Rails version: 7.0
PostgreSQL version: 14
What is the way to find the price by the quantity in the products table?
products table
min_quantity | max_quantity | price
1 | 4 | 200
5 | 9 | 185
10 | 24 | 175
25 | 34 | 150
35 | 999 | 100
1000 | null | 60
Expected result
3 ===> 200
50 ===> 100
2500 ===> 60
You can achieve that with a where condition checking that the given value is between min_quantity and max_quantity;
with products(min_quantity, max_quantity, price) as (
values
(1, 4, 200)
, (5, 9, 185)
, (10, 24, 175)
, (25, 34, 150)
, (35, 999, 100)
, (1000, null, 60)
)
select
price
from products
where
case
when max_quantity is null
then 3 >= min_quantity
else
3 between min_quantity and max_quantity
end
-- 200
But as you might have a null value for max_quantity when the min_quantity is 1000 then you'll need a way to handle that. So you can use a case expression to only compare the input with min_quantity.
If the same applies for min_quantity and it can hold null values, then another branch in the case expression might suffice.
As Rails doesn't have specific support for these situations, you'll be off to go with just "raw" SQL in your where;
where(<<~SQL, input: input)
where
case
when max_quantity is null
then :input > min_quantity
else
:input between min_quantity and max_quantity
end
SQL
I'm trying to query a sheet in google docs. The issue I'm having is that I only want the query to return a list of companies that are either "Customer" or "Referrals". The Customer and Referrals text are in a dropdown menu in a column named "Status" in a tab/sheet named "Contacts". The issue I'm running into is that when the query gets to a row where the value is NOT "Customer" or "Referrals" it automatically populates it with the last known customer. I've tried making changes and in the best case scenario it leaves the rows blank that aren't = "Customer" or "Referrals". What I'm TRYING to acheive is having those rows skipped so there aren't a bunch of blank rows (or a bunch of rows of the same customer) between Customers.
WHAT I'M GETTING:
|Vee Zee Home Services|
|Scott Norris |
|Maple Hill |
|Maple Hill |
|Maple Hill |
|Maple Hill |
|Maple Hill |
|Wright Construction |
|Jenema Builders |
|Jenema Builders |
|Jenema Builders |
|Jenema Builders |
|Costanza Homes |
|A. Rink Architects |
WHAT I WANT:
|Vee Zee Home Services|
|Scott Norris |
|Maple Hill |
|Wright Construction |
|Costanza Homes |
|A. Rink Architects |
Here's the query I'm using:
=QUERY(Contacts!A1:F15,"SELECT A WHERE ((lower(C) = 'customer') OR (lower(C) = 'referrals')) AND ((lower(C) != 'investigage') AND (lower(C) != 'lost') AND (lower(C) != 'bidding') AND (lower(C) != 'suspect') AND (lower(C) != 'prospect') AND (lower(C) != 'contact') AND (lower(C) != 'abandoned')) limit 1", false)
try:
=QUERY(Contacts!A1:F15,
"select A
where lower(C) matches 'customer|referrals'
limit 1", 0)
update:
cell B2:
=QUERY(Contacts!A1:F,
"select A,B where lower(C) matches 'customer|referrals' and A is not null", 0)
cell I2:
=QUERY(Contacts!A1:F,
"select C where lower(C) matches 'customer|referrals' and A is not null", 0)
I'm building a web app to help students with learning Maths.
The app needs to display Maths content that comes from LaTex files.
These Latex files render (beautifully) to pdf that I can convert cleanly to svg thanks to pdf2svg.
The (svg or png or whatever image format) image looks something like this:
_______________________________________
| |
| 1. Word1 word2 word3 word4 |
| a. Word5 word6 word7 |
| |
| ///////////Graph1/////////// |
| |
| b. Word8 word9 word10 |
| |
| 2. Word11 word12 word13 word14 |
| |
|_______________________________________|
Real example:
The web app intent is to manipulate and add content to this, leading to something like this:
_______________________________________
| |
| 1. Word1 word2 | <-- New line break
|_______________________________________|
| |
| -> NewContent1 |
|_______________________________________|
| |
| word3 word4 |
|_______________________________________|
| |
| -> NewContent2 |
|_______________________________________|
| |
| a. Word5 word6 word7 |
|_______________________________________|
| |
| ///////////Graph1/////////// |
|_______________________________________|
| |
| -> NewContent3 |
|_______________________________________|
| |
| b. Word8 word9 word10 |
|_______________________________________|
| |
| 2. Word11 word12 word13 word14 |
|_______________________________________|
Example:
A large single image cannot give me the flexibility to do this kind of manipulations.
But if the image file was broken down into smaller files which hold single words and single Graphs I could do these manipulations.
