def pcolormesh(alt, r, t, title='default', shade='flat'):
fig, ax = plt.subplots(figsize=(8, 2))
im = ax.pcolormesh(t,
alt,
r,
cmap='jet',
shading=shade,
norm=mpl.colors.LogNorm(vmin=5e3, vmax=1e6))
plt.colorbar(im, ax=ax)
ax.set_ylim(0, 5)
converter = mdates.ConciseDateConverter()
munits.registry[datetime.datetime] = converter
fig.autofmt_xdate()
if title == 'default':
ax.set_title('pcolormesh curtain')
else:
try:
ax.set_title(title)
except:
print('Unsupported figure title')
return
def gen_steam_cm_graph(self, graphdata: dict):
"""Make an graph for connection managers"""
mpluse("Agg")
formats = [
"%y", # ticks are mostly years
"%b", # ticks are mostly months
"%d", # ticks are mostly days
"%H:%M", # hrs
"%H:%M", # min
"%S.%f", # secs
]
zero_formats = [""] + formats[:-1]
zero_formats[3] = "%d-%b"
offset_formats = [
"",
"%Y",
"%b %Y",
"%d %b %Y",
"%d %b %Y",
"%d %b %Y %H:%M",
]
munits.registry[datetime] = mdates.ConciseDateConverter(
formats=formats,
zero_formats=zero_formats,
offset_formats=offset_formats)
cur = graphdata["start"]
x = []
for _ in range(len(graphdata["data"])):
cur += graphdata["step"]
x.append(cur)
x = [datetime.utcfromtimestamp(_x / 1000) for _x in x]
y = graphdata["data"]
graphfile = BytesIO()
with pyplot.style.context(
path.join(bundled_data_path(self), "discord.mplstyle")):
fig, ax = pyplot.subplots()
ax.plot(x, y)
ax.set_ylim(bottom=0)
ax.grid()
ax.set(xlabel="Date",
ylabel="%",
title="Steam Connection Managers")
ax.set_yticks(np.arange(0, 100, 5))
fig.savefig(graphfile)
pyplot.close(fig)
graphfile.seek(0)
return graphfile
def formatDateTicks(f, ax):
r"""
Use some reasonable auto-formatting for time-based x ticks.
Taken from here:
https://matplotlib.org/3.1.0/gallery/ticks_and_spines/date_concise_formatter.html
"""
formats = [
'%y', # ticks are mostly years
'%b', # ticks are mostly months
'%d', # ticks are mostly days
'%H:%M', # hrs
'%H:%M', # min
'%S.%f',
] # secs
# these can be the same, except offset by one level....
zero_formats = [''] + formats[:-1]
# ...except for ticks that are mostly hours, then its nice to have month-day
zero_formats[3] = '%d-%b'
offset_formats = [
'',
'%Y',
'%b %Y',
'%d %b %Y',
'%d %b %Y',
'%d %b %Y %H:%M',
]
converter = mdates.ConciseDateConverter(formats=formats,
zero_formats=zero_formats,
offset_formats=offset_formats)
munits.registry[np.datetime64] = converter
munits.registry[datetime.date] = converter
munits.registry[datetime.datetime] = converter
return f, ax
import numpy as np
import pandas as pd
from datetime import datetime
import matplotlib.units as munits
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
converter = mdates.ConciseDateConverter()
munits.registry[np.datetime64] = converter
munits.registry[datetime.date] = converter
munits.registry[datetime] = converter
def seven_day_average(data):
n = 7
ret = np.cumsum(data, dtype=float)
ret[n:] = ret[n:] - ret[:-n]
return ret / n
def first_and_second_by_state(state):
df = pd.read_html(
f"https://covidlive.com.au/report/daily-vaccinations-first-doses/{state.lower()}"
)[1]
first = np.array(df['FIRST'][::-1])
first_dates = np.array(
[np.datetime64(datetime.strptime(d, '%d %b %y'), 'D') for d in df['DATE'][::-1]]
)
df = pd.read_html(
from matplotlib import pyplot as plt
from matplotlib import dates
from matplotlib import units
import pandas as pd
import numpy as np
import datetime
import os
FORMATS = ['%Y', '%b', '%d', '%H:%M', '%H:%M', '%S.%f']
ZERO_FORMATS = ['', '%b\n%Y', '%d %b', '%d %b', '%H:%M', '%H:%M']
OFFSET_FORMATS = ['', '%Y', '%b %Y', '%d %b %Y', '%d %b %Y', '%d %b %Y %H:%M']
converter = dates.ConciseDateConverter(formats=FORMATS,
zero_formats=ZERO_FORMATS,
offset_formats=OFFSET_FORMATS)
units.registry[np.datetime64] = converter
units.registry[datetime.date] = converter
units.registry[datetime.datetime] = converter
SMALL_SIZE = 12
