ENH: Set a default image size across all lectures (#165)

* ENH: Set a default image size across all lectures * Remove size settings * Add global figure setting * Check lecture 1-10 except 6 * Check lecture 11-21 * Check all lectures * Remove unnessesary settings * Update width and remove shadow --------- Co-authored-by: HengCheng <79777246+2789372130@users.noreply.github.com>

Author: mmcky

lectures/_config.yml

@@ -37,6 +37,9 @@ latex:
 sphinx:
  extra_extensions: [sphinx_multitoc_numbering, sphinxext.rediraffe, sphinx_exercise, sphinx_togglebutton, sphinx_proof, sphinx_tojupyter] 
  config:
+ # myst-nb config
+ nb_render_image_options:
+ width: 80%
  html_favicon: _static/lectures-favicon.ico
  html_theme: quantecon_book_theme
  html_static_path: ['_static']

lectures/cobweb.md

@@ -265,7 +265,7 @@ def plot45(model, pmin, pmax, p0, num_arrows=5):
  """
  pgrid = np.linspace(pmin, pmax, 200)
 
- fig, ax = plt.subplots(figsize=(7, 5))
+ fig, ax = plt.subplots()
  ax.set_xlim(pmin, pmax)
  ax.set_ylim(pmin, pmax)
 

lectures/geom_series.md

@@ -3,8 +3,10 @@ jupytext:
  text_representation:
  extension: .md
  format_name: myst
+ format_version: 0.13
+ jupytext_version: 1.14.5
 kernelspec:
- display_name: Python 3
+ display_name: Python 3 (ipykernel)
  language: python
  name: python3
 ---
@@ -652,7 +654,7 @@ approximations, under different values of $T$, and $g$ and $r$ in Python.
 First we plot the true finite stream present-value after computing it
 below
 
-```{code-cell} python3
+```{code-cell} ipython3
 # True present value of a finite lease
 def finite_lease_pv_true(T, g, r, x_0):
  G = (1 + g)
@@ -679,7 +681,13 @@ Now that we have defined our functions, we can plot some outcomes.
 
 First we study the quality of our approximations
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Finite lease present value $T$ periods ahead"
+ name: finite_lease_present_value
+---
 def plot_function(axes, x_vals, func, args):
  axes.plot(x_vals, func(*args), label=func.__name__)
 
@@ -694,10 +702,9 @@ our_args = (T, g, r, x_0)
 funcs = [finite_lease_pv_true,
  finite_lease_pv_approx_1,
  finite_lease_pv_approx_2]
- ## the three functions we want to compare
+ # the three functions we want to compare
 
 fig, ax = plt.subplots()
-ax.set_title('Finite Lease Present Value $T$ Periods Ahead')
 for f in funcs:
  plot_function(ax, T, f, our_args)
 ax.legend()
@@ -713,12 +720,17 @@ However, holding $g$ and r fixed, our approximations deteriorate as $T$ increase
 Next we compare the infinite and finite duration lease present values
 over different lease lengths $T$.
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Infinite and finite lease present value $T$ periods ahead"
+ name: infinite_and_finite_lease_present_value
+---
 # Convergence of infinite and finite
 T_max = 1000
 T = np.arange(0, T_max+1)
 fig, ax = plt.subplots()
-ax.set_title('Infinite and Finite Lease Present Value $T$ Periods Ahead')
 f_1 = finite_lease_pv_true(T, g, r, x_0)
 f_2 = np.full(T_max+1, infinite_lease(g, r, x_0))
 ax.plot(T, f_1, label='T-period lease PV')
@@ -736,11 +748,16 @@ perpetual lease.
 Now we consider two different views of what happens as $r$ and
 $g$ covary
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Value of lease of length $T$"
+ name: value_of_lease
+---
 # First view
 # Changing r and g
 fig, ax = plt.subplots()
-ax.set_title('Value of lease of length $T$')
 ax.set_ylabel('Present Value, $p_0$')
 ax.set_xlabel('$T$ periods ahead')
 T_max = 10
@@ -765,9 +782,15 @@ graph.
 If you aren't enamored of 3-d graphs, feel free to skip the next
 visualization!
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Three period lease PV with varying $g$ and $r$"
+ name: three_period_lease_PV
+---
 # Second view
-fig = plt.figure()
+fig = plt.figure(figsize = [16, 5])
 T = 3
 ax = plt.subplot(projection='3d')
 r = np.arange(0.01, 0.99, 0.005)
@@ -785,8 +808,7 @@ fig.colorbar(surf, shrink=0.5, aspect=5)
 ax.set_xlabel('$r$')
 ax.set_ylabel('$g$')
 ax.set_zlabel('Present Value, $p_0$')
-ax.view_init(20, 10)
-ax.set_title('Three Period Lease PV with Varying $g$ and $r$')
+ax.view_init(20, 8)
 plt.show()
 ```
 
@@ -803,7 +825,7 @@ represents our present value formula for an infinite lease.
 
 After that, we'll use SymPy to compute derivatives
 
-```{code-cell} python3
+```{code-cell} ipython3
 # Creates algebraic symbols that can be used in an algebraic expression
 g, r, x0 = sym.symbols('g, r, x0')
 G = (1 + g)
@@ -814,13 +836,13 @@ print('Our formula is:')
 p0
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 print('dp0 / dg is:')
 dp_dg = sym.diff(p0, g)
 dp_dg
 ```
 
