Średnio')
ax2.plot(shotsPerMatch, shotsPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
ax2.set_title('Strzały na mecz')
ax2.legend()

ax3.plot(onTargetPerMatch, onTargetPerMatch_lo, 'b', linewidth=1.5, label='Mało')
ax3.plot(onTargetPerMatch, onTargetPerMatch_md, 'g', linewidth=1.5, label='Średnio')
ax3.plot(onTargetPerMatch, onTargetPerMatch_hi, 'r', linewidth=1.5, label='Dużo')
ax3.set_title('Strzały w światlo bramki na mecz')
ax3.legend()

ax4.plot(ocena, ocena_lo, 'b', linewidth=1.5, label='Niska')
ax4.plot(ocena, ocena_md, 'g', linewidth=1.5, label='Średnia')
ax4.plot(ocena, ocena_hi, 'r', linewidth=1.5, label='Wysoka')
ax4.set_title('Ocena')
ax4.legend()

# Turn off top/right axes
for ax in (ax0, ax1, ax2, ax3, ax4):
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()

plt.tight_layout()
plt.show()

goals_value=22
xGPerMatch_value=0.9
shotsPerMatch_value=4.43
onTargetPerMatch_value=1.57

goals_level_lo = fuzz.interp_membership(goals, goals_low, goals_value)
goals_level_md = fuzz.interp_membership(goals, goals_md, goals_value)
goals_level_hi = fuzz.interp_membership(goals, goals_hi, goals_value)

xGPerMatch_level_lo = fuzz.interp_membership(xGPerMatch, xGPerMatch_lo, xGPerMatch_value)
xGPerMatch_level_md = fuzz.interp_membership(xGPerMatch, xGPerMatch_md, xGPerMatch_value)
xGPerMatch_level_hi = fuzz.interp_membership(xGPerMatch, xGPerMatch_hi, xGPerMatch_value)

shotsPerMatch_level_lo = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_lo, shotsPerMatch_value)
shotsPerMatch_level_md = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_md, shotsPerMatch_value)
shotsPerMatch_level_hi = fuzz.interp_membership(shotsPerMatch, shotsPerMatch_hi, shotsPerMatch_value)

onTargetPerMatch_level_lo = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_lo, onTargetPerMatch_value)
onTargetPerMatch_level_md = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_md, onTargetPerMatch_value)
onTargetPerMatch_level_hi = fuzz.interp_membership(onTargetPerMatch, onTargetPerMatch_hi, onTargetPerMatch_value)

# Now we take our rules and apply them. Rule 1 concerns bad food OR service.
# The OR operator means we take the maximum of these two.
active_rule1 = np.fmax(goals_level_hi, xGPerMatch_level_hi)
active_rule2=np.fmax(active_rule1, onTargetPerMatch_level_hi)

# Now we apply this by clipping the top off the corresponding output
# membership function with `np.fmin`
ocena_activation_hi = np.fmin(active_rule2, ocena_hi)  # removed entirely to 0

# For rule 2 we connect acceptable service to medium tipping
active_rule3=np.fmax(np.fmin(goals_level_lo,xGPerMatch_level_hi),np.fmin(goals_level_lo, shotsPerMatch_level_hi))
ocena_activation_md = np.fmin(active_rule2, ocena_md)

# For rule 3 we connect high service OR high food with high tipping
ocena_activation_lo = np.fmin(goals_level_lo, ocena_lo)
ocena0 = np.zeros_like(ocena)

# Visualize this
fig, ax0 = plt.subplots(figsize=(8, 3))

ax0.fill_between(ocena, ocena0, ocena_activation_lo, facecolor='b', alpha=0.7)
ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
ax0.fill_between(ocena, ocena0, ocena_activation_md, facecolor='g', alpha=0.7)
ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
ax0.fill_between(ocena, ocena0, ocena_activation_hi, facecolor='r', alpha=0.7)
ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
ax0.set_title('Output membership activity')

# Turn off top/right axes
for ax in (ax0,):
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()

plt.tight_layout()
plt.show()

aggregated = np.fmax(ocena_activation_lo,
                     np.fmax(ocena_activation_md, ocena_activation_hi))

# Calculate defuzzified result
ocena_output = fuzz.defuzz(ocena, aggregated, 'centroid')
ocena_activation = fuzz.interp_membership(ocena, aggregated, ocena_output)  # for plot

# Visualize this
fig, ax0 = plt.subplots(figsize=(8, 3))

ax0.plot(ocena, ocena_lo, 'b', linewidth=0.5, linestyle='--', )
ax0.plot(ocena, ocena_md, 'g', linewidth=0.5, linestyle='--')
ax0.plot(ocena, ocena_hi, 'r', linewidth=0.5, linestyle='--')
ax0.fill_between(ocena, ocena0, aggregated, facecolor='Orange', alpha=0.7)
ax0.plot([ocena_output, ocena_output], [0, ocena_activation], 'k', linewidth=1.5, alpha=0.9)
ax0.set_title('Aggregated membership and result (line)')

# Turn off top/right axes
for ax in (ax0,):
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()

plt.tight_layout()
plt.show()

print(ocena_output)