MEDIUM_SIZE = 14
plt.rc('font', size=SMALL_SIZE)
plt.rc('axes', labelsize=MEDIUM_SIZE)
plt.rc('xtick', labelsize=SMALL_SIZE)
plt.rc('ytick', labelsize=SMALL_SIZE)
plt.rc('legend', fontsize=MEDIUM_SIZE)
plt.rc('figure', autolayout=True)
plt.rc('axes', grid=True)
plt.rc('legend', loc='upper left', borderaxespad=0)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
ax.plot(dates, y)
ax.set_xlim(lims[nn])
axs[0].set_title('Concise Date Formatter')
plt.show()
#############################################################################
# If all calls to axes that have dates are to be made using this converter,
# it is probably most convenient to use the units registry where you do
# imports:
import matplotlib.units as munits
converter = mdates.ConciseDateConverter()
munits.registry[np.datetime64] = converter
munits.registry[datetime.date] = converter
munits.registry[datetime.datetime] = converter
fig, axs = plt.subplots(3, 1, figsize=(6, 6), constrained_layout=True)
for nn, ax in enumerate(axs):
ax.plot(dates, y)
ax.set_xlim(lims[nn])
axs[0].set_title('Concise Date Formatter')
plt.show()
#############################################################################
# Localization of date formats
# ============================
[0.87422, 0.24526, 0.03297], [0.86760, 0.23730, 0.03082],
[0.86079, 0.22945, 0.02875], [0.85380, 0.22170, 0.02677],
[0.84662, 0.21407, 0.02487], [0.83926, 0.20654, 0.02305],
[0.83172, 0.19912, 0.02131], [0.82399, 0.19182, 0.01966],
[0.81608, 0.18462, 0.01809], [0.80799, 0.17753, 0.01660],
[0.79971, 0.17055, 0.01520], [0.79125, 0.16368, 0.01387],
[0.78260, 0.15693, 0.01264], [0.77377, 0.15028, 0.01148],
[0.76476, 0.14374, 0.01041], [0.75556, 0.13731, 0.00942],
[0.74617, 0.13098, 0.00851], [0.73661, 0.12477, 0.00769],
[0.72686, 0.11867, 0.00695], [0.71692, 0.11268, 0.00629],
[0.70680, 0.10680, 0.00571], [0.69650, 0.10102, 0.00522],
[0.68602, 0.09536, 0.00481], [0.67535, 0.08980, 0.00449],
[0.66449, 0.08436, 0.00424], [0.65345, 0.07902, 0.00408],
[0.64223, 0.07380, 0.00401], [0.63082, 0.06868, 0.00401],
[0.61923, 0.06367, 0.00410], [0.60746, 0.05878, 0.00427],
[0.59550, 0.05399, 0.00453], [0.58336, 0.04931, 0.00486],
[0.57103, 0.04474, 0.00529], [0.55852, 0.04028, 0.00579],
[0.54583, 0.03593, 0.00638], [0.53295, 0.03169, 0.00705],
[0.51989, 0.02756, 0.00780], [0.50664, 0.02354, 0.00863],
[0.49321, 0.01963, 0.00955], [0.47960, 0.01583, 0.01055]
]
cmap = mcolors.ListedColormap(turbo_colormap_data)
matplotlib.cm.register_cmap(name='turbo', cmap=cmap)
cmap = mcolors.ListedColormap(turbo_colormap_data[::-1])
matplotlib.cm.register_cmap(name='turbo_r', cmap=cmap)
munits.registry[np.datetime64] = mdates.ConciseDateConverter()
munits.registry[datetime.date] = mdates.ConciseDateConverter()
munits.registry[datetime.datetime] = mdates.ConciseDateConverter()
def Show_Graph(self):
##################################
converter = mdates.ConciseDateConverter()
munits.registry[datetime.date] = converter
munits.registry[datetime.datetime] = converter
##################################
EndDate = date.today()
d = timedelta(days=6)
date1 = EndDate - d
d = timedelta(days=1)
##################################
# MatPlotLib Bar Chart Code
YMin = 0
YMax = 0
Y_calories = []
X_days = []
##################################
for i in range(7):
Y = i * 200
Y_calories.append(Y)
X_days.append(date1)
date1 = date1 + d
if (Y > YMax):
YMax = Y
##################################
plt.clf()
plt.cla()
plt.ylim(YMin, YMax)
width1 = float(0.5)
plt.bar(x=X_days,
height=Y_calories,
width=width1,
label='eaten',
zorder=2)
strNum = 'Past ' + str(7) + ' Days'
##################################
# Show the major grid lines
plt.grid(b=True,
which='major',
color='#444444',
linestyle='-',
alpha=0.5,
zorder=1)
##################################
# Show the minor grid lines
plt.minorticks_on()
