def prettifyLife(ax: mltax.Axes, N: int) -> None:
if N <= 50:
ax.grid()
ax = plt.gca()
ax.set_xticks(np.arange(-.5, N - 1, 1))
ax.set_yticks(np.arange(-.51, N - 1, 1))
ax.set_xticklabels(np.arange(0, N, 1))
ax.set_yticklabels(np.arange(0, N, 1))
elif N > 50 and N < 80:
ax.grid()
ax = plt.gca()
labels = [" " for x in range(N)]
plt.xticks(np.arange(-.5, N - 1, 1), labels)
plt.yticks(np.arange(-.51, N - 1, 1), labels)
def format_plot_3(plt_3: Axes, x_vals: List[int],
state_info_list: List[StateInfo]) -> BarContainer:
"""Adjust all properties of plot 3 to make it look nice.
Add the x & y ticks, format those ticks, set the title, and place the
text on the plot for the priority num plot.
Parameters
----------
plt_3 : `Axes`
The object that describes the graph
x_vals : `List[int]`
The list of ints that shows the states' position's
state_info_list : `List[StateInfo]`
Continually updated list of state calculation info
Returns
-------
`BarContainer`
The objects describing the plotted bars
"""
state_names = extract_state_names(state_info_list)
priority_list = extract_priority(state_info_list)
plt_3_bars: BarContainer = plt_3.bar(x_vals,
priority_list,
align="center",
color="g")
plt_3.set_xticks(x_vals)
plt_3.set_xticklabels(state_names, rotation="vertical")
y_formatter: FuncFormatter = FuncFormatter(comma_format_int())
plt_3.set_ylabel("Priority value")
plt_3.set_yscale("log")
plt_3.get_yaxis().set_major_formatter(y_formatter)
plt_3.text(0.3,
0.9,
"Highlighted, is the state with the highest priority value",
transform=plt_3.transAxes)
plt_3.grid(axis="y", which="major", lw=2)
plt_3.grid(axis="y", which="minor", lw=0.75)
plt_3.grid(axis="x", lw=0.75)
plt_3.set_title("Priority values per state")
return plt_3_bars
def format_plot_2(plt_2: Axes, x_vals: List[int],
state_info_list: List[StateInfo]) -> BarContainer:
"""Adjust all properties of plot 2 to make it look nice.
Add the x & y ticks, format those ticks, set the title, and place the
text on the plot for the number of reps plot.
Parameters
----------
plt_2 : `Axes`
The object that describes the graph
x_vals : `List[int]`
The list of ints that shows the states' position's
state_info_list : `List[StateInfo]`
Continually updated list of state calculation info
Returns
-------
`BarContainer`
The objects describing the plotted bars
"""
state_names = extract_state_names(state_info_list)
reps_list = extract_reps(state_info_list)
plt_2_bars: BarContainer = plt_2.bar(x_vals,
reps_list,
align="center",
color="r")
plt_2.set_xticks(x_vals)
plt_2.set_xticklabels(state_names, rotation="vertical")
y_axis = plt_2.get_yaxis()
minor_loc = MultipleLocator(5)
y_axis.set_minor_locator(minor_loc)
plt_2.set_ylabel("Representatives")
plt_2.set_ylim(top=60, bottom=0)
plt_2.grid(axis="y", which="major", lw=2)
plt_2.grid(axis="y", which="minor", lw=0.75)
plt_2.grid(axis="x", lw=0.75)
plt_2.set_title("Representatives per state")
return plt_2_bars
def format_plot_1(plt_1: Axes, x_vals: List[int],
state_info_list: List[StateInfo]) -> PlotProps:
"""Adjust all properties of plot 1 to make it look nice.
Add the x & y ticks, format those ticks, set the title, draw the mean
line, and place the text on the plot for the pop_per_rep plot.
Parameters
----------
plt_1 : `Axes`
The object that describes the graph
x_vals : `List[int]`
The list of ints that shows the states' positions
state_info_list : `List[StateInfo]`
Continually updated list of state calculation info
Returns
-------
`PlotProps`
A tuple of the plotted bars, text, and line objects
"""
state_names = extract_state_names(state_info_list)
pop_per_rep_list = extract_pop_per_rep(state_info_list)
plt_1_bars: BarContainer = plt_1.bar(x_vals,
pop_per_rep_list,
align="center")
plt_1.set_xticks(x_vals)
plt_1.set_xticklabels(state_names, rotation="vertical")
y_formatter = FuncFormatter(comma_format_int())
plt_1.set_ylabel("People/Representative")
plt_1.set_yscale("log")
plt_1.get_yaxis().set_major_formatter(y_formatter)
plt_1.set_title("People per representative per state")
plt_1.grid(axis="y", which="major", lw=2)
plt_1.grid(axis="y", which="minor", lw=0.75)
plt_1.grid(axis="x", lw=0.75)
mean_pop_per_seat: float = np.mean(pop_per_rep_list)
std_dev_pop_per_seat: float = np.std(pop_per_rep_list)
range_pop_per_seat: float = max(pop_per_rep_list) - min(pop_per_rep_list)
geo_mean_pop_per_seat: float = geometric_mean(pop_per_rep_list)
res_dict: PlotTextDict = {}
res_dict["seat_txt"] = plt_1.text(0.25,
0.75,
f"Seat# 1",
transform=plt_1.transAxes)
res_dict["state_txt"] = plt_1.text(0.15,
0.85,
"State: ",
transform=plt_1.transAxes)
res_dict["mean_txt"] = plt_1.text(0.45,
0.75,
f"Mean: {mean_pop_per_seat:,.2f}",
transform=plt_1.transAxes)
res_dict["std_dev_txt"] = plt_1.text(
0.35,
0.85,
f"Std. Dev. {std_dev_pop_per_seat:,.2f}",
transform=plt_1.transAxes)
res_dict["range_txt"] = plt_1.text(0.70,
0.75,
f"Range: {range_pop_per_seat:,.2f}",
transform=plt_1.transAxes)
res_dict["geo_mean_txt"] = plt_1.text(
0.6,
0.85,
f"Geo. Mean: {geo_mean_pop_per_seat:,.2f}",
transform=plt_1.transAxes)
mean_line: Line2D = plt_1.axhline(y=mean_pop_per_seat,
xmin=0.0,
xmax=1.0,
color="r")
return (plt_1_bars, mean_line, res_dict)