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Merge pull request #10 from ahuang11/add_gif_paused
Fix paused for GIF and add more example recipes
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‎docs/best_practices.md‎

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@@ -170,3 +170,13 @@ No 'max_frames' specified; using the default 50 / 100 frames. Pass `-1` to use a
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## 🧩 Use `processes=False` for rendering HoloViews objects
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This is done automatically! However, in case there's an edge case, note that the kdims/vdims don't seem to carry over properly to the subprocesses when rendering HoloViews objects. It might complain that it can't find the desired dimensions.
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## 📚 Use `threads_per_worker` if flickering
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Matplotlib is not always thread-safe, so if you're seeing flickering, set `threads_per_worker=1`.
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```python
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from streamjoy import stream
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stream(..., threads_per_worker=1)
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```

‎docs/example_recipes/air_temperature.md‎

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@@ -15,6 +15,7 @@ Highlights:
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import xarray as xr
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import streamjoy.xarray
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ds = xr.tutorial.open_dataset("air_temperature")
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ds.streamjoy("air_temperature.mp4")
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if __name__ == "__main__":
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ds = xr.tutorial.open_dataset("air_temperature")
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ds.streamjoy("air_temperature.mp4")
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```

‎docs/example_recipes/co2_timeseries.md‎

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# CO2 timeseries
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<video controls="true" allowfullscreen="true">
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<source src="https://github.com/ahuang11/streamjoy/assets/15331990/1f6fa5ae-9298-452d-ae1c-41d8c9f6cd34" type="video/mp4">
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</video>
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Shows the yearly CO2 measurements from the Mauna Loa Observatory in Hawaii.
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The data is sourced from the [datasets/co2-ppm-daily](https://github.com/datasets/co2-ppm-daily/blob/master/co2-ppm-daily-flow.py).
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Highlights:
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- Uses `wrap_matplotlib` to automatically handle saving and closing the figure.
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- Uses a custom `renderer` function to create each frame of the animation.
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- Uses `Paused` to pause the animation at notable dates.
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.ticker import AutoMinorLocator
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from streamjoy import stream, wrap_matplotlib, Paused
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URL = "https://raw.githubusercontent.com/datasets/co2-ppm-daily/master/data/co2-ppm-daily.csv"
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NOTABLE_YEARS = {
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1958: "Mauna Loa measurements begin",
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1979: "1st World Climate Conference",
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1997: "Kyoto Protocol",
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2005: "exceeded 380 ppm",
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2010: "exceeded 390 ppm",
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2013: "exceeded 400 ppm",
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2015: "Paris Agreement",
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}
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@wrap_matplotlib()
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def renderer(df):
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plt.style.use("dark_background")
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fig, ax = plt.subplots(figsize=(7, 5))
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fig.patch.set_facecolor("#1b1e23")
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ax.set_facecolor("#1b1e23")
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ax.set_frame_on(False)
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ax.axis("off")
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ax.set_title(
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"CO2 Yearly Max",
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fontsize=20,
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loc="left",
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fontname="Courier New",
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color="lightgrey",
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)
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# draw line
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df.plot(
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y="value",
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color="lightgrey", # Line color
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legend=False,
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ax=ax,
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)
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# max date
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max_date = df["value"].idxmax()
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max_co2 = df["value"].max()
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ax.text(
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0.0,
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0.92,
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f"{max_co2:.0f} ppm",
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va="bottom",
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ha="left",
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transform=ax.transAxes,
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fontsize=25,
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color="lightgrey",
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)
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ax.text(
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0.0,
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0.91,
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f"Peaked in {max_date.year}",
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va="top",
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ha="left",
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transform=ax.transAxes,
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fontsize=12,
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color="lightgrey",
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fontname="Courier New",
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)
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# draw end point
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date = df.index[-1]
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co2 = df["value"].values[-1]
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diff = df["diff"].fillna(0).values[-1]
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diff = f"+{diff:.0f}" if diff >= 0 else f"{diff:.0f}"
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ax.scatter(date, co2, color="red", zorder=999)
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ax.annotate(
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f"{diff} ppm",
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(date, co2),
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textcoords="offset points",
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xytext=(-10, 5),
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fontsize=12,
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ha="right",
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va="bottom",
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color="lightgrey",
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)
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# draw source label
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ax.text(
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0.0,
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0.03,
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f"Source: {URL}",
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va="bottom",
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ha="left",
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transform=ax.transAxes,
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fontsize=8,
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color="lightgrey",
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)
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# properly tighten layout
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plt.subplots_adjust(bottom=0, top=0.9, right=0.9, left=0.05)
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# pause at notable years
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year = date.year
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if year in NOTABLE_YEARS:
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ax.annotate(
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f"{NOTABLE_YEARS[year]} - {year}",
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(date, co2),
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textcoords="offset points",
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xytext=(-10, 3),
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fontsize=10.5,
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ha="right",
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va="top",
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color="lightgrey",
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fontname="Courier New",
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)
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return Paused(ax, 2.8)
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else:
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ax.annotate(
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year,
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(date, co2),
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textcoords="offset points",
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xytext=(-10, 3),
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fontsize=10.5,
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ha="right",
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va="top",
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color="lightgrey",
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fontname="Courier New",
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)
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return ax
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if __name__ == "__main__":
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df = (
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pd.read_csv(URL, parse_dates=True, index_col="date")
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.resample("1YE")
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.max()
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.interpolate()
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.assign(
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diff=lambda df: df["value"].diff(),
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)
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)
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stream(df, renderer=renderer, max_frames=-1, threads_per_worker=1).write("co2_emissions.mp4")
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```

