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date | avg_sunspot_area | |
---|---|---|
1875-01-01 | 213.133333333333 | |
1876-01-01 | 109.283333333333 | |
1877-01-01 | 92.8583333333333 | |
1878-01-01 | 22.2166666666667 | |
1879-01-01 | 36.3333333333333 | |
1880-01-01 | 446.75 | |
1881-01-01 | 679.491666666667 | |
1882-01-01 | 968.025 | |
1883-01-01 | 1148.90833333333 |
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<!-- Cal inline embed code begins --> | |
<div style="width:100%;height:100%;overflow:scroll" id="my-cal-inline"></div> | |
<script type="text/javascript"> | |
(function (C, A, L) { let p = function (a, ar) { a.q.push(ar); }; let d = C.document; C.Cal = C.Cal || function () { let cal = C.Cal; let ar = arguments; if (!cal.loaded) { cal.ns = {}; cal.q = cal.q || []; d.head.appendChild(d.createElement("script")).src = A; cal.loaded = true; } if (ar[0] === L) { const api = function () { p(api, arguments); }; const namespace = ar[1]; api.q = api.q || []; typeof namespace === "string" ? (cal.ns[namespace] = api) && p(api, ar) : p(cal, ar); return; } p(cal, ar); }; })(window, "https://app.cal.com/embed/embed.js", "init"); | |
Cal("init", "just-chat", {origin:"https://cal.com"}); | |
Cal.ns["just-chat"]("inline", { | |
elementOrSelector:"#my-cal-inline", | |
calLink: "leima/just-chat", | |
layout: "month_view" |
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import numpy as np | |
import plotly.offline as pyo | |
import plotly.graph_objs as go | |
# Generate a random signal | |
np.random.seed(42) | |
random_signal = np.random.normal(size=100) | |
# Offset the line length by the marker size to avoid overlapping | |
marker_offset = 0.04 |
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import torch | |
from torch.utils.data import Dataset | |
class FakeTimeSeriesDataset(Dataset): | |
def __init__(self, sequence_length, input_length, prediction_length, nodes) -> None: | |
super().__init__() | |
self.sequence_length = sequence_length | |
self.prediction_length = prediction_length |
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name: Docs | |
on: | |
push: | |
branches: [ master ] | |
pull_request: | |
branches: [ master ] | |
jobs: | |
build: | |
name: Build docs |
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import json | |
import os | |
import random | |
import requests | |
from loguru import logger | |
from requests.adapters import HTTPAdapter | |
from requests.packages.urllib3.util.retry import Retry |
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import matplotlib.pyplot as plt | |
import torch | |
from pathlib import Path | |
import torchvision | |
import torchvision.transforms as transforms | |
from loguru import logger | |
from torch import nn | |
import click |
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from itertools import combinations | |
class Graph(object): | |
"""A graph object | |
""" | |
def __init__(self, source_nodes, target_nodes, weights=None): | |
if not isinstance(source_nodes, list): |
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# This is an experiment | |
# It is not working | |
FROM redis:4.0.2-alpine | |
RUN apk add --no-cache python3 py3-pip | |
RUN pip3 install click | |
RUN pip3 install redis | |
RUN mkdir app | |
COPY . /app |
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