def plotting(df, label):
'''
Parameter: DataFrame from query_for_plot which has dates and other labels of interest in columnns;
label: label of interest to be plotted
Function: plots these with the right type of date on axis and title
'''
years = mdates.YearLocator() # every year
months = mdates.MonthLocator() # every month
dates = mdates.DateLocator() #every date
list_of_dates = df.dates.tolist()
No_of_years = list_of_dates[-1].year - list_of_dates[
0].year #value in int of difference in years
fig, ax = plt.subplots(figsize=(12, 7))
#print(df[label])
if (No_of_years <= 1): # if less than 1 year then
yearsFmt = mdates.DateFormatter(r"%b-%y")
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(yearsFmt)
ax.plot(df.dates, df[label])
ax.set_title('Plot of {} from {} to {}'.format(
label, list_of_dates[0].strftime("%B %Y"),
list_of_dates[-1].strftime("%B %Y")))
ax.set_ylabel('{}'.format(label))
ax.set_ylabel('Time')
#ax.xaxis.set_minor_locator() #will be useful if many years and we want months to be minor grid location
datemin = np.datetime64(list_of_dates[0], 'M')
datemax = np.datetime64(list_of_dates[-1], 'M') + np.timedelta64(
1, 'M')
ax.set_xlim(datemin, datemax)
ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
ax.grid(True)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
def line_overlay_plot(data: pd.DataFrame, stocks: [],
plotDateType: PlotDateType):
"""
拆线叠加图
:param data:
:param stocks:
:param plotDateType:
:return:
"""
time = data.loc[(data["symbol"] == stocks[0])]['time']
time = timestamp_2_date(time.tolist())
min_date = time[0]
max_date = time[-1]
df = pd.DataFrame(index=time)
for stock in stocks:
stock_data = data.loc[(data["symbol"] == stock)]
# logging.info(timestamp_2_month(stock_data['time']))
stock_data = list(normalize(stock_data, 'close'))
logging.info('%s %s', stock, len(stock_data))
df[stock] = stock_data
g = sns.lineplot(data=df)
# X轴刻度设置
date_locator = None
if plotDateType == PlotDateType.MONTH:
date_locator = dates.AutoDateFormatter(dates.MonthLocator())
elif plotDateType == PlotDateType.WEEK:
date_locator = dates.AutoDateFormatter(dates.WeekdayLocator())
elif plotDateType == PlotDateType.DAY:
date_locator = dates.AutoDateFormatter(dates.DateLocator())
g.format_xdata = date_locator
# 鼠标hover 事件
# plt.gcf().canvas.mpl_connect('motion_notify_event', onpick)
plt.title('ETF价格叠加图({0} - {1})'.format(date_2_month(min_date),
date_2_month(max_date)))
plt.legend()
plt.show()
def plotTotals(arguments):
"""plot day totals only"""
conn = sqlite3.connect('zeverData.db')
c = conn.cursor()
sql = buildDateQuery("SELECT date, SN, E_TODAY FROM inverterData",
arguments,
post=" ORDER BY date ASC")
sql += " "
data = {}
print(sql)
for row in c.execute(sql):
#SELECT date, SN, PAC_W, E_TODAY, Status FROM inverterData
id = row[1]
if not id in data:
data[id] = {}
# print(row[0])
dt = parser.parse(row[0]).date()
if not (dt in data[id]) or (data[id][dt] < row[2]):
data[id][dt] = row[2] # overwrite with the latest value
#fig, ax = plt.subplots()
for id in data:
xData = [dt for dt in sorted(data[id])]
ticks = [str(dt) for dt in data[id]]
yData = [data[id][dt] for dt in sorted(data[id])]
print(yData)
print(xData)
plt.bar(range(len(yData)), yData, align='center')
plt.xticks(range(len(yData)), ticks)
#plt.bar(xData, yData)
print(xData)
locator = mdates.DateLocator()
#plt.gca().xaxis.set_major_formatter(mdates.AutoDateFormatter(locator))
#plt.gcf().autofmt_xdate()
plt.ylabel(('kWh'))
plt.show()
def plot_date_range(df, start_date, end_date=False):
start_mask = df.index >= start_date
if end_date:
end_mask = df.index <= end_date
display_df = df[start_mask & end_mask].copy()
else:
display_df = df[start_mask].copy()
days = mdates.DateLocator()
fig, ax = plt.subplots(figsize=(12,8))
ax.plot(display_df.index, display_df['exchange_rate'])
# ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d'))
# ax.xaxis.set_minor_locator(mdates.HourLocator())
# ax.yaxis.set_label_coords(-0.115,0.5)
title = np.unique(display_df['from_currency_code'])[0] + ":" + "USD"
plt.suptitle(title, fontweight='bold', fontsize=19)
plt.xlabel('Date', fontweight='bold', fontsize=15)
plt.ylabel("Rate", fontweight='bold', rotation=0, fontsize=15)
fig.autofmt_xdate()
plt.show()
def p_create_pic():
tit = 'Temperature and Depth'
if not os.path.exists('/home/pi/Desktop/Pictures'):
os.makedirs('/home/pi/Desktop/Pictures')
if not os.path.exists('uploaded_files'):
os.makedirs('uploaded_files')
n = 0
try:
files = []
files.extend(sorted(glob.glob('/home/pi/Desktop/towifi/*.csv')))
if not os.path.exists('uploaded_files/mypicfile.dat'):
open('uploaded_files/mypicfile.dat', 'w').close()
with open('uploaded_files/mypicfile.dat', 'r') as f:
content = f.readlines()
f.close()
upfiles = [
line.rstrip('\n')
for line in open('uploaded_files/mypicfile.dat', 'r')
]
dif_data = list(set(files) - set(upfiles))
if dif_data == []:
print 'Standby. When the program detects a probe haul, machine will reboot and show new data.'