##################################
str1 = 'Calories Eaten each Day '
plt.title(str1)
plt.xlabel(strNum)
plt.ylabel('Calories per Day')
plt.gcf()
plt.savefig(fname='./Pic.png', format='png', transparent=True)
##################################
Y_calories.clear()
X_days.clear()
##################################
self.iPic.reload()
if (self.iPic.parent == None):
self.add_widget(self.iPic)
##################################
return
def create_emd_from_lightcurve(self, label, figsize=(20, 28)):
from matplotlib import gridspec as gs
from PyEMD import EMD
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
import glob
from datetime import timedelta
from matplotlib import rc
from multiprocessing import Process
emd = EMD()
font = {"family": "DejaVu", "weight": "normal", "size": 16}
rc("font", **font)
numpyrow, numpycol = self.translate_number(int(label), self.nrowcol)
picture_folder_id = f"{numpycol:02d}_{numpyrow:02d}"
path_to_lcurves = self.global_save_path
path_to_picture = f"{self.global_save_path}{picture_folder_id}/"
dataset = pd.read_csv(f"{path_to_lcurves}complete_lcurves.csv")
time = pd.to_datetime(dataset["Time"])
region = dataset[f"{label}"]
imfs = emd(region.values)
image_list = sorted(glob.glob(f"{path_to_picture}*.png"))
vector_index_div = np.array_split(np.array(np.arange(len(image_list))),
self.n_cpus)
proc = []
colors = ["black", "blue", "red"]
fig = plt.figure(figsize=(10, 20))
time_curr = time[0]
lineset = {}
min_height = 0
max_height = 0
for index, i in enumerate(imfs):
if index < len(imfs) - 1:
if i.min() < min_height:
min_height = i.min()
if i.max() > max_height:
max_height = i.max()
# Draw all IMFs
for index, i in enumerate(imfs):
if index != len(imfs) - 1:
fig.add_subplot(len(imfs), 1, len(imfs) - 1 - index)
ax = plt.gca()
plt.plot(time,
i,
color=colors[0],
linewidth=4,
label=f"IMF {index}")
ybot, ytop = ax.get_ylim()
delta_y = ytop - ybot
move_by = (max_height - min_height) / 2
ax.set_ylim(ybot - move_by, ytop + move_by)
ax.yaxis.set_label_position("right")
ax.set_ylim(min_height, max_height)
ax.set(ylabel=f"IMF {index + 1}", xlabel="", xticks=[])
lineset[f"{index}"] = ax.axvline(x=time_curr,
color="black",
alpha=0.4,
linewidth=5)
# Draw original signal + last IMF (residual)
fig.add_subplot(len(imfs), 1, len(imfs))
ax1 = plt.gca()
plt.plot(time, region, color=colors[1], linewidth=5, label="Signal")
plt.plot(
time,
imfs[len(imfs) - 1],
color=colors[2],
linestyle="dashed",
label="Residual",
linewidth=4,
)
lineset["signal"] = ax1.axvline(x=time_curr,
color="black",
alpha=0.4,
linewidth=5)
ax1.set(ylabel=f"Original Signal")
import matplotlib.dates as mdates
import matplotlib.units as munits
import datetime
converter = mdates.ConciseDateConverter()
munits.registry[np.datetime64] = converter
munits.registry[datetime.date] = converter
munits.registry[datetime.datetime] = converter
plt.gcf().autofmt_xdate()
plt.legend()
ax1.yaxis.set_label_position("right")
save_emd = f"{path_to_picture}/emd_results/"
os.makedirs(save_emd, exist_ok=True)
def multi_proc(index_sublist, save_path=save_emd):
for curr_obs in index_sublist:
# Draw all of the lines#
for k in list(lineset):
lineset[k].set_xdata(time[curr_obs])
fig.canvas.draw()
plt.savefig(f"{save_path}{curr_obs:03d}")
for list_indices_proc in vector_index_div:
if list_indices_proc.size:
# noinspection PyTypeChecker
pr = Process(target=multi_proc, args=([list_indices_proc]))
proc.append(pr)
pr.start()
for process in proc:
process.join()
# Then combine all of these
from Scripts.Imports.Data_analysis.Tools import imgrecomb_any
imgrecomb_any(
path_1=f"{path_to_picture}",
path_2=f"{save_emd}",
save_path=f"{path_to_picture}with_EMD/",
figsize=figsize,
)
def plot_time(dataset,
time_col,
clust_col="prediction",
k=None,
save_path=None):
""" Plot the trend of error messages over time (per each cluster).
