Bertrand  Upton

Bertrand Upton


Learn What Is Template Reference Valuable and How to Use It in Angular

In this angular 12 version video, we learn what is template reference valuable and how to use it in angular 12. This video is made by anil Sidhu in the English language.


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Learn What Is Template Reference Valuable and How to Use It in Angular
Archie  Powell

Archie Powell


Tensorflex: Tensorflow Bindings for The Elixir Programming Language


The paper detailing Tensorflex was presented at NeurIPS/NIPS 2018 as part of the MLOSS workshop. The paper can be found here


How to run

  • You need to have the Tensorflow C API installed. Look here for details.
  • You also need the C library libjpeg. If you are using Linux or OSX, it should already be present on your machine, otherwise be sure to install (brew install libjpeg for OSX, and sudo apt-get install libjpeg-dev for Ubuntu).
  • Simply add Tensorflex to your list of dependencies in mix.exs and you are good to go!:
{:tensorflex, "~> 0.1.2"}

In case you want the latest development version use this:

{:tensorflex, github: "anshuman23/tensorflex"}


Tensorflex contains three main structs which handle different datatypes. These are %Graph, %Matrix and %Tensor. %Graph type structs handle pre-trained graph models, %Matrix handles Tensorflex 2-D matrices, and %Tensor handles Tensorflow Tensor types. The official Tensorflow documentation is present here and do note that this README only briefly discusses Tensorflex functionalities.


Used for loading a Tensorflow .pb graph model in Tensorflex.

Reads in a pre-trained Tensorflow protobuf (.pb) Graph model binary file.

Returns a tuple {:ok, %Graph}.

%Graph is an internal Tensorflex struct which holds the name of the graph file and the binary definition data that is read in via the .pb file.


Used for listing all the operations in a Tensorflow .pb graph.

Reads in a Tensorflex %Graph struct obtained from read_graph/1.

Returns a list of all the operation names (as strings) that populate the graph model.


Creates a 2-D Tensorflex matrix from custom input specifications.

Takes three input arguments: number of rows in matrix (nrows), number of columns in matrix (ncols), and a list of lists of the data that will form the matrix (datalist).

Returns a %Matrix Tensorflex struct type.


Used for accessing an element of a Tensorflex matrix.

Takes in three input arguments: a Tensorflex %Matrix struct matrix, and the row (row) and column (col) values of the required element in the matrix. Both row and col here are NOT zero indexed.

Returns the value as float.


Used for obtaining the size of a Tensorflex matrix.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {nrows, ncols} where nrows represents the number of rows of the matrix and ncols represents the number of columns of the matrix.


Appends a single row to the back of a Tensorflex matrix.

Takes a Tensorflex %Matrix matrix as input and a single row of data (with the same number of columns as the original matrix) as a list of lists (datalist) to append to the original matrix.

Returns the extended and modified %Matrix struct matrix.


Converts a Tensorflex matrix (back) to a list of lists format.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a list of lists representing the data stored in the matrix.

NOTE: If the matrix contains very high dimensional data, typically obtained from a function like load_csv_as_matrix/2, then it is not recommended to convert the matrix back to a list of lists format due to a possibility of memory errors.

float64_tensor/2, float32_tensor/2, int32_tensor/2:

Creates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor from Tensorflex matrices containing the values and dimensions specified.

Takes two arguments: a %Matrix matrix (matrix1) containing the values the tensor should have and another %Matrix matrix (matrix2) containing the dimensions of the required tensor.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor/1, float32_tensor/1, int32_tensor/1, string_tensor/1:

Creates a TF_DOUBLE, TF_FLOAT, TF_INT32, or TF_STRING constant value one-dimensional tensor from the input value specified.

Takes in a float, int or string value (depending on function) as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding tensor data and type.

float64_tensor_alloc/1, float32_tensor_alloc/1, int32_tensor_alloc/1:

Allocates a TF_DOUBLE, TF_FLOAT, or TF_INT32 tensor of specified dimensions.

This function is generally used to allocate output tensors that do not hold any value data yet, but will after the session is run for Inference. Output tensors of the required dimensions are allocated and then passed to the run_session/5 function to hold the output values generated as predictions.

Takes a Tensorflex %Matrix struct matrix as input.

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the potential tensor data and type.


Used to get the datatype of a created tensor.

Takes in a %Tensor struct tensor as input.