time.sleep(5)
pass
##################################
##################################
dif_data.sort(key=os.path.getmtime)
for fn in dif_data:
fn2 = fn
if not os.path.exists('/home/pi/Desktop/Pictures/' +
fn.split('/')[-1][6:14]):
os.makedirs('/home/pi/Desktop/Pictures/' +
fn.split('/')[-1][6:14])
df = pd.read_csv(fn,
sep=',',
skiprows=7,
parse_dates={'datet': [1]},
index_col='datet',
date_parser=parse2) #creat a new Datetimeindex
df2 = df
df2['Depth (m)'] = [x * (-0.5468) for x in df2['Depth (m)'].values]
if len(df2) < 5:
continue
meantemp = round(np.mean(df['Temperature (C)']), 2)
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
time_df2 = gmt_to_eastern(df2.index)
time_df = gmt_to_eastern(df.index)
ax1.plot(
time_df,
df['Temperature (C)'],
'b',
)
ax1.set_ylabel('Temperature (Celius)')
ax1.legend(['temp', 'in the water'])
try:
if max(df.index) - min(
df.index) > Timedelta('0 days 04:00:00'):
ax1.xaxis.set_major_locator(
dates.DateLocator(
interval=(max(df.index) - min(df.index)).seconds /
3600 / 12)) # for hourly plot
ax2.xaxis.set_major_locator(
dates.DateLocator(
interval=(max(df.index) - min(df.index)).seconds /
3600 / 12)) # for hourly plot
else:
ax1.xaxis.set_major_locator(
dates.DateLocator(
interval=(max(df.index) - min(df.index)).seconds /
3600 / 4)) # for hourly plot
ax2.xaxis.set_major_locator(
dates.DateLocator(
interval=(max(df.index) - min(df.index)).seconds /
3600 / 4)) # for hourly plot
except:
print ' '
clim = getclim(
) # extracts climatological values at this place and yearday
if isnan(clim):
txt = 'mean temperature =' + str(round(
meantemp, 1)) + 'F (No Climatology here.)'
else:
txt = 'mean temperature =' + str(round(
c2f(meantemp), 1)) + 'F Climatology =' + str(
round(c2f(clim), 1)) + 'F'
ax1.text(0.9,
0.15,
txt,
verticalalignment='bottom',
horizontalalignment='right',
transform=ax1.transAxes,
color='green',
fontsize=15)
ax1.grid()
ax12 = ax1.twinx()
ax12.set_title(tit)
ax12.set_ylabel('Fahrenheit')
ax12.set_xlabel('')
ax12.set_ylim(
np.nanmin(df['Temperature (C)'].values) * 1.8 + 30,
np.nanmax(df['Temperature (C)'].values) * 1.8 + 36)
ax2.plot(time_df2,
df2['Depth (m)'],
'b',
label='Depth',
color='green')
ax2.legend()
ax2.invert_yaxis()
ax2.set_ylabel('Depth(Fathom)')
ax2.set_ylim(
np.nanmin(df2['Depth (m)'].values) * 1.05,
np.nanmax(df2['Depth (m)'].values) * 0.95)
ax2.yaxis.set_major_formatter(ScalarFormatter(useOffset=False))
ax2.grid()
ax22 = ax2.twinx()
ax22.set_ylabel('Depth(feet)')
ax22.set_ylim(
np.nanmax(df2['Depth (m)'].values) * 6 * 0.95,
np.nanmin(df2['Depth (m)'].values) * 6 * 1.05)
ax22.invert_yaxis()
plt.gcf().autofmt_xdate()
ax2.set_xlabel('TIME ' + time_df[0].astimezone(
pytz.timezone('US/Eastern')).strftime('%m/%d/%Y %H:%M:%S') +
' - ' +
time_df[-1].astimezone(pytz.timezone(
'US/Eastern')).strftime('%m/%d/%Y %H:%M:%S'))
plt.savefig('/home/pi/Desktop/Pictures/' +
fn.split('/')[-1][6:14] + '/' +
fn.split('/')[-1][15:21] + '.png')
plt.close()
a = open('uploaded_files/mypicfile.dat', 'r').close()
a = open('uploaded_files/mypicfile.dat', 'a+')
[a.writelines(i + '\n') for i in dif_data]
a.close()
print 'New data successfully downloaded. Plot will appear.'
return
except:
return