Fix deployment and remove mystnb figure options (#36)

* fix labels

* fix deployment

* fix numbering

Author: kp992

.github/workflows/ci_pr.yml renamed to .github/workflows/ci.yml

@@ -5,7 +5,7 @@ on:
     types: [opened, synchronize, reopened]
   push:
     branches:
-    - theme_updates
+    - main
   workflow_dispatch:
     inputs:
       preview_page:

.github/workflows/deploy.yml

@@ -671,12 +671,6 @@ Now that we have defined our functions, we can plot some outcomes.
 First we study the quality of our approximations
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Finite lease present value $T$ periods ahead"
-    name: finite_lease_present_value
----
 :label: gm-plot-fig-1
 
 def plot_function(axes, x_vals, func, args):
@@ -717,12 +711,6 @@ Next we compare the infinite and finite duration lease present values
 over different lease lengths $T$.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Infinite and finite lease present value $T$ periods ahead"
-    name: infinite_and_finite_lease_present_value
----
 :label: gm-plot-fig-2
 
 # Convergence of infinite and finite
@@ -752,12 +740,6 @@ Now we consider two different views of what happens as $r$ and
 $g$ covary
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Value of lease of length $T$"
-    name: value_of_lease
----
 :label: gm-plot-fig-3
 
 # First view
@@ -793,12 +775,6 @@ If you aren't enamored of 3-d graphs, feel free to skip the next
 visualization!
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Three period lease PV with varying $g$ and $r$"
-    name: three_period_lease_PV
----
 :label: gm-plot-fig-4
 
 # Second view
@@ -879,12 +855,6 @@ time path of $y_t$, given that consumption is a constant fraction
 of national income, and investment is fixed.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Path of aggregate output tver time"
-    name: path_of_aggregate_output_over_time
----
 :label: gm-plot-fig-5
 
 # Function that calculates a path of y
@@ -925,12 +895,6 @@ happen if we vary the so-called **marginal propensity to consume**,
 i.e., the fraction of income that is consumed
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Changing consumption as a fraction of income"
-    name: changing_consumption_as_fraction_of_income
----
 :label: gm-plot-fig-6
 
 bs = (1/3, 2/3, 5/6, 0.9)
@@ -957,12 +921,6 @@ path of output over time.
 Now we will compare the effects on output of increases in investment and government spending.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Different increase on output"
-    name: different_increase_on_output
----
 :label: gm-plot-fig-7
 
 fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6, 10))

lectures/geom_series.md

@@ -86,12 +86,6 @@ We first plot price levels over the period 1600-1914.
 During most years in this time interval, the countries were on a gold or silver standard.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Long run time series of the price level
-    name: lrpl
----
 :label: ih-plot-fig-1
 
 df_fig5_befe1914 = df_fig5[df_fig5.index <= 1914]
@@ -169,12 +163,6 @@ Although we didn't have to use logarithms in our earlier graphs that had stopped
 After the outbreak of the Great War in 1914, the four countries left the gold standard and in so doing acquired the ability to print money to finance government expenditures.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Long run time series of the price level (log)
-    name: lrpl_lg
----
 :label: ih-plot-fig-2
 
 fig, ax = plt.subplots(dpi=200)
@@ -414,12 +402,6 @@ The sources of our data are:
 * Table 3.4, exchange rate with US
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Price index and exchange rate (Austria)
-    name: pi_xrate_austria
----
 :label: ih-plot-fig-3
 
 p_seq = df_aus['Retail price index, 52 commodities']
@@ -440,12 +422,6 @@ Price index and exchange rate (Austria)
 :::
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Monthly inflation rate (Austria)
-    name: inflationrate_austria
----
 :label: ih-plot-fig-4
 
 # Plot moving average
@@ -475,12 +451,6 @@ The source of our data for Hungary is:
 * Table 3.10, price level $\exp p$ and exchange rate
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Price index and exchange rate (Hungary)
-    name: pi_xrate_hungary
----
 :label: ih-plot-fig-5
 
 p_seq = df_hun['Hungarian index of prices']
@@ -501,12 +471,6 @@ Price index and exchange rate (Hungary)
 :::
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Monthly inflation rate (Hungary)
-    name: inflationrate_hungary
----
 :label: ih-plot-fig-6
 
 # Plot moving average
@@ -535,12 +499,6 @@ We dropped the exchange rate after June 1924, when the zloty was adopted. We did
 ```
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Price index and exchange rate (Poland)
-    name: pi_xrate_poland
----
 :label: ih-plot-fig-7
 
 # Splice three price series in different units
@@ -585,12 +543,6 @@ plt.show()
 ```
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Monthly inflation rate (Poland)
-    name: inflationrate_poland
----
 :label: ih-plot-fig-8
 
