Mecsimcalc v0.1.8 documentation
This library is designed to provide a set of functions for handling and converting various types of data, such as base64 encoded data, Pandas DataFrames, and Pillow images.
General
input_to_file
[Source]
input_to_file(input_file, metadata = False)
Description:
Converts a base64 encoded string into a file object and metadata
Arguments:
Argument |
Type |
Description |
input_file |
str |
Base64 encoded string, prefixed with metadata |
metadata |
bool (optional) |
Flag to return metadata with the file. (Defaults to False) |
Raises:
Exception |
Description |
ValueError |
If the input string doesn't contain ';base64,' to separate metadata and file data. |
Returns:
Return Type |
Description |
Condition |
io.BytesIO |
The decoded file data (The thing you get when you open a file in Python) |
metadata is False |
(io.BytesIO, str) |
The decoded file data and its metadata |
metadata is True |
Example:
>>> input_file = inputs['file']
>>> file, metadata = input_to_file(input_file, metadata = True)
>>> print(metadata)
data:image/jpeg;base64,
>>> type(file)
<class '_io.BytesIO'>
metadata_to_filetype
[Source]
metadata_to_filetype(metadata):
Description:
Extracts the file type from the metadata
Arguments:
Argument |
Type |
Description |
metadata |
str |
The metadata string in the form "Data:(MIME type);base64,"(returned from input_to_file ) |
Returns:
Return Type |
Description |
str |
The file type (e.g. "jpeg") |
Example:
>>> input_file = inputs['file']
>>> file, metadata = input_to_file(input_file, metadata = True)
>>> print(metadata)
data:image/jpeg;base64,
>>> download_file_type = metadata_to_filetype(metadata)
>>> print(download_file_type)
jpeg
Text
string_to_file
[Source]
string_to_file(
text
filename= "myfile",
download_text = "Download File",
)
Description:
Generates a downloadable text file containing the given text
Arguments:
Argument |
Type |
Description |
text |
str |
Text to be downloaded |
filename |
str (optional) |
Name of the download file. (Defaults to "myfile") |
download_text |
str (optional) |
Text to be displayed as the download link. (Defaults to "Download File") |
Raises:
Exception |
Description |
TypeError |
If the input text is not a string. |
Returns:
Return Type |
Description |
str |
HTML download link |
Example:
Python
>>> download_link = string_to_file("Hello World!")
>>> return {"download": download_link}
Jinja2
# outputs.downloadLink is the html download link generated by the function
{{ outputs.download }}
Spreadsheets
file_to_dataframe
[Source]
file_to_dataframe(file_data):
Description:
Converts a base64 encoded file data into a pandas DataFrame
Arguments:
Argument |
Type |
Description |
file_data |
io.BytesIO |
Decoded file data (e.g. from input_to_file ) |
Raises:
Exception |
Description |
pd.errors.ParserError |
If the file data cannot be converted to a DataFrame (i.e. file is not an Excel or CSV file or is corrupted) |
Returns:
Return Type |
Description |
pd.DataFrame |
DataFrame created from file data |
Example:
>>> input_file = inputs['file']
>>> decoded_file = input_to_file(input_file)
>>> df = file_to_dataframe(decoded_file)
>>> print(df)
A B C
0 a b c
1 d e f
input_to_dataframe
[Source]
input_to_dataframe(file):
Description:
Converts a base64 encoded file data into a pandas DataFrame
Arguments:
Argument |
Type |
Description |
input_file |
str |
Base64 encoded file data |
get_file_type |
bool |
If True, the function also returns the file type (Defaults to False) |
Returns:
Return Type |
Description |
Condition |
pd.DataFrame |
DataFrame created from file data |
get_file_type is False |
(pd.DataFrame, str) |
Tuple containing the DataFrame and the file type |
get_file_type is True |
Example:
>>> input_file = inputs['file']
>>> df, file_type = input_to_dataframe(input_file, get_file_type = True)
>>> print(df)
A B C
0 a b c
1 d e f
>>> print(file_type)
csv
print_dataframe
[Source]
print_dataframe(
df,
download = False,
download_text = "Download Table",
download_file_name = "mytable",
download_file_type = "csv",
):
Description:
Creates an HTML table and a download link for a given DataFrame
Arguments:
Argument |
Type |
Description |
df |
pd.DataFrame |
DataFrame to be converted |
download |
bool (optional) |
