pandas time series resample option

price.resample('M').count().plot(title='Monthly Counts') 

 

W : weekly frequency
M : month end frequency
SM : semi-month end frequency (15th and end of month)
Q : quarter end frequency

python random function

 import random

mylist = [1, 2, 3]

random.shuffle(mylist) # randomly change the order of the list

print(mylist)

print(random.choice(mylist)) # randomly choose one element from the list

Seismic ghost notch calculation

 

where ${\displaystyle d}$ is the source or receiver depth in meters

print one column after another

 guofyuan @ htpvis-025a$ cat test.txt
AAA    111  123
BBB    222  234
CCC    333  345

guofyuan @ htpvis-025a$ awk '{if(maxc<NF)maxc=NF; for(i=1;i<=NF;i++){(a[i]!=""?a[i]=a[i]RS$i:a[i]=$i)} } END{for(i=1;i<=maxc;i++)print a[i]}' test.txt
AAA
BBB
CCC
111
222
333
123
234
345

 

 gawk '{for (i=1; i<=NF; i++) # loop over columns

           data[i][NR]=$i               # store in data[column][line]
      }
      END {for (i=1;i<=NR;i++)          # loop over lines
                for (j=1;j<=NF;j++)     # loop over columns
                     print data[i][j]   # print the given field
      }' file

https://stackoverflow.com/questions/39287224/how-to-print-columns-one-after-the-other-in-bash

convert a horizontal file to vertical one using awk

 $ cat TBB11-str-ghost-far-tmp.txt | awk '{for(i=1;i<=NF;i++) {print $i}}'

Input

guofyuan @ htpvis-025a$ head TBB11-str-ghost-far-tmp.txt
     0.000     0.000     0.000     0.000     0.000     0.000    -0.001
     0.001    -0.001    -0.002     0.007    -0.013     0.016    -0.012
    -0.006     0.038    -0.076     0.100    -0.087     0.014     0.118
    -0.278     0.401    -0.403     0.213     0.193    -0.762     1.365
    -1.808     1.865    -1.235    -0.757    50.286    57.557     5.330
     5.619   -27.954  -110.350   -86.889   -19.798    -6.313    21.974
    52.920    32.938     6.974     1.216     2.681     6.191     4.807
     2.697     1.438    -0.089     0.905     1.381     0.864     0.263
     0.096     0.493     0.419     0.371     0.348     0.237     0.287
     0.396     0.447     0.389     0.383     0.459     0.519     0.553

output
guofyuan @ htpvis-025a$ head TBB11-str-ghost-far-vertical.txt
0.000
0.000
0.000
0.000
0.000
0.000
-0.001
0.001
-0.001
-0.002

count the frequency of a list (uniq -c)

 guofyuan @ htpvis-025a$ ls Seq* | nl | sed 's/_Subline_/ /g' | sed 's/Seq//g' | awk '{print $2'} | uniq -c | awk '$1<4'

      3 125

ImageIO find intensity

 

# Create left ventricle mask

labels, nlabels = ndi.label(mask)

lv_val = labels[128, 128]

lv_mask = np.where(labels == lv_val, 1, 0)

# Find bounding box of left ventricle

bboxes = ndi.find_objects(lv_mask)

print('Number of objects:', len(bboxes))

print('Indices for first box:', bboxes[0])

# Crop to the left ventricle (index 0)

im_lv = im[bboxes[0]]

# Plot the cropped image

plt.imshow(im_lv, cmap='rainbow')

format_and_render_plot()

 

 

Biomedical Images_1

 Imageio

# Import ImageIO

import imageio

import matplotlib.pyplot as plt 

# Load "chest-220.dcm"

im = imageio.imread("chest-220.dcm")

# Print image attributes

print('Image type:', type(im))

print('Shape of image array:', im.shape)

# Print the available metadata fields

print(im.meta.keys())

print(im.meta['PatientSex']) 

# Draw the image with greater contrast

plt.imshow(im, cmap ='gray', vmin = -200, vmax = 200)

# Remove ticks and axises

plt.axis('off')

# Render the image

plt.show()

# Import ImageIO and NumPy

import imageio

import numpy as np

# Read in each 2D image

im1 = imageio.imread('chest-220.dcm')

im2 = imageio.imread('chest-221.dcm')

im3 = imageio.imread('chest-222.dcm')

# Stack images into a volume

vol = np.stack([im1, im2, im3])

print('Volume dimensions:', vol.shape)

 

# Load the "tcia-chest-ct" directory

vol = imageio.volread("tcia-chest-ct")

# Print image attributes

print('Available metadata:', vol.meta.keys())

print('Shape of image array:', vol.shape)

 

In [5]:

vol.meta['sampling']

Out[5]:

(3.270000000000001, 0.976562, 0.976562)

In [6]:

vol.shape

Out[6]:

(25, 512, 512)

 The field of view = (82, 500, 500)

 

To select a 2D frame, pick a frame for the first axis and select all data 

from the remaining two: vol[0, :, :]

For this exercise, use for loop to plot every 40th slice of vol on a 

separate subplot. matplotlib.pyplot (as plt) has been imported for you.

