index.qmd

---
site-url: https://saforem2.github.io/ambivalent
# editor:
#    render-on-save: true
execute:
   freeze: auto
format:
  html: default
  # revealjs:
  #   output-file: slides.html
  gfm:
    default-image-extension: svg
    author: Sam Foreman
    output-file: "README.md"
---

[~~`opinionated`~~](https://github.com/saforem2/opinionated) $\longrightarrow$ [**`ambivalent`**](https://github.com/saforem2/ambivalent) 🤷🏻‍

Clean, simple style for Matplotlib figures.

Transparent backgrounds with grey text $\textcolor{#838383}{\blacksquare}$ that
are accessible / legible and `{light, dark}`-mode independent.

## Install

```bash
python3 -m pip install ambivalent
```

## Getting Started

```{python}
#| echo: false
#| output: false
# automatically detect and reload local changes to modules
%load_ext autoreload
%autoreload 2

import warnings
warnings.filterwarnings("ignore")

%matplotlib inline
import matplotlib_inline
matplotlib_inline.backend_inline.set_matplotlib_formats('svg', 'retina')
```

<!-- - Use `ambivalend.STYLES['ambivalent']` as the default style for `matplotlib`. -->

```{python}
#| code-fold: false
#| echo: true
import ambivalent
import matplotlib.pyplot as plt
plt.style.use(ambivalent.STYLES['ambivalent'])
```

## Examples

### `seaborn` Tips Dataset

- [Seaborn Gallery](https://seaborn.pydata.org/examples/index.html)
    - [Tips Dataset Example](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)

<!-- <details closed><summary><code>code</code>:</summary> -->

```{python}
#| code-fold: true
#| label: fig-py-tips-density
#| output: true
#| fig-cap: "Tips -- Density Plot"
#| layout: [[100]]
import seaborn as sns

tips = sns.load_dataset("tips")
tips.head()

fig, ax = plt.subplots(figsize=(6, 6))  # , ncols=2)

_ = sns.kdeplot(
   data=tips, x="total_bill", hue="size",
   fill=True, common_norm=False, palette="flare_r",
   alpha=.3, linewidth=0,
   ax=ax,  # [0],
)
_ = ax.set_ylabel('')
plt.show()
```

<!-- </details> -->

### `seaborn` Scatter Plot

<!-- <details closed><summary><code>code</code>:</summary> -->

```{python}
#| code-fold: true
#| label: fig-py-diamonds-scatter
#| output: asis
#| fig-cap: "scatter plot with markers of varying size and color"
#| layout: [[100]]
import seaborn as sns
import matplotlib.pyplot as plt

# Load the example diamonds dataset
diamonds = sns.load_dataset("diamonds")

# Draw a scatter plot while assigning point colors and sizes to different
# variables in the dataset
f, ax = plt.subplots(figsize=(6, 6))
_ = sns.despine(f, left=True, bottom=True)
_ = clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"]
_ = sns.scatterplot(x="carat", y="price",
                hue="clarity", size="depth",
                palette="flare",
                hue_order=clarity_ranking,
                sizes=(1, 8), linewidth=0,
                data=diamonds, ax=ax)
```

<!-- </details> -->

### Histogram + Scatter Plot

<!-- <details closed><summary><code>code</code>:</summary> -->

```{python}
#| code-fold: true
#| label: fig-py-hist-scatter
#| output: true
#| fig-cap: "Combo histogram + Scatter Plot with Density Contours"
#| layout: [[100]]
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# Simulate data from a bivariate Gaussian
n = 10000
mean = [0, 0]
cov = [(2, .4), (.4, .2)]
rng = np.random.RandomState(0)
x, y = rng.multivariate_normal(mean, cov, n).T

# Draw a combo histogram and scatterplot with density contours
f, ax = plt.subplots(figsize=(6, 6))
_ = sns.scatterplot(x=x, y=y, s=5, color="#666666", alpha=0.3)
_ = sns.histplot(x=x, y=y, bins=50, pthresh=.1, cmap="flare_r")
_ = sns.kdeplot(x=x, y=y, levels=5, color="w", linewidths=1)
_ = ax.set_xlabel('x')
_ = ax.set_ylabel('y')
_ = plt.show()
```