-```{code-cell} python3
+```{code-cell} ipython3
 print('dp0 / dr is:')
 dp_dr = sym.diff(p0, r)
 dp_dr
@@ -839,7 +861,13 @@ We will now go back to the case of the Keynesian multiplier and plot the
 time path of $y_t$, given that consumption is a constant fraction
 of national income, and investment is fixed.
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Path of aggregate output tver time"
+ name: path_of_aggregate_output_over_time
+---
 # Function that calculates a path of y
 def calculate_y(i, b, g, T, y_init):
  y = np.zeros(T+1)
@@ -857,7 +885,6 @@ y_init = 0
 T = 100
 
 fig, ax = plt.subplots()
-ax.set_title('Path of Aggregate Output Over Time')
 ax.set_xlabel('$t$')
 ax.set_ylabel('$y_t$')
 ax.plot(np.arange(0, T+1), calculate_y(i_0, b, g_0, T, y_init))
@@ -873,11 +900,16 @@ We now examine what will
 happen if we vary the so-called **marginal propensity to consume**,
 i.e., the fraction of income that is consumed
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Changing consumption as a fraction of income"
+ name: changing_consumption_as_fraction_of_income
+---
 bs = (1/3, 2/3, 5/6, 0.9)
 
 fig,ax = plt.subplots()
-ax.set_title('Changing Consumption as a Fraction of Income')
 ax.set_ylabel('$y_t$')
 ax.set_xlabel('$t$')
 x = np.arange(0, T+1)
@@ -893,7 +925,13 @@ path of output over time.
 
 Now we will compare the effects on output of increases in investment and government spending.
 
-```{code-cell} python3
+```{code-cell} ipython3
+---
+mystnb:
+ figure:
+ caption: "Different increase on output"
+ name: different_increase_on_output
+---
 fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 10))
 fig.subplots_adjust(hspace=0.3)
 

lectures/inequality.md

@@ -244,8 +244,6 @@ mystnb:
  name: lorenz_us
  image:
  alt: lorenz_us
- classes: shadow bg-primary
- width: 75%
 ---
 fig, ax = plt.subplots()
 
@@ -312,8 +310,6 @@ mystnb:
  name: lorenz_gini
  image:
  alt: lorenz_gini
- classes: shadow bg-primary
- width: 75%
 ---
 fig, ax = plt.subplots()
 
@@ -390,8 +386,6 @@ mystnb:
  name: gini_simulated
  image:
  alt: gini_simulated
- classes: shadow bg-primary
- width: 75%
 ---
 plot_inequality_measures(range(k), 
  ginis, 
@@ -465,8 +459,6 @@ mystnb:
  name: gini_wealth_us
  image:
  alt: gini_wealth_us
- classes: shadow bg-primary
- width: 75%
 ---
 xlabel = "year"
 ylabel = "gini coefficient"
@@ -489,8 +481,6 @@ mystnb:
  name: gini_income_us
  image:
  alt: gini_income_us
- classes: shadow bg-primary
- width: 75%
 ---
 xlabel = "year"
 ylabel = "gini coefficient"
@@ -596,8 +586,6 @@ mystnb:
  name: top_shares_us
  image:
  alt: top_shares_us
- classes: shadow bg-primary
- width: 75%
 ---
 xlabel = "year"
 ylabel = "top $10\%$ share"
@@ -677,8 +665,6 @@ mystnb:
  name: top_shares_simulated
  image:
  alt: top_shares_simulated
- classes: shadow bg-primary
- width: 75%
 ---
 plot_inequality_measures(range(len(topshares)), 
  topshares, 
@@ -732,8 +718,6 @@ mystnb:
  name: top_shares_us_al
  image:
  alt: top_shares_us_al
- classes: shadow bg-primary
- width: 75%
 ---
 xlabel = "year"
 ylabel = "top $10\%$ share"