If True, function returns a download link (Defaults to False) |
download_text |
str (optional) |
Text to be displayed as the download link (Defaults to "Download Table") |
download_file_name |
str (optional) |
Name of file when downloaded (Defaults to "mytable") |
download_file_type |
str (optional) |
File type of downloaded file (Defaults to "csv") |
Returns:
Return Type |
Description |
Condition |
str |
HTML table |
download is False |
Tuple[str, str] |
(HTML table, HTML download link) |
download is True |
Example:
Python Code:
>>> input_file = inputs['file']
>>> df = input_to_dataframe(input_file)
>>> table, download = print_dataframe(df, download = True, download_file_name = "FunkyTable", download_text = "Download My Funky Table HERE!", download_file_type = "xlsx")
>>> return {
"table":table,
"download":download,
}
Output using Jinja2 Template:
# outputs.table is the HTML table
Displaying Table
{{ outputs.table }}
# outputs.download is the download link
Downloading Table
{{ outputs.download }}
Tables
table_to_dataframe
[Source]
table_to_dataframe(column_headers, rows) -> pd.DataFrame:
Description:
Create a DataFrame from given rows and column headers
Arguments:
Argument |
Type |
Description |
column_headers |
List[str] |
List of column headers |
rows |
List[List[str]] |
List of rows to be converted into a DataFrame. Each column is a list of strings |
Returns:
Return Type |
Description |
pd.DataFrame |
DataFrame created from headers and rows |
Example:
>>> column_headers = ["A", "B", "C"]
>>> rows = [["a", "b", "c"], ["d", "e", "f"]]
>>> df = table_to_dataframe(column_headers, rows)
>>> print(df)
A B C
0 a b c
1 d e f
print_table
[Source]
print_table(column_headers, rows):
Description:
Creates an HTML table from given rows and column headers
Arguments:
Argument |
Type |
Description |
column_headers |
List[str] |
List of column headers |
rows |
List[List[str]] |
List of rows to be converted into a table. Each column is a list of strings |
index |
bool (optional) |
Whether to use the first column as the DataFrame's index. (Defaults to True) |
Returns:
Return Type |
Description |
str |
HTML table created from rows and headers |
Example:
Python Code:
>>> column_headers = ["A", "B", "C"]
>>> rows = [["a", "b", "c"], ["d", "e", "f"]]
>>> table = print_table(column_headers, rows)
>>> return {
"table":table,
}
Output using Jinja2 Template:
# outputs.table is the HTML table
Displaying Table
{{ outputs.table }}
Images
file_to_PIL
[Source]
file_to_PIL(file):
Description:
Transforms a file into a Pillow Image object
Arguments:
Argument |
Type |
Description |
file |
str |
Decoded file data (returned from input_to_file) |
Raises:
Exception Type |
Description |
ValueError |
If the file does not contain image data |
Returns:
Return Type |
Description |
Image |
Pillow Image object |
Example:
Python Code:
>>> input_file = inputs['file']
>>> decoded_file = input_to_file(input_file)
>>> image = file_to_PIL(decoded_file)
>>> return {
"image":image,
}
Output using Jinja2 Template:
# outputs.image is the Pillow Image object
Displaying Image
{{ outputs.image }}
input_to_PIL
[Source]
input_to_PIL(input_file, get_file_type=False):
Description:
Converts a base64 encoded file data into a pillow image
Arguments:
Argument |
Type |
Description |
input_file |
str |
Base64 encoded file data |
get_file_type |
bool |
If True, the function also returns the file type (Defaults to False) |
Returns:
Return Type |
Description |
Condition |
PIL.Image.Image |
Pillow Image object |
get_file_type is False |
Tuple[PIL.Image.Image, str] |
(pillow image, metadata) |
get_file_type is True |
Example:
>>> input_file = inputs['file']
>>> image, file_type = input_to_PIL(input_file, get_file_type=True)
>>> print(file_type)
jpeg
>>> type(image)
<class 'PIL.JpegImagePlugin.JpegImageFile'>
print_image
[Source]
print_image(
image,
width = 200,
height = 200,
original_size = False,
download = False,
download_text = "Download Image",
download_file_name= "myimg",
download_file_type = "png",
):
Description:
Transforms a Pillow image into an HTML image, with an optional download link
Arguments:
Argument |
Type |
Description |
image |
PIL.Image.Image |
Pillow image |
width |
int (optional) |
Output width of the image in pixels (Defaults to 200) |
