[WASM] Remove numba and quantecon for WASM lectures (#571)

* remove numba from ar1_process

* Replace quantecon with wasm version

* skip lp_intro

* install quantecon_wasm

* Use github url to fetch the graph file data

* add a pass in if statment to avoid failure when pip is commented

* remove fixed files

* fix pip installs

* fix failure

lectures/ar1_processes.md

@@ -409,7 +409,6 @@ Here is one solution:
 ```{code-cell} ipython3
 from scipy.special import factorial2
 
-
 def sample_moments_ar1(k, m=100_000, mu_0=0.0, sigma_0=1.0, seed=1234):
     np.random.seed(seed)
     sample_sum = 0.0

lectures/eigen_II.md

@@ -21,7 +21,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 In this lecture we will begin with the foundational concepts in spectral theory.

lectures/french_rev.md

@@ -62,8 +62,7 @@ This lecture uses data from three spreadsheets assembled by {cite}`sargent_velde
   * [datasets/assignat.xlsx](https://github.com/QuantEcon/lecture-python-intro/blob/main/lectures/datasets/assignat.xlsx)
 
 ```{code-cell} ipython3
-%pip install openpyxl
-%pip install requests
+!pip install openpyxl requests
 ```
 
 ```{code-cell} ipython3

lectures/heavy_tails.md

@@ -19,8 +19,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install --upgrade yfinance pandas_datareader
-%pip install openpyxl
+!pip install yfinance pandas_datareader
 ```
 
 We use the following imports.
@@ -30,7 +29,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import yfinance as yf
 import pandas as pd
-import statsmodels.api as 
+import statsmodels.api as sm
 import pyodide_http
 
 from pandas_datareader import wb

lectures/inequality.md

@@ -82,7 +82,6 @@ We will need to install the following packages
 :tags: [hide-output]
 
 !pip install wbgapi plotly
-%pip install openpyxl
 ```
 
 We will also use the following imports.

lectures/inflation_history.md

@@ -4,7 +4,7 @@ jupytext:
     extension: .md
     format_name: myst
     format_version: 0.13
-    jupytext_version: 1.16.1
+    jupytext_version: 1.16.7
 kernelspec:
   display_name: Python 3 (ipykernel)
   language: python
@@ -22,8 +22,7 @@ The `xlrd` package is used by `pandas` to perform operations on Excel files.
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install xlrd
-!pip install openpyxl
+!pip install xlrd openpyxl
 ```
 
 <!-- Check for pandas>=2.1.4 for Google Collab Compat -->
@@ -36,6 +35,7 @@ from packaging.version import Version
 
 if Version(version("pandas")) < Version('2.1.4'):
     !pip install "pandas>=2.1.4"
+    pass
 ```
 
 We can then import the Python modules we will use.

lectures/input_output.md

@@ -21,7 +21,6 @@ This lecture requires the following imports and installs before we proceed.
 :tags: [hide-output]
 
 !pip install quantecon_book_networks
-!pip install quantecon
 !pip install pandas-datareader
 ```
 

lectures/long_run_growth.md

@@ -65,7 +65,7 @@ be interesting to describe both total GDP and GDP per capita as it evolves withi
 First we will need to install the following package
 
 ```{code-cell} ipython3
-%pip install openpyxl
+!pip install openpyxl
 ```
 
 Now let's import the packages needed to explore what the data says about long-run growth

lectures/markov_chains_I.md

@@ -22,7 +22,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 ## Overview

lectures/markov_chains_II.md

@@ -21,7 +21,7 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-%pip install quantecon_wasm
+!pip install quantecon_wasm
 ```
 
 ## Overview

lectures/mle.md

@@ -13,10 +13,6 @@ kernelspec:
 
