I have a range of values for every hour of year. Which means there are 24 x 365 = 8760 values. I want to plot this information neatly with matplotlib, with x-axis showing January, February......
Here is my current code:
from matplotlib import pyplot as plt
plt.plot(x_data,y_data,label=str("Plot"))
plt.xticks(rotation=45)
plt.xlabel("Time")
plt.ylabel("Y axis values")
plt.title("Y axis values vs Time")
plt.legend(loc='upper right')
axes = plt.gca()
axes.set_ylim([0,some_value * 3])
plt.show()
x_data is a list containing dates in datetime format. y_data contains values corresponding to the values in x_data. How can I get the plot neatly done with months on the X axis? An example:
You could create a scatter plot with horizontal lines as markers. The month is extracted by using the datetime module. In case the dates are not ordered, the plot sorts both lists first according to the date:
#creating a toy dataset for one year, random data points within month-specific limits
from datetime import date, timedelta
import random
x_data = [date(2017, 1, 1) + timedelta(days = i) for i in range(365)]
random.shuffle(x_data)
y_data = [random.randint(50 * (i.month - 1), 50 * i.month) for i in x_data]
#the actual plot starts here
from matplotlib import pyplot as plt
#get a scatter plot with horizontal markers for each data point
#in case the dates are not ordered, sort first the dates and the y values accordingly
plt.scatter([day.strftime("%b") for day in sorted(x_data)], [y for _xsorted, y in sorted(zip(x_data, y_data))], marker = "_", s = 900)
plt.show()
Output
The disadvantage is obviously that the lines have a fixed length. Also, if a month doesn't have a data point, it will not appear in the graph.
Edit 1:
You could also use Axes.hlines, as seen here.
This has the advantage, that the line length changes with the window size. And you don't have to pre-sort the lists, because each start and end point is calculated separately.
The toy dataset is created as above.
from matplotlib import pyplot as plt
#prepare the axis with categories Jan to Dec
x_ax = [date(2017, 1, 1) + timedelta(days = 31 * i) for i in range(12)]
#create invisible bar chart to retrieve start and end points from automatically generated bars
Bars = plt.bar([month.strftime("%b") for month in x_ax], [month.month for month in x_ax], align = "center", alpha = 0)
start_1_12 = [plt.getp(item, "x") for item in Bars]
end_1_12 = [plt.getp(item, "x") + plt.getp(item, "width") for item in Bars]
#retrieve start and end point for each data point line according to its month
x_start = [start_1_12[day.month - 1] for day in x_data]
x_end = [end_1_12[day.month - 1] for day in x_data]
#plot hlines for all data points
plt.hlines(y_data, x_start, x_end, colors = "blue")
plt.show()
Output
Edit 2:
Now your description of the problem is totally different from what you show in your question. You want a simple line plot with specific axis formatting. This can be found easily in the matplotlib documentation and all over SO. An example, how to achieve this with the above created toy dataset would be:
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MonthLocator
ax = plt.subplot(111)
ax.plot([day for day in sorted(x_data)], [y for _xsorted, y in sorted(zip(x_data, y_data))], "r.-")
ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
ax.xaxis.set_minor_locator(MonthLocator())
ax.xaxis.set_major_formatter(DateFormatter("%B"))
plt.show()
Output
Related
I am trying to plot some time dependent data in a polar coordinates system. The problem is that I got such an output that I have no clue where to start: times, locators, … not “correctly” drawn.
I need some major ticks each 2 hours, minor tick every hour, and a day label in correspondence with a day transition. In normal coordinates it seems ok but when switching to polar it seems more complicated and this is very confusing. I miss smt but don’t know what.
I have tried with
p_locator = mpolar.ThetaLocator(mdates.AutoDateLocator(minticks=24, maxticks=24))
p_formatter = mpolar.ThetaFormatter()
or with
p_locator = mdates.AutoDateLocator()
p_formatter = mdates.DateFormatter("%H:%M")
but no success. I think I missed how matplotlib works internally with datetimeobjects. Not just the ticks, locator and co. are "wrong" but the data don't even fit the full circle (-> should I apply a scale transformation?)
I would really appreciate some help to understand the mechanism behind it.