height |
int (optional) |
Output height of the image in pixels (Defaults to 200) |
original_size |
bool (optional) |
If True, the HTML image will be displayed in its original size (Defaults to False) |
download |
bool (optional) |
If True, function returns a download link (Defaults to False) |
download_text |
str (optional) |
The text to be displayed on the download link (Defaults to "Download Image") |
download_file_name |
str (optional) |
The name of the image file when downloaded (Defaults to "myimg") |
download_file_type |
str (optional) |
The file type of the image when downloaded (Defaults to "png") |
Returns:
Return Type |
Description |
Condition |
str |
HTML image |
download is False |
Tuple[str, str] |
(HTML image, download link) |
download is True |
Example:
Python Code:
>>> input_file = inputs['file']
>>> image, metadata = input_to_PIL(input_file)
>>> html_image, download = print_image(image, original_size = True, download = True, download_text = "Download Image Here", download_file_name = "myimage", download_file_type = "jpeg")
>>> return {
"image":html_image,
"download":download,
}
Output using Jinja2 Template:
# outputs.image is the HTML image
Displaying Image
{{ outputs.image }}
# outputs.download is the download link
Downloading Image
{{ outputs.download }}
Plots
print_plot
[Source]
print_plot(
plot_obj,
width = 500,
dpi= 100,
download= False,
download_text = "Download Plot",
download_file_name = "myplot",
)
Description:
Converts a matplotlib.pyplot.axis or matplotlib.figure into an HTML image tag and optionally provides a download link for the image
Arguments:
Argument |
Type |
Description |
plot_obj |
axes or figure |
Matplotlib figure |
width |
int (optional) |
Output width of the image in pixels (Defaults to 500) |
dpi |
int (optional) |
Output dpi of the image in pixels (Defaults to 100) |
download |
bool (optional) |
If True, function returns a download link (Defaults to False) |
download_text |
str (optional) |
The text to be displayed on the download link (Defaults to "Download Plot") |
download_file_name |
str (optional) |
The name of the image file when downloaded (Defaults to "myplot") |
Returns:
Return Type |
Description |
Condition |
str |
HTML image |
download is False |
Tuple[str, str] |
(HTML image, HTML download link) |
download is True |
Example:
Python Code:
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> x = np.linspace(0, 2 * np.pi, 400)
>>> y = np.sin(x)
>>> fig, ax = plt.subplots()
>>> ax.plot(x, y)
>>> ax.set_title('A single plot')
>>> image, download = print_plot(fig, width = 500, dpi = 100, download = True, download_text = "Download Sin Function Plot", download_file_name = "sin(x)")
>>> return {
"image":image,
"download":download,
}
Output using Jinja2 Template:
# outputs.image is the HTML image
Displaying Image
{{ outputs.image }}
# outputs.download is the download link
Downloading Image
{{ outputs.download }}
print_animation
[Source]
print_animation(ani: FuncAnimation, fps: int = 30, save_dir: str = "/tmp/temp_animation.gif") -> str:
Description:
Converts a matplotlib animation into an HTML image tag.
Arguments:
Argument |
Type |
Description |
ani |
FuncAnimation |
The matplotlib animation to be converted. |
fps |
int (optional) |
Frames per second for the animation. Defaults to 30 . |
save_dir |
str (optional) |
The directory to save the animation. Defaults to "/tmp/temp_animation.gif" . (Note: The file will be deleted after the execution of the app is finished.) You can only write to the tmp directory in mecsimcalc. |
Returns:
Return Type |
Description |
str |
The HTML image tag as a string. |
Example:
>>> fig, ax = plt.subplots()
>>> x = np.linspace(0, 10, 1000)
>>> y = np.sin(x)
>>> line, = ax.plot(x, y)
>>> def update(frame):
>>> line.set_ydata(np.sin(x + frame / 100))
>>> ani = FuncAnimation(fig, update, frames=100)
>>> animation = msc.print_animation(ani)
>>> return {
"animation": animation
}
animate_plot
[Source]
animate_plot(
x: np.ndarray,
y: np.ndarray,
duration: int = 5,
fps: int = None,
title: str = "y = f(x)",
show_axes: bool = True,
save_dir: str = "/tmp/temp_animation.gif",
) -> str:
Description:
Creates an animated plot from given x and y data and returns it as an HTML image tag.