 # Maximum Likelihood Estimation
 
-```{code-cell} ipython3
-%pip install openpyxl
-```
-
 ```{code-cell} ipython3
 from scipy.stats import lognorm, pareto, expon
 import numpy as np

lectures/networks.md

@@ -16,7 +16,7 @@ kernelspec:
 ```{code-cell} ipython3
 :tags: [hide-output]
 
-!pip install quantecon-book-networks pandas-datareader
+!pip install quantecon_wasm quantecon-book-networks pandas-datareader
 ```
 
 ## Outline
@@ -54,7 +54,7 @@ import numpy as np
 import networkx as nx
 import matplotlib.pyplot as plt
 import pandas as pd
-import quantecon as qe
+import quantecon_wasm as qe
 
 import matplotlib.cm as cm
 import quantecon_book_networks.input_output as qbn_io

lectures/short_path.md

@@ -3,8 +3,10 @@ jupytext:
   text_representation:
     extension: .md
     format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.16.7
 kernelspec:
-  display_name: Python 3
+  display_name: Python 3 (ipykernel)
   language: python
   name: python3
 ---
@@ -44,7 +46,7 @@ Dynamic programming is an extremely powerful optimization technique that we appl
 
 The only scientific library we'll need in what follows is NumPy:
 
-```{code-cell} python3
+```{code-cell} ipython3
 import numpy as np
 ```
 
@@ -195,7 +197,7 @@ $$
 
 For example, for the simple graph above, we set
 
-```{code-cell} python3
+```{code-cell} ipython3
 from numpy import inf
 
 Q = np.array([[inf, 1,   5,   3,   inf, inf, inf],
@@ -216,7 +218,7 @@ For the sequence of approximations $\{J_n\}$ of the cost-to-go functions, we can
 
 Let's try with this example and see how we go:
 
-```{code-cell} python3
+```{code-cell} ipython3
 nodes = range(7)                              # Nodes = 0, 1, ..., 6
 J = np.zeros_like(nodes, dtype=int)        # Initial guess
 next_J = np.empty_like(nodes, dtype=int)   # Stores updated guess
@@ -249,7 +251,7 @@ But, importantly, we now have a methodology for tackling large graphs.
 :label: short_path_ex1
 ```
 
-The text below describes a weighted directed graph.
+The file data below describes a weighted directed graph.
 
 The line `node0, node1 0.04, node8 11.11, node14 72.21` means that from node0 we can go to
 
@@ -268,108 +270,16 @@ You will be dealing with floating point numbers now, rather than
 integers, so consider replacing `np.equal()` with `np.allclose()`.
 ```
 
