I have this input image and I wan to find pixel maxima and it should look like image2.
I tried contouring but its not working.I also tried scipy pixel_maxima it doesn't gives me result as image2.
here is the code, I have refered this answer (Get coordinates of local maxima in 2D array above certain value)
import numpy as np
import scipy.misc
import scipy.ndimage as ndimage
import scipy.ndimage.filters as filters
import matplotlib.pyplot as plt
import imageio
from skimage.color import rgb2gray
fname = 'slice-0001-trim.jpg'
neighborhood_size = 10
threshold = 12
data = imageio.imread(fname,as_gray=True)
data_max = filters.maximum_filter(data, neighborhood_size)
maxima = (data == data_max)
data_min = filters.minimum_filter(data, neighborhood_size)
diff = ((data_max - data_min) > threshold)
maxima[diff == 0] = 0
labeled, num_objects = ndimage.label(maxima)
slices = ndimage.find_objects(labeled)
x, y = [], []
for dy,dx in slices:
x_center = (dx.start + dx.stop - 1)/2
x.append(x_center)
y_center = (dy.start + dy.stop - 1)/2
y.append(y_center)
plt.imshow(data)
plt.savefig('sample.jpg', bbox_inches = 'tight')
plt.autoscale(False)
plt.plot(x,y, 'ro')
plt.savefig('result.png', bbox_inches = 'tight')
I need this image as result
But, I get this image as output
Related
I plotted some data which has 70 classes, so when I built the color bar it's very difficult to distinguish between each legend as shown below:
The code that I'm using is:
formation_colors = # 70 colors
formation_labels = # 70 labels
data = # the section of the entire dataset which only has 13 labels
data = data.sort_values(by='DEPTH_MD')
ztop=data.DEPTH_MD.min(); zbot=data.DEPTH_MD.max()
cmap_formations = colors.ListedColormap(formation_colors[0:len(formation_colors)], 'indexed')
cluster_f = np.repeat(np.expand_dims(data['Formations'].values,1), 100, 1)
fig = plt.figure(figsize=(2,10))
ax = fig.add_subplot()
im_f = ax.imshow(cluster_f, interpolation='none', aspect='auto', cmap = cmap_formations, vmin=0, vmax=69)
ax.set_xlabel('FORMATION')
ax.set_xticklabels(['']);
divider_f = make_axes_locatable(ax)
cax_f = divider_f.append_axes("right", size="20%", pad=0.05)
cbar_f = plt.colorbar(im_f, cax = cax_f,)
cbar_f.set_ticks(range(0,len(formation_labels))); cbar_f.set_ticklabels(formation_labels)
So far, if I just change:
1. cmap_formations = colors.ListedColormap(formation_colors[0:len(formation_colors)], 'indexed')
2. cbar_f.set_ticks(range(0,len(formation_labels))); cbar_f.set_ticklabels(formation_labels)
to:
cmap_formations = colors.ListedColormap(formation_colors[0:len(data['FORMATION'].unique())], 'indexed')
cbar_f.set_ticks(range(0,len(data['FORMATION'].unique()))); cbar_f.set_ticklabels(data['FORMATION'].unique())
I get, the corresponding colors in the cbar, however the plot is no longer correct and also the legends are out of square
Thank you so much if you have any idea how to do this.
Although not explicitly mentioned in the question, I suppose data['FORMATION'] contains indices from 0 till 69 into the lists of formation_colors and formation_labels
The main problem is that data['FORMATION'] needs to be renumbered to be new indices (with numbers 0 till 12) into the new list of unique colors. np.unique(..., return_inverse=True) returns both the list of unique numbers, and the renumbering for the values.
To be able to reindex the list of colors and of labels, it helps to convert them to numpy arrays.