Returns a tuple {:ok, datatype} where datatype is an atom representing the list of Tensorflow TF_DataType tensor datatypes. Click here to view a list of all possible datatypes.


Loads JPEG images into Tensorflex directly as a TF_UINT8 tensor of dimensions image height x image width x number of color channels.

This function is very useful if you wish to do image classification using Convolutional Neural Networks, or other Deep Learning Models. One of the most widely adopted and robust image classification models is the Inception model by Google. It makes classifications on images from over a 1000 classes with highly accurate results. The load_image_as_tensor/1 function is an essential component for the prediction pipeline of the Inception model (and for other similar image classification models) to work in Tensorflex.

Reads in the path to a JPEG image file (.jpg or .jpeg).

Returns a tuple {:ok, %Tensor} where %Tensor represents an internal Tensorflex struct type that is used for holding the tensor data and type. Here the created Tensor is a uint8 tensor (TF_UINT8).

NOTE: For now, only 3 channel RGB JPEG color images can be passed as arguments. Support for grayscale images and other image formats such as PNG will be added in the future.


Loads high-dimensional data from a CSV file as a Tensorflex 2-D matrix in a super-fast manner.

The load_csv_as_matrix/2 function is very fast-- when compared with the Python based pandas library for data science and analysis' function read_csv on the test.csv file from MNIST Kaggle data (source), the following execution times were obtained:

  • read_csv: 2.549233 seconds
  • load_csv_as_matrix/2: 1.711494 seconds

This function takes in 2 arguments: a path to a valid CSV file (filepath) and other optional arguments opts. These include whether or not a header needs to be discarded in the CSV, and what the delimiter type is. These are specified by passing in an atom :true or :false to the header: key, and setting a string value for the delimiter: key. By default, the header is considered to be present (:true) and the delimiter is set to ,.

Returns a %Matrix Tensorflex struct type.


Runs a Tensorflow session to generate predictions for a given graph, input data, and required input/output operations.

This function is the final step of the Inference (prediction) pipeline and generates output for a given set of input data, a pre-trained graph model, and the specified input and output operations of the graph.

Takes in five arguments: a pre-trained Tensorflow graph .pb model read in from the read_graph/1 function (graph), an input tensor with the dimensions and data required for the input operation of the graph to run (tensor1), an output tensor allocated with the right dimensions (tensor2), the name of the input operation of the graph that needs where the input data is fed (input_opname), and the output operation name in the graph where the outputs are obtained (output_opname). The input tensor is generally created from the matrices manually or using the load_csv_as_matrix/2 function, and then passed through to one of the tensor creation functions. For image classification the load_image_as_tensor/1 can also be used to create the input tensor from an image. The output tensor is created using the tensor allocation functions (generally containing alloc at the end of the function name).

Returns a List of Lists (similar to the matrix_to_lists/1 function) containing the generated predictions as per the output tensor dimensions.


Adds scalar value to matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.


Subtracts scalar value from matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.


Multiplies scalar value with matrix.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.


Divides matrix values by scalar.

Takes two arguments: %Matrix matrix and scalar value (int or float)

Returns a %Matrix modified matrix.


Adds two matrices of same dimensions together.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.


Subtracts matrix2 from matrix1.

Takes in two %Matrix matrices as arguments.

Returns the resultant %Matrix matrix.


Converts the data stored in a 2-D tensor back to a 2-D matrix.

Takes in a single argument as a %Tensor tensor (any TF_Datatype).

Returns a %Matrix 2-D matrix.

NOTE: Tensorflex doesn't currently support 3-D matrices, and therefore tensors that are 3-D (such as created using the load_image_as_tensor/1 function) cannot be converted back to a matrix, yet. Support for 3-D matrices will be added soon.


Examples are generally added in full description on my blog here. A blog post covering how to do classification on the Iris Dataset is present here.


Here we will briefly touch upon how to use the Google V3 Inception pre-trained graph model to do image classficiation from over a 1000 classes. First, the Inception V3 model can be downloaded here:

After unzipping, see that it contains the graphdef .pb file (classify_image_graphdef.pb) which contains our graph definition, a test jpeg image that should identify/classify as a panda (cropped_panda.pb) and a few other files I will detail later.