<!-- </details> -->

### Jointplot

<!-- <details closed><summary><code>code</code>:</summary> -->

```{python}
#| code-fold: true
#| label: fig-py-kde-2d
#| output: true
#| fig-cap: "Joint Distribution with Kernel Density Estimation"
#| layout: [[100]]
import seaborn as sns
# Load the penguins dataset
penguins = sns.load_dataset("penguins")
# Show the joint distribution using kernel density estimation
import matplotlib as mpl
with mpl.rc_context(plt.rcParams.update({'axes.grid': False})):
  g = sns.jointplot(
      data=penguins,
      x="bill_length_mm",
      y="bill_depth_mm",
      hue="species",
      edgecolors='none',
      alpha=0.4,
  )
  _ = plt.grid(False)
  plt.show()
```

<!-- </details> -->

### Matplotlib Histograms

<!-- <details closed><summary><code>code</code>:</summary> -->

```{python}
#| code-fold: true
#| label: fig-py-mpl-hists
#| output: true
#| fig-cap: "Histograms with Matplotlib"
#| layout: [[100]]
import matplotlib.pyplot as plt
import numpy as np

n_bins = 10
x = np.random.randn(1000, 3)

plt.rcParams['axes.grid'] = True

fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2)

colors = ['#333333', '#666666', '#999999']
ax0.hist(x, n_bins, density=True, histtype='bar', color=colors, label=colors)
_ = ax0.legend()
_ = ax0.set_title('bars with legend')

_ = ax1.hist(x, n_bins, density=True, histtype='bar', stacked=True, alpha=0.4)
_ = ax1.set_title('stacked bar')

_ = ax2.hist(x, n_bins, histtype='step', stacked=True, fill=False)
_ = ax2.set_title('stack step (unfilled)')

# Make a multiple-histogram of data-sets with different length.
x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
_ = ax3.hist(x_multi, n_bins, histtype='bar')
_ = ax3.set_title('different sample sizes')

_ = fig.tight_layout()
plt.show()
```

<!-- </details> -->

## Gallery[^examples]

<details closed><summary><italic>More Examples...</italic></summary>

[^examples]: Examples from [Matplotlib Examples](https://matplotlib.org/stable/gallery/index.html)


<!-- ::: {#fig-gallery style="display: flex; flex-direction:row; align-items: flex-end"} -->

::: {layout-nrow=3}

::: {layout-ncol=2}

![[`seaborn.jointplot`](https://seaborn.pydata.org/generated/seaborn.jointplot.html#seaborn.jointplot)](./assets/penguins.svg){#fig-penguins .stretch}

![[spectrum](https://matplotlib.org/stable/gallery/lines_bars_and_markers/spectrum_demo.html#sphx-glr-gallery-lines-bars-and-markers-spectrum-demo-py)](./assets/spectrum.svg){#fig-spectrum .stretch}

:::

<!-- ::: {layout="[[45, 45]]" layout-valign="bottom" style="text-align:center;"} -->
::: {layout-ncol=2}

![`tips` dataset](./assets/tipde-kde_r.svg){#fig-tips-kde .stretch}

![2D KDE](./assets/kde-2d_r.svg){#fig-2d-kde .stretch}

:::

<!-- ::: {layout="[[80]]" style="display: flex; text-align:center;"} -->
::: {layout-ncol=1}

![Histograms](./assets/mpl-hist.svg){#fig-mpl-hist .stretch}

:::

:::


<!-- ::: {.stretch layout="[[31, 31, 31]]" layout-valign="bottom" style="display: flex; text-align:center!important;"} -->

::: {#fig-chains-J layout-ncol=3}

![$|J_{f}|$](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_33_22.svg){.stretch}

![$|J_{b}|$](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_33_23.svg){.stretch}

![$|J|$](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_33_21.svg){.stretch}

$|J|$ during training

:::

