Supply and Demand
Contents
72. Supply and Demand#
Some charts for supply and demand
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import seaborn as sns;sns.set()
sns.set_style("darkgrid", {'axes.grid' : False})
72.1. Supply and Demand Demo Functions#
Demand function, only for illustration purpose.
class Demand():
def __init__(self, **kwargs):
self.params = kwargs
def quantity_of_demand_wrt_price(self, p, **params):
params__income = params.get(
"income",
self.params.get("income", 1)
)
if isinstance(p, (list, tuple,set)):
max_p = max(p)
else:
max_p = max([p])
params__intercept = params.get(
"intercept",
self.params.get("intercept", max_p)
)
return {
"price": p,
"quantity_of_demand": params__intercept-p + params__income
}
def __repr__(self):
return f"Demand Function"
class Supply():
def __init__(self, **kwargs):
self.params = kwargs
def quantity_of_supply_wrt_price(self, p, **params):
params__stimulation = params.get(
"stimulation",
self.params.get("stimulation", 1)
)
return {
"price": p,
"quantity_of_supply": p - params__stimulation
}
def __repr__(self):
return f"Supply Function"
dmd_params_1 = {"income": 1, "intercept": 11}
dmd_params_2 = {"income": 3, "intercept": 11}
dmd_1 = Demand(**dmd_params_1)
dmd_2 = Demand(**dmd_params_2)
dmd_p = np.linspace(1,11,101)
dmd_1_prices = dmd_1.quantity_of_demand_wrt_price(dmd_p)
dmd_2_prices = dmd_2.quantity_of_demand_wrt_price(dmd_p)
fig, ax = plt.subplots(figsize=(10, 10))
ax.plot(
dmd_1_prices["quantity_of_demand"],
dmd_1_prices["price"],
"g-"
)
ax.set_xlabel(r"Quantity of Demand ($Q_d$)")
ax.set_ylabel(r"Price ($P$)")
ax.set_xticks([])
ax.set_yticks([])
[]
fig, ax = plt.subplots(figsize=(10, 10))
ax.plot(
dmd_1_prices["quantity_of_demand"],
dmd_1_prices["price"],
"g-"
)
ax.plot(
dmd_2_prices["quantity_of_demand"],
dmd_2_prices["price"],
"g--"
)
dmd_1_prices_qod_middle = dmd_1_prices["quantity_of_demand"][
int(len(dmd_1_prices["quantity_of_demand"])/2)
]
dmd_1_prices_price_middle = dmd_1_prices["price"][
int(len(dmd_1_prices["quantity_of_demand"])/2)
]
ax.arrow(
dmd_1_prices_qod_middle, dmd_1_prices_price_middle,
1.7, 0,
fc="g", ec="g", head_width=0.2, alpha=0.2
)
ax.set_xlabel(r"Quantity of Demand ($Q_d$)")
ax.set_ylabel(r"Price ($P$)")
ax.set_xticks([])
ax.set_yticks([])
[]
sly_params_1 = {"stimulation": 1}
sly_params_2 = {"stimulation": 3}
sly_1 = Supply(**sly_params_1)
sly_2 = Supply(**sly_params_2)
sly_p = np.linspace(1,11,101)
sly_1_prices = sly_1.quantity_of_supply_wrt_price(sly_p)
sly_2_prices = sly_2.quantity_of_supply_wrt_price(sly_p)
fig, ax = plt.subplots(figsize=(10, 10))
ax.plot(
sly_1_prices["quantity_of_supply"],
sly_1_prices["price"],
"r-"
)
ax.plot(
sly_2_prices["quantity_of_supply"],
sly_2_prices["price"],
"r--"
)
sly_1_prices_qos_middle = sly_1_prices["quantity_of_supply"][
int(len(sly_1_prices["quantity_of_supply"])/2)
]
sly_1_prices_price_middle = sly_1_prices["price"][
int(len(sly_1_prices["quantity_of_supply"])/2)
]
ax.arrow(
sly_1_prices_qos_middle, sly_1_prices_price_middle,
-1.7, 0,
fc="r", ec="r", head_width=0.2, alpha=0.2
)
ax.set_xlabel(r"Quantity of Supply ($Q_s$)")
ax.set_ylabel(r"Price ($P$)")
ax.set_xticks([])
ax.set_yticks([])
[]
fig, ax = plt.subplots(figsize=(10, 10))
ax.plot(
sly_1_prices["quantity_of_supply"],
sly_1_prices["price"],
"r-"
)
ax.plot(
dmd_1_prices["quantity_of_demand"],
dmd_1_prices["price"],
"g-"
)
ax.plot(
dmd_2_prices["quantity_of_demand"],
dmd_2_prices["price"],
"g--"
)
dmd_1_prices_qod_middle = dmd_1_prices["quantity_of_demand"][
int(len(dmd_1_prices["quantity_of_demand"])/2)
]
dmd_1_prices_price_middle = dmd_1_prices["price"][
int(len(dmd_1_prices["quantity_of_demand"])/2)
]
ax.arrow(
dmd_1_prices_qod_middle, dmd_1_prices_price_middle,
1.7, 0,
fc="g", ec="g", head_width=0.2, alpha=0.2
)
ax.set_xlabel(r"Quantity ($Q$)")
ax.set_ylabel(r"Price ($P$)")
ax.set_xticks([])
ax.set_yticks([])
[]
incomes = np.linspace(0.1, 10.1, 10)
incomes
array([ 0.1 , 1.21111111, 2.32222222, 3.43333333, 4.54444444,
5.65555556, 6.76666667, 7.87777778, 8.98888889, 10.1 ])
dmd_p = np.linspace(1,11,101)
dmd_p
array([ 1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. ,
2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1,
3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2,
4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9, 5. , 5.1, 5.2, 5.3,
5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6. , 6.1, 6.2, 6.3, 6.4,
6.5, 6.6, 6.7, 6.8, 6.9, 7. , 7.1, 7.2, 7.3, 7.4, 7.5,
7.6, 7.7, 7.8, 7.9, 8. , 8.1, 8.2, 8.3, 8.4, 8.5, 8.6,
8.7, 8.8, 8.9, 9. , 9.1, 9.2, 9.3, 9.4, 9.5, 9.6, 9.7,
9.8, 9.9, 10. , 10.1, 10.2, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8,
10.9, 11. ])
prices_grid, incomes_grid = np.meshgrid(dmd_p, incomes)
prices_grid
array([[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ],
[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ],
[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ],
...,
[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ],
[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ],
[ 1. , 1.1, 1.2, ..., 10.8, 10.9, 11. ]])
dmd_variable = Demand()
qod_variable = []
for i in range(len(incomes)):
qod_i = []
for j in range(len(dmd_p)):
qod_i.append(
dmd_variable.quantity_of_demand_wrt_price(prices_grid[i,j], income=incomes_grid[i,j], intercept=10)["quantity_of_demand"]
)
qod_variable.append(qod_i)
np.array(qod_variable).shape
(10, 101)
fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(20,20))
# Plot the surface.
surf = ax.plot_surface(qod_variable, incomes_grid, prices_grid, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
# Customize the z axis.
ax.set_zlim(-1.01, 10.01)
ax.zaxis.set_major_locator(LinearLocator(10))
# A StrMethodFormatter is used automatically
ax.zaxis.set_major_formatter('{x:.02f}')
ax.set_xlabel("Quantity of Demand")
ax.set_ylabel("Income")
ax.set_zlabel("Price")
# Add a color bar which maps values to colors.
# fig.colorbar(surf, shrink=0.5, aspect=5)
Text(0.5, 0, 'Price')