Update the axis with
ax.set_xticks(time_ticks)
ax.xaxis.set_major_formatter(p_formatter)
ax.xaxis.set_major_locator(p_locator)
Here a code sample
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import matplotlib.projections.polar as mpolar
import matplotlib.ticker as mticker
import datetime
import random
N_MAJOR_TICKS = 12 # amount of major ticks
IS_MULTI_DAYS = True
def random_daily_hours(n_times, base_date=datetime.datetime.today()): # ordered list of day-hours
secs = sorted([random.randint(0, 24*60*60 - 1) for _ in range(n_times)])
return [base_date + datetime.timedelta(seconds=s) for s in secs]
def uniform_daily_hours(n, base_date=datetime.datetime.today()): # return floats! matplolib format!!!
return [base_date + datetime.timedelta(days=1)*i/n for i in range(n)]
# time ticks
time_ticks = uniform_daily_hours(N_MAJOR_TICKS)
# random values
random.seed(10) # fixing random state for reproducibility
x = random_daily_hours(15)
y = [random.random() for _ in range(len(x))]
# multi days - append a consecutive day
if IS_MULTI_DAYS:
day = datetime.timedelta(days=1)
x += random_daily_hours(15, base_date=datetime.datetime.today() + 1 * day)
y = [random.random() for _ in range(len(x))]
ax = plt.subplot(projection='polar')
# fix scale & orientation
ax.set_rticks([0.5, 1, 1.5, 2]) # set values radial ticks
ax.set_rlabel_position(120) # set location radial scale
ax.set_theta_zero_location('N') # set polar reference direction
ax.set_theta_direction(-1) # set default orientation - clockwise
# attempt 1
ax.set_xticks(np.linspace(0, 2 * np.pi, N_MAJOR_TICKS, endpoint=False))
ax.set_xticklabels(time_ticks)
ax.plot(x, y, '-')
ax.grid(True)
plt.show()
I want to plot date vs time graph using matplot lib. The issue I am facing is that due to access of data many lines are showing on the xaxis and I can't find a way to plot my time on xaxis cleanly with one hour gap. Say i have data in my list as string as ['6:01','6:30','7:20','7:25']. I want to divide my xaxis from 6:00 to 7:00 and the time points between them should be plotted based on time.
Note: time list is just and example I want to do this for whole 24 hour.
I tried to use ticks and many other options to complete my task but unfortunatly I am stuck at this problem. My data is in csv file.
Below is my code:
def arrivalGraph():
from datetime import datetime, timedelta
from matplotlib import pyplot as plt
from matplotlib import dates as mpl_dates
with open("Timetable2021.csv","r") as f:
fileData = f.readlines()
del fileData[0]
date = []
train1 = []
for data in fileData:
ind = data.split(",")
date.append(datetime.strptime(ind[0],"%d/%m/%Y").date())
train1Time = datetime.strptime(ind[1],"%H:%M").time()
train1.append(train1Time.strftime("%H:%M"))
plt.style.use("seaborn")
plt.figure(figsize = (10,10))
plt.plot_date(train1,date)
plt.gcf().autofmt_xdate()#gcf is get current figure - autofmt is auto format
dateformater = mpl_dates.DateFormatter("%b ,%d %Y")
plt.gca().xaxis.set_major_formatter(dateformater) # to format the xaxis
plt.xlabel("Date")
plt.ylabel("Time")
plt.title("Train Time vs Date Schedule")
plt.tight_layout()
plt.show()
When i run the code i get the following output:
output of above code
Assuming that every single minute that every single minute is present in train1 (i.e. train1 = ["00:00", "00:01", "00:02", "00:03", ... , "23:59"]), you can use plt.xticks() by generating an array representing xticks with empty string on every minute which is not 0.
unique_times = sorted(set(train1))
xticks = ['' if time[-2:]!='00' else time for time in unique_times]
plt.style.use("seaborn")
plt.figure(figsize = (10,10))
plt.plot_date(train1,date)
plt.gcf().autofmt_xdate()#gcf is get current figure - autofmt is auto format
dateformater = mpl_dates.DateFormatter("%b ,%d %Y")
# I think you wanted to format the yaxis instead of xaxis
plt.gca().yaxis.set_major_formatter(dateformater) # to format the yaxis
plt.ylabel("Date")
plt.xlabel("Time")
plt.title("Train Time vs Date Schedule")
plt.xticks(range(len(xticks)), xticks)
plt.tight_layout()
plt.show()
If every single minute is not in the train1 array, you have to keep train1 data as an object and generate arrays representing xticks location and values to be used as plt.xticks() parameters.