What I think I need to do is detect whitespace in the image, and slice the image into multiple sub-images, looking something like this:
_______________________________________
| | | | |
| 1. Word1 | word2 | word3 | word4 |
|__________|_______|_______|____________|
| | | |
| a. Word5 | word6 | word7 |
|_____________|_______|_________________|
| |
| ///////////Graph1/////////// |
|_______________________________________|
| | | |
| b. Word8 | word9 | word10 |
|_____________|_______|_________________|
| | | | |
| 2. Word11 | word12 | word13 | word14 |
|___________|________|________|_________|
I'm looking for a way to do this.
What do you think is the way to go?
Thank you for your help!
I would use horizontal and vertical projection to first segment the image into lines, and then each line into smaller slices (e.g. words).
Start by converting the image to grayscale, and then invert it, so that gaps contain zeros and any text/graphics are non-zero.
img = cv2.imread('article.png', cv2.IMREAD_COLOR)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray_inverted = 255 - img_gray
Calculate horizontal projection -- mean intensity per row, using cv2.reduce, and flatten it to a linear array.
row_means = cv2.reduce(img_gray_inverted, 1, cv2.REDUCE_AVG, dtype=cv2.CV_32F).flatten()
Now find the row ranges for all the contiguous gaps. You can use the function provided in this answer.
row_gaps = zero_runs(row_means)
Finally calculate the midpoints of the gaps, that we will use to cut the image up.
row_cutpoints = (row_gaps[:,0] + row_gaps[:,1] - 1) / 2
You end up with something like this situation (gaps are pink, cutpoints red):
Next step would be to process each identified line.
bounding_boxes = []
for n,(start,end) in enumerate(zip(row_cutpoints, row_cutpoints[1:])):
line = img[start:end]
line_gray_inverted = img_gray_inverted[start:end]
Calculate the vertical projection (average intensity per column), find the gaps and cutpoints. Additionally, calculate gap sizes, to allow filtering out the small gaps between individual letters.
column_means = cv2.reduce(line_gray_inverted, 0, cv2.REDUCE_AVG, dtype=cv2.CV_32F).flatten()
column_gaps = zero_runs(column_means)
column_gap_sizes = column_gaps[:,1] - column_gaps[:,0]
column_cutpoints = (column_gaps[:,0] + column_gaps[:,1] - 1) / 2
Filter the cutpoints.
filtered_cutpoints = column_cutpoints[column_gap_sizes > 5]
And create a list of bounding boxes for each segment.
for xstart,xend in zip(filtered_cutpoints, filtered_cutpoints[1:]):
bounding_boxes.append(((xstart, start), (xend, end)))
Now you end up with something like this (again gaps are pink, cutpoints red):
Now you can cut up the image. I'll just visualize the bounding boxes found:
The full script:
import cv2
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import gridspec
def plot_horizontal_projection(file_name, img, projection):
fig = plt.figure(1, figsize=(12,16))
gs = gridspec.GridSpec(1, 2, width_ratios=[3,1])
ax = plt.subplot(gs[0])
im = ax.imshow(img, interpolation='nearest', aspect='auto')
ax.grid(which='major', alpha=0.5)
ax = plt.subplot(gs[1])
ax.plot(projection, np.arange(img.shape[0]), 'm')
ax.grid(which='major', alpha=0.5)
plt.xlim([0.0, 255.0])
plt.ylim([-0.5, img.shape[0] - 0.5])
ax.invert_yaxis()
fig.suptitle("FOO", fontsize=16)
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.97])
fig.set_dpi(200)
fig.savefig(file_name, bbox_inches='tight', dpi=fig.dpi)
plt.clf()
def plot_vertical_projection(file_name, img, projection):
fig = plt.figure(2, figsize=(12, 4))
gs = gridspec.GridSpec(2, 1, height_ratios=[1,5])
ax = plt.subplot(gs[0])
im = ax.imshow(img, interpolation='nearest', aspect='auto')
ax.grid(which='major', alpha=0.5)
ax = plt.subplot(gs[1])
ax.plot(np.arange(img.shape[1]), projection, 'm')
ax.grid(which='major', alpha=0.5)
plt.xlim([-0.5, img.shape[1] - 0.5])
plt.ylim([0.0, 255.0])
fig.suptitle("FOO", fontsize=16)
gs.tight_layout(fig, rect=[0, 0.03, 1, 0.97])
fig.set_dpi(200)
fig.savefig(file_name, bbox_inches='tight', dpi=fig.dpi)
plt.clf()
def visualize_hp(file_name, img, row_means, row_cutpoints):
row_highlight = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
row_highlight[row_means == 0, :, :] = [255,191,191]
row_highlight[row_cutpoints, :, :] = [255,0,0]
plot_horizontal_projection(file_name, row_highlight, row_means)
def visualize_vp(file_name, img, column_means, column_cutpoints):
col_highlight = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
col_highlight[:, column_means == 0, :] = [255,191,191]
col_highlight[:, column_cutpoints, :] = [255,0,0]
plot_vertical_projection(file_name, col_highlight, column_means)
# From https://stackoverflow.com/a/24892274/3962537
def zero_runs(a):