Now for running this in Tensorflex first the graph is loaded:

iex(1)> {:ok, graph} = Tensorflex.read_graph("classify_image_graph_def.pb")
2018-07-29 00:48:19.849870: W tensorflow/core/framework/] Op BatchNormWithGlobalNormalization is deprecated. It will cease to work in GraphDef version 9. Use tf.nn.batch_normalization().
   def: #Reference<0.2597534446.2498625538.211058>,
   name: "classify_image_graph_def.pb"

Then the cropped_panda image is loaded using the new load_image_as_tensor function:

iex(2)> {:ok, input_tensor} = Tensorflex.load_image_as_tensor("cropped_panda.jpg")
   datatype: :tf_uint8,
   tensor: #Reference<0.2597534446.2498625538.211093>

Then create the output tensor which will hold out output vector values. For the inception model, the output is received as a 1008x1 tensor, as there are 1008 classes in the model:

iex(3)> out_dims = Tensorflex.create_matrix(1,2,[[1008,1]])
  data: #Reference<0.2597534446.2498625538.211103>,
  ncols: 2,
  nrows: 1

iex(4)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(out_dims)
   datatype: :tf_float,
   tensor: #Reference<0.2597534446.2498625538.211116>

Then the output results are read into a list called results. Also, the input operation in the Inception model is DecodeJpeg and the output operation is softmax:

iex(5)> results = Tensorflex.run_session(graph, input_tensor, output_tensor, "DecodeJpeg", "softmax")
2018-07-29 00:51:13.631154: I tensorflow/core/platform/] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
  [1.059142014128156e-4, 2.8240500250831246e-4, 8.30648496048525e-5,
   1.2982363114133477e-4, 7.32232874725014e-5, 8.014426566660404e-5,
   6.63459359202534e-5, 0.003170756157487631, 7.931600703159347e-5,
   3.707312498590909e-5, 3.0997329304227605e-5, 1.4232713147066534e-4,
   1.0381334868725389e-4, 1.1057958181481808e-4, 1.4321311027742922e-4,
   1.203602587338537e-4, 1.3130248407833278e-4, 5.850398520124145e-5,
   2.641105093061924e-4, 3.1629020668333396e-5, 3.906813799403608e-5,
   2.8646905775531195e-5, 2.2863158665131778e-4, 1.2222197256051004e-4,
   5.956588938715868e-5, 5.421260357252322e-5, 5.996063555357978e-5,
   4.867801326327026e-4, 1.1005574924638495e-4, 2.3433618480339646e-4,
   1.3062104699201882e-4, 1.317620772169903e-4, 9.388553007738665e-5,
   7.076268957462162e-5, 4.281177825760096e-5, 1.6863139171618968e-4,
   9.093972039408982e-5, 2.611844101920724e-4, 2.7584232157096267e-4,
   5.157176201464608e-5, 2.144951868103817e-4, 1.3628098531626165e-4,
   8.007588621694595e-5, 1.7929042223840952e-4, 2.2831936075817794e-4,
   6.216531619429588e-5, 3.736453436431475e-5, 6.782123091397807e-5,
   1.1538144462974742e-4, ...]

Finally, we need to find which class has the maximum probability and identify it's label. Since results is a List of Lists, it's better to read in the nested list. Then we need to find the index of the element in the new list which as the maximum value. Therefore:

iex(6)> max_prob = List.flatten(results) |> Enum.max

iex(7)> Enum.find_index(results |> List.flatten, fn(x) -> x == max_prob end)

We can thus see that the class with the maximum probability predicted (0.8849328756332397) for the image is 169. We will now find what the 169 label corresponds to. For this we can look back into the unzipped Inception folder, where there is a file called imagenet_2012_challenge_label_map_proto.pbtxt. On opening this file, we can find the string class identifier for the 169 class index. This is n02510455 and is present on Line 1556 in the file. Finally, we need to match this string identifier to a set of identification labels by referring to the file imagenet_synset_to_human_label_map.txt file. Here we can see that corresponding to the string class n02510455 the human labels are giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca (Line 3691 in the file).

Thus, we have correctly identified the animal in the image as a panda using Tensorflex!


A brief idea of what this example entails:

  • The Recurrent Neural Network utilizes Long-Short-Term-Memory (LSTM) cells for holding the state for the data flowing in through the network
  • In this example, we utilize the LSTM network for sentiment analysis on movie reviews data in Tensorflex. The trained models are originally created as part of an online tutorial (source) and are present in a Github repository here.

