I am using ruby to call spreadsheet_by_key from a google document. The first page that I call works great, however when i try to duplicate it and use the second tab on the page it does not work. Let me better explain with some examples.
I am using:
data = session.spreadsheet_by_key("spreadsheetkeygoeshere").worksheets[0]
# Get Graph-Data
(2..data.num_rows).each do |column|
key = data[column, 10]
title = data[column, 2]
current = data[column, 3]
goal = data[column, 4]
send_event(key, title: title, min: 0, max: goal, value: current)
end
This works great and returns all of the expected values. Here is the problem I am having.. this is on the page 1 the first page that loads when you open google docs. Now lets say I wan't to make a new spreadsheet on the same doc just under a new tab with a different name and display that data as well
Here is how i change the code:
data1 = session.spreadsheet_by_key("spreadsheetkeygoeshere").worksheets[1]
# Get Graph-Data
(2..data1.num_rows).each do |column|
key = data[column, 10]
puts key
title = data[column, 1]
current = data[column, 5]
goal = data[column, 6]
send_event(key, title: title, min: 0, max: goal, value: current)
end
SO i changed the .worksheets[0] to .worksheets[1]
also i changed
(2..data.num_rows) to (2..data1.num_rows)
Also i changed the data = to data1 =
Any ideas on what i am doing wrong that causes the second spreadsheet to not get pulled ? Any help is greatly appreciated.
What worked was Cameron suggestion. I went in and changed everything to just data = instead of data1= and that fixed the problem.
Related
I am new to transformer based models. I am trying to fine-tune the following model (https://huggingface.co/Chramer/remote-sensing-distilbert-cased) on my dataset. The code:
enter image description here
and I got the following error:
enter image description here
I will be thankful if anyone could help.
The preprocessing steps I followed:
input_ids_t = []
attention_masks_t = []
for sent in df_train['text_a']:
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens = True,
max_length = 128,
pad_to_max_length = True,
return_attention_mask = True,
return_tensors = 'tf',
)
input_ids_t.append(encoded_dict['input_ids'])
attention_masks_t.append(encoded_dict['attention_mask'])
# Convert the lists into tensors.
input_ids_t = tf.concat(input_ids_t, axis=0)
attention_masks_t = tf.concat(attention_masks_t, axis=0)
labels_t = np.asarray(df_train['label'])
and i did the same for testing data. Then:
train_data = tf.data.Dataset.from_tensor_slices((input_ids_t,attention_masks_t,labels_t))
and the same for testing data
It sounds like you are feeding the transformer_model 1 input instead of 3. Try removing the square brackets around transformer_model([input_ids, input_mask, segment_ids])[0] so that it reads transformer_model(input_ids, input_mask, segment_ids)[0]. That way, the function will have 3 arguments and not just 1.
Error message
Traceback (most recent call last):
File "pred.py", line 134, in
output = model(data)
Runtime Error: Expected 4-dimensional input for 4-dimensional weight [16, 3, 3, 3], but got 3-dimensional input of size [1, 32, 32] instead.
Prediction code
normalize = transforms.Normalize(mean=[0.4914, 0.4824, 0.4467],
std=[0.2471, 0.2435, 0.2616])
train_set = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
model = models.condensenet(args)
model = nn.DataParallel(model)
PATH = "results/savedir/save_models/checkpoint_001.pth.tar"
model.load_state_dict(torch.load(PATH)['state_dict'])
device = torch.device("cpu")
model.eval()
image = Image.open("horse.jpg")
input = train_set(image)
train_loader = torch.utils.data.DataLoader(
input,
batch_size=1,shuffle=True, num_workers=1)
for i, data in enumerate(train_loader):
#input_var = torch.autograd.Variable(data, volatile=True)
#input_var = input_var.view(1, 3, 32,32)
**output = model(data)
topk=(1,5)
maxk = max(topk)
_, pred = output.topk(maxk, 1, True, True)
Am getting this error when am trying to predict on a single image
Image shape/size error message
Link to saved model
Training code repository
Plz uncomment this line #input_var = input_var.view(1, 3, 32,32) so that your input dimension is 4.
I assume that your no. of input channels are 3 if its one then use input_var = input_var.view(1, 1, 32,32) if gray scale
Instead of doing the for loop and train_loader, solved this by just passing the input directly into the model. like this
input = train_set(image)
input = input.unsqueeze(0)
model.eval()
output = model(input)
More details can be found here link
I'm currently working on a Ruby script application using Google::Apis::Sheets. I'm encountering an issue that I'm trying to figure out where is being generated from. Basically when data is append into the Google sheet the data is being inserted twice. So for example, if 50 records are pass to be appended into the Google Sheet, 100 records are created.
# data should be an array of arrays
class Sheets
#some code here
def append(data)
# Initialize the API
service = Google::Apis::SheetsV4::SheetsService.new
service.client_options.application_name = APPLICATION_NAME
service.authorization = authorize
# Prints the names and majors of students in a sample spreadsheet:
# https://docs.google.com/spreadsheets/d/1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms/edit
spreadsheet_id = "XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
range = "Sheet1!A:C"
request_body = Google::Apis::SheetsV4::ValueRange.new
request_body.values = data
service.append_spreadsheet_value(spreadsheet_id, range, request_body, value_input_option: 'USER_ENTERED')
end
end
The above method append_spreadsheet_value appends the values into the spreadsheet. I'm trying to figure out if the error is coming from the range or request_body when data is passed into the request_body.values.