To make the code easier to debug, the following test uses a simple relation between the list of colors and the list of labels.
from matplotlib import pyplot as plt
from matplotlib import colors
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
import numpy as np
import pandas as pd
formation_colors = np.random.choice(list(colors.CSS4_COLORS), 70, replace=False) # 70 random color names
formation_labels = ['lbl_' + c for c in formation_colors] # 70 labels
formation_colors = np.asarray(formation_colors)
formation_labels = np.asarray(formation_labels)
f = np.random.randint(0, 70, 13)
d = np.sort(np.random.randint(0, 5300, 13))
data = pd.DataFrame({'FORMATION': np.repeat(f, np.diff(np.append(0, d))),
'DEPTH_MD': np.arange(d[-1])})
data = data.sort_values(by='DEPTH_MD')
ztop = data['DEPTH_MD'].min()
zbot = data['DEPTH_MD'].max()
unique_values, formation_new_values = np.unique(data['FORMATION'], return_inverse=True)
cmap_formations = colors.ListedColormap(formation_colors[unique_values], 'indexed')
cluster_f = formation_new_values.reshape(-1, 1)
fig = plt.figure(figsize=(3, 10))
ax = fig.add_subplot()
im_f = ax.imshow(cluster_f, extent=[0, 1, zbot, ztop],
interpolation='none', aspect='auto', cmap=cmap_formations, vmin=0, vmax=len(unique_values)-1)
ax.set_xlabel('FORMATION')
ax.set_xticks([])
divider_f = make_axes_locatable(ax)
cax_f = divider_f.append_axes("right", size="20%", pad=0.05)
cbar_f = plt.colorbar(im_f, cax=cax_f)
cbar_f.set_ticks(np.linspace(0, len(unique_values)-1, 2*len(unique_values)+1)[1::2])
cbar_f.set_ticklabels(formation_labels[unique_values])
plt.subplots_adjust(left=0.2, right=0.5)
plt.show()
Here is a comparison plot:
I am trying to minimize the "function()" with respect to two parameters. I have done so by creating mesh arrays and used them in the above "function()" to return similar meshed array values. However, upon using "fmin()" to find the minimum, the output says that the operators could not be broadcasted.
The code is pasted below:
import numpy as np
from scipy.optimize import fmin
import matplotlib.pyplot as plt
i=0
x_values = np.arange(-10,10,2)
y_values = np.arange(-10,10,2)
x_mesh = np.empty((0,len(x_values)))
y_mesh = np.empty((0,len(y_values)))
for i in range(len(x_values)):
y_mesh = np.vstack((y_mesh, y_values))
i=0
for i in range(len(y_values)):
x_mesh = np.vstack((x_mesh, x_values))
y_mesh = np.transpose(y_mesh)
def function(x_mesh, y_mesh):
return (2*x_mesh**2 + y_mesh**2)
''' Want to minimize function '''
x_start = np.zeros((len(x_values), len(y_values)))
y_start = x_start
y = fmin(lamda x_mesh: function(x_mesh, y_mesh), (x_start, y_start), full_output = True, disp = 0)
The output shown was:
File "C:/Users/User/Documents/Year2/Programming/elrter.py", line 42, in function
return (2*x_mesh**2 + y_mesh**2)
ValueError: operands could not be broadcast together with shapes (200,) (10,10)
But why does this happen? What is the solution?
I am trying to produce a figure (shown below) using netcdf data and overlay stippling a 95% using ttest. I want to plot this graph in Python. Any help will be appreciated.
Figure, original source 1:
#!/usr/bin/env python3
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset
from mpl_toolkits.basemap import Basemap
input = Dataset("v2p0chirps_25_1981_2016.nc.nc")
xlat = input.variables["lat"][:]
xlon = input.variables["lon"][:]
xtime = input.variables["time"][:]
precip = input.variables["pr"][:]
latmin = np.min(xlat)
latmax = np.max(xlat)
lonmin = np.min(xlon)
lonmax = np.max(xlon)
m = Basemap(projection='merc',llcrnrlat = latmin, urcrnrlat = latmax,llcrnrlon = lonmin, urcrnrlon = lonmax, resolution='i')
m.drawcoastlines()
m.drawcountries()
parallels = np.arange(latmin, latmax, 10.)
m.drawparallels(parallels,labels=[True, False, True, False],fontsize=12)
meridians = np.arange(lonmin, lonmax, 10.)
m.drawmeridians(meridians, labels=[True,False,False,True],fontsize=12)
lons, lats = np.meshgrid(xlon, xlat)
x, y = m(lons, lats)
cmap_r = mp.cm.get_cmap('autumn_r')
cs = m.contourf(x,y, np.mean(precip[0:6574,:,:], 0) -
np.mean(precip[6574:13145,:,:], 0), cmap="rainbow", extend='both')
plt.savefig("contour_map.eps")
I have a 3D array with the count of number of days past a benchmark date (e.g., 01.01.2000). I am interested in the actual day-of-year (DOY: 1-365/366)rather than the total number of days past a given date.