To do sentiment analysis in Tensorflex however, we first need to do some preprocessing and prepare the graph model (.pb) as done multiple times before in other examples. For that, in the examples/rnn-lstm-example directory there are two scripts: and Prior to explaining the working of these scripts you first need to download the original saved models as well as the datasets:

  • For the model, download from here and then store all the 4 model files in the examples/rnn-lstm-example/model folder
  • For the dataset, download from here. After decompressing, we do not need all the files, just the 2 numpy binaries wordsList.npy and wordVectors.npy. These will be used to encode our text data into UTF-8 encoding for feeding our RNN as input.

Now, for the Python two scripts: and

  • This is used to create our pb model from the Python saved checkpoints. Here we will use the downloaded Python checkpoints' model to create the .pb graph. Just running python after putting the model files in the correct directory will do the trick. In the same ./model/ folder, you will now see a file called frozen_model_lstm.pb. This is the file which we will load into Tensorflex. In case for some reason you want to skip this step and just get the loaded graph here is a Dropbox link
  • Even if we can load our model into Tensorflex, we also need some data to do inference on. For that, we will write our own example sentences and convert them (read encode) to a numeral (int32) format that can be used by the network as input. For that, you can inspect the code in the script to get an understanding of what is happening. Basically, the neural network takes in an input of a 24x250 int32 (matrix) tensor created from text which has been encoded as UTF-8. Again, running python will give you two csv files (one indicating positive sentiment and the other a negative sentiment) which we will later load into Tensorflex. The two sentences converted are:
    • Negative sentiment sentence: That movie was terrible.
    • Positive sentiment sentence: That movie was the best one I have ever seen.

Both of these get converted to two files inputMatrixPositive.csv and inputMatrixNegative.csv (by which we load into Tensorflex next.

Inference in Tensorflex: Now we do sentiment analysis in Tensorflex. A few things to note:

  • The input graph operation is named Placeholder_1
  • The output graph operation is named add and is the eventual result of a matrix multiplication. Of this obtained result we only need the first row
  • Here the input is going to be a integer valued matrix tensor of dimensions 24x250 representing our sentence/review
  • The output will have 2 columns, as there are 2 classes-- for positive and negative sentiment respectively. Since we will only be needing only the first row we will get our result in a 1x2 vector. If the value of the first column is higher than the second column, then the network indicates a positive sentiment otherwise a negative sentiment. All this can be observed in the original repository in a Jupyter notebook here: ```elixir iex(1)> {:ok, graph} = Tensorflex.read_graph "examples/rnn-lstm-example/model/frozen_model_lstm.pb" {:ok, %Tensorflex.Graph{ def: #Reference<0.713975820.1050542081.11558>, name: "examples/rnn-lstm-example/model/frozen_model_lstm.pb" }}

iex(2)> Tensorflex.get_graph_ops graph ["Placeholder_1", "embedding_lookup/params_0", "embedding_lookup", "transpose/perm", "transpose", "rnn/Shape", "rnn/strided_slice/stack", "rnn/strided_slice/stack_1", "rnn/strided_slice/stack_2", "rnn/strided_slice", "rnn/stack/1", "rnn/stack", "rnn/zeros/Const", "rnn/zeros", "rnn/stack_1/1", "rnn/stack_1", "rnn/zeros_1/Const", "rnn/zeros_1", "rnn/Shape_1", "rnn/strided_slice_2/stack", "rnn/strided_slice_2/stack_1", "rnn/strided_slice_2/stack_2", "rnn/strided_slice_2", "rnn/time", "rnn/TensorArray", "rnn/TensorArray_1", "rnn/TensorArrayUnstack/Shape", "rnn/TensorArrayUnstack/strided_slice/stack", "rnn/TensorArrayUnstack/strided_slice/stack_1", "rnn/TensorArrayUnstack/strided_slice/stack_2", "rnn/TensorArrayUnstack/strided_slice", "rnn/TensorArrayUnstack/range/start", "rnn/TensorArrayUnstack/range/delta", "rnn/TensorArrayUnstack/range", "rnn/TensorArrayUnstack/TensorArrayScatter/TensorArrayScatterV3", "rnn/while/Enter", "rnn/while/Enter_1", "rnn/while/Enter_2", "rnn/while/Enter_3", "rnn/while/Merge", "rnn/while/Merge_1", "rnn/while/Merge_2", "rnn/while/Merge_3", "rnn/while/Less/Enter", "rnn/while/Less", "rnn/while/LoopCond", "rnn/while/Switch", "rnn/while/Switch_1", "rnn/while/Switch_2", "rnn/while/Switch_3", ...]