 # Plot moving average
@@ -614,12 +566,6 @@ The sources of our data for Germany are the following tables from chapter 3 of {
 * Table 3.19, exchange rate
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Price index and exchange rate (Germany)
-    name: pi_xrate_germany
----
 :label: ih-plot-fig-9
 
 p_seq = df_deu['Price index (on basis of marks before July 1924,'
@@ -642,12 +588,6 @@ Price index and exchange rate (Germany)
 :::
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Price index (adjusted) and exchange rate (Germany)
-    name: piadj_xrate_germany
----
 :label: ih-plot-fig-10
 
 p_seq = df_deu['Price index (on basis of marks before July 1924,'
@@ -676,12 +616,6 @@ Price index (adjusted) and exchange rate (Germany)
 :::
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: Monthly inflation rate (Germany)
-    name: inflationrate_germany
----
 :label: ih-plot-fig-11
 
 # Plot moving average

lectures/inflation_history.md

@@ -104,12 +104,6 @@ The total height of each bar $i$ is willingness to pay by consumer $i$.
 The orange portion of some of the bars shows consumer surplus.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Willingness to pay (discrete)"
-    name: wpdisc
----
 :label: isd-plot-fig-1
 
 fig, ax = plt.subplots()
@@ -171,12 +165,6 @@ p = 100 e^{-q}
 $$
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Willingness to pay (continuous)"
-    name: wpcont
----
 :label: isd-plot-fig-2
 
 def inverse_demand(q):
@@ -209,12 +197,6 @@ Reasoning by analogy with the discrete case, the area under the demand curve and
 The consumer surplus is shaded in the figure below.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Willingness to pay (continuous) with consumer surplus"
-    name: wpcont_cs
----
 :label: isd-plot-fig-3
 
 # solve for the value of q where demand meets price
@@ -254,12 +236,6 @@ Having discussed demand, let's now switch over to the supply side of the market.
 The figure below shows the price at which a collection of producers, also numbered 1 to 10, are willing to sell one unit of the good in question
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Willingness to sell (discrete)"
-    name: wsdisc
----
 :label: isd-plot-fig-4
 
 fig, ax = plt.subplots()
@@ -310,12 +286,6 @@ The shaded area is the total producer surplus in this continuous model.
 ```
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Willingness to sell (continuous) with producer surplus"
-    name: wscont
----
 :label: isd-plot-fig-5
 
 def inverse_supply(q):
@@ -365,12 +335,6 @@ In general, for a function $f$, the **integral** of $f$ over the interval $[a, b
 This value is written as $\int_a^b f(x) \mathrm{d} x$ and illustrated in the figure below when $f(x) = \cos(x/2) + 1$.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Area under the curve"
-    name: integrate
----
 :label: isd-plot-fig-6
 
 def f(x):
@@ -473,12 +437,6 @@ def inverse_supply(q, model):
 Here is a plot of these two functions using `market`.
 
 ```{code-cell} ipython3
----
-mystnb:
-  figure:
-    caption: "Supply and demand"
-    name: supply_demand
----
 :label: isd-plot-fig-7
 
 market = create_market()
@@ -524,13 +482,9 @@ The next figure illustrates
 
 ```{code-cell} ipython3
 ---
-mystnb:
-  figure:
-    caption: "Supply and demand (consumer surplus)"
-    name: supply_demand_cs
+label: isd-plot-fig-8
 tags: [hide-input]
 ---
-:label: isd-plot-fig-8
 
 q = 1.25
 p = inverse_demand(q, market)
@@ -596,13 +550,9 @@ The next figure illustrates
 
 ```{code-cell} ipython3
 ---
-mystnb:
-  figure:
-    caption: "Supply and demand (producer surplus)"
-    name: supply_demand_ps
+label: isd-plot-fig-9
 tags: [hide-input]
 ---
-:label: isd-plot-fig-9
 
 q = 0.75
 p = inverse_supply(q, market)
@@ -682,13 +632,9 @@ The next figure plots welfare as a function of $q$.
 
 ```{code-cell} ipython3
 ---
-mystnb:
-  figure:
-    caption: "Welfare"
-    name: wf
+label: isd-plot-fig-10
 tags: [hide-input]
 ---
-:label: isd-plot-fig-10
 
 q_vals = np.linspace(0, 1.78, 200)
 fig, ax = plt.subplots()