def get_link_download():
prs = Presentation()
first_slide_layout = prs.slide_layouts[0]
slide = prs.slides.add_slide(first_slide_layout)
slide.shapes.title.text = "Report date"
slide.placeholders[1].text = 'This is 2nd way'
second_slide_layout = prs.slide_layouts[1]
slide1 = prs.slides.add_slide(second_slide_layout)
#slide1.insert_picture(fig.to_image(format = 'JPEG'))
#img_bytes = fig.to_image(format="png")
image1 = BytesIO()
fig.write_image(image1)
image1_toput = Image.frombytes(image1)
slide1.shapes.title.text = "Report date: {}".format(day_daily)
slide1.shapes.add_picute(image1_toput, Inches(1), Inches(1))
#slide1.placeholders[1].(Image(img_bytes), Inches(1), Inches(1))
#img_bytes = fig.to_image(format="png", width = 100, height = 100, scale = 2)
#out_file = base64.b64encode(out_file)
output = BytesIO()
prs.save(output)
output.seek(0)
nuevo = base64.b64encode(output.getbuffer()).decode()
#b = a.write(output.getvalue())
#title.text = "Report date: {}".format(day_daily)
#placeholder = slide.placeholders[0]
#placeholder.insert_picture(img.to_image(format = 'JPEG'))
#buffered = img.to_image(format = 'JPEG')
href = f'<a href="data:file/pptx;base64, {nuevo}">Download report in ptt format</a>'
return href
def toimage(arr,
high=255,
low=0,
cmin=None,
cmax=None,
pal=None,
mode=None,
channel_axis=None):
"""Takes a numpy array and returns a PIL image.
This function is only available if Python Imaging Library (PIL) is installed.
The mode of the PIL image depends on the array shape and the `pal` and
`mode` keywords.
For 2-D arrays, if `pal` is a valid (N,3) byte-array giving the RGB values
(from 0 to 255) then ``mode='P'``, otherwise ``mode='L'``, unless mode
is given as 'F' or 'I' in which case a float and/or integer array is made.
.. warning::
This function uses `bytescale` under the hood to rescale images to use
the full (0, 255) range if ``mode`` is one of ``None, 'L', 'P', 'l'``.
It will also cast data for 2-D images to ``uint32`` for ``mode=None``
(which is the default).
Notes
-----
For 3-D arrays, the `channel_axis` argument tells which dimension of the
array holds the channel data.
For 3-D arrays if one of the dimensions is 3, the mode is 'RGB'
by default or 'YCbCr' if selected.
The numpy array must be either 2 dimensional or 3 dimensional.
"""
#####CRIAR BATCHES COM POTÊNCIAS DE 2 PARA RESOLVER O PROBLEMA DE 450 SAMPLES DA
data = np.asarray(arr)
if np.iscomplexobj(data):
raise ValueError("Cannot convert a complex-valued array.")
shape = list(data.shape)
valid = len(shape) == 2 or ((len(shape) == 3) and ((3 in shape) or
(4 in shape)))
if not valid:
raise ValueError("'arr' does not have a suitable array shape for "
"any mode.")
if len(shape) == 2:
shape = (shape[1], shape[0]) # columns show up first
if mode == 'F':
data32 = data.astype(np.float32)
image = Image.frombytes(mode, shape, data32.tostring())
return image
if mode in [None, 'L', 'P']:
bytedata = bytescale(data,
high=high,
low=low,
cmin=cmin,
cmax=cmax)
image = Image.frombytes('L', shape, bytedata.tostring())
if pal is not None:
image.putpalette(np.asarray(pal, dtype=np.uint8).tostring())
# Becomes a mode='P' automagically.
elif mode == 'P': # default gray-scale
pal = (np.arange(0, 256, 1, dtype=np.uint8)[:, np.newaxis] *
np.ones((3, ), dtype=np.uint8)[np.newaxis, :])
image.putpalette(np.asarray(pal, dtype=np.uint8).tostring())
return image
if mode == '1': # high input gives threshold for 1
bytedata = (data > high)
image = Image.frombytes('1', shape, bytedata.tostring())
return image
if cmin is None:
cmin = np.amin(np.ravel(data))
if cmax is None:
cmax = np.amax(np.ravel(data))
data = (data * 1.0 - cmin) * (high - low) / (cmax - cmin) + low
if mode == 'I':
data32 = data.astype(np.uint32)
image = Image.frombytes(mode, shape, data32.tostring())
else:
raise ValueError(_errstr)
return image
# if here then 3-d array with a 3 or a 4 in the shape length.
# Check for 3 in datacube shape --- 'RGB' or 'YCbCr'
if channel_axis is None:
if (3 in shape):
ca = np.flatnonzero(np.asarray(shape) == 3)[0]
else:
ca = np.flatnonzero(np.asarray(shape) == 4)
if len(ca):
ca = ca[0]
else:
raise ValueError("Could not find channel dimension.")
else:
ca = channel_axis
numch = shape[ca]
if numch not in [3, 4]:
raise ValueError("Channel axis dimension is not valid.")
bytedata = bytescale(data, high=high, low=low, cmin=cmin, cmax=cmax)
if ca == 2:
strdata = bytedata.tostring()
shape = (shape[1], shape[0])
elif ca == 1:
strdata = np.transpose(bytedata, (0, 2, 1)).tostring()
shape = (shape[2], shape[0])
elif ca == 0:
strdata = np.transpose(bytedata, (1, 2, 0)).tostring()
shape = (shape[2], shape[1])
if mode is None:
if numch == 3:
mode = 'RGB'
else:
mode = 'RGBA'
if mode not in ['RGB', 'RGBA', 'YCbCr', 'CMYK']:
raise ValueError(_errstr)
if mode in ['RGB', 'YCbCr']:
if numch != 3:
raise ValueError("Invalid array shape for mode.")
if mode in ['RGBA', 'CMYK']:
if numch != 4:
raise ValueError("Invalid array shape for mode.")
# Here we know data and mode is correct
image = Image.frombytes(mode, shape, strdata)
return image