First we will try for positive sentiment:
iex(3)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixPositive.csv", header: :false)
  data: #Reference<0.713975820.1050542081.13138>,
  ncols: 250,
  nrows: 24

iex(4)> input_dims = Tensorflex.create_matrix(1,2,[[24,250]])
  data: #Reference<0.713975820.1050542081.13575>,
  ncols: 2,
  nrows: 1

iex(5)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals, input_dims)
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.14434>

iex(6)> output_dims = Tensorflex.create_matrix(1,2,[[24,2]])
  data: #Reference<0.713975820.1050542081.14870>,
  ncols: 2,
  nrows: 1

iex(7)> {:ok, output_tensor} = Tensorflex.float32_tensor_alloc(output_dims)
   datatype: :tf_float,
   tensor: #Reference<0.713975820.1050542081.15363>

We only need the first row, the rest do not indicate anything:

iex(8)> [result_pos | _ ] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
  [4.483788013458252, -1.273943305015564],
  [-0.17151066660881042, -2.165886402130127],
  [0.9569928646087646, -1.131581425666809],
  [0.5669126510620117, -1.3842089176177979],
  [-1.4346938133239746, -4.0750861167907715],
  [0.4680981934070587, -1.3494354486465454],
  [1.068990707397461, -2.0195648670196533],
  [3.427264451980591, 0.48857203125953674],
  [0.6307879686355591, -2.069119691848755],
  [0.35061028599739075, -1.700657844543457],
  [3.7612719535827637, 2.421398878097534],
  [2.7635951042175293, -0.7214710116386414],
  [1.146680235862732, -0.8688814640045166],
  [0.8996094465255737, -1.0183486938476563],
  [0.23605018854141235, -1.893072247505188],
  [2.8790698051452637, -0.37355837225914],
  [-1.7325369119644165, -3.6470277309417725],
  [-1.687785029411316, -4.903762340545654],
  [3.6726789474487305, 0.14170047640800476],
  [0.982108473777771, -1.554244875907898],
  [2.248904228210449, 1.0617655515670776],
  [0.3663095533847809, -3.5266385078430176],
  [-1.009346604347229, -2.901120901107788],
  [3.0659966468811035, -1.7605335712432861]

iex(9)> result_pos
[4.483788013458252, -1.273943305015564]

Thus we can clearly see that the RNN predicts a positive sentiment. For a negative sentiment, next:

iex(10)> input_vals = Tensorflex.load_csv_as_matrix("examples/rnn-lstm-example/inputMatrixNegative.csv", header: :false)
  data: #Reference<0.713975820.1050542081.16780>,
  ncols: 250,
  nrows: 24

iex(11)> {:ok, input_tensor} = Tensorflex.int32_tensor(input_vals,input_dims)
   datatype: :tf_int32,
   tensor: #Reference<0.713975820.1050542081.16788>

iex(12)> [result_neg|_] = Tensorflex.run_session(graph, input_tensor,output_tensor, "Placeholder_1", "add")
  [0.7635725736618042, 10.895986557006836],
  [2.205151319503784, -0.6267685294151306],
  [3.5995595455169678, -0.1240251287817955],
  [-1.6063352823257446, -3.586883068084717],
  [1.9608432054519653, -3.084211826324463],
  [3.772461414337158, -0.19421455264091492],
  [3.9185996055603027, 0.4442034661769867],
  [3.010765552520752, -1.4757057428359985],
  [3.23650860786438, -0.008513949811458588],
  [2.263028144836426, -0.7358709573745728],
  [0.206748828291893, -2.1945853233337402],
  [2.913491725921631, 0.8632720708847046],
  [0.15935257077217102, -2.9757845401763916],
  [-0.7757357358932495, -2.360766649246216],
  [3.7359719276428223, -0.7668198347091675],
  [2.2896337509155273, -0.45704856514930725],
  [-1.5497230291366577, -4.42919921875],
  [-2.8478822708129883, -5.541027545928955],
  [1.894787073135376, -0.8441318273544312],
  [0.15720489621162415, -2.699129819869995],
  [-0.18114641308784485, -2.988100051879883],
  [3.342879056930542, 2.1714375019073486],
  [2.906526565551758, 0.18969044089317322],
  [0.8568912744522095, -1.7559258937835693]
iex(13)> result_neg
[0.7635725736618042, 10.895986557006836]

Thus we can clearly see that in this case the RNN indicates negative sentiment! Our model works!