For a single value, the below syntax works. For e.g.,
import numpy as np
import datetime
data = 1595
date = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(data -1)
date.timetuple().tm_yday
134
However, I am having issues with using a 3D array.
import numpy as np
import datetime
data = np.random.randint(5, size = (2,2,2))
data = data + 1595
data
array([[[1596, 1595],
[1599, 1599]],
[[1596, 1599],
[1595, 1595]]])
#Function
def Int_to_DOY(int_array):
date_ = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(int_array - 1)
return date_.timetuple().tm_yday
doy_data = data * 0 #Empty array
for i in range(2):
doy_data[:, :, i] = Int_to_DOY(data[:, :, i])
Here is the error message and I am not able to figure this out.
TypeError: unsupported type for timedelta days component: numpy.ndarray
Thanks for your help.
import numpy as np
import datetime
data = np.random.randint(5, size = (2,2,2))
data = data + 1595
#Function
def Int_to_DOY(int_array):
date_ = datetime.datetime(2000,1,1,0,0) + datetime.timedelta(int(int_array) -1)
return date_.timetuple().tm_yday
doy_data = data.flatten()
for i in range(len(doy_data)):
doy_data[i] = Int_to_DOY(doy_data[i])
doy_data = doy_data.reshape((2,2,2))
Since you tagged pandas:
data = np.array([[[1596, 1595],
[1599, 1599]],
[[1596, 1599],
[1595, 1595]]])
s = pd.to_datetime('2000-01-01') + pd.to_timedelta(data.ravel(), unit='D')
s.dayofyear.values.reshape(data.shape) - 1
Output:
array([[[135, 134],
[138, 138]],
[[135, 138],
[134, 134]]], dtype=int64)
I am trying to generate the plot of a function of two parameters, where one is used as x_axis and for the other I plot n curves, varying the parameter between a min and max value.
The following code works:
import numpy as np
import bokeh
from bokeh.plotting import figure
from bokeh.io import push_notebook, show, output_notebook
output_notebook()
x = np.linspace(0,10,100)
f = figure()
fmin=1
fmax=3
nfreq=4
freq=np.linspace(fmin,fmax,nfreq)
for i in freq:
y = np.sin(i*x)
f.line(x,y)
show(f)
Now I would like to have 3 sliders to interactively vary fmin, fmax and nfreq. I could not figure out how to do it...
Thanks for any help
This example works for Bokeh v1.0.4. Run as: bokeh serve --show app.py
The content of app.py:
import numpy as np
from bokeh.models import Slider, Row, Column
from bokeh.plotting import figure, show, curdoc
from bokeh.models.sources import ColumnDataSource
plot = figure()
layout = Column(plot)
sources, lines = {}, {}
def get_x(n): return [np.linspace(0, 10, 100) for i in range(n)]
def get_y(n): return [np.sin(i * np.linspace(0, 10, 100)) for i in n]
def update(attr, old, new):
update_sources(layout.children[-3].value, layout.children[-2].value, layout.children[-1].value)
def update_sources(fmin, fmax, nfreq):
freq = np.linspace(fmin, fmax, nfreq)
for f, x, y in zip(freq, get_x(len(freq)), get_y(freq)):
data = {'x': x, 'y': y}
if f not in sources:
sources[f] = ColumnDataSource(data)
line = plot.line('x', 'y', source = sources[f])
lines[f] = line
else:
sources[f].data = data
for line in lines:
lines[line].visible = (False if line not in freq else True)
for txt, max in zip(['fmin', 'fmax', 'nfreq'], [3, 4, 5]):
slider = Slider(start = 1, end = max, value = 1, title = txt)
slider.on_change('value', update)
layout.children.append(slider)
update_sources(layout.children[-3].value, layout.children[-2].value, layout.children[-1].value)
[plot.line('x', 'y', source = sources[idx]) for idx in sources]
curdoc().add_root(layout)