‎docs/example_recipes/temperature_anomaly.md‎

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# Temperature anomaly
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<video controls="true" allowfullscreen="true">
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<source src="https://github.com/ahuang11/streamjoy/assets/15331990/069b1826-de92-4643-8be5-6d5a5301d11e" type="video/mp4">
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</video>
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Shows the global temperature anomaly from 1995 to 2024 using the HadCRUT5 dataset. The video pauses at notable dates.
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Highlights:
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- Uses `wrap_matplotlib` to automatically handle saving and closing the figure.
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- Uses a custom `renderer` function to create each frame of the animation.
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- Uses `Paused` to pause the animation at notable dates.
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```python
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import pandas as pd
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import matplotlib.pyplot as plt
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from streamjoy import stream, wrap_matplotlib, Paused
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URL = "https://climexp.knmi.nl/data/ihadcrut5_global.dat"
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NOTABLE_DATES = {
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"1997-12": "Kyoto Protocol adopted",
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"2005-01": "Exceeded 380 ppm",
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"2010-01": "Exceeded 390 ppm",
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"2013-05": "Exceeded 400 ppm",
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"2015-12": "Paris Agreement signed",
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"2016-01": "CO2 permanently over 400 ppm",
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}
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@wrap_matplotlib()
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def renderer(df):
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plt.style.use("dark_background") # Setting the style for dark mode
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fig, ax = plt.subplots()
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fig.patch.set_facecolor("#1b1e23")
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ax.set_facecolor("#1b1e23")
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ax.set_frame_on(False)
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ax.axis("off")
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# Set title
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year = df["year"].iloc[-1]
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ax.set_title(
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f"Global Temperature Anomaly {year} [HadCRUT5]",
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fontsize=15,
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loc="left",
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fontname="Courier New",
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color="lightgrey",
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)
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# draw line
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df.groupby("year")["anom"].plot(
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y="anom", color="lightgrey", legend=False, ax=ax, lw=0.5
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)
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# add source text at bottom right
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ax.text(
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0.01,
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0.05,
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f"Source: {URL}",
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va="bottom",
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ha="left",
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transform=ax.transAxes,
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fontsize=8,
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color="lightgrey",
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fontname="Courier New",
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)
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# draw end point
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jday = df.index.values[-1]
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anom = df["anom"].values[-1]
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ax.scatter(jday, anom, color="red", zorder=999)
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anom_label = f"+{anom:.1f} K" if anom > 0 else f"{anom:.1f} K"
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ax.annotate(
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anom_label,
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(jday, anom),
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textcoords="offset points",
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xytext=(-10, 5),
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fontsize=12,
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ha="right",
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va="bottom",
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color="lightgrey",
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)
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# draw yearly labels
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for year, df_year in df.reset_index().groupby("year").last().iloc[-5:].iterrows():
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if df_year["month"] != 12:
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continue
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ax.annotate(
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year,
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(df_year["jday"], df_year["anom"]),
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fontsize=12,
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ha="left",
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va="center",
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color="lightgrey",
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fontname="Courier New",
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)
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plt.subplots_adjust(bottom=0, top=0.9, left=0.05)
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month = df["date"].iloc[-1].strftime("%b")
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ax.annotate(
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month,
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(jday, anom),
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textcoords="offset points",
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xytext=(-10, 3),
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fontsize=12,
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ha="right",
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va="top",
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color="lightgrey",
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fontname="Courier New",
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)
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date_string = df["date"].iloc[-1].strftime("%Y-%m")
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if date_string in NOTABLE_DATES:
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ax.annotate(
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f"{NOTABLE_DATES[date_string]}",
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xy=(0, 1),
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xycoords="axes fraction",
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xytext=(0, -5),
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textcoords="offset points",
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fontsize=12,
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ha="left",
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va="top",
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color="lightgrey",
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fontname="Courier New",
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)
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return Paused(fig, 3)
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return fig
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df = (
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pd.read_csv(
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URL,
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comment="#",
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header=None,
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sep="\s+",
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na_values=[-999.9],
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)
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.rename(columns={0: "year"})
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.melt(id_vars="year", var_name="month", value_name="anom")
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)
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df.index = pd.to_datetime(
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df["year"].astype(str) + df["month"].astype(str), format="%Y%m"
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)
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df = df.sort_index()["1995":"2024"]
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df["jday"] = df.index.dayofyear
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df = df.rename_axis("date").reset_index().set_index("jday")
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df_list = [df[:i] for i in range(1, len(df) + 1)]
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stream(df_list, renderer=renderer, threads_per_worker=1).write(
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"temperature_anomaly.mp4"
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)
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```