Arguments:
Argument |
Type |
Description |
x |
np.ndarray |
The x-coordinates of the data points. |
y |
np.ndarray |
The y-coordinates of the data points. |
duration |
int (optional) |
The duration of the animation in seconds. Defaults to 5 . |
fps |
int (optional) |
Frames per second for the animation. Defaults to None . (fps = len(x) / duration if fps=None) |
title |
str (optional) |
Title of the plot. Defaults to "y = f(x)" . |
show_axes |
bool (optional) |
Whether to show the x and y axes. Defaults to True . |
save_dir |
str (optional) |
The directory to save the animation. Defaults to "/tmp/temp_animation.gif" . (Note: The file will be deleted after the execution of the app is finished.) You can only write to the tmp directory in mecsimcalc. |
Returns:
Return Type |
Description |
str |
The HTML image tag containing the animated plot. |
Example:
>>> import numpy as np
>>> x = np.linspace(0, 10, 1000)
>>> y = np.sin(x)
>>> animation_html = animate_plot(x, y, duration=5, title="Sine Wave", show_axes=True)
>>> return {
"animation": animation_html
}
Quiz Toolkit
append_to_google_sheet
[Source]
append_to_google_sheet(
service_account_info = {...},
spreadsheet_id = "123abc...",
values = [["name", 12837, ...]],
range_name = 'Sheet1!A1',
include_timestamp = True
)
Description:
This function appends given values to a specified Google Sheet and optionally includes a current timestamp with each entry. It transforms data into a Google Sheets document, facilitating dynamic data entry directly from your application.
Arguments:
Argument |
Type |
Description |
service_account_info |
dict |
The service account credentials used for Google Sheets API authentication. |
spreadsheet_id |
str |
The unique identifier of the target Google Spreadsheet. |
values |
list of lists |
The data to append. Each list element represents a row of data. |
range_name |
str (optional) |
The A1 notation of the range to start appending data (Defaults to 'Sheet1!A1'). |
include_timestamp |
bool (optional) |
If True, appends the current timestamp to each row of data (Defaults to True). |
Returns:
Return Type |
Description |
dict |
The response from the Google Sheets API, containing details of the append operation. |
Example:
Code step:
import mecsimcalc as msc
def main(inputs):
service_account_info = {
# Your service account info here
}
spreadsheet_id = 'your_spreadsheet_id_here'
values = [
[ inputs['input_1'], inputs['input_2'], inputs['input_3'] ],
]
result = msc.append_to_google_sheet(service_account_info, spreadsheet_id, values)
send_gmail
[Source]
send_gmail(
sender_email='sender@example.com',
receiver_email='receiver@example.com',
subject="Quiz",
app_password = "xxxx xxxx xxxx xxxx",
values = [
["name", "grade"]
]
)
Description:
This function sends an email with specified values formatted in the message body, utilizing a service account for authentication.
Arguments:
Argument |
Type |
Description |
sender_email |
str |
The email address of the sender. |
receiver_email |
str |
The email address of the receiver. |
subject |
str |
The subject line of the email. |
app_password |
str |
The app-specific password for the sender's email account. |
values |
list |
A list of lists. Each list contains data to be included in the email body. |
Returns:
Return Type |
Description |
bool |
Returns True if the email was sent successfully, otherwise False. |
Example Usage:
# Example code to use the send_gmail function
import mecsimcalc as msc
def main(inputs):
# Define parameters
sender_email = 'sender@example.com'
receiver_email = 'receiver@example.com'
subject = 'Test Email'
app_password = 'your_app_password_here'
name = inputs['name']
grade = inputs['grade']
values = [
[name, grade]
]
# Send the email
msc.send_gmail(sender_email, receiver_email, subject, app_password, values)