-- params:
dataset (pyspark.sql.dataframe.DataFrame): data frame with predictions and message times
time_col (string): name of the unix time in milliseconds
clust_col (string): name of the cluster prediction column
k (int): number of clusters. If specified the executing time decreases. Default None
save_path (string): where to save output figures. Default None (no saving)
Returns: None
"""
import pyspark.sql.functions as F
import os
import datetime
from pathlib import Path
from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import matplotlib.units as munits
dataset = (
dataset.filter(F.col(time_col) > 0) # ignore null values
.withColumn("datetime_str",
F.from_unixtime(F.col('timestamp_tr_comp') /
1000)) # datetime (string)
.withColumn("datetime",
F.to_timestamp(F.col('datetime_str'),
'yyyy-MM-dd HH:mm')) # datetime (numeric)
.select(clust_col, "datetime"))
if k:
clust_ids = [{"prediction": i} for i in range(0, k)]
else:
clust_ids = dataset.select(clust_col).distinct().collect()
if save_path:
print("Saving time plots to: {}".format(save_path))
for clust_id in clust_ids:
cluster = dataset.filter(
F.col(clust_col) == clust_id[clust_col]).select("datetime")
# cluster = cluster.groupBy("datetime").agg(F.count("datetime").alias("freq")).orderBy("datetime", ascending=True)
cluster = cluster.toPandas()
try:
res_sort = cluster.datetime.value_counts(bins=24 * 6).sort_index()
except ValueError:
print("""WARNING: time column completely empty. Errors time trend
cannot be displayed for cluster {}""".format(
clust_id[clust_col]))
continue
x_datetime = [interval.right for interval in res_sort.index]
converter = mdates.ConciseDateConverter()
munits.registry[datetime.datetime] = converter
fig, ax = plt.subplots(figsize=(10, 5))
# ax.plot(res_sort.index, res_sort)
ax.plot(x_datetime, res_sort.values)
min_h = min(x_datetime) # res_sort.index.min()
max_h = max(x_datetime) # res_sort.index.max()
day_min = str(min_h)[:10]
day_max = str(max_h)[:10]
# title = f"{'Cluster {} - init:'.format(3):>25}{day_min:>15}{str(min_h)[11:]:>12}" + \
# f"\n{' - end:':>25}{day_max:>15}{str(max_h)[11:]:>12}"
title = "Cluster {} - day: {}".format(clust_id[clust_col], day_min)
plt.title(title)
if save_path:
save_path = Path(save_path)
save_path.mkdir(parents=True, exist_ok=True)
outname = save_path / "cluster_{}.png".format(clust_id[clust_col])
# print("Saving time plots to: {}".format(outname))
if os.path.isfile(outname):
os.remove(outname)
fig.savefig(outname, format='png', bbox_inches='tight')
else:
plt.show()
def concise_date_all():
import matplotlib.units as munits
converter = mdates.ConciseDateConverter()
munits.registry[np.datetime64] = converter
def plotting(data,
lim,
title=None,
sav_path=None,
yrange=[5, 0.1],
cscale=[10**3.5, 10**8.5],
save=1):
files = list(data.keys())
if type(sav_path) is not str:
sav_path = r'C:\Users\Magnolia\OneDrive - UMBC\Research\Figures\Preliminary'
i = 0
t = []
alt = []
r = []
alt1 = []
alt2 = []
for file in files:
uL_t, uL_alt, uL_r, uL_alt1, uL_alt2 = lidar.variables(data, file)
if i == 0:
