<jupyter_start><jupyter_text>I wonder if there is any relation between the ratio of AMO and PDO to the snowfall. Let's see.<jupyter_code>hist = ALTA.hist(bins=25, figsize=(15,10))
# Let's see if we can group the AMO and PDO groups together for a better plot
win_avg = win.groupby(['AMOb', 'PDOb']).mean()
win_med = win.groupby(['AMOb', 'PDOb']).median()<jupyter_output><empty_output><jupyter_text>Since these ratios have a lot of values around 0, that creates anomolies that make this graph hard to look at. I'm going to try the difference in normalized values instead.<jupyter_code>#sns.scatterplot(win['ratio'], win['SNOW30'], color='red')
# Let's try a 3D plot of AMO and PDO versus snowfall
#fig = plt.figure()
#ax = fig.add_subplot(111, projection='3d')
#ax.scatter(win_avg['AMO'], win_avg['PDO'], win_avg['SNOW14'], c='red')
#ax.scatter(win_med['AMO'], win_med['PDO'], win_med['SNOW14'], c='blue')<jupyter_output><empty_output><jupyter_text>Snowfall looks a little higher as we get away from 0; let's see the absolute difference. These plots were OK, but I need to scale so that PDO doesn't dominate.<jupyter_code>#win_d = win.groupby(['MONTH','DAY']).mean()
#win_d.head()<jupyter_output><empty_output><jupyter_text>This is very cool to see. Can we plot it?<jupyter_code># plot for 7 day snowfall
# https://www.quantopian.com/posts/plotting-multi-index-pandas-dataframe
#win_d.SNOW7.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));
# those were OK, but I would really like to see the season in one continuous stretch; let's looks at longer stretches
# 14 day snowfall
#win_d.SNOW14.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));
# those were OK, but I would really like to see the season in one continuous stretch; let's try to resort
#plt.plot(x=win_d['MONTH','DAY'], y=win_d['SNOW7']) layout=(10, 2)
# https://www.quantopian.com/posts/plotting-multi-index-pandas-dataframe
#win_d.SNOW30.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));
#win_m = win.groupby(['MONTH','DAY']).median()
#win_m = win.groupby(['MONTH','DAY']).median()
#win_m.describe()
# These stats look far less influenced by outliers. Let's she how the plot looks
#win_m.SNOW7.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));
# These stats look far less influenced by outliers. Let's she how the plot looks
#win_m.SNOW14.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));
# These stats look far less influenced by outliers. Let's she how the plot looks
#win_m.SNOW30.unstack(level=0).plot(subplots=True, figsize=(15, 30), layout=(3, 2));<jupyter_output><empty_output><jupyter_text>There appears to be some trends throughout the season. I will try to plot this better so we could tease out some more elements from it.<jupyter_code>result = np.empty(len(win_415))
for y in win_415['YEAR']:
    
    if win_415.iloc[i]['YEAR'] == win_220.iloc[i]['YEAR']:
        if win_220['SNOW7'] >= win_415['SNOW7']:
            result[i] = True
        else:
            result[i] = False
result
        
# let's see the 7-day plot
plt.rcParams["figure.figsize"] = (20,10)
fig, ax = plt.subplots()
plt.plot(win_mdc7_top.index, win_mdc7_top['PERC'], color='blue')
every_nth = 10
for n, label in enumerate(ax.xaxis.get_ticklabels()):
    if n % every_nth != 0:
        label.set_visible(False)
plt.ylabel("Times that have exceeded 10in in 7-days")
plt.xlabel("Date")
# remove ds becuase scalers don't like datetime
ds = SNOW['ds']
X = SNOW.drop(labels=['y', 'SNOW14', 'SNOW30', 'ds'], axis=1)
# let's create the standard scalar
scaler = StandardScaler()
scaler.fit(X)
X_sc = scaler.transform(X)
# that results in our scaled array, but I don't know that it's necessary for FB Prophet<jupyter_output><empty_output>
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