# Plot the images on a subplots array

fig, axes = plt.subplots(nrows=1, ncols=4)

# Loop through subplots and draw image

for ii in range(4):

im = vol[ii*40, :, :]

axes[ii].imshow(im, cmap='gray')

axes[ii].axis('off')

 

# Select frame from "vol"

im1 = vol[:, 256, :]

im2 = vol[:, :, 256]

# Compute aspect ratios

d0, d1, d2 = vol.meta['sampling']

asp1 = d0 / d2

asp2 = d0/d1

# Plot the images on a subplots array

fig, axes = plt.subplots(nrows=2, ncols=1)

axes[0].imshow(im1, cmap='gray',aspect=asp1)

axes[1].imshow(im2, cmap='gray', aspect=asp2)

plt.show()

 

Intensity: 

# Import SciPy's "ndimage" module

import scipy.ndimage as ndi

# Create a histogram, binned at each possible value

# Change data type could save spaces.

# im_int8 = im.astype(np.unit8)

hist = ndi.histogram(im, min=0, max=256, bins=256)

# Create a cumulative distribution function

cdf = hist.cumsum() / hist.sum()

# Plot the histogram and CDF

fig, axes = plt.subplots(2, 1, sharex=True)

axes[0].plot(hist, label='Histogram')

axes[1].plot(cdf, label='CDF')

format_and_render_plot()

Mask:

# Create skin and bone masks

mask_bone = im>=145

mask_skin = (im>=45)&(im<145)

# Plot the skin (0) and bone (1) masks

fig, axes = plt.subplots(1,2)

axes[0].imshow(mask_skin, cmap='gray')

axes[1].imshow(mask_bone, cmap='gray')

format_and_render_plot()

 

# Import SciPy's "ndimage" module

import scipy.ndimage as ndi

# Screen out non-bone pixels from "im"

mask_bone = im >= 145

im_bone = np.where(mask_bone, im, 0)

# Get the histogram of bone intensities

hist = ndi.histogram(im_bone, min=1, max=255,bins=255)

# Plot masked image and histogram

fig, axes = plt.subplots(2,1)

axes[0].imshow(im_bone)

axes[1].plot(hist)

format_and_render_plot()

 

 

 

 

 

 

 

# Create and tune bone mask

mask_bone = im >=145

mask_dilate = ndi.binary_dilation(mask_bone, iterations=5)

mask_closed = ndi.binary_closing(mask_bone, iterations=5)

# Plot masked images

fig, axes = plt.subplots(1,3)

axes[0].imshow(mask_bone)

axes[1].imshow(mask_dilate)

axes[2].imshow(mask_closed)

format_and_render_plot()

Filters:

# Set filter weights

weights = [[0.11, 0.11, 0.11],

[0.11, 0.11, 0.11],

[0.11, 0.11, 0.11]]

# Convolve the image with the filter

im_filt = ndi.convolve(im, weights)

# Plot the images

fig, axes = plt.subplots(1,2)

axes[0].imshow(im)

axes[1].imshow(im_filt)

format_and_render_plot()

 

 

 

 

 

 

 

 

 

# Smooth "im" with Gaussian filters

im_s1 = ndi.gaussian_filter(im, sigma=1)

im_s3 = ndi.gaussian_filter(im, sigma=3)

# Draw bone masks of each image

fig, axes = plt.subplots(1,3)

axes[0].imshow(im >= 145)

axes[1].imshow(im_s1 >= 145)

axes[2].imshow(im_s3 >= 145)

format_and_render_plot()

 

 

 

 

Edges:
 

# Set weights to detect vertical edges

weights = [[1,0,-1], [1,0,-1], [1,0,-1]]

# Convolve "im" with filter weights

edges = ndi.convolve(im, weights)

# Draw the image in color

plt.imshow(edges, cmap = 'seismic', vmin=-150, vmax=150)

plt.colorbar()

format_and_render_plot()

 

 

 

 

 

 

 

 

 

 

# Apply Sobel filter along both axes

sobel_ax0 = ndi.sobel(im, axis=0)

sobel_ax1 = ndi.sobel(im, axis=1)

# Calculate edge magnitude

edges = np.sqrt(np.square(sobel_ax0) + np.square(sobel_ax1))

# Plot edge magnitude

plt.imshow(edges, cmap='gray', vmax=75)

format_and_render_plot()

 

 

 

 

 

 

 

 

 

 

 

 

 

 

python testEqual function

 testEqual compares what is returned by the distance function and the 0 (the correct answer).

import test

def distance(x1, y1, x2, y2):

    dx = x2 - x1

    dy = y2 - y1

    dsquared = dx**2 + dy**2

    result = dsquared**0.5

    return result

test.testEqual(distance(1,2, 1,2), 0)

test.testEqual(distance(1,2, 4,6), 5)

test.testEqual(distance(0,0, 1,1), 1.41421)

Pass
Pass
Pass

python list range not printing the last one, last one need to be larger than the one you want to print

 For even numbers we want to start at 0 and count by 2’s. So if we wanted the first 10 even numbers we would use range(0,19,2). The most general form of the range is range(start, beyondLast, step).

print(list(range(0, 20, 2)))

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Not printing 20 as you would thought

The randrange function generates an integer between its lower and upper
argument, using the same semantics as range — so the lower bound is included, but
the upper bound is excluded.

diceThrow = random.randrange(1, 7)       

# return an int, one of 1,2,3,4,5,6 But not 7

python math operator

Division and Reminder

 

 total_secs=7350

hours=total_secs//3600 # hours=2

secs_still_remaining=total_secs%3600 # secs_still_remaining=150

minutes= secs_still_remaining // 60 # minutes=2

secs_finally_remaining=secs_still_remaining %60 # seconds=30

vnc resolution

 

$ xrandr

 

 

$ xrandr --newmode "2560x1440_60.00"  312.25  2560 2752 3024 3488  1440 1443 1448 1493 -hsync +vsync

 

$ xrandr --addmode VNC-0 "2560x1440_60.00"

 

 

https://superuser.com/questions/223240/changing-screen-resolution-in-centos