::: {#fig-chains-dQ layout="[[80]]" layout-valign="bottom" style="display: flex; text-align:center;"}

![$\delta Q_{\mathrm{train}}$](./assets/chains.svg){#fig-chains .stretch}

Figure from [`l2hmc-qcd` Notebook](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/l2hmc-2dU1.html#inference)

:::

::: {#fig-xeps style="display: flex; text-align:center;"}

![](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_30_68.svg){.stretch}

$\varepsilon_{x}$ during training

:::

::: {#fig-veps style="display: flex; text-align:center;"}

![](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_30_68.svg){.stretch}

$\varepsilon_{x}$ during training

:::

::: {#fig-combined-chains layout="[[80]]" style="display: flex; text-align:center;"}

![$\sum \delta Q$](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/assets/output_48_1.svg){#fig-dQhist .stretch}

Figure from [`l2hmc-qcd` Notebook](https://saforem2.github.io/l2hmc-qcd/qmd/l2hmc-2dU1/l2hmc-2dU1.html)

:::

</details>


<!-- ```{python} -->
<!-- #| code-fold: true -->
<!-- #| code-summary: "boxenplot" -->
<!-- #| label: fig-py-boxenplot -->
<!-- #| output: true -->
<!-- #| fig-cap: "Seaborn Boxenplot" -->
<!-- #| layout: [[100]] -->
<!---->
<!-- import seaborn as sns -->
<!---->
<!-- diamonds = sns.load_dataset("diamonds") -->
<!-- clarity_ranking = ["I1", "SI2", "SI1", "VS2", "VS1", "VVS2", "VVS1", "IF"] -->
<!---->
<!-- sns.boxenplot( -->
<!--     diamonds, x="clarity", y="carat", -->
<!--     color="b", order=clarity_ranking, width_method="linear", -->
<!-- ) -->
<!-- ``` -->

<!-- ```{python} -->
<!-- import warnings -->
<!-- from ambivalent import STYLES -->
<!-- import matplotlib.pyplot as plt -->
<!-- import numpy as np -->
<!---->
<!-- plt.style.use(STYLES['ambivalent']) -->
<!---->
<!-- # some random data -->
<!-- x = np.random.randn(1000) -->
<!-- y = np.random.randn(1000) -->
<!---->
<!---->
<!-- def scatter_hist(x, y, ax, ax_histx, ax_histy, alpha: float = 0.4): -->
<!--     # no labels -->
<!--     ax_histx.tick_params(axis="x", labelbottom=False) -->
<!--     ax_histy.tick_params(axis="y", labelleft=False) -->
<!---->
<!--     # the scatter plot: -->
<!--     ax.scatter(x, y, alpha=alpha) -->
<!---->
<!--     # now determine nice limits by hand: -->
<!--     binwidth = 0.25 -->
<!--     xymax = max(np.max(np.abs(x)), np.max(np.abs(y))) -->
<!--     lim = (int(xymax/binwidth) + 1) * binwidth -->
<!---->
<!--     bins = np.arange(-lim, lim + binwidth, binwidth) -->
<!--     ax_histx.hist(x, bins=bins) -->
<!--     ax_histy.hist(y, bins=bins, orientation='horizontal') -->
<!-- ``` -->