-```{code-cell} python3
-%%file graph.txt
-node0, node1 0.04, node8 11.11, node14 72.21
-node1, node46 1247.25, node6 20.59, node13 64.94
-node2, node66 54.18, node31 166.80, node45 1561.45
-node3, node20 133.65, node6 2.06, node11 42.43
-node4, node75 3706.67, node5 0.73, node7 1.02
-node5, node45 1382.97, node7 3.33, node11 34.54
-node6, node31 63.17, node9 0.72, node10 13.10
-node7, node50 478.14, node9 3.15, node10 5.85
-node8, node69 577.91, node11 7.45, node12 3.18
-node9, node70 2454.28, node13 4.42, node20 16.53
-node10, node89 5352.79, node12 1.87, node16 25.16
-node11, node94 4961.32, node18 37.55, node20 65.08
-node12, node84 3914.62, node24 34.32, node28 170.04
-node13, node60 2135.95, node38 236.33, node40 475.33
-node14, node67 1878.96, node16 2.70, node24 38.65
-node15, node91 3597.11, node17 1.01, node18 2.57
-node16, node36 392.92, node19 3.49, node38 278.71
-node17, node76 783.29, node22 24.78, node23 26.45
-node18, node91 3363.17, node23 16.23, node28 55.84
-node19, node26 20.09, node20 0.24, node28 70.54
-node20, node98 3523.33, node24 9.81, node33 145.80
-node21, node56 626.04, node28 36.65, node31 27.06
-node22, node72 1447.22, node39 136.32, node40 124.22
-node23, node52 336.73, node26 2.66, node33 22.37
-node24, node66 875.19, node26 1.80, node28 14.25
-node25, node70 1343.63, node32 36.58, node35 45.55
-node26, node47 135.78, node27 0.01, node42 122.00
-node27, node65 480.55, node35 48.10, node43 246.24
-node28, node82 2538.18, node34 21.79, node36 15.52
-node29, node64 635.52, node32 4.22, node33 12.61
-node30, node98 2616.03, node33 5.61, node35 13.95
-node31, node98 3350.98, node36 20.44, node44 125.88
-node32, node97 2613.92, node34 3.33, node35 1.46
-node33, node81 1854.73, node41 3.23, node47 111.54
-node34, node73 1075.38, node42 51.52, node48 129.45
-node35, node52 17.57, node41 2.09, node50 78.81
-node36, node71 1171.60, node54 101.08, node57 260.46
-node37, node75 269.97, node38 0.36, node46 80.49
-node38, node93 2767.85, node40 1.79, node42 8.78
-node39, node50 39.88, node40 0.95, node41 1.34
-node40, node75 548.68, node47 28.57, node54 53.46
-node41, node53 18.23, node46 0.28, node54 162.24
-node42, node59 141.86, node47 10.08, node72 437.49
-node43, node98 2984.83, node54 95.06, node60 116.23
-node44, node91 807.39, node46 1.56, node47 2.14
-node45, node58 79.93, node47 3.68, node49 15.51
-node46, node52 22.68, node57 27.50, node67 65.48
-node47, node50 2.82, node56 49.31, node61 172.64
-node48, node99 2564.12, node59 34.52, node60 66.44
-node49, node78 53.79, node50 0.51, node56 10.89
-node50, node85 251.76, node53 1.38, node55 20.10
-node51, node98 2110.67, node59 23.67, node60 73.79
-node52, node94 1471.80, node64 102.41, node66 123.03
-node53, node72 22.85, node56 4.33, node67 88.35
-node54, node88 967.59, node59 24.30, node73 238.61
-node55, node84 86.09, node57 2.13, node64 60.80
-node56, node76 197.03, node57 0.02, node61 11.06
-node57, node86 701.09, node58 0.46, node60 7.01
-node58, node83 556.70, node64 29.85, node65 34.32
-node59, node90 820.66, node60 0.72, node71 0.67
-node60, node76 48.03, node65 4.76, node67 1.63
-node61, node98 1057.59, node63 0.95, node64 4.88
-node62, node91 132.23, node64 2.94, node76 38.43
-node63, node66 4.43, node72 70.08, node75 56.34
-node64, node80 47.73, node65 0.30, node76 11.98
-node65, node94 594.93, node66 0.64, node73 33.23
-node66, node98 395.63, node68 2.66, node73 37.53
-node67, node82 153.53, node68 0.09, node70 0.98
-node68, node94 232.10, node70 3.35, node71 1.66
-node69, node99 247.80, node70 0.06, node73 8.99
-node70, node76 27.18, node72 1.50, node73 8.37
-node71, node89 104.50, node74 8.86, node91 284.64
-node72, node76 15.32, node84 102.77, node92 133.06
-node73, node83 52.22, node76 1.40, node90 243.00
-node74, node81 1.07, node76 0.52, node78 8.08
-node75, node92 68.53, node76 0.81, node77 1.19
-node76, node85 13.18, node77 0.45, node78 2.36
-node77, node80 8.94, node78 0.98, node86 64.32
-node78, node98 355.90, node81 2.59
-node79, node81 0.09, node85 1.45, node91 22.35
-node80, node92 121.87, node88 28.78, node98 264.34
-node81, node94 99.78, node89 39.52, node92 99.89
-node82, node91 47.44, node88 28.05, node93 11.99
-node83, node94 114.95, node86 8.75, node88 5.78
-node84, node89 19.14, node94 30.41, node98 121.05
-node85, node97 94.51, node87 2.66, node89 4.90
-node86, node97 85.09
-node87, node88 0.21, node91 11.14, node92 21.23
-node88, node93 1.31, node91 6.83, node98 6.12
-node89, node97 36.97, node99 82.12
-node90, node96 23.53, node94 10.47, node99 50.99
-node91, node97 22.17
-node92, node96 10.83, node97 11.24, node99 34.68
-node93, node94 0.19, node97 6.71, node99 32.77
-node94, node98 5.91, node96 2.03
-node95, node98 6.17, node99 0.27
-node96, node98 3.32, node97 0.43, node99 5.87
-node97, node98 0.30
-node98, node99 0.33
-node99,
+```{code-cell} ipython3
+import requests
+
+file_url = "https://raw.githubusercontent.com/QuantEcon/lecture-python-intro/main/lectures/graph.txt"
+graph_file_response = requests.get(file_url)
+```
+
+```{code-cell} ipython3
+graph_file_data = str(graph_file_response.content, 'utf-8')
+print(graph_file_data)
 ```
 