date = []
train1 = []
for data in fileData:
ind = data.split(",")
date.append(datetime.strptime(ind[0],"%d/%m/%Y").date())
train1Time = datetime.strptime(ind[1],"%H:%M")
train1.append(train1Time)
plt.style.use("seaborn")
plt.figure(figsize = (10,10))
plt.plot_date(train1,date)
plt.gcf().autofmt_xdate()#gcf is get current figure - autofmt is auto format
dateformater = mpl_dates.DateFormatter("%b ,%d %Y")
# I think you wanted to format the y axis instead of xaxis
plt.gca().yaxis.set_major_formatter(dateformater) # to format the yaxis
plt.ylabel("Date")
plt.xlabel("Time")
plt.title("Train Time vs Date Schedule")
ax = plt.gca()
xticks_val = []
xticks_loc = []
distance = (ax.get_xticks()[-1] - ax.get_xticks()[0]) / 24
def to_hour_str(x):
x = str(x)
if len(x) < 2:
x = '0' + x
return x + ':00'
for h in range(25):
xticks_val.append(to_hour_str(h))
xticks_loc.append(ax.get_xticks()[0] + h * distance)
plt.xticks(xticks_loc, xticks_val, rotation=90, ha='left')
plt.tight_layout()
plt.show()
Here's the code output using dummy data I generated myself.
For an assignment I need to plot record (min and max) temperatures over the period 2004-2014 using matplotlib. The figure is almost complete (see below) except for the x axis labelling. When plotting, I did not specify the x-axis value so it generated integers from 0-365, thus the number of days in a year. Now I want the months to appear as x-axis labels instead of integers (Jan, Feb, etc.). Can someone help me out?
Record low and high temperatures:
I generated source data as follows:
np.random.seed(13)
dates = pd.date_range(start='2014-01-01', end='2014-12-31')
temp = pd.DataFrame({'tMin': np.random.normal(0, 0.5, dates.size).cumsum() - 10,
'tMax': np.random.normal(0, 0.5, dates.size).cumsum() + 10}, index=dates)
To get the picture with month labels, try the following code:
# Imports
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# Drawing
fig, ax = plt.subplots(figsize=(10, 4))
plt.xlabel('Month')
plt.ylabel('Temp')
plt.title('Temperatures 2014')
ax.xaxis.set_major_locator(mdates.MonthLocator())
fmt = mdates.DateFormatter('%b %Y')
ax.xaxis.set_major_formatter(fmt)
ax.plot(temp.tMin)
ax.plot(temp.tMax)
ax.fill_between(temp.index, temp.tMin, temp.tMax, color='#A0E0A0', alpha=0.2)
plt.setp(ax.get_xticklabels(), rotation=30);
For the above source data I got the following picture:
I have a graph with X as a date and Y as some readings. the X axis has a date interval with an increment of one day. what i want is to show the hours on the x axis between two days(just to set the hours in the yellow area in the graph).
The idea of the code is:
Date=[];Readings=[] # will be filled from another function
dateconv=np.vectorize(datetime.fromtimestamp)
Date_F=dateconv(Date)
ax1 = plt.subplot2grid((1,1), (0,0))
ax1.plot_date(Date_F,Readings,'-')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.grid(True)
plt.xlabel('Date')
plt.ylabel('Readings')
ax1.set_yticks(range(0,800,50))
plt.legend()
plt.show()
You can use MultipleLocator from matplotlib.ticker with set_major_locator and set_minor_locator. See example.
Example
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import datetime
# Generate some data
d = datetime.timedelta(hours=1/5)
now = datetime.datetime.now()
times = [now + d * j for j in range(250)]
ax = plt.gca() # get the current axes
ax.plot(times, range(len(times)))
for label in ax.xaxis.get_ticklabels():
label.set_rotation(30)
# Set the positions of the major and minor ticks
dayLocator = MultipleLocator(1)
hourLocator = MultipleLocator(1/24)
ax.xaxis.set_major_locator(dayLocator)
ax.xaxis.set_minor_locator(hourLocator)
# Convert the labels to the Y-m-d format
xax = ax.get_xaxis() # get the x-axis
adf = xax.get_major_formatter() # the the auto-formatter
adf.scaled[1/24] = '%Y-%m-%d' # set the < 1d scale to Y-m-d
adf.scaled[1.0] = '%Y-%m-%d' # set the > 1d < 1m scale to Y-m-d
plt.show()
Result
I need to create a chart, that has a grid like in the following picture.