Pull Requests Made

Author: anshuman23
Source code:
License: Apache-2.0 license

#elixir #tensorflow #machine-learning 

Chloe  Butler

Chloe Butler


Pdf2gerb: Perl Script Converts PDF Files to Gerber format


Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

#Pdf2Gerb config settings:
#Put this file in same folder/directory as itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)

#configurable settings:
#change values here instead of in main file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)} ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .030,  #heavy-current traces; be careful with these ones!
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
#number of elements in each shape type:
use constant
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,

#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#use Package::Constants;
#use Exporter qw(import); #

#my $caller = "pdf2gerb::";

#sub cfg
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code:

License: GPL-3.0 license


Christa  Stehr

Christa Stehr


Install Angular - Angular Environment Setup Process

Angular is a TypeScript based framework that works in synchronization with HTML, CSS, and JavaScript. To work with angular, domain knowledge of these 3 is required.

  1. Installing Node.js and npm
  2. Installing Angular CLI
  3. Creating workspace
  4. Deploying your First App

In this article, you will get to know about the Angular Environment setup process. After reading this article, you will be able to install, setup, create, and launch your own application in Angular. So let’s start!!!

Angular environment setup

Install Angular in Easy Steps

For Installing Angular on your Machine, there are 2 prerequisites:

  • Node.js
  • npm Package Manager

First you need to have Node.js installed as Angular require current, active LTS or maintenance LTS version of Node.js

Download and Install Node.js version suitable for your machine’s operating system.

Npm Package Manager

Angular, Angular CLI and Angular applications are dependent on npm packages. By installing Node.js, you have automatically installed the npm Package manager which will be the base for installing angular in your system. To check the presence of npm client and Angular version check of npm client, run this command:

  1. npm -v

Installing Angular CLI

  • Open Terminal/Command Prompt
  • To install Angular CLI, run the below command:
  1. npm install -g @angular/cli

installing angular CLI

· After executing the command, Angular CLI will get installed within some time. You can check it using the following command

  1. ng --version

Workspace Creation

Now as your Angular CLI is installed, you need to create a workspace to work upon your application. Methods for it are:

  • Using CLI
  • Using Visual Studio Code
1. Using CLI

To create a workspace:

  • Navigate to the desired directory where you want to create your workspace using cd command in the Terminal/Command prompt
  • Then in the directory write this command on your terminal and provide the name of the app which you want to create. In my case I have mentioned DataFlair:
  1. Ng new YourAppName

create angular workspace

  • After running this command, it will prompt you to select from various options about the CSS and other functionalities.

angular CSS options

  • To leave everything to default, simply press the Enter or the Return key.

angular setup

#angular tutorials #angular cli install #angular environment setup #angular version check #download angular #install angular #install angular cli

Roberta  Ward

Roberta Ward


Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientisttester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade

Nico Jonsson

Nico Jonsson


How to Use DOM Manipulation properly in Angular

If you are coming from the background of working with angularjs, it was quite straight forward to access and manipulate the DOM there. You had access to the DOM node through element injected in the link function of the directive.

function link(scope, element, attrs) {

Or through angular.element which was an AngularJS’s built in subset of jQuery. But this approach had its drawbacks. It made your code tightly coupled with Browser’s API.

The new Angular (2 onwards) works on multiple platforms: mobile, web workers etc. So, they have introduced a number of APIs to work as an abstraction layer between your code and platform APIs. These APIs come in the form of different reference types likeElementRef, TemplateRef, ViewRef, ComponentRef and ViewContainerRef.

In this blog, we will see some examples of how these reference types can be used to manipulate DOM in angular. But before that let’s look at the ways to access these reference types within a Component/Directive.