 ```{exercise-end}
@@ -381,27 +291,30 @@ node99,
 
 First let's write a function that reads in the graph data above and builds a distance matrix.
 
-```{code-cell} python3
+```{code-cell} ipython3
 num_nodes = 100
 destination_node = 99
 
-def map_graph_to_distance_matrix(in_file):
+def map_graph_to_distance_matrix(in_file_data):
 
     # First let's set of the distance matrix Q with inf everywhere
     Q = np.full((num_nodes, num_nodes), np.inf)
 
     # Now we read in the data and modify Q
-    with open(in_file) as infile:
-        for line in infile:
-            elements = line.split(',')
-            node = elements.pop(0)
-            node = int(node[4:])    # convert node description to integer
-            if node != destination_node:
-                for element in elements:
-                    destination, cost = element.split()
-                    destination = int(destination[4:])
-                    Q[node, destination] = float(cost)
-            Q[destination_node, destination_node] = 0
+    lines = in_file_data.split('\n')
+    for line_ in lines:
+        line = line_.strip()
+        if line == '':
+            continue
+        elements = line.split(',')
+        node = elements.pop(0)
+        node = int(node[4:])    # convert node description to integer
+        if node != destination_node:
+            for element in elements:
+                destination, cost = element.split()
+                destination = int(destination[4:])
+                Q[node, destination] = float(cost)
+        Q[destination_node, destination_node] = 0
     return Q
 ```
 
@@ -414,7 +327,7 @@ We'll use the algorithm described above.
 
 The minimization step is vectorized to make it faster.
 
-```{code-cell} python3
+```{code-cell} ipython3
 def bellman(J, Q):
     return np.min(Q + J, axis=1)
 
@@ -442,7 +355,7 @@ dealing with floating point numbers now.
 Finally, here's a function that uses the cost-to-go function to obtain the
 optimal path (and its cost).
 
-```{code-cell} python3
+```{code-cell} ipython3
 def print_best_path(J, Q):
     sum_costs = 0
     current_node = 0
@@ -459,17 +372,17 @@ def print_best_path(J, Q):
 
 Okay, now we have the necessary functions, let's call them to do the job we were assigned.
 
-```{code-cell} python3
-Q = map_graph_to_distance_matrix('graph.txt')
+```{code-cell} ipython3
+Q = map_graph_to_distance_matrix(graph_file_data)
 J = compute_cost_to_go(Q)
 print_best_path(J, Q)
 ```
 
 The total cost of the path should agree with $J[0]$ so let's check this.
 
-```{code-cell} python3
+```{code-cell} ipython3
 J[0]
 ```
 
 ```{solution-end}
-```
+```