t, alt, r, alt1, alt2 = lidar.variables(data, file)
i += 1
if i > 1:
t = np.append(t, uL_t)
alt = np.append(alt, uL_alt)
r = np.hstack((r, uL_r))
alt1 = np.append(alt1, uL_alt1)
alt2 = np.append(alt2, uL_alt2)
# Date Axis Handling #
converter = mdates.ConciseDateConverter()
munits.registry[datetime.datetime] = converter
if type(title) is str: title = title
else: title = 'blank'
fig, ax = plt.subplots(figsize=(12, 3), constrained_layout=True)
im = plt.imshow(r,
extent=[t[0], t[-1],
np.mean(alt2),
np.mean(alt1)],
cmap='jet',
aspect='auto',
norm=LogNorm())
cbar = plt.colorbar(extend='both')
im.set_clim(vmin=10**3.5, vmax=10**8.5)
cbar.set_label('Aerosol Backscatter')
ax.set_xlabel('Datetime (UTC)')
ax.set_ylabel('Altitude (m)')
ax.set_ylim(yrange)
ax.set_title(f'{title}')
plt.gca().invert_yaxis() # Flip the image so its not upside down
ax.xaxis_date()
lims = [np.datetime64(lim[0]), np.datetime64(lim[1])]
ax.set_xlim(lims)
fig.autofmt_xdate()
characters_to_remove = "!()@:"
pattern = "[" + characters_to_remove + "]"
new_string = re.sub(pattern, "", title)
if save == 1: plt.savefig(f"{sav_path}\{new_string}.png", dpi=600)
plt.show()
output = {'t': t, 'alt': alt, 'r': r, 'alt1': alt1, 'alt2': alt}
return output
def plotting(files, lim, title=None, sav_path=None, yrange=[5, 0.1]):
def variables(file):
t = data[file].time
alt = data[file].range
# Gathering the backscatter (i.e. beta_raw)
r = data[file].beta_raw
# Flip the image
r = np.array(r).T
# Filter for negative values
r[r < 0] = np.nan
np.warnings.filterwarnings('ignore')
''' Merge Days (couple days) '''
alt1 = np.min(alt) / 1000
alt2 = np.max(alt) / 1000
x_lims = mdates.date2num(t)
return x_lims, alt, r, alt1, alt2
if type(sav_path) is not str:
sav_path = r'C:\Users\Magnolia\OneDrive - UMBC\Research\Figures\Preliminary'
i = 0
t = []
alt = []
r = []
alt1 = []
alt2 = []
for file in files:
uL_t, uL_alt, uL_r, uL_alt1, uL_alt2 = variables(file)
if i == 0:
t, alt, r, alt1, alt2 = variables(file)
i += 1
if i > 1:
t = np.append(t, uL_t)
alt = np.append(alt, uL_alt)
r = np.hstack((r, uL_r))
alt1 = np.append(alt1, uL_alt1)
alt2 = np.append(alt2, uL_alt2)
# Date Axis Handling #
converter = mdates.ConciseDateConverter()
munits.registry[datetime.datetime] = converter
if type(title) is str: title = title
else: title = file
fig, ax = plt.subplots(figsize=(12, 3), constrained_layout=True)
im = plt.imshow(r,
extent=[t[0], t[-1],
np.mean(alt2),
np.mean(alt1)],
cmap='jet',
aspect='auto',
norm=LogNorm())
cbar = plt.colorbar(extend='both')
im.set_clim(vmin=10**3.5, vmax=10**8.5)
cbar.set_label('Aerosol Backscatter')
ax.set_xlabel('Datetime (UTC)')
ax.set_ylabel('Altitude (m)')
ax.set_ylim([5, 0.1])
ax.set_title(f'{title}')
plt.gca().invert_yaxis()
ax.xaxis_date()
lims = [np.datetime64(lim[0]), np.datetime64(lim[1])]
ax.set_xlim(lims)
fig.autofmt_xdate()
characters_to_remove = "!()@:"
pattern = "[" + characters_to_remove + "]"
new_string = re.sub(pattern, "", title)
plt.savefig(f"{sav_path}\{new_string}.png", dpi=600)
plt.show()
data1 = {'t': t, 'alt': alt, 'r': r, 'alt1': alt1, 'alt2': alt}
return data1