The method append is being called from another file reporting.rb which is hosted on AWS Lambda. The method has the following script.
def self.process(event:, context:, db: MySQL.new)
FileUtils.cp('./token.yaml', '/tmp/token.yaml')
last_day = db.get_last_day()
sheets = Sheets.new
data = []
last_day.each do |row|
data.push([row["created_at"].strftime("%Y-%m-%d"), row["has_email"], row["type"]])
end
sheets.append(data)
api_gateway_resp(statusCode: 204)
end
Basically in the method last_day I'm retrieving some records from a DB via a MySQL2 client. I then iterate over last_day and I add each rwo into data. So basically data is an array of arrays holding the records in the following format.
data = [
[2020-08-06, 1, QUARANTINE],
[2020-08-06, 1, QUARANTINE],
[2020-08-06, 1, POSITIVE],
[2020-08-06, 1, POSITIVE],
[2020-08-06, 1, POSITIVE],
[2020-08-06, 1, QUARANTINE],
[2020-08-06, 0, POSITIVE],
[2020-08-06, 1, QUARANTINE]
]
So if data has 10 records when sheets.append(data) data is append to the Google sheep 20 records are created.
Since my other post got nuked, maybe try replacing
request_body = Google::Apis::SheetsV4::ValueRange.new
with
request_body={
"range":range,
"majorDimension":"ROWS",
"values":values
}
The issue was due to the configuration of the Lambda function trigger being set to run every 10 hours.
I am trying to download public comments and replies from the FACEBOOK public post by page.
my code is working until 5 Feb'18, Now it is showing below error for the "Replies".
Error in data.frame(from_id = json$from$id, from_name = json$from$name, :
arguments imply differing number of rows: 0, 1
Called from: data.frame(from_id = json$from$id, from_name = json$from$name,
message = ifelse(!is.null(json$message), json$message, NA),
created_time = json$created_time, likes_count = json$like_count,
comments_count = json$comment_count, id = json$id, stringsAsFactors = F)
please refer below code I am using.
data_fun=function(II,JJ,page,my_oauth){
test <- list()
test.reply<- list()
for (i in II:length(page$id)){
test[[i]] <- getPost(post=page$id[i], token = my_oauth,n= 100000, comments = TRUE, likes = FALSE)
if (nrow(test[[i]][["comments"]]) > 0) {
write.csv(test[[i]], file = paste0(page$from_name[2],"_comments_", i, ".csv"), row.names = F)
for (j in JJ:length(test[[i]]$comments$id)){
test.reply[[j]] <-getCommentReplies(comment_id=test[[i]]$comments$id[j],token=my_oauth,n = 100000, replies = TRUE,likes = FALSE)
if (nrow(test.reply[[j]][["replies"]]) > 0) {
write.csv(test.reply[[j]], file = paste0(page$from_name[2],"_replies_",i,"_and_", j, ".csv"), row.names = F)
}}}
}
Sys.sleep(10)}
Thanks For Your support In advance.
I had the very same problem as Facebook changed the api rules at the end of January. If you update your package with 'devtools' from Pablo Barbera's github, it should work for you.
I have amended my code (a little) and it works fine now for replies to comments.There is one frustrating thing though, is that Facebook dont appear to allow one to extract the user name. I have a pool of data already so I am now using that to train and predict gender.
If you have any questions and want to make contact - drop me an email at 'robert.chestnutt2#mail.dcu.ie'
By the way - it may not be an issue for you, but I have had challenges in the past writing the Rfacebook output to a csv. Saving output as an .RData file maintains the form a lot better
To get the image dimensions in ruby, I tried to use identify to get image dimensions. I wanted to retrieve the output of this system call and get the output as a string
str = system('identify -format "%[fx:w]x%[fx:h]" image.png')
output = `ls`
print output
But, I'm getting the last lines of output and not the output to this particular system call.
Also, if there is a simpler way to get the image dimensions without external gems or libraries, please suggest as it would be great !
Since you already use an external library (ImageMagick), you could use its Ruby wrapper RMagick:
require 'RMagick'
img = Magick::Image::read('image.png').first
arr = [img.columns, img.rows]
Here's an example of a very simple PNG parser:
data = File.binread('image.png', 100) # read first 100 bytes
if data[0, 8] == [137, 80, 78, 71, 13, 10, 26, 10].pack("C*")
# file has a PNG file signature, let's get the image header chunk
length, chunk_type = data[8, 8].unpack("l>a4")
raise "unknown format, expecting image header" unless chunk_type == "IHDR"
chunk_data = data[16, length].unpack("l>l>CCCCC")
width = chunk_data[0]
height = chunk_data[1]
bit_depth = chunk_data[2]
color_type = chunk_data[3]
compression_method = chunk_data[4]
filter_method = chunk_data[5]
interlace_method = chunk_data[6]
puts "image size: #{width}x#{height}"
else
# handle other formats
end
Okay, I finally found a solution after some experiments.
str = `identify -format "%[fx:w]x%[fx:h]" image.png`
arr = str.split('x')
The array arr now contains dimensions in it [width,height] .
This worked for me ! Please suggest other approaches that might be more easier or simpler.