DOM Queries

Angular has provided two ways to query/access various reference types within a Component/Directive. These are

  • ViewChild/ViewChildren
  • ContentChild/ContentChildren


These are decorators which can be used within a Component/Directive as @ViewChild (returns a single reference) or @ViewChildren (returns a list of references in the form of a QueryList). These will assign the values of reference types from template to the component fields they are applied to. The basic usage is as follow:

@ViewChild(selector, {read: ReferenceType}) fieldName;

A selector can be a string representing a template reference variable, or a Component/Directive class, or a TemplateRef or a provider defined in the child component tree.

@ViewChild("myElem") template: ElementRef;

The second parameter is optional and is only required to query some reference types which can’t be inferred easily by Angular like ViewContainerRef.

@ViewChild("myContainer", {read: ViewContainerRef}) container: ViewContainerRef;


The usage is pretty much similar to that of ViewChild/ViewChildren. The only difference is that it queries within the <ng-content> projected elements of the component while the @ViewChild queries within the template of the component. This will be explained better in the examples of upcoming sections.

DOM access via ElementRef

ElementRef is a very basic abstraction layer on a DOM element in Angular. It’s an angular wrapper around the native element.

You can get hold of ElementRef in a Component or Directive in following ways:

Dependency Injection

Host element of a Component or Directive can be accessed via direct DI in the constructor.

  selector: 'app-test',
  template: '<div>I am a test component</div>'
export class TestComponent implements OnInit {

  constructor(private element: ElementRef) { }

  ngOnInit() {

* Output: 
*   <app-test>
*     <div>I am a test component</div>
*   </app-test>
* */

Using ViewChild and Template Reference Variables

  selector: 'app-test',
  template: `
    <div #child1>First Child</div>
    <div>Second Child</div>
export class TestComponent implements OnInit {

  @ViewChild("child1") firstChild: ElementRef;

  constructor() { }

  ngOnInit() {


* Output: <div>First Child</div>
* */

Using ContentChild

Works in a similar manner as that of @ViewChild, but for <ng-content> projected elements.

// Child Component
  selector: "component-a",
  template: `<ng-content></ng-content>`
export class ComponentA {
  @ContentChild("contentChild") contentChild: ElementRef;
  ngOnInit() {
// Parent Component
  selector: 'app-test',
  template: `
      <div #contentChild>Content Child 1</div>
      <div>Content Child 2</div>
export class TestComponent implements OnInit {}
* Output: <div>Content Child 1</div>
* */

It looks pretty straight forward that you can easily access a DOM element via ElementRef and then manipulate the DOM by accessing the nativeElement. Something like this:

  selector: 'app-test-component',
  template: `
    <div class="header">I am a header</div>
    <div class="body" #body>
    <div class="footer">I am a footer</div>
export class TestComponent implements AfterContentInit {
  @ViewChild("body") bodyElem: ElementRef;

  ngAfterContentInit(): void {
    this.bodyElem.nativeElement.innerHTML = `<div>Hi, I am child added by directly calling the native APIs.</div>`;


However, the direct usage of ElementRef is discouraged by Angular Team because it directly provides the access to DOM which can make your application vulnerable to XSS attacks. It also creates tight coupling between your application and rendering layers which makes is difficult to run an app on multiple platforms.

Everything is a ‘View’ in Angular

A view is the smallest building block of an angular app’s UI. Every component has its own view. You can consider it as a group of elements which can be created and destroyed together.

A view can be classified into two types:

  • Embedded Views — created from templates
  • Host Views — created from components

Displaying a view in UI can be broken down into two steps:

  1. Creating a view from template or component
  2. Rendering a view into a view container

Embedded Views

Embedded views are created from templates defined using <ng-template> element.

Creating an embedded view

First a template needs to be accessed within a component as TemplateRefusing @ViewChild and template reference variable. Then, an embedded view can be created from a TemplateRef by passing a data-binding context.

const viewRef = this.template.createEmbeddedView({
  name: "View 1"

This context is being consumed by the template in<ng-template>.

<ng-template #template let-viewName="name">
  <div>Hi, My name is {{viewName}}. I am a view created from a template</div>

You can also use the $implicit property in the context if you have only a single property to bind.

const viewRef = this.template.createEmbeddedView({
  $implicit: "View 1"

In this case, you can skip assigning values to template variables.

<ng-template #template let-viewName>
  <div>Hi, My name is {{viewName}}. I am a view created from a template</div>

Rendering an embedded view

Till now, we have created only an instance of ViewRef. This view is still not visible in the UI. In order to see it in the UI, we need a placeholder (a view container) to render it. This placeholder is being provided by ViewContainerRef.