# Create an array that is 1 where a is 0, and pad each end with an extra 0.
iszero = np.concatenate(([0], np.equal(a, 0).view(np.int8), [0]))
absdiff = np.abs(np.diff(iszero))
# Runs start and end where absdiff is 1.
ranges = np.where(absdiff == 1)[0].reshape(-1, 2)
return ranges
img = cv2.imread('article.png', cv2.IMREAD_COLOR)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_gray_inverted = 255 - img_gray
row_means = cv2.reduce(img_gray_inverted, 1, cv2.REDUCE_AVG, dtype=cv2.CV_32F).flatten()
row_gaps = zero_runs(row_means)
row_cutpoints = (row_gaps[:,0] + row_gaps[:,1] - 1) / 2
visualize_hp("article_hp.png", img, row_means, row_cutpoints)
bounding_boxes = []
for n,(start,end) in enumerate(zip(row_cutpoints, row_cutpoints[1:])):
line = img[start:end]
line_gray_inverted = img_gray_inverted[start:end]
column_means = cv2.reduce(line_gray_inverted, 0, cv2.REDUCE_AVG, dtype=cv2.CV_32F).flatten()
column_gaps = zero_runs(column_means)
column_gap_sizes = column_gaps[:,1] - column_gaps[:,0]
column_cutpoints = (column_gaps[:,0] + column_gaps[:,1] - 1) / 2
filtered_cutpoints = column_cutpoints[column_gap_sizes > 5]
for xstart,xend in zip(filtered_cutpoints, filtered_cutpoints[1:]):
bounding_boxes.append(((xstart, start), (xend, end)))
visualize_vp("article_vp_%02d.png" % n, line, column_means, filtered_cutpoints)
result = img.copy()
for bounding_box in bounding_boxes:
cv2.rectangle(result, bounding_box[0], bounding_box[1], (255,0,0), 2)
cv2.imwrite("article_boxes.png", result)
The image is top quality, perfectly clean, not skewed, well separated characters. A dream !
First perform binarization and blob detection (standard in OpenCV).
Then cluster the characters by grouping those with an overlap in the ordinates (i.e. facing each other in a row). This will naturally isolate the individual lines.
Now in every row, sort the blobs left-to-right and cluster by proximity to isolate the words. This will be a delicate step, because the spacing of characters within a word is close to the spacing between distinct words. Don't expect perfect results. This should work better than a projection.
The situation is worse with italics as the horizontal spacing is even narrower. You may have to also look at the "slanted distance", i.e. find the lines that tangent the characters in the direction of the italics. This can be achieved by applying a reverse shear transform.
Thanks to the grid, the graphs will appear as big blobs.
I'm developing and iOS game and I want to switch cols and rows
For example here is the stat's table :
StatName (STRING) | StatValue (NUMBER)
--------------------------------------
NbJoker | 12
--------------------------------------
NbPlayed | 71
--------------------------------------
NbPause | 87
--------------------------------------
I need
NbJoker | NbPlayed | NbPause
----------------------------
12 | 71 | 87
Is that possible ?
Thanks!
I do not know SQL Lite. But If the below syntax works.
It's like the same as a Transpose in any SQL.
SELECT SUM(T1) AS NBJOKER, SUM(T2) AS NBPLAYED, SUM(T3) AS NBPAUSE FROM (
SELECT STATVALUE AS T1,O AS T2,0 AS T3 FROM TABLE_X WHERE STATNAME='NBJOKER'
UNION ALL
SELECT 0, STATVALUE, 0 FROM TABLE_X WHERE STATNAME='NBLAYED'
UNION ALL
SELECT 0, 0, STATVALUE FROM TABLE_X WHERE STATNAME='NBPAUSE'
)
For example I have grid.
//grid for answers_for_online
var answersGridForOnline5 = new Ext.grid.GridPanel({
id : 'grid_for_stats',
store : storez3,
columns : answers_columns5,
});
my column:
var answers_columns5 = [{
id: "idz",
header: 'idz',
dataIndex: "idz",
renderer: fun_f
}];
and renderer function
function fun(n, j, k, m, h, i) {
var count = store.snapshot ? store.snapshot.length : store.getCount()
var cez = k.get("scale")
var ce = ( 2 / count ) * 100
return ce + " % "
}
Question: In database I have for example: scales (that user answered on scale-question)
id | scale
1 | 4
2 | 4
3 | 1
4 | 2
How i can sum scales (and group them of course) and put this in my grid?
For example in my grid i should get:
scale | scale %
1 | 25%
2 | 25%
4 | 50%
I advise you don't attempt to do it inside Grid/Store. Instead process the data before loading it to store - for example do it in database with GROUP BY statement.
To get the sum of values in a store, you can use Store.sum()