<!-- ### 2D Density -->
<!---->
<!-- ```{python} -->
<!-- #| code-fold: true -->
<!-- #| code-summary: "Make the plot" -->
<!-- #| label: fig-py-density2d -->
<!-- #| output: true -->
<!-- #| fig-cap: "2D Density plot" -->
<!-- #| layout: [[100]] -->
<!---->
<!-- # Start with a square Figure. -->
<!-- fig = plt.figure(figsize=(6, 6)) -->
<!-- # Add a gridspec with two rows and two columns and a ratio of 1 to 4 between -->
<!-- # the size of the marginal axes and the main axes in both directions. -->
<!-- # Also adjust the subplot parameters for a square plot. -->
<!-- gs = fig.add_gridspec(2, 2,  width_ratios=(4, 1), height_ratios=(1, 4), -->
<!--                       left=0.1, right=0.9, bottom=0.1, top=0.9, -->
<!--                       wspace=0.15, hspace=0.15) -->
<!-- # Create the Axes. -->
<!-- ax = fig.add_subplot(gs[1, 0]) -->
<!-- ax_histx = fig.add_subplot(gs[0, 0], sharex=ax) -->
<!-- ax_histy = fig.add_subplot(gs[1, 1], sharey=ax) -->
<!-- _ = fig.axes[1].grid(False) -->
<!-- _ = fig.axes[2].set_xticklabels([]) -->
<!-- _ = fig.axes[1].set_yticklabels([]) -->
<!-- _ = fig.axes[2].grid(False) -->
<!-- _ = fig.axes[0].set_xticklabels(fig.axes[0].get_xticklabels()) -->
<!-- _ = fig.axes[0].set_yticklabels(fig.axes[0].get_yticklabels()) -->
<!---->
<!-- # Draw the scatter plot and marginals. -->
<!-- _ = scatter_hist(x, y, ax, ax_histx, ax_histy) -->
<!-- _ = plt.show() -->
<!-- ``` -->
<!---->
<!---->



<!-- ```{python} -->
<!-- import numpy as np -->
<!-- import matplotlib.animation as animation -->
<!---->
<!-- # Fixing random state for reproducibility -->
<!-- np.random.seed(19680801) -->
<!---->
<!---->
<!-- def random_walk(num_steps, max_step=0.05): -->
<!--     """Return a 3D random walk as (num_steps, 3) array.""" -->
<!--     start_pos = np.random.random(3) -->
<!--     steps = np.random.uniform(-max_step, max_step, size=(num_steps, 3)) -->
<!--     walk = start_pos + np.cumsum(steps, axis=0) -->
<!--     return walk -->
<!---->
<!---->
<!-- def update_lines(num, walks, lines): -->
<!--     for line, walk in zip(lines, walks): -->
<!--         # NOTE: there is no .set_data() for 3 dim data... -->
<!--         line.set_data(walk[:num, :2].T) -->
<!--         line.set_3d_properties(walk[:num, 2]) -->
<!--     return lines -->
<!---->
<!---->
<!-- # Data: 40 random walks as (num_steps, 3) arrays -->
<!-- num_steps = 30 -->
<!-- walks = [random_walk(num_steps) for index in range(40)] -->
<!---->
<!-- # Attaching 3D axis to the figure -->
<!-- fig = plt.figure() -->
<!-- ax = fig.add_subplot(projection="3d") -->
<!---->
<!-- # Create lines initially without data -->
<!-- lines = [ax.plot([], [], [])[0] for _ in walks] -->
<!---->
<!-- # Setting the axes properties -->
<!-- _ = ax.set(xlim3d=(0, 1), xlabel='X') -->
<!-- _ = ax.set(ylim3d=(0, 1), ylabel='Y') -->
<!-- _ = ax.set(zlim3d=(0, 1), zlabel='Z') -->
<!---->
<!-- # Creating the Animation object -->
<!-- ani = animation.FuncAnimation( -->
<!--     fig, update_lines, num_steps, fargs=(walks, lines), interval=100) -->
<!---->
<!-- plt.show() -->
<!-- ``` -->


::: {.callout-tip icon=false title='[💝 Status]{.dim-text}' collapse="false" style="text-align: left!important; width:100%; border: none!important; border-left: 1px solid var(--dim-text)!important; border-radius: 0pt!important; background-color: var(--bg-transparent); opacity:100%;"}

[![](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fsaforem2.github.io%2Fambivalent&count_bg=%23222222&title_bg=%23303030&icon=&icon_color=%23E7E7E7)]{style="text-align:center;"}

```{python}
#| echo: false
import datetime
from rich import print
now = datetime.datetime.now()
day = now.strftime('%m/%d/%Y')
time = now.strftime('%H:%M:%S')
print(' '.join([
  "[dim italic]Last Updated[/]:",
  f"[#F06292]{day}[/]",
  f"[dim]@[/]",
  f"[#1A8FFF]{time}[/]"
]))
```

:::