Python timeseries animation visualization-CodePudding

I am going to animate a time-series dataset which means there are two variables time(from day to day) and a changeable variable over a day. I used a code that has been written for a function to make animation. But it didn't work for me. I am a beginner at Python. So if there are other methods to make animation for time series datasets that you recommend I really appreciate it if you could comment and describe them to me. Mainly I have chosen this method because it was easy for me to follow.

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.animation import PillowWriter

fig = plt.figure()
l, = plt.plot([], [], 'k--')

plt.xlabel('Time')
plt.ylabel('DO')
plt.title('title')


metadata = dict(title='Movie', artist='codinglikemad')
writer = PillowWriter(fps=15, metadata=metadata)

xlist = []
ylist=[]

with writer.saving(fig, "DOtimeseries.gif", 100):
    for xval in obsprof_ind.index.unique():
        xlist.append(xval)
        ylist.append(obsprof_ind[obsprof_ind.index== xval]['DO_obs'].mean())
#I also print xlist , ylist to ensure they work properly and they did : the export was [Timestamp('2012-06-01 00:00:00'), Timestamp('2012-06-02 00:00:00')] [7.157779211666667, 6.315558422666666]
        l.set_data(xlist,ylist)
        writer.grab_frame()

The original code was this:

import matplotlib
import matplotlib.pyplot as plt
from matplotlib.animation import PillowWriter

fig = plt.figure()
l, = plt.plot([], [], 'k-')

plt.xlabel('xlabel')
plt.ylabel('ylabel')
plt.title('title')

plt.xlim(-5, 5)
plt.ylim(-5, 5)

def func(x):
    return np.sin(x)*3

"""
xlist=np.linspace(-5,5,100)
ylist=func(xlist)
l.set_data(xlist,ylist)
plt.show()
"""
metadata = dict(title='Movie', artist='codinglikemad')
writer = PillowWriter(fps=15, metadata=metadata)

xlist = []
ylist=[]

with writer.saving(fig, "sinWave.gif", 100):
    for xval in np.linspace(-5,5,100):
        xlist.append(xval)
        ylist.append(func(xval))

        l.set_data(xlist,ylist)

        writer.grab_frame()  ```

CodePudding user response：

For animation i would use Plotly instead of trying to animate with GIF files and matplotlib. Saves a lot of time and much more interactive.

Check this : https://plotly.com/python/animations/

Example code :

import plotly.express as px
df = px.data.gapminder()
px.scatter(df, x="gdpPercap", y="lifeExp", animation_frame="year", animation_group="country",
           size="pop", color="continent", hover_name="country",
           log_x=True, size_max=55, range_x=[100,100000], range_y=[25,90])

Full Example


import plotly.graph_objects as go

import pandas as pd

url = "https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv"
dataset = pd.read_csv(url)

years = ["1952", "1962", "1967", "1972", "1977", "1982", "1987", "1992", "1997", "2002",
         "2007"]

# make list of continents
continents = []
for continent in dataset["continent"]:
    if continent not in continents:
        continents.append(continent)
# make figure
fig_dict = {
    "data": [],
    "layout": {},
    "frames": []
}

# fill in most of layout
fig_dict["layout"]["xaxis"] = {"range": [30, 85], "title": "Life Expectancy"}
fig_dict["layout"]["yaxis"] = {"title": "GDP per Capita", "type": "log"}
fig_dict["layout"]["hovermode"] = "closest"
fig_dict["layout"]["updatemenus"] = [
    {
        "buttons": [
            {
                "args": [None, {"frame": {"duration": 500, "redraw": False},
                                "fromcurrent": True, "transition": {"duration": 300,
                                                                    "easing": "quadratic-in-out"}}],
                "label": "Play",
                "method": "animate"
            },
            {
                "args": [[None], {"frame": {"duration": 0, "redraw": False},
                                  "mode": "immediate",
                                  "transition": {"duration": 0}}],
                "label": "Pause",
                "method": "animate"
            }
        ],
        "direction": "left",
        "pad": {"r": 10, "t": 87},
        "showactive": False,
        "type": "buttons",
        "x": 0.1,
        "xanchor": "right",
        "y": 0,
        "yanchor": "top"
    }
]

sliders_dict = {
    "active": 0,
    "yanchor": "top",
    "xanchor": "left",
    "currentvalue": {
        "font": {"size": 20},
        "prefix": "Year:",
        "visible": True,
        "xanchor": "right"
    },
    "transition": {"duration": 300, "easing": "cubic-in-out"},
    "pad": {"b": 10, "t": 50},
    "len": 0.9,
    "x": 0.1,
    "y": 0,
    "steps": []
}

# make data
year = 1952
for continent in continents:
    dataset_by_year = dataset[dataset["year"] == year]
    dataset_by_year_and_cont = dataset_by_year[
        dataset_by_year["continent"] == continent]

    data_dict = {
        "x": list(dataset_by_year_and_cont["lifeExp"]),
        "y": list(dataset_by_year_and_cont["gdpPercap"]),
        "mode": "markers",
        "text": list(dataset_by_year_and_cont["country"]),
        "marker": {
            "sizemode": "area",
            "sizeref": 200000,
            "size": list(dataset_by_year_and_cont["pop"])
        },
        "name": continent
    }
    fig_dict["data"].append(data_dict)

# make frames
for year in years:
    frame = {"data": [], "name": str(year)}
    for continent in continents:
        dataset_by_year = dataset[dataset["year"] == int(year)]
        dataset_by_year_and_cont = dataset_by_year[
            dataset_by_year["continent"] == continent]

        data_dict = {
            "x": list(dataset_by_year_and_cont["lifeExp"]),
            "y": list(dataset_by_year_and_cont["gdpPercap"]),
            "mode": "markers",
            "text": list(dataset_by_year_and_cont["country"]),
            "marker": {
                "sizemode": "area",
                "sizeref": 200000,
                "size": list(dataset_by_year_and_cont["pop"])
            },
            "name": continent
        }
        frame["data"].append(data_dict)

    fig_dict["frames"].append(frame)
    slider_step = {"args": [
        [year],
        {"frame": {"duration": 300, "redraw": False},
         "mode": "immediate",
         "transition": {"duration": 300}}
    ],
        "label": year,
        "method": "animate"}
    sliders_dict["steps"].append(slider_step)


fig_dict["layout"]["sliders"] = [sliders_dict]

fig = go.Figure(fig_dict)

fig.show()

CodePudding user response：

I found out I should have defined the x and y axes limitation.