The key factors being:
The x-axis is time with each tick marking 30 seconds
y-axes labels in the chart repeat at a variable interval
Chart must grow with the amount of data (i.e. for 30 minutes of data, it should be 60 boxes wide)
I have been looking into matplotlib for a bit, and it seems promising. I also managed to fill the chart with data. See my result for 40 Minutes of data.
But before I invest more time into research, I must know if this goal is even possible. If not I'll have to look into other charts. Thanks for your help!
Here is the source for the above image (my_data is actually read from a csv, but filled with random junk here):
from matplotlib import dates
import matplotlib.pyplot as plt
import numpy as np
import time
from datetime import datetime
my_data = list()
for i in range(3000):
my_data.append((datetime.fromtimestamp(i + time.time()), np.random.randint(50, 200), np.random.randint(10, 100)))
hfmt = dates.DateFormatter('%H:%M:%S')
fig = plt.figure()
actg = fig.add_subplot(2, 1, 1) # two rows, one column, first plot
plt.ylim(50, 210)
atoco = fig.add_subplot(2, 1, 2) # second plot
plt.ylim(0, 100)
actg.xaxis.set_minor_locator(dates.MinuteLocator())
actg.xaxis.set_major_formatter(hfmt)
atoco.xaxis.set_minor_locator(dates.MinuteLocator())
atoco.xaxis.set_major_formatter(hfmt)
plt.xticks(rotation=45)
times = []
fhr1 = []
toco = []
for key in my_data:
times.append(key[0])
fhr1.append(key[1])
toco.append(key[2])
actg.plot_date(times, fhr1, '-')
atoco.plot_date(times, toco, '-')
for ax in fig.axes:
ax.grid(True)
plt.tight_layout()
plt.show()
OK, here's something close to what you are after, I think.
I've used dates.SecondLocator(bysecond=[0,30]) to set the grid every 30 seconds (also need to make sure the grid is set on the minor ticks, with ax.xaxis.grid(True,which='both')
To repeat the yticklabels, I create a twinx of the axes for every major tick on the xaxis, and move the spine to that tick's location. I then set the spine color to none, so it doesn't show up, and turn of the actual ticks, but not the tick labels.
from matplotlib import dates
import matplotlib.pyplot as plt
import numpy as np
import time
from datetime import datetime
# how often to show xticklabels and repeat yticklabels:
xtickinterval = 10
# Make random data
my_data = list()
for i in range(3000):
my_data.append((datetime.fromtimestamp(i + time.time()), np.random.randint(120, 160), np.random.randint(10, 100)))
hfmt = dates.DateFormatter('%H:%M:%S')
fig = plt.figure()
actg = fig.add_subplot(2, 1, 1) # two rows, one column, first plot
actg.set_ylim(50, 210)
atoco = fig.add_subplot(2, 1, 2,sharex=actg) # second plot, share the xaxis with actg
atoco.set_ylim(-5, 105)
# Set the major ticks to the intervals specified above.
actg.xaxis.set_major_locator(dates.MinuteLocator(byminute=np.arange(0,60,xtickinterval)))
# Set the minor ticks to every 30 seconds
minloc = dates.SecondLocator(bysecond=[0,30])
minloc.MAXTICKS = 3000
actg.xaxis.set_minor_locator(minloc)
# Use the formatter specified above
actg.xaxis.set_major_formatter(hfmt)
times = []
fhr1 = []
toco = []
for key in my_data:
times.append(key[0])
fhr1.append(key[1])
toco.append(key[2])
print times[-1]-times[0]
# Make your plot
actg.plot_date(times, fhr1, '-')
atoco.plot_date(times, toco, '-')
for ax in [actg,atoco]:
# Turn off the yticklabels on the right hand side
ax.set_yticklabels([])
# Set the grids
ax.xaxis.grid(True,which='both',color='r')
ax.yaxis.grid(True,which='major',color='r')
# Create new yticklabels every major tick on the xaxis
for tick in ax.get_xticks():
tx = ax.twinx()
tx.set_ylim(ax.get_ylim())
tx.spines['right'].set_position(('data',tick))
tx.spines['right'].set_color('None')
for tic in tx.yaxis.get_major_ticks():
tic.tick1On = tic.tick2On = False
plt.tight_layout()
plt.show()