Any element can serve as a view container, however <ng-container> is a better candidate as it is rendered as a comment and doesn’t leave any redundant element in the html DOM.

  selector: 'app-test-component',
  template: `
    <div class="header">I am a header</div>
    <div class="body">
      <ng-container #container></ng-container>
    <div class="footer">I am a footer</div>

    <ng-template #template let-viewName="name">
      <div>Hi, My name is {{viewName}}. I am a view created from a template</div>
export class TestComponent implements AfterContentInit {

  @ViewChild("template") template: TemplateRef;
  @ViewChild("container", {read: ViewContainerRef}) container: ViewContainerRef;

  constructor() { }

  ngAfterContentInit(): void {
    const viewRef = this.template.createEmbeddedView({
      name: "View 1"

Both <ng-container> and <ng-template> elements will be rendered as comments leaving the html DOM neat and clean.

The above 2 steps process of creating a view and adding it into a container can further be reduced by using the createEmbeddedView method available in the ViewContainerRef itself.

this.container.createEmbeddedView(this.template, {
  name: "View 1"

This can be further simplified by moving the whole view creation logic from component class to the template using ngTemplateOutlet and ngTemplateOutletContext.

  selector: 'app-test-component',
  template: `
    <div class="header">I am a header</div>
    <div class="body">
      <ng-container [ngTemplateOutlet]="template" [ngTemplateOutletContext]="{name: 'View 1'}"></ng-container>
    <div class="footer">I am a footer</div>

    <ng-template #template let-viewName="name">
      <div>Hi, My name is {{viewName}}. I am a view created from a template</div>
export class TestComponent {}

Host Views

Host Views are quite similar to Embedded View. The only difference is that the Host Views are created from components instead of templates.

Creating a host view

In order to create a host view, first you need to create a ComponentFactory of the component you want to render using ComponentFactoryResolver.

  private componentFactoryResolver: ComponentFactoryResolver
) {
  this.someComponentFactory = this.componentFactoryResolver.resolveComponentFactory(SomeComponent);

Then, a dynamic instance of the component is created by passing an Injector instance to the factory. Every component should be bound to an instance of Injector. You can use the injector of the parent component for the dynamically created components.

const componentRef = this.someComponentFactory.create(this.injector);
const viewRef = componentRef.hostView;

Rendering a host view

Rendering a host view is almost similar to rendering an embedded view. You can directly insert it into a view container.

  selector: 'app-test-component',
  template: `
    <div class="header">I am a header</div>
    <div class="body">
      <ng-container #container></ng-container>
    <div class="footer">I am a footer</div>
export class TestComponentComponent implements AfterContentInit {

  @ViewChild("container", {read: ViewContainerRef}) container: ViewContainerRef;

  private someComponentFactory: ComponentFactory<SomeComponent>;

    private componentFactoryResolver: ComponentFactoryResolver,
    private injector: Injector
  ) {
    this.someComponentFactory = this.componentFactoryResolver.resolveComponentFactory(SomeComponent);

  ngAfterContentInit(): void {
    const componentRef = this.someComponentFactory.create(this.injector);
    const viewRef = componentRef.hostView;

Or by directly calling the createComponent method of ViewContainerRef and passing the component factory instance.


Now, similar to embedded view, we can also shift the whole logic of host view creation in template itself using ngComponentOutlet.

  selector: 'app-test-component',
  template: `
    <div class="header">I am a header</div>
    <div class="body">
      <ng-container [ngComponentOutlet]="comp"></ng-container>
    <div class="footer">I am a footer</div>
export class TestComponent {
  comp = SomeComponent;

Don’t forget to store the reference of the component class in parent component’s field. The template has access only to the fields of the components.


Here we come to an end. Let’s conclude what we have understood till now.

  • We can access the DOM in Angular using different reference types likeElementRef, TemplateRef, ViewRef, ComponentRef and ViewContainerRef.
  • These reference types can be queried from templates using @ViewChild and @ContentChild.
  • Browser’s native DOM element can be accessed via ElementRef. However, manipulating this element directly is discouraged because of security reasons.
  • Concept of Views.
  • How to create and render an Embedded View.
  • How to create and render a Component View.

So, that’s it for today about understanding DOM manipulation in Angular.

Originally published by medium