‎mkdocs.yml‎

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- Example recipes:
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- Air temperature: example_recipes/air_temperature.md
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- Sine wave: example_recipes/sine_wave.md
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- CO2 timeseries: example_recipes/co2_timeseries.md
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- Temperature anomaly: example_recipes/temperature_anomaly.md
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- Sea ice: example_recipes/sea_ice.md
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- OISST globe: example_recipes/oisst_globe.md
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- Gender gapminder: example_recipes/gender_gapminder.md

‎streamjoy/_utils.py‎

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) -> np.ndarray | Paused:
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imread_kwargs = dict(extension=extension, plugin=plugin)
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if isinstance(uri, Paused):
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return Paused(iio.imread(uri.output, **imread_kwargs), uri.seconds).squeeze()
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return Paused(iio.imread(uri.output, **imread_kwargs).squeeze(), uri.seconds)
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return iio.imread(uri, **imread_kwargs).squeeze()

‎streamjoy/models.py‎

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output = param.Parameter(doc="The output to pause for.")
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seconds = param.Integer(doc="The number of seconds to pause for.")
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seconds = param.Number(doc="The number of seconds to pause for.")
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def __init__(self, output: Any, seconds: int, **params):
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self.output = output

‎streamjoy/streams.py‎

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self._prepend_intro(buf, intro_frame, **write_kwargs)
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for image in images:
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for i, image in enumerate(images):
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image = _utils.get_result(image)
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if isinstance(image, Paused):
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duration[i] = image.seconds * 1000
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image = image.output
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buf.write(image[:, :, :3], **write_kwargs)
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del image
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‎tests/conftest.py‎

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@pytest.fixture(autouse=True, scope="session")
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def default_config():
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config["fps"] = 1
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config["max_frames"] = 3
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config["max_files"] = 2
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config["ending_pause"] = 0

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